Javascript must be enabled to continue!
Evaluation of GSMaP Satellite Precipitation Over Central VietNam in 2000-2010 Period and Correction Ability
View through CrossRef
Abstract: Daily/Monthly precipitation of GSMaP is compared with observation at 10 stations over Central VietNam in 2000-2010 period. Evaluation indices used in this study include correlation coefficient (r), relative bias (B), probability of detection (POD) and false alarm ratio (FAR). The results show that the agreement of the first rainy month over 100mm and the maximum rainy month between GSMaP and observation, however, the duration of rainy months over 100mm of GSMaP is shorter than that of observation. GSMaP precipitationoften underestimates compared to observation in October-December at almost stations.It can be seen that monthly correlation coefficients are often positive at almost stations when evaluating daily precipitation, the lower values normally are found in January and February. Positive relative biases are observed in April-September at most of North Central stations, while those often occur in July-September at South Central stations. Negative relative biases can be found in October until March of next year at almost stations. The good POD and FAR values are given at 0-6mm/day threshold and the worse values are found at 6-16mm/day threshold. After applying correction methods, the GSMaP precipitation is much better agreement with observation, especially in underestimated rainy months.
Key words:Precipitation, GSMaP, evaluation, correction.
Vietnam National University Journal of Science
Title: Evaluation of GSMaP Satellite Precipitation Over Central VietNam in 2000-2010 Period and Correction Ability
Description:
Abstract: Daily/Monthly precipitation of GSMaP is compared with observation at 10 stations over Central VietNam in 2000-2010 period.
Evaluation indices used in this study include correlation coefficient (r), relative bias (B), probability of detection (POD) and false alarm ratio (FAR).
The results show that the agreement of the first rainy month over 100mm and the maximum rainy month between GSMaP and observation, however, the duration of rainy months over 100mm of GSMaP is shorter than that of observation.
GSMaP precipitationoften underestimates compared to observation in October-December at almost stations.
It can be seen that monthly correlation coefficients are often positive at almost stations when evaluating daily precipitation, the lower values normally are found in January and February.
Positive relative biases are observed in April-September at most of North Central stations, while those often occur in July-September at South Central stations.
Negative relative biases can be found in October until March of next year at almost stations.
The good POD and FAR values are given at 0-6mm/day threshold and the worse values are found at 6-16mm/day threshold.
After applying correction methods, the GSMaP precipitation is much better agreement with observation, especially in underestimated rainy months.
Key words:Precipitation, GSMaP, evaluation, correction.
Related Results
Assessing Tropical Cyclone-induced rainfall distributions derived from the TRMM and GSMaP satellite datasets over Vietnam's mainland
Assessing Tropical Cyclone-induced rainfall distributions derived from the TRMM and GSMaP satellite datasets over Vietnam's mainland
In this study, 169 meteorological stations are used as the "ground truth" to assess the Tropical Rainfall Measuring Mission (TRMM) and Global Satellite Mapping of Precipitation (GS...
Near-Real-Time Integration of Multisource Precipitation Products Using a Multiscale Convolutional Neural Network
Near-Real-Time Integration of Multisource Precipitation Products Using a Multiscale Convolutional Neural Network
Abstract
Merging multisource precipitation data based on deep learning models to create an accurate rainfall dataset has received significant interest in recent years. This article...
Global component analysis of errors in five satellite-only global precipitation estimates
Global component analysis of errors in five satellite-only global precipitation estimates
Abstract. Revealing the error components for satellite-only precipitation products (SPPs) can help algorithm developers and end-users substantially understand their error features ...
Characteristics of rainfall distribution induced by tropical cyclones using GSMaP data over the Vietnam region
Characteristics of rainfall distribution induced by tropical cyclones using GSMaP data over the Vietnam region
ABSTRACT
Tropical cyclones (TCs) contribute significantly to rainfall along Vietnam's coast, yet their complex precipitation structures remain poorly...
Spatial and Temporal Evaluation of Satellite Rainfall Estimates Over Vietnam
Spatial and Temporal Evaluation of Satellite Rainfall Estimates Over Vietnam
Abstract
High-resolution reliable rainfall datasets are vital for agricultural, hydrological, and weather-related applications. The accuracy of satellite estimates has a si...
Evaluation and Comparison of the GWR Merged Precipitation and Multi-Source Weighted-Ensemble Precipitation based on High-density Gauge Measurement.
Evaluation and Comparison of the GWR Merged Precipitation and Multi-Source Weighted-Ensemble Precipitation based on High-density Gauge Measurement.
Accurate estimation of precipitation in both space and time is essential
for hydrological research. We compared multi-source weighted ensemble
precipitation (MSWEP) with multi-sour...
Entropy‐based spatiotemporal patterns of precipitation regimes in the Huai River basin, China
Entropy‐based spatiotemporal patterns of precipitation regimes in the Huai River basin, China
ABSTRACTSpatiotemporal patterns of precipitation regimes in terms of precipitation amount and number of precipitation days at different time scales are investigated using the entro...
INFLUENCE OF ATMOSPHERIC PRECIPITATIONS ON THE RUN OF THE PUTIL RIVER
INFLUENCE OF ATMOSPHERIC PRECIPITATIONS ON THE RUN OF THE PUTIL RIVER
Research of precipitation, water balance of river basins, and the impact of precipitation on river runoff remain relevant in the context of global and regional climate change. Nowa...

