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Evaluation of WRF-based Convection-Permitting Multi-Physics Ensemble Forecasts over China for an Extreme Rainfall Event on 21 July 2012 in Beijing


doi: 10.1007/s00376-016-6202-z

  • On 21 July 2012, an extreme rainfall event that recorded a maximum rainfall amount over 24 hours of 460 mm, occurred in Beijing, China. Most operational models failed to predict such an extreme amount. In this study, a convective-permitting ensemble forecast system (CEFS), at 4-km grid spacing, covering the entire mainland of China, is applied to this extreme rainfall case. CEFS consists of 22 members and uses multiple physics parameterizations. For the event, the predicted maximum is 415 mm d-1 in the probability-matched ensemble mean. The predicted high-probability heavy rain region is located in southwest Beijing, as was observed. Ensemble-based verification scores are then investigated. For a small verification domain covering Beijing and its surrounding areas, the precipitation rank histogram of CEFS is much flatter than that of a reference global ensemble. CEFS has a lower (higher) Brier score and a higher resolution than the global ensemble for precipitation, indicating more reliable probabilistic forecasting by CEFS. Additionally, forecasts of different ensemble members are compared and discussed. Most of the extreme rainfall comes from convection in the warm sector east of an approaching cold front. A few members of CEFS successfully reproduce such precipitation, and orographic lift of highly moist low-level flows with a significantly southeasterly component is suggested to have played important roles in producing the initial convection. Comparisons between good and bad forecast members indicate a strong sensitivity of the extreme rainfall to the mesoscale environmental conditions, and, to less of an extent, the model physics.
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  • Bowler N. E., A. Arribas, K. R. Mylne, K. B. Robertson, and S. E. Beare, 2008: The MOGREPS short-range ensemble prediction system. Quart. J. Roy. Meteor. Soc., 134, 703- 722.10.1002/qj.234ccc0a54e7ec3e76d744714d1063e66e1http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1002%2Fqj.234%2Fpdfhttp://onlinelibrary.wiley.com/doi/10.1002/qj.234/pdfNot Available
    Brown B. G., J. H. Gotway, R. Bullock, E. Gilleland , and D. Ahijevych, 2009: The model evaluation tools (MET): Community tools for forecast evaluation. Proc. 25th Conf. Int. Interactive Information and Processing Systems (IIPS) for Meteorology, Oceanography, and Hydrology, Phoenix, AZ, US., American Meteorological Society,Paper 9A. 6.0b367927-ab0a-407d-9df8-aa067711e10a708800e0d44a6d124c5f42d8d26143b3http%3A%2F%2Fams.confex.com%2Fams%2F89annual%2Ftechprogram%2Fpaper_151349.htmhttp://ams.confex.com/ams/89annual/techprogram/paper_151349.htm
    Buizza R., A. Hollingsworth, 2002: Storm prediction over Europe using the ECMWF ensemble prediction system. Meteorological Applications, 9, 289- 305.10.1017/S13504827020030318d04dac776611e6ded50d3d5f7cc24cbhttp%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1017%2FS1350482702003031%2Ffullhttp://onlinelibrary.wiley.com/doi/10.1017/S1350482702003031/fullThree severe storms caused great damage in Europe in December 1999. The first storm hit Denmark and Germany on 3 and 4 December, and the other two storms crossed France and Germany on 26 and 28 December. In this study, the performance of the Ensemble Prediction System (EPS) at the European Centre for Medium-Range Weather Forecast (ECMWF) in predicting these intense storms is investigated. Results indicate that the EPS gave early indications of possible severe storm occurrence, and was especially useful when the deterministic TL319L60 forecasts issued on successive days were highly inconsistent. These results indicate that the EPS is a valuable tool for assessing quantitatively the risk of severe weather and issuing early warnings of possible disruptions.
    Buizza R., D. S. Richardson, and T. N. Palmer, 2003: Benefits of increased resolution in the ECMWF ensemble system and comparison with Poor-man's ensembles. Quart. J. Roy. Meteor. Soc., 129, 1269- 1288.10.1256/qj.02.92cd10b3f39a45ed59dc1842559192c943http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1256%2Fqj.02.92%2Fabstracthttp://onlinelibrary.wiley.com/doi/10.1256/qj.02.92/abstractAbstract In November 2000 the resolution of the forecast model in the operational European Centre for Medium-Range Weather Forecasts Ensemble Prediction System was increased from a 120 km truncation scale (EPS) to an 80 km truncation scale (High-resolution EPS or HEPS). The HEPS performance is compared with that of EPS and with different flavours of poor-man's ensembles. Average results based on Brier skill scores and the potential economic value of probabilistic predictions for 57 winter and 30 summer cases indicate that the new HEPS system is about 12 hours more skilful than the old EPS. Averages over 39 winter cases indicate that HEPS forecasts perform better than five-centre ensemble forecasts. Results also show that if forecasts are transformed into parametrized Gaussian distribution functions centred on the bias-corrected ensemble mean and with re-scaled standard deviation, HEPS-based parametrized forecasts outperform all other configurations. Diagnostics based on parametrized forecast probabilities indicate that the different impact on the probabilistic or deterministic forecast skill is related to the fact that HEPS better represents the daily variation in the uncertainty of the atmosphere, and is not simply a reflection of improved mean bias or of a better level of spread. Copyright 2003 Royal Meteorological Society
    Charles M. E., B. A. Colle, 2009: Verification of extratropical cyclones within the NCEP operational models. Part II: The short-range ensemble forecast system. Wea.Forecasting, 24, 1191- 1214.10.1175/2009WAF2222170.1a1b5ae6e63cd08e849134fcf9801f0c8http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2009WtFor..24.1191Chttp://adsabs.harvard.edu/abs/2009WtFor..24.1191CThis paper verifies the strengths and positions of extratropical cyclones around North America and the adjacent oceans within the Short Range Ensemble Forecast (SREF) system at the National Centers for Environmental Prediction (NCEP) during the 2004 -07 cool seasons (October-March). The SREF mean for cyclone position and central pressure has a smaller error than the various subgroups within SREF and the operational North American Mesoscale (NAM) model in many regions on average, but not the operational Global Forecast System (GFS) for many forecast times. Inclusion of six additional Weather Research and Forecasting (WRF) model members into SREF during the 2006-07 cool season did not improve the SREF mean predictions. The SREF has slightly more probabilistic skill over the eastern United States and western Atlantic than the western portions of the domain for cyclone central pressure. The SREF also has slightly greater probabilistic skill than the combined GFS and NAM for central pressure, which is significant at the 90% level for many regions and thresholds. The SREF probabilities are fairly reliable, although the SREF is overconfident at higher probabilities in all regions. The inclusion of WRF did not improve the SREF probabilistic skill. Over the eastern Pacific, eastern Canada, and western Atlantic, the SREF is overdispersed on average, especially early in the forecast, while across the central and eastern United States the SREF is underdispersed later in the forecast. There are relatively large biases in cyclone central pressure within each SREF subgroup. As a result, the best-member diagrams reveal that the SREF members are not equally accurate for the cyclone central pressure and displacement. Two cases are presented to illustrate examples of SREF developing large errors early in the forecast for cyclones over the eastern United States.
    Chen Y., J. Sun, J. Xu, S. N. Yagn, Z. P. Zong, T. Chen, C. Fang and J. Sheng, 2012: Analysis and thinking on the extremes of the 21 July 2012 torrential rain in Beijing Part I: Observation and thinking. Meteorological Monthly, 38, 1255- 1266. (in Chinese)10.1007/s11783-011-0280-zb27e501d18446cf9ae6f0a6d98ee1418http%3A%2F%2Fen.cnki.com.cn%2FArticle_en%2FCJFDTotal-QXXX201210014.htmhttp://en.cnki.com.cn/Article_en/CJFDTotal-QXXX201210014.htmPrecipitation characteristics,environment conditions,generation and development of the mesoscale convective system that brought about the extreme torrential rain in Beijing on 21 July 2012 were analyzed comprehensively in this paper by using various conventional and unconventional data.The results showed that the extreme torrential rain had the characteristics of long duration,great rainfall and wide coverage area and its process consisted of warm area precipitation and frontal precipitation.The warm area rainfall started earlier,the severe precipitation center was scattered and lasted long while the frontal rainfall process contained several severe rainfall centers with high precipitation efficiency,lasting a short time. Environment conditions of the mesoscale convective system that triggered this extreme severe rainfall were analyzed.The results showed that interactions of high-level divergence,the wind shear and convergence with the vortex in the lower troposphere and the surface wind convergence line provided favorable environment to the severe extreme rain.The warm humid airs from the tropical and sub-tropical zones converged over the torrential rain region,continuous and sufficient water vapor manifested as high atmospheric column of precipitable water and strong low-level water vapor convergence and other extreme vapor conditions for the torrential rain.In addition,the intense precipitation was triggered by the vortex wind shear,wind disturbance on low-level jet,surface wind convergence line and the effect of terrain under the condition of the plentiful water vapour and maintained.With the cold front moved eastward,heavy frontal rainfall was brought by the development and evolution of convective system made by the cold air and the suitable vertical wind shear. Generation and development processes of the mesoscale convective system were also studied.The findings suggested that stratiform cloud precipitation and dispersed convective precipitation occurred firstly in the precipitation process.The warm and steady stratiform cloud precipitation changed to be highly organized convectional precipitation as the cold dry air invaded.Many small-scale and mesoscale convective clusters developed into mesoscale convective complex(MCC),leading to the extreme severe precipitation. Since all the directions of the echo long axis,terrain and echo movement were parallel,train effect was obviously seen in the radar echo imegery during this precipitation process.Meanwhile,the radar echo had the characteristics of backward propagation and low centroid which was similar to tropical heavy rainfalls.Finally, a series of scientific problems were proposed according to the integrated analysis on the observation data of this rare torrential rain event,such as the causes for the extreme torrential rain and the extreme rich water vapor,mechanisms for the warm area torrential rain in the north of China,the mechanism for the train effect and backward propagation,mechanisms for the organization and maintenance of the convective cells,the simulation and analysis ability of the numerical models to extreme torrential rains and so on.
    Clark A. J., W. A. Gallus Jr., M. Xue, and F. Y. Kong, 2009: A comparison of precipitation forecast skill between small convection-allowing and large convection-parameterizing ensembles. Wea.Forecasting, 24, 1121- 1140.10.1175/2009WAF2222222.1c5adbca89ca36611e8f7cc5152f39e67http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2009EGUGA..11.3170Chttp://adsabs.harvard.edu/abs/2009EGUGA..11.3170CComputation of various precipitation skill metrics for probabilistic and deterministic forecasts reveals that ENS4 generally provides more accurate precipitation forecasts than ENS20, with the differences tending to be statistically significant for precipitation thresholds above 0.25 in. at forecast lead times of 9–21 h (0600–1800 UTC) for all accumulation intervals analyzed (1, 3, and 6 h). In addition, an analysis of rank histograms and statistical consistency reveals that faster error growth in ENS4 eventually leads to more reliable precipitation forecasts in ENS4 than in ENS20. For the cases examined, these results imply that the skill gained by increasing to CAR outweighs the skill lost by decreasing the ensemble size. Thus, when computational capabilities become available, it will be highly desirable to increase the ensemble resolution from PCR to CAR, even if the size of the ensemble has to be reduced.
    Clark, A. J., Coauthors, 2012: An overview of the 2010 hazardous weather testbed experimental forecast program spring experiment. Bull. Amer. Meteor. Soc., 93, 55- 74.10.1175/BAMS-D-11-00040.1f4766b545da35c541f04ba5bdb81a5fbhttp%3A%2F%2Fonlinelibrary.wiley.com%2Fresolve%2Freference%2FADS%3Fid%3D2012BAMS...93...55Chttp://onlinelibrary.wiley.com/resolve/reference/ADS?id=2012BAMS...93...55CNot Available
    Doswell C. A., H. E. Brooks, and R. A. Maddox, 1996: Flash flood forecasting: An ingredients-based methodology. Wea.Forecasting, 11, 560- 581.10.1175/1520-0434(1996)0112.0.CO;220332d7be6c4759f89c2f16d8c96a428http%3A%2F%2Fci.nii.ac.jp%2Fnaid%2F80009407343http://ci.nii.ac.jp/naid/80009407343An approach to forecasting the potential for flash flood-producing storms is developed, using the notion of basic ingredients. Heavy precipitation is the result of sustained high rainfall rates. In turn, high rainfall rates involve the rapid ascent of air containing substantial water vapor and also depend on the precipitation efficiency. The duration of an event is associated with its speed of movement and the size of the system causing the event along the direction of system movement. This leads naturally to a consideration of the meteorological processes by which these basic ingredients are brought together. A description of those processes and of the types of heavy precipitation-producing storms suggests some of the variety of ways in which heavy precipitation occurs. Since the right mixture of these ingredients can be found in a wide variety of synoptic and mesoscale situations, it is necessary to know which of the ingredients is critical in any given case. By knowing which of the ingredients is most important in any given case, forecasters can concentrate on recognition of the developing heavy precipitation potential as meteorological processes operate. This also helps with the recognition of heavy rain events as they occur, a challenging problem if the potential for such events has not been anticipated. Three brief case examples are presented to illustrate the procedure as it might be applied in operations. The cases are geographically diverse and even illustrate how a nonconvective heavy precipitation event fits within this methodology. The concept of ingredients-based forecasting is discussed as it might apply to a broader spectrum of forecast events than just flash flood forecasting.
    Du J., S. L. Mullen, and F. Sanders, 1997: Short-range ensemble forecasting of quantitative precipitation. Mon. Wea. Rev., 125, 2427- 2459.10.1175/1520-0493(1997)1252.0.CO;29995d13df2e0b9394aa74d0ec7176fa1http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F1997MWRv..125.2427Dhttp://adsabs.harvard.edu/abs/1997MWRv..125.2427DThe impact of initial condition uncertainty (ICU) on quantitative precipitation forecasts (QPFs) is examined for a case of explosive cyclogenesis that occurred over the contiguous United States and produced widespread, substantial rainfall. The Pennsylvania State University--National Center for Atmospheric Research (NCAR) Mesoscale Model Version 4 (MM4), a limited-area model, is run at 80-km horizontal resolution and 15 layers to produce a 25-member, 36-h forecast ensemble. Lateral boundary conditions for MM4 are provided by ensemble forecasts from a global spectral model, the NCAR Community Climate Model Version 1 (CCM1). The initial perturbations of the ensemble members possess a magnitude and spatial decomposition that closely match estimates of global analysis error, but they are not dynamically conditioned. Results for the 80-km ensemble forecast are compared to forecasts from the then operational Nested Grid Model (NGM), a single 40-km/15- layer MM4 forecast, a single 80-km/29-layer MM4 forecast, and a second 25-member MM4 ensemble based on a different cumulus parameterization and slightly different unperturbed initial conditions. Large sensitivity to ICU marks ensemble QPF. Extrema in 6-h accumulations at individual grid points vary by as much as 3.000. Ensemble averaging reduces the root-mean-square error (rmse) for QPF. Nearly 90% of the improvement is obtainable using ensemble sizes as small as 8--10. Ensemble averaging can adversely affect the bias and equitable threat scores, however, because of its smoothing nature. Probabilistic forecasts for five mutually exclusive, completely exhaustive categories are found to be skillful relative to a climatological forecast. Ensemble sizes of approximately 10 can account for 90% of improvement in categorical forecasts relative to that for the average of individual forecasts. The improvements due to short-range ensemble forecasting (SREF) techniques exceed any due to doubling the resolution...
    Ebert E. E., 2001: Ability of a poor man's ensemble to predict the probability and distribution of precipitation. Mon. Wea. Rev., 129, 2461- 2480.10.1175/1520-0493(2001)1292.0.CO;2bc2e658d8fdb360dba586c4456940d92http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2001MWRv..129.2461Ehttp://adsabs.harvard.edu/abs/2001MWRv..129.2461EA poor man's ensemble is a set of independent numerical weather prediction (NWP) model forecasts from several operational centers. Because it samples uncertainties in both the initial conditions and model formulation through the variation of input data, analysis, and forecast methodologies of its component members, it is less prone to systematic biases and errors that cause underdispersive behavior in single-model ensemble prediction systems (EPSs). It is also essentially cost-free. Its main disadvantage is its relatively small size. This paper investigates the ability of a poor man's ensemble to provide forecasts of the probability and distribution of rainfall in the short range 1-2 days. The poor man's ensemble described here consists of 24- and 48-h daily quantitative precipitation forecasts (QPFs) from seven operational NWP models. The ensemble forecasts were verified for a 28-month period over Australia using gridded daily rain gauge analyses. Forecasts of the probability of precipitation (POP) were skillful for rain rates up to 50 mm day
