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ECMWF recent improvements on scientific and technological fronts will be presented. In 2021 two new operational upgrades of the Integrated Forecasting System (IFS), cycles 47r2 and 47r3, have been introduced. In 2022 the ECMWF High-Performance Computing (HPC) facility has migrated from Reading, UK to a new data centre in Bologna, Italy, and on 18 October 2022 the operational system has been ported to a new supercomputer with enhanced capacity, that will pave the way for an increase in resolution in 2023 with the implementation of IFS cycle 48r1.IFS Cycle 47r2 was first introduced on 11 May 2021 and its key features included changing the vertical resolution of the Ensemble forecast system (ENS) from 91 to 137 levels, the same used in the high-resolution forecast (HRES). This was made possible by introducing single precision arithmetic in both the HRES and ENS forecast systems. The single precision itself is neutral but enabled the ENS change which led to significant forecast skill improvement. Five months later, ECMWF introduced Cycle 47r3 operationally on 12 October 2021. This included major changes to the model physics that had been under development for several years. A more consistent formulation of boundary layer turbulence, new deep convection closure and cloud microphysics changes have increased the realism of the water cycle.The next science upgrade, cycle 48r1, will be implemented in 2023 on our new HPC system in Bologna. This will see an enhancement of the ENS horizontal resolution to the TCo1279 grid (approximately 9km), the same resolution currently used by the HRES. There will also be an increase of the data assimilation resolution used in the incremental 4D-Var minimisation, and the use a new object orientated approach to run the 4D-Var atmospheric data assimilation (OOPS). Other important changes in 48r1 include running a daily 100 members extended range ensembles, introducing a new multi-layer snowpack model, and improving the atmospheric energy and water conservation.Looking further ahead, future higher resolution capabilities will be accelerated by the digital twin developments under the European Commission Destination Earth programme, which will build km-scale capability for a range of potential future HPC architectures. Major efforts have been invested in the code scalability of the Integrated Forecasting System to be able to run on GPUs and investigating alternative dynamical core options. Data assimilation will evolve towards a fully coupled approach to maximise the exploitation of observations and benefit all components of the Earth system (atmosphere, land, ocean) in a consistent way. Machine Learning (ML) will be exploited to enhance the performance and efficiency of our systems. Finally, our Copernicus partnership with the European Commission has just entered its second phase. Synergistic interactions between meteorology and composition will be pursued for the mutual benefit of both and preparatory steps for next ECMWF climate reanalysis, ERA6, and new seasonal forecasting system, SEAS6, have already started. Several major upgrades in ERA6 and SEAS6 will aim at mitigating against systematic model biases to produce climate records with significantly improved time consistency, and enhanced reliability for extended-range predictions.

ECMWF is both a research institute and a 24/7 operational service centre, producing global numerical weather predictions and other data for European Members and Co-operating States as well as the broader meteorological and environmental community. ECMWF develops and operates a global Earth-System model and cutting-edge data assimilation system. Its ensemble forecasts extend from the medium range to the seasonal scales. ECMWF also operates two of the European Union’s Copernicus Earth observation programme services, the Atmosphere Monitoring (CAMS) and the Climate Change (C3S) Services, and contributes to the Copernicus Emergency Management Service (CEMS) as computational centre for the European Flood Awareness System (EFAS) and the Forest fire and wildfire information system (FIRE). Finally, ECMWF partners with ESA and EUMETSAT to deliver the European Union’s Destination Earth project, creating digital twins of the Earth.The science required to make ECMWF’s vision of accurate weather forecasting, reliable data on climate change and usable information about the quality of the air we breathe, the risk of flooding or of forest fires relies on the best science in the world, a science which is developed by our scientists working in close partnership with scientists in all our member states. This collaboration takes place primarily within the national meteorological services, and through academic partnerships which we are developing around the world.Another key aspect of our scientific developments is that they do not happen in silos, they rely on computing science and computing technology. This has always been the case at ECMWF, but the past decade has seen an increased role within ECMWF for computing science and more importantly for a closer relationship between environmental and computing sciences which now work hand in hand. Our experts work with the industry to customise the machines so that they best suit our purposes. For example, a partnership has been established by the creation of a Centre of Excellence between the ECMWF and the company Atos and partners, with joint projects on optimizing the NWP code, using graphics processing units (GPU) and AI.The interaction between ECMWF and its Member States is of prime importance. An example of linking with a community of meteorological services is the fact that ECMWF is supporting and co-developing the South‐East European Multi‐Hazard Early Warning Advisory System (SEE‐MHEWS‐A) project initiated by WMO in 2016 to strengthen the existing early warning capacities in South-East Europe. In the pilot phase of the project, ECMWF contributions to the operational work of several Members and Co‐operating States was instrumental, and it continues to be so in the operationalization phase.Through these various partnerships and collaborations, ECMWF is able to make the most of the expertise available in different communities for the benefit of all.

2021 was a standout year for ECMWF in that not one, but two major upgrades were made to the operational NWP system.Cycle 47r2 (introduced on 11 May) increased the ensemble forecast (ENS) vertical resolution from 91 to 137 levels, bringing it into line with the high-resolution forecast (HRES). The cost of this, which is significant, was offset by running the forecast model in single precision which saved equivalent cost and is meteorologically neutral. Overall validation showed statistically significant skill improvements by the ENS forecasts, for many fields, mostly in the range 0.5-2% RMS error reduction. It also showed improvements for specific meteorological phenomena (e.g. Tropical Cyclones, Madden-Julien Oscillation).Cycle 47r3 (introduced on 12 October) contained model, assimilation and observation usage changes. A major change, and the result of many years of research, was a complete new moist physics package. This brings significant meteorological benefit, and it this aspect users of ECMWF forecasts will see, but it also simplifies and modernizes the physics code in the IFS, and this will facilitate future improvements. This physics package includes too many changes to list here, but includes a more consistent formulation of boundary layer turbulence, shallow convection and sub-grid cloud and a new parametrized deep convection closure with an additional dependence on total advective moisture convergence. On the observation and data assimilation side the new weak constraint 4D-Var approach was applied in the Ensemble of Data Assimilations, and the all-sky observation assimilation approach was extended to a temperature sounder for the first time (AMSU-A), as well as a major update in the radiative transfer model for observation assimilation.Cycle 47r3 validation showed significant improvements. For example, extratropical upper-air geopotential and wind in the first few days of the forecast improved by 1-2% and tropical upper-air winds throughout the medium-range improved by 1-4%. Also, tropical cyclone track errors have been reduced by 10%.Cycle 47r3 is now being ported to the new ATOS HPC in the new ECMWF data centre in Bologna. Following the migration, the first science upgrade will be Cycle 48r1 and will contain some very important changes. The most important from a user perspective will be the ENS resolution change to TCo1279 (~9 km), hence matching the current HRES (which will remain unchanged). There will also be a large number of other changes, including the first use of the OOPS system for 4D-Var. OOPS is a modern code system that encapsulates tasks as objects, enabling both separation of concerns and more flexible interaction between components. The cycle will also see the introduction of a new multi-layer snow scheme (improving predictions of snow and of near-surface temperatures over snow), and enhancements to the use of satellite data over land. This last change represents a step on ECMWF’s strategic direction to get yet more value out of satellite data by moving from an ‘all-sky’ to an ‘all-sky, all-surface’ approach.

