La contribution du spatial en France aux sciences de l'atmosphère et du climat

International audienceOver the last 30 years, satellite data have been playing a key role for the monitoring of the Earth's atmosphere and climate, with an important French and European contribution. This article summarizes the successive stages of a space mission for Earth's observation, describes the geophysical variables that can be derived from the measurements, and presents the societal benefits associated with these observations.Depuis une trentaine d'années, les données satellitaires jouent un rôle déterminant pour l'observation de l'atmosphère terrestre et du climat, avec une contribution française et européenne importante. Cet article reprend les différentes étapes de la définition d'une mission spatiale d'observation de la Terre, donne un aperçu des variables qui peuvent être restituées à partir des données mesurées et présente les applications sociétales qui bénéficient de ces observations

Fostering the development of climate services through Copernicus Climate Change Service (C3S) for agriculture applications

To better understand and manage climate risks in climate-sensitive sectors such as agriculture, it is essential to have access to consistent and reliable data and information products. Tailoring these products to the needs of the users they want to serve facilitate informed decision-making and downstream applications. This requires an in-depth understanding of users' needs and the context in which these users operate. Considering the diversity of the economic sectors and their actors it is extremely challenging if not outright impossible to promote the emergence of climate services without empowering a plethora of intermediate users who can act as one of the steps in a potential long knowledge brokers chain that connect the climate data providers and the end-users. In this context, Copernicus Climate Change Service (C3S) has been designed around the Climate Data Store (CDS), a unique entry point to a huge variety of quality-controlled climate data and high-level utilities to process that data to develop user-driven applications. Through the Sectoral Information System, C3S has then developed a series of sector specific applications, which show how the infrastructure can be used to address specific users’ needs. This paper presents the key elements of the CDS and selected cases of sectoral application of C3S in agriculture.</p

Assimilation and Modeling of the Atmospheric Hydrological Cycle in the ECMWF Forecasting System

International audienceECMWF's preparations for cloud and rain assimilation encompass development of linearized physics, improved satellite data utilization, a new humidity analysis, and another look at the "spindown" problem. E uropean, American, and Japanese satellite agencies have a number of Earth-observation missions with the objective of providing improved measurements of components of the global hydrological cycle-clouds, precipitation, soil moisture, and water vapor-from a range of operational platforms in both polar and geostationary orbits. Significant development of data assimilation methods will be necessary to make full use of both the existing and new types of observations of the water cycle. The small-scal

Skin temperature from the Thermal Infrared Sounder IASI

While long-term temperature time series mostly rely on weather stations, only satellite data are able to provide systematic global temperature data, from pole to pole on a regular basis, over both land and sea. Satellites measure the “skin” temperature derived from upwelling radiation at the Earth’s land surface. The evolution of skin temperature is not yet fully exploited as its measurement is fairly recent.One of the IASI-Flux and Temperature ERC project tasks aims at providing new climate benchmarks by using skin temperature observations from the calibrated radiances measured twice a day at any location by the IASI thermal infrared instrument on the suite of Metop satellites (2006-2025). The uniqueness of this project is that the IASI-data record will be completely independent from third party information, with no other data from observations or models used, and can therefore serve as an independent reference to e.g. reanalysis, or other climate data records. In this presentation, we first describe our iterative method based on entropy reduction combined with artificial neural networks to derive an independent record of IASI temperature, we next compare and validate our novel method with different datasets (e.g. EUMETSAT, ECMWF reanalysis, SEVIRI satellite products and ground measurements). We then show our results of global skin temperature over land and sea and in different regions in the world over the period [2008- present]. The observed trends are analyzed at seasonal and regional scales in order to disentangle natural (weather/dynamical) variability and human-induced climate forcing. Finally, we show how expanding cities are hotspots for skin temperature reflecting the usefulness of skin temperature as a tracer for human-induced land use and climate change.info:eu-repo/semantics/nonPublishe

Artificial Neural Networks to Retrieve Land and Sea Skin Temperature from IASI

International audienceSurface skin temperature (Tskin) derived from infrared remote sensors mounted on board satellites provides a continuous observation of Earth’s surface and allows the monitoring of global temperature change relevant to climate trends. In this study, we present a fast retrieval method for retrieving Tskin based on an artificial neural network (ANN) from a set of spectral channels selected from the Infrared Atmospheric Sounding Interferometer (IASI) using the information theory/entropy reduction technique. Our IASI Tskin product (i.e., TANN) is evaluated against Tskin from EUMETSAT Level 2 product, ECMWF Reanalysis (ERA5), SEVIRI observations, and ground in situ measurements. Good correlations between IASI TANN and the Tskin from other datasets are shown by their statistic data, such as a mean bias and standard deviation (i.e., [bias, STDE]) of [0.55, 1.86 °C], [0.19, 2.10 °C], [−1.5, 3.56 °C], from EUMETSAT IASI L-2 product, ERA5, and SEVIRI. When compared to ground station data, we found that all datasets did not achieve the needed accuracy at several months of the year, and better results were achieved at nighttime. Therefore, comparison with ground-based measurements should be done with care to achieve the ±2 °C accuracy needed, by choosing, for example, a validation site near the station location. On average, this accuracy is achieved, in particular at night, leading to the ability to construct a robust Tskin dataset suitable for Tskin long-term spatio-temporal variability and trend analysis

Northern Hemisphere atmospheric stilling partly attributed to an increase in surface roughness

International audienc