Mesoscale data assimilation and ensemble sensitivity analysis towards improved predictability of dryline convection

Date
2014-12
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Abstract

Ensemble sensitivity analysis (ESA) and its derivative observation targeting, have been tested on dryline convective initiation (CI) forecasts to assess dynamical sensitivities of severe convection forecasts and the value of targeted observations to improve prediction. ESA techniques based on the Ensemble Kalman filter (EnKF) utilize a linear relationship between a scalar forecast metric and a previous model state. Utilizing the Data Assimilation Research Testbed (DART), forecasts may be generated from cycled analyses produced by the EnKF, developing flow-dependent covariances to form a strong relationship between model state variables and the forecast metric. Forecast metrics are chosen to be convective-related parameters critical to the issuance of severe weather alerts for real-time forecasters.

Forecasts are generated for two dryline CI cases in 2012 and 2013 in north-central Texas with a 50-member WRF-DART ensemble. Ensemble sensitivity highlights moisture and temperature regions at the surface, downstream of the response region 0-12 hours prior to CI. Sensitivities are also evident along surface pressure troughs and mesoscale boundaries. Farther aloft the forecasts are sensitive to upper-level trough placement, capping-inversion strength, and synoptic motion characteristics 0-24 hours prior to CI. There exists both magnitude and positional sensitivities with these features. The assimilation of targeted observations shows that impacts to the forecast metric variance do not correlate with the estimated changes from ESA theory due to non-linear mesoscale forecast evolution. These results are discussed in relation to targeting on the mesoscale for convective forecasts.

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Keywords
Dryline, Convection, Sensitivity, Predictability, Data assimilation
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