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The Impact of Satellite Winds on Experimental GFDL Hurricane Model Forecasts

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The Impact of Satellite Winds on Experimental GFDL Hurricane Model Forecasts APRIL 2001 SODEN ET AL. 835 The Impact of Satellite Winds on Experimental GFDL Hurricane Model Forecasts BRIAN J. SODEN Geophysical Fluid Dynamics Laboratory, National Oceanic and Atmospheric Administration, Princeton, New Jersey CHRISTOPHER S. VELDEN Cooperative Institute for Meteorological Satellite Studies, University of WisconsinÐMadison, Madison, Wisconsin ROBERT E. TULEYA Geophysical Fluid Dynamics Laboratory, National Oceanic and Atmospheric Administration, Princeton, New Jersey (Manuscript received 12 April 2000, in ®nal form 22 August 2000) ABSTRACT A series of experimental forecasts are performed to evaluate the impact of enhanced satellite-derived winds on numerical hurricane track predictions. The winds are derived from Geostationary Operational Environmental Satellite-8 (GOES-8) multispectral radiance observations by tracking cloud and water vapor patterns from suc- cessive satellite images. A three-dimensional optimum interpolation method is developed to assimilate the satellite winds directly into the Geophysical Fluid Dynamics Laboratory (GFDL) hurricane prediction system. A series of parallel forecasts are then performed, both with and without the assimilation of GOES winds. Except for the assimilation of the satellite winds, the model integrations are identical in all other respects. A strength of this study is the large number of experiments performed. Over 100 cases are examined from 11 different storms covering three seasons (1996±98), enabling the authors to account for and examine the case-to-case variability in the forecast results when performing the assessment. On average, assimilation of the GOES winds leads to statistically signi®cant improvements for all forecast periods, with the relative reductions in track error ranging from ;5% at 12 h to ;12% at 36 h. The percentage of improved forecasts increases following the assimilation of the satellite winds, with roughly three improved forecasts for every two degraded ones. Inclusion of the satellite winds also dramatically reduces the westward bias that has been a persistent feature of the GFDL model forecasts, implying that much of this bias may be related to errors in the initial conditions rather than a de®ciency in the model itself. Finally, a composite analysis of the deep-layer ¯ow ®elds suggests that the reduction in track error may be associated with the ability of the GOES winds to more accurately depict the strength of vorticity gyres in the environmental ¯ow. These results offer compelling evidence that the assimilation of satellite winds can signi®cantly improve the accuracy of hurricane track forecasts. 1. Introduction vations can substantially improve hurricane forecasts. For example, Franklin and DeMaria (1992) found sta- Numerical prediction of hurricane forecasts requires tistically signi®cant improvements in the predicted accurate representation of the current meteorological storm tracks when dropwindsonde observations were conditions. Unfortunately, conventional measurements included in a barotropic model. Likewise, in a study used to initialize forecast models are unavailable for using the National Centers for Environmental Prediction vast areas of the tropical oceans. The sparsity of ob- (NCEP) global forecast model, Lord (1993) demonstrat- servations, both near the storm center and in the sur- ed a reduction in track error of ;25% due to the in- rounding environment, is a key factor in limiting the clusion of dropwindsonde measurements. Using the accuracy of hurricane forecasts (Burpee et al. 1984, Geophysical Fluid Dynamics Laboratory (GFDL) hur- 1996; Aberson and Franklin 1999). Several studies have ricane model, Tuleya and Lord (1997) reduced forecast demonstrated that the inclusion of near-storm obser- track errors by up to 30% when dropwindsonde data were assimilated into the model. More recently, Aberson and Franklin (1999) also found improvements in the GFDL model track forecasts after including dropwind- Corresponding author address: Dr. Brian J. Soden, Geophysical Fluid Dynamics Laboratory, National Oceanic and Atmospheric Ad- sonde data. Burpee et al. (1996) provide a summary of ministration, P.O. Box 308, Princeton, NJ 08542. recent improvements in hurricane track forecasts due to E-mail: [email protected] the assimilation of dropwindsonde data. q 2001 American Meteorological Society 836 MONTHLY WEATHER REVIEW VOLUME 129 In addition to dropwindsonde measurements, atmo- 2. Description of the forecast model, observational spheric winds over data-void regions of the oceans can data, and assimilation method also be derived by tracking cloud and water vapor fea- a. Forecast model: The GFDL Hurricane Prediction tures