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The Effects of Land Surface Process Perturbations in a Global Ensemble Forecast System


doi: 10.1007/s00376-016-6036-8

  • Atmospheric variability is driven not only by internal dynamics, but also by external forcing, such as soil states, SST, snow, sea-ice cover, and so on. To investigate the forecast uncertainties and effects of land surface processes on numerical weather prediction, we added modules to perturb soil moisture and soil temperature into NCEP's Global Ensemble Forecast System (GEFS), and compared the results of a set of experiments involving different configurations of land surface and atmospheric perturbation. It was found that uncertainties in different soil layers varied due to the multiple timescales of interactions between land surface and atmospheric processes. Perturbations of the soil moisture and soil temperature at the land surface changed sensible and latent heat flux obviously, as compared to the less or indirect land surface perturbation experiment from the day-to-day forecasts. Soil state perturbations led to greater variation in surface heat fluxes that transferred to the upper troposphere, thus reflecting interactions and the response to atmospheric external forcing. Various verification scores were calculated in this study. The results indicated that taking the uncertainties of land surface processes into account in GEFS could contribute a slight improvement in forecast skill in terms of resolution and reliability, a noticeable reduction in forecast error, as well as an increase in ensemble spread in an under-dispersive system. This paper provides a preliminary evaluation of the effects of land surface processes on predictability. Further research using more complex and suitable methods is needed to fully explore our understanding in this area.
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    Buizza R., M. Leutbecher, and L. Isaksen, 2008: Potential use of an ensemble of analyses in the ECMWF ensemble prediction system. Quart. J. Roy. Meteor. Soc., 134(637), 2051-2066, doi: 10.1002/qj.346.10.1002/qj.3461682d3647c1a2fa3f01542d29fc8af49http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1002%2Fqj.346%2Ffullhttp://onlinelibrary.wiley.com/doi/10.1002/qj.346/fullAbstract One of the crucial aspects of the design of an ensemble prediction system is the definition of the ensemble of initial states. This work investigates the use of singular vectors, an ensemble of analyses, and a combination of the two types of perturbations in the ECMWF operational ensemble prediction system. First, the similarity between perturbations generated using initial-time singular vectors (SVs) and analyses from the ensemble data assimilation (EDA) system is assessed. Results show that the EDA perturbations are less localized geographically and have a better coverage of the Tropics. EDA perturbations have also smaller scales than SV-based perturbations, and have a less evident upshear vertical tilt, which explains why they grow less with forecast time. Then, the use of EDA-based perturbations in the ECMWF ensemble prediction system is studied. Results indicate that if used alone, EDA-based perturbations lead to an under-dispersive and less skilful ensemble then the one based on initial-time SVs only. Combining the EDA and the initial-time SVs gives a system with a better agreement between ensemble spread and the error of the ensemble mean, a smaller ensemble-mean error and more skilful probabilistic forecasts than the current operational system based on initial-time and evolved SVs. Copyright 漏 2008 Royal Meteorological Society
    Buizza R., M. Leutbecher, L. Isaksen, and J. Haseler, 2010: Combined use of EDA- and SV-based perturbations in the EPS. ECMWF Newsletter No. 123, 22- 28.
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    Campana K., P. Caplan, 2005: Technical procedures bulletin for the T382 Global Forecast System. Environmental Modeling Center, National Centers for Environmental Prediction. [Available online at http://www.emc.ncep.noaa.gov/gc_wmb/Documentation/TPBoct05/T382.TPB.FINAL.htm].
    Cand ille G., C. Côté, P. L. Houtekamer, G. Pellerin, 2007: Verification of an ensemble prediction system against observations. Mon. Wea. Rev.,135, 2688-2699, doi: 10.1175/MWR 3414.1.10.1175/MWR3414.1a8fb803c013da9200058d82789d3d07bhttp%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2007MWRv..135.2688Chttp://adsabs.harvard.edu/abs/2007MWRv..135.2688CA verification system has been developed for the ensemble prediction system (EPS) at the Canadian Meteorological Centre (CMC). This provides objective criteria for comparing two EPSs, necessary when deciding whether or not to implement a new or revised EPS. The proposed verification methodology is based on the continuous ranked probability score (CRPS), which provides an evaluation of the global skill of an EPS. Its reliability/resolution partition, proposed by Hersbach, is used to measure the two main attributes of a probabilistic system. Also, the characteristics of the reliability are obtained from the two first moments of the reduced centered random variable (RCRV), which define the bias and the dispersion of an EPS. Resampling bootstrap techniques have been applied to these scores. Confidence intervals are thus defined, expressing the uncertainty due to the finiteness of the number of realizations used to compute the scores. All verifications are performed against observations to provide more independent validations and to avoid any local systematic bias of an analysis. A revised EPS, which has been tested at the CMC in a parallel run during the autumn of 2005, is described in this paper. This EPS has been compared with the previously operational one with the verification system presented above. To illustrate the verification methodology, results are shown for the temperature at 850 hPa. The confidence intervals are computed by taking into account the spatial correlation of the data and the temporal autocorrelation of the forecast error. The revised EPS performs significantly better for all the forecast ranges, except for the resolution component of the CRPS where the improvement is no longer significant from day 7. The significant improvement of the reliability is mainly due to a better dispersion of the ensemble. Finally, the verification system correctly indicates that variations are not significant when two theoretically similar EPSs are compared.
    Charron M., G. Pellerin, L. Spacek, P. L. Houtekamer, N. Gagnon, H. L. Mitchell, and L. Michelin, 2010: Toward random sampling of model error in the Canadian ensemble prediction system. Mon. Wea. Rev.,138, 1877-1901, doi: 10.1175/2009 MWR3187.1.10.1175/2009MWR3187.1120ebbfba2e0618833a5b5d67972c723http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2010MWRv..138.1877Chttp://adsabs.harvard.edu/abs/2010MWRv..138.1877CNot Available
    Chen F., Coauthors, 1996: Modeling of land surface evaporation by four schemes and comparison with FIFE observations. J. Geophys. Res., 101( D3), 7251- 7268.10.1029/95JD02165d8487496ae05adc1c65503a636e8b5e4http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F95JD02165%2Ffullhttp://onlinelibrary.wiley.com/doi/10.1029/95JD02165/fullWe tested four land surface parameterization schemes against long-term (5 months) area-averaged observations over the 15 km 脳 15 km First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE) area. This approach proved to be very beneficial to understanding the performance and limitations of different land surface models. These four surface models, embodying different complexities of the evaporation/hydrology treatment, included the traditional simple bucket model, the simple water balance (SWB) model, the Oregon State University (OSU) model, and the simplified Simple Biosphere (SSiB) model. The bucket model overestimated the evaporation during wet periods, and this resulted in unrealistically large negative sensible heat fluxes. The SWB model, despite its simple evaporation formulation, simulated well the evaporation during wet periods, but it tended to underestimate the evaporation during dry periods. Overall, the OSU model ably simulated the observed seasonal and diurnal variation in evaporation, soil moisture, sensible heat flux, and surface skin temperature. The more complex SSiB model performed similarly to the OSU model. A range of sensitivity experiments showed that some complexity in the canopy resistance scheme is important in reducing both the overestimation of evaporation during wet periods and underestimation during dry periods. Properly parameterizing not only the effect of soil moisture stress but also other canopy resistance factors, such as the vapor pressure deficit stress, is critical for canopy resistance evaluation. An overly simple canopy resistance that includes only soil moisture stress is unable to simulate observed surface evaporation during dry periods. Given a modestly comprehensive time-dependent canopy resistance treatment, a rather simple surface model such as the OSU model can provide good area-averaged surface heat fluxes for mesoscale atmospheric models.
    Chen, F. and J. Dudhia, 2001: Coupling an advanced land surface-hydrology model with the Penn State-NCAR MM5 modeling system. Part I: Model implementation and sensitivity. Mon. Wea. Rev.,129, 569-585, doi: 10.1029/95JD02165.0df8bc7b-b6cd-4df3-8626-fe2707242a848413a023b27a68b4f7cf6bfdc15d7b32http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F245810779_Coupling_an_Advanced_Land-SurfaceHydrology_Model_with_the_Penn_StateNCAR_MM5_Modeling_Systemrefpaperuri:(9cc201f9c4e47979a1730595d2c374e6)http://www.researchgate.net/publication/245810779_Coupling_an_Advanced_Land-SurfaceHydrology_Model_with_the_Penn_StateNCAR_MM5_Modeling_SystemPublication &raquo; Coupling an Advanced Land-Surface/Hydrology Model with the Penn State/NCAR MM5 Modeling System.
    Davis C. A., K. W. Manning, R. E. Carbone, S. B. Trier, and J. D. Tuttle, 2003: Coherence of warm-season continental rainfall in numerical weather prediction models. Mon. Wea. Rev.,131, 2667-2679, doi: 10.1175/1520-0493(2003)131<2667: COWCRI>2.0.CO;2.10.1175/1520-0493(2003)1312.0.CO;2a021a861feadfb8823032e56cf77a346http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2003mwrv..131.2667dhttp://adsabs.harvard.edu/abs/2003mwrv..131.2667dNot Available
