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The article describes how a 223-year-old tree in Versailles, Spain, was uprooted by an unusually high wind gust. The tree, a purple beech that was 82 feet tall, collapsed during a severe winter storm that hit France and Spain in January 2009. The fallen tree was eventually utilized as a source of paper making.

Some properties of chaotic dynamical systems can be probed through features of recurrences, also called analogs. In practice, analogs are nearest neighbors of the state of a system, taken from a large database called the catalog. Analogs have been used in many atmospheric applications including forecasts, downscaling, predictability estimation, and attribution of extreme events. The distances of the analogs to the target state usually condition the performances of analog applications. These distances can be viewed as random variables, and their probability distributions can be related to the catalog size and properties of the system at stake. A few studies have focused on the first moments of return-time statistics for the closest analog, fixing an objective of maximum distance from this analog to the target state. However, for practical use and to reduce estimation variance, applications usually require not just one but many analogs. In this paper, we evaluate from a theoretical standpoint and with numerical experiments the probability distributions of the K shortest analog-to-target distances. We show that dimensionality plays a role on the size of the catalog needed to find good analogs and also on the relative means and variances of the K closest analogs. Our results are based on recently developed tools from dynamical systems theory. These findings are illustrated with numerical simulations of well-known chaotic dynamical systems and on 10-m wind reanalysis data in northwest France. Practical applications of our derivations are shown for forecasts of an idealized chaotic dynamical system and for objective-based dimension reduction using the 10-m wind reanalysis data. [ABSTRACT FROM AUTHOR]

This paper deals with the characteristics and effects of digital filter initialization, as implemented in the operational three-dimensional variational data assimilation (3DVAR) system of the Aire Limitéée Adaptation Dynamique Dééveloppement International (ALADIN)-France regional weather forecast model. First, a series of findings on the properties of the initialization of the model are discussed. Examples of initial spinup linked with inertia--gravity wave occurrence are shown, and the major sources for their generation are listed. These experimental results are compared with past and present experiences concerning the use and need for digital filter initialization. Furthermore, the impacts of switching to an incremental formulation of the filter in data assimilation mode are demonstrated. Second, the effects of the filter formulation on the results of an observation impact study are illustrated. The latter consists of implementing screen-level, 10-m horizontal wind information into the ALADIN 3DVAR analysis. There can, indeed, be some delicate interference between observation impact evaluation and the effects of filtering, at least on short-term forecasts. The paper is concluded with some general considerations on the experimental evaluation of spinup and the link between the assimilation system design and model state filtering. [ABSTRACT FROM AUTHOR]

Regional climate models (RCMs) should be evaluated with respect to their ability to downscale large-scale climate information to the local scales, which are sometimes strongly modulated by surface conditions. This is the case for La Réunion (southwest Indian Ocean) because of its island context and its complex topography. Large-scale atmospheric configurations such as tropical cyclones (TCs) may have an amplifying effect on local rainfall patterns that only a very high-resolution RCM, forced by the large scales and resolving finescale processes, may simulate properly. This paper documents the capability of the Weather Research and Forecasting Model (WRF) RCM to regionalize rainfall variability at very high resolution (680 m) over La Réunion island for daily to seasonal time scales and year-to-year differences. Two contrasted wet seasons (November-April) are selected: 2000-01 (abnormally dry) and 2004-05 (abnormally wet). WRF rainfall is compared to a dense network of rain gauge records interpolated onto the WRF grid through the regression-kriging (RK) technique. RK avoids the point-to-grid comparison issue, but produces imperfect estimates due to sampling, so its quality also needs to be tested. Seasonal rainfall amounts and contrasts produced by WRF are fairly realistic. At intraseasonal and daily time scales, differences to RK are more sizable. These differences are not easy to interpret in sectors where the rain gauge network is less dense and the quality of RK more uncertain, as over the eastern slopes of Piton de la Fournaise volcano where WRF seems to simulate more realistic rainfall than RK. Finally, the heavy rainfall associated with TC Ando on 6 January 2001, is documented. WRF shows weak disagreements with RK, indicating its capability to regionalize rainfall during extreme events. [ABSTRACT FROM AUTHOR]

