linear merging methodology for high-resolution precipitation products using spatiotemporal regression

Currently, the only viable option for a global precipitation product is the merger of several precipitation products from different modalities. In this article, we develop a linear merging methodology based on spatiotemporal regression. Four highresolution precipitation products (HRPPs), obtained th...

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Bibliographic Details
Published in:International journal of remote sensing 2012-12, Vol.33 (24), p.7844-7867
Main Authors: Turlapaty, Anish C, Younan, Nicolas H, Anantharaj, Valentine G
Format: Article
Language:English
Subjects:
algorithms
Animal, plant and microbial ecology
Applied geophysics
artificial intelligence
Biological and medical sciences
climate models
data collection
Earth sciences
Earth, ocean, space
Exact sciences and technology
Fundamental and applied biological sciences. Psychology
General aspects. Techniques
Internal geophysics
rain
satellites
spring
summer
Teledetection and vegetation maps
watersheds
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Summary:Currently, the only viable option for a global precipitation product is the merger of several precipitation products from different modalities. In this article, we develop a linear merging methodology based on spatiotemporal regression. Four highresolution precipitation products (HRPPs), obtained through methods including the Climate Prediction Center's Morphing (CMORPH), Geostationary Operational Environmental Satellite-Based Auto-Estimator (GOES-AE), GOES-Based Hydro-Estimator (GOES-HE) and Self-Calibrating Multivariate Precipitation Retrieval (SCAMPR) algorithms, are used in this study. The merged data are evaluated against the Arkansas Red Basin River Forecast Center's (ABRFC's) ground-based rainfall product. The evaluation is performed using the Heidke skill score (HSS) for four seasons, from summer 2007 to spring 2008, and for two different rainfall detection thresholds. It is shown that the merged data outperform all the other products in seven out of eight cases. A key innovation of this machine learning method is that only 6% of the validation data are used for the initial training. The sensitivity of the algorithm to location, distribution of training data, selection of input data sets and seasons is also analysed and presented.
ISSN:1366-5901
0143-1161
1366-5901
DOI:10.1080/01431161.2012.703345