    Fan S. Y., M. Chen, J. Q. Zhong, and Z. F. Zheng, 2009: Performance tests and evaluations of Beijing local high-resolution rapid update cycle system. Torrential Rain and Disasters, 28, 119- 125. (in Chinese)2635dbe4de0b059be33cf98789c0c653http%3A%2F%2Fen.cnki.com.cn%2FArticle_en%2FCJFDTOTAL-HBQX200902005.htmhttp://en.cnki.com.cn/Article_en/CJFDTOTAL-HBQX200902005.htmTo understand clearly the operational performance of Beijing high-resolution Rapid Update Cycle (BJ-RUC) system, the operational forecasts during the flood season of 2007 were verified and evaluated by objective verification method and a precipitation incident was analyzed. Some encouraged conclusions were given as follows: BJ -RUC system had stable forecast performance and could provide good forecast reference; from the verification scores to sounding and surface observations, 3 km- resolution forecast were better than that of 9 km-resolution; the verification scores of precipitation forecast showed that 3 km- resolution one could forecast the precipitation period, location and intensity better, especially for the high intensity precipitation. But precipitation forecast capability of RUC system was still not perfect and the first 6 h forecast was still worse than the latter period forecast. We need more work on it.
    Fels S. B., M. D. Schwarzkopf, 1975: The simplified exchange approximation: A new method for radiative transfer calculations. J. Atmos. Sci., 32, 1476- 1488.10.1175/1520-0469(1975)0322.0.CO;2ad4ee6abeee67b7b4d09a5c6e911ac53http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F1975jats...32.1475fhttp://adsabs.harvard.edu/abs/1975jats...32.1475fA new scheme for the efficient calculation of longwave radiative heating rates is proposed. Its speed and accuracy make it attractive for use in large atmospheric circulation models. Tests using a variety of soundings indicate that for both clear sky and cloudy cases the new approximation is substantially more accurate than either the emissivity or the cool-to-space approximations alone. Deviations from exact calculations are generally under 0.05 K/day. Errors in the calculated flux at the surface are also shown to be small especially with the inclusion of a 'heat from ground' term in the approximation. Some alternate schemes using similar approximations are presented, and their utility is discussed.
    Hamill T. M., 2001: Interpretation of rank histograms for verifying ensemble forecasts. Mon. Wea. Rev., 129, 550.10.1175/1520-0493(2001)129<0550:IORHFV>2.0.CO;28cc805e9481fc0014621eed8f2afc298http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2001mwrv..129..550hhttp://adsabs.harvard.edu/abs/2001mwrv..129..550hRank histograms are a tool for evaluating ensemble forecasts. They are useful for determining the reliability of ensemble forecasts and for diagnosing errors in its mean and spread. Rank histograms are generated by repeatedly tallying the rank of the verification (usually an observation) relative to values from an ensemble sorted from lowest to highest. However, an uncritical use of the rank histogram can lead to misinterpretations of the qualities of that ensemble. For example, a flat rank histogram, usually taken as a sign of reliability, can still be generated from unreliable ensembles, Similarly, a U-shaped rank histogram commonly understood as indicating a lack of variability in the ensemble can also be a sign of conditional bias It is also shown that flat rank histograms can be generated for some model variables if the variance of the ensemble is correctly specified. yet if covariances between model grid points are improperly specified, rank histograms for combinations of model variables may not be flat. Further, if imperfect observations are used for verification the observational errors should be accounted for, otherwise the shape of the rank histogram may mislead the user about the characteristics of the ensemble. If a statistical hypothesis test is to be performed to determine whether the differences from uniformity of rank are statistically significant, then samples used to populate the rank histogram must be located far enough away from each other in time and space to be considered independent.
    Hamill T. M., R. Hagedorn, and J. S. Whitaker, 2008: Probabilistic forecast calibration using ECMWF and GFS ensemble reforecasts. Part II: Precipitation. Mon. Wea. Rev., 136, 2620- 2632.017772092db591d8758a7535aac330f6http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2008MWRv..136.2620Hhttp://xueshu.baidu.com/s?wd=paperuri%3A%28ef5d3f2030150ba6f0ab02495ec9acd3%29&filter=sc_long_sign&tn=SE_xueshusource_2kduw22v&sc_vurl=http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2008MWRv..136.2620H&ie=utf-8&sc_us=2543329239417091382
    Houtekamer P. L., L. Lefaivre, J. Derome, H. Ritchie, and H. L. Mitchell, 1996: A system simulation approach to ensemble prediction. Mon. Wea. Rev., 124, 1225- 1242.67216a5acd633df2dbeb3ccc3f7403d0http%3A%2F%2Fadsabs.harvard.edu%2Fcgi-bin%2Fnph-data_query%3Fbibcode%3D1996MWRv..124.1225H%26db_key%3DPHY%26link_type%3DABSTRACT%26high%3D03696http://xueshu.baidu.com/s?wd=paperuri%3A%28b57031152962600fd4b0f7b5b511d283%29&filter=sc_long_sign&tn=SE_xueshusource_2kduw22v&sc_vurl=http%3A%2F%2Fadsabs.harvard.edu%2Fcgi-bin%2Fnph-data_query%3Fbibcode%3D1996MWRv..124.1225H%26db_key%3DPHY%26link_type%3DABSTRACT%26high%3D03696&ie=utf-8&sc_us=2070151558498488457
    Iyer E. R., A. J. Clark, M. Xue, and F. Y. Kong, 2016: A comparison of 36-60-h precipitation forecasts from convection-allowing and convection-parameterizing ensembles. Wea.Forecasting, 31, 647- 661.10.1175/WAF-D-15-0143.11f0a8f34584d916b7cecf3ac50312e80http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2016WtFor..31..647Ihttp://adsabs.harvard.edu/abs/2016WtFor..31..647INot Available Not Available
    Jiang X. M., H. L. Yuan, M. Xue, X. Chen, and X. G. Tan, 2014: Analysis of a torrential rainfall event over Beijing on 21-22 July 2012 based on high resolution model analyses and forecasts. Acta Meteorologica Sinica, 72, 207- 219.
    Kong F. Y., K. K. Droegemeier, and N. L. Hickmon, 2007: Multiresolution ensemble forecasts of an observed tornadic thunderstorm system. Part II: Storm-scale experiments. Mon. Wea. Rev., 135, 759- 782.10.1175/MWR3323.1ecdcd63fc98e894cfb7f63e91b871c55http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2007MWRv..135..759Khttp://adsabs.harvard.edu/abs/2007MWRv..135..759KIn Part I, the authors used a full physics, nonhydrostatic numerical model with horizontal grid spacing of 24 km and nested grids of 6- and 3-km spacing to generate the ensemble forecasts of an observed tornadic thunderstorm complex. The principal goal was to quantify the value added by fine grid spacing, as well as the assimilation of Doppler radar data, in both probabilistic and deterministic frameworks. The present paper focuses exclusively on 3-km horizontal grid spacing ensembles and the associated impacts on the forecast quality of temporal forecast sequencing, the construction of initial perturbations, and data assimilation. As in Part I, the authors employ a modified form of the scaled lagged average forecasting technique and use Stage IV accumulated precipitation estimates for verification. The ensemble mean and spread of accumulated precipitation are found to be similar in structure, mimicking their behavior in global models. Both the assimilation of Doppler radar data and the use of shorter (1-2 versus 3-5 h) forecast lead times improve ensemble precipitation forecasts. However, even at longer lead times and in certain situations without assimilated radar data, the ensembles are able to capture storm-scale features when the associated control forecast in a deterministic framework fails to do so. This indicates the potential value added by ensembles although this single case is not sufficient for drawing general conclusions. The creation of initial perturbations using forecasts of the same grid spacing shows no significant improvement over simply extracting perturbations from forecasts made at coarser spacing and interpolating them to finer grids. However, forecast quality is somewhat dependent upon perturbation amplitude, with smaller scaling values leading to significant underdispersion. Traditional forecast skill scores show somewhat contradictory results for accumulated precipitation, with the equitable threat score most consistent with qualitative performance.
    Kong, F. Y., Coauthors, 2008: Real-time storm-scale ensemble forecast 2008 spring experiment. Proc. 24th Conf. Severe Local Storms, Savannah, GA, Amer. Meteor. Soc.,Paper 12. 13.29ca41ab-1fcc-4167-953c-4f1801d52ed85a65e304a398ab5731664342191d608dhttp%3A%2F%2Fwww.researchgate.net%2Fpublication%2F228463479_7.3_REAL-TIME_STORM-SCALE_ENSEMBLE_FORECAST_EXPERIMENTrefpaperuri:(58403331ee72c4d973db7f660c0f6b28)http://www.researchgate.net/publication/228463479_7.3_REAL-TIME_STORM-SCALE_ENSEMBLE_FORECAST_EXPERIMENTEnsemble forecasting has proven valuable in medium-range global model forecasts (6-10 days)(Kalnay 2003). Short-range ensemble forecasting (SREF,~ 40 km resolution, 1-3 days) with limited-area models has been operational for some time (Du and Tracton 2001;
    Kong, F. Y., Coauthors, 2014: An overview of CAPS storm-scale ensemble forecast for the 2014 NOAA HWT spring forecasting experiment. Proc. 27th Conf. Severe Local Storms,Madison WI, Amer. Meteor. Soc., Paper 43.5ff9193426ddbcfebd068aecc8297036http%3A%2F%2Fams.confex.com%2Fams%2F27SLS%2Fwebprogram%2FPaper255958.htmlhttp://ams.confex.com/ams/27SLS/webprogram/Paper255958.htmlA major push in 2014 is the experimental EnKF based forecasting that includes a one hour EnKF cycling at 15 min interval from 2300 UTC to 0000 UTC following a 5-h 40-member ensemble forecast initiated from 1800 UTC, over the same CONUS domain as other regular SSEF. In order to provide an ensemble background for EnKF, a separate 4-km ensemble of 5-h forecasts, starting at 1800 UTC, with 40 WRF-ARW members is produced over the CONUS domain. This ensemble is configured with initial perturbations and mixed physics options to provide input for EnKF analysis. Each member uses WSM6 microphysics with different parameter settings. No radar data is analyzed for this set of runs. All members also include random perturbations with recursive filtering of ~20 km horizontal correlations scales, with relatively small perturbations (0.5K for potential temperature and 5% for relative humidity). EnKF analysis (cycling), with radar data and other conventional data, is performed from 23 to 00 UTC every 15 min over the CONUS domain, using as background the 40-member ensemble. A 12- member ensemble forecast (24h) follows using the 00 UTC EnKF analyses. In addition, two deterministic forecasts, one from the ensemble mean analysis and another from 3DVAR analysis, are also produced. Results show that the probability matched mean 3-hourly accumulated precipitation from the EnKF-based analysis outscores the HWT (3DVAR-based) for higher thresholds.
    Li J., J. Du, and Y. Liu, 2015: A comparison of initial condition-, multi-physics- and stochastic physics-based ensembles in predicting Beijing "7.21" excessive storm rain event. Acta Meteorologica Sinica, 73, 50- 71. (in Chinese)b8d22e71f80eeb678b586133aee3ce8ahttp%3A%2F%2Fen.cnki.com.cn%2FArticle_en%2FCJFDTotal-QXXB201501004.htmhttp://en.cnki.com.cn/Article_en/CJFDTotal-QXXB201501004.htmUsing the historical Beijing"7.21"extreme precipitation event as an example,the six ensemble schemes(the initial condition(IC),multi-physics(MULTI),3 stochastic physics as well as a combination of IC and stochastic-physics(COM)were compared in the following three aspects of heavy precipitation forecasts:the performance of ensemble means,ensemble ranges and probabilities with respect to the control forecast,characteristics of ensemble spreads,and spread-forecast error relations.The results show that:(1)In spite of the existence of large systematic forecast error,all the ensembles,especially the IC,MULTI and COM,are able to noticeably improve torrential-rain prediction over the control forecast in both intensity and location,and provide more complete information including the forecast uncertainty for users to make a better decision.(2)Forecast spreads of the three stochastic physics ensembles are similar to each other,generally much less than those of the IC and MULTI ensembles and mainly concentrated near the center of the severe rainfall area.As a result,the ensemble spread is enhanced in the vicinity of the severe precipitation area but little is changed elsewhere after stochastic physics is employed in addition to IC perturbations,which leads to virtually no improvement to the overall spread over a larger domain comparing to that of the IC ensemble.By decomposing spread over the spatial scales,it further shows that the forecast diversity contributed by the stochastic physics is mainly in the smaller-scale(320 km,it could reach to a similar level to those by the IC and MULTI ensembles at scale 160 km),while the contribution from the IC and MULTI perturbations to spread could extend another400-500 km reaching to larger scales such as 1000 km;at smaller scales( 500 km),multi-physics technique could produce larger precipitation spread than IC perturbation does,another advantage of multi-physics approach over other approaches is that it could partially reduce forecast bias.And,(3)the spread spectrum is similar to the forecast error spectrum over spatial scales for all the ensembles,i.e.,decreasing with the increase of the spatial scale.However,the magnitude of the spread spectrum is smaller than that of the forecast error spectrum(indicating under-dispersion),this departure increases rapidly with the decrease of spatial scale and becomes large over the small scales.
    Liu Y. Z., X. S. Shen, and X. L. Li, 2013: Research on the singular vector perturbation of the GRAPES global model based on the total energy norm. Acta Meteorologica Sinica, 71, 517- 526. (in Chinese)10.1061/9780784413128.022d170a8a3699e347579c30e8690d4b32ahttp%3A%2F%2Fen.cnki.com.cn%2FArticle_en%2FCJFDTOTAL-QXXB201303014.htmhttp://en.cnki.com.cn/Article_en/CJFDTOTAL-QXXB201303014.htmAimed to develop the GRAPES global ensemble prediction system with the singular vectors(SVs) as the initial perturbations, the calculation of SVs with total energy norm as weight matrix is performed by using of the tangent linear and adjoint model of the GRAPES global dry dynamical model.The formulation of total energy norm based on the forecast variables of the GRAPES model is derived,and the correctness of SV calculation is verified by the singular vector verification method and linear approximation.The structure of SVs over the middle-high latitude area and its linear evolution are analyzed through the 10-days experiment,and it is found that the GRAPES SVs based on total energy norm can grow fast in the optimal time interval, and they can reflects the characteristics of the atmosphere baroclinic instability over in the middle-high latitude area.The vertical distribution of the total energy of GRAPES SVs and its components(kinetic energy and potential energy) also show that the GRAPES SVs could represent the growing characteristics of the baroclinic instability at the different levels in the troposphere in the middle-high latitudes.
    Molteni F., R. Buizza, T. N. Palmer, and T. Petroliagis, 1996: The ECMWF ensemble prediction system: methodology and validation. Quart. J. Roy. Meteor. Soc., 122, 73- 119.10.1256/smsqj.5290406e58ba7360249148550625e940bddcehttp%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1002%2Fqj.49712252905%2Ffullhttp://onlinelibrary.wiley.com/doi/10.1002/qj.49712252905/fullNot Available
    Murphy A. H., 1973: A new vector partition of the probability score. J. Appl. Meteor., 12, 595- 600.98fc6802469e1f61885d283bfbac4f0chttp%3A%2F%2Fwww.emeraldinsight.com%2Fservlet%2Flinkout%3Fsuffix%3Db28%26dbid%3D16%26doi%3D10.1108%252FEJM-05-2012-0288%26key%3D10.1175%252F1520-0450%281973%290122.0.CO%253B2http://xueshu.baidu.com/s?wd=paperuri%3A%28e7073650750802c7824e422f3b610106%29&filter=sc_long_sign&tn=SE_xueshusource_2kduw22v&sc_vurl=http%3A%2F%2Fwww.emeraldinsight.com%2Fservlet%2Flinkout%3Fsuffix%3Db28%26dbid%3D16%26doi%3D10.1108%252FEJM-05-2012-0288%26key%3D10.1175%252F1520-0450%281973%290122.0.CO%253B2&ie=utf-8&sc_us=10008998936017562578
    Palmer T. N., F. Molteni, R. Mureau, R. Buizza, P. Chapelet, and J. Tribbia, 1993: Ensemble Prediction.ECMWF Seminar Proceedings on Validation of Models over Europe, Vol. I., Reading, U.K., ECMWF, 21- 66.
    Pellerin G., L. Lefaivre, P. Houtekamer, and C. Girard, 2003: Increasing the horizontal resolution of ensemble forecasts at CMC. Nonlinear Processes in Geophysics, 10, 463- 468.10.5194/npg-10-463-2003c7dce1e0fc51a04857721ff8ae595f55http%3A%2F%2Fonlinelibrary.wiley.com%2Fresolve%2Freference%2FXREF%3Fid%3D10.5194%2Fnpg-10-463-2003http://onlinelibrary.wiley.com/resolve/reference/XREF?id=10.5194/npg-10-463-2003Ensemble forecasts are run operationally since February 1998 at the Canadian Meteorological Centre, with outputs up to ten days. The ensemble size was increased from eight to sixteen members in August 1999. The method of producing the perturbed analyses consists of running independent assimilation cycles that use perturbed sets of observations and are driven by eight different models, mainly different in their physical parameterizations. Perturbed analyses are doubled by taking opposite pairs. A multi-model approach is then used to obtain the forecasts. The ensemble output has been used to generate several products. In view of increasing computing facilities, the ensemble prediction system horizontal resolution was increased to TL149 in June 2001. Heights at 500 hPa and mean sea-level pressure maps are regularly used. Charts of precipitation with the probability of precipitation being above various thresholds are also produced at each run. The probabilistic forecast of the 24-h accumulated precipitation has shown skill as demonstrated by the relative operating characteristic (ROC). Verifications of the ensemble forecasts will be presented.
    Pleim J. E., 2006: A simple, efficient solution of flux-profile relationships in the atmospheric surface layer. J. Appl. Meteor. Climatol., 45, 341- 347.10.1175/JAM2339.175617a011f42c93016b91735e84f0ba6http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2006japmc..45..341phttp://adsabs.harvard.edu/abs/2006japmc..45..341pThis note describes a simple scheme for analytical estimation of the surface-layer similarity functions from state variables. What distinguishes this note from the many previous papers on this topic is that this method is specifically targeted for numerical models in which simplicity and economic execution are critical. In addition, it has been in use in a mesoscale meteorological model for several years. For stable conditions, a very simple scheme is presented that compares well to the iterative solution. The stable scheme includes a very stable regime in which the slope of the stability functions is reduced to permit significant fluxes to occur, which is particularly important for numerical models in which decoupling from the surface can be an important problem. For unstable conditions, simple schemes generalized for varying ratios of aerodynamic roughness to thermal roughness (z0/z0h) are less satisfactory. Therefore, a simple scheme has been empirically derived for a fixed z0/z0h ratio, which represents quasi-laminar sublayer resistance.
