PERSIANN-CCS-CDR, a 3-hourly 0.04° global precipitation climate data record for heavy precipitation studies

Abstract

Accurate long-term global precipitation estimates, especially for heavy precipitation rates, at fine spatial and temporal resolutions is vital for a wide variety of climatological studies. Most of the available operational precipitation estimation datasets provide either high spatial resolution with short-term duration estimates or lower spatial resolution with long-term duration estimates. Furthermore, previous research has stressed that most of the available satellite-based precipitation products show poor performance for capturing extreme events at high temporal resolution. Therefore, there is a need for a precipitation product that reliably detects heavy precipitation rates with fine spatiotemporal resolution and a longer period of record. Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record (PERSIANN-CCS-CDR) is designed to address these limitations. This dataset provides precipitation estimates at 0.04° spatial and 3-hourly temporal resolutions from 1983 to present over the global domain of 60°S to 60°N. Evaluations of PERSIANN-CCS-CDR and PERSIANN-CDR against gauge and radar observations show the better performance of PERSIANN-CCS-CDR in representing the spatiotemporal resolution, magnitude, and spatial distribution patterns of precipitation, especially for extreme events.

Error Characteristics and Scale Dependence of Current Satellite Precipitation Estimates Products in Hydrological Modeling

“…PERSIANN-Cloud Classification System-Climate Data Record (hereafter, PCCSCDR) [47] is a merged product, which concentrates PCCS and PCDR and provides a long-term (1983-present) and high-spatiotemporal-resolution (0.04 • × 0.04 • , 3 h) precipitation record.…”

“…PERSIANN and PCCS were among the first generation of global satellite precipitation estimates products, and their algorithm design provided a solid foundation for the advancement of PDIR [48,49]. In addition, they are the foundations of PCDR and PCCSCDR [46,47]. PDIR is a very promising product with a very short delay time (15 to 60 min), relatively high accuracy and the highest spatial resolution (0.04 degree), and it uses the Dynamic Infrared-Rain model, which utilizes climatological data to construct a dynamic cloud-top brightness temperature (T b )-rain rate relationship to retrieve precipitation.…”

“…Although PCCSCDR did not exceed the accuracy of PCDR in this study (YLJ basin), PCCSCDR has a higher spatial resolution (0.04 degree) compared to PCDR. It may exceed PCDR in other regions and can be used for some local studies [47].…”

Assessment of Three Long-Term Satellite-Based Precipitation Estimates against Ground Observations for Drought Characterization in Northwestern China

“…PERSIANN-CDR seems to have difficulty in accurately capturing local terrain precipitation, missing the high precipitation amounts in the Tianshan Mountain and the Kunlun Mountain area. This could be explained by the fact that cloud-top infrared estimates are used in the generation of PERSIANN-CDR [60,61]. CHIRPS performs best in terms of standard deviation, correlation, and RMSE, with the highest CC (0.89) and the lower RB (−0.02%) and RMSE (4.75 mm/month) (Figure 3b).…”

Regional and Seasonal Precipitation and Drought Trends in Ganga–Brahmaputra Basin

“…Satellite observations have more complete coverage, especially across Water 2021, 13, 2218 2 of 19 seas, high altitudes, and isolated locations where gauge data are few or unavailable [4]. Spatio-temporal coverage of reanalysis and remote sensing-based precipitation estimates make them better alternatives of rain gauges [4,5,[9][10][11][12]. Most satellite-based precipitation products such as TRMM [12], IMERG [5], and PERSIANN are available for a short period of time, questioning their significance in climate studies.…”