Evaluation of TRMM 3B42 Precipitation Product using Rain Gauge Data in Meichuan Watershed, Poyang Lake Basin, China

2012 ◽  
Vol 3 (4) ◽  
pp. 359-366 ◽  
Author(s):  
Liu Junzhi ◽  
Zhu A-Xing ◽  
Duan Zheng
Keyword(s):  
Poyang Lake ◽  
Rain Gauge ◽  
Lake Basin ◽  
Rain Gauge Data ◽  
Precipitation Product ◽  
Poyang Lake Basin ◽  
Gauge Data ◽  
Trmm 3B42

Validation of the H-SAF precipitation product H03 over Greece using rain gauge data

2016 ◽  
Vol 131 (1-2) ◽  
pp. 377-398 ◽  
Author(s):  
H. Feidas ◽  
F. Porcu ◽  
S. Puca ◽  
A. Rinollo ◽  
C. Lagouvardos ◽  
...  
Keyword(s):  
Rain Gauge ◽  
Rain Gauge Data ◽  
Precipitation Product ◽  
Gauge Data

scholarly journals Study on quantification of areal mean precipitation using satellite-gauge merging precipitation

2018 ◽  
Vol 12 (5) ◽  
pp. 117-126
Author(s):  
Bui Thi Hieu
Keyword(s):  
Design Method ◽  
Rain Gauge ◽  
Bias Reduction ◽  
Rainfall Runoff ◽  
Sample Design ◽  
Rain Gauge Data ◽  
Rain Gauges ◽  
Precipitation Product ◽  
Gauge Data ◽  
Satellite Rainfall

Satellite based precipitation product (GSMaP-MVK) can be reliably used to estimate the Areal Mean Precipitation error based on “Sample Design method” (Esdd) with the effort to mitigate the problem of sparse data, especially severe in poorly gauged river basins. In addition, the satellite-gauge merging precipitation would reduce significantly the magnitude gaps between the satellite rainfall estimations and the rain gauge data. In this study, the capability of satellite-gauge merging precipitation using GSMaP-MVK and local dense rain gauge data with bias reduction approach to evaluate the AMP is investigated. The main finding is that satellite-gauge blending data which incorporates a dense rain gauge measurements shows the better capability to evaluate AMP using Esdd index than the original satellite only precipitation estimations. However, Esdd quantification performances of satellite-gauge blending precipitation are inferior to the original satellite only precipitation product GSMaP-MVK when the number of blended rain gauges is not large enough. Keywords: areal mean precipitation; remote sensed precipitation product; satellite-gauge merging; rainfall runoff simulations.

scholarly journals Improving Multisensor Precipitation Estimation via Adaptive Conditional Bias–Penalized Merging of Rain Gauge Data and Remotely Sensed Quantitative Precipitation Estimates

2019 ◽  
Vol 20 (12) ◽  
pp. 2347-2365 ◽  
Author(s):  
Ali Jozaghi ◽  
Mohammad Nabatian ◽  
Seongjin Noh ◽  
Dong-Jun Seo ◽  
Lin Tang ◽  
...  
Keyword(s):  
Computational Cost ◽  
Mean Field ◽  
Remotely Sensed ◽  
Rain Gauge ◽  
Rain Gauge Data ◽  
Gauge Data ◽  
Precipitation Estimation ◽  
Precipitation Estimates ◽  
Radar Satellite ◽  
Quantitative Precipitation Estimates

Abstract We describe and evaluate adaptive conditional bias–penalized cokriging (CBPCK) for improved multisensor precipitation estimation using rain gauge data and remotely sensed quantitative precipitation estimates (QPE). The remotely sensed QPEs used are radar-only and radar–satellite-fused estimates. For comparative evaluation, true validation is carried out over the continental United States (CONUS) for 13–30 September 2015 and 7–9 October 2016. The hourly gauge data, radar-only QPE, and satellite QPE used are from the Hydrometeorological Automated Data System, Multi-Radar Multi-Sensor System, and Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR), respectively. For radar–satellite fusion, conditional bias–penalized Fisher estimation is used. The reference merging technique compared is ordinary cokriging (OCK) used in the National Weather Service Multisensor Precipitation Estimator. It is shown that, beyond the reduction due to mean field bias (MFB) correction, both OCK and adaptive CBPCK additionally reduce the unconditional root-mean-square error (RMSE) of radar-only QPE by 9%–16% over the CONUS for the two periods, and that adaptive CBPCK is superior to OCK for estimation of hourly amounts exceeding 1 mm. When fused with the MFB-corrected radar QPE, the MFB-corrected SCaMPR QPE for September 2015 reduces the unconditional RMSE of the MFB-corrected radar by 4% and 6% over the entire and western half of the CONUS, respectively, but is inferior to the MFB-corrected radar for estimation of hourly amounts exceeding 7 mm. Adaptive CBPCK should hence be favored over OCK for estimation of significant amounts of precipitation despite larger computational cost, and the SCaMPR QPE should be used selectively in multisensor QPE.

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