    Pleim J. E., 2007: A combined local and nonlocal closure model for the atmospheric boundary layer. Part I: Model description and testing. J. Appl. Meteor. Climatol., 46, 1383- 1395.10.1175/JAM2539.1261fb6488e3c56b3eafc99bd69a9bfb0http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2007JApMC..46.1383Phttp://adsabs.harvard.edu/abs/2007JApMC..46.1383PThe modeling of the atmospheric boundary layer during convective conditions has long been a major source of uncertainty in the numerical modeling of meteorological conditions and air quality. Much of the difficulty stems from the large range of turbulent scales that are effective in the convective boundary layer (CBL). Both small-scale turbulence that is subgrid in most mesoscale grid models and large-scale turbulence extending to the depth of the CBL are important for the vertical transport of atmospheric properties and chemical species. Eddy diffusion schemes assume that all of the turbulence is subgrid and therefore cannot realistically simulate convective conditions. Simple nonlocal closure PBL models, such as the Blackadar convective model that has been a mainstay PBL option in the fifth-generation Pennsylvania State University-National Center for Atmospheric Research Mesoscale Model (MM5) for many years and the original asymmetric convective model (ACM), also an option in MM5, represent large-scale transport driven by convective plumes but neglect small-scale, subgrid turbulent mixing. A new version of the ACM (ACM2) has been developed that includes the nonlocal scheme of the ongma ACM combined with an eddy diffusion scheme. Thus, the convective is able to represent both the supergrid- and subgrid-scale components of turbulent transport in the convective boundary layer. Testing the ACM2 in one-dimensional form and comparing it with large-eddy simulations and field data from the 1999 Cooperative Atmosphere-Surface Exchange Study demonstrates that the new scheme accurately simulates PBL heights, profiles of fluxes and mean quantities. and surface-level values. The ACM2 performs equally well for both meteorological parameters (e.g., potential temperature, moisture variables, and winds) and trace chemical concentrations, which is an advantage over eddy diffusion models that include a nonlocal term in the form of a gradient adjustment.
    Pleim J. E., A. J. Xiu, 1995: Development and testing of a surface flux and planetary boundary layer model for application in mesoscale models. J. Appl. Meteor., 34, 16- 32.10.1175/1520-0450-34.1.1638e03ff2fd2393a77329bfd861cc6716http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F1995JApMe..34...16Phttp://adsabs.harvard.edu/abs/1995JApMe..34...16P(First International Satellite Land Surface Climatology Project Field Experiment) to demonstrate the model's ability to realistically simulate surface fluxes as well as PBL development. This new surface-PBL model is currently being incorporated into the MM4-MM5 system.
    Ren Z., J. Chen, and H. Tian, 2011: Research on T213 ensemble prediction system stochastic physics perturbation. Meteorological Monthly, 37, 1049- 1059. (in Chinese)10.1007/s00376-010-1000-59762df2b456dfffd59011b685737bd6dhttp%3A%2F%2Fen.cnki.com.cn%2FArticle_en%2FCJFDTotal-QXXX201109003.htmhttp://en.cnki.com.cn/Article_en/CJFDTotal-QXXX201109003.htmThe CMA T213 global ensemble prediction system using BGM initial perturbation scheme has not considered model perturbation thus lags behind the ensemble forecasting system of international advanced technical centers.This paper referring to the ECMWF model perturbation method,designs a T213 global ensemble prediction system stochastic physics perturbation method,and conducts ensemble prediction tests in 20鈥31 July 2008.The results show that,the T213 global ensemble prediction system is very sensitive to stochastic physics perturbation.This is because after physics perturbed,predictor variables change significantly,and the changes expand rapidly with the integration time of growth.In the horizontal direction,the middle and high latitudes are more sensitive than the equatorial regions.In the vertical direction, the variables characterizing the large-scale movements,such as geopotential height,temperature, and wind speed are very sensitive from low to upper levels,and the most sensitive is at 300 hPa,in the middle and high latitudes of north and south hemispheres;while vertical velocity,divergence and other physical variables in the equatorial region are also very sensitive.After the multiple initial condition ensemble added to the stochastic physics perturbations,the spread and RMSE of ensemble mean are improved slightly in the late term of integration,while the improvement of the precipitation forecast is significant, which indicates the prospect of operation to the stochastic physics perturbation is good.The next step will be more test assessments,and the operations of the stochastic physics perturbation are as early as possible to shorten the distance between China and the international advanced technology in the ensemble.
    Ritchie H., C. Beaudoin, 1994: Approximations and sensitivity experiments with a baroclinic semi-Lagrangian spectral model. Mon. Wea. Rev., 122, 2391- 2399.50c4b7128b0145a8f5aec93619cf1b0bhttp%3A%2F%2Fwww.emeraldinsight.com%2Fservlet%2Flinkout%3Fsuffix%3Db10%26dbid%3D16%26doi%3D10.1108%252F09615530110385102%26key%3D10.1175%252F1520-0493%281994%291222.0.CO%253B2http://xueshu.baidu.com/s?wd=paperuri%3A%286d56ded0a689afa6af072bed930d5a63%29&filter=sc_long_sign&tn=SE_xueshusource_2kduw22v&sc_vurl=http%3A%2F%2Fwww.emeraldinsight.com%2Fservlet%2Flinkout%3Fsuffix%3Db10%26dbid%3D16%26doi%3D10.1108%252F09615530110385102%26key%3D10.1175%252F1520-0493%281994%291222.0.CO%253B2&ie=utf-8&sc_us=6247169023562005626
    Schwartz, C. S., Coauthors, 2009: Next-day convection-allowing WRF model guidance: A second look at 2-km versus 4-km grid spacing. Mon. Wea. Rev., 137, 3351- 3372.10.1175/2009MWR2924.1c04fc29309b06c34ac99170c26a9d1cahttp%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2009MWRv..137.3351Shttp://adsabs.harvard.edu/abs/2009MWRv..137.3351SDuring the 2007 NOAA Hazardous Weather Testbed (HWT) Spring Experiment, the Center for Analysis and Prediction of Storms (CAPS) at the University of Oklahoma produced convection-allowing forecasts from a single deterministic 2-km model and a 10-member 4-km-resolution ensemble. In this study, the 2-km deterministic output was compared with forecasts from the 4-km ensemble control member. Other than the difference in horizontal resolution, the two sets of forecasts featured identical Advanced Research Weather Research and Forecasting model (ARW-WRF) configurations, including vertical resolution, forecast domain, initial and lateral boundary conditions, and physical parameterizations. Therefore, forecast disparities were attributed solely to differences in horizontal grid spacing. This study is a follow-up to similar work that was based on results from the 2005 Spring Experiment. Unlike the 2005 experiment, however, model configurations were more rigorously controlled in the present study, providing a more robust dataset and a cleaner isolation of the dependence on horizontal resolution. Additionally, in this study, the 2- and 4-km outputs were compared with 12-km forecasts from the North American Mesoscale (NAM) model. Model forecasts were analyzed using objective verification of mean hourly precipitation and visual comparison of individual events, primarily during the 21- to 33-h forecast period to examine the utility of the models as next-day guidance. On average, both the 2- and 4-km model forecasts showed substantial improvement over the 12-km NAM. However, although the 2-km forecasts produced more-detailed structures on the smallest resolvable scales, the patterns of convective initiation, evolution, and organization were remarkably similar to the 4-km output. Moreover, on average, metrics such as equitable threat score, frequency bias, and fractions skill score revealed no statistical improvement of the 2-km forecasts compared to the 4-km forecasts. These results, based on the 2007 dataset, corroborate previous findings, suggesting that decreasing horizontal grid spacing from 4 to 2 km provides little added value as next-day guidance for severe convective storm and heavy rain forecasters in the United States.
    Skamarock W. C., J. B. Klemp, J. Dudhia, D. O. Gill, D. M. Barker, W. Wang, and J. G. Powers, 2005: A Description of the Advanced Research WRF Version 2. NCAR Technical Note NCAR/TN-468+STR,88 pp.10.5065/D68S4MVH6e1e8ed5238484bf7e6021f9957054e6http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F244955031_A_Description_of_the_Advanced_Research_WRF_Version_2http://www.researchgate.net/publication/244955031_A_Description_of_the_Advanced_Research_WRF_Version_2The development of the Weather Research and Forecasting (WRF) modeling system is a multiagency effort intended to provide a next-generation mesoscale forecast model and data assimilation system that will advance both the understanding and prediction of mesoscale weather and accelerate the transfer of research advances into operations. The model is being developed as a collaborative effort ort among the NCAR Mesoscale and Microscale Meteorology (MMM) Division, the National Oceanic and Atmospheric Administration's (NOAA) National Centers for Environmental Prediction (NCEP) and Forecast System Laboratory (FSL), the Department of Defense's Air Force Weather Agency (AFWA) and Naval Research Laboratory (NRL), the Center for Analysis and Prediction of Storms (CAPS) at the University of Oklahoma, and the Federal Aviation Administration (FAA), along with the participation of a number of university scientists. The WRF model is designed to be a flexible, state-of-the-art, portable code that is an efficient in a massively parallel computing environment. A modular single-source code is maintained that can be configured for both research and operations. It offers numerous physics options, thus tapping into the experience of the broad modeling community. Advanced data assimilation systems are being developed and tested in tandem with the model. WRF is maintained and supported as a community model to facilitate wide use, particularly for research and teaching, in the university community. It is suitable for use in a broad spectrum of applications across scales ranging from meters to thousands of kilometers. Such applications include research and operational numerical weather prediction (NWP), data assimilation and parameterized-physics research, downscaling climate simulations, driving air quality models, atmosphere-ocean coupling, and idealized simulations (e.g boundary-layer eddies, convection, baroclinic waves).*WEATHER FORECASTING
    Stephenson D. B., C. A. S. Coelho, and I. T. Jolliffe, 2008: Two extra components in the brier score decomposition. Wea.Forecasting, 23, 752- 757.10.1175/2007WAF2006116.189e970acdb5dba5c19ac64d274147bc1http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2008WtFor..23..752Shttp://adsabs.harvard.edu/abs/2008WtFor..23..752SAbstract The Brier score is widely used for the verification of probability forecasts. It also forms the basis of other frequently used probability scores such as the rank probability score. By conditioning (stratifying) on the issued forecast probabilities, the Brier score can be decomposed into the sum of three components: uncertainty, reliability, and resolution. This Brier score decomposition can provide useful information to the forecast provider about how the forecasts can be improved. Rather than stratify on all values of issued probability, it is common practice to calculate the Brier score components by first partitioning the issued probabilities into a small set of bins. This note shows that for such a procedure, an additional two within-bin components are needed in addition to the three traditional components of the Brier score. The two new components can be combined with the resolution component to make a generalized resolution component that is less sensitive to choice of bin width than is the traditional resolution component. The difference between the generalized resolution term and the conventional resolution term also quantifies how forecast skill is degraded when issuing categorized probabilities to users. The ideas are illustrated using an example of multimodel ensemble seasonal forecasts of equatorial sea surface temperatures.
    Tao W.-K., J. Simpson, and M. McCumber, 1989: An ice-water saturation adjustment. Mon. Wea. Rev., 117, 231- 235.10.1175/1520-0493(1989)1172.0.CO;2e7bc62ff11834766546f8475eda86e05http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F1989MWRv..117..231Thttp://adsabs.harvard.edu/abs/1989MWRv..117..231TNot Available
    Tao Z. Y., Y. G. Zheng, 2013: Forecasting issues of the extreme heavy rain in Beijing on 21 July 2012. Torrential Rain and Disasters, 32, 193- 201. (in Chinese)aec6a1ce8453060a3b33859d41fe4348http%3A%2F%2Fen.cnki.com.cn%2FArticle_en%2FCJFDTOTAL-HBQX201303001.htmhttp://en.cnki.com.cn/Article_en/CJFDTOTAL-HBQX201303001.htmBased on the upper air sounding,NWP model forecasts,satellite,radar and surface weather data,the extreme heavy rain event in Beijing during July 21-22,2012 is analyzed and its forecasting process is summarized.The results are as follows.(1) The"7-21"heavy rain was produced by a typical mesoscale convective complex(MCC) that occurred in the left front of a low-level jet in the warm and moist southerly,on the right of the entrance jet stream,with deep warm advection and clockwise wind direction with altitude,which are very favorable for the occurrence of MCC.(2) A variety of numerical models have predicted the heavy rain with skill in various degrees,and the lead time is up to 3-4 d.The short-term forecasting with lead time 1-2 d can be more accurately predict the occurrence area and intensity of heavy rainfall.But the prediction of heavy rain's start and end times is noticeably delayed about 6 h.(3) Satellite and radar monitoring indicates that 12 h before the arrival of a large-scale rain band,the initial warm convection has occurred ahead of the front.According to the movement,enhancement and organization of radar echoes,it can be extrapolated that the first stage of the heavy rain will affect Beijing at around noon,and thus timely corrections can be made to the numerical predictions.Using comprehensive analysis of radar echo and the surface weather(such as wind,dew point) fields,we can roughly determine the convective instability condition and the uplift condition favorable for the initial occurrence of convection,which can provide the basis for echo extrapolation forecasts.
    Theis S. E., A. Hense, and U. Damrath, 2005: Probabilistic precipitation forecasts from a deterministic model: a pragmatic approach. Meteorological Applications, 12, 257- 268.10.1017/S1350482705001763a5ee1b38c1f65c187084c4fb3e180731http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1017%2FS1350482705001763%2Fcitedbyhttp://onlinelibrary.wiley.com/doi/10.1017/S1350482705001763/citedbyPrecipitation forecasts from mesoscale numerical weather prediction (NWP) models often contain features that are not deterministically predictable and require a probabilistic forecast approach. However, some forecast providers still refrain from a probabilistic approach in operational forecasting because existing methods are associated with substantial costs. Therefore, a pragmatic, low-budget postprocessing procedure is presented that derives probabilistic precipitation forecasts from deterministic NWP model output. The methodology looks in the spatio-temporal neighbourhood of a point to get a set of forecasts and uses this set to derive a probabilistic forecast at the central point of the neighbourhood. For the sake of low implementation costs and low running costs, the procedure does without ensemble simulations, historical error statistics on the operational interaction of a forecaster. The procedure is applied to the output of the mesoscale model LM, the regional part of the operational modelling system of the German Weather Service (DWD). The probabilistic postprocessed forecast (PPPF) outperforms the deterministic direct model output in terms of forecast consistency, forecast quality and forecast value.
    Toth Z., E. Kalnay, 1993: Ensemble forecasting at NMC: The generation of perturbations. Bull. Amer. Meteor. Soc., 74, 2317- 2330.10.1175/1520-0477(1993)0742.0.CO;2cbe63b780b83c7603b5d4d22f0d6e97fhttp%3A%2F%2Fci.nii.ac.jp%2Fnaid%2F10013126202%2Fhttp://ci.nii.ac.jp/naid/10013126202/Abstract On 7 December 1992, The National Meteorological Center (NMC) started operational ensemble forecasting. The ensemble forecast configuration implemented provides 14 independent forecasts every day verifying on days 1–10. In this paper we briefly review existing methods for creating perturbations for ensemble forecasting. We point out that a regular analysis cycle is a “breeding ground” for fast-growing modes. Based on this observation, we devise a simple and inexpensive method to generate growing modes of the atmosphere. The new method, “breeding of growing modes”, or BGM, consists of one additional, perturbed short-range forecast, introduced on top of the regular analysis in an analysis cycle. The difference between the control and perturbed six-hour (first guess) forecast is scaled back to the size of the initial perturbation and then reintroduced onto the new atmospheric analysis. Thus, the perturbation evolves along with the time dependent analysis fields, ensuring that after a few days of cycl...
    Toth Z., E. Kalnay, 1997: Ensemble forecasting at NCEP and the breeding method. Mon. Wea. Rev., 125, 3297- 3319.10.1175/1520-0493(1997)1252.0.CO;2d77eecdcf56bec983b860925dfc56c2dhttp%3A%2F%2Fci.nii.ac.jp%2Fnaid%2F10013126203%2Fhttp://ci.nii.ac.jp/naid/10013126203/Purpose: The aim of this study was to evaluate the effects of curing mode and viscosity on the biaxial flexural strength (FS) and modulus (FM) of dual resin cements. Methods: Eight experimental groups were created (n=12) according to the dual-cured resin cements (Nexus 2/Kerr Corp. and Variolink II/IvoclarVivadent), curing modes (dual or self-cure), and viscosities (low and high). Forty-eight cement discs of each product (0.5 mm thick by 6.0 mm diameter) were fabricated. Half specimens were light--activated for 40 seconds and half were allowed to self-cure. After 10 days, the biaxial flexure test was performed using a universal testing machine (1.27 mm/min, Instron 5844). Data were statistically analyzed by three-way ANOVA and Tukey's test (5%). Results: Light-activation increased FS and FM of resin cements at both viscosities in comparison with self-curing mode. The high viscosity version of light-activated resin cements exhibited higher FS than low viscosity versions. The viscosity of resin and the type of cement did not influence the FM. Light-activation of dual-polymerizing resin cements provided higher FS and FM for both resin cements and viscosities. Conclusion: The use of different resin cements with different viscosities may change the biomechanical behavior of these luting materials.
