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Variational Analysis of Simulated Ocean Surface Winds from the Cyclone Global Navigation Satellite System (CYGNSS) and Evaluation Using a Regional OSSE
  • Published Date:
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    Journal of Atmospheric and Oceanic Technology, 35(8), 1571-1584.
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  • Description:
    A positive impact of adding directional information to observations from the Cyclone Global Navigation Satellite System (CYNGSS) constellation of microsatellites is observed in simulation using a high-resolution nature run of an Atlantic hurricane for a 4-day period. Directional information is added using a two-dimensional variational analysis method (VAM) for near-surface vector winds that blends simulated CYGNSS wind speeds with an a priori background vector wind field at 6-h analysis times. The resulting wind vectors at CYGNSS data locations are more geophysically self-consistent when using high-resolution 6-h forecast backgrounds from a Hurricane Weather Research and Forecast Model (HWRF) control observing system simulation experiment (OSSE) compared to low-resolution 6-h forecasts from an associated Global Forecast System (GFS) model control OSSE. An important contributing factor is the large displacement error in the center of circulation in the GFS background wind fields that produces asymmetric circulations in the associated VAM analyses. Results of a limited OSSE indicate that CYGNSS winds reduce forecast error in hurricane intensity in 0-48-h forecasts compared to using no CYGNSS data. Assimilation of VAM-CYGNSS vector winds reduces maximum wind speed error by 2-5 kt (1 kt = 0.51 m s(-1)) and reduces minimum central pressure error by 2-5 hPa. The improvement in forecast intensity is notably larger and more consistent than the reduction in track error. The assimilation of VAM-CYGNSS wind vectors constrains analyses of surface wind field structures during OSSE more effectively than wind speeds alone. Because of incomplete sampling and the limitations of the data assimilation system used, CYGNSS scalar winds produce unwanted wind/pressure imbalances and asymmetries more often than the assimilation of VAM-CYGNSS data.
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