Search

Over the last three decades, ensemble forecasts have become an integral part of forecasting the weather. They provide users with more complete information than single forecasts as they permit to estimate the probability of weather events by representing the sources of uncertainties and accounting for the day-to-day variability of error growth in the atmosphere. This paper presents a novel approach to obtain a weather forecast model for ensemble forecasting with machine-learning. AIFS-CRPS is a variant of the Artificial Intelligence Forecasting System (AIFS) developed at ECMWF. Its loss function is based on a proper score, the Continuous Ranked Probability Score (CRPS). For the loss, the almost fair CRPS is introduced because it approximately removes the bias in the score due to finite ensemble size yet avoids a degeneracy of the fair CRPS. The trained model is stochastic and can generate as many exchangeable members as desired and computationally feasible in inference. For medium-range forecasts AIFS-CRPS outperforms the physics-based Integrated Forecasting System (IFS) ensemble for the majority of variables and lead times. For subseasonal forecasts, AIFS-CRPS outperforms the IFS ensemble before calibration and is competitive with the IFS ensemble when forecasts are evaluated as anomalies to remove the influence of model biases.

We introduce GraphDOP, a new data-driven, end-to-end forecast system developed at the European Centre for Medium-Range Weather Forecasts (ECMWF) that is trained and initialised exclusively from Earth System observations, with no physics-based (re)analysis inputs or feedbacks. GraphDOP learns the correlations between observed quantities - such as brightness temperatures from polar orbiters and geostationary satellites - and geophysical quantities of interest (that are measured by conventional observations), to form a coherent latent representation of Earth System state dynamics and physical processes, and is capable of producing skilful predictions of relevant weather parameters up to five days into the future., Comment: 23 pages, 15 figures

Long Short Term Memory networks (LSTMs) are used to build single models that predict river discharge across many catchments. These models offer greater accuracy than models trained on each catchment independently if using the same data. However, the same data is rarely available for all catchments. This prevents the use of variables available only in some catchments, such as historic river discharge or upstream discharge. The only existing method that allows for optional variables requires all variables to be considered in the initial training of the model, limiting its transferability to new catchments. To address this limitation, we develop the Hydra-LSTM. The Hydra-LSTM processes variables used across all catchments and variables used in only some catchments separately to allow general training and use of catchment-specific data in individual catchments. The bulk of the model can be shared across catchments, maintaining the benefits of multi-catchment models to generalise, while also benefitting from the advantages of using bespoke data. We apply this methodology to 1 day-ahead river discharge prediction in the Western US, as next-day river discharge prediction is the first step towards prediction across longer time scales. We obtain state-of-the-art performance, generating more accurate median and quantile predictions than Multi-Catchment and Single-Catchment LSTMs while allowing local forecasters to easily introduce and remove variables from their prediction set. We test the ability of the Hydra-LSTM to incorporate catchment-specific data by introducing historical river discharge as a catchment-specific input, outperforming state-of-the-art models without needing to train an entirely new model.

Data-driven machine learning models for weather forecasting have made transformational progress in the last 1-2 years, with state-of-the-art ones now outperforming the best physics-based models for a wide range of skill scores. Given the strong links between weather and climate modelling, this raises the question whether machine learning models could also revolutionize climate science, for example by informing mitigation and adaptation to climate change or to generate larger ensembles for more robust uncertainty estimates. Here, we show that current state-of-the-art machine learning models trained for weather forecasting in present-day climate produce skillful forecasts across different climate states corresponding to pre-industrial, present-day, and future 2.9K warmer climates. This indicates that the dynamics shaping the weather on short timescales may not differ fundamentally in a changing climate. It also demonstrates out-of-distribution generalization capabilities of the machine learning models that are a critical prerequisite for climate applications. Nonetheless, two of the models show a global-mean cold bias in the forecasts for the future warmer climate state, i.e. they drift towards the colder present-day climate they have been trained for. A similar result is obtained for the pre-industrial case where two out of three models show a warming. We discuss possible remedies for these biases and analyze their spatial distribution, revealing complex warming and cooling patterns that are partly related to missing ocean-sea ice and land surface information in the training data. Despite these current limitations, our results suggest that data-driven machine learning models will provide powerful tools for climate science and transform established approaches by complementing conventional physics-based models., Comment: 14 pages, 4 figures

A data-driven model (DDM) suitable for regional weather forecasting applications is presented. The model extends the Artificial Intelligence Forecasting System by introducing a stretched-grid architecture that dedicates higher resolution over a regional area of interest and maintains a lower resolution elsewhere on the globe. The model is based on graph neural networks, which naturally affords arbitrary multi-resolution grid configurations. The model is applied to short-range weather prediction for the Nordics, producing forecasts at 2.5 km spatial and 6 h temporal resolution. The model is pre-trained on 43 years of global ERA5 data at 31 km resolution and is further refined using 3.3 years of 2.5 km resolution operational analyses from the MetCoOp Ensemble Prediction System (MEPS). The performance of the model is evaluated using surface observations from measurement stations across Norway and is compared to short-range weather forecasts from MEPS. The DDM outperforms both the control run and the ensemble mean of MEPS for 2 m temperature. The model also produces competitive precipitation and wind speed forecasts, but is shown to underestimate extreme events.

While the added value of machine learning (ML) for weather and climate applications is measurable, explaining it remains challenging, especially for large deep learning models. Inspired by climate model hierarchies, we propose that a full hierarchy of Pareto-optimal models, defined within an appropriately determined error-complexity plane, can guide model development and help understand the models' added value. We demonstrate the use of Pareto fronts in atmospheric physics through three sample applications, with hierarchies ranging from semi-empirical models with minimal tunable parameters (simplest) to deep learning algorithms (most complex). First, in cloud cover parameterization, we find that neural networks identify nonlinear relationships between cloud cover and its thermodynamic environment, and assimilate previously neglected features such as vertical gradients in relative humidity that improve the representation of low cloud cover. This added value is condensed into a ten-parameter equation that rivals the performance of deep learning models. Second, we establish a ML model hierarchy for emulating shortwave radiative transfer, distilling the importance of bidirectional vertical connectivity for accurately representing absorption and scattering, especially for multiple cloud layers. Third, we emphasize the importance of convective organization information when modeling the relationship between tropical precipitation and its surrounding environment. We discuss the added value of temporal memory when high-resolution spatial information is unavailable, with implications for precipitation parameterization. Therefore, by comparing data-driven models directly with existing schemes using Pareto optimality, we promote process understanding by hierarchically unveiling system complexity, with the hope of improving the trustworthiness of ML models in atmospheric applications., Comment: 18 pages, 4 figures, submitted to AMS Artificial Intelligence for the Earth Systems (AIES)

