Comparing Impacts of Satellite Data Assimilation and Lateral Boundary Conditions on Regional Model Forecasting: A Case Study of Hurricane Sandy (2012)

Tong Zhu CIRA/Colorado State University, Fort Collins, Colorado, and NOAA/NESDIS/STAR/JCSDA, College Park, Maryland

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Sid Ahmed Boukabara NOAA/NESDIS/STAR, and NOAA/NESDIS/STAR/JCSDA, College Park, Maryland

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Kevin Garrett Riverside Technology, Inc., Fort Collins, Colorado, and NOAA/NESDIS/STAR/JCSDA, College Park, Maryland

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Abstract

The impacts of both satellite data assimilation (DA) and lateral boundary conditions (LBCs) on the Hurricane Weather Research and Forecasting (HWRF) Model forecasts of Hurricane Sandy 2012 were assessed. To investigate the impact of satellite DA, experiments were run with and without satellite data assimilated, as well as with all satellite data but excluding Geostationary Operational Environmental Satellite (GOES) Sounder data. To gauge the LBC impact, these experiments were also run with a variety of outer domain (D-1) sizes. The inclusion of satellite DA resulted in analysis fields that better characterized the tropical storm structures including the warm core anomaly and wavenumber-1 asymmetry near the eyewall, and also served to reduce the forecast track errors for Hurricane Sandy. The specific impact of assimilating the GOES Sounder data showed positive impacts on forecasts of the storm minimum sea level pressure. Increasing the D-1 size resulted in increases in the day 4/5 forecast track errors when verified against the best track and the Global Forecast System (GFS) forecast, which dominated any benefits from assimilating an increased volume of satellite observations due to the larger domain. It was found that the LBCs with realistic environmental flow information could provide better constraints on smaller domain forecasts. This study demonstrated that satellite DA can improve the analysis of a hurricane asymmetry, especially in a shear environment, and then lead to a better track forecast, and also emphasized the importance of the LBCs and the challenges associated with the evaluation of satellite data impacts on regional model prediction.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author e-mail: Dr. Tong Zhu, tong.zhu@noaa.gov

Abstract

The impacts of both satellite data assimilation (DA) and lateral boundary conditions (LBCs) on the Hurricane Weather Research and Forecasting (HWRF) Model forecasts of Hurricane Sandy 2012 were assessed. To investigate the impact of satellite DA, experiments were run with and without satellite data assimilated, as well as with all satellite data but excluding Geostationary Operational Environmental Satellite (GOES) Sounder data. To gauge the LBC impact, these experiments were also run with a variety of outer domain (D-1) sizes. The inclusion of satellite DA resulted in analysis fields that better characterized the tropical storm structures including the warm core anomaly and wavenumber-1 asymmetry near the eyewall, and also served to reduce the forecast track errors for Hurricane Sandy. The specific impact of assimilating the GOES Sounder data showed positive impacts on forecasts of the storm minimum sea level pressure. Increasing the D-1 size resulted in increases in the day 4/5 forecast track errors when verified against the best track and the Global Forecast System (GFS) forecast, which dominated any benefits from assimilating an increased volume of satellite observations due to the larger domain. It was found that the LBCs with realistic environmental flow information could provide better constraints on smaller domain forecasts. This study demonstrated that satellite DA can improve the analysis of a hurricane asymmetry, especially in a shear environment, and then lead to a better track forecast, and also emphasized the importance of the LBCs and the challenges associated with the evaluation of satellite data impacts on regional model prediction.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author e-mail: Dr. Tong Zhu, tong.zhu@noaa.gov
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