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Evaluation of satellite rainfall climatology using CMORPH, PERSIANN‐CDR, PERSIANN, TRMM, MSWEP over Iran

Alijanian, Mohammadali ; Rakhshandehroo, Gholam Reza ; Mishra, Ashok K. ; Dehghani, Maryam

International journal of climatology, 2017-11, Vol.37 (14), p.4896-4914 [Periódico revisado por pares]

Chichester, UK: John Wiley & Sons, Ltd

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Título:
Evaluation of satellite rainfall climatology using CMORPH, PERSIANN‐CDR, PERSIANN, TRMM, MSWEP over Iran
Autor: Alijanian, Mohammadali ; Rakhshandehroo, Gholam Reza ; Mishra, Ashok K. ; Dehghani, Maryam
Assuntos: Artificial neural networks ; Atmospheric precipitations ; categorical statistics ; Climate ; Climate prediction ; Climatic data ; Climatic zones ; Climatology ; CMORPH ; Correlation coefficient ; Correlation coefficients ; Dry spells ; Ensemble precipitation ; Error analysis ; Evaluation ; Gauges ; Heavy rainfall ; Humid climates ; Hydrologic data ; Measuring instruments ; Morphing ; MSWEP ; Neural networks ; PERSIANN ; PERSIANN‐CDR ; Precipitation ; Precipitation estimation ; Rain ; Rainfall ; Rainfall climatology ; Rainfall data ; Rainfall rate ; Remote sensing ; satellite rainfall estimation ; Satellites ; Statistical methods ; TRMM ; Tropical climate ; Tropical rainfall ; Tropical Rainfall Measuring Mission (TRMM) ; Water resources ; Water resources management
É parte de: International journal of climatology, 2017-11, Vol.37 (14), p.4896-4914
Descrição: ABSTRACT In situ rainfall data observed by gauges is the most important data in water resources management. However, these data have some limitations both spatially and temporally. With the advancements in satellite rainfall products, it is now possible to evaluate whether these products can capture the climatology of known rainfall characteristics. In this study, five satellite rainfall estimates (SREs) were evaluated against gauge data based on different rainfall regimes over Iran. The evaluated SREs are Climate Prediction Center Morphing Technique, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) and Tropical Rainfall Measuring Mission (TRMM), PERSIANN Climate Data Record (PERSIANN‐CDR) and the most recently available Multi‐Source Weighted‐Ensemble Precipitation (MSWEP) data. The performance of these five SREs is evaluated with respect to gauge data (total: 958 stations) in eight different climatic zones at daily, monthly, and wet/dry spells during a ten‐year period (2003–2012). Performance of SREs was evaluated using metrics of comparison based on correlation coefficient (CC), root mean square error, and relative error. The study shows that MSWEP has the highest CC (0.72) followed by TRMM (0.46) and PERSIANN‐CDR (0.43) at daily time scale. The performance of SREs varies with respect to climatic regimes, for example, the best correlation was observed in the south, the shore of Persian Gulf with ‘very hot and humid’ climate with CC values of 0.72, 0.70, and 0.82 for MSWEP, TRMM and PERSIANN‐CDR, respectively. Further, the performance of SREs was evaluated using the categorical statistics to capture the rainfall pattern based on different groups (e.g. light, moderate and heavy rainfall events). Results show that MSWEP, PERSIANN‐CDR, and TRMM performed well to distinguish rain from no‐rain condition, whereas for higher rainfall rates, PERSIANN‐CDR outperforms the other SREs. Iran as a country with various geographical features (sea shores, high mountains, and vast deserts) could be a good climatological case study. The exist of mountains on north and west, beside deserts at the center of the country are the reason of insufficient both temporal and spatial distribution of gauge stations. Satellite rainfall estimated (SRE) datasets could be the other sources for climatological studies. In this study, the evaluation of five different SREs (CMORPH, PERSIANN, PERSIANN‐CDR, TRMM, and MSWEP) with comparison to gauge dataset is considered over Iran.
Editor: Chichester, UK: John Wiley & Sons, Ltd
Idioma: Inglês

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Evaluation of satellite rainfall climatology using CMORPH, PERSIANN‐CDR, PERSIANN, TRMM, MSWEP over Iran

Alijanian, Mohammadali ; Rakhshandehroo, Gholam Reza ; Mishra, Ashok K. ; Dehghani, Maryam

Chichester, UK: John Wiley & Sons, Ltd

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