The ECMWF implementation of three‐dimensional variational assimilation (3D‐Var). II: Structure functions

Observations providing three-dimensional information on clouds from space-borne active instruments on board CloudSat and CALIPSO are already available and new satellites, such as EarthCARE, should appear in the near future. This opens new possibilities for exploring the usefulness of this kind of observation, not only for improving model parametrizations but also for investigating their usage in data assimilation to extract information from the data so as to improve the initial atmospheric state. In this study, a 1D + 4D-Var technique has been selected to study the impact of observations related to clouds on 4D-Var analyses and subsequent forecasts. Using this two-step approach, temperature and specific humidity profiles retrieved from 1D-Var assimilation of CloudSat observations have been included in the 4D-Var system. Several experiments have been run for a couple of selected meteorological situations.

Section: Background-error Statisticsmentioning

confidence: 99%

Experimental 1D + 4D‐Var assimilation of CloudSat observations

Janisková

Lopez

Bauer

2011

“…We will also briefly present the changes to the IFS for the implementation of aerosol forecasts, instrumental to the creation of the background statistics presented here. The problem of defining appropriate background statistics is central to any data assimilation technique, and many authors recognise the importance of a correct definition of these statistics (Fisher, 2003;Rabier et al, 1998). At the core of the variational assimilation is the minimisation of a cost function defined as the sum of the quadratic distance between the observations and their model counterpart, and of the background term, weighted by their respective error covariance matrices.…”

Section: Introductionmentioning

confidence: 99%

Background error statistics for aerosols

Benedetti

Fisher

2007

As part of the European initiative for Global Monitoring for Environment and Security (GMES), the Global and regional Earth-system Monitoring using Satellite and in situ data (GEMS) project has been funded to provide forecasts and analysis of atmospheric constituents such as aerosols, and greenhouse and reactive gases. In this context, the development of an aerosol analysis system based on a four-dimensional variational (4D-Var) assimilation was started in March 2005 at the European Centre for Medium-Range Weather Forecasts (ECMWF). As part of the main building blocks of the system, an error background covariance matrix for aerosol mixing ratio was built based on background statistics constructed from aerosol forecasts. These statistics were generated from the differences between the 48-hour and 24-hour forecasts of aerosol mixing ratios for sea salt, desert dust and continental particulate, using the NMC method. This method has been widely used by many numerical weather prediction centres to build background error statistics for state variables. This is the first time, to our knowledge, that it has been applied to a novel variable such as an aerosol mixing ratio, in the context of a global model. Aerosol assimilation systems have been successfully run with either observationally-derived or model-prescribed error covariance matrices with pre-assigned correlation lengths. In this study, the aerosol vertical and horizontal structures of these error correlations as derived with the aid of a state-of-the-art model are investigated and discussed in depth. Successively these error correlations are modelled through a spectral/wavelet approach and used to construct a background error covariance matrix. Application of this background matrix in the context of a single observation 4D-Var experiment produced successful preliminary analyses of the total aerosol mixing ratio.

“…For instance, the impacts of the parametrization of the variance as a function of the background state on the one hand, and of the symmetrization transform on the other hand, could be evaluated separately. Moreover, it would be interesting to compare the variance parametrization as a function of background relative humidity and temperature with variance estimates either based on Rabier et al (1998) or derived from a real-time ensemble. The nonlinearity implied by the symmetrization transform also deserves further studies in terms of advantages but also potential difficulties.…”

Section: Discussionmentioning

confidence: 99%

“…The model grid mesh consisted of 306 × 306 horizontal grid points at 22 km grid spacing and 40 levels. Background-error variances of q are specified as static and homogeneous in the reference HIRLAM system, although a flow-dependent variance specification could be considered for q, following Rabier et al (1998). For radiosonde moisture observations, an empirical regression relation is used to obtain the standard deviations of relative humidity observation errors, with a dependency on the background temperature T b :…”

Section: Model and Experimental Set-upmentioning

confidence: 99%

“…Since most atmospheric models use specific humidity or the mixing ratio to represent the atmospheric moisture content, several modelling groups have chosen to use these model variables also for the analysis. In order to deal with the spatial variability of moisture forecast errors, Rabier et al (1998) introduced empirical models for the moisture background-error standard deviations, depending on the local background temperature and relative humidity fields. Other groups have chosen to use relative humidity as the analysis moisture variable (Hólm et al 2002), thereby solving some of these variability problems.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Use of a nonlinear pseudo‐relative humidity variable in a multivariate formulation of moisture analysis

Gustafsson

Þorsteinsson

Stengel

et al. 2011

We present a reformulation of the humidity part of the HIRLAM (HIgh-Resolution Limited-Area Model) variational data assimilation. The purpose is to rectify some of the shortcomings of the present formulation which uses specific humidity, q, as an assimilation control variable with homogeneous and static covariances. One problem is that specific humidity forecast errors tend to have a non-Gaussian probability distribution, in particular near saturation and near zero humidity. In addition, the variance of the distribution tends to change in space and time due to the dependency of the water vapour saturation pressure on temperature. A modified pseudo-relative humidity variable has been adapted to the statistical balance background constraint, including the associated moisture balance formulation. Background-error statistics for the new moisture control variable and the moisture-related balances were derived, taking differences between forecasts valid at the same time as a proxy for background forecast errors. The background-error statistics were compared with the corresponding statistics for specific humidity as the moisture assimilation control variable. In connection with the nonlinearity of the change of the variable, it was noted that specified background-error standard deviations were chosen to be substantially reduced for nearly dry and saturated states, which can raise difficulties. The impact of the new moisture assimilation control variable is illustrated with simulated observation experiments as well as data assimilation experiments using real observations, for one summer month and one winter month in a 4D-Var assimilation cycle using two outer