Machine Learning–Based Blending of Satellite and Reanalysis Precipitation Datasets: A Multiregional Tropical Complex Terrain Evaluation

This study evaluates a machine learning–based precipitation ensemble technique (MLPET) over three mountainous tropical regions. The technique, based on quantile regression forests, integrates global satellite precipitation datasets from CMORPH, PERSIANN, GSMaP (V6), and 3B42 (V7) and an atmospheric...

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Bibliographic Details
Published in:Journal of hydrometeorology 2019-11, Vol.20 (11), p.2147-2161
Main Authors: Bhuiyan, Md. Abul Ehsan, Nikolopoulos, Efthymios I., Anagnostou, Emmanouil N.
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Atmospheric precipitations
Blending
Datasets
Elevation
Ensemble precipitation
Environmental engineering
Evaluation
Global precipitation
Hydrologic data
International organizations
Learning algorithms
Machine learning
Precipitation
Rain
Rain gauges
Rainfall
Rainfall data
Random errors
Regions
Satellites
Soil
Soil moisture
Spatial discrimination
Spatial resolution
SPECIAL Global Precipitation Measurement (GPM) COLLECTION
Specific humidity
Statistical methods
Studies
Terrain
Tropical climate
Tropical environment
Tropical environments
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Summary:This study evaluates a machine learning–based precipitation ensemble technique (MLPET) over three mountainous tropical regions. The technique, based on quantile regression forests, integrates global satellite precipitation datasets from CMORPH, PERSIANN, GSMaP (V6), and 3B42 (V7) and an atmospheric reanalysis precipitation product (EI_GPCC) with daily soil moisture, specific humidity, and terrain elevation datasets. The complex terrain study areas include the Peruvian and Colombian Andes in South America and the Blue Nile in East Africa. Evaluation is performed at a daily time scale and 0.25° spatial resolution based on 13 years (2000–12) of reference rainfall data derived from dense in situ rain gauge networks. The technique is evaluated using K-fold, separately in each region, and leave-one-region-out validation experiments. Comparison of MLPET with the individual satellite and reanalysis precipitation datasets used for the blending and the recent Multi-Source Weighted-Ensemble Precipitation (MSWEP) global precipitation product exhibited improved systematic and random error statistics for all regions. In addition, it is shown that observations are encapsulated well within the ensemble envelope generated by the blending technique.
ISSN:1525-755X
1525-7541
DOI:10.1175/JHM-D-19-0073.1