Results 1  10
of
47
Ensemble Kalman Filter Assimilation of Doppler Radar Data with a Compressible Nonhydrostatic Model: OSS Experiments
, 2004
"... A Doppler radar data assimilation system is developed based on ensemble Kalman filter (EnKF) method and tested with simulated radar data from a supercell storm. As a first implementation, we assume the forward models are perfect and radar data are sampled at the analysis grid points. A general pur ..."
Abstract

Cited by 127 (78 self)
 Add to MetaCart
A Doppler radar data assimilation system is developed based on ensemble Kalman filter (EnKF) method and tested with simulated radar data from a supercell storm. As a first implementation, we assume the forward models are perfect and radar data are sampled at the analysis grid points. A general purpose nonhydrostatic compressible model is used with the inclusion of complex multiclass ice microphysics. New aspects compared to previous studies include the demonstration of the ability of EnKF method in retrieving multiple microphysical species associated with a multiclass ice microphysics scheme, and in accurately retrieving the wind and thermodynamic variables. Also new are the inclusion of reflectivity observations and the determination of the relative role of radial velocity and reflectivity data as well as their spatial coverage in recovering the full flow and cloud fields. In general, the system is able to reestablish the model storm extremely well after a number of assimilation cycles, and best results are obtained when both radial velocity and reflectivity data, including reflectivity information outside precipitation regions, are used. Significant positive impact of the reflectivity assimilation
A multicase comparative assessment of the ensemble Kalman filter for assimilation of radar observations. Part I: Stormscale analyses.” Monthly Weather Review
, 2009
"... The effectiveness of the ensemble Kalman filter (EnKF) for assimilating radar observations at convective scales is investigated for cases whose behaviors span supercellular, linear, andmulticellular organization. The parallel EnKF algorithm of the Data Assimilation Research Testbed (DART) is used fo ..."
Abstract

Cited by 30 (4 self)
 Add to MetaCart
The effectiveness of the ensemble Kalman filter (EnKF) for assimilating radar observations at convective scales is investigated for cases whose behaviors span supercellular, linear, andmulticellular organization. The parallel EnKF algorithm of the Data Assimilation Research Testbed (DART) is used for data assimilation, while the Weather Research and Forecasting (WRF)Model is employed as a simplified cloud model at 2km horizontal grid spacing. In each case, reflectivity and radial velocity measurements are utilized from a single Weather Surveillance Radar1988 Doppler (WSR88D) within the U.S. operational network. Observations are assimilated every 2 min for a duration of 60 min and correction of folded radial velocities occurs within the EnKF. Initial ensemble uncertainty includes random perturbations to the horizontal wind components of the initial environmental sounding. The EnKF performs effectively and with robust results across all the cases. Over the first 18–30 min of assimilation, the rms and domainaveraged prior fits to observations in each case improve significantly from their initial levels, reaching comparable values of 3–6 m s21 and 7–10 dBZ. Representation of mesoscale uncertainty, albeit in the simplest form of initial sounding perturbations, is a critical part of the assimilation system, as it increases ensemble spread and improves filter performance. In addition, assimilation of ‘‘no precipitation’ ’ observations (i.e., reflectivity observations with values small enough to indicate the absence of precipitation) serves to suppress spurious convection in ensemble members. At the same time, it is clear that the assimilation is far from optimal, as the ensemble spread is consistently smaller than what would be expected from the innovation statistics and the assumed observationerror variance. 1.
2008: Accelerating the spinup of Ensemble Kalman Filtering
"... Ensemble Kalman Filter (EnKF) has that disadvantage that the spinup time needed to reach its asymptotic level of accuracy is longer than the corresponding spinup time in variational methods (3DVar or 4DVar). This is because the ensemble has to fulfill two independent requirements, namely that th ..."
Abstract