The European Centre for Medium-Range Weather Forecasts (ECMWF) mission is to deliver high-quality global medium-range numerical weather predictions and monitoring of the Earth system to its member states. The modelling and forecasting of natural hazards are an important part of this mission. Challenges in this area include the integration of innovative observations into the Earth system; realistic representations of water, energy and carbon cycles; coupling and initialisation of all Earth system components; adequate representation of uncertainties; supporting the development of user-specific products to enable optimal decision-making under uncertainties; and advances in software engineering. The new ECMWF strategy identified three pillars to sustain its future development (ECMWF, 2021a): science and technology (world-leading weather and Earth system science, cutting-edge technology and computational science), impact (high-quality products fit for purpose, efficient and easy access to products), and people (inspiring and hiring the best experts). Progress in all these areas will need enhanced collaboration with member states and partners across Europe and beyond.

Tropical cyclones are some of the most devastating natural hazards and the “three beasts”—Harvey, Irma, and Maria—during the Atlantic hurricane season 2017 are recent examples. The European Centre for Medium-Range Weather Forecasts (ECMWF) is working on fulfilling its 2016–25 strategy in which early warnings for extreme events will be made possible by a high-resolution Earth system ensemble forecasting system. Several verification reports acknowledge deterministic and probabilistic tropical cyclone tracks from ECMWF as world leading. However, producing reliable intensity forecasts is still a difficult task for the ECMWF global forecasting model, especially regarding maximum wind speed. This article will put the ECMWF strategy into a tropical cyclone perspective and highlight some key research activities, using Harvey, Irma, and Maria as examples. We describe the observation usage around tropical cyclones in data assimilation and give examples of their impact. From a model perspective, we show the impact of running at 5-km resolution and also the impact of applying ocean coupling. Finally, we discuss the future challenges to tackle the errors in intensity forecasts for tropical cyclones.

The Observing System Research and Predictability Experiment (THORPEX) was a 10-yr, international research program organized by the World Meteorological Organization’s World Weather Research Program. THORPEX was motivated by the need to accelerate the rate of improvement in the accuracy of 1-day to 2-week forecasts of high-impact weather for the benefit of society, the economy, and the environment. THORPEX, which took place from 2005 to 2014, was the first major international program focusing on the advancement of global numerical weather prediction systems since the Global Atmospheric Research Program, which took place almost 40 years earlier, from 1967 through 1982. The scientific achievements of THORPEX were accomplished through bringing together scientists from operational centers, research laboratories, and the academic community to collaborate on research that would ultimately advance operational predictive skill. THORPEX included an unprecedented effort to make operational products readily accessible to the broader academic research community, with community efforts focused on problems where challenging science intersected with the potential to accelerate improvements in predictive skill. THORPEX also collaborated with other major programs to identify research areas of mutual interest, such as topics at the intersection of weather and climate. THORPEX research has 1) increased our knowledge of the global-to-regional influences on the initiation, evolution, and predictability of high-impact weather; 2) provided insight into how predictive skill depends on observing strategies and observing systems; 3) improved data assimilation and ensemble forecast systems; 4) advanced knowledge of high-impact weather associated with tropical and polar circulations and their interactions with midlatitude flows; and 5) expanded society’s use of weather information through applied and social science research.

In this paper, we review the use of satellite-based remote sensing in combination with in situ data to inform Earth surface modelling. This involves verification and optimization methods that can handle both random and systematic errors and result in effective model improvement for both surface monitoring and prediction applications. The reasons for diverse remote sensing data and products include (i) their complementary areal and temporal coverage, (ii) their diverse and covariant information content, and (iii) their ability to complement in situ observations, which are often sparse and only locally representative. To improve our understanding of the complex behavior of the Earth system at the surface and sub-surface, we need large volumes of data from high-resolution modelling and remote sensing, since the Earth surface exhibits a high degree of heterogeneity and discontinuities in space and time. The spatial and temporal variability of the biosphere, hydrosphere, cryosphere and anthroposphere calls for an increased use of Earth observation (EO) data attaining volumes previously considered prohibitive. We review data availability and discuss recent examples where satellite remote sensing is used to infer observable surface quantities directly or indirectly, with particular emphasis on key parameters necessary for weather and climate prediction. Coordinated high-resolution remote-sensing and modelling/assimilation capabilities for the Earth surface are required to support an international application-focused effort.

Winds, waves and storm surges can induce severe damages in coastal areas. The FP7 IncREO project aims to understand the impact of climate change on coastal areas and also to assess the predictability of such extreme events. Reproduce efficiently past events is the fisrt step to reach this purpose. This paper shows the use of atmospheric downscaling techniques in order to improve waves and storm surge hindcasts. Past storms which caused damages on European coastal areas are investigated using atmosphere, wave and storm surge numerical models and downscaling techniques are based on existing ECMWF reanalyses. The results show clearly that the 10 km resolution wind forcing provided by the downscaled atmospheric model gives better waves and surges hindcast against using wind from the reanalysis. Furthermore, the analysis of the most extreme mid-latitude cyclones indicates that a 4D blending approach improves the whole process as it includes small scale processes in the initial conditions.

