1 Arome, avenir de la prévision régionale François Bouttier Météo-France - Centre national de recherches météorologiques (CNRM) 42, avenue Gaspard-Coriolis Toulouse Cedex Résumé Le système de prévision Arome regroupe les meilleurs composants des modèles MésoNH Aladin, et de l assimilation de données IFS/Arpège. Son premier objectif est la prévision numérique, dès 2008, des phénomènes de convection intense sur la France métropolitaine. Sa haute résolution spatiale, de l ordre du kilomètre, lui permettra de modéliser de nombreux phénomènes importants jusqu alors inaccessibles à la prévision numérique, grâce à une description détaillée des lois physiques en jeu, et une utilisation intensive des réseaux d observation à l échelle régionale. Sa conception originale l ouvre à une large communauté de chercheurs. Abstract The forthcoming Arome regional forecasting system The Arome forecasting system is a blend of the best components from the Méso-NH model, the Aladin model, and the IFS/Arpège data assimilation software. Its focus is on the numerical prediction of intense convective systems over mainland France by Other important weather phenomena will also begin to be reliably forecast, thanks to a high (kilometric) spatial resolution and the use of regional observing systems. The Arome software is designed to be accessible to a wide research community. The method of numerical weather prediction (NWP) founded modern meteorology. For a long time, it has relied on the software implementation of global models with therefore rather coarse-resolution computation grids and on a very simple representation of physical phenomena, because of the computing costs of forecasting software. At Météo-France, this was the case for the Arpège model, whose offshoot, the Aladin regional model, is almost identical, except for a horizontal geometry with a 10 km grid size over mainland France, better than the 25 km in Arpège (in the NWP scheme of early 2007). Aladin uses the large atmospheric scales from the Arpège forecasts to feed its lateral boundary conditions, with the idea in mind that local meteorological phenomena are generally determined by the large-scale atmosphere: depressions, jet streams, ridges, troughs. Why a new model? In this hierarchical vision of NWP, the global model closely controls the design as well as the coupling of the regional model, and neither could be used without the other. This method which was supposed to give a strong advantage to those forecasting centres in control of both a global and a regional model has been given a rough time in the 1990s by the community of cloud physics research. This community designed excellent and often freely usable regional models (MM5, Rams, WRF), widely used operationally by small meteorological centres or even by universities or private companies, without any help from a home-made global model, at kilometre-scale resolutions and with very good results. Another thing that happened was the success of experiments on local initialisation of convective
2 clouds (see e.g. Sun et Crook, 2001; Ducrocq et al., 2000), proving that often assimilation of local data, and not large-scale atmospheric forcing, is decisive to make a good forecast. In France, both of these aspects kilometre-scale resolution and regional assimilation are particularly crucial for forecasts of Cévenol floods and of severe thunderstorms. In 2000, it became clear that these phenomena, which are very important for the society, would not be forecast correctly before many years with the current strategy of continuous improvement of Arpège and Aladin (a strategy launched about 1990, which allowed for great progress in the forecasting of winter storms). So Météo-France abandoned its traditional ways and started a quick jump towards a new system, Arome, which would be a synthesis of the ArpègeAladin software and of recent improvements in fine-mesh modelling. The elaboration of Arome relies on the involvement of research teams (hence the acronym Applications de la Recherche à l Opérationnel à Méso-Echelle = mesoscale applications of research for operational use) in order to create a third operational forecasting system, complementary to Arpège and Aladin, starting from Météo-France s purpose is a complementary relationship between three levels of modelling: - Arpège, aiming at scales larger than about 20 km and forecast ranges of 1 to 3 days in 2008, combined with the IFS model of the European Centre for Medium-range Weather Forecasts (ECMWF), - Aladin, dealing with regional forecasts at a scale of about 10 km, over Western Europe and other areas of the world, - Arome, providing local forecasts and forecasts of dangerous phenomena, primarily over mainland France and for forecast ranges of 3 to 36 hours approximately. Figure 1 Main operational NWP systems (models and data assimilation systems) used by Météo-France starting from Each of these models will have its own ad hoc data assimilation. This choice of numerical systems (figure 1) will allow for the simulation of all significant meteorological phenomena,
3 from winter storms to thunderstorms, except perhaps for very small-scale events, like local fog or some very local anomalies in urban or mountain environments. The intention with Arome is indeed to complement IFS, Arpège and Aladin, and not to replace them. The development work on these forecast models is going on, with noticeable progress every year, which is important for the large-scale forcing that will feed Arome: a regional model can improve a good large-scale forecast, but can rarely correct a bad one. Among the most noteworthy developments, it can be remembered that: Arpège has recently acquired the ability to use most of the modern meteorological satellites, and is getting ready to use Metop, its physical parameterisations have recently begun including prognostic clouds and precipitation, its 4D-Var assimilation system is one of the most state-of-the-art in the world. Aladin has recently been given its own 3D-var data assimilation (Fischer et al., 2005 ; Fischer et al., 2006 ), with the same physics as Arpège. The Arome model will have an even higher level of sophistication, in the spatial resolution of the computations and in the level of detail of the depicted physical processes. A forecast system uses a numerical model of the evolution of the atmosphere, but is also a data assimilation system that initialises model forecasts with the newest possible and most accurate observations. A good large-scale forcing and a good model are not enough to forecast Cévenol precipitation, thunderstorms and fog: the atmosphere has a local-scale memory (figure 2), and in order to make good forecasts, it is essential to insert into the system the precursors of the phenomena, using a fine-scale assimilation of the determining parameters (see box below). In Arome, these are mainly the humidity field, and low-level structures like convergence lines. For that purpose, Arome will assimilate more exhaustively than Aladin the data from radars (Doppler winds, 3D reflectivities), satellites (clear-sky radiances, clouds) and national networks (mainly Radome ground stations). In this area, Meteo-France is at the cutting edge in the world, which will be the main asset of Arome as compared with foreign fine-mesh models. Figure 2 Example of the impact of assimilation on rainfall forecast during a Cévenol precipitation event, at short range (accumulated rainfall between terms 3 h and 6 h): on the left, Arome forecast from a low-resolution (10 km) Aladin analysis; on the right, Arome
4 forecast after 3 h of 3D-Var assimilation in Arome (2.5 km resolution). Green and beige areas are intense rainfall areas. Differences are mainly due to Radome observations of low-level temperature and humidity; the forecast using the Arome assimilation is better. Coupling between models Like the local atmosphere, a regional model reacts to a set of factors, which should all be carefully modelled in order to produce good forecasts. The diagram aside summarises the main information sources needed for Arome forecasts: lateral forcing by the numerical forecast from a larger-scale atmospheric model (Arpège, Aladin or IFS), forcing from below by surface conditions on continents and oceans, whose non-constant parameters (snow, pack ice, vegetation, ground moisture ) have to be initialised adequately, initial state of the atmosphere in the Arome model, to be created by a data assimilation which is itself coupled with a large-scale assimilation. Optionally, the model can be interactively coupled with the evolution of models of upper ocean, dust, chemistry and/or aerosols, in addition to its internal physical parameterisations (radiation, turbulence, surface fluxes, cloud microphysics, subgrid convection). Components of Arome The Arome project has been drafted with specific objectives: improving the forecasting of dangerous convective phenomena (thunderstorms, sudden floods, gust wind, intense precipitation), of local events and of low-level meteorology (wind, temperature, state of the ground, turbulence, visibility ). The base of this development is the Arome software, a brand new model with its own data assimilation (Ducrocq et al., 2005). The novelty of the model is first and foremost the horizontal fineness of the computations (2 to 3 km horizontal grid in the 2008 version of Arome), a more realistic modelling of clouds (prognostic hydrometeors, with an ice phase and graupel) and of turbulence (prognostic turbulent kinetic energy), and also a detailed description of surfaces (mountains, cities, coasts, lakes, snow ). The orography of the Arpège, Aladin and Arome models is shown in figure 3, centred on the Alps. The contribution of assimilation is the use of satellites, radars and regional meteorological networks, in a system similar to Aladin (3D-Var method) but with a stronger spatial resolution, which will mainly improve very short-term forecasts (less than 12 hours).