One of the main objectives of statistical climatology is to extract relevant information hidden in complex spatial-temporal climatological datasets. To identify spatial patterns, most well-known statistical techniques are based on the concept of intra- and intercluster variances (like the k-means algorithm or EOFs). As analyzing quantitative extremes like heavy rainfall has become more and more prevalent for climatologists and hydrologists during these last decades, finding spatial patterns with methods based on deviations from the mean (i.e., variances) may not be the most appropriate strategy in this context of studying such extremes. For practitioners, simple and fast clustering tools tailored for extremes have been lacking. A possible avenue to bridging this methodological gap resides in taking advantage of multivariate extreme value theory, a well-developed research field in probability, and to adapt it to the context of spatial clustering. In this paper, a novel algorithm based on this plan is proposed and studied. The approach is compared and discussed with respect to the classical k-means algorithm throughout the analysis of weekly maxima of hourly precipitation recorded in France (fall season, 92 stations, 1993-2011). [ABSTRACT FROM AUTHOR]

Increasing possibilities for using lightning data-for instance, in monitoring and tracking applications-necessitate proper spatial and temporal mapping of lightning events. It is therefore of importance to assess the capabilities and limitations of a ground-based lightning network of interest to locate electromagnetic signals emitted by lightning discharges. In this paper, data covering two storm seasons, between May and September 2011 and 2012, are used to compare the spatial and temporal lightning observations of three different lightning location systems over an area covering the Benelux and France. The lightning datasets from a regional network employing Surveillance et Alerte Foudre par Interférométrie Radioélectrique (SAFIR) sensors operated by the Royal Meteorological Institute of Belgium (RMIB), a subcontinental network operated by Météorage (MTRG), and the Met Office's long-range Arrival Time Difference network (ATDnet) are considered. It is found that the median location difference among corresponding strokes and flashes between ATDnet and MTRG is 1.9 and 2.8 km, respectively, and increases by a factor of ~3 when comparing ATDnet and/or MTRG to SAFIR. The absolute mean time difference between shared events fluctuates between approximately 25 and 100 μs. Furthermore, lightning data are correlated in terms of relative detection efficiency, quantifying the number of detections that coincide between two different networks. The highest relative values are found among ATDnet and MTRG. In addition, a lower limit of ~25% of ATDnet's flashes are of type inter/intracloud. Finally, it is demonstrated that all three networks are competent in mapping the electrical activity in thunderstorms. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of a hail event that occurred 27 May 2012 over Brignoles, located in southeastern France. The event was observed by an X-band polarimetric radar located in Mont Maurel, 75 km northeast of the hailstorm. Lightning data from the French national network (owned and operated by Météorage) are also used in the study. The analysis highlights that the lightning and radar data provide complementary information that may allow a better microphysical interpretation of the hailstorm and potentially increase the probability of its detection. [ABSTRACT FROM AUTHOR]

Climate change impacts on wave conditions can increase the risk of offshore and coastal hazards. The present paper investigates wave climate multidecadal trends and interannual variability in the Bay of Biscay during the past decades (1958-2001). Wave fields are computed with a wave modeling system based on the WAVEWATCH III code and forced by 40-yr European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-40) wind fields. It provides both an extended spatiotemporal domain and a refined spatial resolution over the Bay of Biscay. The validation of the wave model is based on 11 buoys, allowing for the use of computed wave fields in the analysis of mean and extreme wave height trends and variability. Wave height, period, and direction are examined for a large array of wave conditions (by seasons, high percentiles of wave heights, different periods). Several trends for recent periods are identified, notably an increase of summer significant wave height, a southerly shift of autumn extreme wave direction, and a northerly shift of spring extreme wave direction. Wave fields exhibit high interannual variability, with a normalized standard deviation of seasonal wave height greater than 15%% in wintertime. The relationship with Northern Hemisphere teleconnection patterns is investigated at regional scale, especially along the coast. It highlights a strong correlation between local wave conditions and the North Atlantic Oscillation and the east Atlantic pattern indices. This relationship is further investigated at the local scale with a new method based on bivariate diagrams, allowing the identification of the type of waves (swell, storm, intermediate waves) impacted. These results are discussed in terms of comparison with previous studies and coastal risk implications. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of the rainfall regime of a Mediterranean mountainous region of southeastern France. The rainfall regime is studied on temporal scales from hourly to yearly using daily and hourly rain gauge data of 43 and 16 years, respectively. The domain is 200 × 200 km2 with spatial resolution of hourly and daily rain gauges of about 8 and 5 km, respectively. On average, yearly rainfall increases from about 0.5 m yr−1 in the large river plain close to the Mediterranean Sea to up to 2 m yr−1 over the surrounding mountain ridges. The seasonal distribution is also uneven: one-third of the cumulative rainfall occurs during the autumn season and one-fourth during the spring. At finer time scales, rainfall is studied in terms of rain-no-rain intermittency and nonzero intensity. The monthly intermittency (proportion of dry days per month) and the daily intermittency (proportion of dry hours per day) is fairly well correlated with the relief. The higher the rain gauges are, the lower the monthly and daily intermittencies are. The hourly and daily rainfall intensities are analyzed in terms of seasonal variability, diurnal cycle, and spatial pattern. The difference between regular and heavy-rainfall event is depicted by using both central parameters and maximum values of intensity distributions. The relationship between rain gauge altitudes and rainfall intensity is grossly inverted relative to intermittency and is also far more complex. The spatial and temporal rainfall patterns depicted from rain gauge data are discussed in the light of known meteorological processes affecting the study region. [ABSTRACT FROM AUTHOR]