Most useful weather prediction for the public is near the surface. The processes that are most relevant for near-surface weather prediction are also those that are most interactive and exhibit positive feedback or have key role in energy partitioning. Land surface models (LSMs) consider these processes together with surface heterogeneity and forecast water, carbon and energy fluxes, and coupled with an atmospheric model provide boundary and initial conditions. This numerical parametrization of atmospheric boundaries being computationally expensive, statistical surrogate models are increasingly used to accelerated progress in experimental research. We evaluated the efficiency of three surrogate models in speeding up experimental research by simulating land surface processes, which are integral to forecasting water, carbon, and energy fluxes in coupled atmospheric models. Specifically, we compared the performance of a Long-Short Term Memory (LSTM) encoder-decoder network, extreme gradient boosting, and a feed-forward neural network within a physics-informed multi-objective framework. This framework emulates key states of the ECMWF's Integrated Forecasting System (IFS) land surface scheme, ECLand, across continental and global scales. Our findings indicate that while all models on average demonstrate high accuracy over the forecast period, the LSTM network excels in continental long-range predictions when carefully tuned, the XGB scores consistently high across tasks and the MLP provides an excellent implementation-time-accuracy trade-off. The runtime reduction achieved by the emulators in comparison to the full numerical models are significant, offering a faster, yet reliable alternative for conducting numerical experiments on land surfaces.

Skilful Machine Learned weather forecasts have challenged our approach to numerical weather prediction, demonstrating competitive performance compared to traditional physics-based approaches. Data-driven systems have been trained to forecast future weather by learning from long historical records of past weather such as the ECMWF ERA5. These datasets have been made freely available to the wider research community, including the commercial sector, which has been a major factor in the rapid rise of ML forecast systems and the levels of accuracy they have achieved. However, historical reanalyses used for training and real-time analyses used for initial conditions are produced by data assimilation, an optimal blending of observations with a physics-based forecast model. As such, many ML forecast systems have an implicit and unquantified dependence on the physics-based models they seek to challenge. Here we propose a new approach, training a neural network to predict future weather purely from historical observations with no dependence on reanalyses. We use raw observations to initialise a model of the atmosphere (in observation space) learned directly from the observations themselves. Forecasts of crucial weather parameters (such as surface temperature and wind) are obtained by predicting weather parameter observations (e.g. SYNOP surface data) at future times and arbitrary locations. We present preliminary results on forecasting observations 12-hours into the future. These already demonstrate successful learning of time evolutions of the physical processes captured in real observations. We argue that this new approach, by staying purely in observation space, avoids many of the challenges of traditional data assimilation, can exploit a wider range of observations and is readily expanded to simultaneous forecasting of the full Earth system (atmosphere, land, ocean and composition).

Machine learning-based weather forecasting models have quickly emerged as a promising methodology for accurate medium-range global weather forecasting. Here, we introduce the Artificial Intelligence Forecasting System (AIFS), a data driven forecast model developed by the European Centre for Medium-Range Weather Forecasts (ECMWF). AIFS is based on a graph neural network (GNN) encoder and decoder, and a sliding window transformer processor, and is trained on ECMWF's ERA5 re-analysis and ECMWF's operational numerical weather prediction (NWP) analyses. It has a flexible and modular design and supports several levels of parallelism to enable training on high-resolution input data. AIFS forecast skill is assessed by comparing its forecasts to NWP analyses and direct observational data. We show that AIFS produces highly skilled forecasts for upper-air variables, surface weather parameters and tropical cyclone tracks. AIFS is run four times daily alongside ECMWF's physics-based NWP model and forecasts are available to the public under ECMWF's open data policy.

Weather forecasting is critical for a range of human activities including transportation, agriculture, industry, as well as the safety of the general public. Machine learning models have the potential to transform the complex weather prediction pipeline, but current approaches still rely on numerical weather prediction (NWP) systems, limiting forecast speed and accuracy. Here we demonstrate that a machine learning model can replace the entire operational NWP pipeline. Aardvark Weather, an end-to-end data-driven weather prediction system, ingests raw observations and outputs global gridded forecasts and local station forecasts. Further, it can be optimised end-to-end to maximise performance over quantities of interest. Global forecasts outperform an operational NWP baseline for multiple variables and lead times. Local station forecasts are skillful up to ten days lead time and achieve comparable and often lower errors than a post-processed global NWP baseline and a state-of-the-art end-to-end forecasting system with input from human forecasters. These forecasts are produced with a remarkably simple neural process model using just 8% of the input data and three orders of magnitude less compute than existing NWP and hybrid AI-NWP methods. We anticipate that Aardvark Weather will be the starting point for a new generation of end-to-end machine learning models for medium-range forecasting that will reduce computational costs by orders of magnitude and enable the rapid and cheap creation of bespoke models for users in a variety of fields, including for the developing world where state-of-the-art local models are not currently available.

The conditional generative adversarial rainfall model "cGAN" developed for the UK \cite{Harris22} was trained to post-process into an ensemble and downscale ERA5 rainfall to 1km resolution over three regions of the USA and the UK. Relative to radar data (stage IV and NIMROD), the quality of the forecast rainfall distribution was quantified locally at each grid point and between grid points using the spatial correlation structure. Despite only having information from a single lower quality analysis, the ensembles of post processed rainfall produced were found to be competitive with IFS ensemble forecasts with lead times of between 8 and 16 hours. Comparison to the original cGAN trained on the UK using the IFS HRES forecast indicates that improved training forecasts result in improved post-processing. The cGAN models were additionally applied to the regions that they were not trained on. Each model performed well in their own region indicating that each model is somewhat region specific. However the model trained on the Washington DC, Atlantic coast, region achieved good scores across the USA and was competitive over the UK. There are more overall rainfall events spread over the whole region so the improved scores might be simply due to increased data. A model was therefore trained using data from all four regions which then outperformed the models trained locally.

WeatherBench 2 is an update to the global, medium-range (1-14 day) weather forecasting benchmark proposed by Rasp et al. (2020), designed with the aim to accelerate progress in data-driven weather modeling. WeatherBench 2 consists of an open-source evaluation framework, publicly available training, ground truth and baseline data as well as a continuously updated website with the latest metrics and state-of-the-art models: https://sites.research.google/weatherbench. This paper describes the design principles of the evaluation framework and presents results for current state-of-the-art physical and data-driven weather models. The metrics are based on established practices for evaluating weather forecasts at leading operational weather centers. We define a set of headline scores to provide an overview of model performance. In addition, we also discuss caveats in the current evaluation setup and challenges for the future of data-driven weather forecasting.

