Ensemble Probabilistic Prediction of a Mesoscale Convective System and Associated Polarimetric Radar Variables using Single-Moment and Double-Moment Microphysics Schemes and EnKF Radar Data Assimilation

Ensemble Probabilistic Prediction of a Mesoscale Convective System and Associated Polarimetric... AbstractEnsemble-based probabilistic forecasts are performed for a mesoscale convective system (MCS) that occurred over Oklahoma on 8-9 May 2007, initialized from ensemble Kalman filter analyses using multi-network radar data and different microphysics schemes. Two experiments are conducted, using either a single-moment or double-moment microphysics scheme during the one-hour long assimilation period and in subsequent three-hour ensemble forecasts. Qualitative and quantitative verifications are performed on the ensemble forecasts, including probabilistic skill scores. The predicted dual-polarization (dual-pol) radar variables and their probabilistic forecasts are also evaluated against available dual-pol radar observations, and discussed in relation to predicted microphysical states and structures.Evaluation of predicted reflectivity (Z) fields shows that the double-moment ensemble predicts the precipitation coverage of the leading convective line and stratiform precipitation regions of the MCS with higher probabilities throughout the forecast period compared to the single-moment ensemble. In terms of the simulated differential reflectivity (ZDR) and specific differential phase (KDP) fields, the double-moment ensemble compares more realistically to the observations and better distinguishes the stratiform and convective precipitation regions. ZDR from individual ensemble members indicates better raindrop size-sorting along the leading convective line in the double-moment ensemble. Various commonly used ensemble forecast verification methods are examined for the prediction of dual-pol variables. The results demonstrate the challenges associated with verifying predicted dual-pol fields that can vary significantly in value over small distances. Several microphysics biases are noted with the help of simulated dual-pol variables, such as substantial over-prediction KDP values in single-moment ensembles. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Monthly Weather Review American Meteorological Society

Ensemble Probabilistic Prediction of a Mesoscale Convective System and Associated Polarimetric Radar Variables using Single-Moment and Double-Moment Microphysics Schemes and EnKF Radar Data Assimilation

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Publisher
American Meteorological Society
Copyright
Copyright © American Meteorological Society
ISSN
1520-0493
eISSN
1520-0493
D.O.I.
10.1175/MWR-D-16-0162.1
Publisher site
See Article on Publisher Site

Abstract

AbstractEnsemble-based probabilistic forecasts are performed for a mesoscale convective system (MCS) that occurred over Oklahoma on 8-9 May 2007, initialized from ensemble Kalman filter analyses using multi-network radar data and different microphysics schemes. Two experiments are conducted, using either a single-moment or double-moment microphysics scheme during the one-hour long assimilation period and in subsequent three-hour ensemble forecasts. Qualitative and quantitative verifications are performed on the ensemble forecasts, including probabilistic skill scores. The predicted dual-polarization (dual-pol) radar variables and their probabilistic forecasts are also evaluated against available dual-pol radar observations, and discussed in relation to predicted microphysical states and structures.Evaluation of predicted reflectivity (Z) fields shows that the double-moment ensemble predicts the precipitation coverage of the leading convective line and stratiform precipitation regions of the MCS with higher probabilities throughout the forecast period compared to the single-moment ensemble. In terms of the simulated differential reflectivity (ZDR) and specific differential phase (KDP) fields, the double-moment ensemble compares more realistically to the observations and better distinguishes the stratiform and convective precipitation regions. ZDR from individual ensemble members indicates better raindrop size-sorting along the leading convective line in the double-moment ensemble. Various commonly used ensemble forecast verification methods are examined for the prediction of dual-pol variables. The results demonstrate the challenges associated with verifying predicted dual-pol fields that can vary significantly in value over small distances. Several microphysics biases are noted with the help of simulated dual-pol variables, such as substantial over-prediction KDP values in single-moment ensembles.

Journal

Monthly Weather ReviewAmerican Meteorological Society

Published: Mar 20, 2017

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