Studies carried out worldwide show that topography influences rainfall climatology. As in most western Mediterranean regions, the mountainous Cévennes-Vivarais area in France regularly experiences extreme precipitation that may lead to devastating flash floods. Global warming could further aggravate this situation, but this possibility cannot be confirmed without first improving the understanding of the role of topography in the regional climate and, in particular, for extreme rainfall events. This paper focuses on organized banded rainfall and evaluates its contribution to the rainfall climatology of this region. Stationary rainfall systems made up of such bands are triggered and enhanced by small-scale interactions between the atmospheric flow and the relief. Rainbands are associated with shallow convection and are also present in deep-convection events for specific flux directions. Such precipitation patterns are difficult to observe both with operational weather radar networks, which are not designed to observe low-level convection within complex terrain, and with rain gauge networks, for which gauge spacing is typically larger than the bandwidth. A weather class of banded orographic shallow-convection events is identified, and the contribution of such events to annual or seasonal precipitation over the region is assessed. Moreover, a method is also proposed to quantify the contribution of banded convection during specific deep-convection events. It is shown that even though these orographically driven banded precipitation events produce moderate precipitation intensities they have long durations and therefore represent a significant amount of the rainfall climatology of the region, producing up to 40%% of long-term total precipitation at certain locations. [ABSTRACT FROM AUTHOR]

Snow avalanches are natural hazards strongly controlled by the mountain winter climate, but their recent response to climate change has thus far been poorly documented. In this paper, hierarchical modeling is used to obtain robust indexes of the annual fluctuations of runout altitudes. The proposed model includes a possible level shift, and distinguishes common large-scale signals in both mean- and high-magnitude events from the interannual variability. Application to the data available in France over the last 61 winters shows that the mean runout altitude is not different now than it was 60 yr ago, but that snow avalanches have been retreating since 1977. This trend is of particular note for high-magnitude events, which have seen their probability rates halved, a crucial result in terms of hazard assessment. Avalanche control measures, observation errors, and model limitations are insufficient explanations for these trends. On the other hand, strong similarities in the pattern of behavior of the proposed runout indexes and several climate datasets are shown, as well as a consistent evolution of the preferred flow regime. The proposed runout indexes may therefore be usable as indicators of climate change at high altitudes. [ABSTRACT FROM AUTHOR]