The aim of this study is to test the feasibility of assimilating microwave observations from the Advanced Microwave Sounding Units (AMSU-A and AMSU-B) through the implementation of an appropriate parameterization of sea ice emissivity. AMSU observations are relevant to the description of air temperature and humidity, and their assimilation into numerical weather prediction (NWP) helps better constrain models in regions where very few observations are assimilated. A sea ice emissivity model suitable for AMSU-A and AMSU-B data is described in this paper and its impact is studied through two assimilation experiments run during the period of the Arctic winter. The first experiment is representative of the operational version of the Météo-France NWP model whereas the second simulation uses the sea ice emissivity parameterization and assimilates a selection of AMSU channels above polar regions. The assimilation of AMSU observations over sea ice is shown to have a significant effect on atmospheric analyses (in particular those of temperature and humidity). The effect on temperature induces a warming in the lower troposphere, especially around 850 hPa. This leads to an increase in the Arctic inversion strength over the ice cap by almost 2 K. An improvement in medium-range forecasts is also noticed when the NWP model assimilates AMSU observations over sea ice.

A novel multilevel threshold segmentation method for color satellite images based on Masi entropy is proposed in this paper. Lévy multiverse optimization algorithm (LMVO) has a strong advantage over the traditional multiverse optimization algorithm (MVO) in finding the optimal solution for the segmentation in the three channels of an RGB image. As the work advancement introduces a Lévy multiverse optimization algorithm which uses tournament selection instead of roulette wheel selection, and updates some formulas in the algorithm with mutation factor. Then, the proposal is called TLMVO, and another advantage is that the population diversity of the algorithm in the latest iterations is maintained. The Masi entropy is used as an application and combined with the improved TLMVO algorithm for satellite color image segmentation. Masi entropy combines the additivity of Renyi entropy and the non-extensibility of Tsallis entropy. By increasing the number of thesholds, the quality of segmenttion becomes better, then the dimensionality of the problem also increases. Fitness function value, average CPU running time, Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM) and Feature Similarity Index (FSIM) were used to evaluate the segmentation results. Further statistical evaluation was given by Wilcoxon’s rank sum test and Friedman test. The experimental results show that the TLMVO algorithm has wide adaptability to high-dimensional optimization problems, and has obvious advantages in objective function value, image quality detection, convergence performance and robustness.

The Infrared Atmospheric Sounding Interferometer (IASI) provides 8461 channels in the infrared spectrum. In current numerical weather prediction (NWP) models, it is not feasible to assimilate all channels and it is known that the information content between adjacent channels is redundant. This issue has been addressed in NWP centres by employing a channel selection strategy. The goal of this article is to add new channels to the existing IASI operational channel selection, aimed at improving the data assimilation in cloudy conditions and the simultaneous retrieval of cloud microphysical variables, specifically liquid and ice water contents. Cloudy profiles from the French convective-scale model Applications of Research to Operations at MEsoscale (AROME) are used in the study to focus on the retrieval of cloud variables over the Mediterranean region. Three channel selection methodologies were evaluated in this study: a statistical approach based on the degrees of freedom of the signal (DFS), a physical method based on the channel spectral sensitivity to the cloud variables and a random selection. To validate the new selections, an idealized framework is used with observing system simulation experiments (OSSE) in the context of one-dimensional variational retrievals. The current operational IASI selection has already been shown to provide good retrievals of cloud variables. However, all the different channel selections improve the results with small differences in the 1D-Var retrievals. Based on the physical and DFS methods, the final sets of 134 channels sensitive to cloud variables are proposed for future investigation in operational implementation. Additional tests on temperature and water-vapour retrieval results, air-mass dependence and cloud microphysical parametrization have also been conducted.

International audience; The impact of observations on analysis uncertainty and forecast performance was investigated for austral spring 2010 over the southern polar area for four different systems (NRL, GMAO, ECMWF and Météo-France) at the time of the Concordiasi field experiment. The largest multi-model variance in 500 hPa height analyses is found in the southern sub-Antarctic oceanic region, where there are rapidly evolving weather systems, rapid forecast-error growth, and fewer upper-air wind observation data to constrain the analyses. The total impact of all observations on the model forecast was computed using the 24 h forecast sensitivity-to-observations diagnostic. Observation types that contribute most to the reduction of the forecast error are shown to be AMSU, IASI, AIRS, GPS-RO, radiosonde, surface and atmospheric motion vector observations. For sounding data, radiosondes and dropsondes, one can note a large impact on the analysis and forecasts of temperature at low levels and a large impact of wind at high levels. Observing system experiments using the Concordiasi dropsondes show a large impact of the observations over the Antarctic plateau extending to lower latitudes with the forecast range, with the largest impact around 50-70 • S. These experiments indicate there is a potential benefit from using radiance data better over land and sea-ice and from innovative atmospheric motion vectors obtained from a combination of various satellites to fill the current data gaps and improve numerical weather prediction analyses in this region.

This article focuses on the simulation and the assimilation of satellite infrared observations in convective-scale numerical weather prediction (NWP) systems. A radiative transfer model that includes profiles for liquid-water content, ice-water content and cloud fraction was used to simulate cloud-affected radiances as background equivalents. This approach avoids the use of cloud parameters (cloud-top pressure and effective cloud fraction) deduced from a CO2 slicing algorithm and the modelling of clouds by single-layer clouds. The advanced radiative transfer model was evaluated using infrared observations measured by the Infrared Atmospheric Sounding Interferometer (IASI). The observation-screening procedure that was developed to improve the selection of usable cloudy scenes led to a good agreement between observations and background equivalents. For that purpose, a radiance analysis of collocated Advanced Very High Resolution Radiometer (AVHRR) pixels inside each IASI field of view was used. The goal of this preliminary work is to assess the feasibility of adding the cloud variables (liquid and ice-water contents) to the state vector of the assimilation system. The approach is illustrated with one-dimensional variational (1D-Var) retrievals. The physical consistency of the 1D-Var adjustments is verified with real observations. Then observing-system simulation experiments (OSSE) are used to validate the 1D-Var retrievals.

