We propose determining the degree of success in predicting subseasonal fire weather metrics (hdwi, wind, humidity, soil moisture [as a proxy for fuel moisture], precipitation, and temperature) by…

We propose determining the degree of success in predicting subseasonal fire weather metrics (hdwi, wind, humidity, soil moisture [as a proxy for fuel moisture], precipitation, and temperature) by using the existing retrospective forecasts from UFS S2S and subx models. We will also evaluate the impact of calibration, statistical downscaling, dynamical downscaling, and multiple ensemble members on forecast skill (accuracy, reliability, and uncertainty) and usefulness for decision-making with stakeholders. Purpose for the award: the proposed work will compare two NOAA-sponsored subseasonal forecasts and assess the cost-benefit of different post-processing procedures. Forecast data will not only be evaluated for skill, but will also be used in a case study comparison with sub-daily wildfire growth. Example forecast products will be presented to stakeholders to guide future development of this work for use in real-time decision support and direct stakeholder participation. The…