    Wen Y. R., L. Xue, Y. Li, N. Wei, and A. M. Lü, 2015: Interaction between typhoon Vicente (1208) and the Western Pacific subtropical high during the Beijing extreme rainfall of 21 July 2012. J. Meteor. Res., 29, 293- 304.10.1007/s13351-015-4097-88f4ca49ff9913833ea2fc1e5dc09b99bhttp%3A%2F%2Flink.springer.com%2F10.1007%2Fs13351-015-4097-8http://d.wanfangdata.com.cn/Periodical/qxxb-e201502011最重的降雨在最近六十年在 2012 年 7 月 21 日掉在北京里,在 18 h 以内到达 460 公里的一个记录。这降雨是与台风 Vicente (1208 ) 有关的一个典型遥远的降水事件。观察分析显示 Vicente 由向北方把水蒸汽搬运到北京区域影响了远重降雨。这潮湿运输被在 Vicente 和与形成联系的西方的和平的副热带的高度(WPSH ) 之间的相互作用主要驾驶一低级在东南潮湿隧道。一套数字敏感实验与不同紧张的规定台风被执行在这个暴风雨过程上调查在 Vicente 和 WPSH 和它的效果之间的相互作用。结果显示与不同紧张的台风交往的 WPSH 可以施加对发展的影响的变化的度一在东南潮湿隧道,在北京区域上处于雨率和地点导致一个变化。面对提高的台风,明确地, WPSH 显示出显著退却到东方,它是有利的为一向北方扩展在东南潮湿隧道,为暴风雨的从而增加的潮湿供应。如果台风被削弱或搬迁, WPSH 在一个带的模式向西趋于到段,妨碍向北方潮湿隧道的扩展。因此,降雨区域可以被期望膨胀或收缩,随相应增加或在雨中的减少分别地与加强或变弱的台风在北京区域上评价。
    Whitaker J. S., T. M. Hamill, X. Wei, Y. C. Song, and Z. Toth, 2008: Ensemble data assimilation with the NCEP global forecast system. Mon. Wea. Rev., 136, 463- 482.10.1175/2007MWR2018.1466890a4d7a93ef24c533c99b1072782http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2008mwrv..136..463whttp://adsabs.harvard.edu/abs/2008mwrv..136..463wAbstract Real-data experiments with an ensemble data assimilation system using the NCEP Global Forecast System model were performed and compared with the NCEP Global Data Assimilation System (GDAS). All observations in the operational data stream were assimilated for the period 1 January鈥10 February 2004, except satellite radiances. Because of computational resource limitations, the comparison was done at lower resolution (triangular truncation at wavenumber 62 with 28 levels) than the GDAS real-time NCEP operational runs (triangular truncation at wavenumber 254 with 64 levels). The ensemble data assimilation system outperformed the reduced-resolution version of the NCEP three-dimensional variational data assimilation system (3DVAR), with the biggest improvement in data-sparse regions. Ensemble data assimilation analyses yielded a 24-h improvement in forecast skill in the Southern Hemisphere extratropics relative to the NCEP 3DVAR system (the 48-h forecast from the ensemble data assimilation system was as accurate as the 24-h forecast from the 3DVAR system). Improvements in the data-rich Northern Hemisphere, while still statistically significant, were more modest. It remains to be seen whether the improvements seen in the Southern Hemisphere will be retained when satellite radiances are assimilated. Three different parameterizations of background errors unaccounted for in the data assimilation system (including model error) were tested. Adding scaled random differences between adjacent 6-hourly analyses from the NCEP鈥揘CAR reanalysis to each ensemble member ( additive inflation ) performed slightly better than the other two methods ( multiplicative inflation and relaxation-to-prior ).
    Xue M., F. Y. Kong, K. W. Thomas, J. D. Gao, Y. H. Wang, K. A. Brewster and K. K. Droegemeier, 2013: Prediction of convective storms at convection-resolving 1 km resolution over continental United States with radar data assimilation: An example case of 26 May 2008 and precipitation forecasts from spring 2009. Advances in Meteorology, 2013,Article ID 259052, doi: 10.1155/2013/259052.10.1155/2013/2590527ea5792d591e9fd1ec62f06c5d90cd3dhttp%3A%2F%2Fwww.oalib.com%2Fpaper%2F3066596http://www.oalib.com/paper/3066596For the first time ever, convection-resolving forecasts at 1?km grid spacing were produced in realtime in spring 2009 by the Center for Analysis and Prediction of Storms (CAPS) at the University of Oklahoma. The forecasts assimilated both radial velocity and reflectivity data from all operational WSR-88D radars within a domain covering most of the continental United States. In preparation for the realtime forecasts, 1?km forecast tests were carried out using a case from spring 2008 and the forecasts with and without assimilating radar data are compared with corresponding 4?km forecasts produced in realtime. Significant positive impact of radar data assimilation is found to last at least 24 hours. The 1?km grid produced a more accurate forecast of organized convection, especially in structure and intensity details. It successfully predicted an isolated severe-weather-producing storm nearly 24 hours into the forecast, which all ten members of the 4?km real time ensemble forecasts failed to predict. This case, together with all available forecasts from 2009 CAPS realtime forecasts, provides evidence of the value of both convection-resolving 1?km grid and radar data assimilation for severe weather prediction for up to 24 hours. 1. Introduction Accurate prediction of convective-scale hazardous weather continues to be a major challenge. Efforts to explicitly predict convective storms using numerical models dated back to Lilly [1] and began with the establishment in 1989 of an NSF Science and Technology Center, the Center for Analysis and Prediction of Storms at the University of Oklahoma (CAPS). Over the past two decades, steady progress has been made, aided by steady increases in available computing power. Still, the resolutions of the current-generation operational numerical weather prediction (NWP) models remain too low to explicitly resolve convection, limiting the accuracy of quantitative precipitation forecasts. For over a decade, the research community has been producing experimental real time forecasts at 3-4?km convection-allowing resolutions (e.g., [2–4]). Roberts and Lean [5] documented that convection forecasts of up to 6 hours are more skillful when run on a 1?km grid than on a 12?km grid, and more so than on a 4?km grid. On the other hand, Kain et al. [2] found no appreciable improvement with 2?km forecasts compared to 4?km forecasts beyond 12 hours. In the spring seasons of 2007 and 2008, CAPS conducted more systematic real-time experiments. Daily forecasts of 30?h or more were produced for 10-member 4?km ensembles and 2?km deterministic
    Xue, M., Coauthors, 2007: CAPS realtime storm-scale ensemble and high-resolution forecasts as part of the NOAA Hazardous Weather Testbed 2007 spring experiment. 22nd Conf. Weather Analysis and Forecasting and 18th Conf. Numerical Weather Prediction, Park City, UT, Amer. Meteor. Soc.,CDROM 3B. 1.714e7127-c0a2-4557-84f9-94ee1c6cd49ed69e03e7275470df85ab290fe2c04c56http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F237430741refpaperuri:(a454c0c05b198a00f6d0b6f9091b7261)http://www.researchgate.net/publication/2374307411Accurate prediction of convective-scale hazardous weather continues to be a major challenge, because of the small spatial and temporal scales of the associated weather systems, and the inherent nonlinearity of their
    Xue, M., Coauthors, 2011: CAPS realtime storm scale ensemble and high resolution forecasts for the NOAA hazardous weather testbed 2010 spring experiment. 24th Conf. Weather Forecasting and 20th Conf. Numerical Weather Prediction, Seattle, WA, Amer. Meteor. Soc.,Paper 9A. 2.7d3b3e38-77c6-477a-a6f5-00037f87f244b464c3c4f8e11ce9125addbe6ca63915http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F267369509_STORM-SCALE_ENSEMBLE_FORECASTING_FOR_THE_NOAA_HAZARDOUS_WEATHER_TESTBEDrefpaperuri:(395803b695289cba0d8af897fb5d3e8b)http://www.researchgate.net/publication/267369509_STORM-SCALE_ENSEMBLE_FORECASTING_FOR_THE_NOAA_HAZARDOUS_WEATHER_TESTBEDAccurate prediction of convective-scale hazardous weather events continues to be a major challenge because of the small spatial and temporal scales of the associated weather systems, and the inherent nonlinearity of their dynamics and physics. Uncertainties in
    Yu H. Z., Z. Y. Meng, 2016: Key synoptic-scale features influencing the high-impact heavy rainfall in Beijing, China, on 21 July 2012. Tellus A, 68, 31045.10.3402/tellusa.v68.310452ed6bd2307385c64adf1ff5de29bef45http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F303978386_Key_synoptic-scale_features_influencing_the_high-impact_heavy_rainfall_in_Beijing_China_on_21_July_2012%3Fev%3Dauth_pubhttp://www.researchgate.net/publication/303978386_Key_synoptic-scale_features_influencing_the_high-impact_heavy_rainfall_in_Beijing_China_on_21_July_2012?ev=auth_pubThis work examined quantitatively the key synoptic features influencing the high-impact heavy rainfall event in Beijing, China, on 21 July 2012 using both correlation analysis based on global ensemble forecasts (from TIGGE) and a method previously used for observation targeting. The global models were able to capture the domain-averaged rainfall of >100 mm but underestimated rainfall beyond 200 mm with an apparent time lag. In this particular case, the ensemble forecasts of the National Centres for Environmental Prediction (NCEP) had apparently better performance than those of the European Centre for Medium-Range Weather Forecasts (ECMWF) and the China Meteorological Administration (CMA), likely because of their high accuracies in capturing the key synoptic features influencing the rainfall event. Linear correlation coefficients between the 24-h domain-averaged precipitation in Beijing and various variables during the rainfall were calculated based on the grand ensemble forecasts from ECMWF, NCEP and CMA. The results showed that the distribution of the precipitation was associated with the strength and the location of a mid-level trough in the westerly flow and the associated low-level low. The dominant system was the low-level low, and a stronger low with a location closer to the Beijing area was associated with heavier rainfall, likely caused by stronger low-level lifting. These relationships can be clearly seen by comparing a good member with a bad member of the grand ensemble. The importance of the trough in the westerly flow and the low-level low was further confirmed by the sensitive area identified through sensitivity analyses with conditional nonlinear optimal perturbation method. Keywords: heavy rainfall, sensitivity analysis, TIGGE, CNOP (Published: 14 June 2016) Citation: Tellus A 2016, 68, 31045, http://dx.doi.org/10.3402/tellusa.v68.31045
    Yu X. D., 2012: Investigation of Beijing extreme flooding event on 21 July 2012. Meteorological Monthly, 38, 1313- 1329. (in Chinese)10.1029/JZ068i003p00667d44af233626bdf131965179150c7e32ehttp%3A%2F%2Fen.cnki.com.cn%2FArticle_en%2FCJFDTotal-QXXX201211003.htmhttp://en.cnki.com.cn/Article_en/CJFDTotal-QXXX201211003.htmOn 21 July 2012 Beijing experienced the most severe rainfall event since August 1963.The extreme rainfall induced flooding killed over 100 people and the property damage is over 11.64 billion RMB yuan(about 2 billion U.S.dollars).Based on the routine upper-level and surface observation,the satellite and radar data,a detailed analysis and investigation have been done on this event.The major results are as following:(1) The upper-level trough accompanied by surface cold front moving toward east blocked by the subtropical high provides favorable synoptic scale conditions for torrential rain event in Beijing area. (2) The presence of a tropical cyclone over the South China Sea near the coastline led to the establishment and enhancement of southeastward and southward low-level jet toward Beijing area,providing plenty of water vapor to Beijing area.(3) The development of Hetao vertex on 20 July led to the formation of a meso -a scale MCS over that area,and its high value of vertical helicity made it well organized and longlived. This MCS moved with the upper-level trough eastward,and was over Huabei region(including Beijing area) on the second day(21 July),producing extreme rainfall over Beijing area.(4) The south-east low-level jet constantly triggered new convective cells on the east slope of the Taihang Mountain Range,then moved to northeast direction into Beijing area,leading to the torrential rainfall and severe flooding there.
    Zhang D. L., Y. H. Lin, P. Zhao, X. D. Yu, S. Q. Wang, H. W. Kang, and Y. H. Ding, 2013: The Beijing extreme rainfall of 21 July 2012: "Right results" but for wrong reasons. Geophys. Res. Lett., 40, 1426- 1431.10.1002/grl.50304711d173cd0d50344bc8a35d259d7986bhttp%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1002%2Fgrl.50304%2Fcitedbyhttp://onlinelibrary.wiley.com/doi/10.1002/grl.50304/citedbyThe heaviest rainfall in 6 decades fell in Beijing on 21 July 2012 with a record-breaking amount of 460 mm in 18 h and hourly rainfall rates exceeding 85 mm. This extreme rainfall event appeared to be reasonably well predicted by current operational models, albeit with notable timing and location errors. However, our analysis reveals that the model-predicted rainfall results mainly from topographical lifting and the passage of a cold front, whereas the observed rainfall was mostly generated by convective cells that were triggered by local topography and then propagated along a quasi-stationary linear convective system into Beijing. In particular, most of the extreme rainfall occurred in the warm sector far ahead of the cold front. Evidence from a cloud-permitting simulation indicates the importance of using high-resolution cloud-permitting models to reproduce the above-mentioned rainfall-production mechanisms in order to more accurately predict the timing, distribution, and intensity of such an extreme event.
    Zhang H. B., J. Chen, X. F. Zhi, Y. L. Li, and Y. Sun, 2014: Study on the application of GRAPES regional ensemble prediction system. Meteorological Monthly, 40, 1076- 1087. (in Chinese)4b72468cb96d317bbb93073d40ec83e1http%3A%2F%2Fen.cnki.com.cn%2FArticle_en%2FCJFDTOTAL-QXXX201409005.htmhttp://en.cnki.com.cn/Article_en/CJFDTOTAL-QXXX201409005.htmIn order to develop GRAPES(Global and Regional Assimilation and Prediction System) regional ensemble prediction system(GRAPES-REPS),using ensemble transform Kalman filter(ETKF) as initial perturbation scheme,coupled with multiple physical processes combination as model perturbation,an regional ensemble prediction system is constructed based on the GRAPES_MesoV3.3.2.4 model in this paper.Besides,40 d consecutive ensemble forecast experiments are conducted,the structure and evolution characters of ETKF generated initial perturbation are emphatically investigated,various methods are utilized to evaluate GRAPES-REPS performance and its precipitation forecast capabilities,and a severe rainfall case is also analyzed to further illustrate the precipitation forecast performance of the EPS.The experimental results indicate that GRAPES-REPS can generate promising initial perturbations characterized by flow dependerit structure and good correspondence to the distribution of observation sites,and meanwhile the perturbations are orthogonal.Total energy of perturbations can keep appropriate growth in all forecast lead times.Ensemble forecast verification shows that ensemble forecast outperforms control forecast,the ensemble spread can maintain reasonable growth in 72 h forecast lead time.Comparisons between operational WRF-REPS and GRAPES-REPS on precipitation forecast are carried out,and the results show that GRAPES-REPS outperforms WRF-REPS.Case study indicates that ensemble forecast can provide much better heavy rainfall forecast than control forecast.
    Zhong L. Z., R. Mu, D. L. Zhang, P. Zhao, Z. Q. Zhang, and N. Wang, 2015: An observational analysis of warm-sector rainfall characteristics associated with the 21 July 2012 Beijing extreme rainfall event. J. Geophys. Res., 120, 3274- 3291.10.1002/2014JD022686a4685478468cd17c6aae26ceb017ab4ahttp%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1002%2F2014jd022686%2Ffullhttp://onlinelibrary.wiley.com/doi/10.1002/2014jd022686/fullAn observational analysis of the multiscale processes leading to the extreme rainfall event in Beijing on 21 July 2012 is performed using rain gauge records, Doppler radar, and satellite products, radiosondes, and atmospheric analysis. This rainstorm process included two heavy rainfall stages in the early afternoon [1300–1400 Beijing Standard time (BST) (0500–0600 UTC)] and the evening (1600–1900 BST), respectively. The first stage exhibited warm‐sector rainfall characteristics as it occurred under low‐level warm and moist southeasterly flows ahead of a synoptic‐scale vortex and a cold front. When the southeasterly flows turned northeastward along a southwest‐northeast oriented mountain range in western Beijing, mesoscale convergence centers formed on the windward side of the mountain range in the early afternoon, initiating moist convection. Radar echo showed a northeastward propagation as these flows extended northward. Despite the shallowness of moist convection in the warm sector, atmospheric liquid water content showed the rapid accumulation, and a large amount of supercooled water and/or ice particles was possibly accumulated above the melting level. These appeared to contribute to the occurrence of the largest rainfall rate. During the second stage, as the synoptic‐scale vortex moved across Beijing, with southeastward intrusion of its northwesterly flows, the vortex‐associated lifting caused the generation of strong updrafts aloft and formed deep convection. This facilitated the further accumulation of supercooled water and/or ice particles above the melting level. Radar echo propagated southeastward. Liquid water showed a decrease in the lower troposphere, and there were strong downdrafts due to evaporation of liquid water particles, which resulted in the relatively weak hourly rainfall rates.
    Zhu K. F., Y. Yang, and M. Xue, 2015: Percentile-based neighborhood precipitation verification and its application to a landfalling tropical storm case with radar data assimilation. Adv. Atmos. Sci.,32, 1449-1459, doi: 10.1007/s00376-015-5023-9.10.1007/s00376-015-5023-977fb22322e36c4aacb3b4829b5970042http%3A%2F%2Fwww.cqvip.com%2FQK%2F84334X%2F201511%2F665874469.htmlhttp://d.wanfangdata.com.cn/Periodical/dqkxjz-e201511001The traditional threat score based on fixed thresholds for precipitation verification is sensitive to intensity forecast bias. In this study, the neighborhood precipitation threat score is modified by defining the thresholds in terms of the percentiles of overall precipitation instead of fixed threshold values. The impact of intensity forecast bias on the calculated threat score is reduced. The method is tested with the forecasts of a tropical storm that re-intensified after making landfall and caused heavy flooding. The forecasts are produced with and without radar data assimilation. The forecast with assimilation of both radial velocity and reflectivity produce precipitation patterns that better match observations but have large positive intensity bias. When using fixed thresholds, the neighborhood threat scores fail to yield high scores for forecasts that have good pattern match with observations, due to large intensity bias. In contrast, the percentile-based neighborhood method yields the highest score for the forecast with the best pattern match and the smallest position error. The percentile-based method also yields scores that are more consistent with object-based verifications, which are less sensitive to intensity bias, demonstrating the potential value of percentile-based verification.
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Manuscript received: 29 July 2016
Manuscript revised: 22 August 2016
Manuscript accepted: 02 September 2016
通讯作者: 陈斌, bchen63@163.com
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    沈阳化工大学材料科学与工程学院 沈阳 110142