The Concordiasi campaign aimed to improve satellite data assimilation at high latitudes and, particularly, the assimilation of the Infrared Atmospheric Sounding Interferometer (IASI) radiances over Antarctica. This study focuses on the IASI data retrieval using a 1-D variational data assimilation system, which was carried out at the Concordia station and within the framework of Concordiasi. The study period lasted from November 20 to December 12, 2009. Radiosonde measurements are utilized to validate temperature and water vapor retrieved profiles. Baseline Surface Radiation Network data and manned measurements in Concordia are used to verify skin temperature retrievals and derive information about cloudy conditions. This study assesses the impact of several parameters on the retrieved profile quality. In particular, the background error specification is crucial. The background error covariance matrix is optimally tuned to provide the best possible retrievals, modifying the shape of these covariances for stratospheric temperatures, computing and maximizing the degree of freedom for signal (DFS). The DFS characterizes how the assimilation system uses the observation to pull the signal from the background. For the study period, the humidity and temperature retrieved profiles are optimally improved compared with background profiles, with the largest reduction in error for the skin temperature.

This article compares different methods of deriving cloud properties in the footprint of the Infrared Atmospheric Sounding Interferometer (IASI), onboard the European MetOp satellite. Cloud properties produced by ten operational schemes are assessed and an intercomparison of the products for a 12 h global acquisition is presented. Clouds cover a large part of the Earth, contaminating most of the radiance data. The estimation of cloud top height and effective amount within the sounder footprint is an important step towards the direct assimilation of cloud-affected radiances. This study first examines the capability of all the schemes to detect and characterize the clouds for all complex situations and provides some indications of confidence in the data. Then the dataset is restricted to thick overcast single layers and the comparison shows a significant agreement between all the schemes. The impact of the retrieved cloud properties on the residuals between calculated cloudy radiances and observations is estimated in the long-wave part of the spectrum. Copyright © 2011 Royal Meteorological Society, Crown in the right of Canada, and British Crown copyright, the Met Office

Since July 2008, Infrared Atmospheric Sounder Interferometer (IASI) radiances have been assimilated in the French global model Action de Recherche Petite Echelle Grande Echelle (ARPEGE) and since April 2010 at high density in the French convective-scale model Applications of Research to Operations at MEsoscale (AROME). The impact of the assimilation of clear IASI data on forecast skill is found to be positive for both models. As many observed scenes are cloudy, several ways to characterize the clouds within the observed spectra are investigated. Firstly, a simple approach that enables the determination of both the cloud-top pressure and the effective cloud amount of an equivalent single-layer cloud is followed using a CO2-slicing method. The first assimilation trials in overcast conditions lead to a small positive impact on forecast skill. Another approach would be to take advantage of the fact that cloud water variables are described at high resolution in the convective-scale model AROME. Model cloud fields have to be used in conjunction with a cloudy radiative transfer model. The first simulations using this technique are performed and compared against observations. Copyright © 2011 Royal Meteorological Society

This paper describes several sensitivity studies carried out with the French global 4-D-Var system to check its ability to assimilate surface-sensitive observations over land from the Special Sensor Microwave Imager (SSM/I). As well as a sound knowledge of land-surface parameters, the assimilation of SSM/I observations requires effective rain-detection and bias-correction algorithms. Three sensitivity components are hence analyzed with a special emphasis on the land-surface emissivity at SSM/I frequencies estimated from satellite observations. Several rain algorithms were tested to reject cloudy/rainy observations over land, and the bias-correction scheme was adapted to improve its performance over land and sea surfaces. Once these problems have been outlined, a global 4-D-Var assimilation experiment which assimilates SSM/I observations over land surfaces was run and compared with a control experiment. The impact on forecast scores has been found to be globally positive. Nevertheless, the very high sensitivity of SSM/I to each of the three components presented in this study is characterized by opposite effects that, once clustered together, lead to some residual biases over land due to their combined effects.

International audience; Real-time surface and upper-air observations are crucial to the analysis and forecasting of the West African monsoon (WAM). This paper will focus on the African Monsoon--Multidisciplinary Analyses (AMMA)-driven reactivation and modernisation of the radiosonde network over West Africa, its potential long-term impact on upper-air operations in the region, the influence of the additional data in WAM analyses and forecasting, and the AMMA-related development and usage of the West African Analysis/Forecasting (WASA/F) forecast method.

In the context of its responsibilities as a Regional Specialised Meteorological Centre (RSMC) for the Southwest Indian Ocean, Meteo-France operates a tropical cyclone warning centre which sends advisories to all the concerned countries in the area. In assistance to the forecasters and as part of the operational suite for the short-range forecasts, a limited-area model (ALADIN Reunion) is run with four daily analyses. Assimilated observations include conventional observations – radiosondes, buoys, surface stations, aircraft reports, upper-wind reports – and satellite observations. The latter include QuikSCAT surface winds, atmospheric motion vectors from geostationary satellites, and radiances from polar-orbiting satellites. Assimilation of satellite radiance data is done in clear-sky conditions, and thus cannot be used in the vicinity of tropical cyclones. Two new sources of pseudo-observations are investigated that can bring new information content to those regions: Total Column Water Vapour (TCWV) pseudo-observations deduced from cloudy/rainy SSM/I data, as well as a 3D wind bogus. The TCWV algorithm is obtained from SSM/I brightness temperatures through a simple statistical regression from the 1D-Var analyses of the European Centre for Medium-Range Weather Forecasts, which are derived from complex inversion methods using moist physics and radiative transfer models. The 3D wind bogus is derived from structural information contained in the tropical cyclone advisories issued by the RSMC and contains a low-level vortex composed of two concentric rings of eight winds each, at each of the surface, 850, 700 and 500 hPa levels. Forecast scores and fit of the model to the observations are improved and indicate a positive impact of those new datasets. Structural validation is investigated through the comparison of model and TMI ‘observed’ rain rates: it is found that assimilating and cycling cloudy/rainy TCWV helps achieve more realistic cyclonic features. Copyright © 2008 Royal Meteorological Society

The international programme "THORPEX: a World Weather Research Programme" provides a framework in which to tackle the challenge of improving the forecast skill of high-impact weather through international collaboration between academic institutions, operational forecast centres, and users of forecast products. The objectives of the THORPEX Data Assimilation and Observation Strategy Working Group (DAOS-WG) are two-fold. The primary goal is to assess the impact of observations and various targeting methods to provide guidance for observation campaigns and for the configuration of the Global Observing System. The secondary goal is to setup an optimal framework for data assimilation, including aspects such as targeted observations, satellite data, background error covariances and quality control. The Atlantic THORPEX Regional campaign, ATReC, in 2003, has been very successful technically and has provided valuable datasets to test targeting issues. Various data impact experiments have been performed, showing a small but very slightly positive impact of targeted observations. Projects of the DAOS-WG include working on the AMMA field experiment, in the context of IPY and to prepare the future THORPEX-PARC field campaign in the Pacific by comparing sensitivity of the forecasts to observations between several groups.

