Optimizing Automated Kriging to Improve Spatial Interpolation of Monthly Rainfall over Complex Terrain

LUCAS M. A. T. T. H. E. W. P.; LONGMAN R. Y. A. N. J.; GIAMBELLUCA T. H. O. M. A. S. W.FRAZIER A. B. B. Y. G.MCLEAN J. A. R. E. D.CLEVELAND S. E. A. N. B.HUANG YU-FENLEE J. O. N. G. H. Y. U. N.

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Optimizing Automated Kriging to Improve Spatial Interpolation of Monthly Rainfall over Complex Terrain

机译：Optimizing Automated Kriging to Improve Spatial Interpolation of Monthly Rainfall over Complex Terrain

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Gridded monthly rainfall estimates can be used for a number of research applications, including hydrologic modeling and weather forecasting. Automated interpolation algorithms, such as the "autoKrige" function in R, can produce gridded rainfall estimates that validate well but produce unrealistic spatial patterns. In this work, an optimized geostatistical kriging approach is used to interpolate relative rainfall anomalies, which are then combined with long-term means to develop the gridded estimates. The optimization consists of the following: 1) determining the most appropriate offset (constant) to use when log-transforming data; 2) eliminating poor quality data prior to interpolation; 3) detecting erroneous maps using a machine learning algorithm; and 4) selecting the most appropriate parameterization scheme for fitting the model used in the interpolation. Results of this effort include a 30-yr (1990-2019), highresolution (250-m) gridded monthly rainfall time series for the state of Hawai'i. Leave-one-out cross validation (LOOCV) is performed using an extensive network of 622 observation stations. LOOCV results are in good agreement with observations (R-2 = 0.78; MAE = 55 mm month(-1); -1); however, predictions can underestimate high rainfall observations (bias = 34 mm month(-1); -1) due to a well-known smoothing effect that occurs with kriging. This research highlights the fact that validation statistics should not be the sole source of error assessment and that default parameterizations for automated interpolation may need to be modified to produce realistic gridded rainfall surfaces. Data products can be accessed through the Hawai`i Data Climate Portal (HCDP; http://www.hawaii.edu/climate-data-portal).

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《Journal of hydrometeorology》 |2022年第4期|561-572|共12页
作者
LUCAS M. A. T. T. H. E. W. P.; LONGMAN R. Y. A. N. J.; GIAMBELLUCA T. H. O. M. A. S. W.FRAZIER A. B. B. Y. G.MCLEAN J. A. R. E. D.CLEVELAND S. E. A. N. B.HUANG YU-FENLEE J. O. N. G. H. Y. U. N.;
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作者单位

Univ Hawaii Manoa;

Clark Univ;

Informat & Technol Serv;

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原文格式 PDF
正文语种英语
中图分类水文循环与水文气象;
关键词
Rainfall; Machine learning; Interpolation schemes; PRECIPITATION; TEMPERATURE; CLIMATE; UNCERTAINTY; VARIABILITY; REGRESSION; VARIABLES; SPACE; MODEL;

Optimizing Automated Kriging to Improve Spatial Interpolation of Monthly Rainfall over Complex Terrain

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