This study investigates the scaling properties of rainfall in Tunis over temporal scales ranging from 5 min to 2.5 years using high-resolution rain gauge data from three recording stations. We employ the Universal Multifractal (UM) framework to characterize scaling properties across multiple temporal regimes. The UM model was selected over alternative multifractal approaches because of its parsimonious three-parameter formulation (C1, α, H). It explicitly accounts for non-conservative processes through the Fractionally Integrated Flux (FIF) extension and includes established bias correction methods for highly intermittent signals. This framework has demonstrated universality across diverse climatic conditions and enables direct comparison with existing rainfall studies in Mediterranean environments. Spectral analysis reveals three distinct scaling regimes: micro-scale (5 min–2 h 40 min), meso-scale (2 h 40 min–7 days), and synoptic scale (>7 days). The non-conservative nature of the micro-scale regime is addressed through a multifractal fractionally integrated flux model. A key challenge in applying UM analysis to rainfall data is the prevalence of low and zero rain rates (>98% zeros in our dataset). This extreme intermittency introduces significant bias in parameter estimation. Existing correction methods require either continuous rain sequences—scarce in semi-arid climates—or are limited to moderate intermittency levels. We propose an empirical correction method that extends the existing semi-empirical approach by explicitly linking the percentage of zero values to biased UM parameters through empirical relationships applicable to sequences with as few as 50% rainy observations. This advancement enables reliable parameter estimation from highly intermittent datasets. In such conditions, traditional event-by-event analysis yields insufficient samples (only five continuous events longer than 2 h 40 min over 2.5 years in Tunis). The corrected estimates (α = 1.63, C1 = 0.16 for micro-scales) demonstrate strong consistency with continuous rainfall events and align well with high-resolution studies, validating our approach for extreme intermittency conditions characteristic of Mediterranean semi-arid climates.

Deep generative networks have recently achieved unprecedented performance in precise image and video editing using sophisticated textual prompts. However, the effectiveness of such models heavily depends on access to very large supervised and annotated image datasets, which can be very difficult to obtain. This is particularly true for satellite instruments, which very rarely overlap with labelled data, and suffer from domain shifts in the rare occasions they do. In this paper, we investigate the potential of flow matching models for unsupervised domain adaptation of satellite radiometer images. Our main contribution is a novel unsupervised method that achieves precise domain alignment by leveraging parts of the deterministic ordinary differential equations in flow matching models, conditioned on different satellite instruments. A key strength of our approach is its ability to preserve essential information while adapting across any domains since the perturbations are in theory bijective. Extensive experiments conducted on the GPM-Core constellation show the benefit of our conditional domain adaptation, particularly in improving rain precipitation estimation from radiometer imagery.

Precipitation nowcasting, the forecasting of short-term localized precipitation, is a crucial component of weather forecasting, particularly given the increasing frequency of extreme weather events due to climate change. Building on the critical role of precipitation nowcasting, this study focuses on enhancing deep learning models by optimizing hyper-parameters on well-established models such as convolutional long short-term memory (ConvLSTM) and small attention U-Net (SmaAt-UNet) rather than proposing new architectures. Our dataset consists of 10 years of precipitation maps at 1 3 1 km2, 5-min resolution from M & eacute;t & eacute;o-France's Mosaic product covering the Paris region, providing a robust testbed for training and evaluating models. To better characterize the performance of models over different types of rain events, clustering was carried out to partition the events into four different groups ranging from light rain to intense rain. Four main changes were explored to improve the training process. We show that increasing the temporal context up to 40 min signifi-cantly improves predictive accuracy for high-intensity rainfall events. Second, the introduction of wider spatial observation windows mitigates edge effects during training, enhancing spatial consistency and intensity estimation. Third, extending the output horizon to 90 min to predict precipitation at 60 min offered substantial gains for recurrent models like ConvLSTM, highlighting their capacity to better capture long-term dynamics. Last, the influence of convolution kernel size was studied to maximize the performance of the models. These changes collectively lead to significant forecast improvements especially for extreme precipitation events with metric values increasing compared to baseline configurations. We also show that the proposed model performs better when evaluated at coarser scales, relevant for hydrological applications.

