Probabilistic representation of long-range weather forecasts worked out using the synoptic methods
Demonstrated is the possibility of using the probabilistic approach to synoptic forecasts worked out using the Vangengeim-Girs and Mul’tanovskii-Pagava methods. The approach is realized by analogy with the probabilistic interpretation of ensemble hydrodynamic forecasts based on the Dolgosrochnik-Sinoptik software package. Considered are the concrete examples of the traditional representation of the results of the synoptic forecast of air temperature based on the synoptic methods as well as the variants of the same forecasts presented in terms of probability. The conclusion is made that the application of the probabilistic approach to synoptic forecasts increases their informativeness and extend the visualization potential.
KeywordsSynoptic long-range forecasts probabilistic approach group analog homolog forecast accuracy visualization
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- 1.J. Gill, J. Rubeira, C. Martin, et al., Guidelines on Communicating Forecast Uncertainty, PWS-18, WMO/TD No. 1422 (2008) [Transl. from English].Google Scholar
- 2.A. A. Girs, Macrocirculation Method of Long-range Weather Forecasting (Gidrometeoizdat, Leningrad, 1974) [in Russian].Google Scholar
- 3.G. V. Gruza, E. Ya. Ran’kova, and G. R. Esterle, “A Scheme of Adaptive Statistical Forecast Using the Group of Analogs,” Trudy VNIIGMI-MTsD, No. 13 (1976) [in Russian].Google Scholar
- 4.T. A. Duletova and S. T. Pagava, Fundamentals of the Synoptic Method of Long-range Weather Forecasting (Gidrometeoizdat, Leningrad, 1940) [in Russian].Google Scholar
- 5.E. N. Lorenz, The Nature and Theory of the General Circulation of Atmosphere (Gidrometeoizdat, Leningrad, 1970) [Transl. from English].Google Scholar
- 6.V. M. Mirvis, V. P. Meleshko, V. M. Gavrilina, et al., “Monthly Meteorological Forecasting with the MGO Hydrodynamic Statistical Method. II. Probabilistic Forecast: Analysis and Interpretation of Ensemble Distribution, Forecast Calculation, and Verification,” Meteorol. Gidrol., No. 2 (2006) [Russ. Meteorol. Hydrol., No. 2 (2006)].Google Scholar
- 7.Manual on the Global Data-processing and Forecasting System, Vol. 1 (Appendix IV to WMO Technical Regulations). Global Aspects. WMO No. 485. Attachment II.8. Standardized Verification System (SVS) for Lang-range Forecasts (LRF) (WMO Secretariat, Geneva, 2005) [Transl. from English].Google Scholar
- 8.Manual on Monthly Weather Forecasting (Gidrometeoizdat, Leningrad, 1972) [in Russian].Google Scholar
- 9.A. I. Savichev and V. Yu. Tsepelev, “Monthly Weather Forecasts Based on the Method of Typical Large-scale Processes,” Uchenye Zapiski RGGMU, No. 8 (2008) [in Russian].Google Scholar
- 10.V. Yu. Tsepelev, “Specialized Data-processing System for Analyzing Spatiotemporal Series of Hydrometeorological Characteristics and Its Use for Monthly Weather Forecasting Issues,” in Proceedings of the Scientific Workshop “Problems and Achievements of Long-range Weather Forecasting,” Kiev, October 5–7, 2011 (Nika, Kiev, 2012) [in Russian].Google Scholar
- 11.G. P. Compo, J. S. Whitaker, P. D. Sardeshmukh, et al., “The Twentieth Century Reanalysis Project,” Quart. J. Roy. Meteorol. Soc., 137 (2011).Google Scholar
- 12.E. Kalnay et al., “The NCEP/NCAR 40-year Reanalysis Project,” Bull. Amer. Meteorol. Soc., 77 (1996).Google Scholar