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The recent development of satellite products for observing precipitation based on different technologies (microwaves, infrared, etc.) allows for near-real-time meteorological studies. The purpose of this article is to evaluate 11 satellite products (GHE, PDIR, IMERG-Early v6, IMERG-Late v6, CMORPH v0.x, CMORPH-RT v0.x, GSMaP-NRT v7, GSMaP-NRT-GC v7, GSMaP-NOW v7, GSMaP-NOW-GC v7, and DATABOURG) currently available and compare them to 2 ground-based radar networks (PANTHERE and OPERA) and the French rain-gauge network RADOME. Two case studies of intense precipitation over France (22 to 25 April 2022 and 24 to 29 June 2022) were selected. The radar estimations are closer to the RADOME observations than the satellite-based estimations, which tend to globally underestimate the precipitation amounts over the areas of interest while OPERA tends to strongly overestimate precipitation amounts during the June case study. The PANTHERE radar product and the carrier-to-noise product DATABOURG shows promising results. Near-real-time satellite products tend to have closer precipitation amounts to the reference dataset than satellite products with a shorter latency. The use of these datasets for nowcasting developments is plausible but further analyses must be conducted beforehand. [ABSTRACT FROM AUTHOR]

In this work, Advanced SCATterometer (ASCAT) backscatter data are directly assimilated into the interactions between soil, biosphere, and atmosphere (ISBA) land surface model using Meteo-France's global Land Data Assimilation System (LDAS-Monde) tool in order to jointly analyse soil moisture and leaf area index (LAI). For the first time, observation operators based on neural networks (NNs) are trained with ISBA simulations and LAI observations from the PROBA-V satellite to predict the ASCAT backscatter signal. The trained NN-based observation operators are implemented in LDAS-Monde, which allows the sequential assimilation of backscatter observations. The impact of the assimilation is evaluated over southwestern France. The simulated and analysed backscatter signal, surface soil moisture, and LAI are evaluated using satellite observations from ASCAT and PROBA-V as well as in situ soil moisture observations. An overall improvement in the variables is observed when comparing the analysis with the open-loop simulation. The impact of the assimilation is greater over agricultural areas. [ABSTRACT FROM AUTHOR]

This article presents a new Cloud Radar calibration methodology using solid reference reflectors mounted on masts, developed during two field experiments held in 2018 and 2019 at the SIRTA atmospheric observatory, located in Palaiseau, France, in the framework of the ACTRIS-2 research and innovation program. The experimental setup includes 10 cm and 20 cm triangular trihedral targets installed at the top of 10 m and 20 m masts, respectively. The 10 cm target is mounted on a pan-tilt motor at the top of the 10 m mast to precisely align its boresight with the radar beam. Sources of calibration bias and uncertainty are identified and quantified. Specifically, this work assesses the impact of receiver compression, incomplete antenna overlap, temperature variations inside the radar, clutter and experimental setup misalignment. Setup misalignment is a source of bias previously undocumented in the literature, that can have an impact on the order of tenths of dB in calibration retrievals of W band Radars. A detailed analysis enabled the design of a calibration methodology which can reach a cloud radar calibration uncertainty of 0.3 dB based on the equipment used in the experiment. Among different sources of uncertainty, the two largest terms are due to signal-to-clutter ratio and radar-to-target alignment. The analysis revealed that our 20 m mast setup with an approximate alignment approach is preferred to the 10 m mast setup with the motor-driven alignment system. The calibration uncertainty associated with signal-to-clutter ratio of the former is ten times smaller than for the latter. Cloud radar calibration results are found to be repeatable when comparing results from a total of 18 independent tests. Once calibrated the cloud radar provides valid reflectivity values when sampling mid-tropospheric clouds. Thus we conclude that the method is repeatable and robust, and that the uncertainties are precisely characterized. The method can be implemented under different configurations as long as the proposed principles are respected. It could be extended to reference reflectors held by other lifting devices such as tethered balloons or unmanned aerial vehicles. [ABSTRACT FROM AUTHOR]

