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AHW Ensemble Data Assimilation and Forecasting System

AHW Ensemble Data Assimilation and Forecasting System. Ryan D. Torn, Univ. Albany, SUNY Chris Davis, Wei Wang, Steven Cavallo , Chris Snyder, James Done, NCAR/MMM. Overview.

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AHW Ensemble Data Assimilation and Forecasting System

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  1. AHW Ensemble Data Assimilation and Forecasting System Ryan D. Torn, Univ. Albany, SUNY Chris Davis, Wei Wang, Steven Cavallo, Chris Snyder, James Done, NCAR/MMM

  2. Overview • Since participation in HFIP HRH test, we have been using cycling EnKF approach to create initial conditions for AHW model • Wanted initial conditions that: • Have a good estimate of environment • Have a “decent” estimate of TC structure • Does not lead to significant initialization problem • Since then, we have upgraded the system based on observed flaws in both model and initial conditions

  3. Assimilation System • WRF ARW (v3.3), 36 km horizontal resolution over basin, 96 ensemble members, DART assimilation system. • Observations assimilated each six hours from surface and marine stations (Psfc), rawinsondes, dropsondes > 100 km from TC, ACARS, sat. winds, TC position, MSLP, GPS RO • Initialized system this year on 29 July, continuous cycling using GFS LBC • No vortex bogusing or repositioning, all updates to TC due to observations

  4. AHW Model Setup • WSM6 Microphysics (includes graupel) • Tiedtke cumulus parameterization (includes shallow convection) • YSU PBL, NOAH land surface model • Updated Ck/Cd formulation in Davis et al. 2010 • Pollard 1D Column ocean model • SSTs from NCEP 1/12 degree analysis • HYCOM Mixed-layer depths

  5. Data Assimilation Nesting Strategy • Each time NHC declares an INVEST area, generate a 12 km resolution two-way interactive nest that moves with the system until NHC stops tracking it (1600 km x 1600 km nest) • Observations are assimilated on the nested domain each 6 h • Nest movement determined by extrapolating NHC positions over the previous 6 h • Works better than vortex-following nests, which have largest covariances associated with differences in land position

  6. Nest Example Earl Fiona INVEST Gaston

  7. Cycling Nest Experiments • No Nest (HRH test setup) • Fixed 12 km two-way nest (1000 km x 1000 km) generated for each TC in domain at each analysis time. Discarded at end of model advance (2009 real-time setup) • Moving 12 km two-way nest (1000 km x 1000 km) generated when TD is declared. Nest is cycled along with the coarse domain. Motion based on NHC advisory position over previous 6 hr (2010 real-time setup)

  8. TC Vitals Error RMS Error Bias

  9. Deterministic Forecast • For each TC, choose one analysis ensemble member whose TC properties are closest to ensemble mean (see below) • Remove other 12 km nests, add additional 4 km nest to 12 km for that storm (800 km on a side), runs without cumulus parameterization.

  10. 2011 Retrospective Forecasts Track Maximum Wind Speed

  11. Ensemble Forecasts • Currently running 15 member ensemble for NHC highest priority TC • Take first 15 members of the analysis ensemble since all are equally likely • All members use same lower BC, lateral BC, and model physics (will be relaxed in the future)

  12. 0000 UTC 2 Aug. Ensemble

  13. Lessons to Date • Ensemble can produce fairly large variance in intensity without significant variance in large-scale environment parameters • Biases due to deficiencies in model physics (in particular aerosol and ozone treatment) lead to many situations where truth is outside ensemble

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