Jean-Noël Thépaut

ERA-20C: An Atmospheric Reanalysis of the Twentieth Century

AbstractThe ECMWF twentieth century reanalysis (ERA-20C; 1900–2010) assimilates surface pressure and marine wind observations. The reanalysis is single-member, and the background errors are spatiotemporally varying, derived from an ensemble. The atmospheric general circulation model uses the same configuration as the control member of the ERA-20CM ensemble, forced by observationally based analyses of sea surface temperature, sea ice cover, atmospheric composition changes, and solar forcing. The resulting climate trend estimations resemble ERA-20CM for temperature and the water cycle. The ERA-20C water cycle features stable precipitation minus evaporation global averages and no spurious jumps or trends. The assimilation of observations adds realism on synoptic time scales as compared to ERA-20CM in regions that are sufficiently well observed. Comparing to nighttime ship observations, ERA-20C air temperatures are 1 K colder. Generally, the synoptic quality of the product and the agreement in terms of climat...

ASSIMILATION AND MODELING OF THE ATMOSPHERIC HYDROLOGICAL CYCLE IN THE ECMWF FORECASTING SYSTEM

Several new types of satellite instrument will provide improved measurements of Earths hydrological cycle and the humidity of the atmosphere. In an effort to make the best possible use of these data, the modeling and assimilation of humidity, clouds, and precipitation are currently the subjects of a comprehensive research program at the European Centre for Medium-Range Weather Forecasts (ECMWF). Impacts on weather prediction and climate reanalysis can be expected. The preparations for cloud and rain assimilation within ECMWFs four-dimensional variational data assimilation system include the development of linearized moist physics, the development of fast radiative transfer codes for cloudy and precipitating conditions, and a reformulation of the humidity analysis scheme. Results of model validations against in situ moisture data are presented, indicating generally good agreement—often to within the absolute calibration accuracy of the measurements. Evidence is also presented of shortcomings in ECMWFs h...

Impact of the Digital Filter as a Weak Constraint in the Preoperational 4DVAR Assimilation System of Météo-France

In this paper, a weak constraint formulation of the digital filter based on the Dolph–Chebyshev window is introduced in a preoperational version of the 4DVAR analysis of Meteo-France. The constraint is imposed only on the analysis increments to damp spurious fast oscillations associated with gravity–inertia waves. In the incremental formulation of 4DVAR, the analysis increments are obtained from a global model at a uniform low resolution with a simplified set of physical parameterizations, while the high-resolution forecast is obtained with a model that uses a variable-resolution grid having a higher resolution over France and the complete set of physical parameterizations. Both models have the same vertical resolution. In a set of preliminary experiments using the same background field and the same set of observations, it is shown that the weak constraint imposed only on the low-resolution increments manages to control efficiently the emergence of fast oscillations in the resulting high-resolution forecast while maintaining a closer fit to the observations than is possible if the digital filter initialization is applied externally on the final analysis increments. It is also shown that this weak constraint does not add any significant computer cost to the 4DVAR analysis. Finally, 4DVAR has been cycled over a period of 2 weeks and the results show that, compared to 3DVAR, the initial dynamical imbalances are significantly less in 4DVAR even if no constraint is imposed at all. However, it has been noted that the innovation statistics show a positive impact when a constraint is applied.

Simplified and Regular Physical Parameterizations for Incremental Four-Dimensional Variational Assimilation

Abstract A set of physical parameterizations has been developed for inclusion in incremental four-dimensional variational assimilation (4D-Var). The goal for this physical package is that it be simple, regular (for the efficiency of the minimization in 4D-Var), and at the same time realistic enough. The package contains a simplified computation of radiative fluxes, vertical turbulent diffusion, orographic gravity waves, deep convection, and stratiform precipitation fluxes. Its tangent-linear and adjoint counterparts have also been developed. The validations of the simplified physical parameterizations and of the tangent-linear model with those included have been done. The importance of regularization (removing some thresholds in physical parameterizations that can affect the range of validity of the tangent-linear approximation), which arises during validation of the tangent-linear model, is assessed.

Toward a Consistent Reanalysis of the Climate System

This article reviews past and current reanalysis activities at the European Centre for Medium-Range Weather Forecasts (ECMWF) and describes plans for developing future reanalyses of the coupled climate system. Global reanalyses of the atmosphere, ocean, land surface, and atmospheric composition have played an important role in improving and extending the capabilities of ECMWFs operational forecasting systems. The potential role of reanalysis in support of climate change services in Europe is driving several interesting new developments. These include the production of reanalyses that span a century or more and the implementation of a coupled data assimilation capability suitable for climate reanalysis. Although based largely on ECMWFs achievements, capabilities, and plans, the article serves more generally to provide a review of pertinent issues affecting past and current reanalyses and a discussion of the major challenges in moving to more fully coupled systems.

