1. “1995 Sunrise Fire – Long Island” Using an Ensemble Kalman Filter to Explore Model Performance on Northeast U.S. Fire Weather Days Michael Erickson and Brian A. Colle School of Marine and Atmospheric Sciences, Stony Brook University, Stony Brook, NY

2. What’s a Fire Weather Day? Identifying Fire Weather Days (FWDs) is important for operational and research applications. We would like a quantitative definition attributed to a FWD. Ideally this should come from some sort of Fire Weather Index (FWI). This FWI should be: 1.Simple and intuitive so that it can be applied across a spectrum of data. 2.Have a specific meaning relating weather to fire initiation. Unfortunately many fire related indices are either 1) very complex (NFDRS, CFFDRS) or 2) produce output that is subjective or not well understood (Haines Index, Fosberg Index). For this reason, we have developed a simple FWI. Haines Index SREF Based Forecast

3. What’s a Fire Weather Day? A fire weather day is determined from a Fire Weather Index (FWI). The FWI is a statistical model that uses near-surface weather variables to predict the probability of wildfire occurrence. Several near-surface weather variables are tested using observed fire occurrence data between 1999-2008. Relative humidity and temperature are the only predictors in the FWI. Source: news12.com Domain Temperature

4. What’s a Fire Weather Day? The FWI predicts the probability of wildfire occurrence using only temperature and relative humidity. The FWI has reliable probabilities of wildfire occurrence using independent verification. Using these probabilities, the index is defined as follows: 1.FWI = 1 has a wildfire occurrence probability between 30% and 40%. 2.FWI = 2 has a probability between 40% and 50%. 3.FWI = 3 has a probablility > 50%. Source: news12.com A Fire Weather Day has an FWI of greater than 1 (i.e. a 30% or greater chance of fire initiation). Reliability of Fire Weather Model Look at all of the cases where the model predicts a 45% chance of fire formation… …and compare that to what actually happened. In this case, 45% of the time a fire formed.

5. The Operational Fire Weather Index The FWI is adapted to a model grid to create ensemble forecasts with the National Centers for Environmental Prediction (NCEP) Short Range Ensemble Forecast (SREF) system. This SREF based forecast FWI is available operationally at: http://wavy.somas.stonybrook.edu/fire/ http://wavy.somas.stonybrook.edu/fire/ Source: news12.com FWI Averaged By SREF Core FWI Ensemble Probabilities

6. Although rare, wildfires are dangerous due to a high population density. A forecast FWI could produce reliable probabilistic fire weather forecasts. However, atmospheric models exhibit greater biases (too cold and too wet in the PBL) on fire weather days compared to climatology. There are two ways to address this model bias: 1.Post-process model data via some regime capture method. 2.Explore potential sources of model bias using an Ensemble Kalman Filter. Why Study Fire Weather Days? Model Bias on Fire Weather Days Model Error on Fire Weather Days

7. The Ensemble Kalman Filter (EnKF) is a data assimilation technique that blends observations and a short-term ensemble of models to create a “best- of-both-worlds” analysis. The Global Forecast System (GFS) has been initialized (partially) from an EnKF analysis since 2012. An EnKF uses the variability in the ensemble of models to determine how assimilated observations should impact the analysis. For instance, if a near-surface temperature observation assimilated ahead of a warm front is warmer than the ensemble mean, perhaps the impact of that observation should reflect the temperature structure of a faster warm front. What is the Ensemble Kalman Filter? 1000 hPa Temp and SLP3DVAR IncrementEnKF Increment Whitaker and Hamill

8. This study uses the Pennsylvania State University (PSU) Ensemble Kalman Filter (EnKF) with forward iterations from the Weather Research and Testbed (WRF) model. Observations ingested include mesonet, ASOS, soundings, ACARS, profilers, maritime and satellite winds. Model physics: ACM2 PBL scheme, GFS IC/LBCs, WSM6 microphysics, KF convection, RRTM (Dudhia) SW (LW) radiation. Observations are assimilated using a 45- member ensemble every 6-hours from 20120406 to 20120411. Ensemble Kalman Filter Setup Assimilated Observations Model Domain Initialized: 20120406 00 UTC First Obs. Assim 20120406 12 UTC 1-day EnKF Spin-up 4-day Verification Period Simulation Concluded 20120411 00 UTC

