Thomas Auligné

The Weather Research and Forecasting Model's Community Variational/Ensemble Data Assimilation System: WRFDA

Data assimilation is the process by which observations are combined with short-range NWP model output to produce an analysis of the state of the atmosphere at a specified time. Since its inception in the late 1990s, the multiagency Weather Research and Forecasting (WRF) model effort has had a strong data assimilation component, dedicating two working groups to the subject. This article documents the history of the WRF data assimilation effort, and discusses the challenges associated with balancing academic, research, and operational data assimilation requirements in the context of the WRF effort to date. The WRF Models Community Variational/Ensemble Data Assimilation System (WRFDA) has evolved over the past 10 years, and has resulted in over 30 refereed publications to date, as well as implementation in a wide range of real-time and operational NWP systems. This paper provides an overview of the scientific capabilities of WRFDA, and together with results from sample operation implementations at the U.S. ...

Radar Data Assimilation with WRF 4D-Var. Part I: System Development and Preliminary Testing

AbstractThe major goal of this two-part study is to assimilate radar data into the high-resolution Advanced Research Weather Research and Forecasting Model (ARW-WRF) for the improvement of short-term quantitative precipitation forecasting (QPF) using a four-dimensional variational data assimilation (4D-Var) technique. In Part I the development of a radar data assimilation scheme within the WRF 4D-Var system (WRF 4D-Var) and the preliminary testing of the scheme are described. In Part II the performance of the enhanced WRF 4D-Var system is examined by comparing it with the three-dimensional variational data assimilation system (WRF 3D-Var) for a convective system over the U.S. Great Plains. The WRF 4D-Var radar data assimilation system has been developed with the existing framework of an incremental formulation. The new development for radar data assimilation includes the tangent-linear and adjoint models of a Kessler warm-rain microphysics scheme and the new control variables of cloud water, rainwater, an...

Assimilating Satellite Observations of Clouds and Precipitation into NWP Models

what: Sixty-five experts in numerical weather prediction (NWP) and remote sensing were invited to document progress in cloud and precipitation data assimilation and to recommend pathways for future research and development. when: 15–17 June 2010 where: Reading, United Kingdom S atellite observations in the visible, infrared, and microwave spectrum provide a great deal of information on clouds and precipitation as well as the atmosphere in which the clouds are embedded. A major issue is how to use this information to initialize cloudy and precipitating atmospheric regions in NWP models. Most cloudand/or rain-affected observations are discarded in current data assimilation systems. The major problems are the discontinuous nature, in time and space, of clouds and precipitation, the complex nonlinear and not-well-modeled processes involved in their formation/prediction, and the need for current data assimilation systems to use linearized versions of these nonlinear processes. As a result, cloud/rain-affected radiances are much more difficult to assimilate than clear-sky radiances, which are sensitive to the smoother fields of temperature and water vapor that are controlled by more linear, wellmodeled processes. Since clouds and precipitation often occur in sensitive regions in terms of forecast impact, improvements in their assimilation are likely necessary for continuing significant gains in weather forecasting and, in particular, the prediction of two key weather elements affecting human activities: precipitation and cloudiness (which impacts another key weather factor, surface temperature). In 2005, the National Aeronautics and Space Administration (NASA)–National Oceanic and Atmospheric Administration (NOAA)–Department of Defense (DoD) Joint Center for Satellite Data Assimilation (JCSDA) sponsored an international workshop on assimilating observations in cloudy/ precipitating regions. Papers from that workshop were published in a special section of the November 2007 issue of the Journal of the Atmospheric Sciences AFFILIATIONS: bauer—European Center for Medium Range Weather Forecasting, Reading, United Kingdom; ohrinG—NOAA, Camp Springs, Maryland; Kummerow—Colorado State University, Fort Collins, Colorado; auliGne—University Corporation for Atmospheric Research, Boulder, Colorado CORRESPONDING AUTHOR: George Ohring, NOAA/NESDIS, 5200 Auth Rd., MD 20746 E-mail: george.ohring@noaa.gov

Heterogeneous Convective-Scale Background Error Covariances with the Inclusion of Hydrometeor Variables

Convective-scale models used in NWP nowadays include detailed realistic parameterization for the representation of cloud and precipitation processes. Yet they still lack advanced data assimilation schemes able to efficiently use observations to initialize hydrometeor fields. This challenging task may benefit from a better understanding of the statistical structure of background errors in precipitating areas for both traditional and hydrometeor variables, which is the goal of this study. A special binning has been devised to compute separate background error covariance matrices for precipitating and nonprecipitating areas. This binning is based on bidimensional geographical masks defined by the vertical averaged rain content of the background error perturbations. The sample for computing the covariances is taken from an ensemble of short range forecasts run at 3-km resolution for the prediction of two specific cases of convective storms over the United States. The covariance matrices and associated diagnostics are built on the control variable transform formulation typical of variational data assimilation. The comparison especially highlights the strong coupling of specific humidity, cloud, and rain content with divergence. Shorter horizontal correlations have been obtained in precipitating areas. Vertical correlations mostly reflect the cloud vertical extension due to the convective processes. The statistics for hydrometeor variables show physically meaningful autocovariances and statistical couplings with other variables. Issues for data assimilation of radar reflectivity or more generally of observations linked to cloud and rain content with this kind of background error matrix formulation are thereon briefly discussed.

