International audienceThe collective representation within global models of aerosol, cloud, precipitation, and their radiative properties remains unsatisfactory. They constitute the largest source of uncertainty in predictions of climatic change and hamper the ability of numerical weather prediction models to forecast high-impact weather events. The joint ESA-JAXA EarthCARE satellite mission, scheduled for launch in 2017, will help to resolve these weaknesses by providing global profiles of cloud, aerosol, precipitation, and associated radiative properties inferred from a combination of measurements made by its collocated active and passive sensors. EarthCARE will improve our understanding of cloud and aerosol processes by extending the invaluable dataset acquired by the A-Train satellites CloudSat, CALIPSO, and Aqua. Specifically, EarthCARE's Cloud Profling Radar, with 7 dB more sensitivity than CloudSat, will detect more thin clouds and its Doppler capability will provide novel information on convection, precipitating ice particle and raindrop fall speeds. EarthCARE's 355-nm High Spectral Resolution Lidar will measure directly and accurately cloud and aerosol extinction and optical depth. Combining this with backscatter and polarization information should lead to an unprecedented ability to identify aerosol type. The Multi-Spectral Imager will provide a context for, and the ability to construct the cloud and aerosol distribution in 3D domains around the narrow 2D retrieved cross-section. The consistency of the retrievals will be assessed to within a target of ±10 W m⁻² on the (10 km²) scale by comparing the multi-view Broad-Band Radiometer observations to the top-of-atmosphere fluxes estimated by 3D radiative transfer models acting on retrieved 3D domains

[1] Measurements of the top-of-the-atmosphere outgoing longwave radiation (OLR) for July 2003 from Meteosat-7 are used to assess the performance of the numerical weather prediction version of the Met Office Unified Model. A significant difference is found over desert regions of northern Africa where the model emits too much OLR by up to 35 Wm À2 in the monthly mean. By cloud-screening the data we find an error of up to 50 Wm À2 associated with cloud-free areas, which suggests an error in the model surface temperature, surface emissivity, or atmospheric transmission. By building up a physical model of the radiative properties of mineral dust based on in situ, and surface-based and satellite remote sensing observations we show that the most plausible explanation for the discrepancy in OLR is due to the neglect of mineral dust in the model. The calculations suggest that mineral dust can exert a longwave radiative forcing by as much as 50 Wm À2 in the monthly mean for 1200 UTC in cloud-free regions, which accounts for the discrepancy between the model and the Meteosat-7 observations. This suggests that inclusion of the radiative effects of mineral dust will lead to a significant improvement in the radiation balance of numerical weather prediction models with subsequent improvements in performance.

A new instrument, GERB, is now operating on the European Meteosat-8 spacecraft, making unique, accurate, high-time-resolution measurements of the Earth's radiation budget for atmospheric physics and climate studies.

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Abstract:The Earth Radiation Budget at the top of the atmosphere quantifies how the Earth gains energy from the Sun and loses energy to space. It is of fundamental importance for climate and climate change. In this paper, the current state-of-the-art of the satellite measurements of the Earth Radiation Budget is reviewed. Combining all available measurements, the most likely value of the Total Solar Irradiance at a solar minimum is 1362 W/m 2 , the most likely Earth albedo is 29.8%, and the most likely annual mean Outgoing Longwave Radiation is 238 W/m 2 . We highlight the link between long-term changes of the Outgoing Longwave Radiation, the strengthening of El Nino in the period 1985-1997 and the strengthening of La Nina in the period 2000-2009.

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This study reports on the exploitation of GNSS (Global Navigation Satellite System) and a new potential application for weather forecasts and nowcasting. We focus on GPS observations (post-processing with a time resolution of 5 and 15 min and fast calculations with a time resolution of 5 min) and try to establish typical configurations of the water vapour field which characterise convective systems and particularly which supply precursors of their initiation are associated with deep convection. We show the critical role of GNSS horizontal gradients of the water vapour content to detect small scale structures of the troposphere (i. e. convective cells), and then we present our strategy to obtain typical water vapour configurations by GNSS called "H₂O alert". These alerts are based on a dry/wet contrast taking place during a 30 min time window before the initiation of a convective system. GNSS observations have been assessed for the rainfall event of 28–29 June 2005 using data from the Belgian dense network (baseline from 5 to 30 km). To validate our GNSS H₂O alerts, we use the detection of precipitation by C-band weather radar and thermal infrared radiance (cloud top temperature) of the 10.8-micrometers channel [Ch09] of SEVIRI instrument on Meteosat Second Generation. Using post-processed measurements, our H₂O alerts obtain a score of about 80%. Final and ultra-rapid IGS (International GNSS Service) orbits have been tested and show equivalent results. Fast calculations (less than 10 min) have been processed for 29 June 2005 with a time resolution of 5 min. The mean bias (and standard deviation) between fast and reference post-processed ZTD (zenith total delay) and gradients are, respectively, 0.002 (± 0.008) m and 0.001 (± 0.004) m. The score obtained for the H₂O alerts generated by fast calculations is 65%