9B.3 Impacts of Assimilating WSR-88D Radar Observations on Snow Bands Embedded Within an Intense Northeast U.S. Cyclone

Wednesday, 15 January 2020: 2:00 PM
258A (Boston Convention and Exhibition Center)
Keenan R. Fryer, Stony Brook University, Stony Brook, NY; and B. A. Colle

On 4 January 2018, the Northeast U.S. coast was impacted by a rapidly intensifying cyclone in which a series of snow bands moved across Long Island (LI) and coastal Connecticut. The first bands reached LI between 10 and 11 UTC and was followed by a much stronger band around 12 UTC. These snow bands, which can create have major impacts in this populated region, represent a forecasting challenge since they require high resolution grids and have large model uncertainties likely from both initial conditions and physics.

For this work the Ensemble Kalman Filter (EnKF) implemented within the Data Assimilation Research Testbed (DART) package was employed to run a 40 member WRF continuously cycled data assimilation (CCDA) system. assimilate WSR-88 radar reflectivities and radial velocities from a single radar, OKX, along with conventional observations (radiosondes, satellite winds, ACARS, and surface observations) to a 40 member WRF mesoscale ensemble. This CCDA run was done in two phases. The first, run at 9km with a domain covering about half the U.S. where only conventional observations (radiosondes, satellite winds, ACARS, and surface observations) were assimilated every 6 hours for a 5 day period between 00UTC on 31 Dec to 06UTC on 4 Jan. The resulting 40-member 9km ensemble then was used as the initial conditions for the second phase. This second phase consisted of assimilating, in addition to conventional observations, WSR-88 radar reflectivities and radial velocities from a single radar, OKX, every 15 minutes for a 6 hour period (06-12UTC on 4 Jan) on a downscaled 3km grid which expands from coastal NJ out past Cape Cod, MA. Here we focus mainly on the results of the second phase and compare the resulting 40-member ensemble to a nearly identical ensemble in which only conventional observations were assimilated, and another 40-member forecast ensemble where no observations were assimilated. Finally, going a step further, we utilized the results from the assimilation to initialize a 20 member, 6 hour forecast ensemble.

This presentation will compare the different ensemble results evaluating the structure and intensity of the snow bands along the coast. Preliminary results suggest some relative improvement with the radar DA, but many members struggled to predict the bands within many of the ensembles. For example , weaker bands were the largest challenge, and there was a very large ensemble spread when using band location and intensity as a metric.

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