For its short-range forecasts over Western Europe, Meteo-France runs the limited-area model ALADIN operationally with four daily analyses obtained with a 3D-Var data assimilation system. This system includes, among other observation types, radiances from AMSU-A, AMSU-B, HIRS and SEVIRI radiometers. SEVIRI is on board the geostationary platform Meteosat-8 and provides continuous observations in space and in time over the region of interest at several wavelengths, while the others, which are on board polar-orbiting satellites, have poorer temporal and horizontal resolutions but a better spectral resolution than SEVIRI. Observing System Experiments (OSEs) have been performed with the operational 3D-Var to assess the impact of such satellite data on analyses and on forecasts. DFS (Degrees of Freedom for Signal) have been computed and have shown the complementarity between WV channels from the different radiometers. In the operational version of the 3D-Var, DFS values show that analyses are strongly controlled by SEVIRI data in the mid to high troposphere. This is consistent with the large number of assimilated SEVIRI radiances. HIRS and AMSU-B WV data would provide more information if SEVIRI data were not assimilated and if ATOVS data were used with a higher density. However, using ATOVS data with a higher horizontal resolution makes the analyses more dependent on these data, and it does not appear to be beneficial in this particular context, probably because of a non-optimal bias correction. In that case however, the individual impact of each pixel decreases because of the horizontal correlation lengths of the structure functions. Forecast scores and predicted precipitation patterns display the positive impact of SEVIRI data.

SUMMARY AMSU-A and -B measurements are stillnot extensively used over land surfaces for atmospheric applications. Recent studies have shown that it should now be possible to take advantage of the information content of these instruments provided land emissivity and skin temperature estimates are improved. This paper reports on comparisons between three land-surface schemes using the Meteo-France four-dimensional variational (4D-Var) assimilation system. Firstly, a monthly mean estimated land emissivity atlas using AMSU data is used. A second land-surface scheme based on direct emissivity calculations is developed to obtain dynamically emissivity values. The third approach is based on the first one with the addition of a dynamic skin temperature estimation based on one AMSU-A or AMSU-B window channel. The land-surface schemes described above have been implemented within the 4D-Var system and their results have been compared with those of the operational surface scheme (which uses emissivity models). All land schemes have been evaluated by examining the performances of the observation operator for sounding channels prior to the assimilation. With dynamically varying emissivities and/or skin temperatures or with averaged emissivities, the simulations are clearly improved compared with the operational model and many more data pass the quality-control check.

Desroziers and Ivanov proposed a method to tune error variances used for data assimilation. The implementation of this algorithm implies the computation of the trace of certain matrices which are not explicitly known. A method proposed by Girard, allowing an approximate estimation of the traces without explicit knowledge of the matrices, was then used. This paper proposes a new implementation of the Desroziers and Ivanov algorithm, including a new computation scheme for the required traces. This method is compared to Girard's in two aspects: its use in the implementation of the tuning algorithm, and the computation of a quantification of the observation impacts on the analysis known as Degrees of Freedom for Signal. Those results are illustrated by studies utilizing the French data assimilation/numerical weather-prediction system ARPEGE. The impact of a first quasi-operational tuning of variances on forecasts is shown and discussed. Copyright © 2006 Royal Meteorological Society

An experiment took place during autumn 2003 with the aim of testing the feasibility of an operational targeting of observations over the North Atlantic Ocean in the context of the international programme THORPEX. The purpose of this paper is to evaluate the impact of these additional observations in the French operational model ARPEGE during the last three weeks of the campaign. Results are shown for large regions over and around the North Atlantic Ocean and for specific verification areas. Over Europe, the addition of observations is slightly beneficial for the forecast, mostly in the low troposphere over wide areas and above 100 hPa. However, the impact of extra data is more significant but also more mixed for the dedicated verification areas: they are case, forecast-range and level dependent. In addition, the information content is studied with the Degrees of Freedom for Signal (DFS) for the evaluation of the observation impact on the analysis of one case of December 2003. Firstly, the variations of the DFS have been illustrated in a simplified data assimilation system. It has been found for that case that satellite data have the most important global contribution to the overall analysis, especially the humidity sensitive infrared radiances. For the conventional data, the wind measurements of the aircraft and from the geostationary satellites are the most informative. For the targeted area, the data from aircraft and the dropsondes have the largest DFS. It has been noted that the DFS of the dropsondes located in the sensitivity maximum is larger than the other one even if there is no link between the DFS and the forecast. However, the impact of the dropsondes grows with respect to the forecast range and leads to an improvement of the forecast for this case. Copyright © 2006 Royal Meteorological Society

Recent data assimilation developments which have taken place at numerical weather-prediction centres are briefly discussed, from the perspectives of both the importance of data and algorithmic developments. The increase in quality and quantity of satellite data is seen to play a major role in the improvement of forecast performance, particularly in the southern hemisphere. Further optimization of the use of observations is possible through the proper evaluation of data impact and the optimization of the amount of data to be assimilated. The generalized advent of four-dimensional variational assimilation is presented, and trends in the specification of error statistics are described. Finally, a more interactive forecasting system including an adaptive component is a new challenge to bring additional improvement to the forecasting of high-impact weather. Copyright © 2005 Royal Meteorological Society

The assimilation of the Atmospheric InfraRed Sounder (AIRS) data is expected to improve the quality of NWP products. Currently, operational use of such data is limited to the cloud-free pixels or to the channels far above the clouds for cloudy pixels. This paper focuses on the validation of various cloud-detection schemes applied to AIRS data. The clouds are detected and characterized, in cloud-top and cover, by using the NESDIS, ECMWF, CO2-slicing and MLEV schemes. These four different AIRS cloud descriptions are evaluated by independent information retrieved with the Meteo-France cloud mask applied to MODIS data and taken as our reference. The validation for a ten-day period over the North-east Atlantic is presented. The use of satellite cloudy radiances is a great challenge for numerical weather prediction. Work is in progress to assimilate such data by using enhanced observation operators dealing with clouds. In this work, we try to contribute to this effort by investigating the linearity assumption of an observation operator, with a simple diagnostic cloud scheme, for different cloud types. Copyright © 2005 Royal Meteorological Society