Cited by 18 (5 self)
 Add to MetaCart
Ensemble Kalman Filter (EnKF) has that disadvantage that the spinup time needed to reach its asymptotic level of accuracy is longer than the corresponding spinup time in variational methods (3DVar or 4DVar). This is because the ensemble has to fulfill two independent requirements, namely that the mean be close to the true state, and that the ensemble perturbations represent the “errors of the day”. As a result, there are cases such as radar observations of a severe storm, where EnKF may spinup too slowly to be useful. A scheme is proposed to accelerate the spinup of EnKF applying a nocost Ensemble Kalman Smoother, and using the observations more than once in each assimilation window in order to maximize the initial extraction of information. The performance of this scheme is tested with the Local Ensemble Transform Kalman Filter (LETKF) implemented in a Quasigeostrophic model, which requires a very long spinup time when initialized from a cold start. Results show that with the new “running in place” scheme the LETKF spinsup and converges to the optimal level of error at least as fast as 3DVar or 4DVar. Additional computations (24 iterations for each window) are only required during the initial spinup, since the scheme naturally returns to the original LETKF after spinup is achieved. 1.
The Analysis and Prediction of the 8–9 May 2007 Oklahoma Tornadic Mesoscale Convective System by Assimilating WSR88D and CASA Radar Data Using 3DVAR
 Mon. Wea. Rev
, 2010
"... The Advanced Regional Prediction System (ARPS) model is employed to perform highresolution numerical simulations of a mesoscale convective system and associated cyclonic lineend vortex (LEV) that ..."
Abstract

Cited by 17 (12 self)
 Add to MetaCart
The Advanced Regional Prediction System (ARPS) model is employed to perform highresolution numerical simulations of a mesoscale convective system and associated cyclonic lineend vortex (LEV) that
Performance characteristics of a pseudooperational ensemble Kalman filter
, 2008
"... The 2yr performance of a pseudooperational (real time) limitedarea ensemble Kalman filter (EnKF) based on the Weather Research and Forecasting Model is described. This system assimilates conventional observations from surface stations, rawinsondes, the Aircraft Communications Addressing and Repor ..."
Abstract

Cited by 16 (1 self)
 Add to MetaCart
The 2yr performance of a pseudooperational (real time) limitedarea ensemble Kalman filter (EnKF) based on the Weather Research and Forecasting Model is described. This system assimilates conventional observations from surface stations, rawinsondes, the Aircraft Communications Addressing and Reporting System (ACARS), and cloud motion vectors every 6 h on a domain that includes the eastern North Pacific Ocean and western North America. Ensemble forecasts from this system and deterministic output from operational numerical weather prediction models during this same period are verified against rawinsonde and surface observation data. Relative to operational forecasts, the forecast from the ensemblemean analysis has slightly larger errors in wind and temperature but smaller errors in moisture, even though satellite radiances are not assimilated by the EnKF. Timeaveraged correlations indicate that assimilating ACARS and cloud wind data with flowdependent error statistics provides corrections to the moisture field in the absence of direct observations of that field. Comparison with a control experiment in which a deterministic forecast is cycled without observation assimilation indicates that the skill in the EnKF’s forecasts results from assimilating observations and not from lateral boundary conditions or the model formulation. Furthermore, the ensemble variance is generally in good agreement with the ensemblemean error and the spread increases monotonically with forecast hour.
2009: Coupling ensemble Kalman filter with fourdimensional variational data assimilation
 Advances in Atmospheric Sciences
, 2008
"... This study examines the performance of coupling the deterministic fourdimensional variational assimilation system (4DVAR) with an ensemble Kalman filter (EnKF) to produce a superior hybrid approach for data assimilation. The coupled assimilation scheme (E4DVAR) benefits from using the statedepend ..."
Abstract

Cited by 9 (0 self)
 Add to MetaCart
This study examines the performance of coupling the deterministic fourdimensional variational assimilation system (4DVAR) with an ensemble Kalman filter (EnKF) to produce a superior hybrid approach for data assimilation. The coupled assimilation scheme (E4DVAR) benefits from using the statedependent uncertainty provided by EnKF while taking advantage of 4DVAR in preventing filter divergence: the 4DVAR analysis produces posterior maximum likelihood solutions through minimization of a cost function about which the ensemble perturbations are transformed, and the resulting ensemble analysis can be propagated forward both for the next assimilation cycle and as a basis for ensemble forecasting. The feasibility and effectiveness of this coupled approach are demonstrated in an idealized model with simulated observations. It is found that the E4DVAR is capable of outperforming both 4DVAR and the EnKF under both perfectand imperfectmodel scenarios. The performance of the coupled scheme is also less sensitive to either the ensemble size or the assimilation window length than those for standard EnKF or 4DVAR implementations. Key words: data assimilation, fourdimensional variational data assimilation, ensemble Kalman filter, Lorenz model, hybrid method
Ensemble Kalman Filter: Current Status and Potential
 P. SAKOV AND P. OKE, “IMPLICATIONS OF THE FORM OF THE ENSEMBLE TRANSFORMATION IN THE ENSEMBLE SQUARE ROOT FILTERS” MON.WEA.REV.,136
, 2008
"... ..."
(Show Context)
Diagnostic Pressure Equation as a Weak Constraint in a StormScale Three Dimensional Variational Radar Data Assimilation System
"... A diagnostic pressure equation is incorporated into a stormscale threedimensional variational (3DVAR) data assimilation system in the form of a weak constraint in addition to a mass continuity equation constraint (MCEC). The goal of this diagnostic pressure equation constraint (DPEC) is to couple ..."
Abstract