Data-driven modeling based on machine learning (ML) is showing enormous potential for weather forecasting. Rapid progress has been made with impressive results for some applications. The uptake of ML methods could be a game-changer for the incremental progress in traditional numerical weather prediction (NWP) known as the 'quiet revolution' of weather forecasting. The computational cost of running a forecast with standard NWP systems greatly hinders the improvements that can be made from increasing model resolution and ensemble sizes. An emerging new generation of ML models, developed using high-quality reanalysis datasets like ERA5 for training, allow forecasts that require much lower computational costs and that are highly-competitive in terms of accuracy. Here, we compare for the first time ML-generated forecasts with standard NWP-based forecasts in an operational-like context, initialized from the same initial conditions. Focusing on deterministic forecasts, we apply common forecast verification tools to assess to what extent a data-driven forecast produced with one of the recently developed ML models (PanguWeather) matches the quality and attributes of a forecast from one of the leading global NWP systems (the ECMWF IFS). The results are very promising, with comparable skill for both global metrics and extreme events, when verified against both the operational analysis and synoptic observations. Increasing forecast smoothness and bias drift with forecast lead time are identified as current drawbacks of ML-based forecasts. A new NWP paradigm is emerging relying on inference from ML models and state-of-the-art analysis and reanalysis datasets for forecast initialization and model training.

Due to the increase in computational resources and accessibility of data, an increase in large, deep learning models trained on copious amounts of multi-modal data using self-supervised or semi-supervised learning have emerged. These ``foundation'' models are often adapted to a variety of downstream tasks like classification, object detection, and segmentation with little-to-no training on the target dataset. In this work, we perform a robustness analysis of Visual Foundation Models (VFMs) for segmentation tasks and focus on robustness against real-world distribution shift inspired perturbations. We benchmark seven state-of-the-art segmentation architectures using 2 different perturbed datasets, MS COCO-P and ADE20K-P, with 17 different perturbations with 5 severity levels each. Our findings reveal several key insights: (1) VFMs exhibit vulnerabilities to compression-induced corruptions, (2) despite not outpacing all of unimodal models in robustness, multimodal models show competitive resilience in zero-shot scenarios, and (3) VFMs demonstrate enhanced robustness for certain object categories. These observations suggest that our robustness evaluation framework sets new requirements for foundational models, encouraging further advancements to bolster their adaptability and performance. The code and dataset is available at: \url{https://tinyurl.com/fm-robust}., Comment: This benchmark along with the code and datasets is available at: https://tinyurl.com/fm-robust. Accepted at CVPRW 2024

Statistical post-processing of global ensemble weather forecasts is revisited by leveraging recent developments in machine learning. Verification of past forecasts is exploited to learn systematic deficiencies of numerical weather predictions in order to boost post-processed forecast performance. Here, we introduce PoET, a post-processing approach based on hierarchical transformers. PoET has 2 major characteristics: 1) the post-processing is applied directly to the ensemble members rather than to a predictive distribution or a functional of it, and 2) the method is ensemble-size agnostic in the sense that the number of ensemble members in training and inference mode can differ. The PoET output is a set of calibrated members that has the same size as the original ensemble but with improved reliability. Performance assessments show that PoET can bring up to 20% improvement in skill globally for 2m temperature and 2% for precipitation forecasts and outperforms the simpler statistical member-by-member method, used here as a competitive benchmark. PoET is also applied to the ENS10 benchmark dataset for ensemble post-processing and provides better results when compared to other deep learning solutions that are evaluated for most parameters. Furthermore, because each ensemble member is calibrated separately, downstream applications should directly benefit from the improvement made on the ensemble forecast with post-processing.

A purposely built deep learning algorithm for the Verification of Earth-System ParametERisation (VESPER) is used to assess recent upgrades of the global physiographic datasets underpinning the quality of the Integrated Forecasting System (IFS) of the European Centre for Medium-Range Weather Forecasts (ECMWF), which is used both in numerical weather prediction and climate reanalyses. A neural network regression model is trained to learn the mapping between the surface physiographic dataset plus the meteorology from ERA5, and the MODIS satellite skin temperature observations. Once trained, this tool is applied to rapidly assess the quality of upgrades of the land-surface scheme. Upgrades which improve the prediction accuracy of the machine learning tool indicate a reduction of the errors in the surface fields used as input to the surface parametrisation schemes. Conversely, incorrect specifications of the surface fields decrease the accuracy with which VESPER can make predictions. We apply VESPER to assess the accuracy of recent upgrades of the permanent lake and glaciers covers as well as planned upgrades to represent seasonally varying water bodies (i.e. ephemeral lakes). We show that for grid-cells where the lake fields have been updated, the prediction accuracy in the land surface temperature (i.e mean absolute error difference between updated and original physiographic datasets) improves by 0.37 K on average, whilst for the subset of points where the lakes have been exchanged for bare ground (or vice versa) the improvement is 0.83 K. We also show that updates to the glacier cover improve the prediction accuracy by 0.22 K. We highlight how neural networks such as VESPER can assist the research and development of surface parameterizations and their input physiography to better represent Earth's surface couples processes in weather and climate models., Comment: 26 pages, 16 figures. Accepted for publication in Hydrology and Earth System Sciences (HESS)

Reduced precision floating point arithmetic is now routinely deployed in numerical weather forecasting over short timescales. However the applicability of these reduced precision techniques to longer timescale climate simulations - especially those which seek to describe a dynamical, changing climate - remains unclear. We investigate this question by deploying a global atmospheric, coarse resolution model known as SPEEDY to simulate a changing climate system subject to increased $\text{CO}_2$ concentrations, over a 100 year timescale. Whilst double precision is typically the operational standard for climate modelling, we find that reduced precision solutions (Float32, Float16) are sufficiently accurate. Rounding the finite precision floats stochastically, rather than using the more common ``round-to-nearest" technique, notably improves the performance of the reduced precision solutions. Over 100 years the mean bias error (MBE) in the global mean surface temperature (precipitation) relative to the double precision solution is $+2 \times 10^{-4}$K ($-8 \times 10^{-5}$ mm/6hr) at single precision and $-3.5\times 10^{-2}$ K($-1 \times 10^{-2}$ mm/6hr) at half precision, whilst the inclusion of stochastic rounding reduced the half precision error to +1.8 $\times 10^{-2}$ K ($-8 \times10^{-4}$ mm/6hr). By examining the resultant climatic distributions that arise after 100 years, the difference in the expected value of the global surface temperature, relative to the double precision solution is $\leq 5 \times 10^{-3}$ K and for precipitation $8 \times 10^{-4}$ mm/6h when numerically integrating at half precision with stochastic rounding. Areas of the model which notably improve due to the inclusion of stochastic over deterministic rounding are also explored and discussed. [abridged], Comment: 13 pages, 9 figures. Submitted to QJRMS. Abstract abridged to meet arxiv requirements

