Tuesday, 30 January 2024: 9:30 AM
Key 9 (Hilton Baltimore Inner Harbor)
Accurate forecasts of the development and evolution of deep, moist convection in
convection-allowing models (CAMs) are both a priority and a challenge of the numerical
weather prediction and convective-scale data assimilation communities. Additionally, modeling
of the microphysical and internal structures of convection is difficult, as these can affect the
storm mode, intensity, and longevity. Underused observations from polarimetric weather radars
and all-sky (clear and cloudy affected radiances) satellites have the potential to improve the
forecasts of deep convection in CAM ensembles. Polarimetric radar data offer a wealth of information
about the shape, size, and composition of hydrometeors. Several distinct polarimetric signatures
in early stages of deep convection have been identified, such as the differential reflectivity (ZDR )
column. These columns are vertical protrusions of positive ZDR values above the environmental
melting level and can aid significantly in characterizing storm updrafts. Improved information on
the updraft has the potential to improve CAM representation of convection. In addition, GOES-
16 infrared all-sky brightness temperatures provide complimentary information on cloud
structures and cover that S-band radars are not meant to sense. To explore the benefits of both
types of data, an ensemble data assimilation approach is used with their simultaneous
assimilation. The CAM selected for this study is the Advanced Research version of the Weather
Research and Forecasting (WRF-ARW) model with the High-Resolution Rapid Refresh (HRRR)
configuration, except for the use of the National Severe Storms Laboratory (NSSL) double-
moment microphysics scheme. Observations are assimilated using the Ensemble Kalman Filter
(EnKF). Different sets of observations in the experiments are assimilated jointly and separately,
and all experiments include conventional observations. Analysis is conducted using a real case to
realize the influence of these observations on the prediction of convection initiation, structure,
evolution, and its associated hazards. Sensitivities of ZDR assimilation to the
horizontal and vertical radii of influence and observation error are explored.
convection-allowing models (CAMs) are both a priority and a challenge of the numerical
weather prediction and convective-scale data assimilation communities. Additionally, modeling
of the microphysical and internal structures of convection is difficult, as these can affect the
storm mode, intensity, and longevity. Underused observations from polarimetric weather radars
and all-sky (clear and cloudy affected radiances) satellites have the potential to improve the
forecasts of deep convection in CAM ensembles. Polarimetric radar data offer a wealth of information
about the shape, size, and composition of hydrometeors. Several distinct polarimetric signatures
in early stages of deep convection have been identified, such as the differential reflectivity (ZDR )
column. These columns are vertical protrusions of positive ZDR values above the environmental
melting level and can aid significantly in characterizing storm updrafts. Improved information on
the updraft has the potential to improve CAM representation of convection. In addition, GOES-
16 infrared all-sky brightness temperatures provide complimentary information on cloud
structures and cover that S-band radars are not meant to sense. To explore the benefits of both
types of data, an ensemble data assimilation approach is used with their simultaneous
assimilation. The CAM selected for this study is the Advanced Research version of the Weather
Research and Forecasting (WRF-ARW) model with the High-Resolution Rapid Refresh (HRRR)
configuration, except for the use of the National Severe Storms Laboratory (NSSL) double-
moment microphysics scheme. Observations are assimilated using the Ensemble Kalman Filter
(EnKF). Different sets of observations in the experiments are assimilated jointly and separately,
and all experiments include conventional observations. Analysis is conducted using a real case to
realize the influence of these observations on the prediction of convection initiation, structure,
evolution, and its associated hazards. Sensitivities of ZDR assimilation to the
horizontal and vertical radii of influence and observation error are explored.
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