Capsule: The manuscript addresses the need for tropospheric and stratospheric wind profiles and discusses capabilities to fulfil such need. To follow up the Aeolus mission an international operational UV Doppler Wind Lidar constellation is suggested.

Abstract. A fast method for the Apparent Pressure Retrieval (APR method) over land from satellite data, based on a two band ratio in the oxygen A-band (759-770 nm), is described. This method is devoted to the cloud detection and atmospheric corrections. Parameterizations are performed from line-by-line calculations assuming a pure absorbing medium. Moreover, we defined a corrective factor to account for scattering effects of the atmosphere. We validated this method with measurements of the MOS sensor (Modular Optoelectronic Scanner), whose spectral characteristics are appropriate. Comparisons with ECMWF (European Centre for Medium-Range Weather Forecasts) pressures showed the need to perform in-flight calibrations over a reference scene to account for spectral shifts of filter responses. Therefore we selected bright surfaces for the calibration, such as deserts, because of their major contribution to the satellite signal. After calibration the accuracy of the method is about 10 hPa over bright surfaces. Comparisons for various meteorological and geographical conditions showed that deviations between ECMWF pressures and MOS apparent pressures are generally less than 30 hPa using scattering corrections. These deviations are multiplied by 2 without correction. The APR method has been included in the cloud detection and atmospheric correction algorithms for the MOS data processing over land. Theoretical studies showed that the APR method is suitable for the cloud discrimination and that an error of 30 hPa on the surface pressure retrieval has no noticeable effect on the geophysical products of these algorithms, such as aerosol optical thickness or surface reflectance. Consequently, this method is potentially applicable to the retrieval of the apparent pressure over land with the MOS algorithms, as well as other similar satellite sensors such as the Medium Resolution Imaging Spectrometer. IntroductionThe surface pressure is an important parameter for meteorological applications, as well as an input for algorithms dedicated to the satellite data processing, such as the cloud detection or atmospheric corrections. As an example, the surface pressure is necessary for the Rayleigh correction and, consequently, to evaluate products as surface reflectances or aerosol properties from satellite data next to a nonabsorbing channel (757 nm). Moreover, these close bands allow to check that there is no spectral variation of surface reflectances and atmospheric parameters over the considered spectral range. Indeed, Br•on and Bouffies [1996] showed that a variability on the surface spectral signature yields deviations of _+80 hPa in the retrieval of apparent pressures.In the framework of MOS we performed cloud detection and atmospheric correction algorithms over land [Borde et al., 2000]. The apparent pressure is an important input for these 27,277

Atmospheric Motion Vectors (AMVs) calculated by six different institutions (Brazil Center for Weather Prediction and Climate Studies/CPTEC/INPE, European Organization for the Exploitation of Meteorological Satellites/EUMETSAT, Japan Meteorological Agency/JMA, Korea Meteorological Administration/KMA, Unites States National Oceanic and Atmospheric Administration/NOAA, and the Satellite Application Facility on Support to Nowcasting and Very short range forecasting/NWCSAF) with JMA’s Himawari-8 satellite data and other common input data are here compared. The comparison is based on two different AMV input datasets, calculated with two different image triplets for 21 July 2016, and the use of a prescribed and a specific configuration. The main results of the study are summarized as follows: (1) the differences in the AMV datasets depend very much on the ‘AMV height assignment’ used and much less on the use of a prescribed or specific configuration; (2) the use of the ‘Common Quality Indicator (CQI)’ has a quantified skill in filtering collocated AMVs for an improved statistical agreement between centers; (3) Among the six AMV operational algorithms verified by this AMV Intercomparison, JMA AMV algorithm has the best overall performance considering all validation metrics, mainly due to its new height assignment method: ‘Optimal estimation method considering the observed infrared radiances, the vertical profile of the Numerical Weather Prediction wind, and the estimated brightness temperature using a radiative transfer model’.

Height assignment (HA) is currently the most challenging task in the operational atmospheric motion vectors' (AMV) extraction scheme. Several sources of error are associated with the height assignment step, including the sensitivity of the HA methods to several atmospheric parameters. However, one of the main difficulties is to identify, for the HA calculation, the most significant image pixels used in the feature-tracking process. The most widely used method selects the coldest pixels in a representative target box (e.g., coldest 25%) to infer the height of the detected feature, irrespective of what was tracked. This paper presents a method based on a closer link between the pixels used for tracking and their HA. The individual contribution to the overall tracking cross-correlation coefficient is used to identify the most significant pixels contributing to the tracking. This approach has been implemented operationally at European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) to derive AMVs since September 2012. This paper details the method, gives specific examples, and provides a first glance at its performances and benefits for the operational AMV production.

EUMETSAT, the European Organization for the Exploitation of Meteorological Satellites, is one of the key contributors to global atmospheric motion vector (AMV) production around the world. Its current contribution includes geostationary satellites at 0.0 and 41.5 degrees east, and several products extracted from the Metop low-orbit satellites. These last ones mainly cover high-latitude regions completing the observations from the geostationary ring. In the upcoming years, EUMETSAT will launch a new generation of geostationary and low-orbit satellites. The imager instruments Flexible Combined Imager (FCI) and METImage will take over the nominal AMV production at EUMETSAT around 2022 and 2024. The enhanced characteristics of these new-generation instruments are expected to increase AMV production and to improve the quality of the products. This paper presents an overview of the current EUMETSAT AMV operational production, together with a roadmap of the preparation activities for the new generation of satellites. The characteristics of the upcoming AMV products are described and compared to the current operational AMV products. This paper also presents a recent investigation into AMV extraction using the Sentinel-3 Sea and Land Surface Temperature Radiometer (SLSTR) instrument, as well as the retrieval of wind profiles from infrared sounders.