5 The calendar for Arome is made of a period of real-time testing at Météo-France since June 2005, the implementation of the system on a powerful supercomputer in 2007, and the production of real-time Arome analyses and forecasts starting from The ancestry of the Arome software is interesting. In order to share the research and development with the communities of numerical prediction and atmospheric scientific research, while maintaining our many cooperations (national cooperation with CNRS, international cooperation within Aladin, Hirlam and ECMWF, and cooperations between CNRM teams on Arpège, Aladin, Arome and Méso-NH), Arome is a hybrid between the Aladin software, bringing the dynamical kernel and the assimilation (Bénard et al., 2005 ; Fischer et al., 2005), and the Méso-NH software, bringing the physical parameterisations which are still being improved by the research teams (Lafore et al., 1998). In spite of the unwieldy character of this software mechanism, Arome is a powerful pipe, which is fed by the best developments of IFS, Arpège, Aladin, Méso-NH, and puts them at everybody s disposal in its operational use. It is an opportunity for researchers outside numerical prediction teams to use a powerful prediction and assimilation software in their experiences, and to have their work valued directly in an operational department. Such advantages did not escape the attention of our European and North African partners from the Aladin and Hirlam consortiums, among which many are preparing to use the Arome software themselves, at the price of a few modifications (for instance the Alaro-0 model software, which is Arome with different physical parameterisations). Later on in the process, Arome is assigned the task to supply numerical products to MétéoFrance forecasters for supervised production, and also to application models sensitive to small-scale meteorology, like the Cobel fog model (Bergot et al., 2005), which will benefit from better forcing from Arome in comparison with the presently used Aladin. The surface physics module in Arome is particularly remarkable: Surfex, as it is called, uses a very high-resolution geographical database over Europe (Champeaux et al., 2005) to represent a great cartographic richness: vegetation cover, soil types, oceans, lakes, ice and snow, cities. The meteorological effects can be locally important, for instance on urban heat islands, breezes, or emission of desert dust. Surfex also plays a role in the coupling of Arome with models of hydrology and atmospheric chemistry, which enables it, among other things, to model the cycle of the carbon dioxide.
6 Figure 3 Respective orographies of the models Arpège (left), Aladin (middle) and Arome (right) in the Alpine region. Assimilation Data assimilation is a method of using observations so as to optimise the initial state of forecasts. Observed data are inserted into the model in real time, in order to have it stick better to reality. Small initial errors can make a forecast fail if they happen to be in sensitive areas, which accounts for the fact that about half of failed forecasts can be explained by such errors. They can be improved by refining the mathematical methods for assimilation and by developing observation networks. Several steps Assimilation consists in several steps: first, useful data have to be received in time in the databases of the forecast centre. This is relatively easy for ground observations, but is sometimes problematic for moving satellites, which are not always above the area of interest of the model or within reach of a receiving antenna. Because of the very high number of spatial agencies and meteorological satellites and because of their relatively short lifetime, it may happen that data fluxes from some satellites cannot be used at once after their launch. New radars also experience such mishaps. At the end of the day, only a fraction of the measurements carried out can be used for numerical prediction. The second step in the assimilation is sorting out the data, which is also called quality control. Each use of a measurement in the assimilation has a computing cost, and bears a risk of degrading the forecast if the data is affected by errors. A filtering software is able to only keep the data that almost certainly improve the forecast, which represents about 10% of the data received(1).
