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43
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 ..."
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Cited by 130 (79 self)
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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
Tests of an ensemble Kalman filter for mesoscale and regionalscale data assimilation. Part I: Perfect model experiments.
 Mon. Wea. Rev.,
, 2006
"... ABSTRACT In previous works in this series study, an ensemble Kalman filter (EnKF) was demonstrated to be promising for mesoscale and regionalscale data assimilation in increasingly realistic environments. Parts I and II examined the performance of the EnKF by assimilating simulated observations un ..."
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Cited by 44 (8 self)
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ABSTRACT In previous works in this series study, an ensemble Kalman filter (EnKF) was demonstrated to be promising for mesoscale and regionalscale data assimilation in increasingly realistic environments. Parts I and II examined the performance of the EnKF by assimilating simulated observations under both perfectand imperfectmodel assumptions. Part III explored the application of the EnKF to a realdata case study in comparison to a threedimensional variational data assimilation (3DVAR) method in the Weather Research and Forecasting (WRF) model. The current study extends the singlecase realdata experiments over a period of 1 month to examine the longterm performance and comparison of both methods at the regional scales. It is found that the EnKF systematically outperforms 3DVAR for the 1month period of interest in which both methods assimilate the same standard rawinsonde observations every 12 h over the central United States. Consistent with results from the realdata case study of Part III, the EnKF can benefit from using a multischeme ensemble that partially accounts for model errors in physical parameterizations. The benefit of using a multischeme ensemble (over a singlescheme ensemble) is more pronounced in the thermodynamic variables (including temperature and moisture) than in the wind fields. On average, the EnKF analyses lead to more accurate forecasts than the 3DVAR analyses when they are used to initialize 60 consecutive, deterministic 60h forecast experiments for the month. Results also show that deterministic forecasts of up to 60 h initiated from the EnKF analyses consistently outperform the WRF forecasts initiated from the National Centers for Environmental Prediction final analysis field of the Global Forecast System.
2007: Mesoscale Dynamics
"... 13. Numerical modeling of geophysical fluid systems Chapter 13 Numerical modeling of geophysical fluid systems In the previous chapter, we discussed various numerical approximations of the advection equation. However, to simulate a geophysical fluid system, such as the atmosphere and ocean, within a ..."
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Cited by 20 (4 self)
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13. Numerical modeling of geophysical fluid systems Chapter 13 Numerical modeling of geophysical fluid systems In the previous chapter, we discussed various numerical approximations of the advection equation. However, to simulate a geophysical fluid system, such as the atmosphere and ocean, within a finite region, we need to choose the domain size, grid size, time interval, total integration time, and consider other factors, such as the initial condition and boundary conditions. In addition, when we deal with a real fluid system, the governing equations are much more complicated than the onedimensional, linear advection equation, as considered in the previous chapter. For example, we have to integrate threedimensional nonlinear governing equations with several dependent variables, instead of a onedimensional advection equation with only one variable. When a nonlinear equation is being approximated by numerical methods, one may face new problems such as nonlinear computational instability and nonlinear aliasing. Special numerical techniques are required to avoid these types of problems. Once optimal approximate forms of the equations are selected, it is still necessary to define the domain and grid structure over which the partial differential equations will be approximated. In this chapter, we will also briefly describe on how to build a basic numerical model based on a set of partial differential equations governing a shallow water system, and a hydrostatic or nonhydrostatic continuously stratified fluid system.
Calibrating MultiModel Forecast Ensembles with Exchangeable and Missing Members using Bayesian Model Averaging ∗
, 2009
"... Sloughter for sharing their insights and providing data. This research was sponsored by the National Science Foundation under Joint Ensemble Forecasting System (JEFS) subaward No. S0647225 with the University Corporation for Atmospheric Research (UCAR), as well as grants No. ATM0724721 and No. DMS ..."
