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Open AccessArticle

Precipitation Retrieval over the Tibetan Plateau from the Geostationary Orbit—Part 1: Precipitation Area Delineation with Elektro-L2 and Insat-3D

1
Department of Geography, Laboratory for Climatology and Remote Sensing, Deutschhausstrasse 12, Philipps-Universität Marburg, 35032 Marburg, Germany
2
Faculty of Geosciences, Department of Geography, Luisenstraße 37, Ludwig-Maximilians-Universität München, 80333 München, Germany
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(19), 2302; https://doi.org/10.3390/rs11192302
Received: 6 September 2019 / Revised: 27 September 2019 / Accepted: 29 September 2019 / Published: 2 October 2019
The lack of long term and well distributed precipitation observations on the Tibetan Plateau (TiP) with its complex terrain raises the need for other sources of precipitation data for this area. Satellite-based precipitation retrievals can fill those data gaps. Before precipitation rates can be retrieved from satellite imagery, the precipitating area needs to be classified properly. Here, we present a feasibility study of a precipitation area delineation scheme for the TiP based on multispectral data with data fusion from the geostationary orbit (GEO, Insat-3D and Elektro-L2) and a machine learning approach (Random Forest, RF). The GEO data are used as predictors for the RF model, extensively validated by independent GPM (Global Precipitation Measurement Mission) IMERG (Integrated Multi-satellitE Retrievals for GPM) gauge calibrated microwave (MW) best-quality precipitation estimates. To improve the RF model performance, we tested different optimization schemes. Here, we find that (1) using more precipitating pixels and reducing the amount of non-precipitating pixels during training greatly improved the classification results. The accuracy of the precipitation area delineation also benefits from (2) changing the temporal resolution into smaller segments. We particularly compared our results to the Infrared (IR) only precipitation product from GPM IMERG and found a markedly improved performance of the new multispectral product (Heidke Skill Score (HSS) of 0.19 (IR only) compared to 0.57 (new multispectral product)). Other studies with a precipitation area delineation obtained a probability of detection (POD) of 0.61, whereas our POD is comparable, with 0.56 on average. The new multispectral product performs best (worse) for precipitation rates above the 90th percentile (below the 10th percentile). Our results point to a clear strategy to improve the IMERG product in the absence of MW radiances. View Full-Text
Keywords: precipitation; delineation; GPM IMERG; machine learning; random forest precipitation; delineation; GPM IMERG; machine learning; random forest
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MDPI and ACS Style

Kolbe, C.; Thies, B.; Egli, S.; Lehnert, L.; Schulz, H.M.; Bendix, J. Precipitation Retrieval over the Tibetan Plateau from the Geostationary Orbit—Part 1: Precipitation Area Delineation with Elektro-L2 and Insat-3D. Remote Sens. 2019, 11, 2302.

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