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Evaluation of WRF-based Convection-Permitting Multi-Physics Ensemble Forecasts over China for an Extreme Rainfall Event on 21 July 2012 in Beijing

  • 1. Key Laboratory of Mesoscale Severe Weather/Ministry of Education and School of Atmospheric Sciences, Nanjing University, Nanjing 210093, China
  • 2. Center for Analysis and Prediction of Storms and School of Meteorology, University of Oklahoma, Norman OK 73072, USA

Abstract: On 21 July 2012, an extreme rainfall event that recorded a maximum rainfall amount over 24 hours of 460 mm, occurred in Beijing, China. Most operational models failed to predict such an extreme amount. In this study, a convective-permitting ensemble forecast system (CEFS), at 4-km grid spacing, covering the entire mainland of China, is applied to this extreme rainfall case. CEFS consists of 22 members and uses multiple physics parameterizations. For the event, the predicted maximum is 415 mm d-1 in the probability-matched ensemble mean. The predicted high-probability heavy rain region is located in southwest Beijing, as was observed. Ensemble-based verification scores are then investigated. For a small verification domain covering Beijing and its surrounding areas, the precipitation rank histogram of CEFS is much flatter than that of a reference global ensemble. CEFS has a lower (higher) Brier score and a higher resolution than the global ensemble for precipitation, indicating more reliable probabilistic forecasting by CEFS. Additionally, forecasts of different ensemble members are compared and discussed. Most of the extreme rainfall comes from convection in the warm sector east of an approaching cold front. A few members of CEFS successfully reproduce such precipitation, and orographic lift of highly moist low-level flows with a significantly southeasterly component is suggested to have played important roles in producing the initial convection. Comparisons between good and bad forecast members indicate a strong sensitivity of the extreme rainfall to the mesoscale environmental conditions, and, to less of an extent, the model physics.