    DengG., Coanthors, 2010: Development of mesoscale ensemble prediction system at National Meteorological Center. Journal of Applied Meteorological Science,21(5), 513-523, doi: 10.3969/j.issn.1001-7313.2010.05.001. (in Chinese)10.3724/SP.J.1084.2010.00199c43357b270d45f095c41e3cfe2554e6dhttp%3A%2F%2Fen.cnki.com.cn%2FArticle_en%2FCJFDTOTAL-YYQX201005002.htmhttp://en.cnki.com.cn/Article_en/CJFDTOTAL-YYQX201005002.htmTo improve short-range weather forecast predictability of high-impact weather process,a set of national-level mesoscale ensemble prediction system(MEPS) is developed at National Meteorological Center (NMC).The theory to set up an ensemble prediction system lies in the following facts:There are no perfect forecast models,and atmosphere is a chaotic dynamical system,so any small error in the initial condition will lead to growing errors in the forecast,eventually leading to a total loss of any predictive information. The MEPS at NMC takes advantage of achievements at high resolution deterministic mesoscale prediction model,data assimilation system as well as experience from development of global ensemble prediction system.The error growth features for mesoscale model forecast within China area is explored and it is found that most of the convective-scale weather system develops in weak baroclinic environment and the quick growth errors resulted from baroclinic instability.Considering characteristics of the circulation regime, season and geographical domain,the initial perturbation technique of breeding method is adopted to perturb the initial fields.Furthermore,to reflect uncertainties within physical process as well as systematic errors within mesoscale model,many options of microphysics,convective cumulus parameterization, boundary layer schemes,land surface process schemes and combinations in the model are tested for a certain period to evaluate the performance of different schemes.The experiment indicates that physical process perturbation has equal or even greater impacts on spread of ensemble prediction comparing with initial condition perturbation.Therefore,assembling of different microphysics schemes,cumulus parameterization, and planetary boundary layer processes is applied to build a multi-initial condition,multi-physics ensemble system.The initial conditions and lateral boundary conditions are obtained from global ensemble system at NMC and trickily rescaled during model integration process.To reduce systematic bias in ensemble forecasts,an adaptive Kalman Filtering algorithm is applied as bias correction method and the results is inspiring.Ensemble forecast products include ensemble averages,spread and probability of multiple elements (wind,temperature,humidity,geopotential height,rainfall,etc.) in multiple layers are produced and performance of the ensemble system is evaluated.To evaluate the performance of NMC's regional mesoscale ensemble prediction system,different ensemble verification methods is used to estimate and compare 6 mesoscale ensemble prediction systems within a common forecasting configuration during the WMO/WWRP Beijing 2008 Olympics Mesoscale Ensemble Research and Development Project.Results indicate that the overall predictability of mesoscale ensemble prediction system at NMC is overall comparable to the international participants.The MEPS is still not good enough for fixed site and time-specific forecasts, but it demonstrates good ability to capture the high impact weather event and will play a role in everyday forecast.
    Deng G., H. Tian, X. L. Li, J. Chen, J. D. Gong, and M. Y. Jiao, 2012: A comparison of breeding and ensemble transform vectors for global ensemble generation. Acta Meteorologica Sinica,26(1), 52-61, doi: 10.1007/s13351-012-0105-4. (in Chinese)10.1007/s13351-012-0105-40062401d-dfd7-46fe-bceb-5c5a5ff0624bWOS:000302927800005bfb2de89158e0d0d563e84337f978a26http%3A%2F%2Fd.wanfangdata.com.cn%2FPeriodical%2Fqxxb-e201201005http://d.wanfangdata.com.cn/Periodical/qxxb-e201201005To compare the initial perturbation techniques using breeding vectors and ensemble transform vectors,three ensemble prediction systems using both initial perturbation methods but with different ensemble member sizes based on the spectral model T213/L31 are constructed at the National Meteorological Center,China Meteorological Administration (NMC/CMA).A series of ensemble verification scores such as forecast skill of the ensemble mean,ensemble resolution,and ensemble reliability are introduced to identify the most important attributes of ensemble forecast systems.The results indicate that the ensemble transform technique is superior to the breeding vector method in light of the evaluation of anomaly correlation coefficient (ACC),which is a deterministic character of the ensemble mean,the root-mean-square error (RMSE) and spread,which are of probabilistic attributes,and the continuous ranked probability score (CRPS) and its decomposition.The advantage of the ensemble transform approach is attributed to its orthogonality among ensemble perturbations as well as its consistence with the data assimilation system.Therefore,this study may serve as a reference for configuration of the best ensemble prediction system to be used in operation.
    Gao J. D., M. Xue, 2008: An efficient dual-resolution approach for ensemble data assimilation and tests with simulated Doppler radar data. Mon. Wea. Rev.,136, 945-963, doi: 10.1175/2007MWR2120.1.10.1175/2007MWR2120.1bca7201aeab8250446bb12db44071d83http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2008MWRv..136..945Ghttp://adsabs.harvard.edu/abs/2008MWRv..136..945GA new efficient dual-resolution (DR) data assimilation algorithm is developed based on the ensemble Kalman filter (EnKF) method and tested using simulated radar radial velocity data for a supercell storm. Radar observations are assimilated on both high-resolution and lower-resolution grids using the EnKF algorithm with flow-dependent background error covariances estimated from the lower-resolution ensemble. It is shown that the flow-dependent and dynamically evolved background error covariances thus estimated are effective in producing quality analyses on the high-resolution grid. The DR method has the advantage of being able to significantly reduce the computational cost of the EnKF analysis. In the system, the lower-resolution ensemble provides the flow-dependent background error covariance, while the single-high-resolution forecast and analysis provides the benefit of higher resolution, which is important for resolving the internal structures of thunderstorms. The relative smoothness of the covariance obtained from the lower 4-km-resolution ensemble does not appear to significantly degrade the quality of analysis. This is because the cross covariance among different variables is of first-order importance for 0904retrieving0909 unobserved variables from the radar radial velocity data. For the DR analysis, an ensemble size of 40 appears to be a reasonable choice with the use of a 4-km horizontal resolution in the ensemble and a 1-km resolution in the high-resolution analysis. Several sensitivity tests show that the DR EnKF system is quite robust to different observation errors. A 4-km thinned data resolution is a compromise that is acceptable under the constraint of real-time applications. A data density of 8 km leads to a significant degradation in the analysis.
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    Lewis F. R., 2007: Weather Prediction by Numerical Process. 2nd ed. Cambridge University Press,236pp.10.2136/sssaj1966.03615995003000010006x1abf5df7a676a8b51e14d3c7625030ddhttp%3A%2F%2Fwww.ams.org%2Fmathscinet-getitem%3Fmr%3D2358797http://www.ams.org/mathscinet-getitem?mr=2358797Weather prediction by numerical process by Lewis F. Richardson ; with a foreword by Peter Lynch (Cambridge mathematical library) Cambridge University Press, 2007 2nd ed : pbk
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    McLay J. G., M. K. Flatau, C. A. Reynolds, J. Cummings, T. Hogan, and P. J. Flatau, 2012: Inclusion of sea-surface temperature variation in the U. S. Navy ensemble-transform global ensemble prediction system. J. Geophys. Res.: Atmos., 117,D19120, doi: 10.1029/2011JD016937.10.1029/2011JD016937fde50b756e903f890e1a7c8fd68cbb6ahttp%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F2011JD016937%2Fabstracthttp://onlinelibrary.wiley.com/doi/10.1029/2011JD016937/abstractThe local ensemble transform (ET) analysis perturbation scheme is adapted to generate perturbations to both atmospheric variables and sea-surface temperature (SST). The adapted local ET scheme is used in conjunction with a prognostic model of SST diurnal variation and the Navy Operational Global Atmospheric Prediction System (NOGAPS) global spectral model to generate a medium-range forecast ensemble. When compared to a control ensemble, the new forecast ensemble with SST variation exhibits notable differences in various physical properties including the spatial patterns of surface fluxes, outgoing longwave radiation (OLR), cloud radiative forcing, near-surface air temperature and wind speed, and 24-h accumulated precipitation. The structure of the daily cycle of precipitation also is substantially changed, generally exhibiting a more realistic midday peak of precipitation. Diagnostics of ensemble performance indicate that the inclusion of SST variation is very favorable to forecasts in the Tropics. The forecast ensemble with SST variation outscores the control ensemble in the Tropics across a broad set of metrics and variables. The SST variation has much less impact in the Midlatitudes. Further comparison shows that SST diurnal variation and the SST analysis perturbations are each individually beneficial to the forecast from an overall standpoint. The SST analysis perturbations have broader benefit in the Tropics than the SST diurnal variation, and inclusion of the SST analysis perturbations together with the SST diurnal variation is essential to realize the greatest gains in forecast performance.
    Mullen S. L., R. Buizza, 2002: The impact of horizontal resolution and ensemble size on probabilistic forecasts of precipitation by the ECMWF ensemble prediction system. Wea. Forecasting,17, 173-191, doi:10.1175/1520-0434(2002)017 <0173:TIOHRA>2.0.CO;2.10.1175/1520-0434(2002)0172.0.CO;2be5867472906c8f6b04ee5364b870f4ehttp%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2002WtFor..17..173Mhttp://adsabs.harvard.edu/abs/2002WtFor..17..173MThe effect of horizontal resolution and ensemble size on the ECMWF Ensemble Prediction System (EPS) is assessed for probabilistic forecasts of 24-h accumulated precipitation. Two sets of experiments are analyzed. The primary experiment compares two spectral truncations (total wavenumbers 159 and 255) for 30 summer and 57 winter dates. An auxiliary experiment compares three truncations (total wavenumbers 159, 255, and 319) for 16 initial dates (8 cool- and 8 warm-season events) during which heavy precipitation (>50 mm) occurred over the eastern United States at day 5 of the forecast. Rain gauge data from the River Forecast Centers of NOAA are used for verification. Skill is measured relative to long-term climatic frequencies, and the statistical significance of differences in the accuracy among the forecasts is estimated. Finer model resolution produces statistically significant improvements in EPS performance for ensemble configurations with the same number of members, especially for lighter thresholds (1 and 10 mm day [sup -1]). Performance changes somewhat when ensemble configurations with different resolutions and ensemble sizes, but equivalent computational costs, are compared for the heavier amounts (20 and 50 mm day [sup -1]). Coarser-resolution, larger-member ensembles can outperform higher-resolution, smaller-member ensembles in terms of ability to predict rare (in terms of climatic frequency of occurrence) precipitation events. The overall conclusion is that probabilistic forecasts of precipitation from large ensemble sizes at lower resolution can be more valuable to users and decision makers than probabilistic forecasts from smaller ensemble sizes at higher resolution, particularly when heavy precipitation occurs.
    National Research Council, 1998: The Atmospheric Sciences Entering the Twenty- first Century. National Academy Press,364 pp.10.1016/S1364-6826(99)00127-376c8b711f05568afab8fc05be58e75cahttp%3A%2F%2Fwww.researchgate.net%2Fpublication%2F248353359_The_Atmospheric_Sciences_Entering_the_Twenty-First_Centuryhttp://www.researchgate.net/publication/248353359_The_Atmospheric_Sciences_Entering_the_Twenty-First_Century
