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Since several years, the number of aerosol data coming from lidar has grown and improved in quality. These new datasets are providing a valuable information on the vertical distribution of aerosols which is missing in the AOD (Aerosol Optical Depth), which has been used so far in aerosols analysis. The launch of AEOLUS in 2018 has increased the interest in the assimilation of the aerosol lidar information. In parallel, the ground-based network EARLINET (European Aerosol Research LIdar NETwork) has grown to cover the Europe with good quality data. Assimilation of these data in the ECMWF/CAMS (European Centre for Medium-range Weather Forecasts / Copernicus Atmosphere Monitoring Service) system is expected to provide improvements in the aerosol analyses and forecasts.Three preliminary studies have been done in the past four years using AEOLUS data (A3S-ESA funded) and EARLINET data (ACTRIS-2 and EUNADIC-AV, EU-funded). These studies have allowed the full development of the tangent linear and adjoint code for lidar backscatter in the ECMWF's 4D-VAR system. These developments are now in the operational model version in research mode. The first results are promising and open the path to more intake of aerosol lidar data for assimilation purposes. The future launch of EARTHCARE (Earth-Cloud Aerosol and Radiation Explorer) and later ACCP (Aerosol Cloud, Convention and Precipitation) might even upgrade the use of aerosol lidar data in COMPO-IFS (Composition-Integrated Forecast system).The most recent results using AEOLUS data (for October 2019 and April 2020) and using EARLINET data (October 2020) will be shown in this presentation. The output will be compared to the CAMS operational aerosol forecast as well as to independent data from AERONET (AErosol Robotic NETwork).

Within the Atmospheric Dynamics Mission Aeolus (ADM-Aeolus), the European Space Agency (ESA) has approved a Doppler wind lidar (DWL) to fly on a dedicated platform orbiting dawn to dusk at 400 km altitude, planned for launch in 2008. Rigorous design trade-offs have resulted in a lidar concept capable of delivering high-quality wind component profiles, but with a limited coverage. A companion paper describes the realistic simulation of this DWL, whereas this paper sets out to assess the impact of such a lidar in meteorological analyses and forecasts. To this end, an Observing System Simulation Experiment (OSSE) is run. The superior conventional observation coverage of 1993 is used to simulate all conventional observations, although a limited set of satellite observations is simulated. As a consequence, only the northern hemisphere DWL impact in the OSSE is assumed realistic. Here, over a 15-day period with variable weather, out of 15 daily forecasts, 14 show beneficial impact of the DWL. Although the experiment is limited, it corroborates other practical and theoretical evidence that the ADM DWL will demonstrate a beneficial impact in meteorological analyses and forecasts. Copyright © 2006 Royal Meteorological Society

The directional wind turning across the boundary layer in short-range forecasts made as part of the recent ERA-40 reanalysis project is compared with radiosonde data from oceanic stations in the North Atlantic. Significant errors are identified, particularly in cases of warm advection in which it is found that the observed wind turning is systematically underestimated. Similar problems are found in the higher-resolution operational forecasts, and also in forecasts made with the independent Met Office model. Furthermore, comparisons of forecast wind direction with scatterometer data give consistent results over the North Atlantic and indicate that the same problems are present over other oceans. A large fraction of the cases with the largest errors are shown to have stable boundary layers. In these cases the modelled boundary layers are typically too deep, and sensitivity tests have shown that more realistic structures are obtained by switching to a stable boundary-layer parametrization based on Monin–Obukhov similarity. Some possible problems with the convective boundary layer parametrization in highly baroclinic conditions are also identified. Copyright © 2005 Royal Meteorological Society

In the first of this set of three papers, the formulation of the European Centre for Medium-Range Weather Forecasts (ECMWF) implementation of 3D-Var is described. In the second, the specification of the structure function is presented, and the last is devoted to the results of the extensive numerical experimentation programme which was conducted. The 3D-Var formulation uses a spherical-harmonic expansion, much as the ECMWF optimal interpolation (OI) scheme used an expansion of Bessel functions. This formulation is introduced using a convolution algebra over the sphere expressed directly in spectral space. It is shown that all features of the OI statistical model can be implemented within 3D-Var. Furthermore, a non-separable statistical model is described. In the present formulation, geostrophy is accounted for through a Hough-modes separation of the gravity and Rossby components of the analysis increments. As in OI, the tropical analysis remains essentially non-divergent and with a weak mass-wind coupling. The observations used, as well as their specified statistics of errors, are presented, together with some implementation details. In the light of the results, 3D-Var was implemented operationally at the end of January 1996.

The aim of this study is to make a strict comparison between two assimilation algorithms, sequential and four-dimensional variational, on a 24-hour period extracted from a baroclinic instability situation representative of mid-latitude dynamics. In the case of linear dynamics, and under the hypothesis of a perfect model, these two four-dimensional algorithms are known to lead to the same optimal estimate of the atmosphere at the end of the assimilation period, and both methods can be generalized in the nonlinear case. Because the full sequential algorithm is too resource-demanding to be implemented as such, we shall test the four-dimensional variational method (4D-VAR), and a simplified sequential method based on three-dimensional variational analysis (3D-VAR), deliberately not exceeding the range of validity of the tangent-linear model in the experiments. 4D-VAR is then expected to be almost equivalent to the generalization of the sequential Kalman filter in the nonlinear case, i.e. the extended Kalman filter. As for the simplified sequential algorithm, it can be seen as an approximation of this full extended Kalman filter, for which the forecast error matrices are evaluated only approximately before each analysis, instead of being explicitly computed from the complete dynamical equations. In the four-dimensional variational scheme, the consistency of the propagation of information with the dynamics is illustrated in an experiment assimilating some localized AIREP data. The large impact which these additional observations have over a large geographical area appears to be very beneficial for the quality of the analysis. Comparing the results of both methods in various configurations, we found that 4D-VAR systematically behaved substantially better than the simplified sequential algorithm, and had a more accurate analysis at the end of the assimilation period and a much smaller error growth rate in subsequent forecasts. On the one hand, extremely bad specifications of initial forecast errors were found to be detrimental to both algorithms. On the other hand, the four-dimensional variational algorithm proves to be more robust to the way by which gravity-wave control is implemented.