Cited by 5 (5 self)
 Add to MetaCart
A diagnostic pressure equation is incorporated into a stormscale threedimensional variational (3DVAR) data assimilation system in the form of a weak constraint in addition to a mass continuity equation constraint (MCEC). The goal of this diagnostic pressure equation constraint (DPEC) is to couple different model variables to help build a more dynamic consistent analysis and therefore improve the data assimilation results and subsequent forecasts. Observational System Simulation Experiments (OSSEs) are first performed to examine the impact of the pressure equation constraint on stormscale radar data assimilation using an idealized tornadic thunderstorm simulation. The impact of MCEC is also investigated relative to that of DPEC. It is shown that DPEC can improve the data assimilation results slightly after a given period of data assimilation. Including both DPEC and MCEC yields the best data assimilation results. Sensitivity tests show that MCEC is not very sensitive to the choice of its weighting coefficients in the cost function while DPEC is more sensitive and its weight should be carefully chosen. The updated 3DVAR system with DPEC is further applied to the 5 May 2007 Greensburg, Kansas tornadic supercell storm case assimilating real radar data. It is shown that the use of DPEC can speed up the spinup of precipitation during the intermittent data assimilation process and also improve the followon forecast in terms of the general evolution of storm cells and mesocyclone rotation near the time of observed tornado. 1.
Some Ideas for Ensemble Kalman Filtering
 NOAA, Climate Test Bed Joint Seminar Series, IGES/COLA
, 2008
"... In this seminar we show clean comparisons between EnKF and 4DVar made in Environment Canada, briefly describe the Local Ensemble Transform Kalman Filter (LETKF) as a representative prototype of Ensemble Kalman Filter, and give several examples of how advanced properties and applications that have b ..."
Abstract

Cited by 2 (0 self)
 Add to MetaCart
(Show Context)
In this seminar we show clean comparisons between EnKF and 4DVar made in Environment Canada, briefly describe the Local Ensemble Transform Kalman Filter (LETKF) as a representative prototype of Ensemble Kalman Filter, and give several examples of how advanced properties and applications that have been developed and explored for 4DVar can be adapted to the LETKF without requiring an adjoint model. Although the Ensemble Kalman Filter is less mature than 4DVar, its simplicity and its competitive performance with respect to 4DVar suggest that it could become the method of choice.
2013: A hybrid MPI/OpenMP parallel algorithm and performance analysis for an ensemble square root filter suitable for dense observations
 J
"... A hybrid parallel scheme for the ensemble square root filter (EnSRF) suitable for parallel assimilation of multiscale observations, including those from dense observational networks such as those of radar, is developed based on the domain decomposition strategy. The scheme handles internode communi ..."
Abstract

Cited by 1 (1 self)
 Add to MetaCart
A hybrid parallel scheme for the ensemble square root filter (EnSRF) suitable for parallel assimilation of multiscale observations, including those from dense observational networks such as those of radar, is developed based on the domain decomposition strategy. The scheme handles internode communication through a message passing interface (MPI) and the communication within sharedmemory nodes via Open Multiprocessing (OpenMP) threads. It also supports pure MPI and pure OpenMPmodes. The parallel framework can accommodate highvolume remotesensed radar (or satellite) observations as well as conventional observations that usually have larger covariance localization radii. The performance of the parallel algorithm has been tested with simulated and real radar data. The parallel program shows good scalability in pure MPI and hybrid MPI–OpenMP modes, while pure OpenMP runs exhibit limited scalability on a symmetric sharedmemory system. It is found that inMPImode, better parallel performance is achieved with domain decomposition configurations in which the leading dimension of the state variable arrays is larger, because this configuration allows for more efficient memory access. Given a fixed amount of computing resources, the hybrid parallel mode is preferred to pure MPI mode on supercomputers with nodes containing sharedmemory cores. The overall performance is also affected by factors such as the cache size, memory bandwidth, and the networking topology. Tests with a real data case with a large number of radars confirm that the parallel data assimilation can be done on amulticore supercomputer with a significant speedup compared to the serial data assimilation algorithm. 1.