doi:10.1007/s00376-016-6202-z

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.234

ccc0a54e7ec3e76d744714d1063e66e1

http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1002%2Fqj.234%2Fpdf

http://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-aa067711e10a

708800e0d44a6d124c5f42d8d26143b3

http%3A%2F%2Fams.confex.com%2Fams%2F89annual%2Ftechprogram%2Fpaper_151349.htm

http://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/S1350482702003031

8d04dac776611e6ded50d3d5f7cc24cb

http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1017%2FS1350482702003031%2Ffull

http://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.02.92

cd10b3f39a45ed59dc1842559192c943

http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1256%2Fqj.02.92%2Fabstract

http://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- 1214.10.1175/2009WAF2222170.1

a1b5ae6e63cd08e849134fcf9801f0c8

http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2009WtFor..24.1191C

http://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-z

b27e501d18446cf9ae6f0a6d98ee1418

http%3A%2F%2Fen.cnki.com.cn%2FArticle_en%2FCJFDTotal-QXXX201210014.htm

http://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- 1140.10.1175/2009WAF2222222.1

c5adbca89ca36611e8f7cc5152f39e67

http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2009EGUGA..11.3170C

http://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-11-00040.1

f4766b545da35c541f04ba5bdb81a5fb

http%3A%2F%2Fonlinelibrary.wiley.com%2Fresolve%2Freference%2FADS%3Fid%3D2012BAMS...93...55C

http://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;2

20332d7be6c4759f89c2f16d8c96a428

http%3A%2F%2Fci.nii.ac.jp%2Fnaid%2F80009407343

http://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;2

9995d13df2e0b9394aa74d0ec7176fa1

http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F1997MWRv..125.2427D

http://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;2

bc2e658d8fdb360dba586c4456940d92

http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2001MWRv..129.2461E

http://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)2635dbe4de0b059be33cf98789c0c653

http%3A%2F%2Fen.cnki.com.cn%2FArticle_en%2FCJFDTOTAL-HBQX200902005.htm

http://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;2

ad4ee6abeee67b7b4d09a5c6e911ac53

http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F1975jats...32.1475f

http://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;2

8cc805e9481fc0014621eed8f2afc298

http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2001mwrv..129..550h

http://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- 2632.017772092db591d8758a7535aac330f6

http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2008MWRv..136.2620H

http://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- 1242.67216a5acd633df2dbeb3ccc3f7403d0

http%3A%2F%2Fadsabs.harvard.edu%2Fcgi-bin%2Fnph-data_query%3Fbibcode%3D1996MWRv..124.1225H%26db_key%3DPHY%26link_type%3DABSTRACT%26high%3D03696

http://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-15-0143.1

1f0a8f34584d916b7cecf3ac50312e80

http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2016WtFor..31..647I

http://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/MWR3323.1

ecdcd63fc98e894cfb7f63e91b871c55

http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2007MWRv..135..759K

http://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-4f1801d52ed8

5a65e304a398ab5731664342191d608d

http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F228463479_7.3_REAL-TIME_STORM-SCALE_ENSEMBLE_FORECAST_EXPERIMENT

refpaperuri:(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.

5ff9193426ddbcfebd068aecc8297036

http%3A%2F%2Fams.confex.com%2Fams%2F27SLS%2Fwebprogram%2FPaper255958.html

http://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)b8d22e71f80eeb678b586133aee3ce8a

http%3A%2F%2Fen.cnki.com.cn%2FArticle_en%2FCJFDTotal-QXXB201501004.htm

http://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)10.1061/9780784413128.022

d170a8a3699e347579c30e8690d4b32a

http%3A%2F%2Fen.cnki.com.cn%2FArticle_en%2FCJFDTOTAL-QXXB201303014.htm

http://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.52904

06e58ba7360249148550625e940bddce

http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1002%2Fqj.49712252905%2Ffull

http://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- 600.98fc6802469e1f61885d283bfbac4f0c