A Deep Neural Network (DNN) is used to estimate the Advanced Scatterometer (ASCAT) C-band microwave normalized backscatter (σ 40 o ), slope (σ ′) and curvature (σ ″) over France. The Interactions between Soil, Biosphere and Atmosphere (ISBA) land surface model (LSM) is used to produce land surface variables (LSVs) that are input to the DNN. The DNN is trained to simulate σ 40 o , σ ′ and σ ″ from 2007 to 2016. The predictive skill of the DNN is evaluated during an independent validation period from 2017 to 2019. Normalized sensitivity coefficients (NSCs) are computed to study the sensitivity of ASCAT observables to changes in LSVs as a function of time and space. Model performance yields a near-zeros bias in σ 40 o and σ ′. The domain-averaged values of ρ are 0.84 and 0.85 for σ 40 o and σ ′, compared to 0.58 for σ ″. The domain-averaged unbiased RMSE is 8.6% of the dynamic range for σ 40 o and 13% for σ ′, with land cover having some impact on model performance. NSC results show that the DNN-based model could reproduce the physical response of ASCAT observables to changes in LSVs. Results indicated that σ 40 o is sensitive to surface soil moisture and LAI and that these sensitivities vary with time, and are highly dependent on land cover type. The σ ′ was shown to be sensitive to LAI, but also to root zone soil moisture due to the dependence of vegetation water content on soil moisture. The DNN could potentially serve as an observation operator in data assimilation to constrain soil and vegetation water dynamics in LSMs. • A Deep Neural Network was trained to predict ASCAT backscatter, slope and curvature. • Input variables describing the soil and vegetation were obtained from the ISBA LSM. • Excellent agreement was achieved between observed and predicted ASCAT observables. • Sensitivity of ASCAT observables to inputs is consistent with microwave theory. • The DNN can be used as a measurement operator to assimilate ASCAT data into ISBA. [ABSTRACT FROM AUTHOR]

The ability to perform multiple-Doppler wind synthesis from operational weather radar systems on an operational basis has been investigated by the French Weather Service since 2006 using a (sub-)network of six Doppler radars covering the Greater Paris area. This analysis has been recently extended to the entire French radar network so as to implement a nationwide, three-dimensional reflectivity and wind field mosaic to be eventually delivered to forecasters and modellers, as well as automatic nowcasting systems for air traffic management purposes. In this composite analysis, data collected by all 24 radars of the French radar network are collected, pre-processed and combined in real time at intervals of 15 minutes to retrieve the complete wind vector ( u, v, w) and reflectivity fields at a horizontal resolution of 2.5 km within a domain of approximately 1000 × 1000 × 12 km3. In order to illustrate the performance of this new product, examples of 3-D wind and reflectivity output produced during the passage of two significant weather events are presented: the extratropical storm Klaus, which struck France on 24 January 2009 with hurricane-force gusts, and a typical orographic high-impact precipitation event that developed on 21 October 2009. The evaluation of real-time radar outputs against operational analyses attests to the consistency of the retrieved wind fields. [ABSTRACT FROM AUTHOR]

An algorithm for the detection of horizontal wind shear at low levels was developed. The algorithm makes use of data collected by all radars from the Application Radar à la Météorologie Infra-Synoptique (ARAMIS) operational network, in order to build a complete mosaic of wind shear over metropolitan France. The product provides an estimation of the maximum horizontal wind shear detected in the low levels, between 0 and 2 km AGL. Examination of the wind shear mosaic for different cases shows that the product is able to retrieve small-scale wind shear signatures that can be linked to either convergence lines ahead of convective cells, which are indicative of gust fronts, or strong convergence areas inside intense cells. A statistical evaluation of the wind shear mosaic was performed, by comparing horizontal wind shear observed inside the area defined by convective objects with wind gusts recorded along their trajectory by weather stations. A link between those different observations was clearly established. Therefore, the use of wind shear for wind gust prediction was tested in combination with other parameters: an estimation of the energetic potential of density currents, the cell surface with reflectivity over 51 dB Z, relative helicity, and cell propagation speed. Different wind gust warning rules were tested on 468 convection nowcasting objects (CONOs). The results clearly highlighted the benefits of using wind shear for wind gust estimation, and also demonstrated the improvement in forecasting skill when combining different parameters. The wind shear mosaic will be produced operationally before the end of 2013 and will be used to improve wind gust warnings provided to end users. [ABSTRACT FROM AUTHOR]

