Abstract
Satellite precipitation estimates (SPE) product with high spatiotemporal resolution is a potential alternative to traditional ground-based gauge precipitation. However, SPE is frequently biased due to its indirect measurement, and thus bias correction is necessary before applying to a specific region. An improved distribution mapping method, i.e., Extended Mixture Distribution (EMD) of censored Gamma and generalized Pareto distributions, was established. The advantage of EMD method is that it describes both moderate and extreme values well and carries on the traditional censored, shifted Gamma distribution to combine the precipitation occurrence/non-occurrence events together. Then the EMD method was applied to the Integrated Multi-satellitE Retrievals for GPM product (IMERG) as statistical post-processing over Yangtze River basin. The Version-2 Gridded dataset of daily Surface Precipitation from China Meteorological Administration (GSP-CMA) was taken as reference. The adequacy of bias corrected IMERG precipitation was assessed and the results showed that (1) the Root Mean Squared Error and Relative Bias between bias-corrected IMERG precipitation and reference are significantly reduced relative to the raw IMERG estimates; (2) the performance of extreme values of IMERG in Yangtze River basin is enhanced since both the under- and over-estimation of the raw IMERG are compromised, due to the generalized Pareto distribution introduced in EMD which is enable to describe the extreme value distribution. This highlights the improved distribution mapping method, EMD is flexible and robust to bias correct the IMERG precipitation to obtain higher accuracy of SPE despite the coarse resolution of reference.
Highlights
Satellite precipitation estimates (SPE) are important alternatives to the traditional precipitation measurements and have been increasingly applied to many scientific and social-economic fields, such as, hydro-meteorological and ecological system modeling, flood forecast, water resources development and conservation, point- and nonpoint-source pollutant management [1, 2]
The daily average precipitation of raw Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) product (IMERG) and GSPCMA data over Yangtze River basin was showed in Fig. 1(a) and (b)
The underand over-estimation of IMERG are compromised after using Extended Mixture Distribution (EMD) to bias correct
Summary
Satellite precipitation estimates (SPE) are important alternatives to the traditional precipitation measurements and have been increasingly applied to many scientific and social-economic fields, such as, hydro-meteorological and ecological system modeling, flood forecast, water resources development and conservation, point- and nonpoint-source pollutant management [1, 2]. Other mainstream SPE products include TRMM Multisatellite Precipitation Analysis product (TMPA), Precipitation Estimation from Remote Sensed Information Using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS), Global Satellite Mapping of Precipitation product (GSMap), and Climate Prediction Center (CPC) morphing technique product (CMORPH). They were originally designed to adjust GCM data, but can be used to correct SPE. A Censored, Shifted Gamma distribution (CSGD) was proposed to describe and map the RCM data [11], and later used to correct TMPA data [12]. It is not quite adequate to model the tail of the distribution since it underestimates large values
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