1. Introduction
  • On 21 July 2012, an extreme rainfall event occurred in Beijing, China. The average 24-h accumulated rainfall across rain gauge stations in the city of Beijing was 190 mm, which is the highest in the recorded history of Beijing since 1951 (Chen et al., 2012). The maximum rainfall among all meteorological and hydrologic sites was 460 mm, at a hydrological station in Hebei Township of Fangshan District in the southwest suburb of Beijing. The maximum recorded hourly rainfall was 100.3 mm (Chen et al., 2012). Such excessive rainfall caused major urban flooding in Beijing; 79 people died and millions of people were affected. Direct financial loses were estimated to be about 2 billion U.S dollars. As a reference, the mean annual rainfall amount from 1949 to 2010 is only 600 mm in the Beijing region. For this event, the Beijing Meteorological Bureau issued an orange-color rainstorm warning, which is the second from highest level. The actual, extreme amount of rainfall was, however, not expected by forecasters. The city population was not well prepared.

    Operational NWP models predicted a general rain pattern and high probability of heavy rain in the Beijing area for that day. However, the rain intensity predicted varied greatly by forecast model, forecast lead time, as well as model resolution (Tao and Zheng, 2013; Jiang et al., 2014). The rain intensity was significantly under-predicted by global models from various NWP centers (maximum <200 mm d-1) (Zhang et al., 2013; Yu and Meng, 2016). The convection-permitting Rapid Update Cycle of Beijing Meteorological Bureau (BJ-RUC) (Fan et al., 2009), which has a horizontal resolution of 3 km, did predict 24-h rain intensity of more than 300 mm (Jiang et al., 2014). However, the predicted maximum was outside Beijing. Moreover, BJ-RUC is a deterministic forecasting system. The prediction by a single model of an extreme rainfall amount that was about half of the average annual rainfall amount did not give decision-makers much confidence without additional probabilistic guidance.

    Ensemble forecasting is well-established as an effective way of providing uncertainty estimates of weather forecasting, and for forecasting the probability of certain events occurring. Most operational NWP centers, including the ECMWF (Palmer et al., 1993; Molteni et al., 1996; Buizza et al., 2003), NCEP (Toth and Kalnay, 1993; Du et al., 1997; Toth and Kalnay, 1997), UKMO (Bowler et al., 2008) and MSC (Ritchie and Beaudoin, 1994; Houtekamer et al., 1996; Pellerin et al., 2003), have developed, and are running, global and regional operational ensemble prediction systems (EPSs), although the resolutions are generally too coarse to resolve convection. The China Meteorological Administration (CMA) has also developed global (Ren et al., 2011; Liu et al., 2013) and regional (Zhang et al., 2014) EPSs. Both have 14 members. For this case, the ensemble forecasts of the NCEP global EPS are more accurate than those of the ECMWF and CMA global EPSs for the peak rainfall stage (Yu and Meng, 2016).

    Studies have shown that ensemble-derived quantitative precipitation forecasts are often more skillful than a single forecast (e.g., Buizza and Hollingsworth, 2002; Theis et al., 2005; Charles and Colle, 2009). (Li et al., 2015) conducted a series of mesoscale (horizontal grid spacing of 9 km) ensemble forecasts for this extreme event. Among all the ensemble forecasts, the experiment that used initial perturbation, multiple physics and multiple initial and boundary conditions was the best and was much better than the deterministic forecast in the prediction of rain intensity as well as the location. However, the rain intensities were still underestimated. For precipitation forecasting, it is desirable to use model resolutions that allow direct prediction of convection instead of relying on convective parameterization, which is a great source of uncertainty. Since 2007, the Center for Analysis and Prediction of Storms (CAPS) at the University of Oklahoma has been producing experimental storm-scale ensemble forecasts (SSEFs) over the continental United States at 3- to 4-km grid spacing (considered convection-permitting resolutions) (Xue et al., 2007), for evaluation at NOAA's Hazardous Weather Testbed (Clark et al., 2012). The SSEF system has been increasing in sophistication over the years (Xue et al., 2011; Kong et al., 2014), and has included multiple dynamic cores, and multiple physics options in combination with perturbed initial and boundary conditions. Such convection-permitting ensembles have been shown to have better abilities in providing severe weather forecasting guidance over the continental United States (e.g., Clark et al., 2009; Schwartz et al., 2009; Xue et al., 2013; Iyer et al., 2016).

    To the best of the authors' knowledge, ensemble forecasting at convection-permitting resolutions has so far not been applied to the forecasting of heavy or extreme precipitation in China——at least not over continental China as a whole. For extreme but low-probability rainfall events in Beijing, it is even more important to assess the ability of state-of-the-art non-hydrostatic models running at a convection-permitting resolution in predicting such events, and to assess the uncertainty of such predictions.

    Even though BJ-RUC (which was based on the WRF model) predicted extreme rainfall amounts associated with this event, it did not place the maximum precipitation in Beijing. (Zhang et al., 2013) pointed out that most operational model forecasts failed to capture the precipitation associated with the convection in the warm sector ahead of an approaching cold front, which was a key contributor to the total extreme precipitation (Tao and Zheng, 2013). The prediction of warm-sector convection remains a major challenge (Zhong et al., 2015). An extended goal of our study is to understand the physical processes responsible for producing the historical rainfall amount at a particular location and time, by analyzing output from an ensemble of forecasts that have different initial and boundary conditions, and different model physics. As the first step towards this goal, we document and evaluate in this paper the performance of a 4-km ensemble of forecasts, in both the probabilistic and deterministic sense. For brevity, the goal of obtaining a physical understanding is left for a separate paper.

    Specifically, we apply a similar strategy used by CAPS's SSEF to the Beijing case. The WRF model (Skamarock et al., 2005) is used with its various physics parameterizations. As part of an ongoing effort to establish and evaluate a convection-permitting/convection-resolving ensemble forecasting system (CEFS) suitable for warm-season precipitation forecasting over China, we use a model domain that covers the whole of continental China (see Fig. 1). The initial and boundary conditions are interpolated from those of an experimental version of the NCEP Global Ensemble Forecasting System (GEnFS), whose initial conditions were produced by the ensemble Kalman filter data assimilation method. To help understand the sensitivity of the prediction of extreme rainfall to model physics (relative to the initial and boundary conditions), we perform a second set of ensemble forecasts in which only the physics parameterization schemes differ. To the best of our knowledge, this high-impact event has not been studied from the perspective of a convection-permitting ensemble.

    The rest of this paper is organized as follows: Section 2 provides an overview of the 21 July 2012 extreme rainfall event in Beijing. In section 3, the model configurations and ensemble experiments are described, and the probability matching (PM) ensemble mean algorithm and verification metrics are described. The ensemble forecasts are examined in section 4, together with an investigation of the warm-sector rain. The sensitivity of precipitation to the model physics is examined in section 5. Finally, we conclude in section 6 with a summary and suggestions for future work.

    Figure 1.  The 500-hPa geopotential height (blue lines; units: gpm), 925-hPa winds (barbs; units: m s$^-1$) and 925-hPa horizontal water vapor flux [color-shaded; untis: g (hPa cm s)$^-1$] at (a) 0600 UTC 21 July and (b) 1200 UTC 21 July 2012 (black box indicates the location of Beijing, typhoon Vicente is located in the south China sea), and the 24-h accumulated rainfall (units: mm) from 0000 UTC 21 July to 0000 UTC 22 July 2012 from (c) rain gauge observations and (d) NCEP GFS forecast. The magenta box in (c) indicates one of the two verification areas, with the other being mainland China. For the observation plot, we use distance-weighted interpolation to obtain the gridded data, and the maximum after interpolation is 382 mm. The corresponding maximum value of GFS is 195 mm.

2. Overview of the extreme rainfall event
  • The extreme rainfall event in Beijing on 21 July 2012 started at 0200 UTC [1000 LST (local standard time)] and ended at 1800 UTC (0200 LST). According to Li et al. (2013), there were two main stages of the heavy rainfall. The first stage was from 0200 UTC to 1200 UTC, which is defined as the period of warm-sector rainfall. During this period, a cold front was slowly moving into the Beijing region from the west. Southeasterly air with very high moisture content converged onto the windward slope of the Taihang Mountain Range west of Beijing. A large number of storm cells formed in the southwest part of Beijing near the mountain range and moved northeastwards, and organized into a quasi-linear mesoscale convective system (MCS) that was more or less parallel to the southwest-northeast-oriented mountain range (Yu, 2012). New cells continuously formed at the southern end of the linear MCS, and moved northeast along the MCS, exhibiting back-building and echo-training processes (Doswell et al., 1996; Yu, 2012). As intense storm cells continually moved over similar areas, extreme precipitation was produced. The maximum hourly rainfall was observed around 1300 UTC (2100 LST), when it exceeded 100 mm h-1 (Chen et al., 2012). After 1200 UTC, the cold front moved into Beijing, producing a smaller amount of rainfall, and the precipitation system moved out of the Beijing region by 1800 UTC.

    Figure 1 shows the synoptic weather charts at 0600 UTC and 1200 UTC, together with the observed (Fig. 1c) and predicted (NCEP GFS; Fig. 1d) 24-h accumulated precipitation between 0000 UTC 21 July and 0000 UTC 22 July 2012. At 0600 UTC, Beijing (enclosed in the black box in Fig. 1a) was located ahead of a major 500 hPa trough, which moved eastward to be closer to Beijing over the next 6 h (Fig. 1b). From the wind and horizontal water vapor flux fields, one can see two channels of water vapor transportation into the Beijing region: a stronger channel associated with the south-southeasterly flows, which brought in water vapor from the East China Sea; and a weaker one associated with the south-southwesterly flows, which transported water vapor through Southwest China from the Bay of Bengal. At this time, the northwestern Pacific subtropical high was located off the East China Sea, while the intensifying tropical storm Vicente was present over the South China Sea, moving towards the coastal province of Guangdong. The northward moisture transportation towards northern China is believed to have been strengthened by Vicente (Yu, 2012; Wen et al., 2015), which created a strong channel of flow between its cyclonic circulation and the anticyclonic circulation of the subtropical high (Fig. 1b).

    The observed 24-h accumulated rainfall (Fig. 1c) shows a band of precipitation stretching from Southwest China through Northeast China, with amounts mostly below 50 mm. This band was associated with a cold front. Over most of the Beijing area, total rainfall exceeded 100 m, and the heaviest rain was east of the frontal rainband. Based on this objectively analyzed precipitation map (with smoothing effect), the maximum 24-h rainfall was 382 mm. For this event, the NCEP GFS successfully predicted heavy rain over the Beijing area (Fig. 1d), although the maximum amount of 190 mm was not enough to indicate the occurrence of a historical extreme rainfall event.

3. Experimental design and verification methods
  • The design of our 4-km CEFS follows the CAPS SSEF (Kong et al., 2007; Xue et al., 2007), but with some differences. Only a single WRF model (WRF3.3.1) (Skamarock et al., 2005) dynamic core is used. The model domain, with 4-km horizontal grid spacing, has 1500× 1100 grid points in the horizontal direction and 50 levels in the vertical direction (see Fig. 1)—— large enough to cover all of mainland China. The ensemble forecasts start from 0000 UTC 21 July and end at 0000 UTC 22 July 2012, covering both precipitation stages.

    The initial and lateral boundary conditions are from the experimental global ensemble forecast system (referred to as GEnFS to distinguish it from the operational NCEP GEFS) produced by NOAA's ESRL, which is initialized by a global ensemble Kalman filter (EnKF) data assimilation system, as described in (Whitaker et al., 2008). This EnKF system has been shown to produce better initial conditions and subsequent tropical cyclone forecasts (Whitaker et al., 2008) than the operational GSI three-dimensional variational system used at that time.

    GEnFS has 80 members and the data assimilation cycle is 6 h. The gridded horizontal resolution of the data is 0.5°, and there are 27 vertical levels. The first 20 members are used here to initialize our ensemble forecasts and to provide the boundary conditions (BCs). Additionally, we add two special members: one uses the operational NCEP GFS analysis and forecast as the initial conditions (ICs) and BCs, and the other uses the GEnFS ensemble mean analysis and corresponding forecast for the same purposes. We name these two members g0 and e0, respectively (Table 1). Therefore, there are a total of 22 members in the 4-km CEFS.

    As with the CAPS SSEF systems, multiple physics suites are used within CEFS to form a multi-physics ensemble. Table 1 lists the physics configurations of the CEFS members. We follow two simple principles to select the physics configurations: one is to make the physics configurations as diverse as possible, and the other is to have two or more differences in the physics configurations between any two members. In the early stages of our study, we also performed additional experiments based on other physics configurations. The Pleim-Xiu land surface model (LSM) became the preferred choice as it produced better precipitation forecasts for this case in general. We do not claim that the current configurations of the ensemble members are optimal——an important purpose of this study is to evaluate the performance of the ensemble system as configured.

