GSTDTAP  > 地球科学
DOI10.1016/j.atmosres.2018.10.020
Improving ECMWF-based 6-hours maximum rain using instability indices and neural networks
Manzato, Agostino; Pucillo, Arturo; Cicogna, Andrea
2019-03-01
发表期刊ATMOSPHERIC RESEARCH
ISSN0169-8095
EISSN1873-2895
出版年2019
卷号217页码:184-197
文章类型Article
语种英语
国家Italy
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- R-2 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%).


英文关键词6-Hour rain forecast ECMWF downscaling Neural networks
领域地球科学
收录类别SCI-E
WOS记录号WOS:000452931100018
WOS关键词SOUNDING-DERIVED INDEXES ; PRECIPITATION ; CLIMATOLOGY
WOS类目Meteorology & Atmospheric Sciences
WOS研究方向Meteorology & Atmospheric Sciences
引用统计
文献类型期刊论文
条目标识符http://119.78.100.173/C666/handle/2XK7JSWQ/38072
专题地球科学
作者单位OSMER Osservatorio Meteorol Reg ARPA Friuli Venez, Via Natisone 43, I-33057 Palmanova, UD, Italy
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Manzato, Agostino,Pucillo, Arturo,Cicogna, Andrea. Improving ECMWF-based 6-hours maximum rain using instability indices and neural networks[J]. ATMOSPHERIC RESEARCH,2019,217:184-197.
APA Manzato, Agostino,Pucillo, Arturo,&Cicogna, Andrea.(2019).Improving ECMWF-based 6-hours maximum rain using instability indices and neural networks.ATMOSPHERIC RESEARCH,217,184-197.
MLA Manzato, Agostino,et al."Improving ECMWF-based 6-hours maximum rain using instability indices and neural networks".ATMOSPHERIC RESEARCH 217(2019):184-197.
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