7 (1) Rejected data are for the most part satellite radiances which cannot be used, because they are affected by clouds or surfaces in a way that we are still unable to model. It may also be data of too high density locally (e.g. most aircraft measurements in the vicinity of airports) as compared with the numerical resolution of the models. But these data are still precious, either for future versions of the modelling system or for other applications, like nowcasting. The third step is analysis, which serves to insert the measured information into the model grid. It is the computation of a weighted average between the most recent model forecast and the corrections suggested by the measurements, which are spatially smoothed so as to comply with the properties of the meteorological fluid: hydrostatic and geostrophic quasi-equilibrium, Ekman pumping, positivity and absence of supersaturation for humidity, for instance. In Arome, acquisition and sorting out of observations are similar to the Arpège and Aladin systems, using a higher density of data over France, since Arome has a stronger resolution there. Analysis is realised by the variational technique 3D-Var, refined in order to properly take into account the temporal distribution of observations. In Arome, each observation is used to correct the atmosphere inside an area of about 30 km in radius and 2,000 m in altitude. Various observation classes Among the observation classes most important for Arome, we can note measurements of precipitation systems (radars, microwave satellite radiances), of low-level humidity (today Radome automatic stations and GPS, tomorrow maybe lidars and radars), of low-level wind (Radome stations, Doppler radars and satellite scatterometers), of upper-level wind and temperature (Cris and Iasi satellite radiances, aircraft wind profiles, radiosoundings and profiling radars). The amount of effort assigned to the use of each observation type depends on the quality of the measurement, on how informative it is about the state of the atmosphere, and on the quality of the network coverage: for instance, a measuring device, even an excellent one, with only few measurement points over France is interesting for scientific studies, but is rarely useful for weather prediction The use of satellite radiances and of conventional in-situ observations, in spite of its importance, does not fundamentally differ between Arome and larger-scale systems like IFS, Arpège and Aladin. The main thing to note about it is that the better spatial resolution enables Arome to use high-density observations, partially getting rid of representativeness problems due to local disturbances in the measurements, such as mountains and clouds. On the other hand, the use of radars will be radically new. It is particularly fortunate that its development coincides with the renewal of the radar network in mainland France (Panthere project at Météo-France, which includes equipping several radars with measurements of Doppler wind and polarimetric reflectivity performing a three-dimensional scan of the troposphere). Doppler winds are wind measurements restricted to certain directions, in lower layers and clouds, in the vicinity of French radars (figure 4); their relevance to prediction consists mainly in the description of the convergence lines associated with the birth and evolution of large convective clouds, which is important for forecasting isolated thunderstorms and for aeronautical assistance. Arpège and Aladin take into account convective systems mainly in an implicit form called parameterisation of subgrid convection (see box on page 15): these models describe the net effect of convection, rather than the often complex three-dimensional wind circulation associated with cloud cells. The use of Doppler radar winds in Arome will be much more efficient than in these older models. The synergy between the model and the Doppler radars will probably be the most impressive contribution of Arome to regional
8 numerical prediction, and also in a more distant future to nowcasting, that is, forecasting for up to a few hours, which has been so far quite dependant on the extrapolation of radar and satellite images. These developments are presently studied in the framework of the Flysafe European project for improvement of air traffic security. Figure 4 Low-level radial Doppler wind observed by the Trappes radar (top) and simulated by a model (bottom). Colours represent different values of the radial component of the wind relative to the radar antenna (located in the Paris area). Data are interpolated at the same spatial resolution (10 km). White zones are unobserved. This ambiguous information is difficult to visualise, but is easily used in the 3D-Var assimilation to improve the initialisation of wind in Arome forecasts. The convection grey zone The size of the smallest phenomena described by a numerical model is about twice the size of the computation grid. In Arome, this mesh size is 2.5 km horizontally and about 300 m vertically (17 m near the ground). Mesh fineness is constrained by the processing power of the computer used for the forecast. This mesh allows explicit calculation of the structure of large convective clouds such as cumulonimbus clouds, which are controlled by air circulations represented rather well on the Arome grid. These circulations produce threedimensional cloud structures which are important to forecast certain physical processes (hail, gust winds, anvils, rain-snow limit, transportation of a cloud far from its formation area, diurnal cycle ). In a lower resolution model such as Arpège or Aladin, but also in Arome for small clouds, the calculation grid is too coarse, and the idea of simulating the details of these clouds was given up; only the net effect is represented at grid scale (production of rain, of turbulent mixing, of partial cloud coverage, of latent heat ), which is a stopgap measure for forecasting the cloud-affected meteorological parameters. This net effect is calculated by rather empirical formulas, called parameterisations, which deduce them from characteristics of the atmosphere at grid scale (proximity of the air to moist saturation, vertical thermal instability, occurrence of conditions often associated with a given cloud type ).
9 Radar reflectivity data and precipitation Another challenge in radar data assimilation is the use of reflectivity data, which give information on the distribution and intensity of upper-level precipitation. This idea is particularly difficult to work out, because precipitation is the result, rather than the cause, of the cloud mechanics: the assimilation of reflectivity data should ideally figure out threedimensional structures for wind, temperature and humidity from precipitation images, which is generally out of reach of all known assimilation methods, for various reasons (starting with non-linearity of small-scale cloud microphysics, which precludes, at first sight, the use of classical algorithms like 4D-Var). In the Arome project, the effort now focuses on Bayesian non-linear assimilation techniques, which can provide a rather simple repositioning of convective columns in favourable meteorological situations. To make further progress, an ambitious research programme is preparing to be developed at Météo-France, based on the automatic identification of coherent structures in the atmosphere and on an intelligent method to take into account moist processes in data assimilation. The biggest challenges for the future of assimilation will be realistic assimilation of precipitation, use of cloud observations, and taking account of the spatio-temporal variations in the radius of influence of observations, for instance across inversions at the top of the boundary layer (which are absolutely necessary for the analysis of fog and low clouds), near mountains and coasts. However, the value of Arome forecasts will not only stem from its assimilation. Large-scale assimilations (such as Arpège) will still dominate the forecasting of dynamical phenomena like winter storms, which come from over the borders of the Arome domain. Modelling the surfaces will still be essential to predict phenomena forced by lower layers: coastal breezes, fog formation, urban heat islands, effects of snow on the ground and of frost. Assimilation will thus concentrate on precursors of the changing phenomena which have a strong regional memory: formation and propagation of the convective systems and their gust fronts, arrangement of precipitation systems in cells and bands, evolution of inversions, of fog banks and of low clouds.
10 Figure 5 Accumulated rainfall over three hours during the Cévenol flood event of 6 September Top: Aladin forecast; middle: Arome forecast without own assimilation; bottom: observations (radar data with rain gauge correction) of actual rainfall. The crucial zone (most intense rain) is in yellow and orange. The Arome model performs better than Aladin, though the latter gives the former its initial conditions and lateral boundary conditions.
