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Estimating the spatial cumulative distribution of rain from precipitation amounts
Author(s) -
Luini L.,
Capsoni C.
Publication year - 2012
Publication title -
radio science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.371
H-Index - 84
eISSN - 1944-799X
pISSN - 0048-6604
DOI - 10.1029/2011rs004832
Subject(s) - rain gauge , precipitation , environmental science , meteorology , cumulative distribution function , radar , quantitative precipitation estimation , spatial distribution , data set , probability density function , confidence interval , numerical weather prediction , statistics , mathematics , remote sensing , geography , computer science , telecommunications
This contribution describes SPET (Spatial P ( R ) Estimation Technique), a methodology aimed at estimating the spatial rain rate complementary cumulative distribution function, P S ( R ), from Numerical Weather Prediction (NWP) rain precipitation data. SPET has been calibrated making use of a large database of rain maps derived from the S‐band weather radar sited in Spino d'Adda, Italy, while its performance has been assessed against an independent data set derived from the NIMROD C‐band radar network. Results indicate that SPET performance improves as both the observation area and reference time interval increase, which adds confidence to its use to estimate P S ( R ) starting from global rainfall data produced by meteorological re‐analyses. SPET also proved to correctly predict the fractional rainy area, showing an RMS of the relative error that falls approximately between 15% and 20% for area and time interval values typical of NWP re‐analysis data. As a final step, the proposed technique has been used to predict long‐term rainfall statistics collected by rain gauges worldwide, receiving as input rainfall data extracted from 10 years of ERA40 products (ergodicity of the rain field). Very good performances have been obtained, only limited by the poor quality of the input data.