    As will be seen later, the skills of the individual members of the CEFS ensemble in predicting the extreme rainfall in the Beijing region are quite different. To gain some understanding on the relative sensitivity of the extreme rainfall forecast to the ICs and BCs versus the model physics, we perform another set of forecasts that share the same ICs and BCs as the most skillful member (mem13) of CEFS, but differ in the physics packages used. Table 2 lists these experiments, which can be divided into three groups using: different microphysics (MPY), different planetary boundary layer (PBL), and different radiation (RAD) parameterizations. To distinguish from the CEFS members, we refer to these experiments with the prefix "mpy". So, experiments mpy01 through mpy09 differ in the microphysics schemes used; mpy10 through mpy13 differ in the surface layer and PBL schemes used; and mpy14 through mpy18 differ in the radiation schemes used. Here, mpy01, with Goddard Lin microphysics (Tao et al., 1989), a Pleim-Xiu surface layer (Pleim, 2006), an Asymmetrical Convective Model version 2 (ACM2) PBL (Pleim, 2007), the Pleim-Xiu LSM (Pleim and Xiu, 1995), and the GFDL short- and longwave radiation schemes (Fels and Schwarzkopf, 1975), is considered the control experiment for the purpose of physics configuration.

  • Unlike forecast variables, such as geopotential height, which often show near Gaussian ensemble distributions, precipitation forecasts tend to have a positively skewed distribution (Hamill et al., 2008). With non-Gaussian distributions and often-present location errors, a simple ensemble mean of precipitation fields tends to smooth out precipitation peak values and results in underestimation of maximum rainfall, especially for extreme rainfall events. (Ebert, 2001) proposed several alternatives to generating ensemble mean precipitation, including weighted averaging, median forecasts, bias reduction and the probability matching (PM) approach. Among the proposed methods, the PM mean gives the best prediction of size, shape, intensity and location of heavy rain (Ebert, 2001). It is calculated as follows: \begin{eqnarray} \label{eq1} {x}&=&(x_1,x_2,\ldots,x_m,x_{m+1},x_{m+2},\ldots,x_{2m},\ldots,x_{(n-1)m},\qquad\nonumber\\ &&x_{(n-1)m+1},\ldots,x_{nm}) ,(1)\\ \label{eq2} \bar{x}_{\rm PM}&=&(\bar{x}_{\rm 1,PM},\bar{x}_{\rm 2,PM},\ldots,\bar{x}_{n,{\rm PM}}) ,(2) \end{eqnarray} where \begin{eqnarray} \bar{x}_{j,{\rm PM}}&=&{\rm Median}(x_{(j-1)m+1},x_{(j-1)m+2},\ldots,x_{(j-1)m+m}),\qquad\nonumber\\ &&j=1,\ldots,n ,(3)\\ \label{eq3} \bar{x}_{\rm SM}&=&(\bar{x}_{\rm 1,SM},\bar{x}_{\rm 2,SM},\ldots,\bar{x}_{n,{\rm SM}}) . (4)\end{eqnarray} Here, m is the ensemble size and n is the total number of grid points. The vector x contains the forecast rainfall amounts at all grid points and for all ensemble members, and the amounts have been sorted in descending order from largest to smallest values. The operators \(\overline{( \quad )}_{SM}\) and \(\overline{( \quad )}_{PM}\) denote the simple ensemble mean and an intermediate PM mean, respectively. To calculate the PM mean, one value, \(\bar x_j,\rm PM\), is picked out of every m values from x by taking the median value according to Eq. (3). The \(\bar x_j,\rm PM\) values make \(\bar x_{PM}\) up the intermediate PM mean vector. Then, the simple ensemble rainfall amounts at every grid point are similarly ranked from largest to smallest into the vector \(\bar x_{SM}\), but with the location information of each grid-point stored along with its rank. Finally, \(\bar x_{j,SM}\) is reassigned the value of \(\bar x_{j,PM}\) and put back to its original grid point location, and this is done for all values of j. This procedure yields the PM mean rainfall field, \(\bar x_{PM}\). In the PM field, at the grid-point where the largest (smallest) simple ensemble mean is found, the largest (smallest) value from the intermediate PM mean vector \(\bar x_{PM}\) is assigned. The PM mean has been found to be more skillful than the simple ensemble mean for the CAPS SSEF forecasts (e.g., Kong et al., 2008; Clark et al., 2009). In this study, the PM mean of 24-h accumulated rainfall will be presented and compared with the simple ensemble mean precipitation.

  • In this study, the ensemble forecasts of CEFS are verified against surface and upper-air observations. The GEnFS forecasts are used as a reference. For the verification, we employ the Model Evaluation Tools developed by the U.S. Developmental Testbed Center (Brown et al., 2009), which contains a comprehensive suite of verification metrics for both deterministic and ensemble-based probabilistic forecasts. Two domains are chosen for our verification: one is the entire forecast domain covering the whole of mainland China (referred to as "FULL"), which serves to verify the forecasts of environmental conditions for the Beijing extreme rainfall event; and the other is enclosed by the magenta box labeled A in Fig. 1c. This domain covers Beijing and its surrounding areas, and is mainly used to verify precipitation forecasts in the Beijing region.

    Over 20 000 surface rain gauge observations over China are collected, quality controlled and used to verify the precipitation forecasts. The verifications are carried out in both the "FULL" and "Box-A" domain. Ensemble-based verification scores, such as the rank histogram (Hamill, 2001) and Brier Score (Murphy, 1973), are used to evaluate the probabilistic forecasting skills of CEFS. Upper-air sounding observations are used to verify the forecast RH, temperature (T), and model zonal and meridional wind components (U and V, respectively), while surface observations are used to verify the 2-m RH and T, and the 10-m U and V; and these verifications are performed in the "FULL" domain.

4. Evaluation of CEFS ensemble forecasts
  • Figure 2 shows the postage stamp charts of 24-h accumulated rainfall of individual ensemble members together with observed rainfall, from 0000 UTC 21 July through 0000 UTC 22 July 2012, within the "Box-A" domain. The observed heavy rain center, with a maximum of 382 mm, is located in southwest Beijing (as pointed to by the black arrow in Fig. 2a). The forecasts show a high level of diversity among the members. Most members predict rainfall of more than 100 mm d-1, but the location and spatial coverage vary. Heavy rain exceeding 250 mm d-1 is captured by members g0, e0, mem13 and mem16, and the maximum centers are generally located in south or southwest Beijing, close to the observed maximum location. A maximum of 452 mm is predicted by member g0, which is the member initialized from the operational GFS analysis. We will refer to these four members as "good" members.

    Figure 2.  Postage stamp plots of 24-h accumulated rainfall (units: mm) from 0000 UTC 21 July to 0000 UTC 22 July 2012, observed (OBS), and from the 4-km CEFS members (see Table 1 for the definition of members). In this, and some of the later figures, Beijing City is shown by the bold-black border.

    Figure 3.  Spaghetti plots of 24-h accumulated rainfall contours of (a) 100 mm and (b) 150 mm. Different colors represents different members.

    In contrast, members such as mem06 and mem03 only predict small patches of rainfall exceeding 100 mm, and very few patches are located within the city of Beijing. In fact, the maximum amount predicted by mem06 over Beijing is less than 50 mm, while the maximum barely exceeds 100 m over Beijing according to mem03. Other members predict maximum rainfall amounts of between 100 and 250 mm, but the maximum centers have various displacement errors. In mem05, the precipitation system seems to have moved too rapidly towards the southeast, while that in mem12 seems to have moved a little too slow, placing the maximum accumulated precipitation too far southeast and northwest of Beijing, respectively. Overall, about half of the members predict heavy precipitation of more than 200 mm over a large enough area close to Beijing, while the other half predict precipitation that is too weak.

    The spaghetti plots of 100 and 150 mm 24-h rainfall contours from the ensemble members are shown in Fig. 3. For the 100-mm contours, most members cluster over the Beijing area and several are scattered around Beijing, indicating a high degree of certainty in predicting medium to heavy precipitation by the ensemble, although uncertainty does exist (Fig. 3a). There are two other clusters of 100-mm contours in the forecast domain——one in the eastern part of Sichuan Province, which is at the southern end of the frontal system stretching through the Beijing region; and the other over the South China Sea, which is associated with typhoon Vincente. For the 150-mm rainfall, the contours cluster quite tightly over the Beijing area, and there are more contours over the southern part of Beijing (Fig. 3b), suggesting a high probability of heavy rain in Beijing. The ensemble probabilities of precipitation (POPs) exceeding 100 mm and 150 mm are shown in Fig. 4 for the zoomed "Box-A" domain. While the spaghetti plots give a subjective view of the precipitation patterns and distributions, the POP provides quantitative probabilistic forecasts. For this event, the maximum POP is 82% for 100 mm d-1 and 50% for 150 mm d-1. The highest probability is located in the southwest part of Beijing for both thresholds, which is consistent with the observed heaviest rainfall. Such ensemble forecast products, if available in real time, would have greatly enhanced forecasters' confidence in the occurrence of very heavy rainfall in Beijing.

    Figure 5 shows the simple ensemble mean and PM mean 24-h precipitation forecasts together with observed precipitation. Given the diversity of the members in predicting the intensity and location of the maximum rainfall, the simple ensemble mean significantly underestimates the rain intensity, giving an ensemble mean maximum of only 151 mm d-1 (Fig. 5b). The PM ensemble mean, however, is able to capture the maximum intensity as well as the spatial distribution of the heavy precipitation much better; the PM mean maximum is as high as 415 mm d-1 and is located correctly near the southwest corner of Beijing, matching the observations very well. Based on the PM mean algorithm, the PM mean maximum is usually close to the maximum value predicted by all ensemble members at all grid-points. For this reason, the choice of domain used for calculating the PM mean does matter, and the calculation domain should be chosen to cover only the relevant precipitation systems, as we do here with the "Box-A" domain.

    Figure 4.  Probability of 24-h accumulated rainfall of (a) $\geq$100 mm and (b) $\geq$150 mm.

    Figure 5.  24-h accumulated rainfall (units: mm) from (a) rain gauge observations, (b) the simple ensemble mean, and (c) the PM ensemble mean.

  • In this subsection, CEFS forecasts are evaluated using the GEnFS as a reference (the much coarser-resolution GEnFS is not expected to out-perform the convection-permitting CEFS ensemble, but it helps to have a reference when evaluating the performance. NCEP global forecast products are used routinely by forecasters in China as one of the references when producing operational forecasts). Figure 6 shows the maximum and the 5th-percentile rainfall amounts within the "Box-A" region, and the rank histograms of 24-h precipitation forecasts of the CEFS members and the corresponding GEnFS members used to provide the CEFS ICs and BCs. The use of the 5th-percentile rainfall amount here helps alleviate the effects of precipitation biases among the members, as is discussed in (Zhu et al., 2015).

    For the maximum rainfall, GEnFS, not surprisingly, significantly underestimates the amount. The gridded maximum value of observations is 382 mm, while very few members of GEnFS predict more than 150 mm of rainfall (Fig. 6b). The intensity is greatly improved in CEFS. All except one member predict more than, or very close to, 200 mm of rainfall, though only 6 members predict close to or more than 300 mm. Member e0 predicts a maximum that is closest to the maximum in the objectively analyzed rainfall field, while g0 and mem13 predict maximum values of 440-450 mm, which are close to the station-observed maximum of 460 mm (see Chen et al., 2012).

    For the 5th-percentile rainfall, which represents the lower end of the rainfall intensity, all GEnFS members over-predict the light-rain amount (Fig. 6d), while the predictions of CEFS are distributed around the observed value of about 0.7 mm (Fig. 6c), indicating that CEFS members also predict light rain better. For the rank histograms of rainfall, GEnFS shows pronounced and more or less symmetric "U" shapes (Fig. 6f) for both the "FULL" and "Box-A" domains, indicating under-prediction of high precipitation values and over-prediction of low precipitation values. The ensemble system is seriously under-dispersive for rainfall. For CEFS (Fig. 6e), the occurrence frequencies near both ends of the histograms are greatly reduced for both domains, and more so for the "Box-A" region. Most interestingly, the histogram of CEFS shows an almost uniform distribution for the "Box-A" region, apart from a slight underestimation of high values. Larger intensity forecast errors in other parts of the domain contribute to more underestimation of high values for the "FULL" domain.

    Figure 6.  24-h accumulated rainfall amounts for (a, c, e) CEFS and (b, d, f) GEnFS members for the (a, b) maximum values and (c, d) 5th-percentile values within the "Box-A" region, and (e, f) the rank histograms for CEFS and GEnFS. The rank histograms are plotted for both the "Box-A" and "FULL" regions.

    The rank histogram informs us of the ensemble forecast distribution as compared to that of observations; it does not, however, reveal the sharpness of the ensemble forecasts, which is also an important property of an ensemble. (Hamill, 2001) suggests that it should be used in conjunction with other probabilistic ensemble skill scores. Here, the Brier score and its components for reliability, resolution and uncertainty (Stephenson et al., 2008) are calculated for the 24-h accumulated rainfall for the "Box-A" region and given in Fig. 7. The Brier score is similar to the RMSE but is calculated as the mean-square difference of forecast probability and that of corresponding observations. It can be decomposed into three components representing reliability, resolution and uncertainty of the forecast. For the Brier score and its reliability component, a smaller value is better; while for resolution, a larger value is better. The value of uncertainty is not related to the forecast but is a function of the frequency of the events occurring. For the 50 mm d-1 threshold, the Brier scores and reliabilities of CEFS are all smaller (better) than those of GEnFS, while its resolution is higher. This indicates that the precipitation probability forecast of CEFS is better than that of GEnFS. For the 100 mm d-1 threshold, CEFS again has a smaller Brier score overall (0.1 versus 0.157) and higher resolution (0.07 versus 0.01) when compared to GEnFS. However, the reliability value is somewhat higher than that of GEnFS. This appears to be due to the overestimation of the rainfall area above 100 mm d-1 (see Fig. 3a), but the reliability difference is much smaller than that of the resolution. Overall, the Brier score and its components show that CEFS is better than GEnFS for the probabilistic forecasting of heavy precipitation for the Beijing region in this case.

    We also check the ensemble forecasts of other atmospheric variables. They are verified against surface and sounding observations within the "FULL" domain. In general, for RH, temperature, and wind components, CEFS yields a larger ensemble spread but smaller RMSEs than GEnFS, especially at the surface and lower levels (not shown). The rank histogram distributions of surface variables show that CEFS, while still under-dispersive, greatly reduces the forecast sampling biases at both low and high ends, especially for the longer range forecasts (again not shown).

    Figure 7.  Brier scores and their components for 24-h accumulated rainfall for the "Box-A" domain for (a) threshold $\ge$50 mm and (b) threshold $\ge$100 mm.

