A framework for developing a spatial high-resolution daily precipitation dataset over a data-sparse region

Abstract

This paper outlines a framework in order to provide a reliable and up-to date local precipitation dataset over Sistan and Baluchestan province, one of the poorly rain gauged areas in Iran. Initially, the accuracy of GPCC data, as the reference dataset, was evaluated. Next, the performance of eight gridded precipitation products (namely, CHIRPS, CMORPH-RAW, ERA5, ERA-Interim, GPM-IMERG, GSMaP-MVK, PERSIANN and TRMM3B42) were compared based on the GPCC observations during 1982–2016 over the study area. The evaluation was done by using eight commonly used statistical and categorical metrics. Then, among the products, the most suitable ones on the basis of their better performance and least time delay in providing data, were utilized as the constituent members of the proposed hybrid dataset. Using several statistical/machine learning approaches (namely, NSGA II, ETROPY and TOPSIS), daily weights of the chosen datasets were estimated, while the correlation coefficient and the estimation error of the data were maximized and minimized, respectively. Finally, the efficiency of the proposed hybrid precipitation dataset was investigated. Results indicate that the developed hybrid dataset (2014-present), using the estimates of the chosen ensemble members (GPM-IMERG, GSMaP-MVK and PERSIANN) and their respective weighting coefficients, provides accurate local daily precipitation data with a spatial resolution of 0.25°, representing the minimum time delay, compared to the other available datasets.

Evaluation of IMERG and ERA5 precipitation products over the Mongolian Plateau

“…However, further improvements were still needed for their estimation and detection capabilities. Specifically, ERA5 generally overestimated precipitation and mis-detected many non-precipitation events, which is previously reported in the Tibetan Plateau 124 , southern China 125 , Austria 46 , North America 48 , and Iran 58 . This probably stems from the imperfections in the cumulus parameterizations over steep mountain slopes 126 .…”

“…Tang et al concluded that IMERG generally outperformed ERA5 across China, and can better reproduce precipitation diurnal cycles 55 . Other studies were presented in Central Asia 56 , India 57 , Turkey 47 , Iran 58 , and the United States 59 . Most studies have shown that IMERG outperforms ERA5, but the superiority of each dataset varies by regions, precipitation intensity, and altitude.…”

Satellite-based meteorological drought indicator to support food security in Java Island

“…PERSIANN-CDR performed better than CHIRPS for semiarid regions of Brazil [ 26 ]. GPM, GSMaP, and PERSIANN were all found to offer reliable near-real-time meteorological estimation of data-sparse regions in Iran [ 27 ]. Climate Prediction Center Morphing Method (CMORPH) outperformed TRMM and Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG) for daily estimation of the precipitation in the Yellow River basin of China, while GSMaP and CHIRPS performed the worst.…”

Climatology of icing conditions over Colombia based on ERA5 reanalysis and in situ observations

“…The overestimation could also be related to the fact that ERA5 overestimates precipitation over high mountain ranges such as the Andes and Himalayas (Hassler & Lauer, 2021). Other studies have reported that ERA5 tends to overestimate precipitation and incorrectly identify numerous non‐precipitation events in various regions, including Austria (Sharifi et al, 2019), southern China (Gao et al, 2020), North America (Tarek et al, 2020), Iran (Yazdandoost et al, 2020), the Tibetan Plateau (Zhang et al, 2022) and the Mongolian Plateau (Xin et al, 2022). This is likely due to the limitations in the representation of cumulus parameterizations in high‐gradient mountain slopes (Zhang et al, 2022).…”