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Monitoring the fuel moisture content (FMC) of 10 h dead vegetation is crucial for managing and mitigating the impact of wildland fires. The combination of in situ FMC observations, numerical weather prediction (NWP) models, and satellite retrievals has facilitated the development of machine learning (ML) models to estimate 10 h dead FMC retrievals over the contiguous US (CONUS). In this study, ML models were trained using variables from the National Water Model, the High-Resolution Rapid Refresh (HRRR) NWP model, and static surface properties, along with surface reflectances and land surface temperature (LST) retrievals from the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument on the Suomi-NPP satellite system. Extensive hyper-parameter optimization resulted in skillful FMC models compared to a daily climatography RMSE (+44%) and an hourly climatography RMSE (+24%). Notably, VIIRS retrievals played a significant role as predictors for estimating 10 h dead FMC, demonstrating their importance as a group due to their high band correlation. Conversely, individual predictors within the HRRR group exhibited relatively high importance according to explainability techniques. Removing both HRRR and VIIRS retrievals as model inputs led to a significant decline in performance, particularly with worse RMSE values when excluding VIIRS retrievals. The importance of the VIIRS predictor group reinforces the dynamic relationship between 10 h dead fuel, the atmosphere, and soil moisture. These findings underscore the significance of selecting appropriate data sources when utilizing ML models for FMC prediction. VIIRS retrievals, in combination with selected HRRR variables, emerge as critical components in achieving skillful FMC estimates.

This paper investigates the sensitivities of the Weather Research and Forecasting (WRF) model simulations to different parameterization schemes (atmospheric boundary layer, microphysics, cumulus, longwave and shortwave radiations and other model configuration parameters) on a domain centered over the inter-mountain western United States (U.S.). Sensitivities are evaluated through a multi-model, multi-physics and multi-perturbation operational ensemble system based on the real-time four-dimensional data assimilation (RTFDDA) forecasting scheme, which was developed at the National Center for Atmospheric Research (NCAR) in the United States. The modeling system has three nested domains with horizontal grid intervals of 30 km, 10 km and 3.3 km. Each member of the ensemble system is treated as one of 48 sensitivity experiments. Validation with station observations is done with simulations on a 3.3-km domain from a cold period (January) and a warm period (July). Analyses and forecasts were run every 6 h during one week in each period. Performance metrics, calculated station-by-station and as a grid-wide average, are the bias, root mean square error (RMSE), mean absolute error (MAE), normalized standard deviation and the correlation between the observation and model. Across all members, the 2-m temperature has domain-average biases of −1.5–0.8 K; the 2-m specific humidity has biases from −0.5–−0.05 g/kg; and the 10-m wind speed and wind direction have biases from 0.2–1.18 m/s and −0.5–4 degrees, respectively. Surface temperature is most sensitive to the microphysics and atmospheric boundary layer schemes, which can also produce significant differences in surface wind speed and direction. All examined variables are sensitive to data assimilation.