FY4QPE-MSA: An All-Day Near-Real-Time Quantitative Precipitation Estimation Framework Based on Multispectral Analysis From AGRI Onboard Chinese FY-4 Series Satellites
Ziqiang Ma, Siyu Zhu, Jun Yang
Abstract
Accurate and near-real-time rain information at fine scales is critical for forecasting local weather and floods. Traditional classical infrared (IR) cloud-top brightness temperature data alone do not contain sufficient precipitation-related information, and the introduction of visible (VIS) observations limits their applications to daytime. Methods for the efficient and comprehensive utilization of multichannel IR observations for accurately retrieving all-day near-real-time rain rates with consistent high quality warrant further exploration. In this study, we propose an all-day near-real-time quantitative precipitation estimation framework based on multispectral analysis (MSA) from the Advanced Geosynchronous Radiation Imager (AGRI) onboard Chinese FY-4 series satellites; the proposed framework is called FY4QPE- MSA. Multiple IR bands are comprehensively and efficiently considered by adopting the principal component analysis technique to reduce the dimensionality to a few independent features while preserving most of the variations. The main conclusions include, but are not limited to, the following aspects: 1) the MSA from IR channels provides valuable information that facilitates the more accurate delineations of the precipitation occurrences; 2) FY4AQPE -MSA outperforms FY4AQPE- Offical [<inline-formula> <tex-math notation="LaTeX">$\sim 20$ </tex-math></inline-formula>% gain in the Pearson correlation coefficient (CC), <inline-formula> <tex-math notation="LaTeX">$\sim 25$ </tex-math></inline-formula>% gain in the root mean square error (RMSE), and <inline-formula> <tex-math notation="LaTeX">$\sim 15$ </tex-math></inline-formula>% gain in the critical success index (CSI)], FY4AQPE-Single (<inline-formula> <tex-math notation="LaTeX">$\sim 10$ </tex-math></inline-formula>% gain in CC, <inline-formula> <tex-math notation="LaTeX">$\sim 15$ </tex-math></inline-formula>% gain in RMSE, and <inline-formula> <tex-math notation="LaTeX">$\sim 15$ </tex-math></inline-formula>% gain in CSI), and Precipitation Estimation from Remotely Sensed Information using artificial neural network-cloud classification system (PERSIANN-CCS) (<inline-formula> <tex-math notation="LaTeX">$\sim 30$ </tex-math></inline-formula>% gain in CC, <inline-formula> <tex-math notation="LaTeX">$\sim 20$ </tex-math></inline-formula>% gain in RMSE, and <inline-formula> <tex-math notation="LaTeX">$\sim 25$ </tex-math></inline-formula>% gain in CSI); and 3) compared with the international IR-based baseline precipitation product, i.e., PERSIANN-CCS, FY4AQPE-MSA demonstrates no spatial gaps over the Tibetan Plateau. In addition, the results of this study suggest that the proposed general framework is promising and applicable for Chinese FY-4 series satellites in generating all-day near-real-time rain-rate information.