The method for tuning observational or background error statistics is presented and some of its properties are exposed using theoretical considerations and experiments carried out in a simplified framework. In particular, the method is shown to be equivalent to a maximum-likelihood evaluation and its efficiency is seen to depend on the number of observations. The results of several experiments carried out with the variational assimilation system of the French numerical weather-prediction system ARPEGE, both with simulated and actual datasets involving satellite radiances, are also presented. The temporal stability of the results and their consistency with the known quality of the measurements are shown. Copyright © 2004 Royal Meteorological Society

The cloudiness in simulated Infrared Atmospheric Sounding Interferometer (IASI) pixels deduced from the Advanced Very-High-Resolution Radiometer (AVHRR) satellite imager is studied specifically in meteorologically sensitive areas during the Fronts and Atlantic Storm-Track Experiment. It is found that few clear AVHRR observations are located in the IASI pixels in these regions, which are covered by high-level and low-level clouds. The IASI channel selection is then studied in the context of the sensitive areas for the pixels with low-level clouds. The Entropy Reduction (ER) method, which was previously studied in a general context, is compared with two other channel selection methods using selection criteria based on adjoint sensitivity: the sensitivity to observations and the so-called Kalman-filter sensitivity. It is found that, even though the ‘sensitive’ methods give slightly better results than the ER one, the latter performs quite robustly and at a lower computational cost. The robustness to the specification of the background-error covariance matrix is then studied. It is shown that channel selection based on the ER method is particularly robust to the specification of the background-error covariance matrix, but the analysis step itself requires an accurate determination of the background-error covariance matrix. In addition it is shown that an independently computed constant channel set gives comparable results to the optimal channel set. Copyright © 2004 Royal Meteorological Society.

The skill of numerical weather prediction depends to a large extent upon the quantity of globally available observations. Only a fraction of the available observations (especially high-density observations) is used in current operational assimilation systems. In this paper, the potential of high-density observations is studied in a practical four-dimensional variational assimilation context. Two individual meteorological situations are used to examine the impact of different observation densities on the analysis and the forecast. A series of observing-system simulation experiments are performed. Both direct observations (temperature and surface pressure) and indirect observations (radiance) are simulated, with uncorrelated or correlated errors. In general, it is verified that a small reduction (increase) of the initial error in a sensitive area can produce a considerable improvement (degradation) of the targeted forecast. In particular, the results show that increasing the observation density for the uncorrelated-error case can generally improve the analysis and the forecast. However, for correlated observation errors and the use of a diagonal observation-error covariance matrix in the assimilation, an increase in the observation number such that the error correlation between two adjacent observations becomes greater than a threshold value (around 0.2) degrades the analysis and the forecast. Posterior diagnostics of the sub-optimality of the assimilation scheme for correlated observation errors are analysed. Finally, it is shown that a risk of using high-density observations and poor vertical resolution is that deficiencies in the background-error statistics can lead to unrealistic analysis increments at some levels where no observations are present, and so produce a degradation of the analysis at these levels. Copyright © 2003 Royal Meteorological Society

In this paper, the optimal configurations of model resolution, observation resolution and observation density are investigated in a simple one-dimensional framework. In this context, the representativeness error is formalized and estimated before being used in the analysis-error formulation. Some optimal and suboptimal assimilation-schemes, differing from different approximations of observation-error covariance and observation operator, are compared. The optimal observation-extent is determined as a function of model resolution. Increasing the observation density is usually beneficial, except for suboptimal schemes similar to the ones used in operational practice. The impact of thinning the observations with correlated error is also studied from a suboptimal viewpoint. Copyright © 2002 Royal Meteorological Society

International audience; Advanced infrared sounders will provide thousands of radiance data at every observation location. The number of individual pieces of information is not usable in an operational numerical weather-prediction context, and we have investigated the possibilities of choosing an optimal subset of data. These issues have been addressed in the context of optimal linear estimation theory, using simulated Infrared Atmospheric Sounding Interferometer data. Several methods have been tried to select a set of the most useful channels for each individual atmospheric profile. These are two methods based on the data resolution matrix, one method based on the Jacobian matrix, and one iterative method selecting sequentially the channels with largest information content. The Jacobian method and the iterative method were found to be the most suitable for the problem. The iterative method was demonstrated to always produce the best results, but at a larger cost than the Jacobian method. To test the robustness of the iterative method, a variant has been tried. It consists in building a mean channel selection aimed at optimizing the results over the whole database, and then applying to each profile this constant selection. Results show that this constant iterative method is very promising, with results of intermediate quality between the ones obtained for the optimal iterative method and the Jacobian method. The practical advantage of this method for operational purposes is that the same set of channels can be used for various atmospheric profiles.

This chapter discusses some of the implementation details that are necessary to apply data assimilation in the context of numerical weather prediction (NWP). It is divided into three parts. The first part addresses the processing of observations, which includes the transformation of raw data into a form that can be processed by a data assimilation system, quality control, and data thinning. The second part discusses two important aspects of data assimilation for NWP: (i) filtering of the analysis to remove spurious inertia–gravity waves and (ii) methods to handle nonlinearities and non-Gaussian error statistics. The third part discusses the development of parallel algorithms for four-dimensional variational data assimilation (4D-VAR), in order to better exploit the parallel nature of the computers on which it is run and to maintain its status as an important and viable NWP data assimilation algorithm into the foreseeable future.

The use of the Infrared Atmospheric Sounding Interferometer (IASI) to improve short-range forecasts by the analysis of cloud variables is presented. After the retrieval of cloud microphysical variables, a one-dimensional (1D) version of the French convective scale model AROME is used here to discuss the persistence of the cloudy increment in the numerical weather prediction (NWP) model. The analysis of cloud variables is shown to significantly improve the forecasts of cloud variables, temperature and humidity during 3 h. This maintenance of the cloud information in the NWP model is expected to have a high impact on the forecasts of clouds and heavy rainfall events.