    Nicholson S. E., 1988: Land Surface Atmosphere Interaction: Physical processes and Surface Changes and their Impact. Progress in Physical Geography,12, 36-65, doi: 10.1177/ 030913338801200102.10.1177/030913338801200102ee327e8c4be4aa6bbefa54a70af65666http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F237970060_Land_surface_atmosphere_interaction_physical_processes_and_surface_changes_and_their_impacthttp://www.researchgate.net/publication/237970060_Land_surface_atmosphere_interaction_physical_processes_and_surface_changes_and_their_impactRelative dispersibility of Tilia americana L., Acer saccharum Marsh. and Fraxinus pennsylvanica Marsh. was inferred from the ratio among species-specific regression coefficients (beta) computed from seedling density-distance plots. Density counts were made in spatially-uniform old fields adjacent to single seed sources or monotypic fencerows. Resultant seedling shadow curves approximate the negative exponential form expected for many seeds (log y = a-beta X). This basic curve shape fit species of differing dispersibility, dispersal under a range of wind directions and one-year-old or all-aged cohorts. The ratios of beta were 1:2.6:3.2 for Tilia, Acer and Fraxinus, respectively, in order of increasing dispersibility. Vegetation patches isolated from seed sources by several hundred meters or more should have extremely low input of seeds, especially Tilia and Acer. The finding that Fraxinus disperses farther than Acer was unexpected, since the samaras of the former have faster terminal velocities. The relationship can be explained by better performance of Fraxinus samaras in the stronger winds experienced by trees in open landscapes, poorer formation of the samara abscission layer, and release of samaras following leaf abscission and during the winter when winds are the strongest. Both the samara plan and dispersal phenology need to be considered in estimating relative dispersibility among species.
    Pan H.-L., L. Mahrt, 1987: Interaction between soil hydrology and boundary layer development. Bound.-Layer Meteor.,38, 185-202, doi: 10.1007/BF00121563.10.1007/BF001215636d1764a3c8e01ebf874c5509b508326dhttp%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F1987BoLMe..38..185Phttp://adsabs.harvard.edu/abs/1987BoLMe..38..185PA two-layer model of soil hydrology and thermodynamics is combined with a one-dimensional model of the planetary boundary layer to study various interactions between evolution of the boundary layer and soil moisture transport. Boundary-layer moistening through surface evaporation reduces the potential and actual surface evaporation as well as the boundary-layer growth. With more advanced stages of soil drying, the restricted surface evaporation allows greater sensible heat flux which enhances boundary-layer growth and entrainment drying. Special individual cases are studied where the wind speed is strong, solar radiation is reduced, transpiration is important, the soil is thin, or the soil is covered with organic debris.
    Pielke R. A., 2001: Influence of the spatial distribution of vegetation and soils on the prediction of cumulus Convective rainfall. Rev. Geophys.,39, 151-177, doi: 10.1029/1999RG 000072.10.1029/1999RG000072a3a1619744d5408b32d80ae49f3e8bb4http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F1999RG000072%2Fabstracthttp://onlinelibrary.wiley.com/doi/10.1029/1999RG000072/abstractThis paper uses published work to demonstrate the link between surface moisture and heat fluxes and cumulus convective rainfall. The Earth's surface role with respect to the surface energy and moisture budgets is examined. Changes in land-surface properties are shown to influence the heat and moisture fluxes within the planetary boundary layer, convective available potential energy, and other measures of the deep cumulus cloud activity. The spatial structure of the surface heating, as influenced by landscape patterning, produces focused regions for deep cumulonimbus convection. In the tropics, and during midlatitude summers, deep cumulus convection has apparently been significantly altered as a result of landscape changes. These alterations in cumulus convection teleconnect to higher latitudes, which significantly alters the weather in those regions. The effect of tropical deforestation is most clearly defined in the winter hemisphere. In the context of climate, landscape processes are shown to be as much a part of the climate system as are atmospheric processes.
    Richardson D. S., 2000: Skill and relative economic value of the ECMWF ensemble prediction system. Quart. J. Roy. Meteor. Soc., 126, 649- 667.10.1002/qj.4971265631314f9db02ceffc9a0ec0f00cc22afbb65http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1002%2Fqj.49712656313%2Ffullhttp://onlinelibrary.wiley.com/doi/10.1002/qj.49712656313/fullThis case study shows how interactive marketing campaigns can be systematically fine-tuned and made more productive through adaptive experimentation. It details the use of adaptive experimentation in a viral marketing campaign at Plaxo, Inc., a company that provides Internet-based updating of personal contact information. The experiences of Plaxo highlight that even for a product that is amenable to viral marketing, growth is not guaranteed. To achieve a desired level of growth, Plaxo identified the product features that contributed to greater adoption conversion of recipients of its marketing message and improved them through continuous experimentation. To overcome potential negative side effects of aggressive viral growth, the company used a carefully crafted feedback loop via internet alert services that tapped into the blogging community. This practice allowed management to better understand negative perceptions of the product and take timely corrective actions.
    Stanski H. R., L. J. Wilson, and W. R. Burrows, 1989: Survey of common verification methods in meteorology. World Weather Watch Tech. Rept. No.8,WMO/TD No.358, WMO, Geneva, 114 pp.871590666fa75358bc35b6a4b1699156http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F264158612_Survey_of_Common_Verification_Methods_in_Meteorologyhttp://www.researchgate.net/publication/264158612_Survey_of_Common_Verification_Methods_in_MeteorologyABSTRACT This is part 1.
    Sutton C., T. M. Hamill, and T. T. Warner, 2006: Will perturbing soil moisture improve warm-season ensemble forecasts? A proof of concept. Mon. Wea. Rev.,134, 3174-3189, doi: 10.1175/MWR3248.1.10.1175/MWR3248.s186621188eb034330e4d4a087fba43f1fhttp%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2006MWRv..134.3174Shttp://adsabs.harvard.edu/abs/2006MWRv..134.3174SNot Available
    Tennant W., S. Beare, 2014: New schemes to perturb sea-surface temperature and soil moisture content in MOGREPS. Quart. J. Roy. Meteor. Soc.,140, 1150-1160, doi: 10.1002/qj. 2202.10.1002/qj.2202d5918fa022fe8141f4ae1664b9054386http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1002%2Fqj.2202%2Fcitedbyhttp://onlinelibrary.wiley.com/doi/10.1002/qj.2202/citedbyThis article investigates two schemes that perturb sea‐surface temperatures (SSTs) and soil moisture content (SMC) in the Met Office Global and Regional Ensemble Prediction System (MOGREPS), to address a known deficiency of a lack of ensemble spread near the surface. Results from a two‐month‐long trial during the Northern Hemisphere summer show positive benefits from these schemes. These include a decrease in the spread deficit of surface temperature and improved probabilistic verification scores. SST perturbations exhibit a stronger impact than SMC perturbations but, when combined, the increased spread from the two schemes is cumulative. A regional ensemble system driven by the global ensemble members largely reflects the same changes seen in the global ensemble but cycling fields, like SMC, between successive regional forecasts does show some benefit.
    Toth Z., O. Talagrand , and Y. J. Zhu, 2006: The attributes of forecast systems: a general framework for the evaluation and calibration of weather forecasts. Tim Palmer and Renate Hagedorn, Predictability of Weather and Climate, Ed., Cambridge University Press, 584- 595.10.1017/CBO9780511617652.0234c2dccbc836e4b0d21ec3b819a8f7bachttp%3A%2F%2Fdx.doi.org%2F10.1017%2FCBO9780511617652.023http://dx.doi.org/10.1017/CBO9780511617652.023
    Viterbo P., A. C. M. Beljaars, 1995: An improved land surface parameterization scheme in the ECMWF model and its validation. J. Climate,8, 2716-2748, doi: 10.1175/1520-0442 (1995)008<2716:AILSPS>2.0.CO;2.10.1175/1520-0442(1995)0082.0.CO;2eac2109858ddaa6ea43cb0ba3c58aebehttp%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F1995JCli....8.2716Vhttp://adsabs.harvard.edu/abs/1995JCli....8.2716VA new version of the ECMWF land surface parameterization scheme is described. It has four prognostic layers in the soil for temperature and soil moisture, with a free drainage and a zero heat flux condition at the bottom as a boundary condition. The scheme has been extensively tested in stand-alone mode with the help of long observational time series from three different experiments with different climatological regimes: the First ISLSCP (International Satellite Land Surface Climatology Project) Field Experiment in the United States, Cabauw in the Netherlands, and the Amazonian Rainforest Meteorological Experiment in Brazil. The emphasis is on seasonal timescales because it was felt that the main deficiencies in the old ECMWF land surface scheme were related to its capability of storing precipitation in spring and making it available for evaporation later in the year. It is argued that the stand-alone testing is particularly important, because it allows one to isolate problems in the land surface scheme without having to deal with complicated interactions in the full three-dimensional model.
    Wang X. G., C. H. Bishop, 2003: A comparison of breeding and ensemble transform Kalman filter ensemble forecast schemes. J. Atmos. Sci.,60, 1140-1158, doi: 10.1175/1520-0469(2003)060<1140:ACOBAE>2.0.CO;2.10.1175/1520-0469(2003)0602.0.CO;2e944df1a5d6726e5668650fe360597c4http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2003EAEJA.....8087Whttp://adsabs.harvard.edu/abs/2003EAEJA.....8087WThe ensemble transform Kalman filter (ETKF) ensemble forecast scheme is introduced and compared with both a simple and a masked breeding scheme. Instead of directly multiplying each forecast perturbation with a constant or regional rescaling factor as in the simple form of breeding and the masked breeding schemes, the ETKF transforms forecast perturbations into analysis perturbations by multiplying by a transformation matrix. This matrix is chosen to ensure that the ensemble-based analysis error covariance matrix would be equal to the true analysis error covariance if the covariance matrix of the raw forecast perturbations were equal to the true forecast error covariance matrix and the data assimilation scheme were optimal. For small ensembles (鈭100), the computational expense of the ETKF ensemble generation is only slightly greater than that of the masked breeding scheme. Version 3 of the Community Climate Model (CCM3) developed at National Center for Atmospheric Research (NCAR) is used to test and compare these ensemble generation schemes. The NCEP-NCAR reanalysis data for the boreal summer in 2000 are used for the initialization of the control forecast and the verifications of the ensemble forecasts. The ETKF and masked breeding ensemble variances at the analysis time show reasonable correspondences between variance and observational density. Examination of eigenvalue spectra of ensemble covariance matrices demonstrates that while the ETKF maintains comparable amounts of variance in all orthogonal and uncorrelated directions spanning its ensemble perturbation subspace, both breeding techniques maintain variance in few directions. The growth of the linear combination of ensemble perturbations that maximizes energy growth is computed for each of the ensemble subspaces. The ETKF maximal amplification is found to significantly exceed that of the breeding techniques. The ETKF ensemble mean has lower root-mean-square errors than the mean of the breeding ensemble. New methods to measure the precision of the ensemble-estimated forecast error variance are presented. All of the methods indicate that the ETKF estimates of forecast error variance are considerably more accurate than those of the breeding techniques.
