Abstract
Different methods are known for interpolating spatial data. Introduced a few years ago, the initial version of the Most Probable Precipitation Method (MPPM) proved to be a valuable competitor against the Thiessen Polygons Method, Inverse Distance Weighting and kriging for estimating the regional trend of precipitation series. Climate Analyzer, introduced here, is a user-friendly toolkit written in Matlab, which implements the initial and modified version of MPPM and new selection criteria of the series that participate in estimating the regional precipitation series. The software provides the graphical output of the estimated regional series, the modeling errors and the comparisons of the results for different segmentations of the time interval used in modeling. This article contains the description of Climate Analyzer, accompanied by a case study to exemplify its capabilities.
Highlights
An extensive Implementation Plan for the Grand Challenge on Understanding and Predicting Weather and Climate Extremes should focus on integrated observations, improved models, new process understanding of the physical drivers of extremes and fast-track attribution [1]
Wu et al [10] compared the performances of Inverse Distance Weighting (IDW), Ordinary Kriging (OK), Local Polynomial Interpolation, Radial Basis Function and some versions of IDW and UK for interpolating data from the Mississippi River Basin
In the same idea of the spatial interpolation of the precipitation series [22,23,24,25,26], Bărbulescu [22] introduced a new method, called the Most Probable Precipitation Method (MPPM) and compared its results with those provided by some well-known spatial interpolation techniques—the Thiessen Polygons Method (TPM), Inverse Distance Weighting (IDW) and Ordinary Kriging (OK)—for estimating the regional precipitation in Dobrogea, Romania
Summary
An extensive Implementation Plan for the Grand Challenge on Understanding and Predicting Weather and Climate Extremes should focus on integrated observations, improved models, new process understanding of the physical drivers of extremes and fast-track attribution [1]. It was shown that there is no best method for all the studied problems since that the modeling quality depends on the series characteristics [19,20,21] All these approaches aim at providing accurate estimations of precipitation as well as possible. In the same idea of the spatial interpolation of the precipitation series [22,23,24,25,26], Bărbulescu [22] introduced a new method, called the Most Probable Precipitation Method (MPPM) and compared its results with those provided by some well-known spatial interpolation techniques—the Thiessen Polygons Method (TPM), Inverse Distance Weighting (IDW) and Ordinary Kriging (OK)—for estimating the regional precipitation in Dobrogea, Romania. The study of the precipitation occurrence has been performed on daily precipitation [25,28,29] for the same region
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