Dissecting Performances of PERSIANN-CDR Precipitation Product over Huai River Basin, China

Abstract

Satellite-based precipitation products, especially those with high temporal and spatial resolution, constitute a potential alternative to sparse rain gauge networks for multidisciplinary research and applications. In this study, the validation of the 30-year Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) daily precipitation dataset was conducted over the Huai River Basin (HRB) of China. Based on daily precipitation data from 182 rain gauges, several continuous and categorical validation statistics combined with bias and error decomposition techniques were employed to quantitatively dissect the PERSIANN-CDR performance on daily, monthly, and annual scales. With and without consideration of non-rainfall data, this product reproduces adequate climatologic precipitation characteristics in the HRB, such as intra-annual cycles and spatial distributions. Bias analyses show that PERSIANN-CDR overestimates daily, monthly, and annual precipitation with a regional mean percent total bias of 11%. This is related closely to the larger positive false bias on the daily scale, while the negative non-false bias comes from a large underestimation of high percentile data despite overestimating lower percentile data. The systematic sub-component (error from high precipitation), which is independent of timescale, mainly leads to the PERSIANN-CDR total Mean-Square-Error (TMSE). Moreover, the daily TMSE is attributed to non-false error. The correlation coefficient (R) and Kling–Gupta Efficiency (KGE) respectively suggest that this product can well capture the temporal variability of precipitation and has a moderate-to-high overall performance skill in reproducing precipitation. The corresponding capabilities increase from the daily to annual scale, but decrease with the specified precipitation thresholds. Overall, the PERSIANN-CDR product has good (poor) performance in detecting daily low (high) rainfall events on the basis of Probability of Detection, and it has a False Alarm Ratio of above 50% for each precipitation threshold. The Equitable Threat Score and Heidke Skill Score both suggest that PERSIANN-CDR has a certain ability to detect precipitation between the second and eighth percentiles. According to the Hanssen–Kuipers Discriminant, this product can generally discriminate rainfall events between two thresholds. The Frequency Bias Index indicates an overestimation (underestimation) of precipitation totals in thresholds below (above) the seventh percentile. Also, continuous and categorical statistics for each month show evident intra-annual fluctuations. In brief, the comprehensive dissection of PERSIANN-CDR performance reported herein facilitates a valuable reference for decision-makers seeking to mitigate the adverse impacts of water deficit in the HRB and algorithm improvements in this product.

Evaluation the Performance of Several Gridded Precipitation Products over the Highland Region of Yemen for Water Resources Management

“…For instance, the high estimation of rainfall rates by TRMM 3B42 is consistent with other studies on neighboring regions such as Ethiopia [79], which indicated overestimation of rainfall rates on the highland region of Ethiopia. High rainfall rates produced by PERSIANN-CDR, particularly the low rainfall events, is consistent with other studies from Iran highlands and high region of China [41,80]. A large degree of variability of reanalysis dataset, mainly CFSR, agrees with the result presented by Qiaohong Sun and C. Maio 2017 [81], which covers around 30 global precipitation datasets and mentions the large difference in precipitation estimates, especially in complex mountain areas and high latitude regions.…”

“…Estimates of precipitation are produced using the PERSIANN algorithm on GridSat B1 infrared satellite data, and the National Centers for Environmental Prediction (NCEP) stage IV hourly precipitation data are used for training the artificial neural network [19,41]. The adjustment of PERSIANN-CDR is done using the Global Precipitation Climatology Project (GPCP) monthly product version 2.2 (GPCPv2.2).…”

Evaluation of precipitation datasets available on Google earth engine over India

“…The estimates from VIS/IR can only reflect bulk properties of clouds ignoring the characteristics of cloud morphology and microphysics and hence does not correlate well with gauge precipitation (Ebert et al ., 1996; Joyce et al ., 2004; Sapiano and Arkin, 2009). It is also possible that the actual precipitating region may not be consistent with the cloud shield of the precipitation system (Sun et al ., 2019). It is also observed that heavy precipitation usually occurs at the edges of the cloud system which are characterized by relatively warmer cloud top temperature in comparison to the central core of the clouds (Scofield and Kuligowski, 2003; Behrangi et al ., 2010; Tao et al ., 2016).…”

“…Type of cloud is closely related to precipitation intensities and can significantly improve the estimates but unfortunately, these are complex three‐dimensional structures and high‐frequency estimates from VIS/IR sensors does not contain much information on this respect. Although the identification of the type of cloud is a complex process, numerous studies have pointed out on its importance (Sun et al ., 2019). Evaporation of hydrometeors from subcloud or cloud base is also a crucial aspect which is ignored in the satellite‐based precipitation estimates.…”

An Analysis of Long-Term Rainfall Trends and Variability in the Uttarakhand Himalaya Using Google Earth Engine

“…However, Ashouri et al [38] established the proficiency of PERSIANN-CDR in terms of long-term rainfall-runoff modelling with low bias. In recent years, there has been growing literature in the performance of PERSIANN-CDR daily rainfall product to acquire rainfall information in Asian mountainous regions and assess its efficiency in terms of long-term performance for rainfall and hydrological modelling [32,37,[65][66][67][68][69].…”