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Explore a cutting-edge probabilistic assimilation-prediction system for ensemble forecasting, optimizing perturbations, and estimating model errors. Enhance forecasts by coupling data assimilation and ensemble forecasting, providing accurate analysis and model error covariance management. Utilize advanced methods like maximum likelihood and state augmentation for optimal results. Collaborate with experts in conducting experiments and parameter estimations in mesoscale and storm-scale processes using satellite observations. Unlock new knowledge on atmospheric processes through uncertainty information derived from ensemble forecasting.
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Probabilistic (ensemble) assimilation-prediction system • Data assimilation and ensemble forecasting fully coupled - optimal perturbations for ensemble forecasting - flow-dependent forecast error covariance for data assimilation • Maximum likelihood approach - cost function minimization in ensemble-spanned subspace - no adjoint needed • Model error estimation - empirical model parameters - serially correlated model error (bias) • Forecast model and observation operators of any complexity - non-linearity, mesoscale and storm scale processes - satellite observations (GOES-R, AIRS, CrIS) ftp://ftp.cira.colostate.edu/Zupanski/manuscripts/MLEF_MWR.pdf ftp://ftp.cira.colostate.edu/Zupanski/manuscripts/MLEF_model_err.pdf
Model error estimation • Assimilation of real observations - model error cannot be neglected - use information from the observations to learn about model error • State augmentation approach - model state(x) augmented to include model error () - estimate optimal initial conditions and model error - update forecast and model error covariance matrix in each analysis cycle x0– initial conditions ;bk – model bias; n – model time step; k – analysis cycle; a - constant Parameter estimation is a special case of state augmentation approach! Dusanka Zupanski, CIRA/CSU Zupanski@CIRA.colostate.edu
Experiments with KdVB model (PARAMETER estimation impact) 10 obs 101 obs
Results with assimilation of global analyses employing NASA’s GEOS column model • Work in progress under NASA’s TRMM project • D. Zupanski (CSU/CIRA) • A. Hou and S. Zhang (NASA/GMAO) • C. Kummerow (CSU/Atmos. Sci.) Preliminary results including parameter estimation: R1/2 = 1/2 e R1/2 = e Choice of observation errors directly impacts innovation statistics. Observation error covariance R is the only given input to the system!
Plans to apply probabilistic assimilation-prediction algorithm to RAMS and WRF models • Use the probabilistic assimilation-prediction system with RAMS and/or WRF to assess GOES-R sounder capabilities using simulated data (identical twin experiments) • Use a similar framework to assimilate AIRS soundings for mesoscale case studies (severe weather, tropical cyclone, lake effect snow) • Continue the mesoscale studies when CrIS soundings become available from the NPP mission. • Uncertainty information provided by ensemble forecasting can be used to gain new knowledge about atmospheric processes • New knowledge about model errors and observation errors can also be obtained Dusanka Zupanski, CIRA/CSU Zupanski@CIRA.colostate.edu
Probabilistic assimilation-prediction framework Forecast error covariance Observations First guess Data assimilation (Init. Cond. and Model Error adjust.) Analysis error Covariance (in ensemble subspace) Init. Cond. and Model Error opt. estimates 4DVAR framework Ens. forecasting Forecast error covariance Observations First guess Data assimilation (Init. Cond. and Model Error adjust.) Init. Cond. and Model Error opt. estimates In probabilistic assimilation-prediction framework model error does not depend on assumptions regarding forecast error covariance; data assimilation problem is solved in ensemble subspace Dusanka Zupanski, CIRA/CSU Zupanski@CIRA.colostate.edu