11 First results The most awaited contribution of Arome is the ability to properly forecast Cévenol-type intense Mediterranean precipitation. The first evaluations made in 2005 and 2006 naturally focused on recent Cévenol events most present in the media. Although the system is still incomplete (the synergy between radars, assimilation and high-resolution model has not yet been tested), these tests confirmed that Arome is clearly more realistic than its predecessors (IFS, Arpège and Aladin) when forecasting Cévenol precipitation 6 to 24 hours in advance, particularly concerning intensity distribution, timing and location of the most intense precipitation, which are the most important criteria to anticipate flood risks. The positive contribution of Arome (figure 5) is clear, both for the direct use of forecast maps and for the subjective interpretation of the risk of intense precipitation: other models only describe the risk areas, leaving the forecaster with the heavy responsibility of correcting their tendencies to under- or over-predict the accumulated intense precipitation, depending on the case. Arome provides precipitation structures with intensities closer to reality, so that the residual error left for the forecaster to correct mainly consists in the exact location of the event. These conclusions cannot be generalised to all Cévenol events, which have varying mechanisms: some can be easily predicted by all models; others are entirely determined by local orographic forcing and do not need any particular effort on assimilation; some do not produce significant precipitation in spite of the misleadingly alarming meteorological context; and still others produce impressive and unexpected showers which still elude all modelling efforts, in spite of the research studies to understand their causes. The tests available today have for the most part been achieved using a direct coupling of Arome with Aladin, without high-resolution assimilation. The first experiments with assimilation of Cévenol events in Arome show en extra improvement of forecasts at the regional scale. The future use of radars and satellites on the western Mediterranean will probably make it possible to get even more predictability at the small scale and at very short range. Numerical systems are nowadays the handmaidens of meteorological prediction: besides tests of Cévenol events, Arome forecasts still had to be evaluated in a large range of atmospheric conditions, and with several evaluation criteria. Since the summer of 2005, two Arome forecasts a day have been run at Météo-France, over various areas, and have been compared to Aladin. The high spatial resolution of the model dramatically improves the geographical detail of low-level forecasts, as illustrated in the figures. They display the meteorological effects of towns, coasts, or large valleys: urban heat islands (figure 6) over large cities, diurnal cycle of coastal and valley breezes, local orographic winds, shelter effect in the vicinity of mountain ranges (figure 7), fog banks in basins, frost areas (figure 6). In some situations, Arome builds atmospheric structures which were so far inaccessible to operational models, such as thunderstorm gust fronts (figure 8), cumulonimbus anvils, multilayer clouds, turbulent areas of Autan and Mistral winds (figure 7), sudden showers (figure 9), cumulus streets.
12 Figure 6 Arome simulation of low-level temperatures (coloured, frost areas in white) and wind (blue arrows) over north-eastern France on 10 February Arome shows the urban heat islands (dark pink) in Paris, Lyon, Grenoble, Genève, the relative mildness of Lake Léman, and the frost areas on the Langres plateau and in mountain ranges. Figure 7 Aladin (left) and Arome (right) simulations of low-level wind over the Var department during a Mistral event. Colours represent the strength of the wind, white areas are places of relative calm. Arome shows more clearly than Aladin the area of influence of the Mistral, the shelter and rotation effects in the vicinity of the mountains, the wind gradient on the coasts.
13 Figure 8 Arome simulation of a group of thunderstorms in the Paris area on 4 August 1994 at 18:00. Left: patches of rain on the ground; middle: potential temperature (coloured zones, cool air in blue and green, warm air in red and magenta) and low-level wind vector (black arrows); right: perspective visualisation of the associated cloud volume (grey: cloud liquid water, blue: cloud ice, magenta: rain). Notice the inflow of cool air from the English Channel, the cooling under the thunderstorm, the divergent gust fronts associated with density currents, the characteristic tower and anvil shape of cumulonimbus clouds. Figure 9 Accumulated rainfall (top) and snow (bottom) during 24 hours in one of the first tests of Arome over the whole of mainland France, on 10 March 2006, a winter storm day. Red areas indicate the strongest precipitation. Notice strong accumulations on the Pyrenees range and upstream (orographic blocking). The striated appearance of the fields is due to the sudden showers.
14 Although these structures are not always judiciously created, comparison with observations shows that they are improvements in most cases. Research teams are presently correcting the identified weaknesses of the model: priority is given to stratiform clouds and light precipitation. Today, the most impressive recognised strengths of Arome are intense Mediterranean convective precipitation and orographically forced precipitation structures (figure 10), sometimes very far downstream from the mountains. It is a safe bet that there will be many other such strengths over time, with the growing power of the Arome assimilation and of the tools for objective verification of small-scale forecasts. Figure 10 Example of an orographically forced precipitation structure. It is a rain band related to a convergence zone downstream the Pyrenees, extending until Lyon in a south-western flow. Left: rainfall from Arome simulation; right: rainfall from radar observations. (Note that colour ranges are different.) What is the contribution for the user of the model? It was mentioned above that Arome contributes to the anticipation of dangerous Cévenol-type convective events. Experimentation on other kinds of phenomena that need a special vigilance has made less progress, but first results are promising. The high resolution of the model ensures better relevance and improved fineness for the local forecasts of storms, snowfalls, frost, heat waves, strong winds. First evaluations of the forecasts of low-level temperatures (figure 11) and wind suggest that Arome, in extreme cases but also on average, will noticeably improve derived products: air quality, aeronautics, road conditions, pollutant dispersion, hydrology, oceanography and coastal or mountain meteorology, just to quote a few applications.
15 Figure 11 Objective comparison of Arome (red) and operational Aladin (black) forecasts of temperature, over three days in August 2006 in south-eastern France. Forecasts are launched every day at midnight and are evaluated over the first twenty-four hours. Continuous lines are mean-square forecast errors, dashed lines are errors averaged over all of the Radome observation network. This graph shows that, in spite of strong mean errors (related to a teething problem of Arome which is being corrected), the Arome model, in the evaluated preliminary version, is already better than Aladin. Like its elder brother Aladin, Arome will be a transferable system. Except for assimilation of the observations specific to mainland France, the whole system can be easily cloned to any area of the world, by coupling it with Arpège and Aladin and, if necessary, with the help of the IFS model for the most remote areas. These distant applications will be limited only by the allocated computing power. Already now, interesting tests have been performed by MétéoFrance over Northern Africa, Equatorial Africa and Turkey, and also in Central Europe and Scandinavia in the countries of our foreign partners from the Aladin and Hirlam consortiums. These partners will help us optimise Arome for a rich diversity of climates: Arctic, Nordic, continental, dry Mediterranean, Middle-East, Sahara. A particular effort will be made for future use in the French overseas departments and territories. Fog forecasting is a crucial stake at institutional level for Météo-France. It almost totally eludes current numerical models. Study of about ten fog-prone cases in the winter of (figure 12) showed that Arome already brings a clear added value, which is still being optimised by improving its initialisation, its modelling, and the coupling with the local fog model Cobel. During the winter of , Arome has been involved in the experimental measurement campaign Parisfog, which is devoted to understanding and forecasting fog in the Paris area. The ability of Arome to model three-dimensional boundary layer forcing, local orography and lakes, and to finely analyse low-level humidity, opens exciting perspectives for the improvement of fog formation and dissipation in the next years.