Abstract Microwave brightness temperatures (TB) measured by satellites have played a significant role in observing various water cycle components. Until now, the measurement of TB has been used to retrieve many geophysical variables with Bayesian or, more recently, supervised deep learning approaches. These retrieval methods rely on the availability and the quality of the target variable and could be affected by changes in the relationship between TB and the target variable under climate change. This study aims to show that an unsupervised semantic image segmentation model of TB images offers meaningful segments that represent geophysical variables by interpreting and analyzing the resulting segmentation. The unsupervised segmentation methods explored include fully convolutional networks and pixelwise approaches with k -means. The main challenge tackled here is evaluating the model’s performance without any ground-truth labeling. The proposed evaluation requires external reference variables, including precipitation and sea surface temperatures (SSTs). The segmentation classes that represent these two geophysical variables are first compared to reference precipitation and SST data using segmentation metrics. The second step is to investigate the behavior of these classes. Results show that the precipitation classes capture the pattern of monsoon precipitation, while the ocean classes show the seasonality of SST. With an unsupervised approach, it is essential to verify the properties of the obtained segments extensively. The segmentation results in this study show that unsupervised learning is a key approach to extracting decades-long information from TB observations. Significance Statement Unsupervised learning has the potential to extract information from the climatic record in a large database of microwave brightness temperatures. However, the lack of labeled data means that the resulting clusters or representations require careful interpretation, as they are not inherently associated with known geophysical classes. While certain architectural and algorithmic choices may introduce useful constraints, validating the results against geophysical knowledge remains essential to ensure their physical relevance. This study aims to provide a rigorous evaluation process and a geophysical interpretation for the obtained segmentation. This is an important step before using these unsupervised segments to study climate change in the decades-long brightness temperature observations.

Weather radar mosaics provide high-resolution precipitation estimates but are often degraded by beam-blockage artefacts caused by terrain or infrastructure. These artefacts introduce systematic attenuation of radar reflectivity and distort precipitation patterns, particularly in complex topographic environments. Traditional correction approaches typically rely on geometric modeling or multi-radar fusion and often treat detection and correction as separate problems. In this work, we propose a guided conditional diffusion framework for correcting radar beam-blockage artefacts. The model reconstructs the precipitation field through a conditional reverse diffusion process guided by the corrupted radar observation and its associated quality map. During training, beam-blockage patterns are simulated on clean radar sectors to generate corrupted observations while preserving the original precipitation fields as references. This enables the model to learn both the spatial characteristics of blockage and the structure of precipitation fields. The method is evaluated on real radar mosaics containing both simulated and naturally occurring beam-blockage effects. Quantitative results show that the proposed approach reduces reconstruction errors within blocked regions while preserving precipitation values in unaffected areas. Qualitative analysis further demonstrates the recovery of coherent precipitation structures in degraded regions.

Retrieval of rain from Passive Microwave radiometers data has been a challenge ever since the launch of the first Defense Meteorological Satellite Program in the late 70s. Enormous progress has been made since the launch of the Tropical Rainfall Measuring Mission (TRMM) in 1997 but until recently the data were processed pixel-by-pixel or taking a few neighboring pixels into account. Deep learning has obtained remarkable improvement in the computer vision field, and offers a whole new way to tackle the rain retrieval problem. The Global Precipitation Measurement (GPM) Core satellite carries similarly to TRMM, a passive microwave radiometer and a radar that share part of their swath. The brightness temperatures measured in the 37 and 89 GHz channels are used like the RGB components of a regular image while rain rate from Dual Frequency radar provides the surface rain. A U-net is then trained on these data to develop a retrieval algorithm: Deep-learning RAIN (DRAIN). With only four brightness temperatures as an input and no other a priori information, DRAIN is offering similar or slightly better performances than GPROF, the GPM official algorithm, in most situations. These performances are assumed to be due to the fact that DRAIN works on an image basis instead of the classical pixel-by-pixel basis.