The first two papers of this series describe the development of the operational four-dimensional variational assimilation (4D-Var) configuration implemented at the European Centre for Medium-Range Weather Forecasts (ECMWF). The basic features are a 6-hour incremental 4D-Var set-up with two minimization steps, using very simplified physics in the first minimization and a more complete set of linear physics in the second. This paper describes the validation of this configuration. Prior to implementation, 12 weeks of experimentation showed a consistent improvement relative to 3D-Var. After an additional 6 weeks of encouraging parallel operation with the then current operational suite, 4D-Var with physics was introduced in operations at ECMWF in November 1997. The difference in scores is statistically significant, and the fast-growing components of the 4D-Var analysis errors are shown to be smaller than their 3D-Var counterparts. The performance of this new operational assimilation system is studied for the month of January 1998, for which the 4D-Var analyses exhibit more realistic baroclinic waves than the 3D-Var, especially in the Pacific area. A case-study illustrates the improvement one can expect in forecast terms in the mid latitudes. The 4D-Var system improved the forecast skill in the Tropics in general. Observing-system experiments show that the current 4D-Var operational system benefits from the assimilation both of satellite data and conventional observations.

Results of four-dimensional variational assimilations, 4D-Var, in cycling mode, over a few two-week assimilation periods are presented. 4D-Var is implemented in its incremental formulation, with a high-resolution model with the full physical parametrization package to compare the atmospheric states with the observations, and a low-resolution model with simplified physics to minimize the cost-function. The comparison of 4D-Var using several assimilation windows (6, 12 and 24 hours) with 3D-Var (the equivalent of 4D-Var with no time-dimension) over a two-week period shows a clear benefit from using 4D-Var over a 6 or 12—hour window compared to the static 3D-Var scheme. It also exhibits some problems with the forecasts started using 4D-Var over a 24-hour window. The poorer performance of 4D-Var over a relatively long assimilation window can be partly explained by the fact that, in these experiments, the tangent-linear and adjoint models used in the minimization are only approximations of the assimilating model (having lower resolution and crude physics). The error these approximations introduce in the time evolution of a perturbation affects the convergence of the incremental 4D-Var, with larger discontinuities in the values of the cost-function when going from low to high resolution for longer assimilation windows. Additional experiments are performed comparing 4D-Var using a 6-hour window with the 3D-Var system. Two additional 2-week periods show a consistent improvement in extratropical forecast scores with the 4D-Var system. The main 4D-Var improvements occur in areas where the 3D-Var errors were the largest. Local improvement can be as large as 35% for the root-mean-square of the 5-day-forecast error, averaged over a two-week period. A comparison of key analysis errors shows that, indeed, 4D-Var using a 6-hour window is able to reduce substantially the amplitude of its fast-growing error components. The overall fit to observations of analyses and short-range forecasts from 3D-Var and 4D-Var is comparable. In active baroclinic areas, the fit of the background to the data is considerably better for the 4D-Var system, resulting in smaller increments. It appears that in these areas (and in particular over the west Atlantic), 4D-Var is able to better use the information contained in the observations. The ability of 4D-Var to extrapolate some aircraft data in the vertical with a baroclinic tilt is illustrated. Problems exist in the tropics and mountainous areas due partly to a lack of physics in the tangent-linear model. Possible improvements to the system (the introduction of more physics; better behaviour of the incremental approach owing to a line search at high resolution) are also discussed.

Satellite microwave radiometers provide measurements of precipitable water content (PWC) over the oceans, with a horizontal resolution of a few tens of kilometres. These data represent the water vapour content integrated over the atmospheric column. We assess the value of this source of information for numerical weather prediction systems. We used the European Centre for Medium-Range Weather Forecasts Integrated Forecasting System with a four-dimensional variational method to assimilate the Special Sensor Microwave/Imager (SSM/I) PWC data over a 24-hour period. Our experiments use an incremental variational method. Comparison with observations is made using a multi-level global primitive-equation T106 spectral model with physical parametrizations. Minimization is performed using a T63 adiabatic dynamics model which includes only simplified physics (horizontal diffusion, a simple surface drag and a vertical diffusion scheme). Comparing the control and assimilation experiments with aircraft and other data shows that the use of PWC data from SSM/I improves the analysis. We also obtain a slight improvement in short-range forecasts of almost all parameters when SSM/I-PWC data are used in the assimilation.

Structure functions for the 3D-Var assimilation scheme of the European Centre for Medium-Range Weather Forecasts are evaluated from statistics of the differences between two forecasts valid at the same time. Results compare satisfactorily with those reported in the existing literature. Non-separability of the correlation functions is a pervasive feature. Accounting for non-separability in 3D-Var is necessary to reproduce geostrophic characteristics of the statistics, such as the increase of length-scale with height for the horizontal correlation of the mass variable, sharper vertical correlations for wind than for mass and shorter horizontal length-scales for temperature than for mass. In our non-separable 3D-Var, the vertical correlations vary with total wave-number and the horizontal correlation functions vary with vertical level.

An iteration procedure minimizing the short-range forecast error leads, after some iterations, to so-called key analysis errors. These are estimates of the part of analysis errors that is largely responsible for the short-range forecast errors. The first step of the minimization procedure provides a scaled gradient of the two-day forecast errors for which the ‘energy’ inner-product provides an efficient way of identifying the analysis errors at scales that are relevant for forecast error growth. By using an ‘enstrophy’ like inner-product as an alternative to ‘energy’ the sensitivity gradient obtains an unrealistically large scale. Performing a few more steps in the minimization provides better estimates of the analysis error in the directions spanned by the leading singular vectors of the tangent-linear model. On a case study it is shown that three steps provide key analysis increments which, when added to the analysis, both significantly improve the fit to the available data, and substantially improve the subsequent model integration. It does not appear to be beneficial to do more steps of the minimization because of the uncertainty in the definition of the short-range forecast error, and of approximations in the tangent-linear model. Key analysis errors represent an improved estimate of analysis errors compared to the scaled gradient of day-2 forecast errors. In particular the geographical distribution shows the stability dependence of the scaled gradient. The projection of the gradient on the fastest growing errors limits maximum sensitivity to the major baroclinic zones. The close correspondence of evolved key analysis errors and forecast errors shows that key analysis errors are more realistically projecting on to full analysis errors. The close link between the stability of the flow and the gradient of the forecast errors implies an unreasonably strong seasonal variation of analysis errors estimates. In contrast, key analysis errors are nearly seasonally independent, which means that their detrimental effect on forecast errors in absolute terms in summer and winter is comparable.

