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Assimilation of Simulated Polarimetric Radar Data for a Convective Storm Using the Ensemble Kalman Filter. Part I: Observation Operators for Reflectivity and Polarimetric Variables // [Mon.
, 2008
"... Abstract A data assimilation system based on the ensemble squareroot Kalman filter (EnSRF) is extended to include the additional capability of assimilating polarimetric radar variables. It is used to assess the impact of simulating additional polarimetric observations on convective storm analysis ..."
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Abstract A data assimilation system based on the ensemble squareroot Kalman filter (EnSRF) is extended to include the additional capability of assimilating polarimetric radar variables. It is used to assess the impact of simulating additional polarimetric observations on convective storm analysis in an OSSE (Observing System Simulation Experiment) framework. The polarimetric variables considered include differential reflectivity Z DR , reflectivity difference Z dp , and specific differential phase K DP . To simulate the observational data more realistically, a new error model is introduced for characterizing the errors of the nonpolarimetric and polarimetric radar variables. The error model includes both correlated and uncorrelated error components for reflectivities at horizontal and vertical polarizations (Z H and Z V ). It is shown that the storm analysis is improved when polarimetric variables are assimilated in addition to Z H or in addition to both Z H and radial velocity V r . Positive impact is largest when Z DR , Z dp , and K DP are assimilated all together. Improvement is generally larger in vertical velocity, water vapor and rainwater mixing ratios. The rain water field benefits the most while the impacts on horizontal wind components and snow mixing ratios are smaller. Improvement is found at all model levels even though the polarimetric data, after the application of thresholds, are mostly limited to the lower levels. Among Z DR , Z dp , and K DP , Z DR is found to produce the largest positive impact on the analysis. It is suggested that Z DR provides more independent information than the other variables. The impact of polarimetric data is also expected to be larger when they are used to retrieve drop size distribution parameters. This study is believed to be the first to directly assimilate (simulated) polarimetric data into a numerical model. 1
Ensemblebased sensitivity analysis
 MON. WEA. REV
, 2008
"... The sensitivity of forecasts to observations is evaluated using an ensemble approach with data drawn from a pseudooperational ensemble Kalman filter. For Gaussian statistics and a forecast metric defined as a scalar function of the forecast variables, the effect of observations on the forecast metr ..."
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Cited by 16 (1 self)
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The sensitivity of forecasts to observations is evaluated using an ensemble approach with data drawn from a pseudooperational ensemble Kalman filter. For Gaussian statistics and a forecast metric defined as a scalar function of the forecast variables, the effect of observations on the forecast metric is quantified by changes in the metric mean and variance. For a single observation, expressions for these changes involve a product of scalar quantities, which can be rapidly evaluated for large numbers of observations. This technique is applied to determining climatological forecast sensitivity and predicting the impact of observations on sea level pressure and precipitation forecast metrics. The climatological 24h forecast sensitivity of the average pressure over western Washington State shows a region of maximum sensitivity to the west of the region, which tilts gently westward with height. The accuracy of ensemble sensitivity predictions is tested by withholding a single buoy pressure observation from this region and comparing this perturbed forecast with the control case where the buoy is assimilated. For 30 cases, there is excellent agreement between these forecast differences and the ensemble predictions, as measured by the forecast metric. This agreement decreases for increasing numbers of observations. Nevertheless, by using statistical confidence tests to address sampling error, the impact of thousands of observations on forecastmetric variance is shown to be well estimated by a subset of the O(100) most significant observations.
Robust Characterization of Model Physics Uncertainty for Simulations of Deep Moist Convection
, 2009
"... does not require the AMS’s permission. Republication, systematic reproduction, posting in electronic form, such as on a web site or in a searchable database, or other uses of this material, except as exempted by the above statement, requires written permission or a license from the ..."
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Cited by 5 (2 self)
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does not require the AMS’s permission. Republication, systematic reproduction, posting in electronic form, such as on a web site or in a searchable database, or other uses of this material, except as exempted by the above statement, requires written permission or a license from the
Nonlinear Parameter Estimation: Comparison of an Ensemble Kalman Smoother with a Markov Chain Monte Carlo Algorithm
, 2011
"... brief excerpts from this work in scientific and educational works is hereby granted provided that the source is acknowledged. Any use of material in this work that is determined to be “fair use ” under Section 107 of the U.S. Copyright Act or that satisfies the conditions specified in Section 108 of ..."
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Cited by 5 (0 self)
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brief excerpts from this work in scientific and educational works is hereby granted provided that the source is acknowledged. Any use of material in this work that is determined to be “fair use ” under Section 107 of the U.S. Copyright Act or that satisfies the conditions specified in Section 108 of the U.S. Copyright Act (17 USC §108, as revised by P.L. 94553) does not require the AMS’s permission. Republication, systematic reproduction, posting in electronic form, such as on a web site or in a searchable database, or other uses of this material, except as exempted by the above statement, requires written permission or a license from the
2012), Assimilation of radial velocity and reflectivity data from coastal WSR88D radars using ensemble Kalman filter for the analysis and forecast of landfalling hurricane Ike (2008
, 1002
"... Ensemble Kalman filter (EnKF) assimilation and forecasting experiments are performed for the case of Hurricane Ike (2008), the third most destructive hurricane hitting the USA. Data from two coastal WSR88D radars are carefully quality controlled before assimilation. In the control assimilation ex ..."