Recent observations of supermassive black holes have brought us new information on their magnetospheres. In this study we attempt a theoretical modelling of the coupling of black holes with their jets and discs, via three innovations. First, we propose a semi-analytical MHD description of a steady relativistic inflow-outflow structure characteristic to the extraction of the hole rotational energy. The mass-loading is ensured in a thin layer, the stagnation surface, by a two-photon pair production originating to a gamma-ray emission from the surrounding disc. The double flow is described near the polar axis by an axisymmetric meridionally self-similar MHD model. Second, the inflow and outflow solutions are crossing the MHD critical points and are matched at the stagnation surface. Knowledge of the MHD field on the horizon give us the angular momentum and energy extracted from the black hole. Finally, we illustrate the model with three specific examples of double-flow solutions by varying the energetic interaction between the MHD field and the rotating black hole. When the isorotation frequency is half of the black hole one, the extracted Poynting flux is comparable to the one obtained using the force-free assumption. In two of the presented solutions, the Penrose process dominates at large colatitudes, while the third is Poynting flux dominated at mid colatitudes. Mass injection rate estimations, from disk luminosity and inner radius, give an upper limit just above the values obtained for two solutions. This model is pertinent to describe the flows near the polar axis where pair production is more efficient., Comment: 22 page

We have seen a great progress in video action recognition in recent years. There are several models based on convolutional neural network (CNN) and some recent transformer based approaches which provide top performance on existing benchmarks. In this work, we perform a large-scale robustness analysis of these existing models for video action recognition. We focus on robustness against real-world distribution shift perturbations instead of adversarial perturbations. We propose four different benchmark datasets, HMDB51-P, UCF101-P, Kinetics400-P, and SSv2-P to perform this analysis. We study robustness of six state-of-the-art action recognition models against 90 different perturbations. The study reveals some interesting findings, 1) transformer based models are consistently more robust compared to CNN based models, 2) Pretraining improves robustness for Transformer based models more than CNN based models, and 3) All of the studied models are robust to temporal perturbations for all datasets but SSv2; suggesting the importance of temporal information for action recognition varies based on the dataset and activities. Next, we study the role of augmentations in model robustness and present a real-world dataset, UCF101-DS, which contains realistic distribution shifts, to further validate some of these findings. We believe this study will serve as a benchmark for future research in robust video action recognition., Comment: Accepted in 2023 Conference on Computer Vision and Pattern Recognition (CVPR)

Despite continuous improvements, precipitation forecasts are still not as accurate and reliable as those of other meteorological variables. A major contributing factor to this is that several key processes affecting precipitation distribution and intensity occur below the resolved scale of global weather models. Generative adversarial networks (GANs) have been demonstrated by the computer vision community to be successful at super-resolution problems, i.e., learning to add fine-scale structure to coarse images. Leinonen et al. (2020) previously applied a GAN to produce ensembles of reconstructed high-resolution atmospheric fields, given coarsened input data. In this paper, we demonstrate this approach can be extended to the more challenging problem of increasing the accuracy and resolution of comparatively low-resolution input from a weather forecasting model, using high-resolution radar measurements as a "ground truth". The neural network must learn to add resolution and structure whilst accounting for non-negligible forecast error. We show that GANs and VAE-GANs can match the statistical properties of state-of-the-art pointwise post-processing methods whilst creating high-resolution, spatially coherent precipitation maps. Our model compares favourably to the best existing downscaling methods in both pixel-wise and pooled CRPS scores, power spectrum information and rank histograms (used to assess calibration). We test our models and show that they perform in a range of scenarios, including heavy rainfall., Comment: Revised version 28/7/22

We propose two approaches to obtain an isometric embedding of the poloidal Kerr sub-manifold. The first one relies on the convex integration process using the corrugation from a primitive embedding. This allows us to obtain one parameter family of embeddings reaching the limits of an isometric embedding. The second one consists in consecutive numerical resolutions of the Gauss-Codazzi-Mainardi and frame equations. This method requires geometric assumptions near the equatorial axis of the poloidal sub-manifold to get initial and boundary conditions. The second approach allows to understand some physical properties in the vicinity of a Kerr black hole, in particular the fast increasing ergoregion extent with angular momentum., Comment: 32 page, 13 Figures

Motivated by recent advances in operational weather forecasting, we study the efficacy of low-precision arithmetic for climate simulations. We develop a framework to measure rounding error in a climate model which provides a stress-test for a low-precision version of the model, and we apply our method to a variety of models including the Lorenz system; a shallow water approximation for flow over a ridge; and a coarse resolution global atmospheric model with simplified parameterisations (SPEEDY). Although double precision (52 significant bits) is standard across operational climate models, in our experiments we find that single precision (23 sbits) is more than enough and that as low as half precision (10 sbits) is often sufficient. For example, SPEEDY can be run with 12 sbits across the entire code with negligible rounding error and this can be lowered to 10 sbits if very minor errors are accepted, amounting to less than 0.1 mm/6hr for the average grid-point precipitation, for example. Our test is based on the Wasserstein metric and this provides stringent non-parametric bounds on rounding error accounting for annual means as well as extreme weather events. In addition, by testing models using both round-to-nearest (RN) and stochastic rounding (SR) we find that SR can mitigate rounding error across a range of applications. Thus our results also provide evidence that SR could be relevant to next-generation climate models. While many studies have shown that low-precision arithmetic can be suitable on short-term weather forecasting timescales, our results give the first evidence that a similar low precision level can be suitable for climate.

