Open AccessAn optimal merging technique for high‐resolution precipitation products
Author(s) -
Shrestha Roshan,
Houser Paul R.,
Anantharaj Valentine G.
Publication year - 2011
Publication title -
journal of advances in modeling earth systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.03
H-Index - 58
ISSN - 1942-2466
DOI - 10.1029/2011ms000062
Subject(s) - quantitative precipitation estimation , data assimilation , computer science , precipitation , downscaling , environmental science , data mining , meteorology , physics
Precipitation products are currently available from various sources at higher spatial and temporal resolution than any time in the past. Each of the precipitation products has its strengths and weaknesses in availability, accuracy, resolution, retrieval techniques and quality control. By merging the precipitation data obtained from multiple sources, one can improve its information content by minimizing these issues. However, precipitation data merging poses challenges of scale‐mismatch, and accurate error and bias assessment. In this paper we present Optimal Merging of Precipitation (OMP), a new method to merge precipitation data from multiple sources that are of different spatial and temporal resolutions and accuracies. This method is a combination of scale conversion and merging weight optimization, involving performance‐tracing based on Bayesian statistics and trend‐analysis, which yields merging weights for each precipitation data source. The weights are optimized at multiple scales to facilitate multiscale merging and better precipitation downscaling. Precipitation data used in the experiment include products from the 12‐km resolution North American Land Data Assimilation (NLDAS) system, the 8‐km resolution CMORPH and the 4‐km resolution National Stage‐IV QPE. The test cases demonstrate that the OMP method is capable of identifying a better data source and allocating a higher priority for them in the merging procedure, dynamically over the region and time period. This method is also effective in filtering out poor quality data introduced into the merging process.