THE USE OF AUTOMATED SYNOPTIC TYPING FOR CONDITIONAL VERIFICATION OF NUMERICAL WEATHER PREDICTION IN THE PERM REGION
DOI:
https://doi.org/10.17072/2079-7877-2021-1-68-80Keywords:
conditional verification, automated typing, synoptic type, GFS model, GEM model, 2-meter air temperature, Perm regionAbstract
The article discusses the possibility of verification of short-term 2-meter air temperature forecasts with the Global Forecast System and Global Environment Multiscale numerical weather prediction models depending on the observed synoptic type (a case study of the Perm region for the period 2018–2019). As part of the study, we have developed a system of automated determination of synoptic type based on a two-stage procedure, including decomposition of mean sea level pressure fields via principal component analysis and the subsequent clustering of decomposition coefficients using K-means. It has been established that GFS forecasts are more dependent on synoptic type in summer than in winter. The decline of forecast quality, expressed in systematic underestimation of forecast temperature by 0.6°–1.2°, is noted for synoptic types associated with warm air advection.: In contrast, GEM forecasts tend to lack accuracy in winter. A sharp decrease in forecast quality has been discovered in the central area of anticyclone at night, when the forecast accuracy drops to 44%. The obtained results could be useful in operational forecasting and model postprocessing.References
Аухадеев Т.Р. Барико-циркуляционный режим Приволжского федерального округа // Географический вестник. 2014. № 2 (29). С. 50–59.
Базовые требования к технологии подготовки краткосрочных прогнозов погоды. РД 52.27.723-2009. Обнинск: ИГ-СОЦИН, 2009. 32 с.
Калинин Н.А., Пищальникова Е.В., Шихов А.Н., Быков А.В. Прогноз сильных снегопадов на Урале с использованием численных моделей атмосферы // Географический вестник. 2019. №1(48). С. 93–106. doi: 10.17072/2079-7877-2019-1-93-106.
Костарев С.В., Русин И.Н. Оценка качества численного прогноза температуры воздуха в зависимости от синоптической ситуации в Пермском крае // Географический вестник. 2019. №3(50). С. 48–62. doi: 10.17072/2079-7877-2019-3-48-62.
Проведение производственных (оперативных) испытаний новых и усовершенствованных методов гидрометеорологических и гелиогеофизических прогнозов РД № 52.27.284-91: метод. указания. Л.: Гидрометеоиздат, 1991. 149 с.
Смирнов Ч.П., Вайновский П.А., Титов Ю.Э. Статистический диагноз и прогноз океанологических процессов. СПб.: Гидрометеоиздат, 1992. 200 с.
Bertolani L., Salerno R., Dipierro G. Self-organizing maps: an application to NWP models verification // Primo Congresso Nazionale AISAM. Bologna, 2018. P. 105.
Bundel A.Yu., Astakhova E.D., Rozinkina I.A., Alferov D.Yu., Semenov A.E. Verification of Short- and Medium-range Precipitation Forecasts from the Ensemble Modeling System of the Hydrometcenter of Russia // Russ. Meteorol. Hydrol. 2011. V. 36. No. 10. P. 653–662. doi: 0.3103/S1068373911100025.
Casati B., Haiden T., Brown B., Nurmi P., Lemieux J.-F. Verification of environmental prediction in polar regions: Recommendations for the Year of Polar Prediction. WWRP 2017-1. Geneva: WMO, 2017. 44 p.
Casati B., Wilson L.J., Stephenson D.B., Nurmi P., Ghelli A., Pocernich M., Damrath U., Ebert E.E., Brown B.G., Mason S. Forecast verification: current status and future directions//Meteorol. Appl. 2008. V. 15. No. 1. P. 3–18. doi: 10.1002/met.52.
Compo G.P., Whitaker J.S., Sardeshmukh P.D., Matsui N., Allan R.J., Yin X., Gleason B.E., Vose R.S., Rutledge G., Bessemoulin P., Brönnimann S., Brunet M., Crouthamel R.I., Grant A.N., Groisman P.Y., Jones P.D., Kruk M.C., Kruger A.C., Marshall G.J., Maugeri M., Mok H.Y., Nordli Ø., Ross T.F., Trigo R.M., Wang X.L., Woodruff S.D., Worley S.J. The twentieth century reanalysis project // Q. J. Roy. Meteorol. Soc. 2011. V. 137. No. 654. P. 1–28. doi: 10.1002/qj.776.