16 Figure 12 Example of Arome forecast of fog (left, predicted fog is represented in grey) and the corresponding observation (right, Météosat satellite visible image, light grey areas are fog ), over south-western France on 11 November Arome, a tool for atmospheric research The institutional and software structure of Arome is completely innovative, since a founding feature of the project is its openness to the research world. This fundamentally sets it apart from the Aladin system and from rival foreign models, which are usually characterised by complete control of the software by operational departments or research teams. Arome takes the bet that there is more to win than to lose by using a hybrid group of developers, based on the numerical forecasting community, but also on some of the research teams most competent to keep some state-of-the-art components of the system at the highest possible level. Indeed, with the growing sophistication of the processes and observations required for meteorological modelling (continental surfaces, oceans, aerosols, cloud microphysics, radars, hydrology ), it is becoming impossible for numerical prediction groups to meet all needs on their own. Involving specialised researchers means an increasing quality and quantity of available brains. This of course implies some clever organisation of the work. In practice, the Méso-NH model, which is better adapted to research laboratory methods, is preferentially used for the very high-resolution experiments needed to improve Arome. Modifications of the physical components of Méso-NH are introduced in Arome on a regular basis. Conversely, some computational optimisation techniques crucial to real-time numerical prediction are being developed in parallel in Méso-NH and in the IFS/Arpège/Aladin/Arome system, so as to ensure operational efficiency of the latter. The Méso-NH/Arome couple is thus a convenient way for researchers to have their modelling developments valued quickly in an operational weather prediction system. And it is also an interesting tool for the upstream research, for two reasons. Firstly, the particular organisation of the computations in Arome enables it to make forecasts with a numerical cost 5 to 50 times lower than Méso-NH and other similar models. Although Arome does not offer all the freedom and flexibility of a research software, it makes it possible to carry out experiments that otherwise could not be made because they would be too costly. These are the experiments implying a great number of simulations (as opposed to case studies) or the handling of a great number of chemical species and aerosols; for this latter application, a research version with
17 atmospheric chemistry has been created. Arome will become even more useful for research when it can carry out simulations with a horizontal grid finer than one kilometre, which is a development currently under study. Another feature important for research is the ability of Arome to assimilate high-resolution data, with very good capabilities since it is based on the IFS software which has a very good reputation in this matter, particularly for its use of satellite data. Such assimilation software are often difficult to cope with for research laboratories, because of their complexity and their dependence on an operational observation database. To solve this difficulty, Arome has been integrated into an experiment configuration system developed at Météo-France, called Olive. In its most classic use, Olive allows for recalculation of a forecast or of an operational assimilation period at a later time, after modifying some parameters (number of assimilated observations, model settings, software itself, etc.). An automatic comparison with the archive of operational observations and forecasts makes it possible to easily check whether the modifications tested in this numerical experiment would have benefited the operational production system or not. It is even possible to reproduce a colleague s experiment and then change the desired parameters. For a researcher, it is a convenient way to study the practical behaviour of the operational system and to carry out more theoretical studies, for instance on assimilation techniques. Olive will also be a fantastic tool to add value to the measurement campaigns during which many experimental observations are made but are seldom assimilated by operational meteorological centres. Olive makes it possible for laboratories to insert their own standard or experimental observations (such as new GPS, radar and lidar instruments), study the value of these observations, and produce reference reanalyses for the measurement campaigns. When properly carried out, these reanalyses are better than the operational analyses and are ideal to study the phenomena covered by these campaigns, thanks to the spatialisation of observations that is brought by the data assimilation. Olive presently makes it possible to perform Arome forecasts and assimilations (and Aladin and Arpège as well) at the Météo-France computer centre via the Internet, using if necessary the ECMWF supercomputer. Porting to other computers, for instance PCs or clusters, is under way. The use of Arome with a scientific purpose is firmly established at Météo-France, where research teams have been contributing to the Méso-NH and Arpège/Aladin systems for more than ten years, which directly benefits Arome. This use has recently spread to European and North African scientists from the Aladin and Hirlam consortiums. The Arome model has been deployed in quasi-real time, as a demonstration, for the measurement campaigns Amma (African monsoon, summer 2006) and Parisfog (fog in the Île-de-France region, winter ). It has also been used for past situations from the MAP campaign (Mesoscale Alpine Experiment, 1999). During the summer of 2007, the full Arome system with data assimilation will be employed for the international campaigns Cops (Convective and Orographic Precipitation Study) and MAP D-phase, orientated towards forecasting of convective rain and hydrology in Europe, which are issues at the heart of the Arome project. These campaigns will include the study of new measuring instruments: GPS and lidars.
