QRF4P‐NRT: Probabilistic Post‐Processing of Near‐Real‐Time Satellite Precipitation Estimates Using Quantile Regression Forests.

Accurate and reliable near‐real‐time satellite precipitation estimation is of great importance for operational large‐scale flood forecasting and drought monitoring. The state‐of‐the‐art precipitation post‐processing model is based on a deterministic approach to construct relationships between satellites estimates and ground observations. We propose a probabilistic postprocessor, the Probabilistic Post‐Processing of Near‐Real‐Time Satellite Precipitation Estimates using Quantile Regression Forests (QRF4P‐NRT), based on quantile modeling, yielding both deterministic and probabilistic predictions. The experimental design incorporates different solutions of near‐real‐time predictors to further improve the model performance. Using the Integrated Multi‐satellitE Retrievals Early Run for Global Precipitation Measurement Mission (IMERG‐E) product as an example, we illustrate that the proposed method significantly improves the overall quality of the raw IMERG‐E and is also superior to the bias‐corrected product (IMERG Final Run, IMERG‐F) at daily scale in a complex mountain basin. Evaluations of the corrected IMERG‐E, raw IMERG‐E, and IMERG‐F using ground observation show that the corrected IMERG‐E improves correlation coefficients (0.7), mean error (−0.14 mm/day) and root mean square error (3.3 mm/day) relative to the raw IMERG‐E (0.31, −0.72 and 5.5 mm/day) and IMERG‐F (0.34, −0.09 and 6.0 mm/day). The error decomposition further confirms that the QRF4P‐NRT improves on the various deficiencies of the raw IMERG‐E product. The ensemble assessment also demonstrates that the quantile outputs provide reliable prediction spread and sharp prediction intervals. The promising results indicate the great potential of the proposed method for probabilistic post‐processing for near‐real‐time satellite precipitation estimates, and for further applications such as hydrological ensemble forecasting. Plain Language Summary: Errors in near‐real‐time satellite precipitation estimates limit their applications. The use of error correction models is better able to reduce the errors. However, current deterministic error correction models reduce errors while losing uncertain information. In this study, we propose a probabilistic error correction method that has been used in the field of ensemble numerical weather forecasts. While reducing the error, it is also possible to quantify the probabilistic information. Our method obtains the best score compared to both the raw product and bias‐corrected product. This is of great interest for the application of near‐real‐time satellite precipitation estimates and can be further applied to operational flood forecasting and drought monitoring. Key Points: An ensemble postprocessor based on quantile regression forests for bias correction of satellite precipitation estimates is proposedThe Probabilistic Post‐Processing of Near‐Real‐Time Satellite Precipitation Estimates using Quantile Regression Forests remarkably improved raw satellite precipitation estimates and provide reliable probabilistic outputs in a near‐real‐time wayA static proxy of dynamic near‐real‐time predictor is an acceptable solution for operational application [ABSTRACT FROM AUTHOR]