9. Ensemble Kalman Filter Example – 20120409 at 18 UTC 6 Hr WRF Forecast 2-m Temperature, 10-m Wind and Sea Level Pressure EnKF Analysis 2-m Temperature, 10-m Wind and Sea Level Pressure

10. The EnKF requires some “tuning” to optimize its performance. Several runs were tested that adjusted the localization (radius of influence of the observations) and the impact of surface observations. These trial runs include: 1.Default run: 1300 km localization aloft, 200 km localization at the surface. 2.Same as 1) but with surface observations localization doubled. 3.Same as 1) but with surface observations localization halved. 4.No surface observations assimilated. 5.Observational error variance (i.e. confidence in the quality of the surface observations) reduced by half. For simplicity, results will just be presented for temperature only. Ensemble Kalman Filter – Trial Runs Region of Study

11. Trial 5 is the least biased EnKF run in the Update and is comparable to the RUC. Trial Run Results – Mean Error by Obs. Type 6 Hour WRF Forecast minus Observations EnKF Analysis minus Observations RUC Analysis minus Observations

12. Trial 5 also has the lowest MAE at the surface compared to all other EnKF runs. 6 Hour WRF Forecast MAE EnKF Analysis MAE RUC Analysis MAE Trial Run Results – MAE by Obs. Type

13. In the vertical, the largest error develops in the lower levels of the atmosphere for the 6-hour WRF forecast. Trial Run Results – MAE Vertical 6-hr WRF Forecast ACARS ACARS Profile

14. The EnKF analysis is comparable to or slightly better than the RUC. Trial 5 (reduced surface observational error) is the best performing run. Trial Run Results – MAE Vertical 6-hr WRF Forecast ACARS ACARS ProfileACARS ACARS Profile

15. Optimal Run EnKF Performance on FWD’s – Mean Error Fire Weather Days have a colder (warmer) near-surface (aloft) model bias. 6 Hour WRF Forecast minus Observations EnKF Analysis minus Observations RUC Analysis minus Observations

16. These biases impact mean absolute error, with the EnKF analysis comparable to or better than the RUC analysis. 6 Hour WRF Forecast MAE EnKF Analysis MAE RUC Analysis MAE Optimal Run EnKF Performance on FWD’s – MAE

17. Optimal Run EnKF Performance on FWD’s – Spatial Mean Error For Mesonet Observations 6-hour WRF Forecast - Non-Fire Weather Days 6-hour WRF Forecast - Fire Weather Days Spatially Fire Weather Days have a much greater cool bias, even for a 6 hour WRF forecast.

18. Optimal Run EnKF Performance on FWD’s – Spatial Mean Error For Mesonet Observations EnKF Analysis - Non-Fire Weather Days EnKF Analysis - Fire Weather Days The EnKF analysis corrects this cool bias in most locations.

19. Optimal Run EnKF Performance on FWD’s – Spatial Mean Error For Mesonet Observations RUC Analysis - Non-Fire Weather Days RUC Analysis - Fire Weather Days Although the RUC and EnKF analyses are comparable on the average, the RUC has a slight cool bias over Long Island.

20. Optimal Run EnKF Performance on FWD’s – Spatial MAE for Mesonet Observations 6-hour WRF Forecast - Non-Fire Weather Days 6-hour WRF Forecast - Fire Weather Days The cool bias on fire weather days negatively impact MAE.

21. Optimal Run EnKF Performance on FWD’s – Spatial MAE for Mesonet Observations EnKF Analysis - Non-Fire Weather Days EnKF Analysis - Fire Weather Days The EnKF in most cases reduces spatial MAE below 1 o C.

22. Optimal Run EnKF Performance on FWD’s – Spatial MAE for Mesonet Observations RUC Analysis - Non-Fire Weather Days RUC Analysis - Fire Weather Days The RUC likewise reduces temperature MAE below 1 o C.

23. A statistical Fire Weather Index (FWI) has been developed over the Northeast U.S. to reliably predict the probability of wildfire formation. The FWI has been adapted for use with the Short Range Ensemble Forecast (SREF) and is available operationally. Unfortunately model biases are greater on fire weather days, which can degrade operational fire weather forecasts. An Ensemble Kalman Filter has been tested and optimized using the FWI to isolate fire weather days over the Northeast U.S. to explore potential sources of model error. It is now time to put this EnKF to good use! The EnKF can be used to both estimate relevant parameters embedded in WRF model physics while creating an optimal analysis. Our next step will implement this technique to optimize parameters in the ACM2 PBL scheme within WRF. Take Home Points Future Work