Precipitation nowcasting plays an essential role in operational weather forecasting services. Sudden precipitation events have significant socio-economic impacts, including natural disasters like flash floods. This challenge is becoming increasingly critical as climate change alters weather patterns and the frequency of extreme weather events continues to increase.Over the last decade, radar observations, offering high temporal and spatial resolution, have facilitated the development of machine learning methods for precipitation nowcasting. Once trained, these methods are well suited to processing large datasets with low latency, especially in a real-time context. Recent advances in the field of nowcasting have focused on optimizing model architectures, improving loss functions for imbalanced data, and integrating multivariate inputs, including radar and satellite observations.This study explores some critical hyperparameters, such as temporal context length, edge effect during training, influence of the output horizons prediction, and convolution kernel size. To do this, we investigate the performance of several models, including both machine learning approaches from different families, in particular SmaAt-Unet, ConvLSTM , and DGMR (trained on UK rains) , as well as non-machine learning methods such as STEPS. An eleven years consistent radar precipitation dataset covering the Paris region was set up from Météo-France mosaic. Nine years were used for training machine learning models, and two years were reserved to evaluate the models’ performances. To assess the model in different weather conditions, the data set is divided into four groups with distinct characteristics corresponding to various meteorological phenomena. To ensure consistent evaluation, we evaluated the models on the same two-year test dataset, focusing on three criteria, namely: spatial consistency (Pearson correlation coefficient), location accuracy (CSI), and precipitation intensity (MSE).Our analysis reveals that machine learning models consistently outperform traditional optical flow methods, with notable variations in performance across timescales and rainfall intensities. We also highlight that performance is nearly identical for all models in the presence of stratiform rain, while there are substantial differences in the convective rain group. Additionally, we show that for deep learning models, considering edge effects during training prevents the propagation of inevitable errors and helps avoid the appearance of ghost rain cells at the edges of the map. Furthermore, we show that the size of the kernels of the first layers plays an important role and must be large enough to allow correlation between distant pixels.Finally, our study provides guidelines for the development of precipitation nowcasting models.

This study proposes using a data-driven statistical model to freeze errors due to differences in environmental forcing when evaluating surface turbulent heat fluxes from weather and climate numerical models with observations. It takes advantage of continuous acquisition over approximately 10 years of near-surface sensible and latent heat fluxes (H and LE respectively) together with ancillary parameters at the M & eacute;t & eacute;opole flux station, a supersite of the Aerosol, Clouds and Trace Gases Research Infrastructure in France (ACTRIS-FR), located in Toulouse. The statistical model consists of several multi-layer perceptrons (MLPs) with the same architecture. A total of 13 variables characterizing environmental forcing in the surface layer on an hourly timescale are used as input parameters to estimate the observed H and LE simultaneously. The MLPs are trained using 5-year observational data under a 5-fold cross-validation. The remaining data are used to test the estimates under unknown conditions. The performance of the statistical model ranges within the state-of-the-art surface parameterization schemes on hourly and seasonal timescales. It also has a good generalization ability, but it hardly estimates negative H and large LE. A case study is conducted with data from a regional climate simulation. The statistical model is used to evaluate the simulated fluxes in the simulated environment to better examine the flaws of their numerical formulation throughout the simulation. Comparison of simulated fluxes with observed and MLP-based fluxes shows different results. According to MLP-based fluxes in the simulated environment, the land surface scheme of this climate model tends to underestimate large sensible heat flux. Thus, it incorrectly partitions between surface heating and evaporation during the late summer. Our innovative method provides insight into different techniques for evaluating simulated near-surface turbulent heat fluxes when a long period of comprehensive observations is available. It can usefully support ongoing efforts to improve surface parameterization schemes.

In this paper, we investigate the performance of several models, including both machine learning approaches, in particular SmaAt-Unet, ConvLSTM and DGMR, as well as non-machine learning methods such as Lagrangian persistence, S-PROG, and STEPS, using a consistent radar precipitation dataset covering the Paris region over a period of two years. In our analysis, the dataset is partitioned into four groups with distinct characteristics, shedding light on the diverse behaviors of these models. Our findings reveal that machine learning models outperform traditional optical flow models, with noticeable variations in performance across different time scales and rain intensities. This improvement by machine learning can be particularly observed in light rain events. On the other hand, when considering heavy rain events, the results are closer from a model to another, especially for horizons exceeding 15 minutes.

