Assimilation of Simulated Doppler Radar Observations with an Ensemble Kalman Filter

Assimilation of Doppler radar data into cloud models is an important obstacle to routine numerical weather prediction for convective-scale motions; the difficulty lies in initializing fields of wind, temperature, moisture, and condensate given only observations of radial velocity and reflectivity from the radar. This paper investigates the potential of the ensemble Kalman filter (EnKF), which estimates the covariances between observed variables and the state through an ensemble of forecasts, to assimilate radar observations at convective scales. In the basic experiment, simulated observations are extracted from a reference simulation of a splitting supercell and assimilated using the EnKF and the same numerical model that produced the reference simulation. The EnKF produces accurate analyses, including the unobserved variables, after roughly 30 min (or six scans) of radial velocity observations. Additional experiments, in which forecasts are made from the ensemble-mean analysis, reveal that forecast errors grow significantly in this simple system, so that the ability of the EnKF to track the reference solution is not simply because of stable system dynamics. It is also found that the covariances between radial velocity and temperature, moisture, and condensate are important to the quality of the analyses, as is the initialization chosen for the ensemble members prior to assimilating the first observations. These results are promising, especially given the ease of implementing the EnKF. A number of important issues remain, however, including the initialization of the ensemble prior to the first observation, the treatment of uncertainty in the environmental sounding, the role of error in the forecast model (particularly the microphysical parameterizations), and the treatment of lateral boundary conditions.

[1]  R. P. Pearce,et al.  Large-Scale Dynamical Processes in the Atmosphere , 1983 .

[2]  James M. Murphy,et al.  The impact of ensemble forecasts on predictability , 1988 .

[3]  G. Evensen.  Sequential data assimilation with a nonlinear quasi‐geostrophic model using Monte Carlo methods to forecast error statistics , 1994 .

[4]  Juanzhen Sun,et al.  Dynamical and Microphysical Retrieval from Doppler Radar Observations Using a Cloud Model and Its Adjoint. Part I: Model Development and Simulated Data Experiments. , 1997 .

[5]  Stephen E. Cohn,et al.  An Introduction to Estimation Theory (gtSpecial IssueltData Assimilation in Meteology and Oceanography: Theory and Practice) , 1997 .

[6]  Philippe Courtier,et al.  Unified Notation for Data Assimilation : Operational, Sequential and Variational , 1997 .

[7]  S. Cohn,et al.  An Introduction to Estimation Theory , 1997 .

[8]  Juanzhen Sun,et al.  Dynamical and Microphysical Retrieval from Doppler Radar Observations Using a Cloud Model and Its Adjoint. Part II: Retrieval Experiments of an Observed Florida Convective Storm , 1998 .

[9]  P. Houtekamer,et al.  Data Assimilation Using an Ensemble Kalman Filter Technique , 1998 .

[10]  Peter Jan van Leeuwen,et al.  Comment on ''Data Assimilation Using an Ensemble Kalman Filter Technique'' , 1999 .

[11]  P. Houtekamer,et al.  An Adaptive Ensemble Kalman Filter , 2000 .

[12]  T. Hamill,et al.  A Hybrid Ensemble Kalman Filter-3D Variational Analysis Scheme , 2000 .

[13]  Jeffrey L. Anderson.  An Ensemble Adjustment Kalman Filter for Data Assimilation , 2001 .

[14]  Thibaut Montmerle,et al.  Simulation of a Midlatitude Convective Storm Initialized with Bistatic Doppler Radar Data , 2001 .

[15]  P. Houtekamer,et al.  A Sequential Ensemble Kalman Filter for Atmospheric Data Assimilation , 2001 .

[16]  J. Whitaker,et al.  Distance-dependent filtering of background error covariance estimates in an ensemble Kalman filter , 2001 .

[17]  C. Leith.  Predictability in theory and practice , 2001 .

[18]  Q. Xu,et al.  Simple adjoint method for three‐dimensional wind retrievals from single‐Doppler radar , 2001 .

[19]  J. Whitaker,et al.  Ensemble Data Assimilation without Perturbed Observations , 2002 .

[20]  M. Rienecker,et al.  Initial testing of a massively parallel ensemble Kalman filter with the Poseidon isopycnal ocean general circulation model , 2002 .

[21]  Alan Shapiro,et al.  Retrieval of Model Initial Fields from Single-Doppler Observations of a Supercell Thunderstorm. Part I: Single-Doppler Velocity Retrieval , 2002 .

[22]  P. Houtekamer,et al.  Ensemble size, balance, and model-error representation in an ensemble Kalman filter , 2002 .

[23]  K. Brusdala,et al.  A demonstration of ensemble-based assimilation methods with a layered OGCM from the perspective of operational ocean forecasting systems , 2003 .