doi:10.1007/s00376-016-6036-8

Atger F., 1999: The skill of ensemble prediction systems. Mon. Wea. Rev.,127, 1941-1953, doi: 10.1175/1520-0493(1999) 127<1941:TSOEPS>2.0.CO;2.10.1175/1520-0493(1999)1272.0.CO;2

13075551f0f4138b9d22d57cc8501e6a

http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F1999MWRv..127.1941A

http://adsabs.harvard.edu/abs/1999MWRv..127.1941ANot Available

Beljaars A., G. Balsamo, A. Betts, and P. Viterbo, 2007: Atmosphere/surface interactions in the ECMWF model at high latitudes. Proc. of ECMWF Seminar on Polar Meteorology, 4-8 September 2006, Reading, ECMWF, 153- 168.91622371-1a87-4d37-982b-8f71b42a77b5

1caba74c35f90de65a359fc5ad8f25b6

http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F229827219_Atmospheresurface_interactions_in_the_ECMWF_model_at_high_latitudes

refpaperuri:(0952c5f0804429b3596802369c8757ad)

http://www.researchgate.net/publication/229827219_Atmospheresurface_interactions_in_the_ECMWF_model_at_high_latitudesThe surface boundary condition is an essential aspect of an atmospheric model as it controls the surface fluxes of momentum, heat and moisture into the atmosphere. The ocean represents a relatively simple boundary condition with a slowly varying sea surface

Betts A. K., J. H. Ball, A. C. M. Beljaars, M. J. Miller, and P. A. Viterbo, 1996: The land surface-atmosphere interaction: A review based on observational and global modeling perspectives. J. Geophys. Res.,101(D3), 7209-7225, doi: 10.1029/ 95JD02135.10.1029/95JD02135

5b3ca8583ad256f8ed484e32c11ae770

http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F95JD02135%2Ffull

http://onlinelibrary.wiley.com/doi/10.1029/95JD02135/fullCiteSeerX - Scientific documents that cite the following paper: The land-surface atmosphere interaction: A review based on observational and global modeling perspectives

Bishop C. H., Z. Toth, 1999: Ensemble transformation and adaptive observations. J. Atmos. Sci.,56, 1748-1765, doi: 10.1175/1520-0469(1999)056<1748:ETAAO>2.0.CO;2.766d4dfae61ff755e9f7e006911a2b70

http%3A%2F%2Fnsr.oxfordjournals.org%2Fexternal-ref%3Faccess_num%3D10.1175%2F1520-0469%281999%290562.0.CO%3B2%26link_type%3DDOI

http://nsr.oxfordjournals.org/external-ref?access_num=10.1175/1520-0469(1999)0562.0.CO;2&link_type=DOI

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.346

1682d3647c1a2fa3f01542d29fc8af49

http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1002%2Fqj.346%2Ffull

http://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.
[ Available from http://www.ecmwf.int/sites/default/files/elibrary/2010/14602-newsletter-no123-spring-2010.pdf].e17b42214a3d23df8fafbb5944738872

http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F290162226_Combined_use_of_EDA-_and_SV-based_perturbations_in_the_EPS

http://www.researchgate.net/publication/290162226_Combined_use_of_EDA-_and_SV-based_perturbations_in_the_EPS

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/MWR3414.1

a8fb803c013da9200058d82789d3d07b

http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2007MWRv..135.2688C

http://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 MWR3187.1.10.1175/2009MWR3187.1

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http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2010MWRv..138.1877C

http://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- 7268.10.1029/95JD02165

d8487496ae05adc1c65503a636e8b5e4

http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F95JD02165%2Ffull

http://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-fe2707242a84

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http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F245810779_Coupling_an_Advanced_Land-SurfaceHydrology_Model_with_the_Penn_StateNCAR_MM5_Modeling_System

refpaperuri:(9cc201f9c4e47979a1730595d2c374e6)

http://www.researchgate.net/publication/245810779_Coupling_an_Advanced_Land-SurfaceHydrology_Model_with_the_Penn_StateNCAR_MM5_Modeling_SystemPublication » 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;2

