Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1016/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 |
ISSN | 0169-8095 |
EISSN | 1873-2895 |
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 |
推荐引用方式 GB/T 7714 | 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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