Recent increases in the frequency and extent of wildland fires assert a critical need for improved forecasts of fire-weather models as an essential element of mitigating the risks to public health,…

This Cooperative Agreement awarded by the National Oceanic and Atmospheric Administration (NOAA) under the Weather and Air Quality Research program (CFDA 11.459) provides $450,445 in funding to Northeastern University to improve the weather forecasting capabilities of NOAA's Rapid Refresh Forecast System (RRFS).

The key objectives of this 2-year project are to: 1) Implement a new parameterization in RRFS that couples fire and meteorology to better account for fire-induced atmospheric boundary layer processes, 2) Develop improved estimates of fire emission rates from NOAA's weather satellite data to enhance modeling of fire plume dynamics and air quality impacts, and 3) Incorporate two-way feedbacks between fire weather and smoke emissions into RRFS. These enhancements aim to address critical deficiencies in the current RRFS model's representation of wildfire-atmosphere interactions and improve short-term wildfire forecasting capabilities.

Recent increases in the frequency and extent of wildland fires assert a critical need for improved forecasts of fire-weather models as an essential element of mitigating the risks to public health, civil infrastructure, and terrestrial ecosystem function. Despite advances in the spatial resolution and data assimilation capabilities of noaas rapid refresh forecast system (RRFS; next generation regional version of the unified forecast system (UFS)), such advances are yet to be complemented with model development towards improved physical parameterization of coupling between the fire, atmospheric boundary-layer (ABL) turbulence, emission rates, and vegetation structure, all of which are sub-grid scale processes at the current 3-KM model resolution. The absence of this fire-ABL feedback leads to model deficiencies including: 1) limited influence of fire-induced secondary flows associated with convective heat and moisture exchange between the fire and ambient air on fire rate of spread and ABL mixing; and 2) biased estimates of fire emission rates resulting in inaccurate predictions of fire plume dynamics and downwind air quality impacts. This project responds to noaas competition on fire-weather (NOAA-OAR-WPO- 2024-2008082) to address these deficiencies as highly relevant to competition priorities FW-4 (PRIMARY) and FW-1, with the overarching objective of enhancing rrfss short-term wildfire forecasting capability by improving rrfss representation of fire-ABL interactions and incorporating two-way feedbacks between fire weather and smoke emissions. Specific tasks are 1) implementation of a new parameterization in UFS/RRFS that couples fire and meteorology through convective heat/moisture exchange to account for fireinduced secondary circulations in the ABL (relevant to competition priority FW-4); 2) improved estimates of fire emission rates from noaas weather satellite products (RAVE) and influence of new fire-ABL scheme on modeled plume rise dynamics (FW-4); and 3) simulating the evolution of an active fire to evaluate the new parameterization in UFS/RRFS coupled with the community multiscale air quality modeling system (UFS/RRFS-CMAQ) against intensively instrumented fire episodes over the western US (firex-AQ 2019 campaign) (FW-1). Project products include an enhanced RRFS model version with higher fidelity in forecasting fire-weather evolution (tasks 1-2) and for evaluating impacts on air quality (task 3), and the production of publicly accessible datasets from simulation output for model diagnosis, testing, and validation. In coordination with ncep/EMC, these products will advance the project readiness level from RL3 to RL5 by year 1 (tasks 1-2; model improvement), and to RL7 by project completion (task 3; model validation and impacts), which will provide an accurate modeling system for NOAA forecasters, forest/emergency response managers, and local and state agencies to address wildfire risks and impacts (intended benefits/outcomes). Albeit not part of standard reporting for this project, the improved modeling capability of fire-weather in RRFS can be extended in the future to global models for medium-range weather and subseasonal-toseasonal (S2S) applications. A total budget of $700,000 is requested to achieve the project goals.