in geostationary satellite imagery. The ability to System provide high-density wind coverage over large regions of the Tropics makes satellite winds particularly useful The dynamical model used in the hurricane prediction for studying tropical cyclones (Velden et al. 1998; system is an outgrowth of a research model developed LeMarshall 1998). Although satellite-derived winds at GFDL and adopted by the National Weather Service have been produced operationally for more than a de- as an operational hurricane forecast model in 1995. A cade, recent enhancements in spatial resolution and ra- brief summary of its distinctive features is provided diometric sensitivity from the new generation of Geo- here; a more complete description can be found in Ku- stationary Operational Environmental Satellites (GOES) rihara et al. (1998) and references therein. The predic- have signi®cantly improved both the accuracy and den- tion system uses a limited area, three-dimensional model sity of the wind products (Velden et al. 1997). Yet de- that solves the primitive equations using a ®nite-differ- spite their obvious utility for measuring tropical winds, ence method in spherical coordinates with 18 vertical there have been relatively few attempts to determine the levels in sigma coordinates. To resolve the interior struc- impact of GOES winds on numerical hurricane track ture of a hurricane, a multiply nested grid system is 1 forecasts, particularly for the Atlantic. Velden et al. used consisting of two inner movable meshes ( /68 and 1 (1992) demonstrated that the assimilation of satellite /38 resolution) nested within a coarser 7583758 outer winds into the VICBAR model resulted in modest (2%± mesh (18 resolution). The outer nest remains geograph- 6%) reductions in the mean track error, although none ically ®xed during a forecast, while the inner two nests of the improvements were considered to be statistically remain centered on the storm. Further details on the signi®cant. Leslie et al. (1998) demonstrated that the nested grid system are provided in Kurihara et al. assimilation of high-density satellite winds can greatly (1998). improve track forecasts in a high-resolution model, al- The initial and lateral boundary conditions are de®ned though their study was limited to only two cases. Re- by the NCEP global analysis and prediction model. cently, Goerss et al. (1998) examined the impact of an Tropical cyclone structure information is determined experimental high-density GOES wind product on hur- from the National Hurricane Center (NHC) storm mes- ricane track predictions from the Navy Oceanographic sage. The global NCEP analyses (18 resolution) are spa- Global Atmospheric Prediction System (NOGAPS) tially interpolated onto the two inner nested regional forecast model. Based upon results from four tropical domains of the model. To produce realistic storm-scale Atlantic storms from the 1995 season, this study found circulations near the center of the hurricane, a synthetic that assimilation of the experimentally derived GOES vortex speci®cation procedure is used that is compatible winds reduced the track errors in the NOGAPS model with the nested mesh model (Kurihara et al. 1993). After by 12%±14%. Consequently, operational assimilation of insertion of the idealized vortex, small adjustments are these winds into the NOGAPS model was initiated in made to the surface pressure and temperature ®elds to 1996 and has continued since. minimize imbalances with the existing wind ®eld by In recent years one of the most accurate models for solving the reverse balance equation (Kurihara et al. predicting hurricane tracks has been the GFDL Hurri- 1995). The resulting wind, mass, and thermodynamic cane Prediction System (Kurihara et al. 1998). In con- ®elds provide the initial conditions for the nested model trast to the NOGAPS model, the GFDL Hurricane Pre- forecast. The model is then integrated for 72 h yielding diction System uses a limited-area, multiply nested both track and intensity forecasts. These steps are sum- model designed speci®cally for tropical cyclone pre- marized in Fig. 1. It is important to note that since 1996 diction. Given these distinctions, and in light of its dem- NCEP has routinely included any available dropwind- onstrated skill in track prediction, this study seeks to sonde data from aircraft ¯ights into the global analysis. determine the extent to which GFDL model forecasts Hence, the background ®eld used for both the control can bene®t from the direct assimilation of GOES winds. and satellite-wind experiments already contains the im- Toward this end, a series of over 100 parallel forecasts provements resulting from these additional observations (with and without GOES winds) were performed span- for cases in which they were available. The high-density ning 11 storms and three Atlantic hurricane seasons GOES winds, however,
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