DOI: 10.1016/j.atmosres.2018.10.004
Scopus记录号: 2-s2.0-85055908991
论文题名: Potential of an EnKF Storm-Scale Data Assimilation System Over Sparse Observation Regions with Complex Orography
作者: Carrió D.S. ; Homar V. ; Wheatley D.M.
刊名: Atmospheric Research
ISSN: 1698095
出版年: 2019
卷: 216 起始页码: 186
结束页码: 206
语种: 英语
英文关键词: Data assimilation
; EnKF
; HyMeX
; Radar reflectivity
; Western mediterranean
; WRF-DART
Scopus关键词: Coastal zones
; Radar
; Reflection
; Weather information services
; Data assimilation
; EnKF
; HyMeX
; Radar reflectivities
; Western Mediterranean
; WRF-DART
; Weather forecasting
; algorithm
; data assimilation
; ensemble forecasting
; Kalman filter
; radar
; weather forecasting
; Corse
; France
; Italy
; Mediterranean Region
英文摘要: High-impact weather events over sparse data regions with complex orography, such as the Mediterranean region, remain a challenge for numerical weather prediction. This study evaluates, for the first time, the ability of a multiscale ensemble-based data assimilation system to reproduce a heavy precipitation episode that occurred during the first Special Observation Period (SOP1) of the Hydrological cycle in the Mediterranean Experiment (HyMeX). During the Intense Observation Period (IOP13) from 14 to 15 October 2012, convective maritime activity associated with an advancing cold front affected coastal areas of southern France, Corsica and Italy. With the main objective of improving forecasts of this weather event, a data assimilation (DA) system using the Ensemble Kalman Filter (EnKF) algorithm is implemented. The potential impact of assimilating conventional in-situ observations (METAR, aircrafts, buoys and rawinsondes) and single-Doppler reflectivity data to improve numerical representation of growing convective maritime structures that will evolve towards coastal populated areas is evaluated. Results indicate that information provided by both observation sources contribute to initiation and subsequent evolution of convective structures not captured by the conventional runs. Notably, data assimilation experiments produce the best quantitative verification scores for the short range (6–8 h) forecasts of accumulated precipitation. Beyond 6–8 h, data assimilation experiments and those without data assimilation are indistinguishable. Sensitivity experiments, evaluating the impact of increasing the length of the radar data assimilation period, reveal the importance of assimilating high-frequency reflectivity data during a mid-term period (6 h approx.) to better depict deep convective structures initiated over the sea that evolve towards populated coastal areas. © 2018 Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/122378
Appears in Collections: 气候变化事实与影响
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作者单位: Universitat de les Illes Balears, Physics Department, Carretera de Valldemossa km 7.5, Palma de Mallorca, 07122, Spain; Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, NOAA/OAR/National Severe Storms Laboratory, Norman, OK, United States
Recommended Citation:
Carrió D.S.,Homar V.,Wheatley D.M.. Potential of an EnKF Storm-Scale Data Assimilation System Over Sparse Observation Regions with Complex Orography[J]. Atmospheric Research,2019-01-01,216