Validation of TRMM-3B42 precipitation product over the tropical Indian Ocean using rain gauge data from the RAMA buoy array

2013 ◽  
Vol 115 (3-4) ◽  
pp. 451-460 ◽  
Author(s):  
Satya Prakash ◽  
R. M. Gairola
Keyword(s):  
Indian Ocean ◽  
Tropical Indian Ocean ◽  
Rain Gauge ◽  
Rain Gauge Data ◽  
Precipitation Product ◽  
Gauge Data ◽  
Rama Buoy ◽  
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.

scholarly journals Experiences in using the TMPA-3B42R satellite data to complement rain gauge measurements in the Ecuadorian coastal foothills

2013 ◽  
Vol 17 (7) ◽  
pp. 2905-2915 ◽  
Author(s):  
M. Arias-Hidalgo ◽  
B. Bhattacharya ◽  
A. E. Mynett ◽  
A. van Griensven
Keyword(s):  
Satellite Data ◽  
Bias Correction ◽  
Hydrological Modeling ◽  
New Technologies ◽  
Simple Procedure ◽  
Rain Gauge ◽  
Experimental Methodology ◽  
Rain Gauge Data ◽  
Gauge Data ◽  
Satellite Rainfall

Abstract. At present, new technologies are becoming available to extend the coverage of conventional meteorological datasets. An example is the TMPA-3B42R dataset (research – v6). The usefulness of this satellite rainfall product has been investigated in the hydrological modeling of the Vinces River catchment (Ecuadorian lowlands). The initial TMPA-3B42R information exhibited some features of the precipitation spatial pattern (e.g., decreasing southwards and westwards). It showed a remarkable bias compared to the ground-based rainfall values. Several time scales (annual, seasonal, monthly, etc.) were considered for bias correction. High correlations between the TMPA-3B42R and the rain gauge data were still found for the monthly resolution, and accordingly a bias correction at that level was performed. Bias correction factors were calculated, and, adopting a simple procedure, they were spatially distributed to enhance the satellite data. By means of rain gauge hyetographs, the bias-corrected monthly TMPA-3B42R data were disaggregated to daily resolution. These synthetic time series were inserted in a hydrological model to complement the available rain gauge data to assess the model performance. The results were quite comparable with those using only the rain gauge data. Although the model outcomes did not improve remarkably, the contribution of this experimental methodology was that, despite a high bias, the satellite rainfall data could still be corrected for use in rainfall-runoff modeling at catchment and daily level. In absence of rain gauge data, the approach may have the potential to provide useful data at scales larger than the present modeling resolution (e.g., monthly/basin).

scholarly journals On evaluation of ensemble precipitation forecasts with observation-based ensembles

2007 ◽  
Vol 10 ◽  
pp. 139-144 ◽  
Author(s):  
B. Ahrens ◽  
S. Jaun
Keyword(s):  
Rain Gauge ◽  
Performance Measure ◽  
Ensemble Prediction ◽  
Limited Area ◽  
Large Area ◽  
Ensemble Prediction System ◽  
Rain Gauge Data ◽  
Forecast Lead Time ◽  
Gauge Data ◽  
The One

Abstract. Spatial interpolation of precipitation data is uncertain. How important is this uncertainty and how can it be considered in evaluation of high-resolution probabilistic precipitation forecasts? These questions are discussed by experimental evaluation of the COSMO consortium's limited-area ensemble prediction system COSMO-LEPS. The applied performance measure is the often used Brier skill score (BSS). The observational references in the evaluation are (a) analyzed rain gauge data by ordinary Kriging and (b) ensembles of interpolated rain gauge data by stochastic simulation. This permits the consideration of either a deterministic reference (the event is observed or not with 100% certainty) or a probabilistic reference that makes allowance for uncertainties in spatial averaging. The evaluation experiments show that the evaluation uncertainties are substantial even for the large area (41 300 km2) of Switzerland with a mean rain gauge distance as good as 7 km: the one- to three-day precipitation forecasts have skill decreasing with forecast lead time but the one- and two-day forecast performances differ not significantly.

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