    Wang Y., A. Kann, M. Bellus, J. Pailleux, and C. Wittmann, 2010: A strategy for perturbing surface initial conditions in LAMEPS. Atmos. Sci. Lett.,11(2), 108-113, doi: 10.1002/ asl.260.10.1002/asl.26040d796621308f03a28309d6a6d225705http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1002%2Fasl.260%2Ffullhttp://onlinelibrary.wiley.com/doi/10.1002/asl.260/fullAbstract Top of page Abstract 1.Introduction 2.Configuration of ALADIN-LAEF 3.Noncycling surface breeding 4.Results 5.Summary and conclusions Acknowledgements References The lack or inadequate representation of uncertainties in the surface initial conditions (ICs) affects the quality of ensemble forecast, in particular the near surface temperature and precipitation. In this paper, a strategy for perturbing surface ICs in limited area model ensemble prediction system, noncycling surface breeding (NCSB) is proposed. The strategy combines short-range surface forecasts driven by perturbed atmospheric forcing and the breeding method for generating the perturbation to surface ICs. NCSB is implemented and tested in Aire Limit茅e Adaptation dynamique D茅veloppement InterNational-limited area ensemble forecasting (ALADIN-LAEF). Statistical verification demonstrates that the application of NCSB improves the ALADIN-LAEF 2 m temperature and precipitation forecast. Positive impacts are also obtained for temperature and specific humidity in the lower atmosphere. Copyright 漏 2010 Royal Meteorological Society
    Wei M. Z., Z. Toth, R. Wobus, Y. J. Zhu, C. H. Bishop, and X. G. Wang, 2005: Ensemble Transform Kalman Filter-based ensemble perturbations in an operational global prediction system at NCEP. Tellus A,58(1), 28-44, doi: 10.1111/j.1600-0870.2006.00159.x.10.1111/j.1600-0870.2006.00159.x9327d5ee3404291fa0c4ad6c9a0f1523http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1111%2Fj.1600-0870.2006.00159.x%2Fcitedbyhttp://onlinelibrary.wiley.com/doi/10.1111/j.1600-0870.2006.00159.x/citedbyThe initial perturbations used for the operational global ensemble prediction system of the National Centers for Environmental Prediction are generated through the breeding method with a regional rescaling mechanism. Limitations of the system include the use of a climatologically fixed estimate of the analysis error variance and the lack of an orthogonalization in the breeding procedure. The Ensemble Transform Kalman Filter (ETKF) method is a natural extension of the concept of breeding and, as shown by Wang and Bishop, can be used to generate ensemble perturbations that can potentially ameliorate these shortcomings. In the present paper, a spherical simplex 10-member ETKF ensemble, using the actual distribution and error characteristics of real-time observations and an innovation-based inflation, is tested and compared with a 5-pair breeding ensemble in an operational environment.
    Wei M. Z., Z. Toth, R. Wobus, and Y. J. Zhu, 2008: Initial perturbations based on the ensemble transform (ET) technique in the NCEP global operational forecast system. Tellus A,60, 62-79, doi: 10.1111/j.1600-0870.2007.00273.x.10.1111/j.1600-0870.2007.00273.xc630b34e2d19a77224c2d239c5c9c31dhttp%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1111%2Fj.1600-0870.2007.00273.x%2Fcitedbyhttp://onlinelibrary.wiley.com/doi/10.1111/j.1600-0870.2007.00273.x/citedbyABSTRACT Since modern data assimilation (DA) involves the repetitive use of dynamical forecasts, errors in analyses share characteristics of those in short-range forecasts. Initial conditions for an ensemble prediction/forecast system (EPS or EFS) are expected to sample uncertainty in the analysis field. Ensemble forecasts with such initial conditions can therefore (a) be fed back to DA to reduce analysis uncertainty, as well as (b) sample forecast uncertainty related to initial conditions. Optimum performance of both DA and EFS requires a careful choice of initial ensemble perturbations.鈥僁A can be improved with an EFS that represents the dynamically conditioned part of forecast error covariance as accurately as possible, while an EFS can be improved by initial perturbations reflecting analysis error variance. Initial perturbation generation schemes that dynamically cycle ensemble perturbations reminiscent to how forecast errors are cycled in DA schemes may offer consistency between DA and EFS, and good performance for both. In this paper, we introduce an EFS based on the initial perturbations that are generated by the Ensemble Transform (ET) and ET with rescaling (ETR) methods to achieve this goal. Both ET and ETR are generalizations of the breeding method (BM).鈥僒he results from ensemble systems based on BM, ET, ETR and the Ensemble Transform Kalman Filter (ETKF) method are experimentally compared in the context of ensemble forecast performance. Initial perturbations are centred around a 3D-VAR analysis, with a variance equal to that of estimated analysis errors. Of the four methods, the ETR method performed best in most probabilistic scores and in terms of the forecast error explained by the perturbations. All methods display very high time consistency between the analysis and forecast perturbations. It is expected that DA performance can be improved by the use of forecast error covariance from a dynamically cycled ensemble either with a variational DA approach (coupled with an ETR generation scheme), or with an ETKF-type DA scheme.
    Weng Y. H., F. Q. Zhang, 2012: Assimilating airborne Doppler radar observations with an ensemble Kalman filter for convection-permitting hurricane initialization and prediction: Katrina (2005). Mon. Wea. Rev.,140, 841-859, doi: 10.1175/2011MWR3602.1.10.1175/2011MWR3602.1580e11bb8cde69c55146f2780c3c3ecbhttp%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2012MWRv..140..841Whttp://adsabs.harvard.edu/abs/2012MWRv..140..841WThrough a Weather Research and Forecasting model (WRF)-based ensemble Kalman filter (EnKF) data assimilation system, the impact of assimilating airborne radar observations for the convection-permitting analysis and prediction of Hurricane Katrina (2005) is examined in this study. A forecast initialized from EnKF analyses of airborne radar observations had substantially smaller hurricane track forecast errors than NOAA's operational forecasts and a control forecast initialized from NCEP analysis data for lead times up to 120 h. Verifications against independent in situ and remotely sensed observations show that EnKF analyses successfully depict the inner-core structure of the hurricane vortex in terms of both dynamic (wind) and thermodynamic (temperature and moisture) fields. In addition to the improved analyses and deterministic forecast, an ensemble of forecasts initiated from the EnKF analyses also provided forecast uncertainty estimates for the hurricane track and intensity. Also documented here are the details of a series of data thinning and quality control procedures that were developed to generate superobservations from large volumes of airborne radial velocity measurements. These procedures have since been implemented operationally on the NOAA hurricane reconnaissance aircraft that allows for more efficient real-time transmission of airborne radar observations to the ground.
    Xue M., M. J. Tong, and K. K. Droegemeier, 2006: An OSSE framework based on the ensemble square root Kalman filter for evaluating impact of data from radar networks on thunderstorm analysis and forecasting. J. Atmos. Oceanic Technol.,23, 46-66, doi: 10.1175/JTECH1835.1.10.1175/JTECH1835.1dd7184bd218b673cdc280fdc4baadec9http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1002%2Fajpa.1330160106%2Fpdfhttp://onlinelibrary.wiley.com/doi/10.1002/ajpa.1330160106/pdfAbstract A framework for Observing System Simulation Experiments (OSSEs) based on the ensemble square root Kalman filter (EnSRF) technique for assimilating data from more than one radar network is described. The system is tested by assimilating simulated radial velocity and reflectivity data from a Weather Surveillance Radar-1988 Doppler (WSR-88D) radar and a network of four low-cost radars planned for the Oklahoma test bed by the new National Science Foundation (NSF) Engineering Research Center for Collaborative Adaptive Sensing of the Atmosphere (CASA). Such networks are meant to adaptively probe the lower atmosphere that is often missed by the existing WSR-88D radar network, so as to improve the detection of low-level hazardous weather events and to provide more complete data for the initialization of numerical weather prediction models. Different from earlier OSSE work with ensemble Kalman filters, the radar data are sampled on the radar elevation levels and a more realistic forward operator based on ...
    Yuan H. J., J. A. McGinley, P. J. Schultz, C. J. Anderson, and C. G. Lu, 2008: Short-range precipitation forecasts from time-lagged multimodel ensembles during the HMT-West-2006 campaign. Journal of Hydrometeorology,9, 477-491, doi: 10.1175/2007JHM879.1.10.1175/2007JHM879.153c08fdc176de6faba5730f9680f9fc5http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2008jhyme...9..477yhttp://adsabs.harvard.edu/abs/2008jhyme...9..477yNot Available
    Yuan H. L., C. G. Lu, J. A. McGinley, P. J. Schultz, B. D. Jamison, L. Wharton, and C. J. Anderson, 2009: Evaluation of short-range quantitative precipitation forecasts from a time-lagged multimodel ensemble. Wea. Forecasting,24, 18-38, doi: 10.1175/2008WAF2007053.1.10.1175/2008WAF2007053.1ecc6f51a65e9cb34fd3e12ca00e69601http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2009WtFor..24...18Yhttp://adsabs.harvard.edu/abs/2009WtFor..24...18YShort-range quantitative precipitation forecasts (QPFs) and probabilistic QPFs (PQPFs) are investigated for a time-lagged multimodel ensemble forecast system. One of the advantages of such an ensemble forecast system is its low-cost generation of ensemble members. In conjunction with a frequently cycling data assimilation system using a diabatic initialization [such as the Local Analysis and Prediction System (LAPS)], the time-lagged multimodel ensemble system offers a particularly appealing approach for QPF and PQPF applications. Using the NCEP stage IV precipitation analyses for verification, 6-h QPFs and PQPFs from this system are assessed during the period of March-May 2005 over the west-central United States. The ensemble system was initialized by hourly LAPS runs at a horizontal resolution of 12 km using two mesoscale models, including the fifth-generation Pennsylvania State University-National Center for Atmospheric Research Mesoscale Model (MM5) and the Weather Research and Forecast (WRF) model with the Advanced Research WRF (ARW) dynamic core. The 6-h PQPFs from this system provide better performance than the NCEP operational North American Mesoscale (NAM) deterministic runs at 12-km resolution, even though individual members of the MM5 or WRF models perform comparatively worse than the NAM forecasts at higher thresholds and longer lead times. Recalibration was conducted to reduce the intensity errors in time-lagged members. In spite of large biases and spatial displacement errors in the MM5 and WRF forecasts, statistical verification of QPFs and PQPFs shows more skill at longer lead times by adding more members from earlier initialized forecast cycles. Combing the two models only reduced the forecast biases. The results suggest that further studies on time-lagged multimodel ensembles for operational forecasts are needed.
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Manuscript received: 25 May 2016
Manuscript revised: 20 July 2016
Manuscript accepted: 25 July 2016
通讯作者: 陈斌, bchen63@163.com
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The Effects of Land Surface Process Perturbations in a Global Ensemble Forecast System

