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Turini, N.; Thies, B.; Horna, N. &amp; Bendix, J. (2021): <b>Random forest-based rainfall retrieval for Ecuador using GOES-16 and IMERG-V06 data</b>. <i>European Journal of Remote Sensing</i> <b>54</b>(1), 117-139.

Resource Description

Title: Random forest-based rainfall retrieval for Ecuador using GOES-16 and IMERG-V06 data
FOR816dw ID: 1918
Publication Date: 2021-01-01
License and Usage Rights:
Resource Owner(s):
Individual: Nazli Turini
Contact:
Individual: Boris Thies
Contact:
Individual: Natalia Horna
Contact:
Individual: Jörg Bendix
Contact:
Abstract:
A new satellite-based algorithm for rainfall retrieval in high spatio-temporal resolution fo<br/> Ecuador is presented. The algorithm relies on the precipitation information from the Integrated<br/> Multi-SatEllite Retrieval for the Global Precipitation Measurement (GPM) (IMERG) and infrared<br/> (IR) data from the Geostationary Operational Environmental Satellite-16 (GOES-16). It wa<br/> developed to (i) classify the rainfall area (ii) assign the rainfall rate. In each step, we selected<br/> the most important predictors and hyperparameter tuning parameters monthly. Between 19<br/> April 2017 and 30 November 2017, brightness temperature derived from the GOES-16 IR<br/> channels and ancillary geo-information were trained with microwave-only IMERG-V06 using<br/> random forest (RF). Validation was done against independent microwave-only IMERG-V06<br/> information not used for training. The validation results showed the new rainfall retrieva<br/> technique (multispectral) outperforms the IR-only IMERG rainfall product. This offers using<br/> the multispectral IR data can improve the retrieval performance compared to single-spectrum<br/> IR approaches. The standard verification scored a median Heidke skill score of ~0.6 for the rain<br/> area delineation and R between ~0.5 and ~0.62 for the rainfall rate assignment, indicating<br/> uncertainties for Andes’s high elevation. Comparison of RF rainfall rates in 2 km2 <br/> resolution<br/> with daily rain gauge measurements reveals the correlation of R = ~0.33.
Keywords:
| random forest | rainfall | GOES |
Literature type specific fields:
ARTICLE
Journal: European Journal of Remote Sensing
Volume: 54
Issue: 1
Page Range: 117-139
Publisher: Taylor & Francis
Metadata Provider:
Individual: Jörg Bendix
Contact:
Online Distribution:
Download File: http://www.lcrs.de/publications.do?citid=1918


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