Abstract:
A new satellite-based algorithm for rainfall retrieval in high spatio-temporal resolution fo
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 wa
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 retrieva
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 km2
resolution
with daily rain gauge measurements reveals the correlation of R = ~0.33.