The differential phase (ΦDP) measured by polarimetric radars is recognized to be a very good indicator of the path integrated by rain. Moreover, if a linear relationship is assumed between the specific differential phase (KDP) and the specific attenuation (AH) and specific differential attenuation (ADP), then attenuation can easily be corrected. The coefficients of proportionality, γH and γDP, are, however, known to be dependent in rain upon drop temperature, drop shapes, drop size distribution, and the presence of large drops causing Mie scattering. In this paper, the authors extensively apply a physically based method, often referred to as the “Smyth and Illingworth constraint,” which uses the constraint that the value of the differential reflectivity ZDR on the far side of the storm should be low to retrieve the γDP coefficient. More than 30 convective episodes observed by the French operational C-band polarimetric Trappes radar during two summers (2005 and 2006) are used to document the variability of γDP with respect to the intrinsic three-dimensional characteristics of the attenuating cells. The Smyth and Illingworth constraint could be applied to only 20% of all attenuated rays of the 2-yr dataset so it cannot be considered the unique solution for attenuation correction in an operational setting but is useful for characterizing the properties of the strongly attenuating cells. The range of variation of γDP is shown to be extremely large, with minimal, maximal, and mean values being, respectively, equal to 0.01, 0.11, and 0.025 dB °-1. Coefficient γDP appears to be almost linearly correlated with the horizontal reflectivity (ZH), differential reflectivity (ZDR), and specific differential phase (KDP) and correlation coefficient (ρHV) of the attenuating cells. The temperature effect is negligible with respect to that of the microphysical properties of the attenuating cells. Unusually large values of γDP, above 0.06 dB °-1, often referred to as “hot spots,” are reported for 15%—a nonnegligible figure—of the rays presenting a significant total differential phase shift (ΔφDP > 30°). The corresponding strongly attenuating cells are shown to have extremely high ZDR (above 4 dB) and ZH (above 55 dBZ), very low ρHV (below 0.94), and high KDP (above 4° km-1). Analysis of 4 yr of observed raindrop spectra does not reproduce such low values of ρHV, suggesting that (wet) ice is likely to be present in the precipitation medium and responsible for the attenuation and high phase shifts. Furthermore, if melting ice is responsible for the high phase shifts, this suggests that KDP may not be uniquely related to rainfall rate but can result from the presence of wet ice. This hypothesis is supported by the analysis of the vertical profiles of horizontal reflectivity and the values of conventional probability of hail indexes. [ABSTRACT FROM AUTHOR]

This paper presents a study undertaken in preparation of the work leading up to the assimilation of Soil Moisture and Ocean Salinity (SMOS) observations into the land surface model (LSM) Interaction Soil Biosphere Atmosphere (ISBA) at Météo-France. This study consists of an intercomparison experiment of different space-borne platforms providing surface soil moisture information [Advanced Microwave Scanning Radiometer for Earth Observing (AMSR-E) and European Remote Sensing Satellite Scatterometer (ERS-Scat)] with the reanalysis soil moisture predictions over France from the model suite of Système d'analyse fournissant des renseignements atmosphériques à la neige (SAFRAN), ISBA, and coupled model (MODCOU; SIM) of Météo-France for the years of 2003–05. Both modeled and remotely sensed data are initially validated against in situ observations obtained at the experimental soil moisture monitoring site Surface Monitoring of the Soil Reservoir Experiment (SMOSREX) in southwestern France. Two different AMSR-E soil moisture products are compared in the course of this study—the official AMSR-E product from the National Snow and Ice Data Center (NSIDC) and a new product developed at the Vrije Universiteit Amsterdam and NASA (VUA–NASA)—which were obtained using two different retrieval algorithms. This allows for an additional assessment of the different algorithms while using identical brightness temperature datasets. This study shows that a good correlation generally exists between AMSR-E (VUA–NASA), ERS-Scat, and SIM for low altitudes and low-to-moderate vegetation covers (1.5–3 kgm-2 vegetation water content), with a reduction in the correlation in mountainous regions. It also shows that the AMSR-E (NSIDC) soil moisture product has significant differences when compared to the other datasets. [ABSTRACT FROM AUTHOR]

