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Atmospheric Motion Vectors (AMVs) derived from Low-Earth-Orbit (LEO) images are essential to understand atmospheric dynamics, in particular in polar areas where other types of measurements are scarce. EUMETSAT computes AMVs from several sensors on board LEO satellites, including Metop – Advanced Very High Resolution Radiometer (AVHRR) and Sentinel-3 - Sea and Land Surface Temperature Radiometer (SLSTR). Wind observations from these sensors have a positive impact on the Numerical Weather Prediction (NWP) systems in which they are assimilated, but show strong speed biases when compared to the forecast model, especially in the tropics and the mid-latitude regions. Studies have tried to characterize these biases from a physical perspective and have hypothesized that a wrong height assignment could be responsible for these biases. In this article, we show however that the real culprit is the tracking algorithm. Indeed, our analysis of an SLSTR AMV dataset revealed that an approximation on the guess vector, combined with a lack of robustness of cross-correlation matching, is responsible for the patterns observed on our AMV bias world maps. As a side product of this finding, we elaborate on the necessity to investigate new algorithms to derive AMVs from LEO platforms. [ABSTRACT FROM AUTHOR]

Atmospheric motion vectors (AMVs) are derived operationally at EUMETSAT from the AVHRR/3 instrument on the Polar System satellite MetOp-A since 2011. The launch of MetOp-B in 2012 allowed for doubling of the production of AMVs over the polar regions using both MetOp-A and MetOp-B satellite data. In addition to the single AVHRR polar wind product, in 2014 EUMETSAT developed a new global AVHRR wind product extracted from a pair of MetOp-A and MetOp-B images. This new product is extracted using the large overlap in the imagery data obtained from the tandem configuration of the two satellites on the same orbital plane but with a phase difference of about 50 min. The tandem configuration also provides the possibility to derive wind vectors over polar areas using a triplet of AVHRR images, keeping the same time period necessary to derive the single MetOp polar wind product but allowing for a temporal consistency check in the calculation of the AMV quality index. Three different AMV products are currently extracted from AVHRR imagery at EUMETSAT, using two or three images taken by one or two satellites having different coverage and time integration. This paper describes the scientific concept of the AVHRR wind extraction algorithm developed at EUMETSAT and presents the performances of the various AVHRR wind products. Intercomparisons of these different products highlight the role of the temporal gap between the images used to extract the wind and the impact of the consistency check on the calculation of the quality index. [ABSTRACT FROM AUTHOR]

EUMETSAT has been deriving atmospheric motion vectors (AMV) operationally from the EUMETSAT Polar System satellite MetOp over polar regions since 2011. The launch of MetOp-B in 2012 permitted doubling the frequency of extracting AMVs using AVHRR imagery data of both MetOp-A and -B satellites. The tandem configuration with two satellites on the same orbital plane, but with a phase difference, provided an interesting opportunity to create global AMVs from the satellites with a significant overlap in imagery data. EUMETSAT has therefore developed a new global AVHRR winds product derived from a pair of MetOp-A and MetOp-B images that has been operational since January 2015. The temporal gap between the two images used for tracking clouds is about 50 min. The global coverage of this new wind product allows for a homogeneous retrieval of wind product over the whole globe, including the polar regions. This clearly helps fill the gaps between 55° and 70° latitude north and south, where only few wind observations are currently available for assimilation into numerical weather prediction models. The new global AVHRR wind product can be directly compared with AMVs derived from geostationary satellites. This paper describes the scientific concept of wind extraction using dual MetOp satellites. It highlights the performance of the new global AVHRR wind product by comparing with collocated AMVs extracted from the EUMETSAT geostationary satellites Meteosat-7 and Meteosat-10. [ABSTRACT FROM AUTHOR]

The goal of this paper is to show the impact of the tracer size and the temporal gap between images in atmospheric motion vector (AMV) extraction schemes. A test has been performed using NWC SAF/High Resolutions Winds AMV software for different configurations with a tracer size varying between 8 × 8 and 40 × 40 pixels and a temporal gap between images varying between 5 and 90 min. AMVs have been extracted for four different MSG/SEVIRI channels (HRVIS, VIS0.8, WV6.2, and IR10.8) over the European and Mediterranean area for a 6-month period (January-June 2010). The AMV performances have been tested against radiosonde winds and ECMWF model analysis winds. The results show a small impact of the tracer size on the number of valid AMVs, which is, however, more significant for clear air AMVs, and a significant impact of the temporal gap between images. The largest number of valid AMVs has been found in general for a temporal gap of 5 min for the 1-km pixel scale and for a temporal gap of 10 min for the 3-km pixel scale. Results also show a decrease of the mean AMV speed and the normalized BIAS (NBIAS) with larger tracer sizes, and a relatively small impact of the temporal gap on these parameters. Finally, the results show minimum values of the normalized root-mean-square vector difference (NRMSVD) for intermediate temporal gaps between 15 and 30 min with a relatively small impact of the tracer size on this parameter. [ABSTRACT FROM AUTHOR]