Cloud radars are capable of providing continuous high-resolution observations of the cloud. These observations are related to the microphysical properties of clouds. Power law relations in the form of Z = a · LWCb are generally used to estimate liquid water content (LWC) profiles. The constants a and b from the power-law relation vary with the cloud type and cloud characteristics. Due to the variety of such parameterizations, selecting the most appropriate Z-LWC relation for a continuous cloud system is complicated. Additional information such as Liquid water path (LWP) from a co-located microwave radiometer is used to scale the LWC of the cloud profile. An algorithm for estimating the LWC of warm clouds using radar-microwave radiometer synergy in a variational framework is presented. This method also accounts for attenuation due to cloud droplets and retrieves a suitable scaling factor (lna) of the profile in addition to the LWC. The optimal estimation techniques incorporate a priori information of desired variables, and the forward model converts these variables into observation parameters. In this algorithm formulation, the measure of uncertainty in observations, forward model and, a priori acts as weights in the retrieved quantities. These uncertainties in the retrieval are analyzed in the sensitivity analysis of the algorithm. The retrieval algorithm is first tested on a synthetic profile for different perturbations in sensitivity parameters. The sensitivity study has shown that this method is susceptible to LWP information. The algorithm is then implemented to various cloud and fog cases at SIRTA observatory to estimate LWC and the scaling factor. The scaling factor changes for each cloud profile, and the range of lna are consistent with suggested values in literature. The validation of such an algorithm is challenging, as we need reference measurements of LWC co-located with the retrieved values. During the SOFOG-3D campaign (South-West of France, October 2019 to March 2020), in-situ measurements of LWC were collected in the vicinity of a cloud radar and a microwave radiometer, allowing comparison of retrieved and measured LWC. The comparison demonstrated that the cloud-fog heterogeneity was playing a key role in the assessment. The proposed synergistic retrieval algorithm is applied to 39 cloud and fog cases at SIRTA, and the behavior of the scaling factor is studied. This statistical analysis of scaling is carried out to develop a radar-only retrieval method. The climatology revealed that the scaling factor can be linked to the maximum reflectivity of the profile. From climatology, the statistical relations for scaling factor are proposed for fog and cloud. Thanks to the variational framework, a stand-alone radar version of the algorithm is adapted from the synergistic retrieval algorithm, which incorporates the climatology of scaling factor as a priori information to estimate the LWC of warm cloud. [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]

The French Defence Radiation Protection Service (SPRA) conducted a study to assess the radiation exposure of personnel assigned to the maintenance of the Palmier radar in an Air Force Base. The aim of the study was the assessment of the annual effective doses received by personnel assigned to these maintenance operations, and the measurement of equivalent dose rates in the area in order to realise radiological zoning. In two measurement campaigns, the annual individual effective doses, measured by passive whole-body OSL Inlight® dosimeters, consolidated the results obtained by radiometric measurements. Moreover, the equivalent dose rate shows wide variations in relation to the position of the operator in the emitter's area. From these results, the authors propose recommendations for categorisation of workers, radiological zoning and dose monitoring procedures. [ABSTRACT FROM AUTHOR]

This study offers an approach to estimate the rainfall kinetic energy (KE) by rain intensity (R) and radar reflectivity factor (Z) separately or jointly on the basis of a one- or two-moment scaled raindrop size distribution (DSD) formulation, which contains (1) R and/or Z observations and (2) the dimensionless probability density function (pdf) of a scaled raindrop diameter. The key point is to explain all variability of the DSD by the evolution of the explaining moments (R and Z); hence the pdf is considered as constant. A robust method is proposed to estimate the climatological values of the parameters with a 28 month DSD data set collected in the Cévennes-Vivarais region of France. Three relationships (KE-R, KE-Z, and KE-RZ), which link the observations (R and/or Z) to rainfall kinetic energy (KE), are established. As expected, the assessment using the disdrometer data indicates that (1) because of the proximity of the moment orders, the KE-Z relationship exhibits less variability than the KE-R relationship and (2) the combination of R and Z yields a significant improvement of the estimation of KE compared to the single-moment formulations. Subsequently, a first attempt to spatialize the kinetic energy using radar and rain gauge measurements is presented for a convective event, showing a promising potential for erosion process studies. Different from the application with the disdrometer data, the performance of the KE-Z relationship degrades compared to the KE-R relationship as a result of a bias and/or the sampling characteristics of the radar data. [ABSTRACT FROM AUTHOR]

Summary: Important damages occur in small headwater catchments when they are hit by severe storms with complex spatio-temporal structure, sometimes resulting in flash floods. As these catchments are mostly not covered by sensor networks, it is difficult to forecast these floods. This is particularly true for road submersions, representing major concerns for flood event managers. The use of Quantitative Precipitation Estimates and Forecasts (QPE/QPF) especially based on radar measurements could particularly be adequate to evaluate rainfall-induced risks. Although their characteristic time and space scales would make them suitable for flash flood modelling, the impact of their uncertainties remain uncertain and have to be evaluated. The Gard region (France) has been chosen as case study. This area is frequently affected by severe flash floods, and an application devoted to the road network has also been recently developed for the North part of this region. This warning system combines distributed hydro-meteorological modelling and susceptibility analysis to provide warnings of road inundations. The warning system has been tested on the specific storm of the 29–30 September 2007. During this event, around 200mm dropped on the South part of the Gard and many roads were submerged. Radar-based QPE and QPF have been used to forecast the exact location of road submersions and the results have been compared to the effective road submersions actually occurred during the event as listed by the emergency services. Used on an area it has not been calibrated, the results confirm that the road submersion warning system represents a promising tool for anticipating and quantifying the consequences of storm events at ground. It rates the submersion risk with an acceptable level of accuracy and demonstrates also the quality of high spatial and temporal resolution radar rainfall data in real time, and the possibility to use them despite their uncertainties. However because of the quality of rainfall forecasts falls drastically with time, it is not often sufficient to provide valuable information for lead times exceeding 1h. [ABSTRACT FROM AUTHOR]

