Journal
JOURNAL OF HYDROMETEOROLOGY
Volume 23, Issue 4, Pages 561-572Publisher
AMER METEOROLOGICAL SOC
DOI: 10.1175/JHM-D-21-0171.1
Keywords
Rainfall; Machine learning; Interpolation schemes
Categories
Funding
- Hawai'i EPSCoR Program, a National Science Foundation Research Infrastructure Improvement (RII) Track-1: 'Ike Wai: Securing Hawai's Water Future Award [OIA-1557349]
- National Science Foundation MRI [CNS-1920304]
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This study applies an optimized geostatistical kriging approach to obtain high-resolution gridded monthly rainfall time series for Hawaii. The results are validated using cross-validation and show good agreement with observations, although there may be underestimation of high rainfall events due to the smoothing effect of kriging. The study highlights the importance of considering additional sources of error assessment and modifying parameterizations for realistic gridded rainfall surfaces.
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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