A Terrain-Aware Two-Stage Correction Framework for Near-Real-Time Improvement of GSMaP-NRT Precipitation Estimates

Remote sensing technology is essential for real-time monitoring of spatiotemporal precipitation patterns. However, inherent limitations in indirect observation lead to significant errors in satellite-based precipitation products. Most existing correction methods depend on real-time ground observations, which limits their applicability for high-precision, operational use. To address this, we propose a two-stage synergistic correction framework specifically for the Global Satellite Mapping of Precipitation Near Real-Time product (GSMaP-NRT), with the goal of systematically enhancing the accuracy of its daily-scale estimates worldwide. Central to this framework is the Terrain-aware Two-stage Correction Framework (TTCF-NRT). In the first stage (historical modeling and real-time correction), we jointly utilize historical GSMaP-NRT and CPC merged precipitation data to train an improved Cumulative Distribution Function (CDF) matching model. Once trained, the model operates independently, requiring only real-time GSMaP-NRT data to perform rapid correction without needing concurrent CPC or ground-based inputs. In the second stage (near-real-time spatial refinement), we integrate the contemporaneous CPC product as a spatial reference into the first-stage corrected output. An improved Convolutional Neural Network (CNN) model, trained and validated through rigorous cross-validation, is then applied for spatial enhancement. This step significantly improves the characterization of precipitation spatial distribution, especially over complex terrain. Using the TTCF-NRT framework, we produced a daily corrected precipitation dataset for global land areas from 2020 to 2024 at a 0.5° spatial resolution. Comprehensive evaluation shows that: (1) globally, the TTCF-RT product significantly outperforms both the original GSMaP-NRT and its gauge-adjusted version (GSMaP-Gauge-NRT) in terms of Root Mean Square Error (RMSE) and Relative Bias (BIAS); (2) regionally, TTCF-NRT excels over the Continental United States (CONUS) and Western Europe. It also demonstrates consistent improvement at independent validation sites across China, though performance can still be enhanced, partly due to the limited spatial representativeness of the training data. In summary, the TTCF-NRT framework effectively combines historically calibrated real-time CDF correction with CNN-driven near-real-time spatial fusion. It offers an efficient, robust, and operationally viable correction solution for GSMaP-NNRT that does not rely on real-time external data. This approach substantially improves the accuracy and practical utility of satellite-derived precipitation estimates on a global scale, particularly in regions with complex topography.