Coastal mesoscale eddies were evidenced during a high-frequency radar campaign in the Gulf of Lions (GoL), northwestern Mediterranean Sea, from June 2005 to January 2007. These anticyclonic eddies are characterized by repeated and intermittent occurrences as well as variable lifetime. This paper aims at studying the link between these new surface observations with similar structures suggested at depth by traditional acoustic Doppler current profiler measurements and investigates the eddy generation and driving mechanisms by means of an academic numerical study. The influence of the wind forcing on the GoL circulation and the eddy generation is analyzed, using a number of idealized configurations in order to investigate the interaction with river discharge, buoyancy, and bathymetric effects. The wind forcing is shown to be crucial for two different generation mechanisms: A strong northerly offshore wind (Mistral) generates a vortex column due to the bathymetric constraint of a geostrophic barotropic current, which can surface after the wind relaxes; a southerly onshore wind generates a freshwater bulge from the Rhône river discharge, which detaches from the coast and forms a well-defined surface anticyclonic eddy based on buoyancy gradients. These structures are expected to have important consequences in terms of dispersion or retention of biogeochemical material at local scales. [ABSTRACT FROM AUTHOR]

Ground-penetrating radar has not been applied widely to the recognition of ancient carbonate platform geometries. This article reports the results of an integrated study performed on an Upper Jurassic outcrop from the south-east Paris basin, where coral bioherms laterally change into prograding depositional sequences. Ground-penetrating radar profiles illustrate the different bedding planes and major erosional unconformities visible at outcrop. A ground-penetrating radar profile conducted at the base of the cliff displays a palaeotopographic surface on which the outcropping bioherms settled. The excellent penetration depths of the ground-penetrating radar (20 m with a monostatic 200 MHz antenna) images the carbonate platform geometries, ranging between outcrop workscale (a few metres) and seismic scale (several hundreds of metres). This study supports recent evidence of icehouse conditions and induced sea-level fluctuations controlling the Upper Jurassic carbonate production. [ABSTRACT FROM AUTHOR]

Dans le cadre des multifractals universels, il est possible de caracteriser la variabilite spatio-temporelle de la pluie sur une grande gamme d'echelle a l'aide de trois parametres invariants d'echelles. Dans cette etude, nous avons estime ces parametres multifractals sur des simulations numeriques effectuees avec le modele meso-echelle Meso-NH, developpe par Meteo-France et le Laboratoire d'Aerologie (Univ. P. Sabatier, Toulouse, France), et des images radar composites, couvrant le meme evenement pluvieux, a savoir un orage particulierement violent, dit de type Cevenol, ayant eu lieu sur la partie sud de la France du 5 au 9 Septembre 2005. La comparaison des resultats montre que les deux types de donnees presentent des domaines d'invariance d'echelle relativement similaires, et dont les proprietes sont en accord avec les modeles de precipitation spatio-temporels unifies et scalants les plus simples. Neanmoins l'evaluation de leurs exposants conduit a des valeurs parfois fortement differentes. Citation Gires, A., Tchiguirinskaia, I., Schertzer, D. & Lovejoy, S. (2011) Analyses multifractales et spatio-temporelles des precipitations du modele Meso-NH et des donnees radar. Hydrol. Sci. J. 56(3), 380-396. [ABSTRACT FROM AUTHOR]

Summary: A method to estimate antecedent soil moisture conditions is proposed to improve the accuracy of flash flood forecasts at ungauged locations. The method combines two indexes: a ‘climatic’ temporal index calculated in each cell using an uncalibrated soil moisture accounting (SMA) scheme and a spatial ‘statistical’ index giving the “average saturation state” usually encountered before a flood. The proposed method was carried out on two different models: an event-based distributed model, and a REGRESSION model. Simulation results were analysed for 562 individual events, issued from 160 catchments located in the South of France. The presented method improved the efficiency of both models. This indicates that the both proposed indexes give relevant information for estimating antecedent soil moisture conditions at ungauged locations. Furthermore, a contingency statistical analysis based on a 2-year flood threshold showed that alerts were better forecasted by the distributed model. These encouraging results suggest that further efforts should be made for the development of the method and its operational application. [ABSTRACT FROM AUTHOR]

