Comprehensive Analysis of PERSIANN Products in Studying the Precipitation Variations over Luzon

Abstract

This study evaluated the capability of satellite precipitation estimates from five products derived from Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (including PERSIANN, PERSIANN-CCS, PERSIANN-CDR, PERSIANN-CCS-CDR, and PDIR-Now) to represent precipitation characteristics over Luzon. The analyses focused on monthly and daily timescales from 2003–2015 and adopted surface observations from the Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) platform as the evaluation base. Among the five satellite precipitation products (SPPs), PERSIANN-CDR was observed to possess a better ability to qualitatively and quantitatively estimate spatiotemporal variations of precipitation over Luzon for the majority of the examined features with the exception of the extreme precipitation events, for which PERSIANN-CCS-CDR is superior to the other SPPs. These results highlight the usefulness of the addition of the cloud patch approach to PERSIANN-CDR to produce PERSIANN-CCS-CDR to depict the characteristics of extreme precipitation events over Luzon. A similar advantage of adopting the cloud patch approach in producing extreme precipitation estimates was also revealed from the comparison of PERSIANN, PERSIANN-CCS, and PDIR-Now. Our analyses also highlighted that all PERSIANN-series exhibit improved skills in regard to detecting precipitation characteristics over west Luzon compared to that over east Luzon. To overcome this weakness, we suggest that an adjustment in the cloud patch approach (e.g., using different cloud temperature thresholds or different brightness temperature and precipitation rate relationships) over east Luzon may be helpful.

Capability of satellite data to estimate observed precipitation in southeastern South America

“…On the other hand, CCS‐CDR and PDIR‐NOW (Figure 3k,l) better represent the winter precipitation pattern, though they slightly overestimate observed precipitation in southern Brazil and southern Uruguay. This is consistent with the findings of Salehi et al (2022) and Hsu et al (2022) who found that CCS‐CDR and PDIR‐NOW perform better in the winter months, when the cloud patch approach significantly improves quantitative precipitation estimation.…”

“…This result may be attributed to the challenges faced by GPM satellites in accurately representing the warm rain processes prevalent in this region, as documented by Gadelha et al (2019). Moreover, the cloud patch approach of CCS‐CDR and PDIR‐NOW is more sensitive to precipitation associated with deep convection than to warm rain (Hsu et al, 2022).…”

“…The performance of IMERG is better during the rainy season due to the effectiveness of passive microwave sensors in deep convection systems, as they rely on scattering by ice particles in the atmosphere (Derin et al, 2016). On the other hand, winter precipitation is better represented by CCS-CDR and PDIR-NOW, in agreement with Salehi et al (2022) and Hsu et al (2022). These products use the cloud patch approach with a temperature threshold of 253 K for CCS-CDR and 263 K for PDIR-NOW (Hsu et al, 2022).…”

Advantages of GSMaP Data for Multi‐Timescale Precipitation Estimation in Luzon

“…Numerous studies (Hsu et al., 2022; Peralta et al., 2020; Sunilkumar et al., 2019; Veloria et al., 2021) have been conducted to evaluate the accuracy of GPM products over Luzon and the nearby regions. Among these studies, Sunilkumar et al.…”

“…Numerous studies (Hsu et al, 2022;Peralta et al, 2020;Sunilkumar et al, 2019;Veloria et al, 2021) have been conducted to evaluate the accuracy of GPM products over Luzon and the nearby regions. Among these studies, Sunilkumar et al (2019) compared the performance of Tropical Rainfall Measuring Mission (TRMM) 3B42 v7 and IMERG-F v05 during 2014-2015, and they determined that IMERG-F can represent rainfall measurements better than can TRMM 3B42 over Luzon and the nearby regions.…”