Yunji Zhang;
Fuqing Zhang;
David J. Stensrud
Pennsylvania State University
The 8th EnKF Data Assimilation Workshop
Assimilating All-Sky Infrared Radiance from GOES-16 ABI using Ensemble Kalman Filter for Convection-Permitting Severe Thunderstorm Predictions
Talk:
20180509_EnKF_Workshop_YZhang.pptx
The Advanced Baseline Imager (ABI) onboard the GOES-16 satellite, with its high spatial and temporal resolutions, provides a great opportunity to improve monitoring and prediction of severe convective thunderstorms. Focusing on the severe weather event with multiple tornadic supercells in Wyoming and Nebraskaon on 12-13 June 2017, this study is the first successful attempt to assimilate realistic all-sky infrared radiance (brightness temperature) observations from geosynchronous satellites for a realistic severe weather event using ensemble Kalman filter in a convection-permitting resolution. It is found that the vast majority of spurious clouds, which occurred in deterministic forecast from HRRR analysis and ensemble forecast with GEFS perturbations, was removed for 5-10 cycles of EnKF that assimilated satellite observations every 5 minutes before observed convection initiation. After initiation, continuos assimilation of satellite observations further improved location and structure of the storms as well as surrounding environment. As a result, both deterministic and ensemble forecasts showed persistent improvement when more cycles were applied, with simulated mid-level mesocyclone and low-level vortex tracks in close proximity to reported tornado locations. Further analysis indicate that more improvements were observed in ice particles than liquid particles, which is consistent with a stronger correlation between ice particles and satellite radiance. These promising results suggest the possibility to include satellite infrared radiance observations in future storm-scale data assimilation and severe thunderstorm prediction systems.
The Advanced Baseline Imager (ABI) onboard the GOES-16 satellite, with its high spatial and temporal resolutions, provides a great opportunity to improve monitoring and prediction of severe convective thunderstorms. Focusing on the severe weather event with multiple tornadic supercells in Wyoming and Nebraskaon on 12-13 June 2017, this study is the first successful attempt to assimilate realistic all-sky infrared radiance (brightness temperature) observations from geosynchronous satellites for a realistic severe weather event using ensemble Kalman filter in a convection-permitting resolution. It is found that the vast majority of spurious clouds, which occurred in deterministic forecast from HRRR analysis and ensemble forecast with GEFS perturbations, was removed for 5-10 cycles of EnKF that assimilated satellite observations every 5 minutes before observed convection initiation. After initiation, continuos assimilation of satellite observations further improved location and structure of the storms as well as surrounding environment. As a result, both deterministic and ensemble forecasts showed persistent improvement when more cycles were applied, with simulated mid-level mesocyclone and low-level vortex tracks in close proximity to reported tornado locations. Further analysis indicate that more improvements were observed in ice particles than liquid particles, which is consistent with a stronger correlation between ice particles and satellite radiance. These promising results suggest the possibility to include satellite infrared radiance observations in future storm-scale data assimilation and severe thunderstorm prediction systems.
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