The goal of this paper is to show the impact of the use of the wind guess (WG) in atmospheric motion vector (AMV) extraction schemes. The study has been performed using the Satellite Application Facility on Support to Nowcasting and Very Short Range Forecasting (NWCSAF) High Resolution Winds AMV software. Target box sizes varying from 8 × 8 to 40 × 40 pixels and temporal gaps varying from 5 to 60 min have been considered for two configurations that use WG and do not use the wind guess (NWG) to locate the search area in the tracking process. AMVs have been extracted for four different Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI) channels [high-resolution visible (HRVIS), visible 0.8 μm (VIS0.8), water vapor 6.2 μm (WV6.2), and infrared 10.8 μm (IR10.8)] over the European and Mediterranean area for a 6-month period (January-June 2010). The AMVs' performances have been tested against radiosonde wind observations and ECMWF NWP model wind analysis. The results show an impact on the amount of valid AMVs extracted by each configuration. Not using the wind guess produces more valid AMVs when large target boxes and short temporal gaps are used. It is the opposite when small target boxes and long temporal gaps are used. The results also show a general increase in the mean AMV speed, and a general reduction of the normalized bias and the normalized root-mean-square vector difference for all the tested channels and configurations, when the wind guess is not used to locate the search area. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper presents the sensitivity to various atmospheric parameters of two height assignment methods that aim to retrieve the cloud-top height of semitransparent clouds. The use of simulated Meteosat-8 radiances has the advantage that the pressure retrieved by a given method can be compared to the initial pressure set to the cloud in the model, which is exactly known. The methods retrieve the pressure of a perfectly opaque cloud to within a few hectopascals. However, considering more realistic ice clouds, methods are sensitive to all of the tested atmospheric parameters and, especially, to the cloud microphysics, which can bias the results of the CO2-slicing method by several tens of hectopascals. The cloud-top pressure retrieval is especially difficult for thinner clouds with optical thicknesses smaller than 2, for which the errors can reach several tens of hectopascals. The methods have also been tested after introducing realistic perturbations in the temperature and humidity profiles and on the clear-sky surface radiances. The corresponding averages of errors on the retrieved pressures are also very large, especially for thin clouds. In multilayer cloud situations the height assignment methods do not work properly, placing the cloud-top height somewhere between the two cloud layers for most cirrus cloud layers with optical thicknesses between 0.1 and 10. [ABSTRACT FROM AUTHOR]

A key problem in aerosol retrieval is to distinguish between surface and atmospheric contributions to the variability in the satellite signal. A major contribution in the surface-related variability is caused by the non-Lambertian nature of the Earth surface reflectance and the fact that the illumination/observation geometry varies considerably between successive observations of the same area (with a polar orbiting sensor). In principle, if the surface boundary condition can be specified with sufficient accuracy by means of a bidirectional reflectance distribution function (BRDF), the two contributions can be unfolded and aerosol information retrieved. This approach has been tested using combined datasets made of satellite measured “top of atmosphere” (TOA) radiance and corresponding ground estimation of the aerosol optical thickness. Studying a time series of data, taking into account geometrical conditions and assuming the ground BRDF to be constant during the time period, a coupled surface/atmosphere model was used to investigate the retrieval of aerosol optical thickness (AOT) over several sites. By fitting a subset of satellite observations associated with ground photometer data, a best fit of BRDF model parameters could be determined. This surface characterization is then used to reduce the model unknowns to AOT only and thereby to permit its retrieval from the satellite data alone, by means of a simple inversion process. The study was conducted on three European AERONET sites and using satellite data from both the VEGETATION and Sea viewing Wide Field of view (SeaWiFS) sensors. In all cases, the AOT retrieved from satellite was in good agreement with the measurements. [Copyright &y& Elsevier]

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. [ABSTRACT FROM AUTHOR]