Fugitive methane (CH4) emissions occur in the whole chain of oil and gas production, including from extraction, transportation, storage, and distribution. Such emissions are usually detected and quantified by conducting surveys as close as possible to the source location. However, these surveys are labour-intensive, are costly, and fail to not provide continuous emissions monitoring. The deployment of permanent sensor networks in the vicinity of industrial CH4 emitting facilities would overcome the limitations of surveys by providing accurate emission estimates, thanks to continuous sampling of emission plumes. Yet high-precision instruments are too costly to deploy in such networks. Low-cost sensors using a metal oxide semiconductor (MOS) are presented as a cheap alternative for such deployments due to their compact dimensions and to their sensitivity to CH4. In this study, we demonstrate the ability of two types of MOS sensors (TGS 2611-C00 and TGS 2611-E00) manufactured by Figaro® to reconstruct a CH4 signal, as measured by a high-precision reference gas analyser, during a 7 d controlled release campaign conducted by TotalEnergies® in autumn 2019 near Pau, France. We propose a baseline voltage correction linked to atmospheric CH4 background variations per instrument based on an iterative comparison of neighbouring observations, i.e. data points. Two CH4 mole fraction reconstruction models were compared: multilayer perceptron (MLP) and second-degree polynomial. Emission estimates were then computed using an inversion approach based on the adjoint of a Gaussian dispersion model. Despite obtaining emission estimates comparable with those obtained using high-precision instruments (average emission rate error of 25 % and average location error of 9.5 m), the application of these emission estimates is limited to adequate environmental conditions. Emission estimates are also influenced by model errors in the inversion process.

A major issue limiting the successful deployment of deep learning algorithms in geophysical applications is their inability to generalize to new contexts. Regarding the quantitative precipitation estimation (QPE) from the Global Precipitation Mission (GPM) satellite constellation, the GPM Microwave Imager (GMI) contains enough co-located brightness temperatures and rain rates data to train a deep learning inverse model to retrieve precipitation intensity. However, the difference in instrumental configurations makes it impossible to directly apply this inverse operator to another space-borne radiometric imager. A domain adaptation is thus necessary to solve the domain shift problem encountered when applying the model trained on one satellite to another satellite. The present paper tests a method to map the SSMI/S data to the GMI data. In the absence of sufficient paired images between the two satellites, we applied a Cycle consistent Generative Adversarial Network (CycleGAN), which allows for an Unsupervised Domain Adaptation approach. Evaluating the quality of adapted images is a complex problem. This paper employs two tactics: a brief evaluation of adapted radiometric images and a qualitative/quantitative evaluation of rain retrieval. Over several case studies, the results show that the domain adaptation step produces adapted SSMI/S images that retain the majority of the rain structure. Next, the rain detection score and intensity bias are then compared using 847 overpasses. The same analysis is carried out over mainland France by comparing the results with rainfall products supplied by Météo-France. In both comparisons, the adapted images allow the inverse operator to provide a better score in rain detection and intensity.

Abstract. Fugitive methane (CH4) emission occur in the whole chain of oil and gas production, from the extraction, transportation, storage and distribution. The detection and quantification of such emissions are conducted usually from surveys as close as possible to the source location. However, these surveys are labor intensive, costly and they do not provide continuous monitoring of the emissions. The deployment of permanent networks of sensors in the vicinity of industrial facilities would overcome the limitations of surveys by providing accurate estimates thanks to continuous sampling of the plumes. High precision instruments are too costly to deploy in such networks. Low-cost sensors like Metal oxide semiconductors (MOS) are presented as a cheap alternative for such deployments due to its compact dimensions and to its sensitivity to CH4. In this study we test the ability of two types of MOS sensors from the manufacturer Figaro® (TGS 2611-C00 and TGS 2611-E00) deployed in six chambers to reconstruct an actual signal from a source in open air corresponding to a series of controlled CH4 releases and we assess the accuracy of the emission estimates computed from reconstructed CH4 mole fractions from voltages measurements of these sensors. A baseline correction of the voltage linked to background variations is presented based on an iterative comparison of neighboring observations. Two reconstruction models were compared, multilayer perceptron (MLP) and 2nd degree polynomial, providing similar performances meeting our target requirement on all the chambers when the input variable is the TGS 2611-C00 sensor. The emission estimates were then computed using an inversion approach based on the adjoint of a Gaussian dispersion model obtaining promising results with an emission rate error of 25 % and a location error of 9.5 m.