It has long been suggested that the mid-latitude atmospheric circulation possesses what has come to be known as `weather regimes', loosely categorised as regions of phase space with above-average density and/or extended persistence. Their existence and behaviour has been extensively studied in meteorology and climate science, due to their potential for drastically simplifying the complex and chaotic mid-latitude dynamics. Several well-known, simple non-linear dynamical systems have been used as toy-models of the atmosphere in order to understand and exemplify such regime behaviour. Nevertheless, no agreed-upon and clear-cut definition of a `regime' exists in the literature. We argue here for an approach which equates the existence of regimes in a dynamical system with the existence of non-trivial topological structure of the system's attractor. We show using persistent homology, an algorithmic tool in topological data analysis, that this approach is computationally tractable, practically informative, and identifies the relevant regime structure across a range of examples., Comment: Major revisions and improvements to exposition. More mathematical details in the Appendix. New title

We assess the value of machine learning as an accelerator for the parameterisation schemes of operational weather forecasting systems, specifically the parameterisation of non-orographic gravity wave drag. Emulators of this scheme can be trained to produce stable and accurate results up to seasonal forecasting timescales. Generally, more complex networks produce more accurate emulators. By training on an increased complexity version of the existing parameterisation scheme we build emulators that produce more accurate forecasts. {For medium range forecasting we find evidence our emulators are more accurate} than the version of the parametrisation scheme that is used for operational predictions. Using the current operational CPU hardware our emulators have a similar computational cost to the existing scheme, but are heavily limited by data movement. On GPU hardware our emulators perform ten times faster than the existing scheme on a CPU.

Extreme precipitation events, such as violent rainfall and hail storms, routinely ravage economies and livelihoods around the developing world. Climate change further aggravates this issue. Data-driven deep learning approaches could widen the access to accurate multi-day forecasts, to mitigate against such events. However, there is currently no benchmark dataset dedicated to the study of global precipitation forecasts. In this paper, we introduce \textbf{RainBench}, a new multi-modal benchmark dataset for data-driven precipitation forecasting. It includes simulated satellite data, a selection of relevant meteorological data from the ERA5 reanalysis product, and IMERG precipitation data. We also release \textbf{PyRain}, a library to process large precipitation datasets efficiently. We present an extensive analysis of our novel dataset and establish baseline results for two benchmark medium-range precipitation forecasting tasks. Finally, we discuss existing data-driven weather forecasting methodologies and suggest future research avenues., Comment: Work completed during the 2020 Frontier Development Lab research accelerator, a private-public partnership with NASA in the US, and ESA in Europe. Accepted as a spotlight/long oral talk at both Climate Change and AI, as well as AI for Earth Sciences Workshops at NeurIPS 2020

The proliferation of turbulence in subcritical wall-bounded shear flows involves spatially localised coherent structures. Turbulent spots correspond to finite-time nonlinear responses to pointwise disturbances and are regarded as seeds of turbulence during transition. The rapid spatial decay of the turbulent fluctuations away from a spot is accompanied by large-scale flows with a robust structuration. The far field velocity field of these spots is investigated numerically using spectral methods in large domains in four different flow scenarios (plane Couette, plane Poiseuille, Couette-Poiseuille and a sinusoidal shear flow). At odds with former expectations, the planar components of the velocity field decay algebraically. These decay exponents depend only on the symmetries of the system, which here depend on the presence of an applied gradient, and not on the Reynolds number. This suggests an effective two-dimensional multipolar expansion for the far field, dominated by a quadrupolar flow component or, for asymmetric flow fields, by a dipolar flow component., Comment: Article accepted for publication in Physical Review Fluids

High resolution radio imaging of AGN have revealed that some sources present motion of superluminal knots and transverse stratification of their jet. Recent observational projects have provided new observational constraints on the central region of rotating black holes in AGN, suggesting that there is an inner- or spine-jet surrounded by a disk wind. This relativistic spine-jet is likely to be composed of electron - positron pairs extracting energy from the black hole. In this article we present an extension and generalization to relativistic jets in Kerr metric of the meridional self similar mechanism. We aim at modeling the inner spine-jet of AGN as the relativistic light outflow emerging from a spherical corona surrounding a Kerr black hole. The model is built by expanding the metric and the forces with colatitude to first order in the magnetic flux function. Conversely to previous models, effects of the light cylinder are not neglected. Solutions with high Lorentz factor are obtained and provide spine-jet models up to the polar axis. As in previous publications, we calculate the magnetic collimation efficiency parameter, which measures the variation of the available energy across the field lines. This collimation efficiency is an integral of the model, generalizing to Kerr metric the classical magnetic rotator efficiency criterion. We study the variation of the magnetic efficiency and acceleration with the spin of the black hole and show their high sensitivity to this integral. These new solutions model collimated or radial, relativistic or ultra-relativistic outflows. We discuss the relevance of our solutions to model the M87 spine-jet. We study the efficiency of the central black hole spin to collimate a spine-jet and show that the jet power is of the same order with that determined by numerical simulations., Comment: Accepted for publication in Astronomy and Astrophysics

We examine the onset of turbulence in Waleffe flow -- the planar shear flow between stress-free boundaries driven by a sinusoidal body force. By truncating the wall-normal representation to four modes, we are able to simulate system sizes an order of magnitude larger than any previously simulated, and thereby to attack the question of universality for a planar shear flow. We demonstrate that the equilibrium turbulence fraction increases continuously from zero above a critical Reynolds number and that statistics of the turbulent structures exhibit the power-law scalings of the (2+1)-D directed percolation universality class.

Luminosity profiles of galaxies acting as strong gravitational lenses can be tricky to study. Indeed, strong gravitational lensing images display several lensed components, both point-like and diffuse, around the lensing galaxy. Those objects limit the study of the galaxy luminosity to its inner parts. Therefore, the usual fitting methods perform rather badly on such images. Previous studies of strong lenses luminosity profiles using software such as GALFIT or IMFITFITS and various PSF-determining methods have resulted in discrepant results. The present work aims at investigating the causes of those discrepancies, as well as at designing more robust techniques for studying the morphology of early-type lensing galaxies with the ability to subtract a lensed signal from their luminosity profiles. We design a new method to independently measure each shape parameter, namely, the position angle, ellipticity, and half-light radius of the galaxy. Our half-light radius measurement method is based on an innovative scheme for computing isophotes that is well suited to measuring the morphological properties of gravitational lensing galaxies. Its robustness regarding various specific aspects of gravitational lensing image processing is analysed and tested against GALFIT. It is applied to a sample of systems from the CASTLES database. Simulations show that when restricted to small, inner parts of the lensing galaxy, the technique presented here is more trustworthy than GALFIT. It gives more robust results than GALFIT, which shows instabilities regarding the fitting region, the value of the Sersic index, and the signal-to-noise ratio. It is therefore better suited than GALFIT for gravitational lensing galaxies. It is also able to study lensing galaxies that are not much larger than the PSF. New values for the half-light radius of the objects in our sample are presented and compared to previous works., Comment: Accepted for publication in A&A on september 28th, 2015