a021a861feadfb8823032e56cf77a346

http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2003mwrv..131.2667d

http://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.1084.2010.00199

c43357b270d45f095c41e3cfe2554e6d

http%3A%2F%2Fen.cnki.com.cn%2FArticle_en%2FCJFDTOTAL-YYQX201005002.htm

http://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/s13351-012-0105-4

0062401d-dfd7-46fe-bceb-5c5a5ff0624b

WOS:000302927800005

bfb2de89158e0d0d563e84337f978a26

http%3A%2F%2Fd.wanfangdata.com.cn%2FPeriodical%2Fqxxb-e201201005

http://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: 10.1175/2007MWR2120.1.10.1175/2007MWR2120.1

bca7201aeab8250446bb12db44071d83

http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2008MWRv..136..945G

http://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.

Global Climate and Weather Modeling Branch, 2003: The GFS Atmospheric Model. NCEP Office Note 442, Environmental Modeling Center. [Available online at http://www.nws.noaa.gov/ost/climate/STIP/AGFS_DOC_1103.pdf]

Hamill T. M., S. J. Colucci, 1997: Verification of Eta-RSM short-range ensemble forecasts. Mon. Wea. Rev., 125, 1312- 1327, doi: 10.1175/1520-0493(1997)125<1312:VOERSR>2. 0.CO;2.07f555bfc5cc56243f8973eff7547d70

http%3A%2F%2Fadsabs.harvard.edu%2Fcgi-bin%2Fnph-data_query%3Fbibcode%3D1997MWRv..125.1312H%26db_key%3DPHY%26link_type%3DABSTRACT

http://xueshu.baidu.com/s?wd=paperuri%3A%28c8e2f86bcf21144f84f9e788783fcdfb%29&filter=sc_long_sign&tn=SE_xueshusource_2kduw22v&sc_vurl=http%3A%2F%2Fadsabs.harvard.edu%2Fcgi-bin%2Fnph-data_query%3Fbibcode%3D1997MWRv..125.1312H%26db_key%3DPHY%26link_type%3DABSTRACT&ie=utf-8&sc_us=5849885618847077309

Hamill T. M., C. Snyder, and R. E. Morss, 2000: A comparison of probabilistic forecasts from bred,singular-vector, and perturbed observation ensembles. Mon. Wea. Rev., 128, 1835-1851, doi: 10.1175/1520-0493(2000)128<1835:ACOPFF>2. 0.CO;2.9b79bcbb8a9439ebd9f6df3f49b53dfe

http%3A%2F%2Fadsabs.harvard.edu%2Fcgi-bin%2Fnph-data_query%3Fbibcode%3D2000MWRv..128.1835H%26db_key%3DPHY%26link_type%3DEJOURNAL

http://xueshu.baidu.com/s?wd=paperuri%3A%284f3795c7fddb596f40673b9e0399f4c8%29&filter=sc_long_sign&tn=SE_xueshusource_2kduw22v&sc_vurl=http%3A%2F%2Fadsabs.harvard.edu%2Fcgi-bin%2Fnph-data_query%3Fbibcode%3D2000MWRv..128.1835H%26db_key%3DPHY%26link_type%3DEJOURNAL&ie=utf-8&sc_us=10653996544642623098

Hou D., and Coauthors,., 2016: Current State of NCEP Global Ensemble. 7th NCEP Ensemble Users Workshop, 13-15 June 2016, College Park, MD.

Langford S., H. H. Hendon, 2011: Assessment of international seasonal rainfall forecasts for Australia and the benefit of multi-model ensembles for improving reliability. CAWCR Technical Report No. 039.03a4cd6621028bed317450689aa94b8f

http%3A%2F%2Fciteseerx.ist.psu.edu%2Fviewdoc%2Fdownload%3Bjsessionid%3D9D2AB1C1D60EA7FAF82EA564E4BAEE46%3Fdoi%3D10.1.1.431.6605%26rep%3Drep1%26type%3Dpdf

http://xueshu.baidu.com/s?wd=paperuri%3A%28c39f7e3eb2ede8083639106ea946dfee%29&filter=sc_long_sign&tn=SE_xueshusource_2kduw22v&sc_vurl=http%3A%2F%2Fciteseerx.ist.psu.edu%2Fviewdoc%2Fdownload%3Bjsessionid%3D9D2AB1C1D60EA7FAF82EA564E4BAEE46%3Fdoi%3D10.1.1.431.6605%26rep%3Drep1%26type%3Dpdf&ie=utf-8&sc_us=950310281084147025

