Downscaling of Rainfall Extremes From Satellite Observations

Abstract

The estimation of the frequency of intense rainfall events is a crucial step for quantifying their impact on human societies and on the environment. This process is hindered by large gaps in ground observational networks at the global scale, such that extensive areas remain ungauged. The increasing availability of satellite‐based rainfall estimates, while providing data with unprecedented resolution and global coverage, also introduces new challenges: the scale disparity between gridded and rain‐gauge precipitation products on the one hand, and the short length of the available satellite records on the other. Here we propose a statistical framework for the estimation of rainfall extremes that is specifically designed to simultaneously address these two key issues, providing a new way of estimating extreme rainfall magnitudes from space. A downscaling procedure is here introduced to recover the spatial correlation and the probability density function of daily rainfall at the point (gauge) scale from coarse‐scale satellite estimates. The results are then combined with a recent statistical model of extremes (the Metastatistical Extreme Value distribution), which optimizes the use of the information obtained from relatively short satellite observational time series. The methodology is tested using data from the Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis over the Little Washita River, Oklahoma. We find that our approach satisfactorily reproduces downscaled daily rainfall probability density functions and can significantly improve the Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis‐based estimation of quantiles with return times larger than the length of the available data set (19 years here), which are especially important for several water‐related applications.

Validation of GPM IMERG V05 and V06 Precipitation Products over Iran

“…Moreover, applications of SPPs in a broad range of studies indicated their noteworthy potentials in various fields, for example, extreme precipitation analysis, drought monitoring, hydrological modeling, etc. (Camici et al 2018;Hazra et al 2019;Lai et al 2019; Maggioni and Massari 2018;Wang et al 2019;Yong et al 2016;Zhang et al 2019;Zhu et al 2019; Zorzetto and Marani 2019;Zubieta et al 2019).…”

Global distribution of the intensity and frequency of hourly precipitation and their responses to ENSO

“…Since the 1990s, global gridded high-resolution subhourly satellite precipitation estimates have been developed, e.g., the 30-min, 8 km resolution CMORPH global satellite precipitation dataset (Joyce et al 2004). This dataset offers global coverage and extends back to 1998, which provides valuable information for the analysis of global hourly precipitation climatology, including extremes (e.g., Curtis et al 2007;Sun et al 2018;Zorzetto and Marani 2019). Additionally, an increasing number of weather stations have recorded precipitation on sub-daily time steps, enabling new, global-scale analyses.…”

Estimation of Daily Rainfall Extremes Through the Metastatistical Extreme Value Distribution: Uncertainty Minimization and Implications for Trend Detection

“…The optimal choice of EW is thus potentially a function of the average number of events per year, its interannual variability, and the magnitude of the actual interannual variability in the probability distribution of ordinary values. The original formulation of the MEVD, as well as subsequent rainfall applications (Marra et al., 2018; Zorzetto et al., 2016; Zorzetto & Marani, 2019) assume EW =1 year. Here, we seek to determine if an optimal choice of EW exists that balances estimation uncertainty reduction (i.e., reduction of spurious interannual variability) and the ability to resolve short time scale interannual variability (actual interannual variability).…”