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Combining 2-m Temperature Nowcasting and Short Range Ensemble Forecasting : Volume 18, Issue 6 (02/12/2011)

By Kann, A.

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Book Id: WPLBN0003982842
Format Type: PDF Article :
File Size: Pages 8
Reproduction Date: 2015

Title: Combining 2-m Temperature Nowcasting and Short Range Ensemble Forecasting : Volume 18, Issue 6 (02/12/2011)  
Author: Kann, A.
Volume: Vol. 18, Issue 6
Language: English
Subject: Science, Nonlinear, Processes
Collections: Periodicals: Journal and Magazine Collection (Contemporary), Copernicus GmbH
Historic
Publication Date:
2011
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications

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Haiden, T., Wittmann, C., & Kann, A. (2011). Combining 2-m Temperature Nowcasting and Short Range Ensemble Forecasting : Volume 18, Issue 6 (02/12/2011). Retrieved from http://hawaiilibrary.net/


Description
Description: Central Institute for Meteorology and Geodynamics, Vienna, Austria. During recent years, numerical ensemble prediction systems have become an important tool for estimating the uncertainties of dynamical and physical processes as represented in numerical weather models. The latest generation of limited area ensemble prediction systems (LAM-EPSs) allows for probabilistic forecasts at high resolution in both space and time. However, these systems still suffer from systematic deficiencies. Especially for nowcasting (0–6 h) applications the ensemble spread is smaller than the actual forecast error. This paper tries to generate probabilistic short range 2-m temperature forecasts by combining a state-of-the-art nowcasting method and a limited area ensemble system, and compares the results with statistical methods. The Integrated Nowcasting Through Comprehensive Analysis (INCA) system, which has been in operation at the Central Institute for Meteorology and Geodynamics (ZAMG) since 2006 (Haiden et al., 2011), provides short range deterministic forecasts at high temporal (15 min–60 min) and spatial (1 km) resolution. An INCA Ensemble (INCA-EPS) of 2-m temperature forecasts is constructed by applying a dynamical approach, a statistical approach, and a combined dynamic-statistical method. The dynamical method takes uncertainty information (i.e. ensemble variance) from the operational limited area ensemble system ALADIN-LAEF (Aire Limitée Adaptation Dynamique Développement InterNational Limited Area Ensemble Forecasting) which is running operationally at ZAMG (Wang et al., 2011). The purely statistical method assumes a well-calibrated spread-skill relation and applies ensemble spread according to the skill of the INCA forecast of the most recent past. The combined dynamic-statistical approach adapts the ensemble variance gained from ALADIN-LAEF with non-homogeneous Gaussian regression (NGR) which yields a statistical \mbox{correction} of the first and second moment (mean bias and dispersion) for Gaussian distributed continuous variables. Validation results indicate that all three methods produce sharp and reliable probabilistic 2-m temperature forecasts. However, the statistical and combined dynamic-statistical methods slightly outperform the pure dynamical approach, mainly due to the under-dispersive behavior of ALADIN-LAEF outside the nowcasting range. The training length does not have a pronounced impact on forecast skill, but a spread re-scaling improves the forecast skill substantially. Refinements of the statistical methods yield a slight further improvement.

Summary
Combining 2-m temperature nowcasting and short range ensemble forecasting