Interactions of dynamics and observations in a four-dimensional variational assimilation

Abstract A four-dimensional (4D) variational assimilation (4DVAR) seeks an optimal balance between observations scattered in time and space over a finite 4D analysis volume and a priori information. In some cases, 4DVAR is able to closely fit both observations and the a priori initial estimate by making very small changes to the initial conditions that correspond to those rapidly growing perturbations that have large amplitude at the observation locations and times. Some observations may occur at locations and times for which the amplitudes of rapidly growing perturbations are not large. To fit such data, larger changes to the initial conditions are necessary. Such cases may result in amplification of the analysis increments away from the observation locations. This situation occurs generally for surface data, because of the damping effect of surface exchange processes. These interactions are seen in experiments using single observations. To further explore the impact of surface data in 4DVAR, experiments...

The Spatial Structure of Observation Errors in Atmospheric Motion Vectors from Geostationary Satellite Data

Abstract This study investigates and quantifies in detail the spatial correlations of random errors in atmospheric motion vectors (AMVs) derived by tracking structures in imagery from geostationary satellites. A good specification of the observation error is essential to assimilate any kind of observation for numerical weather prediction in a near-optimal way. For AMVs, height assignment, tracking of similar cloud structures, or quality control procedures may introduce spatially correlated errors. The spatial structure of the error correlations is investigated based on a 1-yr dataset of pairs of collocations between AMVs and radiosonde observations. Assuming spatially uncorrelated sonde errors, the spatial AMV error correlations are obtained over dense sonde networks. Results for operational infrared and water vapor wind datasets from Meteosat-5 and -7, Geostationary Operational Environmental Satellite-8 and -10 (GOES-8 and -10), and Geostationary Meteorological Satellite-5 (GMS-5) are presented. Winds fr...

Estimating atmospheric CO2 from advanced infrared satellite radiances within an operational 4D‐Var data assimilation system: Methodology and first results

[1] Atmospheric CO2 concentrations have been obtained from the Atmospheric Infrared Sounder (AIRS) radiance data within the European Centre for Medium-Range Weather Forecasts data assimilation system. A subset of channels from the AIRS instrument on board the NASA Aqua platform has been assimilated providing estimates of tropospheric and stratospheric column-average CO2 mixing ratios. Although global estimates are obtained, the information content of the tropospheric estimates at middle and high latitudes is limited, and results are therefore only presented for the tropical region. First results for February and August 2003 show considerable geographical variability compared to the background with values ranging between 371 and 380 ppmv. These CO2 values are representative for a layer between the tropopause and about 600 hPa. The monthly mean random error is about 1%. Careful error analysis has been carried out to minimize any systematic errors. This study has demonstrated the feasibility of global CO2 estimation using AIRS data in a numerical weather prediction data assimilation system. In the future the system will be improved to treat CO2 as a full three-dimensional atmospheric variable, including transport. INDEX TERMS: 3337 Meteorology and Atmospheric Dynamics: Numerical modeling and data assimilation; 0365 Atmospheric Composition and Structure: Troposphere—composition and chemistry; 1610 Global Change: Atmosphere (0315, 0325); KEYWORDS: AIRS, carbon dioxide, data assimilation

The Concordiasi Project in Antarctica

The Concordiasi project is making innovative observations of the atmosphere above Antarctica. The most important goals of the Concordiasi are as follows: To enhance the accuracy of weather prediction and climate records in Antarctica through the assimilation of in situ and satellite data, with an emphasis on data provided by hyperspectral infrared sounders. The focus is on clouds, precipitation, and the mass budget of the ice sheets. The improvements in dynamical model analyses and forecasts will be used in chemical-transport models that describe the links between the polar vortex dynamics and ozone depletion, and to advance the under understanding of the Earth system by examining the interactions between Antarctica and lower latitudes. To improve our understanding of microphysical and dynamical processes controlling the polar ozone, by providing the first quasi-Lagrangian observations of stratospheric ozone and particles, in addition to an improved characterization of the 3D polar vortex dynamics. Techni...

A Comparison of Variational and Ensemble-Based Data Assimilation Systems for Reanalysis of Sparse Observations

Abstract An observing system experiment, simulating a surface-only observing network representative of the 1930s, is carried out with three- and four-dimensional variational data assimilation systems (3D-VAR and 4D-VAR) and an ensemble-based data assimilation system (EnsDA). It is found that 4D-VAR and EnsDA systems produce analyses of comparable quality and that both are much more accurate than the analyses produced by the 3D-VAR system. The EnsDA system also produces useful estimates of analysis error, which are not directly available from the variational systems.