<p>Due to climate change, understanding the changes in the water cycle has become a pressing issue. It is increasingly important to study prolonged periods of intense precipitation or dry spells to better manage water supply, infrastructure and agriculture. However, obtaining fine-scale precipitation data is challenging due to the intermittent nature of rain in time and space. Ground-based instruments could have mismatches between different regions due to spatial distribution, calibration, and complex topography. On the other hand, space-borne observations have uncertainties in their retrieval algorithms. This study proposes to deal directly with microwave images from space remote sensing, as this type of data makes it possible to study the evolution of the atmospheric water cycle on a global scale and with a temporal coverage of several decades by avoiding the uncertainties from retrieval methods. In recent years, convolutional neural networks have shown promising capabilities in identifying cyclones and weather fronts in large labelled climate datasets. However, these models required large labelled datasets for training and testing. The present study aims to test unsupervised segmentation approaches of microwave images, which are thus segmented into different classes. Instead of focusing only on one aspect, for example, precipitation, the obtained classes contain many physical properties. This is due to the fact that microwave brightness temperatures contain essential information relative to the atmospheric water cycle that can be used to derive many products such as rain intensity, water vapour, cloud fraction, and sea surface temperature. The unsupervised segmentation model consists of blocks of fully convolutional networks serving as feature extractors. Without labels, pseudo-targets from the feature extractors are used to train the model. The performance of the model in terms of intra-class and inter-class distances is compared with those of simpler models such as Kmeans. A major challenge in the unsupervised approach is validating and interpreting the resulting classes. Most of the obtained cluster patterns provide geographically coherent regions whose mode of variability of geophysical quantities can be highlighted. The presented study will then explore how the different classes computed by the unsupervised methods can be labelled and how the properties of the said classes change through time and space.</p>

We focus in this paper on the use of satellite attenuation in Ku-frequency band for measuring rainfall, using low-cost sensors allowing to measure only the total received power. This implies that the measured powers are not only composed of the satellite signal, but also include a radiometric contribution, which is the atmospheric radiation and the receiving system noise. In case of heavy rainfall, while the satellite signal weakens, the atmospheric radiation becomes more important and neglecting it can lead to a significant underestimation of rainfall. In this paper, we introduce the theoretical framework of these measurements, and derive a simple method for correcting this underestimation, based on the measurements of the received power on two channels (different frequencies and / or polarizations), one with strong satellite signal and the other one with no or weak satellite signal. Then we do a preliminary assessment of this method in Abidjan, Ivory Coast, where such a sensor has been deployed during 9 months close to a rain gauge. These preliminary results show that this method seems to lead to rainfall assessments better than using a single-channel approach, but only after a calibration phase to assess the difference in the gain of the system between the two channels we use.

Deploying a dense network of sensors around emitting industrial facilities allows to detect and quantify possible CH4 leaks and monitor the emissions continuously. Designing such a monitoring network with highly precise instruments is limited by the elevated cost of instruments, requirements of power consumption and maintenance. Low cost and low power metal oxide sensor could come handy to be an alternative to deploy this kind of network at a fraction of the cost with satisfactory quality of measurements for such applications. Recent studies have tested Metal Oxide Sensors (MOx) on natural and controlled conditions to measure atmospheric methane concentrations and showed a fair agreement with high precision instruments, such as those from Cavity Ring Down Spectrometers (CRDS). Such results open perspectives regarding the potential of MOx to be employed as an alternative to measure and quantify CH4 emissions on industrial facilities. However, such sensors are known to drift with time, to be highly sensitive to water vapor mole fraction, have a poor selectivity with several known cross-sensitivities to other species and present significant sensitivity environmental factors like temperature and pressure. Different approaches for the derivation of CH4 mole fractions from the MOx signal and ancillary parameter measurements have been employed to overcome these problems, from traditional approaches like linear or multilinear regressions to machine learning (ANN, SVM or Random Forest). Most studies were focused on the derivation of ambient CH4 concentrations under different conditions, but few tests assessed the performance of these sensors to capture CH4 variations at high frequency, with peaks of elevated concentrations, which corresponds well with the signal observed from point sources in industrial sites presenting leakage and isolated methane emission. We conducted a continuous controlled experiment over four months (from November 2019 to February 2020) in which three types of MOx Sensors from Figaro® measured high frequency CH4 peaks with concentrations varying between atmospheric background levels up to 24 ppm at LSCE, Saclay, France. We develop a calibration strategy including a two-step baseline correction and compared different approaches to reconstruct CH4 spikes such as linear, multilinear and polynomial regression, and ANN and random forest algorithms. We found that baseline correction in the pre-processing stage improved the reconstruction of CH4 concentrations in the spikes. The random forest models performed better than other methods achieving a mean RMSE = 0.25 ppm when reconstructing peaks amplitude over windows of 4 days. In addition, we conducted tests to determine the minimum amount of data required to train successful models for predicting CH4 spikes, and the needed frequency of re-calibration / re-training under these controlled circumstances. We concluded that for a target RMSE <= 0.3 ppm at a measurement frequency of 5s, 4 days of training are required, and a recalibration / re-training is recommended every 30 days. Our study presents a new approach to process and reconstruct observations from low cost CH4 sensors and highlights its potential to quantify high concentration releases in industrial facilities.

