Christopher Kerr;
Xuguang Wang
University of Oklahoma
The 8th EnKF Data Assimilation Workshop
Impact of Multi-function Phased Array Radar Targeted Observations on Ensemble-based Numerical Prediction of Convective Storms
Talk:
EnKFworkshop2018_radarDA_Wang_Kerr_v3.pptx
Targeted observation techniques predict where and how to observe the atmosphere to best improve NWP model forecasts. Targeted observations will be a crucial component of future MPAR data assimilation since the observation platform is capable of adaptive scanning and will serve many purposes. The ensemble based targeted observation method in this study reveals which observations should be collected and assimilated at a future time by a MPAR. One major obstacle is the non-linearity associated with radar data assimilation and convective scale ensemble forecasts. The ensemble based targeted observation algorithm is based on linear regression which usually fails on the convective scale. Another obstacle is the sampling error associated with the use of a limited number of ensemble members. The technique’s ability to provide meaningful insight into scanning strategies is investigated. In this study, the targeted observation algorithm is applied to an idealized supercell thunderstorm. Synthetic radar observations of radar reflectivity and Doppler radial velocity are harvested from a truth run and assimilated using an ensemble Kalman filter (EnKF). A 50-member ensemble is created by perturbing the truth run’s homogeneous environment. Various forecast experiments are compared to evaluate the predictions made via the targeting algorithm. The results show advantages to utilizing the targeting algorithm for sub-hour convective scale forecasts.
Targeted observation techniques predict where and how to observe the atmosphere to best improve NWP model forecasts. Targeted observations will be a crucial component of future MPAR data assimilation since the observation platform is capable of adaptive scanning and will serve many purposes. The ensemble based targeted observation method in this study reveals which observations should be collected and assimilated at a future time by a MPAR. One major obstacle is the non-linearity associated with radar data assimilation and convective scale ensemble forecasts. The ensemble based targeted observation algorithm is based on linear regression which usually fails on the convective scale. Another obstacle is the sampling error associated with the use of a limited number of ensemble members. The technique’s ability to provide meaningful insight into scanning strategies is investigated. In this study, the targeted observation algorithm is applied to an idealized supercell thunderstorm. Synthetic radar observations of radar reflectivity and Doppler radial velocity are harvested from a truth run and assimilated using an ensemble Kalman filter (EnKF). A 50-member ensemble is created by perturbing the truth run’s homogeneous environment. Various forecast experiments are compared to evaluate the predictions made via the targeting algorithm. The results show advantages to utilizing the targeting algorithm for sub-hour convective scale forecasts.
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