Assessment of rainfall forecasts based on canonical correlations of satellite remote sensing data

Alcely Lau1, Ali Behrangi1
1Department of Hydrology and Atmospheric Sciences, University of Arizona

Climate predictions are essential tools for reducing the vulnerability of climate variability risks. For this reason, National Meteorological and Hydrological Services (NMHSs), supported by the World Meteorological Organization (WMO), implemented the regular operation of 19 Regional Climate Outlook Forums (RCOFs) around the world since 1997. The RCOFs are operational platforms that provide consensual-regional climate predictions over climatologically homogenous zones. In Central America, South America, and the Caribbean’s RCOFs, teleconnections modeling through Canonical Correlation Analysis (CCA) between weather station observations and Sea Surface Temperatures (SSTs) is the most widely applied method to generate seasonal rainfall forecasts. However, the RCOFs’ approach faces 2 main issues: a decrease in weather station data availability and a communication deficiency in providing sub-seasonal forecasts. This research aims to assess the reliability of seasonal rainfall forecasts based on CCA of different satellite products, such as CHIRPS and IMERG datasets. Satellite information is advantageous over weather station observations as it provides a continuous high spatial resolution dataset with full coverage for at least 20 years. Thus, the Panama Republic was selected as the forecast domain and the Global SST as the CCA predictor. The forecast process is repeated 4 times, alternating the timescale, from 1 month to 3 months, and the predictand, first from CHIRPS and then from IMERG. Resulting in 4 cases: CHIRPS_1M, CHIRPS_3M, IMERG_1M, and IMERG_3M. Lastly, we will compare the CCA’s prediction skill of the 4 cases against the traditional forecast that relies on weather station observations. Considering that the population and stakeholders need more information at intra-seasonal timescales, the study expects to demonstrate the potential of satellite remote sensing data in RCOF’s operational seasonal and sub-seasonal forecasts.

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