Performance of High Resolution Satellite Rainfall Products over Data Scarce Parts of Eastern Ethiopia

Gebere, Shimelis B.; Alamirew, Tena; Merkel, Broder J.; Melesse, Assefa M.

doi:10.3390/rs70911639

Cited by 61 publications

(40 citation statements)

References 54 publications

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“…The dekadal and monthly data were further classified to validate the satellite products at different elevations and for each of 12 months over the UBN basin, respectively. The double mass curve techniques and correlation coefficient analysis (similar to Gebere et al, 2015) confirmed the consistency and homogeneity of rain gauge observations, respectively.…”

Section: Methodssupporting

confidence: 54%

Validation of new satellite rainfall products over the Upper Blue Nile Basin, Ethiopia

Ayehu¹,

Tadesse²,

Gessesse³

et al. 2017

Preprint

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Abstract. Accurate measurement of rainfall is vital to analyze the spatial and temporal patterns of precipitation at variousscales. However, the conventional rain gauge observations in many parts of the world such as Ethiopia are sparse and unevenly distributed. An alternative to traditional rain gauge observations could be satellite-based rainfall estimates. Satellite 15 rainfall estimates could be used as a sole product (e.g. in areas with no (poor) ground observations) or through integrating with rain gauge measurements. In this study, the newly available Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) data has been evaluated in comparison to rain gauge data for the period of 2000 to 2015 across the Upper Blue Nile basin in Ethiopia. Besides, the Tropical Applications of Meteorology using SATellite and ground-based observations (TAMSAT) version 2 and 3 (TAMSAT 2 and TAMSAT 3) and the African Rainfall Climatology (ARC 2) products have 20 been used as a benchmark and compared with CHIRPS. The TAMSAT 2 rainfall estimate was used in this study mainly to assess the improvements made with the recent version of a TAMSAT product (TAMSAT 3). From the overall analysis at dekadal and monthly temporal scale, CHIRPS exhibited higher skills and the best bias value in comparison to ARC 2 but overestimates the frequency of rainfall occurrence particularly during the dry months. On the other hand, TAMSAT 3 has shown very comparable performance with that of CHIRPS product, particularly with regards to bias. The ARC 2 product 25 was found to have the weakest performance underestimating rainfall amounts by about 24%. The skill of CHIRPS is less affected by variation in elevation in comparison to TAMSAT 3 and ARC 2 products. While ARC 2 was found to be affected by elevation with the average biases of 1.53, 0.86 and 0.77 at lower (< 1000 m a.s.l), medium (1000 to 2000 m a.s.l) and higher elevation (> 2000 m a.s.l), respectively. Comparing the overall performance of the three products, CHIRPS exhibited the best performance followed closely by TAMSAT 3. This validation study also shows that the TAMSAT 3 has overcome 30 the main weaknesses of TAMSAT 2, which is underestimation of high rainfall amounts by up to 31% in this study. Overall, the finding of this validation study shows the potentials of CHIRPS product to be used for various operational applications such as rainfall pattern and variability study in the Upper Blue Nile basin in Ethiopia.Atmos. Meas. Tech. Discuss., https://doi

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Section: Methodssupporting

confidence: 54%

Validation of new satellite rainfall products over the Upper Blue Nile Basin, Ethiopia

Ayehu¹,

Tadesse²,

Gessesse³

et al. 2017

Preprint

View full text Add to dashboard Cite

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“…The difference between the 3B42RT data and actual precipitation varied over the years. The 3B42RT observations were generally higher than the ground-based measurements before 2007, and the difference between the two was greater than 10 The difference between GWR-estimated and actual precipitation was lower that between the 3B42RT data and actual precipitation. The 3B42RT data overestimated precipitation before 2008, whereas the GWR model results were better correlated with the actual precipitation.…”

Section: Monthly Precipitation Distribution Estimationsmentioning

confidence: 71%

A Rainfall Model Based on a Geographically Weighted Regression Algorithm for Rainfall Estimations over the Arid Qaidam Basin in China

