摘要：A new satellite-based algorithm for rainfall retrieval in high spatio-temporal resolution for Ecuador is presented. The algorithm relies on the precipitation information from the Integrated Multi-SatEllite Retrieval for the Global Precipitation Measurement (GPM) (IMERG) and infrared (IR) data from the Geostationary Operational Environmental Satellite-16 (GOES-16). It was developed to (i) classify the rainfall area (ii) assign the rainfall rate. In each step, we selected the most important predictors and hyperparameter tuning parameters monthly. Between 19 April 2017 and 30 November 2017, brightness temperature derived from the GOES-16 IR channels and ancillary geo-information were trained with microwave-only IMERG-V06 using random forest (RF). Validation was done against independent microwave-only IMERG-V06 information not used for training. The validation results showed the new rainfall retrieval technique (multispectral) outperforms the IR-only IMERG rainfall product. This offers using the multispectral IR data can improve the retrieval performance compared to single-spectrum IR approaches. The standard verification scored a median Heidke skill score of ~0.6 for the rain area delineation and R between ~0.5 and ~0.62 for the rainfall rate assignment, indicating uncertainties for Andes’s high elevation. Comparison of RF rainfall rates in 2 km 2 resolution with daily rain gauge measurements reveals the correlation of R = ~0.33.
关键词：Ecuador GOES-16 GPM IMERG machine learning rainfall retrieval random forest