Summary: In the general context of field experiment design, this paper presents a modeling study that quantifies the respective impact of rainfall estimation and soil variability on the simulated discharge for an extreme event in southern France. The CVN distributed hydrological model, built within the LIQUID® modeling platform is used. The method is illustrated for two medium sized catchments, Saumane (99km2) and Uzès (88km2) using raingauges and two radar estimates. The soil properties are extracted from an existing soil database provided for the whole region. The model parameter specification uses available observation and a priori hydrological knowledge. No parameter adjustment is performed. For model evaluation on the regional scale, simulated maximum peak discharges are compared with post-flood estimations for 32 catchments. The area of these catchments ranges from 2.5 to 99km2 and model results are satisfactory. Then, the study focuses on the Saumane and Uzès catchments. A sensitivity analysis highlights the role of the Manning roughness coefficient on the simulated hydrographs dynamics. The impact of the bottom boundary condition of the infiltration and water redistribution module is also shown for the gauged Saumane catchment. Then the impact of rainfall input and soil spatial variability is presented. The results show that (i) the use of radar data is necessary to properly simulate the flood dynamics; (ii) although radar volume-scanning strategy has been shown to give more accurate results on a pixel/gauge comparison of the rainfall estimations, it is not necessarily the case when catchment averaged amounts are considered, especially for catchments in mountainous areas; (iii) the impact of the variability in soil properties on the simulated discharges is of the same order of magnitude as the impact of differences in rainfall estimation; (iv) the flood dynamics presents two phases: the first one, mainly controlled by the soil properties and the second one, since the soils are saturated, controlled by the rainfall variability. Therefore, uncertainties on both observations need to be mitigated in order to improve flash-flood understanding. [Copyright &y& Elsevier]

Summary: Characterisation of the error structure of radar quantitative precipitation estimation (QPE) is a major issue for applications of radar technology in hydrological modelling. Due to the variety of sources of error in radar QPE and the impact of correction algorithms, the problem can only be addressed practically by comparison of radar QPEs with reference values derived from ground-based measurements. Using the radar and raingauge datasets of the Bollène-2002 experiment, a preliminary investigation of this subject has been carried out within a geostatistical framework in the context of the Cévennes–Vivarais Mediterranean Hydrometeorological Observatory. First, raingauge measurements were critically analysed using variograms to detect erroneous squared differences between pairs of raingauge values. The anisotropic block kriging technique was then used to compute and select the most reliable reference values. The statistical distribution and the spatial and temporal structure of the residuals between radar and reference values was established and analysed. The error variance separation concept was also tested to estimate the variance of the residuals between the radar estimates and the true unknown rainfall. A preliminary version of the radar QPE error model was established for 1-km2 domains and time steps ranging from 1 to 12h using the limited data sample of the Bollène-2002 experiment. The error model is dependent on the time scales considered and needs to be conditioned on the rain rate and rainfall type as well as on the radar range. [ABSTRACT FROM AUTHOR]

L- and P-band airborne polarimetric synthetic aperture radar (SAR) data acquired by the RAMSES system over different height maritime pine (Pinus Pinaster Ait.) stands of the Nezer forest (Landes, France) have been evaluated for forest biophysical parameter estimation. A pseudolinear correlation has been brought to evidence at P-band between polarimetric anisotropy and mean tree height, which is also linked to other biophysical parameters in the Nezer forest, meaning that SAR polarimetry constitutes a promising tool for forest parameter retrieval at low frequency. The spatial conditions have been evaluated through the quantification of the impact of signal-to-noise ratio diminution and resolution degradation on the forest height inversion. It has been shown that the inversion accuracy remains acceptable for N E σo, representing the noise level of the SAR image, which is lower than -- 15 dB, and for spatial resolution increasing up to 15 m. [ABSTRACT FROM AUTHOR]

