Bias adjustment of infrared‐based rainfall estimation using Passive Microwave satellite rainfall data

Abstract

This study explores using Passive Microwave (PMW) rainfall estimation for spatial and temporal adjustment of Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks‐Cloud Classification System (PERSIANN‐CCS). The PERSIANN‐CCS algorithm collects information from infrared images to estimate rainfall. PERSIANN‐CCS is one of the algorithms used in the Integrated Multisatellite Retrievals for GPM (Global Precipitation Mission) estimation for the time period PMW rainfall estimations are limited or not available. Continued improvement of PERSIANN‐CCS will support Integrated Multisatellite Retrievals for GPM for current as well as retrospective estimations of global precipitation. This study takes advantage of the high spatial and temporal resolution of GEO‐based PERSIANN‐CCS estimation and the more effective, but lower sample frequency, PMW estimation. The Probability Matching Method (PMM) was used to adjust the rainfall distribution of GEO‐based PERSIANN‐CCS toward that of PMW rainfall estimation. The results show that a significant improvement of global PERSIANN‐CCS rainfall estimation is obtained.

Probabilistic precipitation rate estimates with space‐based infrared sensors

“…The accuracy of these precipitation estimates tends to increase when the estimates are integrated over larger time-and space-scales, but remains inadequate at the resolution of the IR measurements. Although PMW-based estimates generally provide higher accuracy, the spatial and temporal resolution provided by current constellations of PMW sensors is limited (Karbalaee et al, 2017). As part of the Global Precipitation Measurement (GPM) mission (https://www.nasa.gov/mission_pages/ GPM/main/; Hou et al, 2014;Skofronick-Jackson et al, 2017), the Integrated Multi-satellitE Retrievals for GPM (IMERG: Huffman et al, 2015) combines the IR-based Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS: Hong et al, 2004) algorithm and retrievals from PMW sensors on board LEO satellites to produce near-real time estimates at high spatial and temporal resolution with quasi-global coverage.…”

Investigating the Potential of Using Satellite‐Based Precipitation Radars as Reference for Evaluating Multisatellite Merged Products

“…This is mainly attributed to the complex orography at high altitudes, where no‐rain clouds are falsely identified as rainy clouds by the IR algorithms (Maggioni et al, ; Tian et al, ) and to snow cover on the mountains, which may affect the rain estimate (Maggioni et al, ). Moreover, the current IR algorithm is challenged in identifying warm precipitating clouds (Joyce et al, ; Karbalaee et al, ). For most climate regions, biases are greater than 1.…”

“…At seasonal scale, arid‐desert, arid‐steppe, and continental climates with warm summer manifest largest variability in terms of both categorical and continuous statistics. This analysis on the performance of the IR estimates in different climatic regions calls for climatology‐based improvements in the IR algorithms (Karbalaee et al, ).…”

Assessment of Satellite and Radar Quantitative Precipitation Estimates for Real Time Monitoring of Meteorological Extremes Over the Southeast of the Iberian Peninsula

“…Since PERSIANN-CCS is available in near-real time, it is suitable for use in flood warning and management applications [3,23], especially in large river systems such as the Segura or Guadalentín river basins where the one-hour temporal resolution has little impact on hydrological analysis compared to smaller flash floods prone basins [24]. Very promising advances have been made in the calibration of PERSIANN-CCS with data from other satellite data [25], but, unfortunately, these products are not available in near-real time.…”