Inhomogeneous Background Error Modeling and Estimation over Antarctica

Abstract The structure of the analysis increments in a variational data assimilation scheme is strongly driven by the formulation of the background error covariance matrix, especially in data-sparse areas such as the Antarctic region. The gridpoint background error modeling in this study makes use of regression-based balance operators between variables, empirical orthogonal function decomposition to define the vertical correlations, gridpoint variances, and high-order efficient recursive filters to impose horizontal correlations. A particularity is that the regression operators and the recursive filters have been made spatially inhomogeneous. The computation of the background error statistics is performed with the Weather Research and Forecast (WRF) model from a set of forecast differences. The mesoscale limited-area domains of interest cover Antarctica. Inhomogeneities of background errors are shown to be related to the particular orography and physics of the area. Differences seem particularly pronounce...

Impact of Microphysics Scheme Complexity on the Propagation of Initial Perturbations

AbstractThe study of evolution characteristics of initial perturbations is an important subject in four-dimensional variational data assimilation (4DVAR) and mesoscale predictability research. This paper evaluates the impact of microphysical scheme complexity on the propagation of the perturbations in initial conditions for warm-season convections over the central United States. The Weather Research and Forecasting Model (WRF), in conjunction with four schemes of the Morrison microphysics parameterization with varying complexity, was used to simulate convective cases using grids nested to 5-km horizontal grid spacing. Results indicate that, on average, the four schemes show similar perturbation evolution in amplitude and spatial pattern during the first 2 h. After that, the simplified schemes introduce significant error in amplitude and spatial pattern. The simplest (liquid only) and most complex schemes show almost the same growth rate of initial perturbations with different amplitudes during 6-h forecas...

Toward a new cloud analysis and prediction system

What: Reviewing current and recent cloud analysis efforts and evaluating the state of the science, synergies, and collaborations in modeling clouds, 40 experts in cloud modeling, observations, and data assimilation met to move decisively toward a realization of cloud analysis systems for operational use. When: 1–3 September 2009 Where: Boulder, Colorado TOWARD A NEW CLOUD ANALYSIS AND PREDICTION SYSTEM

Adjoint-Derived Observation Impact Using WRF in the Western North Pacific

AbstractAn increasing number of observations have contributed to the performance of numerical weather prediction systems. Accordingly, it is important to evaluate the impact of these observations on forecast accuracy. While the observing system experiment (OSE) requires considerable computational resources, the adjoint-derived method can evaluate the impact of all observational components at a lower cost. In this study, the effect of observations on forecasts is evaluated by the adjoint-derived method using the Weather Research and Forecasting Model, its adjoint model, and a corresponding three-dimensional variational data assimilation system in East Asia and the western North Pacific for the 2008 typhoon season. Radiance observations had the greatest total impact on forecasts, but conventional wind observations had the greatest impact per observation. For each observation type, the total impact was greatest for radiosonde and each Advanced Microwave Sounding Unit (AMSU)-A satellite, followed by surface s...

Multivariate Minimum Residual Method for Cloud Retrieval. Part I: Theoretical Aspects and Simulated Observation Experiments

AbstractA new method is presented for cloud detection and the retrieval of three-dimensional cloud fraction from satellite infrared radiances. This method, called multivariate minimum residual (MMR), is inspired by the minimum residual technique by Eyre and Menzel and is especially suitable for exploiting the large number of channels from hyperspectral infrared sounders. Its accuracy is studied in a theoretical framework where the observations and the numerical model are supposed perfect. Of particular interest is the number of independent information that can be found on the cloud according to the number of channels used. The technical implementation of the method is also briefly discussed. The MMR scheme is validated with the Atmospheric Infrared Sounder (AIRS) instrument using simulated observations. This new method is compared with the cloud-detection scheme from McNally and Watts that is operational at the European Centre for Medium-Range Weather Forecasts (ECMWF) and considered to be the state of th...

Optimized Localization and Hybridization to Filter Ensemble-Based Covariances

AbstractLocalization and hybridization are two methods used in ensemble data assimilation to improve the accuracy of sample covariances. It is shown in this paper that it is beneficial to consider them jointly in the framework of linear filtering of sample covariances. Following previous work on localization, an objective method is provided to optimize both localization and hybridization coefficients simultaneously. Theoretical and experimental evidence shows that if optimal weights are used, localized-hybridized sample covariances are always more accurate than their localized-only counterparts, whatever the static covariance matrix specified for the hybridization. Experimental results obtained using a 1000-member ensemble as a reference show that the method developed in this paper can efficiently provide localization and hybridization coefficients consistent with the variable, vertical level, and ensemble size. Spatially heterogeneous optimization is shown to improve the accuracy of the filtered covarian...