Zhou

2016

Remote Sensing

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Accurate rainfall estimations based on ground-based rainfall observations and satellite-based rainfall measurements are essential for hydrological and environmental modeling in the Qaidam Basin of China. We evaluated the accuracy of daily and monthly scale Tropical Rainfall Measuring Mission (TRMM) rainfall products in the Qaidam Basin. A Geographically Weighted Regression (GWR) was used to estimate the spatial distribution of the TRMM product error using altitude and geographical latitude and longitude as independent variables. Finally, a rainfall model was developed by combining ground-based and satellite-based rainfall measurements, and the model precision was validated with a cross-validation method based on rainfall gauge measurements. The TRMM precipitation observations may contain errors compared with the ground-measured precipitation, and the error for daily data was higher than that for monthly data. A time series of TRMM rainfall measurements at the same location showed errors at certain time intervals. The ground-based and satellite-based rainfall GWR model improved the error in the TRMM rainfall products. This rainfall estimation model with a 1-km spatial resolution is applicable in the Qaidam Basin in which there is a sparse network of rainfall gauges, and is significant for spatial investigations of hydrology and climate change.

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“…TMPA products, which combine remote sensing data from various passive microwave (PMW) and infrared (IR) sensors with gauge accumulation corrections based on the Global Precipitation Climatology Center (GPCC) monthly gauge analysis, aim to provide the global precipitation estimates from 50°N to 50°S at the TRMM-era. TMPA products have been widely examined and were proved to obtain a splendid success [26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Evaluation of GPM-IMERG, CMORPH, and TMPA Precipitation Products with Gauged Rainfall over Mainland China

Guang-hua

Lü

Crow

et al. 2018

Advances in Meteorology

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over China. Moreover, IMERG V05B was compared with IMERG V04A, the Tropical Rainfall Measuring Mission (TRMM) 3B42, and the Climate Prediction Center Morphing technique (CMORPH)-CRT in this study. Categorical verification techniques and statistical methods are used to quantify their performance. Results illustrate the following.(1) Except for IMERG V04A's severe underestimation over the Tibetan Plateau (TP) and Xinjiang (XJ) with high negative relative biases (RBs) and CMORPH-CRT's overestimation over XJ with high positive RB, the four satellite-based precipitation products generally capture the same spatial patterns of precipitation over China.(2) At the annual scale over China, the IMERG products do not show an advantage over its predecessor (TRMM 3B42) in terms of RMSEs, RRMSEs, and Rs; meanwhile, the performance of IMERG products is worse than TRMM 3B42 in spring and summer according to the RMSE, RRMSE, and R metrics. Between the two IMERG products, IMERG V05B shows the anticipated improvement (over IMERG V04A) with a decrease in RMSE from 0.4496 to 0.4097 mm/day, a decrease of RRMSE from 16.95% to 15.44%, and an increase of R from 0.9689 to 0.9759 during the whole study period. Similar results are obtained at the seasonal scale. Among the four satellite products, CMORPH-CRTshows the worst seasonal performance with the highest RMSE (0.6247 mm/day), RRMSE (23.55%), and lowest R (0.9343) over China. (3) Over XJ and TP, IMERG V05B clearly improves the strong underestimation of precipitation in IMERG V04A with the RBs of 5.2% vs. −21.8% over XJ, and 2.78% vs. −46% over TP. Results at the annual scale are similar to those obtained at the seasonal scale, except for summer results over XJ. While, over the remaining subregions, the two IMERG products have a close performance; meanwhile, IMERG V04A slightly improves IMERG V05B's overestimation according to RBs (except for HN) at the annual scale. However, all four products are unreliable over XJ at both an annual and seasonal scale. (4) Across all products, TRMM 3B42 best reproduces the probability density function (PDF) of daily precipitation intensity. (5) According to the categorical verification technique in this study, both IMERG products yield better results for the detection of precipitation events on the basis of probability of detection (POD) and critical success index (CSI) categorical evaluations compared to TRMM 3B42 and CMORPH-CRT over China and across most of the subregions. However, all four products have room for further improvement, especially in high-latitude and dry climate regions. ese findings provide valuable feedback for both IMERG algorithm developers and data set users.

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Performance of High Resolution Satellite Rainfall Products over Data Scarce Parts of Eastern Ethiopia

Cited by 61 publications

References 54 publications

Validation of new satellite rainfall products over the Upper Blue Nile Basin, Ethiopia

Validation of new satellite rainfall products over the Upper Blue Nile Basin, Ethiopia

A Rainfall Model Based on a Geographically Weighted Regression Algorithm for Rainfall Estimations over the Arid Qaidam Basin in China

Comprehensive Evaluation of GPM-IMERG, CMORPH, and TMPA Precipitation Products with Gauged Rainfall over Mainland China

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