Turbulent-laminar intermittency, typically in the form of bands and spots, is a ubiquitous feature of the route to turbulence in wall-bounded shear flows. Here we study the idealised shear between stress-free boundaries driven by a sinusoidal body force and demonstrate quantitative agreement between turbulence in this flow and that found in the interior of plane Couette flow -- the region excluding the boundary layers. Exploiting the absence of boundary layers, we construct a model flow that uses only four Fourier modes in the shear direction and yet robustly captures the range of spatiotemporal phenomena observed in transition, from spot growth to turbulent bands and uniform turbulence. The model substantially reduces the cost of simulating intermittent turbulent structures while maintaining the essential physics and a direct connection to the Navier-Stokes equations. We demonstrate the generic nature of this process by introducing stress-free equivalent flows for plane Poiseuille and pipe flows which again capture the turbulent-laminar structures seen in transition., Comment: 13 pages, 9 figures

We consider a 9-PDE (1-space and 1-time) model of plane Couette flow in which the degrees of freedom are severely restricted in the streamwise and cross-stream directions to study spanwise localisation in detail. Of the many steady Eckhaus (spanwise modulational) instabilities identified of global steady states, none lead to a localized state. Localized periodic solutions were found instead which arise in saddle node bifurcations in the Reynolds number. These solutions appear global (domain filling) in narrow (small spanwise) domains yet can be smoothly continued out to fully spanwise-localised states in very wide domains. This smooth localisation behaviour, which has also been seen in fully-resolved duct flow (Okino 2011), indicates that an apparently global flow structure needn't have to suffer a modulational instability to localize in wide domains., Comment: 14 pages, 18 figures

The aim in the dynamical systems approach to transitional turbulence is to construct a scaffold in phase space for the dynamics using simple invariant sets (exact solutions) and their stable and unstable manifolds. In large (realistic) domains where turbulence can co-exist with laminar flow, this requires identifying exact localized solutions. In wall-bounded shear flows the first of these has recently been found in pipe flow, but questions remain as to how they are connected to the many known streamwise-periodic solutions. Here we demonstrate the origin of the first localized solution in a modulational symmetry-breaking Hopf bifurcation from a known global travelling wave that has 2-fold rotational symmetry about the pipe axis. Similar behaviour is found for a global wave of 3-fold rotational symmetry, this time leading to two localized relative periodic orbits. The clear implication is that all global solutions should be expected to lead to more realistic localised counterparts through such bifurcations, which provides a constructive route for their generation., Comment: 5 pages, 6 figures

In linearly stable shear flows at moderate Re, turbulence spontaneously decays despite the existence of a codimension-one manifold, termed the edge of chaos, which separates decaying perturbations from those triggering turbulence. We statistically analyse the decay in plane Couette flow, quantify the breaking of self-sustaining feedback loops and demonstrate the existence of a whole continuum of possible decay paths. Drawing parallels with low-dimensional models and monitoring the location of the edge relative to decaying trajectories we provide evidence, that the edge of chaos separates state space not globally. It is instead wrapped around the turbulence generating structures and not an independent dynamical structure but part of the chaotic saddle. Thereby, decaying trajectories need not cross the edge, but circumnavigate it while unwrapping from the turbulent saddle., Comment: 11 pages, 6 figures

It is generally thought that galaxies are embedded in dark matter halos extending well beyond their luminous matter. The existence of these galactic halos is mainly derived from the larger than expected velocities of stars and gas in the outskirts of spiral galaxies. Much less is known about dark matter around early-type (elliptical or lenticular) galaxies. We use gravitational lensing to derive the masses of early-type galaxies deflecting light of background quasars. This provides a robust measurement of the total mass within the Einstein ring, a circle whose diameter is comparable to the separation of the different quasar images. We find that the mass-to-light ratio of the lensing galaxies does not depend on radius, from inner galactic regions out to several half-light radii. Moreover, its value does not exceed the value predicted by stellar population models by more than a factor two, which may be explained by baryonic dark matter alone, without any need for exotic matter. Our results thus suggest that, if dark matter is present in early-type galaxies, its amount does not exceed the amount of luminous matter and its density follows that of luminous matter, in sharp contrast to what is found from rotation curves of spiral galaxies., Comment: 9 pages, 2 figures, 1 table, submitted to ApJ Letters

(abridged) Gravitationally lensed quasars can be used as powerful cosmological and astrophysical probes. We can (i) infer the Hubble constant based on the time-delay technique, (ii) unveil substructures along the l.o.s. toward distant galaxies, and (iii) compare the shape and the slope of baryons and dark matter distributions in galaxies. To reach these goals, we need high-accuracy astrometry and morphology measurements of the lens. In this work, we first present new astrometry for 11 lenses with measured time delays. Using MCS deconvolution on NIC2 HST images, we reached an astrometric accuracy of about 1-2.5 mas and an accurate shape measurement of the lens galaxy. Second, we combined these measurements with those of 14 other systems to present new mass models of these lenses. This led to the following results: 1) In 4 double-image quasars, we show that the influence of the lens environment on the time delay can easily be quantified and modeled, hence putting these lenses with high priority for time-delay determination. 2) For quadruple-image quasars, the difficulty often encountered in reproducing the image positions to milli-arcsec accuracy (astrometric anomaly) is overcome by explicitly including the nearest visible galaxy in the model. However, one anomalous system (J1131-1231) does not show any luminous perturber in its vicinity, and three others (WFI2026-4536, WFI2033-4723, and B2045+265) have problematic modeling. These 4 systems are the best candidates for a pertubation by a dark matter substructure. 3) We find a significant correlation between the PA of the light and of the mass distributions in lensing galaxies. In contrast with other studies, we find that the ellipticity of the light and of the mass also correlate well, suggesting that the overall spatial distribution of matter is not very different from the baryon distribution in the inner \sim 5 kpc of lensing galaxies., Comment: Accepted for publication in Astronomy and Astrophysics abridged abstract

We present here a new robotic telescope called TRAPPIST (TRAnsiting Planets and PlanetesImals Small Telescope). Equipped with a high-quality CCD camera mounted on a 0.6 meter light weight optical tube, TRAPPIST has been installed in April 2010 at the ESO La Silla Observatory (Chile), and is now beginning its scientific program. The science goal of TRAPPIST is the study of planetary systems through two approaches: the detection and study of exoplanets, and the study of comets. We describe here the objectives of the project, the hardware, and we present some of the first results obtained during the commissioning phase., Comment: To appear in Detection and Dynamics of Transiting Exoplanets, Proceedings of Haute Provence Observatory Colloquium (23-27 August 2010), eds. F. Bouchy, R.F. Diaz & C.Moutou, Platypus press 2011