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Ensemble Kalman filter (EnKF) assimilation and forecasting experiments are performed for the case of Hurricane Ike (2008), the third most destructive hurricane hitting the USA. Data from two coastal WSR88D radars are carefully quality controlled before assimilation. In the control assimilation experiment, reflectivity (Z) and radial velocity (Vr) data from two radars are assimilated at 10 min intervals over a 2 h period shortly before Ike made landfall. A 32member forecast ensemble is initialized by introducing both mesoscale and convectivescale perturbations to the initial National Centers for Environmental Prediction (NCEP) operational global forecast system (GFS) analysis background, and the ensemble spread during the analysis cycles is maintained using multiplicative covariance inflation and posterior additive perturbations. The radar data assimilation results in much improved vortex intensity and structure analysis over the corresponding GFS analysis. Compared with the forecast starting from the GFS analysis, the forecast intensity, track and structure of Ike over a 12 h period are much improved in both deterministic and ensemble forecasts. Assimilation of either Vr or Z leads to improvement in the forecasts, with Vr data exhibiting much greater impacts than Z data. With the 2 h assimilation window, 30 min assimilation intervals produced results similar to 10 min intervals, while 60 min intervals were found to be too long. The ensemble forecasts starting from the EnKF analyses are found to bemostly better than the corresponding deterministic forecast, especially after ensemble postprocessing, such as probability matching for precipitation. Precipitation equitable threat scores were
The Analysis and Impact of Simulated HighResolution Surface Observations for Convective Storms with Ensemble Kalman Filter: Perfect Model Experiments
, 2007
"... A series of observing system simulation experiments (OSSEs) are performed using the ARPS model and its EnKF (ensemble Kalman filter) data assimilation system to investigate the impact of surface observations on the analysis and forecast of convective storms in addition to Doppler radar data. A truth ..."
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Cited by 4 (3 self)
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A series of observing system simulation experiments (OSSEs) are performed using the ARPS model and its EnKF (ensemble Kalman filter) data assimilation system to investigate the impact of surface observations on the analysis and forecast of convective storms in addition to Doppler radar data. A truth simulation is created for a supercell storm at a 2 km horizontal resolution. This storm is sampled using a radar emulator that assumes a standard WSR88D radar scanning mode and in the vertical uses a realistic radar beam weighting function. A single radar is located at different distances from the storm. Partly due to the earth curvature effect, the lowlevel coverage of radar data decreases as the radar distance increases, causing the loss of coverage on important lowlevel features including the cold pool and gust front. When the radar is located far away (185 and 115 km) from the main convective storm, clear positive impact on storm analysis and forecast is achieved by mesonetlike surface observations of 20 km spacings, and such impact increases when the station spacing increases to 12 or 6 km. Through the background error covariance estimated from the ensemble and through dynamical interactions in the prediction model, the surface observations not only correct the
2013: A fourdimensional asynchronous ensemble squareroot filter (4DEnSRF) and tests with simulated radar data. Tellus
"... algorithm and tests with simulated radar data ..."
Quantification of Cloud Microphysical Parameterization Uncertainty Using Radar Reflectivity
, 2011
"... brief excerpts from this work in scientific and educational works is hereby granted provided that the source is acknowledged. Any use of material in this work that is determined to be “fair use ” under Section 107 of the U.S. Copyright Act or that satisfies the conditions specified in Section 108 of ..."
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Cited by 2 (1 self)
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brief excerpts from this work in scientific and educational works is hereby granted provided that the source is acknowledged. Any use of material in this work that is determined to be “fair use ” under Section 107 of the U.S. Copyright Act or that satisfies the conditions specified in Section 108 of the U.S. Copyright Act (17 USC §108, as revised by P.L. 94553) does not require the AMS’s permission. Republication, systematic reproduction, posting in electronic form, such as on a web site or in a searchable database, or other uses of this material, except as exempted by the above statement, requires written permission or a license from the
Study on the optimal scanning strategies of phasearray radar through ensemble Kalman filter assimilation of simulated data
 33rd International Conference on Radar Meteorology
, 2007
"... 1 The phasedarray radar (PAR) of the National Weather Radar Testbed (NWRT) in Norman, Oklahoma represents a paradigm shift for weather radar ..."
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1 The phasedarray radar (PAR) of the National Weather Radar Testbed (NWRT) in Norman, Oklahoma represents a paradigm shift for weather radar
Evolved and random perturbation methods for calculating model sensitivities and covariances
"... Different ways of perturbing the initial condition of an ensemble of forecasts for the purpose of calculating sensitivity or covariance fields of model variables are examined. The three methods considered are: random perturbations at each gridpoint, smoothed random perturbations, and perturbations ..."
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Cited by 1 (1 self)
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Different ways of perturbing the initial condition of an ensemble of forecasts for the purpose of calculating sensitivity or covariance fields of model variables are examined. The three methods considered are: random perturbations at each gridpoint, smoothed random perturbations, and perturbations that are evolved by the model through time from an earlier set of perturbations. A very large ensemble of model runs using spatially discrete perturbations is also compared for validation purposes. An ensemble size of 2000 members is used so as to reduce the noise in sensitivity fields. Covariances found from the three methods are highly accurate and nearly identical for any perturbation method. The calculation of sensitivity fields, however, is more dependent on the perturbation method. For the cases of evolved or smoothed perturbations, the spatial correlation of the perturbations leads to an inherent smoothing of the sensitivity fields. Sensitivity structures of scales smaller than the perturbation correlation distance can not be found. This is a particular problem for the evolved perturbations in the boundary layer. Furthermore, the spatial correlation of initial perturbations makes the calculation of sensitivity values inaccurate unless the complicated problem of separating the combined effects of correlated perturbations on the forecast is dealt with. Consequently, mathematically correct sensitivity values are only found by using initial perturbation fields that are spatially completely random.