A Regional Blended Precipitation Dataset over Pakistan Based on Regional Selection of Blending Satellite Precipitation Datasets and the Dynamic Weighted Average Least Squares Algorithm

Abstract

Substantial uncertainties are associated with satellite precipitation datasets (SPDs), which are further amplified over complex terrain and diverse climate regions. The current study develops a regional blended precipitation dataset (RBPD) over Pakistan from selected SPDs in different regions using a dynamic weighted average least squares (WALS) algorithm from 2007 to 2018 with 0.25° spatial resolution and one-day temporal resolution. Several SPDs, including Global Precipitation Measurement (GPM)-based Integrated Multi-Satellite Retrievals for GPM (IMERG), Tropical Rainfall Measurement Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) 3B42-v7, Precipitation Estimates from Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), ERA-Interim (reanalysis dataset), SM2RAIN-CCI, and SM2RAIN-ASCAT are evaluated to select appropriate blending SPDs in different climate regions. Six statistical indices, including mean bias (MB), mean absolute error (MAE), unbiased root mean square error (ubRMSE), correlation coefficient (R), Kling–Gupta efficiency (KGE), and Theil’s U coefficient, are used to assess the WALS-RBPD performance over 102 rain gauges (RGs) in Pakistan. The results showed that WALS-RBPD had assigned higher weights to IMERG in the glacial, humid, and arid regions, while SM2RAIN-ASCAT had higher weights across the hyper-arid region. The average weights of IMERG (SM2RAIN-ASCAT) are 29.03% (23.90%), 30.12% (24.19%), 31.30% (27.84%), and 27.65% (32.02%) across glacial, humid, arid, and hyper-arid regions, respectively. IMERG dominated monsoon and pre-monsoon seasons with average weights of 34.87% and 31.70%, while SM2RAIN-ASCAT depicted high performance during post-monsoon and winter seasons with average weights of 37.03% and 38.69%, respectively. Spatial scale evaluation of WALS-RPBD resulted in relatively poorer performance at high altitudes (glacial and humid regions), whereas better performance in plain areas (arid and hyper-arid regions). Moreover, temporal scale performance assessment depicted poorer performance during intense precipitation seasons (monsoon and pre-monsoon) as compared with post-monsoon and winter seasons. Skill scores are used to quantify the improvements of WALS-RBPD against previously developed blended precipitation datasets (BPDs) based on WALS (WALS-BPD), dynamic clustered Bayesian model averaging (DCBA-BPD), and dynamic Bayesian model averaging (DBMA-BPD). On the one hand, skill scores show relatively low improvements of WALS-RBPD against WALS-BPD, where maximum improvements are observed in glacial (humid) regions with skill scores of 29.89% (28.69%) in MAE, 27.25% (23.89%) in ubRMSE, and 24.37% (28.95%) in MB. On the other hand, the highest improvements are observed against DBMA-BPD with average improvements across glacial (humid) regions of 39.74% (36.93%), 38.27% (33.06%), and 39.16% (30.47%) in MB, MAE, and ubRMSE, respectively. It is recommended that the development of RBPDs can be a potential alternative for data-scarce regions and areas with complex topography.

Assessment of Merged Satellite Precipitation Datasets in Monitoring Meteorological Drought over Pakistan

“…The considered SPDs are the final version of Global Precipitation Measurement (GPM)-based Integrated Multi-Satellite Retrievals for GPM (IMERG-V06) [76], TMPA 3B42V7 [32], and PERSIANN-CDR [37], and a re-analysis dataset ERA-Interim [39]. The MSPDs include DBMA-MSPD [54], DCBA-MSPD [18], WALS-MSPD [55], and RWALS-MSPD [56]. Note that post-real time versions of SPDs have been used to develop the MSPDs.…”

“…The SPDs performed best are selected to developed RWALS-MSPD for that particular region. Readers are referred to Rahman and Shang [56] for detailed information about RWALS-MSPD.…”

“…DCBA-MSPD, DBMA-MSPD, WALS-MSPD, and RWALS-MSPD are developed and extensively evaluated over the complex topography and diverse climate of Pakistan. A previous study has shown that RWALS-MSPD is the best MSPD developed across Pakistan, followed by WALS and DCBA MSPDs [56]. RWALS-MSPD addresses the high magnitude of SPDs errors (across glacial and humid regions) on a regional scale by proposing different sets of SPDs evaluated using several statistical indices.…”

“…Very few studies are available that developed MSPDs over the complex topography of Pakistan characterized by diverse climate. The developed MSPDs across Pakistan include Dynamic Bayesian Model Averaging (DBMA-MSPD) [54], Dynamic Clustered Bayesian Model Averaging (DCBA-MSPD) [18], Dynamic Weighted Average Least Square (WALS-MSPD) [55], and Regional Dynamic Weighted Average Least Square (RWALS-MSPD) [56]. The results reported that MSPDs have significant improvements in precipitation estimates and higher potential for hydrological applications [57,58].…”

Superiority of Dynamic Weights against Fixed Weights in Merging Multi-Satellite Precipitation Datasets over Pakistan

“…Researchers suggested the dynamic variation of weights with time, and the weights are varied both in space and time using the moving averaging technique by collecting estimations of the average precipitation over 40 days [6]. Recently, Rahman, Shang [26] and Rahman and Shang [30] merged four SPPs using DCBA and dynamic Bayesian model averaging (DBMA) across four climate (glacial, humid, arid, and hyper-arid) zones of Pakistan. Each of the studies documented a notable enhancement in performance along with a considerable decrease in uncertainties.…”