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

http://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-10-463-2003

c7dce1e0fc51a04857721ff8ae595f55

http%3A%2F%2Fonlinelibrary.wiley.com%2Fresolve%2Freference%2FXREF%3Fid%3D10.5194%2Fnpg-10-463-2003

http://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/JAM2339.1

75617a011f42c93016b91735e84f0ba6

http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2006japmc..45..341p

http://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/JAM2539.1

261fb6488e3c56b3eafc99bd69a9bfb0

http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2007JApMC..46.1383P

http://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- 32.10.1175/1520-0450-34.1.16

38e03ff2fd2393a77329bfd861cc6716

http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F1995JApMe..34...16P

http://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/s00376-010-1000-5

9762df2b456dfffd59011b685737bd6d

http%3A%2F%2Fen.cnki.com.cn%2FArticle_en%2FCJFDTotal-QXXX201109003.htm

http://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- 2399.50c4b7128b0145a8f5aec93619cf1b0b

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

http://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- 3372.10.1175/2009MWR2924.1

c04fc29309b06c34ac99170c26a9d1ca

http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2009MWRv..137.3351S

http://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/D68S4MVH

6e1e8ed5238484bf7e6021f9957054e6

http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F244955031_A_Description_of_the_Advanced_Research_WRF_Version_2

http://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- 757.10.1175/2007WAF2006116.1

89e970acdb5dba5c19ac64d274147bc1

http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2008WtFor..23..752S

http://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;2

e7bc62ff11834766546f8475eda86e05

http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F1989MWRv..117..231T

http://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)aec6a1ce8453060a3b33859d41fe4348

http%3A%2F%2Fen.cnki.com.cn%2FArticle_en%2FCJFDTOTAL-HBQX201303001.htm

http://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/S1350482705001763

a5ee1b38c1f65c187084c4fb3e180731

http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1017%2FS1350482705001763%2Fcitedby

http://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;2

cbe63b780b83c7603b5d4d22f0d6e97f

http%3A%2F%2Fci.nii.ac.jp%2Fnaid%2F10013126202%2F

http://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;2

d77eecdcf56bec983b860925dfc56c2d

http%3A%2F%2Fci.nii.ac.jp%2Fnaid%2F10013126203%2F

http://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/s13351-015-4097-8

8f4ca49ff9913833ea2fc1e5dc09b99b

http%3A%2F%2Flink.springer.com%2F10.1007%2Fs13351-015-4097-8

http://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- 482.10.1175/2007MWR2018.1

466890a4d7a93ef24c533c99b1072782

http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2008mwrv..136..463w

http://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: 10.1155/2013/259052.10.1155/2013/259052

7ea5792d591e9fd1ec62f06c5d90cd3d

http%3A%2F%2Fwww.oalib.com%2Fpaper%2F3066596

http://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-c0a2-4557-84f9-94ee1c6cd49e

d69e03e7275470df85ab290fe2c04c56

http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F237430741

refpaperuri:(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-a6f5-00037f87f244

b464c3c4f8e11ce9125addbe6ca63915

http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F267369509_STORM-SCALE_ENSEMBLE_FORECASTING_FOR_THE_NOAA_HAZARDOUS_WEATHER_TESTBED

refpaperuri:(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.v68.31045

2ed6bd2307385c64adf1ff5de29bef45

http%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_pub

http://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/JZ068i003p00667

d44af233626bdf131965179150c7e32e

http%3A%2F%2Fen.cnki.com.cn%2FArticle_en%2FCJFDTotal-QXXX201211003.htm

http://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.50304

711d173cd0d50344bc8a35d259d7986b

http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1002%2Fgrl.50304%2Fcitedby

http://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)4b72468cb96d317bbb93073d40ec83e1

http%3A%2F%2Fen.cnki.com.cn%2FArticle_en%2FCJFDTOTAL-QXXX201409005.htm

http://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- 3291.10.1002/2014JD022686

a4685478468cd17c6aae26ceb017ab4a

http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1002%2F2014jd022686%2Ffull

http://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/s00376-015-5023-9

77fb22322e36c4aacb3b4829b5970042

http%3A%2F%2Fwww.cqvip.com%2FQK%2F84334X%2F201511%2F665874469.html

http://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.