Lewis F. R., 2007: Weather Prediction by Numerical Process. 2nd ed. Cambridge University Press,236pp.10.2136/sssaj1966.03615995003000010006x

1abf5df7a676a8b51e14d3c7625030dd

http%3A%2F%2Fwww.ams.org%2Fmathscinet-getitem%3Fmr%3D2358797

http://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

Li X. F., C. Z. Zou, 2009: Effects of sea surface temperature,radiation, cloud microphysics, and diurnal variations on vertical structures of tropical tropospheric temperature: A two-dimensional equilibrium cloud-resolving modeling study. Meteor. Atmos. Phys., 105, 85-98, doi: 10.1007/ s00703-009-0039-2.

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: 10.1029/2011JD016937.10.1029/2011JD016937

fde50b756e903f890e1a7c8fd68cbb6a

http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F2011JD016937%2Fabstract

http://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;2

be5867472906c8f6b04ee5364b870f4e

http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2002WtFor..17..173M

http://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)00127-3

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http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F248353359_The_Atmospheric_Sciences_Entering_the_Twenty-First_Century

http://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/ 030913338801200102.10.1177/030913338801200102

ee327e8c4be4aa6bbefa54a70af65666

http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F237970060_Land_surface_atmosphere_interaction_physical_processes_and_surface_changes_and_their_impact

http://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/BF00121563

6d1764a3c8e01ebf874c5509b508326d

http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F1987BoLMe..38..185P

http://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 000072.10.1029/1999RG000072

a3a1619744d5408b32d80ae49f3e8bb4

http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F1999RG000072%2Fabstract

http://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.49712656313

14f9db02ceffc9a0ec0f00cc22afbb65

http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1002%2Fqj.49712656313%2Ffull

http://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.871590666fa75358bc35b6a4b1699156

http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F264158612_Survey_of_Common_Verification_Methods_in_Meteorology

http://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.s1

86621188eb034330e4d4a087fba43f1f

http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2006MWRv..134.3174S

http://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.2202

d5918fa022fe8141f4ae1664b9054386

http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1002%2Fqj.2202%2Fcitedby

http://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/CBO9780511617652.023

4c2dccbc836e4b0d21ec3b819a8f7bac

http%3A%2F%2Fdx.doi.org%2F10.1017%2FCBO9780511617652.023

http://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;2

eac2109858ddaa6ea43cb0ba3c58aebe

http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F1995JCli....8.2716V

http://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;2

e944df1a5d6726e5668650fe360597c4

http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2003EAEJA.....8087W

http://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.260

40d796621308f03a28309d6a6d225705

http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1002%2Fasl.260%2Ffull

http://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.x

9327d5ee3404291fa0c4ad6c9a0f1523

http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1111%2Fj.1600-0870.2006.00159.x%2Fcitedby

http://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.x

c630b34e2d19a77224c2d239c5c9c31d

http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1111%2Fj.1600-0870.2007.00273.x%2Fcitedby

http://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: 10.1175/2011MWR3602.1.10.1175/2011MWR3602.1

580e11bb8cde69c55146f2780c3c3ecb

http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2012MWRv..140..841W

http://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/JTECH1835.1

dd7184bd218b673cdc280fdc4baadec9

http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1002%2Fajpa.1330160106%2Fpdf

http://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: 10.1175/2007JHM879.1.10.1175/2007JHM879.1

53c08fdc176de6faba5730f9680f9fc5

http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2008jhyme...9..477y

http://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: 10.1175/2008WAF2007053.1.10.1175/2008WAF2007053.1

ecc6f51a65e9cb34fd3e12ca00e69601

http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2009WtFor..24...18Y

http://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.