We present accurate time delays for the quadruply imaged quasar HE 0435-1223. The delays were measured from 575 independent photometric points obtained in the R-band between January 2004 and March 2010. With seven years of data, we clearly show that quasar image A is affected by strong microlensing variations and that the time delays are best expressed relative to quasar image B. We measured Delta_t(BC) = 7.8+/-0.8 days, Delta_t(BD) = -6.5+/-0.7 days and Delta_t_CD = -14.3+/-0.8 days. We spacially deconvolved HST NICMOS2 F160W images to derive accurate astrometry of the quasar images and to infer the light profile of the lensing galaxy. We combined these images with a stellar population fitting of a deep VLT spectrum of the lensing galaxy to estimate the baryonic fraction, $f_b$, in the Einstein radius. We measured f_b = 0.65+0.13-0.10 if the lensing galaxy has a Salpeter IMF and f_b = 0.45+0.04-0.07 if it has a Kroupa IMF. The spectrum also allowed us to estimate the velocity dispersion of the lensing galaxy, sigma_ap = 222+/-34 km/s. We used f_b and sigma_ap to constrain an analytical model of the lensing galaxy composed of an Hernquist plus generalized NFW profile. We solve the Jeans equations numerically for the model and explored the parameter space under the additional requirement that the model must predict the correct astrometry for the quasar images. Given the current error bars on f_b and sigma_ap, we did not constrain H0 yet with high accuracy, i.e., we found a broad range of models with chi^2 < 1. However, narrowing this range is possible, provided a better velocity dispersion measurement becomes available. In addition, increasing the depth of the current HST imaging data of HE 0435-1223 will allow us to combine our constraints with lens reconstruction techniques that make use of the full Einstein ring that is visible in this object., Comment: 12 pages, 10 figures, final version accepted for publication by A&A

We apply the iterative MCS deconvolution method (ISMCS) to near-IR HST archives data of seven gravitationally lensed quasars currently monitored by the COSMOGRAIL collaboration: HE 0047-1756, RX J1131-1231, SDSS J1138+0314, SDSS J1155+6346, SDSS J1226-0006, WFI J2026-4536 and HS 2209+1914. In doing so, we obtain relative positions for the lensed images and shape parameters for the light distribution of the lensing galaxy in each system. The lensed image positions are derived with 1-2 mas accuracy. To predict time delays and to test the ability of simple mass models to reproduce the observed configuration, isothermal and de Vaucouleurs mass models are calculated for the whole sample using state-of-the-art modeling techniques. The effect of the lens environment on the lens mass models is taken into account with a shear term. Doubly imaged quasars are equally well fitted by each of these models. A large amount of shear is necessary to reproduce SDSS J1155+6346 and SDSS J1226-006. In the latter case, we identify a nearby galaxy as the dominant source of shear. The quadruply imaged quasar SDSS J1138+0314 is well reproduced by simple lens models, which is not the case for the two other quads, RX J1131-1231 and WFI J2026-4536. This might be the signature of astrometric perturbations due to massive substructures in the lensing galaxy unaccounted for by the models. Other possible explanations are also presented., Comment: 14 pages, 1 figure (with 28 frames), 5 tables, accepted for publication in Astronomy & Astrophysics (08/07/2010)

(Abridged) The luminous z=0.286 quasar HE0450-2958 is interacting with a companion galaxy at 6.5 kpc distance and the whole system is a ULIRG. A so far undetected host galaxy triggered the hypothesis of a mostly "naked" black hole (BH) ejected from the companion by three-body interaction. We present new HST/NICMOS 1.6micron imaging data at 0.1" resolution and VLT/VISIR 11.3micron images at 0.35" resolution that for the first time resolve the system in the near- and mid-infrared. We combine these with existing optical HST and CO maps. (i) At 1.6micron we find an extension N-E of the quasar nucleus that is likely a part of the host galaxy, though not its main body. If true, this places HE0450-2958 directly onto the M_BH-M_bulge-relation for nearby galaxies. (ii) HE0450-2958 is consistent with lying at the high-luminosity end of Narrow-Line Seyfert 1 Galaxies, and more exotic explanations like a "naked quasar" are unlikely. (iii) All 11.3micron radiation in the system is emitted by the quasar nucleus, which is radiating at super-Eddington rate, L/L_Edd=6.2+3.8-1.8, or 12 M_sun/yr. (iv) The companion galaxy is covered in optically thick dust and is not a collisional ring galaxy. It emits in the far infrared at ULIRG strength, powered by Arp220-like star formation (strong starburst-like). An M82-like SED is ruled out. (v) With its black hole accretion rate HE0450-2958 produces not enough new stars to maintain its position on the M_BH-M_bulge-relation, and star formation and black hole accretion are spatially disjoint; the bulge has to grow by redistribution of preexisting stars. (vi) Systems similar to HE0450-2958 with spatially disjoint ULIRG-strength star formation and quasar activity are rare. At z<0.43 we only find <4% (3/77) candidates for a similar configuration., Comment: 12 pages, 6 figures, accepted for publication in ApJ

We present observations of the afterglow of GRB 080319B at optical, mm and radio frequencies from a few hours to 67 days after the burst. Present observations along with other published multi-wavelength data have been used to study the light-curves and spectral energy distributions of the burst afterglow. The nature of this brightest cosmic explosion has been explored based on the observed properties and it's comparison with the afterglow models. Our results show that the observed features of the afterglow fits equally good with the Inter Stellar Matter and the Stellar Wind density profiles of the circum-burst medium. In case of both density profiles, location of the maximum synchrotron frequency $\nu_m$ is below optical and the value of cooling break frequency $\nu_c$ is below $X-$rays, $\sim 10^{4}$s after the burst. Also, the derived value of the Lorentz factor at the time of naked eye brightness is $\sim 300$ with the corresponding blast wave size of $\sim 10^{18}$ cm. The numerical fit to the multi-wavelength afterglow data constraints the values of physical parameters and the emission mechanism of the burst., Comment: 8 Pages, 3 Figures, Accepted for publication to Astronomy and Astrophysics on 02/04/2009