Study region: The study is carried out for northern Tunisia. Study focus: Precipitations are often analysed via intensity or accumulation for a specified time -scale (e.g., annual, seasonal, etc). We propose in this study to analyse regional rainfall variability by adopting a variable time step through the rain event concept. This event-based approach, ensures the integration of information related to rain intermittency, which is one of the fundamental properties of precipitations. This study focuses essentially on wet spells characteristics derived from the aggregation of daily winter dataset over a 50 years period (1960-2009). The multivariate analysis, based on the combination of two clustering approaches, i.e., self-organizing map and hierarchical clustering, allows the identification of different rainfall regimes. This study helps to understand rainfall variability patterns and to address rainfall regionalization and water use management issues. New hydrological insights for the region: The winter precipitations of northern Tunisia are classified into 4 typical situations: Extremely dry seasons with a few short and weak rainfall events, dry seasons, with high frequency of weak events, intermediate seasons with medium amount of rain and intermittent events and rainiest seasons with long and intense events. The regionalization yields two geographical regions: northern sector characterized by rainy seasons, whereas the stations of the southern sector are mostly dry. The temporal variability analysis shows that the dry season classes dominate extending over three consecutive decades from 1970 to 2000.

The surface turbulent fluxes, namely sensible and latent heat fluxes, are keys factors governing the boundary layer processes. Therefore, their correct representation in numerical models is crucial for accurate weather forecasts and climate projections. However, the formulation of these fluxes in such models is the second source of uncertainty, leading to incorrect surface-atmosphere interactions in the simulations. Model evaluation is essential to draw development perspectives. Existing methods mostly consist of direct comparison between observed and modelled fluxes, blending other sources of errors such as incoherent grid-scale representation (soil and vegetation types) and inaccurate environmental forcing (radiative fluxes, temperature, moisture and wind speed). Thus, quantifying errors solely due to fluxes formulation is still challenging. This study, within the framework of the French project MOSAI (Model and Observation for Surface-Atmosphere Interactions), aims at proposing a novel evaluation approach to better identify the weakness of numerical models in surface turbulent fluxes formulation. The concept is to freeze the errors due to other sources by using a machine-learning model, notably a multi-layer perceptron, trained to estimate the fluxes from variables describing the conditions in the surface layer. Hourly data collected over several years at three operational instrumented sites of ACTRIS-France research infrastructure (SIRTA in Paris, Météo-pole in Toulouse and P2OA in Lannemezan), are used. Then, after adaptation to the outputs of numerical models involved in the MOSAI project (RegIPSL, LMDZ, AROME and ARPEGE), the trained-perception will be applied to assess their surface turbulent fluxes.

Raw data issued from meteorological radars are often corrupted by unwanted signals generically called clutter. Hills, tall buildings, atmospheric turbulence, birds, and insects yield patterns that complicate the interpretation of radar images and might add bias in the quantitative precipitation estimates (QPE). Clutter differs from precipitating echoes by both their polarimetric signatures and their particular shapes. This work deals with the removal of clutter. The core idea is to use a fully convolutional network (FCN) to take clutter shapes into account. For a straightforward approach by supervised learning, one would need radar images and their cleaned counterpart, which are not available. We developed a weakly supervised learning method that allows circumventing this issue. This method only requires auxiliary data from rain gauges. The additivity of the reflectivity allows presenting the learning problem in the form of a supervised restoration task with noisy targets. This problem is solved by successive training of a standard FCN (U-net). As ground truth is missing, standard metrics cannot be employed to make a global evaluation. Nevertheless, our method is quantitatively assessed on two clutter classes: ground clutter and interferences. A case study completes the evaluation. A qualitative comparison with the Météo-France algorithm is also performed on a couple of difficult cases.