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Brian Etherton and Pablo Santos

Abstract

This study presents results from an experiment conducted to measure the impact of locally initializing a numerical weather prediction model on that model’s ability to predict precipitation and other surface parameters. The study consisted of quantifying the impact of initializing the Weather and Research Forecast (WRF) model with the Advanced Weather Interactive Processing System (AWIPS) Local Analysis and Prediction System (LAPS) diagnostic analyses. In the experiment, WRF was run for two different initial times: 0600 and 1800 UTC. For each initial time, the model was run twice, once using LAPS for the initial conditions, and once using the North American Mesoscale model (NAM; also known as the Eta Model at the time of the experiment). The impact of the local LAPS initialization on the model forecast of surface parameters is presented. Additionally, the model’s quantitative precipitation forecast (QPF) skill is compared for three different model configurations: 1) WRF initialized with LAPS, 2) WRF initialized with NAM, and 3) the standard NAM/Eta Model. The experiment ran from 1 June 2005 to 31 July 2005.

Results show that WRF forecasts initialized by LAPS have a more accurate representation of convection in the short range. LAPS-initialized forecasts also offer more accurate forecasts of 2-m temperature and dewpoint, 10-m wind, and sea level pressure, particularly in the short range. Most significantly, precipitation forecasts from WRF runs initialized by LAPS are more accurate than WRF runs initialized by NAM. WRF initialized with LAPS also demonstrates higher QPF skill than does the NAM/Eta Model, particularly in the short range when the precipitation thresholds are higher (0.25 in. in 3 h versus 0.10 in. in 3 h), and when forecasts are initialized at 0600 UTC rather than initialized at 1800 UTC.

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Brian J. Etherton

Abstract

An ensemble Kalman filter (EnKF) estimates the error statistics of a model forecast using an ensemble of model forecasts. One use of an EnKF is data assimilation, resulting in the creation of an increment to the first-guess field at the observation time. Another use of an EnKF is to propagate error statistics of a model forecast forward in time, such as is done for optimizing the location of adaptive observations. Combining these two uses of an ensemble Kalman filter, a “preemptive forecast” can be generated. In a preemptive forecast, the increment to the first-guess field is, using ensembles, propagated to some future time and added to the future control forecast, resulting in a new forecast. This new forecast requires no more time to produce than the time needed to run a data assimilation scheme, as no model integration is necessary. In an observing system simulation experiment (OSSE), a barotropic vorticity model was run to produce a 300-day “nature run.” The same model, run with a different vorticity forcing scheme, served as the forecast model. The model produced 24- and 48-h forecasts for each of the 300 days. The model was initialized every 24 h by assimilating observations of the nature run using a hybrid ensemble Kalman filter–three-dimensional variational data assimilation (3DVAR) scheme. In addition to the control forecast, a 64-member forecast ensemble was generated for each of the 300 days. Every 24 h, given a set of observations, the 64-member ensemble, and the control run, an EnKF was used to create 24-h preemptive forecasts. The preemptive forecasts were more accurate than the unmodified, original 48-h forecasts, though not quite as accurate as the 24-h forecast obtained from a new model integration initialized by assimilating the same observations as were used in the preemptive forecasts. The accuracy of the preemptive forecasts improved significantly when 1) the ensemble-based error statistics used by the EnKF were localized using a Schur product and 2) a model error term was included in the background error covariance matrices.

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Sim D. Aberson and Brian J. Etherton

Abstract

Two operational synoptic surveillance missions were conducted by the National Oceanic and Atmospheric Administration into Hurricane Humberto (2001). Forecasts from two leading dynamical hurricane track forecast models were improved substantially during the watch and warning period before a projected landfall by the assimilation of the additional dropwindsonde data. Feasibility tests with a barotropic model suggest that further improvements may be obtained by the use of the ensemble transform Kalman filter for assimilating these additional data into the model. This is the first effort to assimilate data into a hurricane model using the ensemble transform Kalman filter.

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Betsy Weatherhead, Brian Etherton, and Paul Markowski
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Brian J. Etherton and Craig H. Bishop

Abstract

Previous idealized numerical experiments have shown that a straightforward augmentation of an isotropic error correlation matrix with an ensemble-based error correlation matrix yields an improved data assimilation scheme under certain conditions. Those conditions are (a) the forecast model is perfect and (b) the ensemble accurately samples the probability distribution function of forecast errors. Such schemes blend characteristics of ensemble Kalman filter analysis schemes with three-dimensional variational data assimilation (3DVAR) analysis schemes and are called hybrid schemes. Here, we test the robustness of hybrid schemes to model error and ensemble inaccuracy in the context of a numerically simulated two-dimensional turbulent flow. The turbulence is produced by a doubly periodic barotropic vorticity equation model that is constantly relaxing to a barotropically unstable state. The types of forecast models considered include a perfect model, a model with a resolution error, and a model with a parameterization error. The ensemble generation schemes considered include the breeding scheme, the singular vector scheme, the perturbed observations system simulation scheme, a gridpoint noise scheme, and a scheme based on the ensemble transform Kalman filter (ETKF). For all combinations examined, it is found that the hybrid schemes outperform the 3DVAR scheme. In the presence of model error a perturbed observations hybrid and a singular vector hybrid perform best, though the ETKF ensemble is competitive.