The QSO HE0450-2958 was brought to the front scene by the non-detection of its host galaxy and strong upper limits on the latter's luminosity. The QSO is also a powerful infrared emitter, in gravitational interaction with a strongly distorted UltraLuminous InfraRed companion galaxy. We investigate the properties of the companion galaxy, through new near- and mid-infrared observations of the system obtained with NICMOS onboard HST, ISAAC and VISIR on the ESO VLT. The companion galaxy is found to harbour a point source revealed only in the infrared, in what appears as a hole or dark patch in the optical images. Various hypotheses on the nature of this point source are analyzed and it is found that the only plausible one is that it is a strongly reddened AGN hidden behind a thick dust cloud. The hypothesis that the QSO supermassive black hole might have been ejected from the companion galaxy in the course of a galactic collision involving 3-body black holes interaction is also reviewed, on the basis of this new insight on a definitely complex system., Comment: 8 pages, 8 figures, accepted for publication in MNRAS

We report on the redshift of the lensing galaxy and of the quasar QJ 0158-4325 and on the lens model of the system. A deep VLT/FORS2 spectrum and HST/NICMOS-F160W images are deconvolved. From the images we derive the light profile of the lensing galaxy and an accurate relative astrometry for the system. In addition we measure the flux ratio between the quasar images in the MgII emission line to constrain the mass model. From the spectrum we measure the redshift of the lensing galaxy (z=0.317+/-0.001) and of the quasar (z=1.294+/-0.008). Using the flux ratio in the lens model allows to discard the SIE as a suitable approximation of the lens potential. On the contrary the truncated-PIEMD gives a good fit to the lens and leads to a time delay of t(A-B)=-14.5+/-0.1 days, with H0=73 km/s/Mpc. Using the flux ratio to constrain the mass model favors the truncated-PIEMD over the SIE, while ignoring this constraint leaves the choice open., Comment: Research Note, Accepted for publication in A&A

Gravitationally lensed quasars can be used to map the mass distribution in lensing galaxies and to estimate the Hubble constant H0 by measuring the time delays between the quasar images. Here we report the measurement of two independent time delays in the quadruply imaged quasar WFI J2033-4723 (z = 1.66). Our data consist of R-band images obtained with the Swiss 1.2 m EULER telescope located at La Silla and with the 1.3 m SMARTS telescope located at Cerro Tololo. The light curves have 218 independent epochs spanning 3 full years of monitoring between March 2004 and May 2007, with a mean temporal sampling of one observation every 4th day. We measure the time delays using three different techniques, and we obtain Dt(B-A) = 35.5 +- 1.4 days (3.8%) and Dt(B-C) = 62.6 +4.1/-2.3 days (+6.5%/-3.7%), where A is a composite of the close, merging image pair. After correcting for the time delays, we find R-band flux ratios of F_A/F_B = 2.88 +- 0.04, F_A/F_C = 3.38 +- 0.06, and F_A1/F_A2 = 1.37 +- 0.05 with no evidence for microlensing variability over a time scale of three years. However, these flux ratios do not agree with those measured in the quasar emission lines, suggesting that longer term microlensing is present. Our estimate of H0 agrees with the concordance value: non-parametric modeling of the lensing galaxy predicts H0 = 67 +13/-10 km s-1 Mpc-1, while the Single Isothermal Sphere model yields H0 = 63 +7/-3 km s-1 Mpc-1 (68% confidence level). More complex lens models using a composite de Vaucouleurs plus NFW galaxy mass profile show twisting of the mass isocontours in the lensing galaxy, as do the non-parametric models. As all models also require a significant external shear, this suggests that the lens is a member of the group of galaxies seen in field of view of WFI J2033-4723., Comment: 14 pages, 12 figures, published in A&A

A high accuracy photometric reduction method is needed to take full advantage of the potential of the transit method for the detection and characterization of exoplanets, especially in deep crowded fields. In this context, we present DECPHOT, a new deconvolution-based photometry algorithm able to deal with a very high level of crowding and large variations of seeing. It also increases the resolution of astronomical images, an important advantage for the discrimination of false positives in transit photometry., Comment: 6 pages, 2 figures; to be published in Transiting Extrasolar Planets Workshop, Eds: Cristina Afonso, David Weldrake & Thomas Henning

Archival HST/NICMOS-2 images of the Cloverleaf gravitational lens (H1413+117), a quadruply imaged quasar, have been analysed with a new method derived from the MCS deconvolution algorithm (Magain et al., 1998). This method is based on an iterative process which simultaneously allows to determine the Point Spread Function (PSF) and to perform a deconvolution of images containing several point sources plus extended structures. As such, it is well adapted to the processing of gravitational lens images, especially in the case of multiply imaged quasars. Two sets of data have been analysed : the first one, which has been obtained through the F160W filter in 1997, basically corresponds to a continuum image, while the second one, obtained through the narrower F180M filter in 2003, is centered around the forbidden [OIII] emission lines at the source redshift, thus probing the narrow-line region of the quasar. The deconvolution gives astrometric and photometric measurements in both filters and reveals the primary lensing galaxy as well as a partial Einstein ring. The high accuracy of the results is particularly important in order to model the lensing system and to reconstruct the source undergoing the strong lensing. The reliability of the method is checked on a synthetic image similar to H1413+117., Comment: 8 pages, 25 figures

A new method is presented for determining the Point Spread Function (PSF) of images that lack bright and isolated stars. It is based on the same principles as the MCS (Magain, Courbin, Sohy, 1998) image deconvolution algorithm. It uses the information contained in all stellar images to achieve the double task of reconstructing the PSFs for single or multiple exposures of the same field and to extract the photometry of all point sources in the field of view. The use of the full information available allows to construct an accurate PSF. The possibility to simultaneously consider several exposures makes it very well suited to the measurement of the light curves of blended point sources from data that would be very difficult or even impossible to analyse with traditional PSF fitting techniques. The potential of the method for the analysis of ground-based and space-based data is tested on artificial images and illustrated by several examples, including HST/NICMOS images of a lensed quasar and VLT/ISAAC images of a faint blended Mira star in the halo of the giant elliptical galaxy NGC5128 (Cen A)., Comment: Institutes: (1) Institut d'Astrophysique et de Geophysique, Universite de Liege, allee du 6 Aout 17, B-4000 Liege, Belgium; (2) Ecole Polytechnique Federale de Lausanne (EPFL), Laboratoire d'Astrophysique, Observatoire, CH-1290 Sauverny, Switzerland; (3) Observatoire de Geneve, 51 Chemin des Maillettes, CH-1290 Sauverny, Switzerland. 8 pages, 8 figures. Accepted for publication in A&A