  • 1. National Meteorological Center, China Meteorological Administration, Beijing 100081, China
  • 2. Environmental Modeling Center, National Centers for Environmental Prediction, 5830 University Research Court, College Park, MD 20740, USA
  • 3. Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China

Abstract: Atmospheric variability is driven not only by internal dynamics, but also by external forcing, such as soil states, SST, snow, sea-ice cover, and so on. To investigate the forecast uncertainties and effects of land surface processes on numerical weather prediction, we added modules to perturb soil moisture and soil temperature into NCEP's Global Ensemble Forecast System (GEFS), and compared the results of a set of experiments involving different configurations of land surface and atmospheric perturbation. It was found that uncertainties in different soil layers varied due to the multiple timescales of interactions between land surface and atmospheric processes. Perturbations of the soil moisture and soil temperature at the land surface changed sensible and latent heat flux obviously, as compared to the less or indirect land surface perturbation experiment from the day-to-day forecasts. Soil state perturbations led to greater variation in surface heat fluxes that transferred to the upper troposphere, thus reflecting interactions and the response to atmospheric external forcing. Various verification scores were calculated in this study. The results indicated that taking the uncertainties of land surface processes into account in GEFS could contribute a slight improvement in forecast skill in terms of resolution and reliability, a noticeable reduction in forecast error, as well as an increase in ensemble spread in an under-dispersive system. This paper provides a preliminary evaluation of the effects of land surface processes on predictability. Further research using more complex and suitable methods is needed to fully explore our understanding in this area.

