Improving warm rain estimation in the PERSIANN-CCS satellite-based retrieval algorithm

Abstract:

The Precipitation Estimation from remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS) is one of the algorithms being integrated in the IMERG (Integrated Multi-Satellite Retrievals for the Global Precipitation Mission GPM) to estimate precipitation at 0.04 lat-long scale every 30-minute. PERSIANN-CCS extracts features from infrared cloud image segmentation from three brightness temperature thresholds (220K, 235K, and 253K). Warm raining clouds with brightness temperature higher than 253K are not covered from the current algorithm. To improve rain detection from warm rain, in this study, the cloud image segmentation threshold to cover warmer clouds is extended from 253K to 300K. Several other temperature thresholds between 253K and 300K were also examined. K-means cluster algorithm was used to classify extracted image features to 400 groups. Rainfall rates from each cluster were retrained using radar rainfall measurements. Case studies were carried out over CONUS to investigate the ability to improve detection of warm rainfall from segmentation and image classification using warmer temperature thresholds. Satellite image and radar rainfall data in both summer and winter seasons were used in the experiments in year 2012 as a training data. Overall results show that rain detection from warm clouds is significantly improved. However, it also shows that the false rain detection is also relatively increased when the segmentation temperature is increased.