Cuell, C., Bonsal B. An assessment of climatological synoptic typing by principal component analysis and kmeans clustering // Theor. Appl. Climatol. 2009. V. 98. P. 361–373. doi: 10.1007/s00704-009-0119-8.
Dahni R.R. An automated synoptic typing system using archived and real-time NWP model output // 19th International Conference on Interactive Information and Processing Systems (IIPS) for Meteorology, Oceanography and Hydrology. Long Beach, California, 2003.
Huth R. Properties of the circulation classification scheme based on the rotated principal component analysis // Meteorol. Atmos. Phys. 1996. V. 59. P. 217–233. doi: 10.1007/BF01030145.
Jiang N. A new objective procedure for classifying New Zealand synoptic weather types during 1958-2008 // Int. J. Climatol. 2011. V. 31. P. 863–879. doi: 10.1002/joc.2126.
Jiang N., Cheung K., Luo K., Beggs P.J., Zhou W. On two objective procedures for classifying synoptic weather types over east Australia // Int. J. Climatol. 2012. V. 32. P. 1475–1494. doi: 10.1002/joc.2373.
Kalinin N.A., Kislov A.V., Babina E.D., Vetrov A.L. Estimation of air temperature reproduction quality by the MM5 model in the Urals in July // Russ. Meteorol. Hydrol. 2010. V. 35. No. 10. P. 659–664. doi: 10.3103/S106837391010002X.
Key J., Crane R.G. A comparison of synoptic classification schemes based on ‘objective’ procedures. // Journal of Climatology. 1986. V. 6. P. 375–388. doi: 10.1002/joc.3370060404.
Kirchhofer W. Classification of European 500 mb patterns // Arbeitsbericht der Schweizerischen Meteorologischen Zentralanstalt. 1973. V. 43. P. 1–16.
Lund I.A. Map-pattern classification by statistical methods // J. Appl. Meteorol. 1963. V. 2. P. 56–65. doi: 10.1175/1520-0450(1963)002<0056:MPCBSM>2.0.CO;2.
McMurdie L.A., Casola J. Weather regimes and forecast errors in the Pacific Northwest // Weather Forecast. 2009. V. 24. No. 3. P. 829–842. doi: 10.1175/2008WAF2222172.1.
NCEP. List of GFS implementations. URL: www.emc.ncep.noaa.gov/emc/pages/numerical_forecast_systems/gfs/implementations.php (дата обращения: 04.10.2020).
Neal R., Fereday D., Crocker R., Comer R.E. A flexible approach to defining weather patterns and their application in weather forecasting over Europe // Meteorol. Appl. 2016. V. 23. No 3. P. 389–400. doi: 10.1002/met.1563.
Pedregosa F., Varoquaux G., Gramfort A., Michel V., Thirion B., Grisel O., Blondel M., Prettenhofer P., Weiss R., Dubourg V., Vanderplas J., Passos A., Cournapeau D., Brucher M., Perrot M., Duchesnay E. Scikit-learn: Machine Learning in Python // J. Mach. Learn. Res. 2011. V. 12. P. 2825–2830.
Qaddouri A., Lee V. The Canadian Global Environmental Multiscale model on the Yin-Yang grid system // Q. J. Roy. Meteorol. Soc. 2011. V. 137. No. 660. P. 1913–1926. doi: 10.1002/qj.873.
Rossa A., Nurmi P., Ebert E. Overview of methods for the verification of quantitative precipitation forecasts // Precipitation: Advances in Measurement, Estimation and Prediction / Ed. S.C., Michaelides. Berlin: Springer-Verlag Berlin Heidelberg, 2008. С. 419–452. doi: 10.1007/978-3-540-77655-0.
Rousseeuw, P.J. Silhouettes: A Graphical Aid to the Interpretation and Validation of Cluster Analysis // Comput. Appl. Math. 1987. V. 20. P. 53–65. doi: 10.1016/0377-0427(87)90125-7.