The Bollène-2002 Experiment was aimed at developing the use of a radar volume-scanning strategy for conducting radar rainfall estimations in the mountainous regions of France. A developmental radar processing system, called Traitements Régionalisés et Adaptatifs de Données Radar pour l’Hydrologie (Regionalized and Adaptive Radar Data Processing for Hydrological Applications), has been built and several algorithms were specifically produced as part of this project. These algorithms include 1) a clutter identification technique based on the pulse-to-pulse variability of reflectivity Z for noncoherent radar, 2) a coupled procedure for determining a rain partition between convective and widespread rainfall R and the associated normalized vertical profiles of reflectivity, and 3) a method for calculating reflectivity at ground level from reflectivities measured aloft. Several radar processing strategies, including nonadaptive, time-adaptive, and space–time-adaptive variants, have been implemented to assess the performance of these new algorithms. Reference rainfall data were derived from a careful analysis of rain gauge datasets furnished by the Cévennes–Vivarais Mediterranean Hydrometeorological Observatory. The assessment criteria for five intense and long-lasting Mediterranean rain events have proven that good quantitative precipitation estimates can be obtained from radar data alone within 100-km range by using well-sited, well-maintained radar systems and sophisticated, physically based data-processing systems. The basic requirements entail performing accurate electronic calibration and stability verification, determining the radar detection domain, achieving efficient clutter elimination, and capturing the vertical structure(s) of reflectivity for the target event. Radar performance was shown to depend on type of rainfall, with better results obtained with deep convective rain systems (Nash coefficients of roughly 0.90 for point radar–rain gauge comparisons at the event time step), as opposed to shallow convective and frontal rain systems (Nash coefficients in the 0.6–0.8 range). In comparison with time-adaptive strategies, the space–time-adaptive strategy yields a very significant reduction in the radar–rain gauge bias while the level of scatter remains basically unchanged. Because the Z–R relationships have not been optimized in this study, results are attributed to an improved processing of spatial variations in the vertical profile of reflectivity. The two main recommendations for future work consist of adapting the rain separation method for radar network operations and documenting Z–R relationships conditional on rainfall type. [ABSTRACT FROM AUTHOR]

Abstract: Two very high-frequency radars (VHFRs), operating in the southern Channel Isles region (English Channel) in February–March 2003, provided a continuous 27-day long dataset of surface currents at 2km resolution over an area extending approximately 20km offshore. The tidal range in the region of study is one of the highest in the world and the coastal circulation is completely dominated by tides. The radar data resolve two modes which account for 97% of the variability of the surface current velocities, with the major contribution of the first mode. This mode accounts for oscillating tidal currents whereas the second mode represents motions emerging from the interaction of tidal currents with capes and islands (eddy in the vicinity of the Point of Grouin and jet south of Chausey). A fortnightly modulation of the modal amplitudes causes the exceptional (more than 600%) variability of currents which is well captured by the VHFR observations. The radar data revealed that tidal circulation in the region is flood-dominated with a strong asymmetry of current velocity curve. Wind events and fortnightly variability affect the course of tidal cycle by modifying the magnitude and duration of ebb and flood. In addition to expected features of coastal circulation (tidally dominated flow, eddies) and high wind-current coupling, the residual currents revealed a strong cross-shore structure in the mean and a significant variability which has the same order of magnitude. [Copyright &y& Elsevier]

In the last few years, radar technology has been given strong consideration for sea level monitoring. France has been a pioneer in the use of radar technology for its tide gauge network, and the first results of an 8-year-long experience are presented. The performance of the radar sensors has been assessed using the Van de Casteele test, which has been revealed to be an efficient method for detecting the main deficiencies of the installations and evaluate the data quality. The experience so far shows that radar tide gauges present interesting advantages with regard to ease of operation and that their accuracy is consistent with GLOSS 1-cm requirements, provided the installation is correctly performed. [ABSTRACT FROM AUTHOR]

The French Transportable Laser Ranging System (FTLRS) was deployed in the calibration site of satellite radar altimeters in Corsica over the 2002 and 2005 campaigns. The paper describes the different steps of SLR data processing. The average arcs RMS obtained are about 1-2 cm for Lageos-1&-2, Starlette and Stella satellites; it is shown that the best results of satellite orbits determination and geocentric positioning are obtained with Eigen-Grace03s gravity model. The difference of FTLRS absolute 3D positioning, between 2002 and 2005, of about 7.7 mm (i.e., 2.6 mm/yr) is less than residual errors of ITRF2005 velocities (of about 4.3 mm/yr). [ABSTRACT FROM AUTHOR]

Radar reflectivity (Zh), differential reflectivity (Zdr), and specific differential phase (Kdp) measured from the operational, polarimetric weather radar located in Trappes, France, were used to examine the effects of radar beam shielding on rainfall estimation. The objective of this study is to investigate the degree of immunity of Kdp-based rainfall estimates to beam shielding for C-band radar data during four typical rain events encountered in Europe. The rain events include two cold frontal rainbands with average rainfall rates of 7 and 17 mm h-1, respectively, and two summertime convective rain events with average rainfall rates of 11 and 22 mm h-1. The large effects of beam shielding on rainfall accumulation were observed for algorithms using Zh and Zdr with differences of up to ∼2 dB (40%) compared to a Kdp-based algorithm over a power loss range of 0–8 dB. This analysis reveals that Zdr and Kdp are not affected by partial beam shielding. Standard reflectivity corrections based on the degree of beam shielding would have overestimated rainfall rates by up to 1.5 dB for less than 40% beam shielding and up to 3 dB for beam shielding less than 75%. The investigation also examined the sensitivity of beam shielding effects on rainfall rate estimation to (i) axis–ratio parameterization and drop size distribution, (ii) methods used to smooth profiles of differential propagation phase (φdp) and estimate Kdp, and (iii) event-to-event variability. Although rainfall estimates were sensitive to drop size distribution and axis–ratio parameterization, differences between Zh- and Kdp-based rainfall rates increased independently from those parameters with amount of shielding. Different approaches to smoothing φdp profiles and estimating Kdp were examined and showed little impact on results. [ABSTRACT FROM AUTHOR]

