DOI: 10.1016/j.atmosres.2018.10.020
Scopus记录号: 2-s2.0-85056725447
论文题名: Improving ECMWF-based 6-hours maximum rain using instability indices and neural networks
作者: Manzato A. ; Pucillo A. ; Cicogna A.
刊名: Atmospheric Research
ISSN: 1698095
出版年: 2019
卷: 217 起始页码: 184
结束页码: 197
语种: 英语
英文关键词: 6-Hour rain forecast
; ECMWF downscaling
; Neural networks
Scopus关键词: Forecasting
; Neural networks
; Convective rain
; Down-scaling
; Instability index
; Multi-regression model
; Non-linear methods
; Statistical downscaling
; Vertical profile
; Vertical resolution
; Rain
; artificial neural network
; climatology
; downscaling
; rainfall
; raingauge
; weather forecasting
; Alps
; Eastern Alps
; Friuli-Venezia Giulia
; Italy
; Julian Alps
英文摘要: Friuli Venezia Giulia (FVG, NE Italy) is an area of maximum rainfall in the whole Alpine chain territory, reaching more than 3200 mm of mean annual rain in the Julian Prealps. According to recent climatological studies, the same area is also one of the European spot in recent lightning climatologies, meaning that convective rain plays an important role in the total rainfall. A network of 104 raingauges placed around the FVG territory is used to extract the absolute maximum rain accumulated every 6 hours in four subareas of FVG. In an attempt to improve the original ECMWF maximum rain, these data have been targeted to develop 32 statistical downscaling models, according to the period of the day, of the year and specific sub-area. ECMWF 6-hour rain forecasts available for all the gridpoints encompassed in the FVG territory and some derived variables (absolute values, anomalies, standardized values, plus mean, max and SD in time and/or space) have been used as predictors. With respect to a previous version of this work, here also the instability pseudo-indices (derived from the vertical profile with the maximum vertical resolution available in the ECMWF hybrid levels) are used as candidate predictors. Moreover, also non-linear methods, namely neural networks, are implemented, together with exhaustive multiregression models. Results show that the 32 models improve -on average- R2 of 12% on the validation sample and of 5% on the 2017 test sample, with respect to the ECMWF rain forecast, but the improvement is particularly notable during the convective season (18%). © 2018 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/122360
Appears in Collections: 气候变化事实与影响
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作者单位: OSMER – Osservatorio Meteorologico Regionale dell'ARPA Friuli Venezia Giulia, Via Natisone 43, Palmanova, UD I-33057, Italy
Recommended Citation:
Manzato A.,Pucillo A.,Cicogna A.. Improving ECMWF-based 6-hours maximum rain using instability indices and neural networks[J]. Atmospheric Research,2019-01-01,217