scholarly journals Diurnal cycle of tropical precipitation in Tropical Rainfall Measuring Mission (TRMM) satellite and ocean buoy rain gauge data

2005 ◽  
Vol 110 (D21) ◽  
Author(s):  
Kenneth P. Bowman ◽  
J. Craig Collier ◽  
Gerald R. North ◽  
Qiaoyan Wu ◽  
Eunho Ha ◽  
...  
Keyword(s):  
Diurnal Cycle ◽  
Tropical Rainfall Measuring Mission ◽  
Rain Gauge ◽  
Rain Gauge Data ◽  
Tropical Rainfall ◽  
Tropical Precipitation ◽  
Gauge Data

scholarly journals Improving Satellite-Based Subhourly Surface Rain Estimates Using Vertical Rain Profile Information

2019 ◽  
Vol 20 (5) ◽  
pp. 1015-1026 ◽  
Author(s):  
Nobuyuki Utsumi ◽  
Hyungjun Kim ◽  
F. Joseph Turk ◽  
Ziad. S. Haddad
Keyword(s):  
Rain Rate ◽  
Tropical Rainfall Measuring Mission ◽  
Rain Gauge ◽  
Rain Gauge Data ◽  
Rain Gauges ◽  
Gauge Data ◽  
Surface Rain Rate ◽  
Trmm Precipitation ◽  
Time Lagged ◽  
Rain Rates

Abstract Quantifying time-averaged rain rate, or rain accumulation, on subhourly time scales is essential for various application studies requiring rain estimates. This study proposes a novel idea to estimate subhourly time-averaged surface rain rate based on the instantaneous vertical rain profile observed from low-Earth-orbiting satellites. Instantaneous rain estimates from the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) are compared with 1-min surface rain gauges in North America and Kwajalein atoll for the warm seasons of 2005–14. Time-lagged correlation analysis between PR rain rates at various height levels and surface rain gauge data shows that the peak of the correlations tends to be delayed for PR rain at higher levels up to around 6-km altitude. PR estimates for low to middle height levels have better correlations with time-delayed surface gauge data than the PR’s estimated surface rain rate product. This implies that rain estimates for lower to middle heights may have skill to estimate the eventual surface rain rate that occurs 1–30 min later. Therefore, in this study, the vertical profiles of TRMM PR instantaneous rain estimates are averaged between the surface and various heights above the surface to represent time-averaged surface rain rate. It was shown that vertically averaged PR estimates up to middle heights (~4.5 km) exhibit better skill, compared to the PR estimated instantaneous surface rain product, to represent subhourly (~30 min) time-averaged surface rain rate. These findings highlight the merit of additional consideration of vertical rain profiles, not only instantaneous surface rain rate, to improve subhourly surface estimates of satellite-based rain products.

Calibration of TRMM Derived Rainfall Over Nepal During 1998-2007

2010 ◽  
Vol 4 (1) ◽  
pp. 12-23 ◽  
Author(s):  
Md. Nazrul Islam ◽  
Someshwar Das ◽  
Hiroshi Uyeda
Keyword(s):  
Daily Rainfall ◽  
Tropical Rainfall Measuring Mission ◽  
Calibration Procedure ◽  
Skill Score ◽  
Rain Gauge ◽  
Probability Of Detection ◽  
Similar Distribution ◽  
Rain Gauge Data ◽  
Gauge Data ◽  
Adjustment Factors

In this study rainfall is calculated from Tropical Rainfall Measuring Mission (TRMM) Version 6 (V6) 3B42 datasets and calibrated with reference to the observed daily rainfall by rain-gauge collected at 15 locations over Nepal during 1998-2007. In monthly, seasonal and annual scales TRMM estimated rainfalls follow the similar distribution of historical patterns obtained from the rain-gauge data. Rainfall is large in the Southern parts of the country, especially in the Central Nepal. Day-to-day rainfall comparison shows that TRMM derived trend is very similar to the observed data but TRMM usually underestimates rainfall on many days with some exceptions of overestimation on some days. The correlation coefficient of rainfalls between TRMM and rain-gauge data is obtained about 0.71. TRMM can measure about 65.39% of surface rainfall in Nepal. After using calibration factors obtained through regression expression the TRMM estimated rainfall over Nepal becomes about 99.91% of observed data. TRMM detection of rainy days is poor over Nepal; it can approximately detect, under-detect and over-detect by 19%, 72% and 9% of stations respectively. False alarm rate, probability of detection, threat score and skill score are calculated as 0.30, 0.68, 0.53 and 0.55 respectively. Finally, TRMM data can be utilized in measuring mountainous rainfall over Nepal but exact amount of rainfall has to be calculated with the help of adjustment factors obtained through calibration procedure. This preliminary work is the preparation of utilization of Global Precipitation Measurement (GPM) data to be commencing in 2013.

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).

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