Comparison of PERSIANN and V7 TRMM Multi-satellite Precipitation Analysis (TMPA) products with rain gauge data over Iran

2013 ◽  
Vol 34 (22) ◽  
pp. 8156-8171 ◽  
Author(s):  
Saber Moazami ◽  
Saeed Golian ◽  
M. Reza Kavianpour ◽  
Yang Hong
Keyword(s):  
Rain Gauge ◽  
Rain Gauge Data ◽  
Satellite Precipitation ◽  
Gauge Data ◽  
Precipitation Analysis

scholarly journals Evaluation of Satellite Precipitation Estimates over Australia

2020 ◽  
Vol 12 (4) ◽  
pp. 678 ◽  
Author(s):  
Zhi-Weng Chua ◽  
Yuriy Kuleshov ◽  
Andrew Watkins
Keyword(s):  
Rainfall Intensity ◽  
Austral Summer ◽  
Rain Gauge ◽  
Reference Dataset ◽  
Rain Gauge Data ◽  
Satellite Precipitation ◽  
Gauge Data ◽  
Precipitation Estimates ◽  
Notable Increase ◽  
Climate Prediction Center

This study evaluates the U.S. National Oceanographic and Atmospheric Administration’s (NOAA) Climate Prediction Center morphing technique (CMORPH) and the Japan Aerospace Exploration Agency’s (JAXA) Global Satellite Mapping of Precipitation (GSMaP) satellite precipitation estimates over Australia across an 18 year period from 2001 to 2018. The evaluation was performed on a monthly time scale and used both point and gridded rain gauge data as the reference dataset. Overall statistics demonstrated that satellite precipitation estimates did exhibit skill over Australia and that gauge-blending yielded a notable increase in performance. Dependencies of performance on geography, season, and rainfall intensity were also investigated. The skill of satellite precipitation detection was reduced in areas of elevated topography and where cold frontal rainfall was the main precipitation source. Areas where rain gauge coverage was sparse also exhibited reduced skill. In terms of seasons, the performance was relatively similar across the year, with austral summer (DJF) exhibiting slightly better performance. The skill of the satellite precipitation estimates was highly dependent on rainfall intensity. The highest skill was obtained for moderate rainfall amounts (2–4 mm/day). There was an overestimation of low-end rainfall amounts and an underestimation in both the frequency and amount for high-end rainfall. Overall, CMORPH and GSMaP datasets were evaluated as useful sources of satellite precipitation estimates over Australia.

scholarly journals Validasi Nilai Erosivitas Hujan Dari Data Penginderaan Jauh TRMM 3B42 Di Bali Selatan

1970 ◽  
Vol 16 (1) ◽  
Author(s):  
I Wayan Sandi Adnyana ◽  
Abd. Rahman As-syakur
Keyword(s):  
Rain Gauge ◽  
Monthly Rainfall ◽  
Bias Error ◽  
Rainfall Erosivity ◽  
Rain Gauge Data ◽  
Point Analysis ◽  
Gauge Data ◽  
High Bias ◽  
Precipitation Analysis ◽  
Very High

Rainfall erosivity is a measure for the erosive force of rainfall. Rainfall kinetic energydetermines the erosivity and is in turn greatly dependent on rainfall intensity. Research hasbeen conducted to validate monthly rainfall erosivity derived from the Tropical Rainfall MeasuringMission (TRMM) Multisatellite Precipitation Analysis(TMPA)3B43 version 7 usingraingauge data analysis from 2003 to 2012. Rain gauge located in the south Bali regions wereemployedto monitor erosivity value from two different methods that are base on Bols (1978)andAbdurachman(1989). Therelationship of erosivity and their other factor from TRMM3B43andrain gauge data statistical analysis measures consisted of the linear correlation coefficient,themean bias error (MBE), and the root mean square error (RMSE). Data validation wasconductedwith point-by-point analysis. The results of these analyses indicate that satellitedatahave lower values than the gauge estimation values. The point-by-point analysis indicatedsatellite data values of high to very high correlation, while values of MBE and RMSEtendedto indicate underestimations with high square errors. Moreover,monthly rainfall erosivityderived from TRMM give high correlation from both methods, with has high bias androot-mean-squareerror. In general, the data from TRMM3B43 version 7 are potentially usabletoreplace rain gauge data based on erosivity estimation, but after inconsistencies and errorsaretaken into account.

scholarly journals Comparison of two successive versions 6 and 7 of TMPA satellite precipitation products with rain gauge data over Swat Watershed, Hindukush Mountains, Pakistan

2016 ◽  
Vol 17 (4) ◽  
pp. 270-279 ◽  
Author(s):  
Muhammad Naveed Anjum ◽  
Yongjian Ding ◽  
Donghui Shangguan ◽  
Adnan Ahmad Tahir ◽  
Mudassar Iqbal ◽  
...  
Keyword(s):  
Rain Gauge ◽  
Rain Gauge Data ◽  
Satellite Precipitation ◽  
Gauge Data

scholarly journals Mapping the Daily Rainfall over an Ungauged Tropical Micro-Watershed: A Downscaling Algorithm Using GPM Data

Water ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1661 ◽  
Author(s):  
Mohd. Rizaludin Mahmud ◽  
Aina Afifah Mohd Yusof ◽  
Mohd Nadzri Mohd Reba ◽  
Mazlan Hashim
Keyword(s):  
High Resolution ◽  
Daily Rainfall ◽  
Rain Gauge ◽  
Rainfall Data ◽  
Precipitation Data ◽  
Rain Gauge Data ◽  
Satellite Precipitation ◽  
Daily Rainfall Data ◽  
Gauge Data

In this study, half-hourly Global Precipitation Mission (GPM) satellite precipitation data were downscaled to produce high-resolution daily rainfall data for tropical coastal micro-watersheds (100–1000 ha) without gauges or with rainfall data conflicts. Currently, daily-scale satellite rainfall downscaling techniques rely on rain gauge data as corrective and controlling factors, making them impractical for ungauged watersheds or watersheds with rainfall data conflicts. Therefore, we used high-resolution local orographic and vertical velocity data as proxies to downscale half-hourly GPM precipitation data (0.1°) to high-resolution daily rainfall data (0.02°). The overall quality of the downscaled product was similar to or better than the quality of the raw GPM data. The downscaled rainfall dataset improved the accuracy of rainfall estimates on the ground, with lower error relative to measured rain gauge data. The average error was reduced from 41 to 27 mm/d and from 16 to 12 mm/d during the wet and dry seasons, respectively. Estimates of localized rainfall patterns were improved from 38% to 73%. The results of this study will be useful for production of high-resolution satellite precipitation data in ungauged tropical micro-watersheds.

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