1. Introduction
  • The importance of land surface processes to numerical weather prediction (NWP) has been recognized in recent years. The first few meters of ground below Earth's surface has a thermal capacity comparable to 1/10 of the entire atmospheric column, which could mean the change in atmospheric temperature through this layer is considerable (Lewis, 2007). It is generally agreed that land surface processes have a substantial influence on both large-scale and mesoscale circulation (Chen and Dudhia, 2001). Large-scale weather patterns are influenced by land surface processes as a consequence of change in moisture influx, static stability, convergence and divergence of flow patterns, vertical motions, and latent heating (Nicholson, 1988; Betts et al., 1996; Li and Zou, 2009). An improved understanding of atmosphere-land interaction, along with accurate measurements of land-surface properties, especially soil moisture, would constitute a major intellectual advantage. And potentially, such progress could lead to dramatic improvements in tackling a number of forecasting problems, including the location and timing of deep convection over land, quantitative precipitation forecasting, and seasonal climate prediction (National Research Council, 1998).

    Among all currently available numerical prediction methods, ensemble forecasting has developed at a particularly fast pace during the last decade, and is expected to continue to play an increasingly important role in weather forecasting compared with other approaches. In ensemble forecasts, a set of different states is discretely sampled from a probability density function to account for uncertainty in the initial conditions. To achieve a reliable probabilistic weather forecast system, a series of schemes have been tested and applied by various NWP centers and researchers. For instance: the time-lagged method, which is a very simple but effective method (Yuan et al., 2008, 2009); the combined application of ensemble data assimilation and the singular vector based perturbations method at the European Centre for Medium-Range Weather Forecasts (Buizza et al., 2008, 2010); the ensemble Kalman filter (EnKF) method plus stochastic perturbation, which is operated at NCEP (Hou et al., 2016); the EnKF with a four-dimensional data method plus a kinetic energy backscatter algorithm, used at the Meteorological Service of Canada (Charron et al., 2010); and bred vectors, employed at the National Meteorological Center, China Meteorological Administration (Deng et al., 2010). Among these methods, at the present moment in time, the EnKF is particularly widely studied and applied as an initial condition perturbation or data assimilation method (Xue et al., 2006; Gao and Xue, 2008; Weng and Zhang, 2012).

    Although ensemble products are playing an increasingly important role in daily probabilistic forecasts, the issue of unreliability and under-dispersion remains a known problem in the field of ensemble forecasting (Hamill and Colucci, 1997). (Sutton et al., 2006) attributed the problems to the inadequate resolution of ensemble members (Mullen and Buizza, 2002), suboptimal methods for generating initial conditions (Hamill et al., 2000; Wang and Bishop, 2003), model biases related to problems in the parameterization of surface and boundary layer effects and the diurnal cycle (Davis et al., 2003), or a lack of perturbation in the characteristics of the land surface state. On the other hand, since atmospheric variability is driven not only by internal dynamics, but also by external forcing factors, such as soil states, SST, snow and sea-ice cover etc., consideration of the uncertainties and effects of land surface processes on the performance of an ensemble prediction system (EPS) is of great importance for improving its forecasting skill. Most methods dealing with the uncertainties are related to the initial state of the atmosphere, but only a small amount of work to perturb the initial state of the land surface in ensemble systems has been carried out thus far. Therefore, in most EPSs, the initial state of soil moisture and soil temperature is the same for each member in most currently available operational ensemble prediction systems (Wang et al., 2010). (Sutton et al., 2006) tried to perturb the soil moisture to test its effect on temperature forecasts and precipitation forecasts; (Wang et al., 2010) generated perturbations of surface variables, such as soil moisture content and surface temperature etc., to represent uncertainties in the surface initial conditions; (McLay et al., 2012) introduced SST variation in the U.S. Navy's GEFS; while at the UK Met Office, its operational EPS contains SST (stochastic process) and soil-moisture perturbations (Tennant and Beare, 2014). Until now, most research related to land surface perturbation has been carried out in regional ensemble forecast systems. But what is the effect at the global scale (i.e. in a GEFS)? Furthermore, most studies have focused on the effects on near-surface variables, but what are the effects on forecast variables in the middle or upper levels? And what is the effect if we consider only the soil uncertainties in the ensemble forecast system? In the present work, using the addition of a module into NCEP's GEFS (Wei et al., 2005, 2008) to perturb the soil moisture and soil temperature, and comparing the results of a set of parallel experiments involving different configurations of land surface and atmospheric perturbation, we investigated whether or not the perturbation of soil states only could improve the system's forecasting skill. The aim in carrying out this study was to expand upon the relatively limited knowledge regarding land surface process perturbations in EPSs.

    The remainder of the paper is organized as follows: Section 2 provides a brief description of the parallel experimental design in the GEFS. Section 3 reports the uncertainties and changes in variables as a result of land surface perturbation. A probabilistic verification of the results regarding the predictability of the GEFS under the different configurations is presented in section 4. Finally, discussion and conclusions are provided in section 5.

2. Configuration of the GEFS
  • The NCEP's GEFS (http://www.emc.ncep.noaa.gov/gmb/yzhu/html/ENS_IMP.html) was developed based on the earlier Global Forecast System (GFS) (Version 8.0.0, T126L28, NCEP Office Note 442) (Global Climate and Weather Modeling Branch, 2003). The horizontal resolution is approximately 110 km in both the analysis and forecast model for the four GFS cycles at 0000, 0600, 1200 and 1800 UTC. The vertical resolution is 64 hybrid layers for the entire 16-day forecast. The GFS land-surface model component is the Noah Land Surface Model (Noah LSM; Chen et al., 1996). Its land-surface parameterization has four subsurface layers (10, 40, 100 and 200 cm). The model also contains an improved algorithm of frozen soil, ground heat flux, and energy/water balance at the surface, along with reformulated infiltration and runoff functions and an upgraded vegetation fraction. The heat capacity, thermal and hydraulic diffusivity, and hydraulic conductivity coefficients are a function of the soil moisture content (Pan and Mahrt, 1987). To obtain initial values of soil moisture and soil temperature, Noah LSM cycles continuously on itself in the Global Data Assimilation System cycles. Values are updated at each model forecast integration time step in response to land-surface forcing (precipitation, surface solar radiation, and near-surface parameters: temperature, humidity, and wind speed) (Campana and Caplan, 2005).

    Figure 1.  Configuration of the atmosphere perturbation (control) run and the atmosphere/surface perturbation (replacement and rescaled) runs.

    The initial perturbations of the GEFS are generated by an ensemble transform (ET) with rescaling technique, and the methods are the same as employed in (Bishop and Toth, 1999), (Wei et al., 2008) and (Deng et al., 2012). To test the effect of land surface process perturbations on the forecasting skill of the GEFS, parallel experiments were devised (Fig. 1). On one side, perturbations were included only in the atmospheric component, named the "control run". Its characteristics included: initial perturbation (ET technique) in the atmospheric component, with four ensemble members; and the tropical cyclone relocation technique. On the other side, in the sensitivity run, perturbations were included in both the atmospheric and land surface processes, besides all the characteristics in the control run, with two methods: the "replacement run" began with the control surface analysis (i.e. cold start), and then, after one cycle (6 h), each member used its forecasted soil temperature and soil moisture from the previous cycle for its initial surface condition, and so on until the end of the experiment; in the "rescaled run", the soil moisture and temperature differences between each forecast member and the deterministic GFS model forecast were added. To maintain the values of the perturbations within a reasonable range, the maximum amplitude of the perturbations was scaled to the climate reference values. For all the perturbation tests, the variables perturbed included soil temperature (four layers: 0-10 cm; 10-40 cm; 40-100 cm; 100-200 cm) and soil moisture (four layers, similar to soil temperature, including soil volumetric water content in the fraction and liquid soil moisture). To avoid the model drifting after long-term integration (several months later), an exponential function of soil moisture (as well as soil temperature) perturbation and soil climate was devised in the experiment [after the land surface process devised in the Global Spectral Forecast Model (T213/T639) at the National Meteorological Center, China Meteorological Administration]. That is, at the beginning of the model integration period, the perturbation part was maintained as a comparatively larger component, and then the climate states gradually dominated; after three months of integration, the soil states would finally convert to the model climate value. As for the rescaled run, because the sum of soil states perturbations was near zero, it would also prevent model from drifting (Tennant and Beare, 2014). The test period was from 1200 UTC 22 August 2006 to 1200 UTC 24 September 2006.