Excerpt
Brozkova, R., Klaric, D., Ivatek-Sahdan, S., Geleyn, J. F., Casse, V., Siroka, M., Radnoti, G., Janousek, M., Stadlbacher, K., and Seidl, H.: DFI Blending, an alternative tool for preparation of the initial conditions for LAM. PWRP Report Series No. 31 (CAS/JSCWGNE Report), WMO-TD, No. 1064, 1.7, 2001.; Buizza, R., Miller, M., and Palmer, T. N.: Stochastic simulation of model uncertainties, Q. J. Roy. Meteor. Soc., 125, 2887–2908, 1999.; Cui, B., Toth, Z., Zhu, Y., Hou, D., and Wobus, R.: Bias correction methods – adjusting moments, Geophys. Res. Abstr., Vol. 7, 05997, 2005.; Buizza, R., Houtekamer, P. L., Toth, Z., Pellerin, G., Wei, N., and Zhu, Y.: A comparison of the ECMWF, MSC, and NCEP Global Ensemble Prediction Systems, Mon. Weather Rev., 133, 1076–1097, 2005.; Dance, S., Ebert, E., and Scurrah, D.: Thunderstorm strike probability nowcasting, J. Atmos. Ocean. Tech., 27, 79–93, 2010.; Gneiting, T., Raftery, A. E., Westveld, A. H., and Goldman, T.: Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation, Mon. Weather Rev., 133, 1098–1118, 2005.; Haiden, T., Kann, A., Wittmann, C., Pistotnik, G., Bica, B., and Gruber, C.: The Integrated Nowcasting through Comprehensive Analysis (INCA) system and its validation over the Eastern Alpine region. Weather Forecas., 26, 166–183, doi:10.1175/2010WAF2222451.1, 2011.; Hagedorn, R., Hamill, T. M., and Whitaker, J. S.: Probabilistic forecast calibration using ECMWF and GFS ensemble reforecasts, Part I: temperature, Mon. Weather Rev., 136, 2608–2619, 2008.; Hamill, T. M., Snyder, C., and Morss, R. E.: A comparison of probabilistic forecasts from bred, singular-vector, and perturbed observation ensembles, Mon. Weather Rev., 128, 1835–1851, 2000.; Hamill, T. M., Hagedorn, R., and Whitaker, J. S.: Probabilistic forecast calibration using ECMWF and GFS ensemble reforecasts. Part II: precipitation, Mon. Weather Rev., 136, 2620–2632, 2008.; Hersbach, H.: Decomposition of the continuous ranked probability score for ensemble prediction systems, Weather Forecast., 15, 559–570, 2000.; Homar, V., Stensrud, D. J., Levit, J. J., and Bright, D. R.: Value of human-generated perturbations in short-range ensemble forecasts of severe weather, Weather Forecast., 21, 347–363, 2006.; Kann, A., Wittmann, C., Wang, Y., and Ma, X.: Calibrating 2-m Temperature of Limited Area Ensemble Forecasts Using High-Resolution Analysis, Mon. Weather Rev., 137, 3373–3387, 2009.; Leutbecher, M. and Palmer, T. N.: Ensemble forecasting, J. Comput. Phys., 227, 3515–3539, 2008.; Lu, C., Yuan, H., Schwartz, B. E., and Benjamin, S. G.: Short-range numerical weather prediction using time-lagged ensembles, Weather Forecast., 22, 580–595, 2007.; Stanski, H. R., Wilson, L. J., and Burrows, W. R.: Survey of common verification methods in meteorology. WMO/WWW Tech. Rep. 8, 114 pp., 1989.; Thorarinsdottir, T. L. and Gneiting, T.: Probabilistic forecasts of wind speed: Ensemble model output statistics by using heteroskedastic censored regression, J. R. Stat. Soc. A. Sta., 173, 371–388, 2010.; Wang, Y., Haiden, T., and Kann, A.: The operational Limited Area Modelling system at ZAMG: ALADIN-AUSTRIA, Österr. Beiträge Meteorol. Geophys., 37, ISSN 1016-6254, 2006.; Wang, Y., Kann, A., Bellus, M., Pailleux, J., and Wittmann, C.: A strategy for perturbing surface initial conditions in LAMEPS, Atmos. Sci. Lett., 11, 108–113, doi:10.1002/asl.260, 2010.; Wang, Y., Bellus, M., Wittmann, C., Steinheimer, M., Weidle, F., Kann, A., Ivatek-Sahdan, S., Tian, W., Ma, X., Tascu, S., and Bazile, E.: The Central European limited-area ensemble forecasting system: ALADIN-LAEF, Q. J. Roy. Meteor. Soc., 137, 483–502, doi:10.1002/qj.751, 2011.; Zhu, Y., Toth, Z., Wobus, R., Richardson, D., and Mylne, K.: The economic value of ensemble

 

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