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 adjoint equations allow computation of the sensitivity of one output parameter of a model to all input parameters. After a brief introduction of this technique, its main applications to numerical weather prediction are described. The most trivial application of the adjoint model is to investigate the sensitivity to initial conditions or model parameters. The fact that the adjoint method allows computation of the gradient of a cost function with respect to some parameters in an efficient way makes the minimization of a cost function using descent algorithms possible. This may be applied to estimation problems like variational assimilation. Another use of the adjoint model is the evaluation of the covariances of forecast error in the Kalman filtering context. Finally, the estimation of the singular vectors of a linearized model using its adjoint is relevant for predictability studies.

International audience; Constellations of driftsonde systems— gondolas floating in the stratosphere and able to release dropsondes upon command— have so far been used in three major field experiments from 2006 through 2010. With them, high-quality, high-resolution, in situ atmospheric profiles were made over extended periods in regions that are otherwise very difficult to observe. The measurements have unique value for verifying and evaluating numerical weather prediction models and global data assimilation systems; they can be a valuable resource to validate data from remote sensing instruments, especially on satellites, but also airborne or ground-based remote sensors. These applications for models and remote sensors result in a powerful combination for improving data assimilation systems. Driftsondes also can support process studies in otherwise difficult locations—for example, to study factors that control the development or decay of a tropical disturbance, or to investigate the lower boundary layer over the interior Antarctic continent. The driftsonde system is now a mature and robust observing system that can be combined with flight-level data to conduct multidisciplinary research at heights well above that reached by current research aircraft. In this article we describe the development and capabilities of the driftsonde system, the exemplary science resulting from its use to date, and some future applications.

International audience; During the international African Monsoon Multidisciplinary Analysis (AMMA) project, stratospheric balloons carrying gondolas called driftsondes capable of dropping meteorological sondes were deployed over West Africa and the tropical Atlantic Ocean. The goals of the deploymentwere to test the technology and to study the African easterly waves, which are often the forerunners of hurricanes. Between 29 August and 22 September 2006, 124 sondes were dropped over the seven easterly waves that moved across Africa into the Atlantic between about 10° and 20°N, where almost no in situ vertical information exists. Conditions included waves that developed into Tropical Storm Florence and Hurricanes Gordon and Helene. In this study, a selection of numerical weather prediction model outputs has been compared with the dropsondes to assess the effect of some developments in data assimilation on the quality of analyses and forecasts. By comparing two different versions of the Action de Recherche Petite Echelle Grande Echelle (ARPEGE) model of Météo-France with the dropsondes, first the benefits of the last data assimilation updates are quantified. Then comparisons are carried out using the ARPEGE model and the Integrated Forecast System (IFS) model of the European Centre for Medium-Range Weather Forecasts. It is shown that the two models represent very well the vertical structure of temperature and humidity over both land and sea, and particularly within the Saharan air layer, which displays humidity below 5%-10%. Conversely, the models are less able to represent the vertical structure of the meridional wind. This problem seems to be common to ARPEGE and IFS, and its understanding still requires further investigations. © 2013 American Meteorological Society.

Validation of ozone profiles measured from a nadir looking satellite instrument over Antarctica is a challenging task due to differences in their vertical sensitivity with ozonesonde measurements. In this paper, ozone observations provided by the Infrared Atmospheric Sounding Interferometer (IASI) instrument onboard the polar-orbiting satellite MetOp are compared with ozone profiles collected between August and October 2010 at McMurdo Station, Antarctica, during the Concordiasi measurement campaign. The main objective of the campaign was the satellite data validation. With this aim 20 zero-pressure sounding balloons carrying ozonesondes were launched during this period when the MetOp satellite was passing above McMurdo. This makes the dataset relevant for comparison, especially because the balloons covered the entire altitude range of IASI profiles. The validation methodology and the collocation criteria vary according to the availability of global positioning system auxiliary data with each electro-chemical cell ozonesonde observation. The relative mean difference is shown to depend on the vertical range investigated. The analysis shows a good agreement in the troposphere (below 10 km) and middle stratosphere (25-40 km), where the differences are lower than 10%. However a significant positive bias of about 10-26% is estimated in the lower stratosphere at 10-25 km, depending on altitude. The positive bias in the 10-25 km range is consistent with previously reported studies comparing in situ data with thermal infrared satellite measurements. This study allows for a better characterization of IASI-retrieved ozone over the polar region during ozone depletion/recovery processes. © Author(s) 2013., SCOPUS: ar.j, info:eu-repo/semantics/published

The adjoint method has been used to calculate the sensitivity of short-range forecast errors to the initial conditions. The gradient of the energy of the day 2 forecast error with respect to the initial conditions can be interpreted as a sum of rapidly growing components of the analysis error. An analysis modified by subtracting an appropriately scaled vector, proportional to the gradient, provides initial conditions for a ‘sensitivity integration’ that can be used to diagnose the effect of initial-data errors on forecast errors. Statistics of sensitivity calculations for the month of April 1994 characterize the sensitivity patterns as small-scale, middle or lower tropospheric structures which are tilted in the vertical. The general pattern of these structures is known to be associated with the fastest possible growth of forecast error. When used as initial perturbations, they evolve rapidly into synoptic-scale structures, propagating both downstream and to higher atmospheric levels. On average, the sensitivity integration corrects for about a tenth of the day 2 forecast error, which indicates that indeed not all of the error is in the fastest-amplifying modes. But the fraction of the error corrected at day 2 is important for an improvement in the medium-range, as this fraction continues to grow substantially in the non-linear regime. These results have proved that there is still scope for great improvement in the medium-range forecast, particularly over Europe, by a better description of the initial conditions. The sensitivity experimentation suggests that many cases of major forecast-errors may be explained by defects in the analysis. A small but well-chosen change in the analysis can frequently improve the forecast quality.