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Cited by 16 (5 self)
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Sloughter for sharing their insights and providing data. This research was sponsored by the National Science Foundation under Joint Ensemble Forecasting System (JEFS) subaward No. S0647225 with the University Corporation for Atmospheric Research (UCAR), as well as grants No. ATM0724721 and No. DMS0706745. Bayesian model averaging (BMA) is a statistical postprocessing technique that generates calibrated and sharp predictive probability density functions (PDFs) from forecast ensembles. It represents the predictive PDF as a weighted average of PDFs centered on the biascorrected ensemble members, where the weights reflect the relative skill of the individual members over a training period. This work adapts the BMA approach to situations that arise frequently in practice, namely, when one or more of the member forecasts are exchangeable, and when there are missing ensemble members. Exchangeable members differ in random perturbations only, such as the members of bred ensembles, singular vector ensembles, or ensemble Kalman filter systems. Accounting for exchangeability simplifies the BMA approach, in that the BMA weights and the parameters of the component PDFs can be assumed to
Boundary conditions for limitedarea ensemble Kalman filters
, 2006
"... One aspect of implementing a limitedarea ensemble Kalman filter (EnKF) involves the specification of a suitable ensemble of lateral boundary conditions. We propose two classes of methods to populate a boundary condition ensemble. In the first class, the ensemble of boundary conditions is provided b ..."
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Cited by 14 (5 self)
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One aspect of implementing a limitedarea ensemble Kalman filter (EnKF) involves the specification of a suitable ensemble of lateral boundary conditions. We propose two classes of methods to populate a boundary condition ensemble. In the first class, the ensemble of boundary conditions is provided by an EnKF on a larger domain and is approximately a random draw from the probability distribution function for the forecast (or analysis) on the limitedarea domain boundary given the available observations. The second class perturbs around a deterministic estimate of the state using assumed spatial and temporal covariance relationships. Methods in the second class are relatively flexible and easy to implement. Experiments that test the utility of these methods are performed for both an idealized lowdimensional model and limitedarea simulations using the Weather Research and Forecasting (WRF) model; all experiments employ simulated observations under the perfect model assumption. The performance of the ensemble boundary condition methods is assessed by comparing the results of each experiment against a control “global ” EnKF that extends beyond the limitedarea domain. For all methods tested, results show that errors for the
Integration of data assimilation subsystem into environmental model of harmful substances propagation
 in Harmo11  11th Internal Conf
, 2007
"... The paper describes present state of implementation of assimilation subsystem within software system HARP designed as decision support tool for fast assessment of radiological consequences of accidental releases of radionuclides into living environment. The product is presented here from viewpoint o ..."
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Cited by 12 (10 self)
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The paper describes present state of implementation of assimilation subsystem within software system HARP designed as decision support tool for fast assessment of radiological consequences of accidental releases of radionuclides into living environment. The product is presented here from viewpoint of its architecture and possibility of its utilization such an educational tool for training purposes in radiation protection field and for decision support staff. A review of assimilation methods implemented in the subsystem is presented here. ASSIMILATION METHODS The goal of data assimilation is to provide analysis which relies on so called background field from a model forecast and observations. Other inputs to data assimilation process can be physical constraints on the problem or any additional prior knowledge not included in the model. Merging of these contending sources of information had shown to be very promising in many branches of contemporary Earth sciences. In data assimilation we try to adjust model according to measured values what represents research effort to move from isolated model predictions forward reality. An automatic procedure for bringing observations into the model is called objective analysis. The major progress of objective analysis was achieved in the field
2007: A data assimilation case study using a limitedarea ensemble Kalman filter
"... Ensemble Kalman filter (EnKF) data assimilation experiments are conducted on a limitedarea domain over the Pacific Northwest region of the United States, using the Weather Research and Forecasting model. Idealized surface pressure, radiosoundings, and aircraft observations are assimilated every 6 h ..."