The paper describes a four-dimensional variational assimilation using a multilevel global primitive-equation spectral model. The experiments consist in minimizing the distance between the model solution and conventional observations spread over 24 hours. A first set of experiments was performed at low resolution (spectral truncation T21). The model solution converges to fit the observations satisfactorily over the whole assimilation period. The meteorological quality of the analyses, in terms of the large-scale part of the flow, is comparable with the ECMWF operational analysis. Confirming early results, the control of gravity waves in the solution can be easily handled using a combination of a penalty term and a normal-mode initialization scheme within the forward-backward process of the variational assimilation. Experiments excluding data over an area with a strong baroclinic development show that the information contained in the dynamics of the model is used successfully in the analysis over this area. In addition, the dynamics are able to inter implicitly flow-dependent structure functions. Higher-resolution experiments (spectral truncation T42) were performed and compared with optimum interpolation (OI) analyses performed under the same conditions. Although the lack of a first-guess term in the cost function leads to small-scale noise generation if the minimization is pursued, the quality of the variational assimilation is comparable with OI, and the error growth of the following 24-hour forecast is smaller when performed from the variational analysis. Moreover, experiments excluding wind data from aircraft show a clear advantage for the variational approach against OI in using, in a consistent way, the information coming from the model and the observations.

Titre traduit en français : "Correction de biais des mesures d'humidité réalisées par radiosonde au-dessus de l'Afrique de l'Ouest en vue des réanalyses effectuées au CEPMMT spécialement pour la campagne AMMA"; International audience; During the African Monsoon Multidisciplinary Analysis (AMMA) field experiment in 2006 there was a large increase in the number of radiosonde data over West Africa. This has the potential of improving the numerical weather prediction (NWP) analysis/forecast and the water budget studies over that region. However, it is well known that the humidity from radiosondes can have some errors depending on sonde type, relative humidity (RH), temperature and the age of the sensor and can give rise to dry biases that are typically between 5% and 30% for RH. Three main sonde types were used in the AMMA field experiment: Vaisala RS80A, Vaisala RS92 and MODEM. In this article, a new empirical method is presented by using the operational European Centre for Medium-Range Weather Forecasts (ECMWF) short-range forecast as an intermediary dataset for computing biases. The validation of the correction method using global positioning system (GPS) total columnar water vapour (TCWV) confirms that the method is able to correct for a large part of the dry biases associated with the different sonde types. Results from analysis experiments show how the correction of humidity is particularly important in the West African region due to its impact on the development of convection in NWP models. The proposed radiosonde humidity bias correction has been applied to the special AMMA reanalysis experiment performed at ECMWF for the 2006 West African wet monsoon season. This is expected to benefit a wide number of AMMA-related studies that make use of the reanalysis, in particular those focusing on the water cycle. Copyright © 2009 Royal Meteorological Society

Snow water equivalent and snow extent are key parameters for the earth's energy and water budget. In this study, the current operational snow-depth analysis (2D spatial Cressman interpolation) at the European Centre for Medium-Range Weather Forecasts (ECMWF), which relies on real-time observations of snow depth, the short-range forecast, and snow-depth climatic data, is presented. The operational product is compared with satellite-derived snow cover. It is found that the total area of grid boxes affected by snow is approximately 10% larger in the analysis than in the National Oceanic and Atmospheric Administration National Environmental Satellite, Data, and Information Service (NOAA/NESDIS) snow-extent product. The differences are persistent in time and space and cover the entire Northern Hemisphere. They comprise areas with intermittent and/or patchy snow cover, for example, the Tibetan Plateau, the edges of snow fields, and areas with a low density of observations, which are difficult to captur...

SUMMARY In this third and final paper of a series, we assess the performance of the three-dimensional variational data assimilation scheme, in the light of the results from the extensive pre-operational programme of numerical experimentation. Its performance is compared with that of the previous operational scheme at the European Centre for Medium-Range Weather Forecasts, which was based on Optimal Interpolation. The main features of the new scheme are illustrated, in particular the effects of non-separable structure functions and the improved data usage. TIROS-N Operational Vertical Sounder cloud-cleared radiances, for example, are used directly without a separate retrieval step. Scatterometer data are assimilated in the form of ambiguous winds with the ambiguity removal taking place within the analysis itself. Problems encountered during the tests are discussed and the solutions implemented are explained. The overall impact on forecast accuracy in the troposphere of the northern hemisphere extratropics is neutral for geopotential and positive for wind and temperature. The impact is neutral in the tropics, and significantly positive in the southern hemisphere. Analyses and forecasts for the stratosphere have improved in all regions. Other positive results include a clear improvement in analyses of near-surface winds over oceans, particularly in the vicinity of tropical storms. This is predominantly because of the assimilation of scatterometer wind-data.