A full radar simulator for high-resolution (1-5 km) nonhydrostatic models has been developed within the research nonhydrostatic mesoscale atmospheric (Meso-NH) model. This simulator is made up of building blocks, each of which describes a particular physical process (scattering, beam bending, etc.). For each of these blocks, several formulations have been implemented. For instance, the radar simulator offers the possibility to choose among Rayleigh, Rayleigh-Gans, Mie, or T-matrix scattering methods, and beam bending can be derived from an effective earth radius or can depend on the vertical gradient of the refractive index of air. Moreover, the radar simulator is fully consistent with the microphysical parameterizations used by the atmospheric numerical model. Sensitivity experiments were carried out using different configurations for the simulator. They permitted the specification of an observation operator for assimilation of radar reflectivities by high-resolution nonhydrostatic numerical weather prediction systems, as well as for their validation. A study of the flash flood of 8-9 September 2002 in southeastern France, which was well documented with volumetric data from an S-band radar, serves to illustrate the capabilities of the radar simulator as a validation tool for a mesoscale model. [ABSTRACT FROM AUTHOR]

In anticipation of X-band polarimetric Synthetic Aperture Radar (SAR) data from future sensors, we investigated the potential of X-band fully polarimetric data for discriminating between the principal classes present in a study site near Avignon, France. Decomposition and analysis techniques have been applied to a dataset acquired by the ONERA airborne RAMSES (Radar Aéroporté Multi-Spectral d'Etude des Signatures) SAR. Results show that X-band provides some discrimination capability. The polarimetric parameters, entropy and , show clearly that these signature classes are grouped into five clusters corresponding to physical scattering characteristics. The introduction of the anisotropy parameter does not increase our ability to distinguish between different classes whose clusters are in the same zone. The correlation observed between the radar signal and the surface roughness over bare soils is very low. [ABSTRACT FROM AUTHOR]

On 11 August 1999, a near-total solar eclipse (80%) was observed in Campistrous, France. The influence of this particular event on the atmospheric boundary layer was observed with a UHF-RASS radar, a sodar and an instrumented mast. The changes in turbulence intensity, radar reflectivity, and temperature on the radiative budget are described in relation to collocated ground meteorological data. The impact of the eclipse induces a clear response of the atmosphere, with a time lag of 15 to 30 min, perceptible in several mean and turbulent meteorological variables up to the top of the atmospheric boundary layer. [ABSTRACT FROM AUTHOR]

• Contributions and complementarities of the different frequencies for detecting 4 types of land use (and two states of bare soils. • The use of one mono-frequency images provides limited overall results. • A significant improvement in the classifications by fusing images delivered by optical and radar sensors, for most of the classes studied. • The complementary nature of the contributions made by L-band and optical waves. The monitoring of different crops (cultivated plots) and types of surface (bare soils, etc.) is a crucial economic and environmental issue for the management of resources and human activity. In this context, the objective of this study is to evaluate the contribution of multispectral satellite imagery (optical and radar) to land use and land cover classification. Object-oriented supervised classifications, based on a Random Forest algorithm, and majority zoning post-processing are used. This study emerges from the experiment on multi-sensor crop monitoring (MCM'10, Baup et al., 2012) conducted in 2010 on a mixed farming area in the southwest of France, near Toulouse. This experiment enabled the regular and quasi-synchronous collection of multi-sensor satellite data and in situ observations, which are used in this study. 211 plots with contrasting characteristics (different slopes, soil types, aspects, farming practices, shapes and surface areas) were monitored to represent the variability of the study area. They can be grouped into four classes of land cover: 39 grassland areas, 100 plots of wheat, 13 plots of barley, 20 plots of rapeseed, and 2 classes of bare soil: 23 plots of small roughness and 16 plots of medium roughness. Satellite radar images in the X-, C- and L-bands (HH polarization) were acquired between 14 and 18 April 2010. Optical images delivered by Formosat-2 and corresponding field data were acquired on 14 April 2010. The results show that combining images acquired in the L-band (Alos) and the optical range (Formosat-2) improves the classification performance (overall accuracy = 0.85, kappa = 0.81) compared to the use of radar or optical data alone. The results obtained for the various types of land cover show performance levels and confusions related to the phenological stage of the species studied, with the geometry of the cover, the roughness states of the surfaces, etc. Performance is also related to the wavelength and penetration depth of the signal providing the images. Thus, the results show that the quality of the classification often increases with increasing wavelength of the images used. [ABSTRACT FROM AUTHOR]

