Geography
Document Type
Article
Abstract
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 (R2 = 0.78; MAE = 55 mm month21; 1.4%); however, predictions can underestimate high rainfall observations (bias = 34 mm month21; 21%) 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).
Publication Title
Journal of Hydrometeorology
Publication Date
4-1-2022
Volume
23
Issue
4
First Page
561
Last Page
572
ISSN
1525-755X
DOI
10.1175/JHM-D-21-0171.1
Keywords
interpolation schemes, machine learning, rainfall
Repository Citation
Lucas, Matthew P.; Longman, Ryan J.; Giambelluca, Thomas W.; Frazier, Abby G.; McLean, Jared; Cleveland, Sean B.; Huang, Yu Fen; and Lee, Jonghyun, "Optimizing Automated Kriging to Improve Spatial Interpolation of Monthly Rainfall over Complex Terrain" (2022). Geography. 5.
https://commons.clarku.edu/faculty_geography/5
Copyright Conditions
© Copyright 2022 American Meteorological Society (AMS). For permission to reuse any portion of this Work, please contact permissions@ametsoc.org. Any use of material in this Work that is determined to be “fair use” under Section 107 of the U.S. Copyright Act (17 U.S. Code § 107) or that satisfies the conditions specified in Section 108 of the U.S. Copyright Act (17 USC § 108) does not require the AMS’s permission. Republication, systematic reproduction, posting in electronic form, such as on a website or in a searchable database, or other uses of this material, except as exempted by the above statement, requires written permission or a license from the AMS. All AMS journals and monograph publications are registered with the Copyright Clearance Center (https://www.copyright.com). Additional details are provided in the AMS Copyright Policy statement, available on the AMS website (https://www.ametsoc.org/PUBSCopyrightPolicy). Must link to published article: https://doi.org/10.1175/JHM-D-21-0171.1
