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
“1995 Sunrise Fire – Long Island”
What’s a Fire Weather Day?
•A fire weather day is
determined from a Fire Weather
Index (FWI).
Domain
•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.
Temperature
Source: news12.com
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%.
• 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
…and compare
that to what
actually
happened. In
this case, 45%
of the time a
fire formed.
Look at all of the
cases where the
model predicts a
45% chance of
fire formation…
Source: news12.com
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/
FWI Averaged By SREF Core
FWI Ensemble Probabilities
Source: news12.com
Why Study Fire Weather Days?
Model Bias on Fire Weather Days
•Although rare, wildfires are
dangerous due to a high population
density.
•A forecast FWI could produce
reliable probabilistic fire weather
forecasts.
Model Error on Fire Weather Days
•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.
What is the Ensemble Kalman Filter?
• The Ensemble Kalman Filter (EnKF) is a data assimilation technique that
blends observations and a short-term ensemble of models to create a “bestof-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.
1000 hPa Temp and SLP
Whitaker and Hamill
3DVAR Increment
EnKF Increment
Ensemble Kalman Filter Setup
• This study uses the Pennsylvania State
University (PSU) Ensemble Kalman Filter
(EnKF) with forward iterations from the
Weather Research and Testbed (WRF) model.
Model Domain
• Observations ingested include mesonet,
ASOS, soundings, ACARS, profilers, maritime
and satellite winds.
Assimilated Observations
• Model physics: ACM2 PBL scheme, GFS
IC/LBCs, WSM6 microphysics, KF convection,
RRTM (Dudhia) SW (LW) radiation.
• Observations are assimilated using a 45member ensemble every 6-hours from
20120406 to 20120411.
Initialized:
20120406 00 UTC
First Obs. Assim
20120406 12 UTC
1-day EnKF Spin-up
Simulation Concluded
20120411 00 UTC
4-day Verification Period
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
Ensemble Kalman Filter – Trial Runs
• 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.
Region of Study
Trial Run Results – Mean Error by Obs. Type
6 Hour WRF Forecast minus Observations
EnKF Analysis minus Observations
RUC Analysis minus Observations
• Trial 5 is the least biased EnKF run in the Update and is comparable to the
RUC.
Trial Run Results – MAE by Obs. Type
6 Hour WRF Forecast MAE
EnKF Analysis MAE
RUC Analysis MAE
• Trial 5 also has the lowest MAE at the surface compared to all other EnKF
runs.
Trial Run Results – MAE Vertical 6-hr WRF Forecast
ACARS
ACARS Profile
• 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
• The EnKF analysis is comparable to or slightly better than the RUC.
• Trial 5 (reduced surface observational error) is the best performing run.
Optimal Run EnKF Performance on FWD’s – Mean Error
6 Hour WRF Forecast minus Observations
EnKF Analysis minus Observations
RUC Analysis minus Observations
• Fire Weather Days have a colder (warmer) near-surface (aloft) model bias.
Optimal Run EnKF Performance on FWD’s – MAE
6 Hour WRF Forecast MAE
EnKF Analysis MAE
RUC Analysis MAE
• These biases impact mean absolute error, with the EnKF analysis
comparable to or better than the RUC analysis.
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.
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.
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.
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.
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 1oC.
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 1oC .
Take Home Points
•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.
Future Work
•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.