Intercomparison of PERSIANN-CDR and TRMM-3B42V7 precipitation estimates at monthly and daily time scales

Abstract

In the first part of this paper, monthly precipitation data from Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) and Tropical Rainfall Measuring Mission 3B42 algorithm Version 7 (TRMM-3B42V7) are evaluated over Iran using the Generalized Three-Cornered Hat (GTCH) method which is self-sufficient of reference data as input. Climate Data Unit (CRU) is added to the GTCH evaluations as an independent gauge-based dataset thus, the minimum requirement of three datasets for the model is satisfied. To ensure consistency of all datasets, the two satellite products were aggregated to 0.5°spatial resolution, which is the minimum resolution of CRU. The results show that the PERSIANN-CDR has higher Signal to Noise Ratio (SNR) than TRMM-3B42V7 for the monthly rainfall estimation, especially in the northern half of the country. All datasets showed low SNR in the mountainous area of southwestern Iran, as well as the arid parts in the southeast region of the country. Additionally, in order to evaluate the efficacy of PERSIANN-CDR and TRMM-3B42V7 in capturing extreme daily-precipitation amounts, an in-situ rain-gauge dataset collected by the Islamic Republic of the Iran Meteorological Organization (IRIMO) was employed. Given the sparsity of the rain gauges, only 0.25°pixels containing three or more gauges were used for this evaluation. There were 228 such pixels where daily and extreme rainfall from PERSIANN-CDR and TRMM-3B42V7 could be compared. However, TRMM-3B42V7 overestimates most of the intensity indices (correlation coefficients; R between 0.7648-0.8311, Root Mean Square Error; RMSE between 3.29mm/day-21.2mm/5day); PERSIANN-CDR underestimates these extremes (R between 0.6349-0.7791 and RMSE between 3.59mm/day-30.56mm/5day). Both satellite products show higher correlation coefficients and lower RMSEs for the annual mean of consecutive dry spells than wet spells. The results show that TRMM-3B42V7 can capture the annual mean of the absolute indices (the number of wet days in which daily precipitation > 10 mm, 20 mm) better than PERSIANN-CDR. The results of daily evaluations show that the similarity of Empirical Cumulative Density Function (ECDF) of satellite products and IRIMO gauges daily precipitation, as well as dry spells with different thresholds in some selected pixels (include at least five gauges), are significant. The results also indicate that ECDFs become more significant when threshold increases. In terms of regional analyses, the higher SNR of the products on monthly (based on the GTCH method) and daily evaluations (significant ECDFs) is mostly consistent.

Comparing Multiple Precipitation Products against In-Situ Observations over Different Climate Regions of Pakistan

“…GPPs produced closer distribution of higher percentile with those of in-situ as a reference, however deviations and differences were obvious for MSWEP and PERSIANN-CDR. The capability of GPPs to locate higher percentile precipitation estimates closely resemble in-situ data, especially TMPA and CHIRPS [28,47].…”

“…In-situ data represents point-scale observations, which are not truly representative of the area averaged precipitation of the GPPs; thus, the interpretation of a direct comparison should consider such a problem. To overcome this, numerous studies have limited the comparisons to the pixels where an appropriate number of stations were present [12,47], with a minimum of one to more than five stations in each pixel, according to the purpose of the study. In this study, we used minimum of one station to maximum of four in-situ stations in one degree-pixel.…”

“…The grouped precipitation observations were then normalized and plotted against station observations, where the frequency count (precipitating days) was placed on the y-axis and magnitude is shown on the x-axis. For daily scale ECDF, the cumulative probability derived for regional precipitation from in-situ and GPPs were compared using extreme precipitation thresholds of 2.5, 5 and 10 mm/day following [47]. To compare the similarity of precipitation between in-situ data and GPPs, a nonparametric Kolmogorov-Smirnov (K-S) significance test with 95% confidence level was applied, assuming that both in-situ data and GPPs have similar distributions.…”

Applications of Satellite-Based Rainfall Estimates in Flood Inundation Modeling—A Case Study in Mundeni Aru River Basin, Sri Lanka

“…In addition, the RRI model has been utilized with GSMaP to simulate the 2008 flood event in Pakistan [20]. In the meantime, SRE products have been compared with each other and their characteristics and accuracy have also been evaluated [21][22][23]. However, evaluation among multiple SREs in terms of flood inundation reproducibility has not been done yet.…”

Validation of TRMM 3B42V7 Rainfall Product under Complex Topographic and Climatic Conditions over Hexi Region in the Northwest Arid Region of China

“…Yang et al evaluated the TRMM 3B42V7 rainfall product using gauge precipitation observations and then investigated its application in supporting reservoir operation and water resources management, considered that the 3B42V7 can capture well the spatial and temporal characteristics of precipitation and perform well in water allocation decision-making over the Hanjiang basin in China [23]. Different studies show that the applicability and accuracy of the satellite rainfall products in different regions are significantly differ for diverse inversion algorithm and underlying surface [19,[24][25][26].…”