18 Prospects In accordance with its institutional commitments, Météo-France will deploy Arome for realtime forecasting over the whole of mainland France in This system will complement Arpège and Aladin, it will be used by forecasters and for the elaboration of finalised products. So the year 2007 will be a phase of final integration and exhaustive evaluation of the quality of the system. Arome will not stop there, however: like any numerical prediction system, it is meant to improve, diversify towards new applications, and exchange software components with other numerical prediction centres. Likely evolutions include most notably: application to nowcasting using a frequent refresh of the model; application to climatology using reanalyses; design of a high-resolution probabilistic forecast system; growing use of the model outside mainland France; its evolution towards very high-resolution local modelling on interest sites, such as industrial sites, airports and large urban areas, for an integrated modelling of the environment. Thanks The Arome project is the result of the work of dozens of different staff members, institutes and countries. We are particularly grateful to the whole CNRM/GMAP and CNRM/GMME teams, the Laboratoire d aérologie of CNRS, the numerical prediction teams from Aladin partner institutes and from ECMWF, as well as the Météo-France staff and management involved in writing the contrats d objectifs (performance contracts) from 2000 to 2008, who gave this project its founding impulse. Bibliography Bénard P., J. Masek and J. Vivoda, 2005 : Stability of Leap-Frog Constant-Coefficients Semi-Implicit Schemes for the Fully Elastic System of Euler Equations. Case with Orography. Mon. Wea. Rev., 133, Bergot T., D. Carrer, J. Noilhan and P. Bougeault, 2005 : Improved site-specific numerical of fog and low clouds: a feasibility study. Weather and Forecasting, 20, Champeaux J.-L., V. Masson and R. Chauvin, 2005 : Ecoclimap: a global database of land surface parameters at 1 km resolution. Meteorological Applications, 12, Ducrocq V., J.-P. Lafore, J.-L. Redelsperger and F. Orain, 2000 : Initialisation of a fine scale model for convective system prediction: a case study. Quart. J. Roy. Meteor. Soc., 126, Ducrocq V., F. Bouttier, S. Malardel, T. Montmerle and Y. Seity, 2005 : Le projet Arome, crues méditerranéennes : les réponses scientifiques et techniques de l État. [The Arome project, Mediterranean floods: scientific and technical answers from the (French) state.] La Houille Blanche, 2,
19 Fischer C., T. Montmerle, L. Auger and B. Lacroix, 2006 : L assimilation opérationnelle de données régionales à Météo-France. [Operational assimilation of regional data at MétéoFrance.] La Météorologie, 8e série, 54, Fischer C., T. Montmerle, L. Berre, L. Auger and S. E. Stefanescu, 2005 : An overview of the variational assimilation in the Aladin/France NWP system. Quart. J. Roy. Meteor. Soc., 613, Lafore J.-P., J. Stein, N. Asencio, P. Bougeault, V. Ducrocq, C. Fischer, P. Héreil, J.-L. Redelsperger, E. Richard and J. Vilà-Guerau de Arellano, 1998 : The Méso-NH atmospheric simulation system. Part I: adiabatic formulation and control simulations. Ann. Geophys., 16, Sun J. and N. A. Crook, 2001 : Real-time low-level wind and temperature analysis using single WSR-88D data. Weather Forecast, 16, Internet sites La Météorologie - n 58 - août 2007
WWW TECHNICAL PROGRESS REPORT ON THE GLOBAL DATA- PROCESSING AND FORECASTING SYSTEM (GDPFS), AND THE ANNUAL NUMERICAL WEATHER PREDICTION (NWP) PROGRESS REPORT FOR THE YEAR 2005 HUNGARIAN METEOROLOGICAL
Page 18 Heavy Precipitation and Thunderstorms: an Example of a Successful Forecast for the South-East of France François Saïx, Bernard Roulet, Meteo France Abstract In spite of huge improvements in numerical
Status of AROME news since Bratislava '05 based on contributions from CNRM GMAP & GMME teams,lab. Aérologie, aladinists 1. System & workforce 2. Model & software 3. Assimilation 4. The Future 1 1. Multiscale
Towards an NWP-testbed Ewan O Connor and Robin Hogan University of Reading, UK Overview Cloud schemes in NWP models are basically the same as in climate models, but easier to evaluate using ARM because:
SATELLITE USES FOR PURPOSE OF NOWCASTING Kedir, Mohammed National Meteorological Agency of Ethiopia Introduction The application(uses) of satellite sensing data deals to obtain information about the basic
Evaluation of a Fully Coupled Atmospheric Hydrological Modeling System for the Sissili Watershed in the West African Sudanian Savannah Titelmasterformat durch Klicken June, 11, 2014 1 st European Fully
High Performance Computing for Numerical Weather Prediction Isabella Weger European Centre for Medium-Range Weather Forecasts Slide 1 ECMWF ECMWF A European organisation with headquarters in the UK Established
The role of Earth Observation Satellites to observe rainfall Riko Oki National Space Development Agency of Japan Outline 1. Importance of rain measurement 2. TRMM and Its Achievements 3. Outline of GPM
Introduction HIRLAM 7.2 Differences between the current operational HIRLAM version 7.0 and the next operational version 7.2 are described in this document. I will try to describe the effect of this change
Operational Mesoscale NWP at the Japan Meteorological Agency Tabito HARA Numerical Prediction Division Japan Meteorological Agency NWP at JMA JMA has been operating two NWP models. GSM (Global Spectral
Large Eddy Simulation (LES) & Cloud Resolving Model (CRM) Françoise Guichard and Fleur Couvreux Cloud-resolving modelling : perspectives Improvement of models, new ways of using them, renewed views And
University: Florida Institute of Technology Name of University Researcher Preparing Report: Sen Chiao NWS Office: Las Vegas Name of NWS Researcher Preparing Report: Stanley Czyzyk Type of Project (Partners
Developing Continuous SCM/CRM Forcing Using NWP Products Constrained by ARM Observations S. C. Xie, R. T. Cederwall, and J. J. Yio Lawrence Livermore National Laboratory Livermore, California M. H. Zhang
for WP4.1.3 // meeting, 22 Sept 2005 morning, Francoise Guichard some inferences from the EUROCS project EUROCS: european project on cloud systems in NWP/climate models European Component of GCSS (GEWEX
STATEMENT OF GUIDANCE FOR HIGH-RESOLUTION NUMERICAL WEATHER PREDICTION (NWP) (T. Montmerle) (Version updated by the PoC the 8 th of January, 2016, reviewed by IPET-OSDE-2, April 2016, and approved by the
REGIONAL CLIMATE AND DOWNSCALING Regional Climate Modelling at the Hungarian Meteorological Service ANDRÁS HORÁNYI (horanyi( horanyi.a@.firstname.lastname@example.org) Special thanks: : Gabriella Csima,, Péter Szabó, Gabriella
JP1J.1 TOWARDS A 1D+3DVAR ASSIMILATION OF RADAR REFLECTIVITIES: ONGOING RESULTS Olivier Caumont 1, Éric Wattrelot 1, Véronique Ducrocq 1, François Bouttier 1, Claudine Guéguen 2, and Guy L Hénaff 2 1 CNRM/GAME
(Picture: http://www.dagbladet.no/nyheter/2007/02/24/493138.html) A study of model performance for the February 2007 snowstorm on Sørlandet. Part I: Evaluation against observations Thor Erik Nordeng (P.O.