3. Uncertainties and variation resulting from land surface perturbation
  • As described in section 2, the four members in the control experiments used the same initial land surface conditions, whereas they were different in the sensitivity run. Therefore, the differences in the results of the three experiments could only result from the uncertainties in the soil temperature and soil moisture. To investigate whether these uncertainties impose any impacts on the GEFS, the changes in the land surface processes and free atmosphere were explored through comparison with the control experiment.

  • To analyze the effects of perturbing land surface variables on the predictability of the GEFS, we began by examining the variation in soil properties due to land surface perturbation. Firstly, the average volumetric soil moisture difference between the four perturbation members (replacement or rescaled run) and the control experiment (four members) at the start and at a later time (e.g., one week later) was examined (not shown). It was found that, at the very beginning of model integration (second integration cycle after a cold start), the differences between the two experiments were apparent. This indicated that land surface process uncertainties had been introduced into the ensemble system and the interaction between land surface processes and the atmosphere subsequently took place. Although the soil moisture difference was small at the beginning, the difference grew rapidly as the model integrated, indicating strong soil moisture exchange among land surface processes and the atmosphere compared with the control experiment. It is interesting to note that large soil moisture differences did not necessarily correspond to large soil temperature differences, and vice-versa. This indicated that, although the soil moisture and temperature interacted with the atmosphere above, the uncertainties varied temporally and spatially for different elements. Similar characteristics were found in all the other soil levels. However, in the deeper soil layers, the change in soil temperature or moisture decreased rapidly compared with the levels above (Fig. 2 and 3); that is, the deeper down in the soil, the less of a difference was obtained between the perturbed and control runs. An explanation for this might be the fact that deeper soil layers possess more stable thermodynamic and humid characteristics. It was noticed, for instance, that uncertainties in both soil moisture and temperature were large in high-altitude mountain areas, such as the Tibetan Plateau and Iranian Plateau, which may have resulted from fewer high quality surface observations, but nevertheless affected the model's integration and forecasting ability. To investigate the soil variation due to land surface perturbation more thoroughly, the time series of soil spread across the Northern Hemisphere during the experimental period were examined. The ensemble spread was used to measure forecast uncertainties, which was calculated by the deviation of ensemble forecasts from their mean.

    Figure 2.  Temporal evolution of soil temperature spread in the perturbation experiments at different soil depths: (a) 0–10cm; (b) 10–40 cm; (c) 40–100 cm; (d) 100–200 cm (units: K).

    Figure 3.  As in Fig. 2 but for soil moisture (units: %).

    Figure 4.  Time series of average surface latent heat flux (units: W m$^-2$) for the (a) difference between the perturbed and control experiments, and (b) spread for each test, within the Eurasian continent.

    Figure 2 presents the time series of soil temperature spread for the four-member rescaled perturbation, replacement perturbation, and control experiments, at different soil depths. Because there was no soil perturbation in the control test, the spread for the control was close to zero. It is clear that the spread at the near-surface soil level reached a steady state immediately, despite significant fluctuation [Fig. 2a, 0-10 cm, which reflects the range of probable soil temperature uncertainties at this level; the calculation area was the Eurasian continent (20°-80°N, 0°-150°E)]. By contrast, the soil temperature spread at deeper layers (Figs. 2b-d) presented a rapid increase with time, meaning the uncertainties of soil temperature at these levels did not even reach a saturation value within the experimental period. This phenomenon could be explained by the fact that the timescales at which the land surface interacts and responds to atmospheric forcing differ greatly with soil depth (Viterbo and Beljaars, 1995; Beljaars et al., 2007). Studies indicate that the timescales at which the atmosphere and land surface processes interact range from instantaneous to seasonal (Beljaars et al., 2007). Furthermore, (Viterbo and Beljaars, 1995) tried to deduce the timescales associated with each soil layer by using the soil heat budget function, and concluded that the timescales of interaction among soil layers depend on the soil depth, the heat capacity, and soil moisture; for any given layer, the interactions with lower layers operate at longer timescales than interactions with upper layers, and the timescales range from fractions of a day to around 150 days. Therefore, the timescales of interactions between the atmosphere and land surface processes in our experiments were expected to differ from the diurnal to seasonal scale, since there are four soil layers in the land surface processes of the GEFS. At the top level, the timescale of interactions between land surface processes and the atmosphere was very short (not longer than one day), so their interactions reached a balanced state very quickly. In the latter stages of the experiment, there was a tendency for the spread to decrease slightly compared with the earlier period, and this phenomenon probably resulted from the imperfections of our experimental design: if the perturbations had been devised more strategically, such as the non-cycling surface breeding in Wang et al. (2010), the effect would probably have been more obvious. At the second soil level (10-40 cm), the spread grew steadily in the later stages of the experiment, and for the third and last layer, the slopes were larger, implying a longer timescale of interactions. The evolution of spread for soil moisture was similar to that of soil temperature, albeit there were some differences in the variation range and slope (Fig. 3). Given the finding that the spread of soil moisture and temperature continued to increase with soil depth, to determine the overall effect of land surface process perturbations on the predictability of the GEFS should require a longer model integration time.

    Figure 5.  As in Fig. 4 but for sensible heat flux (units: W m$^-2$).

  • It is known that land surface processes play an important role in NWP: as the surface heats up during the day, sensible energy is transferred to the atmosphere, moisture evaporates from the soil or transpires from plants (latent heating), and soil in the lower levels is heated. Changes in land-surface properties have been shown to influence the heat and moisture fluxes within the PBL, which influences convective available potential energy and other measures of deep cumulus cloud activity (Pan and Mahrt, 1987; Pielke, 2001; Sutton et al., 2006). The effect of land surface processes in a numerical prediction system is reflected explicitly and inexplicitly in the boundary layer dynamic and thermodynamic equations; for example, the friction term in the momentum equation, the sensible and latent heating in the energy equation, and the local water vapor budget in the moisture conservation equation. The soil moisture and temperature interact with the atmosphere above in the form of sensible heat flux and evapotranspiration (latent heat flux heat flux). The latent and sensible heat flux within the PBL affect the development of convection and precipitation——a mechanism that operates globally (Pielke, 2001). Therefore, discussing the distribution of sensible and latent flux is key to understanding how land surface processes affect the forecasting skill of tools such as the GEFS.

    Figure 4 shows the difference between the two perturbed tests and control experiments (ensemble mean), and the spread for each test within the Eurasian continent. A positive value in Fig. 4a means that the overall forecasted surface latent heat flux in the perturbed run was larger than in the control run, while a negative value indicates a lower overall latent heat exchange. It is clear that uncertainties in the GEFS resulted in a comparatively larger surface latent heat flux during the one-month experiment over the area; and in view of the spread, the two perturbation tests were obviously larger than the test without soil perturbation. Meanwhile, for surface sensible heat flux, we found a reduction between the control test and the two perturbations (Fig. 5). The positive or negative value between the perturbation tests and the control run was not the point of our focus, but by combining with the spread we were able to find that the perturbation of land surface processes did indeed contribute to obvious variation in forecasted heat fluxes. Therefore, we can confidently conclude that uncertainties in land surface processes tend to change the exchanges of surface sensible and latent heat flux in systems such as the GEFS, therefore affecting the development of atmospheric processes.

    To investigate the effects of land surface process perturbations on day-to-day forecasting, the 5-day lead time forecast with the initial date of 1 September was arbitrarily selected (Fig. 6). It can be seen that all three of these groups of ensemble means differed obviously from one another; for instance, geopotential height and 2-m temperature. Furthermore, the time series of average relative humidity in the replacement perturbation, rescaled perturbation and control tests over the area of focus were calculated (not shown), and the results also indicated that uncertainties in land surface processes contributed to quite different forecast results from day to day, therefore affecting the performance of the ensemble forecasts. Because land surface uncertainties within the GEFS result in a change in the surface energy budget, it follows that partitioning of thermal energy between latent and sensible heat flux (Dr. Jun DU, NCEP, 2006, personal communication), and further alteration of PBL processes, convection, radiation, and other processes in the free atmosphere, will also take place. At the same time, these variations in free-atmospheric processes will feed back to the land surface in perturbation experiments, and thus the interactions between land surface processes and the atmosphere cycles and induces forecast differences between the perturbation and control experiments.