This paper investigates to what extent soil moisture and vegetation density information can be extracted from the Advanced Scatterometer (ASCAT) satellite-derived radar backscatter (σ°) in a data assimilation context. The impact of independent estimates of the surface soil moisture (SSM) and leaf area index (LAI) of diverse vegetation types on ASCAT σ° observations is simulated over southwestern France using the water cloud model (WCM). The LAI and SSM variables used by the WCM are derived from satellite observations and from the Interactions between Soil, Biosphere, and Atmosphere (ISBA) land surface model, respectively. They permit the calibration of the four parameters of the WCM describing static soil and vegetation characteristics. A seasonal analysis of the model scores shows that the WCM has shortcomings over karstic areas and wheat croplands. In the studied area, the Klaus windstorm in January 2009 damaged a large fraction of the Landes forest. The ability of the WCM to represent the impact of Klaus and to simulate ASCAT σ° observations in contrasting land-cover conditions is explored. The difference in σ° observations between the forest zone affected by the storm and the bordering agricultural areas presents a marked seasonality before the storm. The difference is small in the springtime (from March to May) and large in the autumn (September to November) and wintertime (December to February). After the storm, hardly any seasonality was observed over four years. This study shows that the WCM is able to simulate this extreme event. It is concluded that the WCM could be used as an observation operator for the assimilation of ASCAT σ° observations into the ISBA land surface model. [ABSTRACT FROM AUTHOR]

Recent studies have highlighted the need for high resolution rainfall measurements for better modelling of urban and peri-urban catchment responses. In this work, we used a fully-distributed model called "Multi-Hydro" to study small-scale rainfall variability and its hydrological impacts. The catchment modelled is a semi-urban area located in the southwest region of Paris, an area that has been previously partially validated. At this time, we make some changes to the model, henceforth using its drainage system globally, and we investigate the influence of small-scale rainfall variability by modelling three rainfall events with two different rainfall data inputs: the C-band radar data provided by Météo-France at a 1 km × 1 km × 5 min resolution, and the new X-band radar (recently installed at Ecole des Ponts, France) data at a resolution of 250 m × 250 m × 3.41 min, thereby presenting the gains of better resolution (with the help of Universal Multifractals). Finally, we compare the Multi-Hydro hydrological results with those obtained using an operational semi-distributed model called "Optim Sim" over the same area to revalidate Multi-Hydro modelling, and discuss the model's limitations and the impacts of data quality and resolution, observing the difficulties associated with semi-distributed models when accounting the spatial variability of weather radar data. This work concludes that it may be useful in future to improve rainfall data acquisition, aiming for better spatio-temporal resolution (now achieved by the weather dual-polarized X-band radars) and data quality when considering small-scale rainfall variability, and to merge deterministic, fully-distributed and stochastic models into a hybrid model which would be capable of taking this small-scale rainfall variability into account. [ABSTRACT FROM AUTHOR]

This paper investigates the impact of leaf area index (LAI) and surface soil moisture (SSM) on satellite-derived radar backscatter (sigma0) observations over southwestern France. Observations from the Advanced Scatterometer (ASCAT) are compared to simulated sigma0 values produced by the Water Cloud Model (WCM) coupled to the Interactions between Soil, Biosphere, and Atmosphere (ISBA) land surface model (LSM). The LAI and SSM variables used by the WCM are derived from satellite observations and from ISBA simulations, respectively. Values of four parameters of the WCM are calibrated. The ability of the WCM to simulate ASCAT sigma0 observations in contrasting land cover conditions is explored. The response of observed and simulated sigma0 to LAI and SSM is analyzed across seasons. Two ways of representing vegetation in the WCM are compared. The impact of the Klaus storm on the ASCAT observations over the Les Landes forest in January 2009 is examined. The difference in sigma0 between the forest zone affected by the storm and the bordering agricultural areas presents a marked seasonality before the storm and hardly any seasonality during four years after the storm. From 2013 onward, a regeneration phase is visible, with the return of seasonality in the forest vs. non-forest sigma0 difference. The ability of the WCM to represent this phenomenon is investigated. It is shown that the WCM can be used as an observation operator for the assimilation of ASCAT sigma0 observations into the ISBA LSM. [ABSTRACT FROM AUTHOR]