MICROPHYSICS COMPLEXITY EFFECTS ON STORM EVOLUTION AND ELECTRIFICATION Blake J. Allen National Weather Center Research Experience For Undergraduates, Norman, Oklahoma and Pittsburg State University, Pittsburg,
Global modeling of weather and applications for Dr. Karl G. Gutbrod TPH Basel 2016-01-21 www.meteoblue.com 16-01-26 1 meteoblue AG, Basel a private Weather Information Management company based in Basel,
(1) Global Cloud Resolving Model Simulations toward Numerical Weather Forecasting in the Tropics (FY2005-2010) (2) Scale Interaction and Large-Scale Variation of the Ocean Circulation (FY2006-2011) (3)
Proposals of Summer Placement Programme 2015 Division Project Title Job description Subject and year of study required A2 Impact of dual-polarization Doppler radar data on Mathematics or short-term related
ANALYSIS OF TOURISM POTENTIAL FOR CRETE A. Matzarakis 1, P. Nastos 2 N. Karatarakis 3, A. Sarantopoulos 3 1 Meteorological Institute, University of Freiburg, Germany 2 Laboratory of Climatology and Atmospheric
3 Working Group on Verification and Case Studies 56 Comparative Evaluation of High Resolution Numerical Weather Prediction Models COSMO-WRF Bogdan Alexandru MACO, Mihaela BOGDAN, Amalia IRIZA, Cosmin Dănuţ
376 THE SIMULATION OF TROPICAL CONVECTIVE SYSTEMS William M. Frank and Charles Cohen Department of Meteorology The Pennsylvania State University University Park, PA, 16801 -U.S.A. ABSTRACT IN NUMERICAL
Federal Department of Home Affairs FDHA Federal Office of Meteorology and Climatology Meteorological forecasting Introduction to the COSMO model Urs Germann Head Radar/Satellite/Nowcasting,, Locarno-Monti
Ján Kaňák Slovak Hydrometeorological Institute Jan.email@example.com Overview of the IR channels and their applications EUMeTrain, 14 June 2011 Ján Kaňák, SHMÚ 1 Basics in satellite Infrared image interpretation
Verification working group Sander Tijm Gwenaelle Hello With contributions from Mariska Derkova and Doinia Banciu Contents of talk Plans of cooperation between Hirlam and Aladin in physics Why verification
Weather Radar Basics RADAR: Radio Detection And Ranging Developed during World War II as a method to detect the presence of ships and aircraft (the military considered weather targets as noise) Since WW
Investigations on COSMO 2.8Km precipitation forecast Federico Grazzini, ARPA-SIMC Emilia-Romagna Coordinator of physical aspects group of COSMO Outline Brief description of the COSMO-HR operational suites
All-sky assimilation of microwave imager observations sensitive to water vapour, cloud and rain A.J. Geer, P. Bauer, P. Lopez and D. Salmond European Centre for Medium-Range Weather Forecasts, Reading,
Geography Grade 10 Lesson 1 A low pressure system over an area can cause rain and strong winds! The table below gives us some examples of low pressure systems over an area. Severe storms over South Coast
GCMs with Implicit and Explicit cloudrain processes for simulation of extreme precipitation frequency In Sik Kang Seoul National University Young Min Yang (UH) and Wei Kuo Tao (GSFC) Content 1. Conventional
Nowcasting: analysis and up to 6 hours forecast Very high resoultion in time and space Better than NWP Rapid update Application oriented NWP problems for 0 6 forecast: Incomplete physics Resolution space
Hazard Manager User guide Making use of the weather layers How will this guide help me? This guide will help you understand the information that is available within Hazard Manager and how to make use of
Satellite Weather And Climate (SWAC) Satellite and cloud interpretation Vermont State Climatologist s Office University of Vermont Dr. Lesley-Ann Dupigny-Giroux Vermont State Climatologist firstname.lastname@example.org
WEATHER THEORY Temperature, Pressure And Moisture Air Masses And Fronts Weather Theory- Page 77 Every physical process of weather is a result of a heat exchange. The standard sea level temperature is 59
A new positive cloud feedback? Bjorn Stevens Max-Planck-Institut für Meteorologie KlimaCampus, Hamburg (Based on joint work with Louise Nuijens and Malte Rieck) Slide 1/31 Prehistory [W]ater vapor, confessedly
TROPICAL SYNOPTIC METEOROLOGY Gary M. Lackmann Department of Marine, Earth, and Atmospheric Sciences North Carolina State University Keywords: Hadley cell, trade winds, easterly waves, intertropical convergence
Cirrus (CI) Detached clouds in the form of delicate white filaments or white patches or narrow bands. These clouds have a fibrous or hair like appearance, or a silky sheen or both. with frontal lifting
Climate Extremes Research: Recent Findings and New Direc8ons Kenneth Kunkel NOAA Cooperative Institute for Climate and Satellites North Carolina State University and National Climatic Data Center h#p://assessment.globalchange.gov
Evalua&ng Downdra/ Parameteriza&ons with High Resolu&on CRM Data Kate Thayer-Calder and Dave Randall Colorado State University October 24, 2012 NOAA's 37th Climate Diagnostics and Prediction Workshop Convective
11 th International Conference on Hydroinformatics HIC 2014, New York City, USA FLOODALERT: A SIMPLIFIED RADAR-BASED EWS FOR URBAN FLOOD WARNING XAVIER LLORT (1), RAFAEL SÁNCHEZ-DIEZMA (1), ÁLVARO RODRÍGUEZ
13 CHAPTER TWO SOME CONCEPTS IN CLIMATOLOGY 2.1 The Adiabatic Process An important principle to remember is that, in the troposphere, the first layer of the atmosphere, temperature decreases with altitude.