    Figure 6.  A 5-day lead time forecast ensemble mean for the three ensemble tests at 500 hPa (initial time is 1 September 2006): (a) geopotential height (units: gpm); (b) 2-m temperature (units: K).

    Figure 7.  ROC area for the rescaled perturbation (green), replacement perturbation (red) and control (black) experiments for (a) 1000 hPa geopotential height and (b) 500 hPa geopotential height, averaged over the verification domain (Eurasian continent) and over the verification period from 23 August to 24 September 2006 (E4s: control run; E4x: replacement run; E4u: rescaled run).

4. Evaluation of predictability due to land surface perturbation
  • For probabilistic forecasts, there are many existing verification methods to help with judging the quality of a forecast system. Some measures assess the reliability or resolution, while others provide a combined measure of both. No single verification measure provides complete information on the quality of a product (Stanski et al., 1989). The resolution is defined as a forecast system's ability to distinguish, ahead of time, different outcomes of the real atmosphere. Resolution, as the inherent predictive value of a forecast system, is one of two important forecast attributes most sought after by developers of forecast systems, could be only enhanced through improving forecast system. Reliability, however, is equally important in real-world applications (Toth et al., 2006). It refers to the ability to provide unbiased probability estimates for forecasts. To assess the effect of land surface processes on the GEFS, various scores that evaluate the performance of probability forecasts were calculated for the three experiments.

  • The Relative Operating Characteristic (ROC) curve is a plot of the hit rate as a function of the false alarm rate of a series of deterministic forecasts, obtained from the probability distribution by considering several probability thresholds, from p=0% (event systematically forecasted) to p=100% (event never forecasted) (Atger, 1999). It measures the ability of the forecast to discriminate between events and non-events, and indicates the characteristic attribute of resolution. The area under the curve (the "ROC area") is a useful summary measure of forecast skill, and for a perfect ensemble prediction system, ROC area = 1 (Richardson, 2000). Figures 7a and b show the ROC area scores for the 1000 hPa and 500 hPa geopotential heights, respectively. It can be seen that, at the short forecast lead times (1-5 days), there was no obvious difference among the three experiments; as the forecast lead time increased, from day 4 to day 9, the ROC area in the two perturbation experiments was slightly better than in the control. As the lead time increased beyond 10 days, however, the scores of the rescaled and control experiments were almost the same. From the low level (1000 hPa) to the mid-level (500 hPa), the effects seemed to grow larger. This can be explained by the fact that soil moisture and temperature uncertainties affect PBL and radiation processes, among others, directly. As height increases, more complex physics is involved, and thus the effects are enlarged. A lower level of improvement in forecast skill between the perturbation and control tests resides in the fact that there were too few members (four members for each test, due to limitations in computing resources); it is known that the skill of an ensemble forecast generally increases with an increase in the number of members (Langford and Hendon, 2011), Moreover, from the ROC area score, it seems that considering the variation in land surface processes could slightly increase the resolution of global prediction systems.

    Figure 8.  As in Fig. 7 but for the CRPSS.

    Figure 9.  As in Fig. 7 but for the evolution of spread (SP, dashed) and RMSE (RM, solid).

  • The continuous ranked probability score (CRPS) measures the difference between the forecasted and observed cumulative density functions of scalar variables (Candille et al., 2007). It evaluates both the reliability and resolution; furthermore, the CRP skill score (CRPSS) has an advantage of being sensitive to a whole range of values of the parameter of interest, that does not depend on predefined classes at the same time. It evaluates the characteristics of both the resolution and reliability. Similar to the ROC area, the CRPSSs for the perturbation experiments were slightly larger than for the control run at most forecast lead times (beyond day 5, Fig. 8). Likewise, a tendency was found for a higher CRPSS at higher levels (vertically) in the model, illustrating that perturbation of soil moisture and soil temperature contribute to an overall slight improvement of forecast skill in resolution and reliability. However, the replacement test produced a lower score than the other two at the lead times of 15 and 16 days, indicating that perturbations were too large, in comparison with the rescaled perturbation test.

  • Due to the deficiency in the ensemble technique and the limited number of ensemble members, almost all current EPSs are under-dispersive, which remains as a great challenge in ensemble forecasts. For the ensemble verification score, it shows that the ensemble spread (distance between the ensemble mean and ensemble members) is less than the ensemble RMSE (distance between the ensemble mean and the analysis). Figure 9 compares the ensemble mean and spread of the 500 hPa and 1000 hPa geopotential heights. The results indicated that, unlike the slight improvement in forecast skill in terms of resolution and reliability, the perturbation of surface variables in the GEFS contributes to a noticeable reduction in forecast error, as well as an increase in ensemble spread in an under-dispersive system.

    Besides the scores mentioned above, other verification methods were employed to evaluate the performance of the probability forecasts, and the results were similar. All in all, mostly positive results were obtained for the GEFS when considering the uncertainties of land surface processes. This is due to the fact that, unlike the direct perturbation of atmospheric variables, it takes time for land surface process uncertainties to play a role, through interactions between the atmosphere and the land surface, i.e., the characteristics of soil are much more stable than those of air, and so there is a clear time delay in the saturation of soil spread. Finally, due to the limitations of computing resources, too few ensemble members were used in the experiments (four members for each group), which more than likely affected the results (Sutton et al., 2006). Furthermore, a more wisely devised perturbation scheme, more ensemble members, and a longer experiment period are expected to improve the forecast skill.

5. Discussion and conclusions
  • Land surface processes have a profound impact on the overlying atmosphere on all time scales, including the storm scale, meso-scale, weather, sub-seasonal to seasonal, and climate scales. This study took into account the uncertainties in land surface processes by adding a module into the NCEP's GEFS and testing the influence on its predictability. Three experiments were conducted, and the preliminary results can be summarized as follows:

    (1) The variations of soil temperature and soil moisture in the GEFS were examined to illustrate the uncertainties in land surface processes. The spread of the soil states reflected the timescales of interactions between the atmosphere and land surface processes, ranging from fractions of a day to the seasonal scale. The ensemble spread reached a steady state immediately at the near-surface soil level; but with deeper soil underneath the surface, the time it took for the spread to saturate increased. Therefore, a successive integration period of more than 6 months is required in the GEFS to fully represent the effects of land surface perturbation.

    (2) Land surface process uncertainties resulted in large sensible and latent heat flux changes in the perturbation experiments compared to the control run, and the influences of land surface processes propagated to the upper troposphere via PBL processes, convection, and other activities. Locally, or in terms of day-to-day forecasting, there were great differences between the perturbed and control experiments.

    (3) To assess the effects of land surface process perturbations on the GEFS, various scores, such as the ROC area, CRPSS, ensemble mean forecast error and spread, were calculated to evaluate the performance of the probability forecasts. The results indicated that the perturbation of surface variables in the GEFS contributes to slight improvement in forecast skill in terms of resolution and reliability, a noticeable reduction in forecast error, as well as an increase in ensemble spread in an under-dispersive system. The improvement is small at the surface, but the effect becomes increasingly obvious with depth due to interactions or feedback among surface processes and the free atmosphere. Considering the small number of ensemble members in the experiments, we expect the land surface perturbations to potentially have a greater impact in baroclinic zones, which is important for increasing ensemble spread in under-dispersive systems.

    (4) Two different perturbation schemes were designed in this study. It seems that the rescaled experiment showed more skill than the replacement experiment, indicating that it is necessary to control the ranges of perturbation. Moreover, a state-of-the-art land surface perturbation might help to further improve the GEFS' forecast skills. For both schemes, the effects of interactions between land surface processes and the atmosphere differed with variables (soil moisture, soil temperature, geopotential height, temperature field, wind fields etc.), due to the timescales and mechanisms of interactions involved. Limited by computing resources, there were only four members for each ensemble group, which would have greatly affected the results. Therefore, this paper serves only as a preliminary exploration in this field. More complex and suitable methods need to be devised and applied to examine the effects of land surface process perturbations, such as the ET method for land surface processes, the perturbation of more variables (SST, sea-ice, near-surface temperatures, humidity etc.), a longer testing period, and more ensemble members.

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