The current state of the art in three-dimensional chemistry-transport models allows them to be considered as mature and reliable enough to be combined with observations networks for implementing integrated air quality monitoring systems over large territories. A cooperative initiative of tour research and operational organizations in France has led to the creation of an integrated air quality platform providing near-real-time and forecasted information using last model developments. Since summer 2003, this system, Prévisions et Observations de la Qualité de l'Air en France et en Europe (PRBV AIR; available online at www.prevair. org), has been delivering daily air quality observations short-term (0-3 days) forecasts, and real-time analyses of ozone, nitrogen dioxide, and particulate matter (PM) at the global and European scales, with a focus over France. It has become a powerful scientific and communication tool to inform the public, sensitive population about the potential occurrence of air pollutant concentrations exceeding regulatory thresholds. The architecture of the operational system is described in this paper. Its setup allows the partners to cope with stringent operational constraints regarding the timely availability of the forecasts and the reliability and the quality of the products delivered. The system relies on a chain of numerical models and methods, and on real-time air quality observations, it uses National Centers for Environmental Predictions (NCEP) and Météo-France weather forecasts. Methods used for producing air pollutant concentration forecasts and analyses are described, as well as the final products, with case examples. A synthesis of the operational system skill results obtained over three consecutive years of operation (2003-06) is given. Lessons learned are presented and issues related to the use of the PREV'AIR system as a complementary tool for air quality management are discussed. [ABSTRACT FROM AUTHOR]

Ensemble streamflow prediction systems are emerging in the international scientific community in order to better assess hydrologic threats. Two ensemble streamflow prediction systems (ESPSs) were set up at Météo-France using ensemble forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) Ensemble Prediction System for the first one, and from the Prévision d’Ensemble Action de Recherche Petite Echelle Grande Echelle (PEARP) ensemble prediction system of Météo-France for the second. This paper presents the evaluation of their capacities to better anticipate severe hydrological events and more generally to estimate the quality of both ESPSs on their globality. The two ensemble predictions were used as input for the same hydrometeorological model. The skills of both ensemble streamflow prediction systems were evaluated over all of France for the precipitation input and streamflow prediction during a 569-day period and for a 2-day short-range scale. The ensemble streamflow prediction system based on the PEARP data was the best for floods and small basins, and the ensemble streamflow prediction system based on the ECMWF data seemed the best adapted for low flows and large basins. [ABSTRACT FROM AUTHOR]

A persistent challenge for small-scale air quality modeling is the assessment of health impact and population exposure studies. Despite progress in computation and in the quality of model input (i.e., high-resolution information on land use and emission patterns), the uncertainty associated with input parameters cannot be eliminated. The aim of this paper is to study different sources of uncertainty that affect model results as the resolution increases. Mesoscale chemistry transport simulations at different resolutions are used and modeled 03 concentrations are compared with surface measurements. The case study consists of CHIMERE model simulations over the city of Paris. It is shown that the principal source of noise in model results is the resolution of the input emission fluxes. The O3 concentrations modeled with simulations forced by several horizontal resolutions of input emission data (from Δx = 48 km to Δx = 6 km) indicate that model results do not improve monotonously with resolution, but that after a certain point discrepancies become larger. Based on this result and as an alternative to the deterministic downscaling that resolves explicitly the finer scale (beyond the 1-km range), the authors propose a subgrid-scale approach that uses a statistical description of spatial scales finer than model resolution. As an example, the subgrid variability of modeled O3 concentration has been quantified, when modeled dry deposition processes occur over subgrid surfaces (land use fractions). The implementation of this modified calculation gives access to subgrid fluxes and subgrid surface concentrations instead of the mean values provided by the commonly used model calculation. [ABSTRACT FROM AUTHOR]

Système d’analyse fournissant des renseignements atmosphériques à la neige (SAFRAN) is a mesoscale atmospheric analysis system for surface variables. It produces an analysis at the hourly time step using ground data observations. One of SAFRAN’s main features is that it is based on climatically homogeneous zones and is able to take vertical variations into account. Originally intended for mountainous areas, it was later extended to cover France. This paper focuses on the validation of the extended version. The principle of the analysis is described and its quality was tested for five parameters (air temperature, humidity, wind speed, rainfall, and incoming radiation), using Météo-France’s observation network and data of some well-instrumented stations. Moreover, SAFRAN’s rainfall was compared with another analysis, known as analyse utilisant le relief pour l’hydrométéorologie (Aurelhy). Last, two different versions of SAFRAN were compared for mountain conditions. Temperature and relative humidity were well reproduced, presenting no bias. Wind speed was also well reproduced; however, its bias was -0.3 m s–1. The interpolation from the 6-h time step of the analysis to the 1-h time step was one of the sources of error. The precipitation analysis was robust and not biased; its root-mean-square error was 2.4 mm day-1. This error was mainly due to the spatial heterogeneity of the precipitation within the geographical zones of analysis (1000 km2). The analysis of incoming solar radiation presented some biases, especially in coastal areas. The results of the comparison with some well-instrumented sites were encouraging. SAFRAN is being run operationally at Météo-France on a real-time basis for various applications. [ABSTRACT FROM AUTHOR]