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Scott Sandgathe, Barbara Brown, Brian Etherton, and Edward Tollerud
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Craig H. Bishop, Brian J. Etherton, and Sharanya J. Majumdar

Abstract

A suboptimal Kalman filter called the ensemble transform Kalman filter (ET KF) is introduced. Like other Kalman filters, it provides a framework for assimilating observations and also for estimating the effect of observations on forecast error covariance. It differs from other ensemble Kalman filters in that it uses ensemble transformation and a normalization to rapidly obtain the prediction error covariance matrix associated with a particular deployment of observational resources. This rapidity enables it to quickly assess the ability of a large number of future feasible sequences of observational networks to reduce forecast error variance. The ET KF was used by the National Centers for Environmental Prediction in the Winter Storm Reconnaissance missions of 1999 and 2000 to determine where aircraft should deploy dropwindsondes in order to improve 24–72-h forecasts over the continental United States. The ET KF may be applied to any well-constructed set of ensemble perturbations.

The ET KF technique supercedes the ensemble transform (ET) targeting technique of Bishop and Toth. In the ET targeting formulation, the means by which observations reduced forecast error variance was not expressed mathematically. The mathematical representation of this process provided by the ET KF enables such things as the evaluation of the reduction in forecast error variance associated with individual flight tracks and assessments of the value of targeted observations that are distributed over significant time intervals. It also enables a serial targeting methodology whereby one can identify optimal observing sites given the location and error statistics of other observations. This allows the network designer to nonredundantly position targeted observations. Serial targeting can also be used to greatly reduce the computations required to identify optimal target sites. For these theoretical and practical reasons, the ET KF technique is more useful than the ET technique. The methodology is illustrated with observation system simulation experiments involving a barotropic numerical model of tropical cyclonelike vortices. These include preliminary empirical tests of ET KF predictions using ET KF, 3DVAR, and hybrid data assimilation schemes—the results of which look promising. To concisely describe the future feasible sequences of observations considered in adaptive sampling problems, an extension to Ide et al.’s unified notation for data assimilation is suggested.

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Brian J. Etherton, Sean C. Arms, Larry D. Oolman, Gary M. Lackmann, and Mohan K. Ramamurthy

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Sim D. Aberson, Sharanya J. Majumdar, Carolyn A. Reynolds, and Brian J. Etherton

Abstract

In 1997, the National Oceanic and Atmospheric Administration’s National Hurricane Center and the Hurricane Research Division began operational synoptic surveillance missions with the Gulfstream IV-SP jet aircraft to improve the numerical guidance for hurricanes that threaten the continental United States, Puerto Rico, the U.S. Virgin Islands, and Hawaii. The dropwindsonde observations from these missions were processed and formatted aboard the aircraft and sent to the National Centers for Environmental Prediction and the Global Telecommunications System to be ingested into the Global Forecasting System, which serves as initial and boundary conditions for regional numerical models that also forecast tropical cyclone track and intensity. As a result of limited aircraft resources, optimal observing strategies for these missions are investigated. An Observing System Experiment in which different configurations of the dropwindsonde data based on three targeting techniques (ensemble variance, ensemble transform Kalman filter, and total energy singular vectors) are assimilated into the model system was conducted. All three techniques show some promise in obtaining maximal forecast improvements while limiting flight time and expendables. The data taken within and around the regions specified by the total energy singular vectors provide the largest forecast improvements, though the sample size is too small to make any operational recommendations. Case studies show that the impact of dropwindsonde data obtained either outside of fully sampled, or within nonfully sampled target regions is generally, though not always, small; this suggests that the techniques are able to discern in which regions extra observations will impact the particular forecast.

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Edward I. Tollerud, Brian Etherton, Zoltan Toth, Isidora Jankov, Tara L. Jensen, Huiling Yuan, Linda S. Wharton, Paula T. McCaslin, Eugene Mirvis, Bill Kuo, Barbara G. Brown, Louisa Nance, Steven E. Koch, and F. Anthony Eckel
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