6.4 Exploring Ensemble Kalman Filter Performance on Fire Weather Days over the Northeast United States

Wednesday, 6 May 2015: 11:15 AM
Great Lakes Ballroom (Crowne Plaza Minneapolis Northstar)
Michael J. Erickson, WPC/CIRES, College Park, MD; and J. J. Charney and B. Colle

Data assimilation with an Ensemble Kalman Filter (EnKF) is becoming a popular tool for improving model initial conditions or exploring model error. One such unexplored pathway with the EnKF is to examine high fire threat days defined by a Fire Weather Index (FWI), since these days typically exhibit less complex meteorological conditions with fewer clouds and a deeper planetary boundary layer (PBL). However, model verification on FWI days over the Northeast United States (NEUS) have much cooler and wetter biases in the PBL compared to climatology using the National Centers for Environmental Prediction (NCEP) Short Range Ensemble Forecast (SREF). These biases can result in significantly underestimated FWI values when made directly from forecast ensembles like the SREF. For this reason, an EnKF is used to both correct and explore potential biases and error with a focus in the modeled PBL.

The data assimilation platform comes from the Pennsylvania State University Ensemble Kalman Filter (PSU-EnKF) Version 4.0 with forward iterations performed using the Weather Research and Forecasting (WRF) Advanced Research WRF (ARW) core Version 3.5. Observations are assimilated into the WRF-EnKF system every 6 hours and include data from buoys, ships, mesonet stations, profilers, rawinsonde soundings, satellite winds, Standard Aviation Observation (SAO), Aircraft Communication Addressing and Reporting System (ACARS) and Automated Surface Observation System (ASOS) stations. WRF physics include the Symmetric Convective Model Version 2 (ACM2) Planetary Boundary Layer (PBL), WRF Single Moment 6-class (WSM6) microphysics, Kain-Fritsch (KF) cumulus parameterization, Rapid Radiative Transfer Model (RRTM) for short wave radiation, Dudhia long wave radiation and the Noah Land Surface Model (LSM).

This study conducts two separate 45-member ensemble runs throughout April of 2012 (during a period of drought and abundant FWI days) employing the PSU-EnKF system; one with no parameter estimation and one with Simultaneous State and Parameter Estimation (SSPE) of physically relevant parameters within the ACM2 PBL scheme. As recommended by Nielsen-Gammon et al. (2010), parameters controlling minimum allowed mixing and governing mixing profile in the daytime PBL are estimated and explored on FWI days. Results comparing the impact of data assimilation and SSPE on FWI days are discussed.

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