The three-dimensional structure and dynamics of the combination of the sea breeze and the mistral at the Rhône Valley exit, in southeastern France, have been investigated experimentally and numerically on 22 June 2001. The mistral refers to a severe northerly wind that develops along the Rhône Valley. The exit of this valley is located near the Mediterranean Sea where sea-breeze circulation often develops. The sea breeze and the mistral coexist this day because of the weakness of this mistral event. The event was documented in the framework of the Expérience sur Site pour Contraindre les Modèles de Pollution Atmosphérique et de Transport d'Emissions (ESCOMPTE) field experiment. Several important data sources are used (airborne Doppler lidar, UHF wind profilers, radiosoundings, and surface stations) as well as nonhydrostatic mesoscale simulations. This paper examines the various mechanisms that drive the time and spatial variability of the mistral and the sea breeze in various regions of the Rhône Valley. In the morning, the sea breeze penetrates inland near the western side of the Rhône Valley then moves back because of the reinforcement of the mistral flow caused by the deepening of the leeward surface low due to convection at noon. At midday, the sea breeze penetrates inland in the middle of the Rhône Valley only. In contrast to pure sea-breeze episodes when the sea breeze can extend inland over a horizontal range of more than 150 km, the presence of the mistral prevents the sea breeze from penetrating more than 40 km onshore. In the late afternoon, the sea breeze reaches the eastern side of the Rhône Valley but over a smaller horizontal range because of higher local topography and because the mistral is more intense in this part of the Rhône Valley. The situations of sea-breeze–mistral interactions can have a severe impact on regional air quality. Indeed, the southerly sea breeze, which advects toward the countryside the pollutants emitted from the large coastal city of Marseille, France, and its industrialized suburbs, cannot penetrate far inland because of the mistral blowing in the opposite direction. This leads to the stagnation of the pollutants near the area of emission that is also the most densely inhabited area of the region (over one million inhabitants). [ABSTRACT FROM AUTHOR]

This paper presents results of numerical experiments on five precipitating events: two cases of convective systems over the northern French plains that induced localized flooding, two cases of quasi-stationary systems over the southern French mountainous areas with recorded precipitation above 100 mm in less than 3-4 h, and the extreme flooding case of 12-13 November 1999 with daily precipitation having reached 550 mm, which led to more than 30 deaths. The ability of a high-resolution (2.5 km) model to forecast the quantitative precipitation associated with these five events is evaluated. Both qualitative evaluations that compare observed and modeled reflectivities or surface precipitation, and quantitative evaluations based on classical scores are performed to assess the quality of the numerical experiments. Starting from the same analysis as the operational models, the high-resolution model improves the results for one of the cases, the extreme flash flood case. For the four other cases, higher resolution and more advanced physics than those currently used in operational models do not improve the results. In order to verify that the failures of the high-resolution simulations are due to the initial conditions, the initial state of the simulations are modified for these four cases by using more mesoscale observations, such as mesonet surface observations, radar reflectivities, and Meteosat data. With these modified initial conditions, the high-resolution model produces more realistic precipitation fields. The description of the humidity field in the initial state is crucial for the forecast of the convective systems. Information, such as the presence of a mid- to upper-tropospheric saturated area associated with the developing convective clouds, or nearly saturated low levels, is essential. [ABSTRACT FROM AUTHOR]

From 30 September to 2 October 1999 a workshop was held in Gif–sur–Yvette, France, with the central objective to develop a research strategy for the next 3–5 years, aiming at a systematic description of root functioning, rooting depth, and root distribution for modeling root water uptake from local and regional to global scales. The goal was to link more closely the weather prediction and climate and hydrological models with ecological and plant physiological information in order to improve the understanding of the impact that root functioning has on the hydrological cycle at various scales. The major outcome of the workshop was a number of recommendations, detailed at the end of this paper, on root water uptake parameterization and modeling and on collection of root and soil hydraulic data. [ABSTRACT FROM AUTHOR]