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Cited by 9 (2 self)
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Ensemble Kalman filter (EnKF) data assimilation experiments are conducted on a limitedarea domain over the Pacific Northwest region of the United States, using the Weather Research and Forecasting model. Idealized surface pressure, radiosoundings, and aircraft observations are assimilated every 6 h for a 7day period in January 2004. The objectives here are to study the performance of the filter in constraining analysis errors with a relatively inhomogeneous, sparseobservation network and to explore the potential for such a network to serve as the basis for a realtime EnKF system dedicated to the Pacific Northwest region of the United States. When only a single observation type is assimilated, results show that the ensemblemean analysis error and ensemble spread (standard deviation) are significantly reduced compared to a control ensemble without assimilation for both observed and unobserved variables. Analysis errors are smaller than background errors over nearly the entire domain when averaged over the 7day period. Moreover, comparisons of background errors and observation increments at each assimilation step suggest that the flowdependent filter corrections are accurate in both scale and amplitude. An illustrative example concerns a misspecified mesoscale 500hPa shortwave trough moving along the British Columbia coast, which is corrected by surface pressure observations alone. The relative impact of each observation type upon different variables and vertical levels is also discussed. 1.
Data Assimilation for Geophysical Fluids
"... The ultimate purpose of environmental studies is the forecast of its natural evolution. A prerequisite before a prediction is to retrieve at best the state of the environment. Data assimilation is the ensemble of techniques which, starting from heterogeneous information, permit to retrieve the initi ..."
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Cited by 8 (0 self)
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The ultimate purpose of environmental studies is the forecast of its natural evolution. A prerequisite before a prediction is to retrieve at best the state of the environment. Data assimilation is the ensemble of techniques which, starting from heterogeneous information, permit to retrieve the initial state of a flow. In the first part, the mathematical models governing geophysical flows are presented together with the networks of observations of the atmosphere and of the ocean. In variational methods, we seek for the minimum of a functional estimating the discrepancy between the solution of the model and the observation. The derivation of the optimality system, using the adjoint state, permits to compute a gradient which is used in the optimization. The definition of the cost function permits to take into account the available statistical information through the choice of metrics in the space of observation and in the space of the initial condition. Some examples are presented on simplified models, especially an application in oceanography. Among the tools of optimal control, the adjoint model permits to carry out sensitivity studies, but if we look for the sensitivity of the prediction with respect to the observations, then a secondorder analysis should be considered. One of the first methods used for assimilating data in oceanography is the nudging method, adding a forcing term in the equations. A variational variant of nudging method is described and also a socalled Computational Methods for the Atmosphere and the Oceans
Ensemble forecasting and data assimilation: two problems with the same solution?
 PREDICTABILITY OF WEATHER AND CLIMATE
, 2005
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The Overamplification of Gravity Waves in Numerical Solutions to Flow over Topography
, 2008
"... The tendency of highresolution numerical weather prediction (NWP) models to overpredict the strength of vertically propagating mountain waves is explored. Discrete analytic mountainwave solutions are presented for the classical problem of crossmountain flow in an atmosphere with constant wind spe ..."
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Cited by 5 (1 self)
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The tendency of highresolution numerical weather prediction (NWP) models to overpredict the strength of vertically propagating mountain waves is explored. Discrete analytic mountainwave solutions are presented for the classical problem of crossmountain flow in an atmosphere with constant wind speed and stability. Timedependent linear numerical solutions are also obtained for more realistic atmospheric structures. On one hand, using secondorderaccurate finite differences on an Arakawa C grid to model nonhydrostatic flow over what might be supposed to be an adequately resolved 8Dxwide mountain can lead to an overamplification of the standing mountain wave by 30%–40%. On the other hand, the same finitedifference scheme underestimates the wave amplitude in hydrostatic flow over an 8Dxwide mountain. Increasing the accuracy of the advection scheme to the fourth order significantly reduces the numerical errors associated with both the hydrostatic and nonhydrostatic discrete solutions. The Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) model is used to generate two 70member ensemble simulations of a mountainwave event during the TerrainInduced Rotor Experiment. It is shown that switching from secondorder advection to fourthorder advection leads to as much as a 20 m s 21 decrease in vertical velocity on the lee side of the Sierra Nevada, and that the weaker fourthorder solutions are more consistent with observations. 1.