Improving Station-Based Ensemble Surface Meteorological Analyses Using Numerical Weather Prediction: A Case Study of the Oroville Dam Crisis Precipitation Event
Final Published Version
In situ atmospheric observations
Numerical weather prediction/forecasting
Probabilistic Quantitative Precipitation Forecasting (PQPF)
MetadataShow full item record
PublisherAmerican Meteorological Society
CitationBunn, P. T. W., Wood, A. W., Newman, A. J., Chang, H.-I., Castro, C. L., Clark, M. P., & Arnold, J. R. (2022). Improving Station-Based Ensemble Surface Meteorological Analyses Using Numerical Weather Prediction: A Case Study of the Oroville Dam Crisis Precipitation Event. Journal of Hydrometeorology, 23(7), 1155–1169.
JournalJournal of Hydrometeorology
RightsCopyright © 2022 American Meteorological Society.
Collection InformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at email@example.com.
AbstractSurface meteorological analyses serve a wide range of research and applications, including forcing inputs for hydrological and ecological models, climate analysis, and resource and emergency management. Quantifying uncertainty in such analyses would extend their utility for probabilistic hydrologic prediction and climate risk applications. With this motivation, we enhance and evaluate an approach for generating ensemble analyses of precipitation and temperature through the fusion of station observations, terrain information, and numerical weather prediction simulations of surface climate fields. In particular, we expand a spatial regression in which static terrain attributes serve as predictors for spatially distributed 1/168 daily surface precipitation and temperature by including forecast outputs from the High-Resolution Rapid Refresh (HRRR) numerical weather prediction model as additional predictors. We demonstrate the approach for a case study domain of California, focusing on the meteorological conditions leading to the 2017 flood and spillway failure event at Lake Oroville. The approach extends the spatial regression capability of the Gridded Meteorological Ensemble Tool (GMET) and also adds cross validation to the uncertainty estimation component, enabling the use of predictive rather than calibration uncertainty. In evaluation against out-of-sample station observations, the HRRR-based predictors alone are found to be skillful for the study setting, leading to overall improvements in the enhanced GMET meteorological anal-yses. The methodology and associated tool represent a promising method for generating meteorological surface analyses for both research-oriented and operational applications, as well as a general strategy for merging in situ and gridded observations. © 2022 American Meteorological Society.
Note6 month embargo; published online: 15 July 2022
VersionFinal published version