The Impact of Assimilating Satellite-Derived Precipitation Rates on Numerical Simulations of the ERICA IOP 4 Cyclone

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  • 1 Research and Data Systems and Laboratory for Atmospheres, NASA/Goddard Space Flight Center, Greenbelt, Maryland
  • | 2 Laboratory for Atmospheres, NASA/Goddard Space Flight Center, Greenbelt, Maryland
  • | 3 Laboratory for Atmospheres, NASA/Goddard Space Flight Center, Greenbelt, Maryland, and University Space Research Association, Columbia, Maryland
  • | 4 Laboratory for Atmospheres, NASA/Goddard Space Flight Center, Greenbelt, Maryland
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Abstract

The present study uses a regional-scale numerical model to test the impact of dynamically assimilating, satellite-derived precipitation rates on the numerical simulations of one of the deepest extratropical cyclones to develop south of 40°N in this century. This cyclone event occurred during the Experiment on Rapidly Intensifying Cyclones over the Atlantic (ERICA) intensive observing period 4 and has been selected because of the strength of the cyclone and the availability of the special ERICA data in addition to the Special Sensor Microwave/Imager (SSM/I) and Geostationary Operational Environmental Satellite (GOES) infrared (IR) satellite data.

The unique methodology developed herein to synthesize the SSM/I and GOES IR satellite data produces precipitation estimates that have realistic spatial and temporal structure. The assimilation of satellite-derived precipitation is accomplished by scaling the internally generated model profiles of total latent heating. At points where the model is not producing precipitation, the vertical distribution of total latent heating given by satellite precipitation is specified from instantaneous model-based profiles at adjacent points using a search algorithm. The technique does not assume a priori that the satellite-estimated precipitation corresponds to either convective or stratiform model precipitation, and uses heating profiles that are consistent with the model's parameterization of either type of precipitation since they are not specified from externally based parabolic or other structure functions.

Several simulations are performed with and without satellite data assimilation at varying horizontal and vertical model resolutions. The results from the 80-km 40-layer control and assimilation runs demonstrate that the assimilation of satellite precipitation 1) does not introduce noise into the simulations at any time during or after the data assimilation period, 2) forces the model to reproduce the magnitude and distribution of satellite precipitation, and 3) improves the simulated central mean sea level pressure (MSLP) minima slightly, frontal positions, and, to a greater extent, the low-level vertical-motion patterns when compared with subjective analyses and satellite imagery. The model retains the information introduced by the assimilation of satellite-derived precipitation 8.5 h after the end of the data assimilation period.

An increase in the vertical and horizontal model resolution further reduces the errors in simulating the MSLP minima but does not consistently improve the cyclone position errors in the assimilation runs. Either the exclusion of the search algorithm, the doubling of satellite precipitation, or an eastward shift of satellite precipitation by 400 km increases the MSLP and position users; therefore, the impact of assimilating satellite precipitation depends on model resolution, the use of the search algorithm, and the magnitude and position of satellite precipitation. The increase in horizontal resolution generates the largest reduction in MSLP errors, while the shifting of satellite precipitation generates the largest increase in MSLP errors. The results confirm the findings of earlier studies that the impact of assimilating satellite precipitation on the subsequent simulations is less sensitive to errors in magnitude rather than to the distribution of satellite-derived precipitation and depends on the relative accuracy with which the model simulates the cyclone in the control run. Despite the fact that this study focuses on a single case, it does demonstrate the promise of using combined infrared and microwave satellite precipitation estimates to produce sustained positive impacts in mesoscale model forecasts of midlatitude cyclogenesis over data-sparse oceanic regions.

Abstract

The present study uses a regional-scale numerical model to test the impact of dynamically assimilating, satellite-derived precipitation rates on the numerical simulations of one of the deepest extratropical cyclones to develop south of 40°N in this century. This cyclone event occurred during the Experiment on Rapidly Intensifying Cyclones over the Atlantic (ERICA) intensive observing period 4 and has been selected because of the strength of the cyclone and the availability of the special ERICA data in addition to the Special Sensor Microwave/Imager (SSM/I) and Geostationary Operational Environmental Satellite (GOES) infrared (IR) satellite data.

The unique methodology developed herein to synthesize the SSM/I and GOES IR satellite data produces precipitation estimates that have realistic spatial and temporal structure. The assimilation of satellite-derived precipitation is accomplished by scaling the internally generated model profiles of total latent heating. At points where the model is not producing precipitation, the vertical distribution of total latent heating given by satellite precipitation is specified from instantaneous model-based profiles at adjacent points using a search algorithm. The technique does not assume a priori that the satellite-estimated precipitation corresponds to either convective or stratiform model precipitation, and uses heating profiles that are consistent with the model's parameterization of either type of precipitation since they are not specified from externally based parabolic or other structure functions.

Several simulations are performed with and without satellite data assimilation at varying horizontal and vertical model resolutions. The results from the 80-km 40-layer control and assimilation runs demonstrate that the assimilation of satellite precipitation 1) does not introduce noise into the simulations at any time during or after the data assimilation period, 2) forces the model to reproduce the magnitude and distribution of satellite precipitation, and 3) improves the simulated central mean sea level pressure (MSLP) minima slightly, frontal positions, and, to a greater extent, the low-level vertical-motion patterns when compared with subjective analyses and satellite imagery. The model retains the information introduced by the assimilation of satellite-derived precipitation 8.5 h after the end of the data assimilation period.

An increase in the vertical and horizontal model resolution further reduces the errors in simulating the MSLP minima but does not consistently improve the cyclone position errors in the assimilation runs. Either the exclusion of the search algorithm, the doubling of satellite precipitation, or an eastward shift of satellite precipitation by 400 km increases the MSLP and position users; therefore, the impact of assimilating satellite precipitation depends on model resolution, the use of the search algorithm, and the magnitude and position of satellite precipitation. The increase in horizontal resolution generates the largest reduction in MSLP errors, while the shifting of satellite precipitation generates the largest increase in MSLP errors. The results confirm the findings of earlier studies that the impact of assimilating satellite precipitation on the subsequent simulations is less sensitive to errors in magnitude rather than to the distribution of satellite-derived precipitation and depends on the relative accuracy with which the model simulates the cyclone in the control run. Despite the fact that this study focuses on a single case, it does demonstrate the promise of using combined infrared and microwave satellite precipitation estimates to produce sustained positive impacts in mesoscale model forecasts of midlatitude cyclogenesis over data-sparse oceanic regions.

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