ahw ensemble data assimilation and forecasting system
DESCRIPTION
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. - PowerPoint PPT PresentationTRANSCRIPT
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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
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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
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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
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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
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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
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Nest Example
Earl
Fiona
Gaston
INVEST
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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)
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TC Vitals ErrorR
MS
Err
or
Bia
s
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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.
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2011 Retrospective Forecasts
Track Maximum Wind Speed
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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)
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0000 UTC 2 Aug. Ensemble
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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