Analyze Weather in Cold Regions and Mountainous Terrain Terminal Learning Objective Action: Analyze weather of cold regions and mountainous terrain Condition: Given a training mission that involves a specified
Climate of Utah Robert R. Gillies R. Douglas Ramsey Climate in Utah is greatly influenced by its location within the North American Continent with significant local modifications due to topography. Northern,
Estimation of satellite observations bias correction for limited area model Roger Randriamampianina Hungarian Meteorological Service, Budapest, Hungary email@example.com Abstract Assimilation of satellite radiances
[ Climate Data Collection and Forecasting Element ] An Advanced Monitoring Network In Support of the FloodER Program December 2010 1 Introduction Extreme precipitation and the resulting flooding events
Chapter 7 Stability and Cloud Development Atmospheric Stability 1 Cloud Development - stable environment Stable air (parcel) - vertical motion is inhibited if clouds form, they will be shallow, layered
The horizontal diffusion issue in CRM simulations of moist convection Wolfgang Langhans Institute for Atmospheric and Climate Science, ETH Zurich June 9, 2009 Wolfgang Langhans Group retreat/bergell June
A system of direct radiation forecasting based on numerical weather predictions, satellite image and machine learning. 31st Annual International Symposium on Forecasting Lourdes Ramírez Santigosa Martín
Operational and other ALADIN activities in Meteorological and Hydrological Service of Croatia Martina Tudor and Stjepan Ivatek-Šahdan 1. Summary The operational suite has suffered from numerous problems
IMPACT OF SAINT LOUIS UNIVERSITY-AMERENUE QUANTUM WEATHER PROJECT MESONET DATA ON WRF-ARW FORECASTS M. J. Mueller, R. W. Pasken, W. Dannevik, T. P. Eichler Saint Louis University Department of Earth and
Stability & Cloud Development This chapter discusses: 1. Definitions and causes of stable and unstable atmospheric air 2. Processes that cause instability and cloud development Stability & Movement A rock,
Title: Met Éireann High Resolution Reanalysis for Ireland Author(s): Eoin Whelan and Emily Gleeson This article is provided by the author(s) and Met Éireann in accordance with publisher policies. Please
Road weather forecast for different customer systems in Switzerland André-Charles Letestu MeteoSwiss Introduction Switzerland is a mountainous country and its motorways lie in a range of altitudes extending
Diurnal Wind, Relative Humidity and Temperature Variation in the Stellenbosch-Groot Drakenstein Wine-Growing Area* V. Bonnardot 1, O. Planchon 2, V. Carey 3 ** and S. Cautenet 4 1) ARC Institute for Soil,
VALIDATION OF SAFNWC / MSG CLOUD PRODUCTS WITH ONE YEAR OF SEVIRI DATA M.Derrien 1, H.Le Gléau 1, Jean-François Daloze 2, Martial Haeffelin 2 1 Météo-France / DP / Centre de Météorologie Spatiale. BP 50747.
Climatology and Monitoring of Dust and Sand Storms in the Arabian Peninsula Mansour Almazroui Center of Excellence for Climate Change Research (CECCR) King Abdulaziz University, Jeddah, Saudi Arabia E-mail:
Cloud-Resolving Simulations of Convection during DYNAMO Matthew A. Janiga and Chidong Zhang University of Miami, RSMAS 2013 Fall ASR Workshop Outline Overview of observations. Methodology. Simulation results.
Unit 2: Weather Weather Lore: ways to keep track of common trends in weather patterns some have scientific facts, while others are more about coincidence weather lore may vary from place to place as different
Tropical Cloud Population Before Satellites Visual Observation View from and aircraft flying over the South China Sea Radiosonde Data Hot tower hypothesis Riehl & Malkus 1958 Satellite Observations Post
Kingdom Schools Science Department Grade 5- Term 2 Name: Date: Section: Summary Booklet Topic 8 Weather Patterns Lesson 1 :How does Air move? Skill 8-1: Understand that air pressure is related to altitude,
Monsoon Variability and Extreme Weather Events M Rajeevan National Climate Centre India Meteorological Department Pune 411 005 firstname.lastname@example.org Outline of the presentation Monsoon rainfall Variability
Weather Vocabulary Weather- The condition of the atmosphere at a certain time and place. Meteorologist-A scientist who studies weather Weather maps- a map or chart showing the meteorological conditions,
Introduction to parametrization development Anton Beljaars (ECMWF) Thanks to: The ECMWF Physics Team and many others Outline Introduction Physics related applications Does a sub-grid scheme have the correct
Quantitative Precipitation Estimation and Quantitative Precipitation Forecasting by the Japan Meteorological Agency Kazuhiko NAGATA Forecast Division, Forecast Department Japan Meteorological Agency 1.
VALIDATION OF SATELLITE-DERIVED RAINFALL ESTIMATES AND NUMERICAL MODEL FORECASTS OF PRECIPITATION OVER THE UNITED STATES J.E. Janowiak, P Xie, R.J. Joyce, M. Chen, and Y. Yarosh Climate Prediction Center/NOAA
These 3 climate graphs and tables of data show average temperatures and precipitation for each month in Victoria, Winnipeg and Whitehorse: Figure 1.1 Month J F M A M J J A S O N D Year Precipitation 139
reflect You re going on vacation in a week and you have to start thinking about what clothes you re going to pack for your trip. You ve read the weather reports for your vacation spot, but you know that
Chapter Overview CHAPTER 6 Air-Sea Interaction The atmosphere and the ocean are one independent system. Earth has seasons because of the tilt on its axis. There are three major wind belts in each hemisphere.
Module 2.3 Wind Energy 2.3 Wind Energy Utilization Gerhard J. Gerdes Workshop on Renewable Energies March 9, 2005 Majuro, Republic of the Marshall Islands Content Global and local wind systems Influence
Name: Class: Date: Meteorology Study Guide Modified True/False Indicate whether the sentence or statement is true or false. If false, change the identified word or phrase to make the sentence or statement
Introduction to Wildland Fire Management Fireline Safety 101: Keep informed on fire weather conditions and forecasts REM 244: Introduction to Wildland Fire Management 3. Introduction to Fire Weather Surface
Turbulent mixing in clouds latent heat and cloud microphysics effects Szymon P. Malinowski1*, Mirosław Andrejczuk2, Wojciech W. Grabowski3, Piotr Korczyk4, Tomasz A. Kowalewski4 and Piotr K. Smolarkiewicz3
Mountain Leader Training Introduction to mountain weather Mountain Weather Why worry about the weather! How is weather created! The atmosphere Weather systems Airmass modification Depressions and fronts
SOLAR IRRADIANCE FORECASTING, BENCHMARKING of DIFFERENT TECHNIQUES and APPLICATIONS of ENERGY METEOROLOGY Wolfgang Traunmüller 1 * and Gerald Steinmaurer 2 1 BLUE SKY Wetteranalysen, 4800 Attnang-Puchheim,
Weather Notes with Poole Sailing It is a requirement of the SOLAS rules that we have a passage plan whenever we take a boat to sea, and an essential part of that plan is an understanding of the weather