Developing a Near Real-Time Satellite-Based Global Drought Monitoring System

Reliable drought monitoring necessitates long-term and continuous precipitation data. High-resolution satellite measurements provide valuable precipitation information on a quasi-global scale, yet their short record lengths limit their applicability for drought monitoring. Additionally, long-term low-resolution satellite-based gauge-adjusted data sets, such as the Global Precipitation Climatology Project (GPCP), are not available in near real-time for timely drought assessments. This study bridges the gap between low-resolution long-term satellite gauge-adjusted data and emerging high-resolution satellite precipitation data sets to create a comprehensive long-term climate data record for droughts. To achieve this, a Bayesian correction algorithm is employed to combine GPCP data with real-time satellite precipitation data sets, enhancing drought monitoring and analysis capabilities. The results indicate that the combined data sets, after applying the Bayesian correction, show significant improvements over the uncorrected data. Additionally, this combined approach successfully detected several recent major droughts, including the 2011 Texas drought, the 2010 Amazon drought, and the 2010 Horn of Africa drought. These findings underscore the potential of satellite precipitation data for regional to global drought monitoring. The final product is a real-time data-driven satellite-based standardized precipitation index (SPI) that can be utilized for drought monitoring, particularly in remote and/or ungauged regions. This innovative approach offers a reliable tool for timely and accurate drought assessments, addressing the limitations of existing data sets.