  • As mentioned in the introduction, most operational models missed the precipitation in the pre-frontal warm sector over Beijing before 1200 UTC 21 July 2012. Figure 8 shows the observed and CEFS forecasted hourly rainfall amounts valid at 0600 UTC 21 July 2012, together with the forecasted surface winds. The forecasts for the two "control" members g0 and e0, and from those of the "good" member mem13 and "bad" member mem06, are shown. As can be seen from Fig. 6d, mem13 and mem06 have the best and worst maximum and light rainfall forecasts overall among the CEFS members, and are therefore examined in more detail here. At this time, a cold front is located about 200 km west of Beijing, and a band of light to moderate rain can be found along the front (Fig. 8a). The band of frontal precipitation is predicted by all of the members shown in Fig. 8, including mem06. In the observations, however, the most significant precipitation at this time is found over and to the southwest of Beijing (Fig. 8a), and this area of precipitation has been referred to as the (pre-frontal) warm-sector precipitation (Chen et al., 2012,Zhang et al., 2013), which marked the onset of heavy precipitation over Beijing. It, together with the later, quasi-linearly organized MCS, contributed significantly to the total amount of precipitation.

    Figure 8.  (a) Observed hourly rainfall at 0600 UTC 21 July 2012 and (b-e) forecasted hourly rainfall and surface winds for members g0, e0, mem13 and mem06, respectively. The single thick black line in each panel indicates the convergence line in the forecasted surface winds, which roughly corresponds to a surface cold front. The black contours of medium thickness represent the terrain heights of 100 m and 1000 m.

    The warm-sector precipitation at this stage is best predicted by mem13 (Fig. 8d), followed by g0 (Fig. 8b), and is almost completed missed by mem06 (Fig. 8e). The prediction of e0 is also relatively poor for the warm-sector rain at this time. (Yu and Meng, 2016) study showed that the low-level (850 hPa) low contributed most to the heavy rain in Beijing. The heavy rainfall was likely caused by strong low-level lifting. To gain some further understanding, we show in Fig. 9 the 850-hPa wind and horizontal water vapor flux fields of mem13 and mem06 at 0000 and 0600 UTC. The star symbol in Fig. 9 indicates the location of the observed maximum 24-h accumulated rainfall, which is in the southwest part of Beijing City, at the eastern edge of the mountain range that has a mean height of about 1.5 km (the mountain range is part of the large Taihang Mountain Range).

    At 0000 UTC, both mem13 and mem06 show a concentrated band of water vapor flux coming from the south into the Beijing region. In mem13, the winds associated with the band are mostly southerly, with a slight easterly component (Fig. 9a), but those in mem06 have a noticeable westerly component, making the flow more parallel to the southwest-northeast-oriented Taihang Mountain Range (Fig. 9b), reducing the range-normal wind component. As a result, there is more convergence of flows on the eastern slope of the Taihang Mountain Range, south of the maximum precipitation spot, due to mountain blocking, in mem13, giving rise to strong moisture flux convergence there (Fig. 9a). Meanwhile, in mem06, the more-or-less mountain-range parallel flow moves further north until it encounters the eastward-extending Yanshan Mountains north of Beijing City, creating large moisture flux convergence south of the Yanshan Mountains. For such flow configurations, one would expect much more precipitation on the upwind slope of the Taihang Mountains in mem13 at the location of the largest moisture flux convergence, as is the case in the forecast.

    By 0600 UTC, there is a significant enhancement to the southerly and southeasterly flows at 850 hPa, with a corresponding increase in the moisture flux, in mem13 (Fig. 9c). To the southeast of the maximum precipitation point, the flows are almost all southeasterly, creating an almost 90° incident angle to the Taihang Mountain Range, and strong lifting of low-level moist air. As a result, a more-or-less southwest-northeast-oriented band of heavy precipitation forms near the foot of the mountain range (Fig. 8d), in a way very similar to observations (Fig. 8a). In comparison, the much more southerly flows found in mem06 (Fig. 9d) create little rainfall along the Taihang Mountain Range; rainfall stronger than observed is instead created along the cold front to the northwest, apparently due to less depletion of moisture by precipitation before the air stream reaches the cold front. These examinations suggest that the environmental flows south and southeast of the Beijing region, and especially the flow directions before 0600 UTC 21 July, together with the associated orographic lift, played important roles in producing the pre-frontal, warm-sector heavy rainfall at this stage, which marked the onset of the extreme rainfall event in Beijing. Properly sampling the uncertainties in the environmental conditions is important for convective-scale ensemble forecasting. In the next section, we further explore the sensitivity of the precipitation forecast to the model physics.

    Figure 9.  The 850-hPa wind vectors (units: m s$^-1$) and horizontal water vapor flux (color-shaded) fields [units: g (hPa cm s)$^-1$] at the (a, b) IC time of 0000 UTC 21 July 2012 and (c, d) 6-h forecast time of 0600 UTC 21 July 2012, of the (a, c) "good" member mem13 and (b, d) "bad" member mem06. The thick dark-gray contours indicate the terrain heights of 100 m and 1000 m, respectively.

    Figure 10.  Time series of average hourly rainfall in a domain from 38.5$^\circ$N to 41.5$^\circ$N and 114.5$^\circ$E to 117.5$^\circ$E for (a) observation and ensemble members of CEFS, and for mem13 and members of the set of sensitivity forecasts (see Table 2) with (b) different microphysics schemes, (c) different PBL schemes, and (d) different radiation schemes. The sensitivity forecasts all use the same ICs and BCs as mem13.

    Figure 11.  Time series of observed and predicted accumulated rainfall at the grid-point with largest 24-h accumulated rainfall (of each member) within a 100-km radius of the observed maximum, from (a) members of CEFS, and for mem13 and members of the set of sensitivity forecasts (see Table 2) with (b) different microphysics schemes, (c) different PBL schemes, and (d) different radiation schemes.

5. Sensitivity of precipitation to physics schemes
  • The second set of sensitivity experiments listed in Table 2 aims to examine the relative sensitivity of extreme precipitation to model physics. Figure 10 shows the average hourly precipitation within a domain from 38.5°N to 41.5°N and from 114.5°E to 117.5°E, where heavy rainfall occurred. The area is a little larger than the Beijing region in order to account for the forecast position error. For the observed rainfall, there are two rapid rainfall intensification periods: from 0300 to 0700 UTC and from 0900 to 1300 UTC. The former is related to the first stage of warm-sector rainfall from less-organized convective cells, while the latter occurred when the well-organized quasi-linear MCS with back-building and echo-training characteristics was established (Zhang et al., 2013). Member mem13 of CEFS successfully captures the rapid intensification in both periods with a very good timing of onset. It ends the rapid intensification at 1000 UTC, one hour earlier than observed. Member g0 produces a larger domain-average maximum hourly precipitation than mem13, but the rapid intensification phase is delayed by two to three hours. It produces a total amount of precipitation within the average domain that is closer to observation. For most other members, the total amount of precipitation is significantly underestimated, and the onset of heavy precipitation is also significantly delayed.

    Figures 10b-d show the results of the sensitivity experiments with different physics schemes but with the same ICs and BCs as mem13. It can be seen that, among all the physics sensitivity experiments, the average precipitation differences in the first nine hours of the forecast are small. As a comparison, the average hourly precipitation is very similar up to 1000 UTC when the forecast intensity peak is reached, except for the WDM5 (mpy07) and WDM6 (mpy08) members, which reach lower peaks one hour earlier (Fig. 10b). There is little sensitivity of precipitation to radiation physics throughout the forecast period (Fig. 10d). Among the PBL schemes, the Pleim-Xiu scheme produces more sustained precipitation (Fig. 10c). There is a much larger sensitivity of precipitation after 0900 UTC among the microphysics members, which is not very surprising (Fig. 10b) since microphysics has a much bigger opportunity to affect the precipitation process when the precipitation system is fully developed. Despite the noticeable sensitivity to the microphysics during the later period, the overall precipitation timing and amount, however, have much smaller variability across the physics-difference members than the members of CEFS that have both IC and BC differences and physics differences. These results clearly indicate that, for the Beijing extreme precipitation case, the synoptic and mesoscale environmental conditions, as determined by the ICs and BCs, have a much larger influence on the forecast precipitation, which is consistent with our earlier discussions when comparing the results of CEFS members. Given the rather large (full-China domain) computational domain used and the relatively short forecast time examined, the ICs should have a much larger impact than the BCs in this case.

    The time series of the observed maximum accumulated rainfall at a meteorological station (the 460-mm maximum was reported by a hydrological station whose time series data are not available), and the predicted accumulated rainfall at the grid-point with the largest 24-h rainfall (of each member) within a 100 km radius of the observed maximum, are plotted in Fig. 11 for CEFS members and the physics sensitivity forecasts. We tested search radii up to 800 km; most members have the same maximum locations, except for a few "bad" members such as mem06.

    Consistent with the hourly rainfall, the observed station maximum has two main rain accumulation stages (Fig. 11a). Among all CEFS members, mem13 performs best. It successfully captures both stages, though there are delays of two to four hours. The second best member is g0. It predicts the largest rainfall amount, although most is contributed by the second stage. The precipitation of the first stage is significantly underestimated. Most other CEFS members perform similarly to member g0 for the first stage, but significantly under-predict the rainfall amount in the second stage. Therefore, most of them predict no more than 300 mm of rainfall. Figures 11b-d are the results of different physics schemes. The maximum difference among all the members in each group is also given in each panel. For the precipitation at the maximum accumulation locations, different microphysics and PBL schemes produce a high level of diversity, even in the first nine hours of the forecast. The maximum differences in these two groups are 164 mm and 130 mm, respectively (Figs. 11b and c). The differences among the forecasts with different radiation physics are much smaller (Fig. 11d).

    Figure 12 shows the locations of the maximum accumulated rainfall for all the forecast members. The observed maximum is located at the foot of the Taihang Mountains (marked by the star in Fig. 12). For most CEFS members, the maximum locations are generally close to the Taihang Mountains, although there is significant scattering in the locations (Fig. 12a). The "good" member g0 has a better maximum location than the "good" member 13. There is also scattering in the maximum locations with the microphysics and PBL members, but overall the scattering is much smaller than that of full perturbation members in CEFS. Compared to domain-average rainfall, the intensity and location of the maximum rainfall have clearly larger forecast uncertainties.

    Figure 12.  Locations of maximum 24-h accumulated rainfall of (a) observed and ensemble forecasts of CEFS, and (b) ensemble forecasts with different physics schemes. In (b), the color of the numbers represents different groups.

6. Summary and future plan
  • This paper studies the extreme rainfall event that occurred in Beijing on 21 July 2012 (maximum rainfall of 460 mm) using a convection-permitting ensemble forecasting system, CEFS. The system consists of 22 members based on the WRF model and uses multiple physics schemes. Its domain covers the whole of mainland China and has a 4-km horizontal grid spacing and 51 vertical levels. The forecasts of this extreme rainfall event are evaluated in terms of both deterministic and probabilistic forecasting.

    For this event, CEFS predicts a high probability of torrential rain in the Beijing region, especially in its southwestern part, consistent with observations. The predicted highest probability of 100 mm d-1 and 150 mm d-1 precipitation is 82% and 50%, respectively. The highest predicted value of the PM ensemble mean is 415 mm d-1, while the simple ensemble mean gives only 151 mm d-1. Note that we did not perform any calibration for the ensemble. Such a high forecast probability, together with other ensemble forecast products, if available in real-time, would have been very useful for decision-making and public warning for this historically extreme heavy rain event that caused the loss of 79 lives.

    The precipitation forecasts of CEFS are evaluated using ensemble-based verification scores. The precipitation forecasts of a global ensemble forecasting system initialized from EnKF data assimilation (GEnFS) are used as a reference. Two verification domains are used. One is the "FULL" domain covering the whole of mainland China, and the other is a small domain covering Beijing and its surrounding areas. For both verification domains, GEnFS displays pronounced "U"-shaped rank histograms, while CEFS exhibits shapes that are almost flat, especially for the small verification domain, though there is still a small level of under-prediction of high values. In terms of probability verification scores, CEFS achieves a lower (higher) Brier score and a higher resolution than GEnFS, indicating that the probability forecasts of CEFS are more reliable than those of GEnFS.

    For this extreme rainfall event, most of the rainfall came from convection in MCSs that occurred in the warm sector east of an approaching cold front. A few members of CEFS successfully reproduced the MCS precipitation. A "good" member from CEFS is specifically compared with a "bad" member that produced too little rain in the warm sector. It is believed that the first-stage precipitation in the warm sector was mostly induced by orographic lift. For the "good" member, there is more of an easterly component in the low-level southeasterly flow that brought in rich moisture to converge onto the southeast-facing slope of the southwest-northeast-oriented Taihang Mountain Range on the west side of Beijing, producing strong orographic lift. In contrast, the low-level flow in the "bad" member is mostly southerly, and is weaker. The low-level moisture is therefore transported further north of Beijing, producing little rain in the Beijing area. These results indicate a strong sensitivity of the extreme rainfall in this event to the mesoscale environmental conditions.

    The relative sensitivity of the forecasted precipitation to model physics is investigated by running another set of 18 forecasts using the ICs and BCs as the best member of CEFS, but with different microphysics, PBL and radiation schemes. The precipitation forecast is found to be not very sensitive to the radiation scheme used, but sensitive to the microphysics scheme after the peak rainfall intensity is reached. The Plaim-Xiu PBL scheme produces more precipitation than other PBL schemes. There is more sensitivity to the physics parameterizations in terms of the maximum precipitation amount and location than domain-average rainfall. Overall, environmental conditions as given by the ICs produce more precipitation diversity than physics parameterizations.

    In this study, although some of the members of our 4-km CEFS perform quite well in capturing the extreme rainfall that occurred in Beijing on 21 July 2012, demonstrating higher skill than the coarse-resolution GEnFS in terms of ensemble forecasting, large uncertainties still exist across the ensemble members. Some members completely miss the warm-sector rainfall and even the "good" members have intensity and position errors. Ensemble-based methods assimilating high-resolution local observations will likely both increase the forecast accuracy and reduce the forecast uncertainty. Also, ensemble probabilistic calibration, if properly performed, can further improve the probabilistic forecasting skill. However, ensemble probabilistic calibration of precipitation suitable for predicting extreme rainfall will require large data samples. In this paper, we did not attempt to fully analyze and understand the physical processes responsible for the extreme rainfall, or the successes and failures of individual members in predicting them. We plan to further analyze this dataset in future work to gain greater insights along these lines.

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