We derived an atmospheric motion vector (AMV) algorithm for the Geostationary Korea Multipurpose Satellite (GEO-KOMPSAT-2A; GK-2A) launched on 4 December 2018, using the Advanced Himawari Imager (AHI) onboard Himawari-8, which is very similar to the Advanced Meteorological Imager onboard GK-2A. This study clearly describes the main steps in our algorithm and optimizes it for the target box size and height assignment methods by comparing AMVs with numerical weather prediction (NWP) and rawinsonde profiles for July 2016 and January 2017. Target box size sensitivity tests were performed from 8 × 8 to 48 × 48 pixels for three infrared channels and from 16 × 16 to 96 × 96 pixels for one visible channel. The results show that the smaller box increases the speed, whereas the larger one slows the speed without quality control. The best target box sizes were found to be 16 × 16 for CH07, 08, and 13, and 48 × 48 pixels for CH03. Height assignment sensitivity tests were performed for several methods, such as the cross-correlation coefficient (CCC), equivalent blackbody temperature (EBBT), infrared/water vapor (IR/WV) intercept, and CO2 slicing methods for a cloudy target as well as normalized total contribution (NTC) and normalized total cumulative contribution (NTCC) for a clear-air target. For a cloudy target, the CCC method is influenced by the quality of the cloud's top pressure. Better results were found when using EBBT and IR/WV intercept methods together rather than individually. Furthermore, CO2 slicing had the best statistics. For a clear-air target, the combined use of NTC and NTCC had the best statistics. Additionally, the mean vector difference, root-mean-square (RMS) vector difference, bias, and RMS error (RMSE) between GK-2A AMVs and NWP or rawinsonde were smaller by approximately 18.2% on average than in the case of the Communication, Ocean and Meteorology Satellite (COMS) AMVs. In addition, we verified the similarity between GK-2A and Meteosat Third Generation (MTG) AMVs using the AHI of Himawari-8 from 21 July 2016. This similarity can provide evidence that the GK-2A algorithm works properly because the GK-2A AMV algorithm borrows many methods of the MTG AMV algorithm for geostationary data and inversion layer corrections. The Pearson correlation coefficients in the speed, direction, and height of the prescribed GK-2A and MTG AMVs were larger than 0.97, and the corresponding bias/RMSE were0.07/2.19 m/s, 0.21/14.8°, and 2.61/62.9 hPa, respectively, considering common quality indicator with forecast (CQIF) > 80. [ABSTRACT FROM AUTHOR]

The Aeolus mission objectives are to improve numerical weather prediction (NWP) and enhance the understanding and modeling of atmospheric dynamics on global and regional scale. Given the first successes of Aeolus in NWP, it is time to look forward to future vertical wind profiling capability to fulfill the rolling requirements in operational meteorology. Requirements for wind profiles and information on vertical wind shear are constantly evolving. The need for high-quality wind and profile information to capture and initialize small-amplitude, fast-evolving, and mesoscale dynamical structures increases, as the resolution of global NWP improved well into the 3D turbulence regime on horizontal scales smaller than 500 km. In addition, advanced requirements to describe the transport and dispersion of atmospheric constituents and better depict the circulation on climate scales are well recognized. Direct wind profile observations over the oceans, tropics, and Southern Hemisphere are not provided by the current global observing system. Looking to the future, most other wind observation techniques rely on cloud or regions of water vapor and are necessarily restricted in coverage. Therefore, after its full demonstration, an operational Aeolus-like follow-on mission obtaining globally distributed wind profiles in clear air by exploiting molecular scattering remains unique. [ABSTRACT FROM AUTHOR]

The European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) MetOp-A and MetOp-B satellites fly in the same polar orbit with a 180° phase difference, which enables the global retrieval of atmospheric motion vectors (AMVs, or 'winds') by tracking clouds in a pair of Advanced Very High Resolution Radiometer (AVHRR) infrared-window-channel images taken successively by the tandem platforms within their swath overlap area. This novel global wind product has been operational since 2015. As part of ongoing validation efforts, two months of MetOp global AMVs were compared with a suite of independent wind data, including AMVs from geostationary and polar-orbiter satellites as well as radiosonde and model winds. The performance of the new wind product is generally comparable to that of more established satellite winds. In the tropics, however, high-level MetOp global AMVs show a strong fast speed bias, increased root-mean-square difference, and considerably reduced speed correlation relative to all comparison datasets-an as-yet-unexplained drop in retrieval quality that warrants further investigation. A best-fit wind analysis also indicates that selectively applied height adjustments, such as cloud-base and inversion methods, can be a significant source of discrepancy, leading to very poor height correlation among low-level satellite AMVs. Height assignment is more consistent and better correlated at mid- to high levels, although MetOp heights derived from window-channel brightness temperatures have a bias toward lower heights because of the lack of semitransparency corrections. Collocated Infrared Atmospheric Sounding Interferometer CO2-slicing heights significantly improve the best-fit height-difference statistics at higher altitudes but are available for only ~5% of MetOp AMVs. [ABSTRACT FROM AUTHOR]

The objective of this study is to improve the characterization of satellite-derived atmospheric motion vectors (AMVs) and their errors to guide developments in the use of AMVs in numerical weather prediction. AMVs tend to exhibit considerable systematic and random errors that arise in the derivation or the interpretation of AMVs as single-level point observations of wind. One difficulty in the study of AMV errors is the scarcity of collocated observations of clouds and wind. This study uses instead a simulation framework: geostationary imagery for Meteosat-8 is generated from a high-resolution simulation with the Weather Research and Forecasting regional model, and AMVs are derived from sequences of these images. The forecast model provides the 'truth' with a sophisticated description of the atmosphere. The study considers infrared and water vapor AMVs from cloudy scenes. This is the first part of a two-part paper, and it introduces the framework and provides a first evaluation in terms of the brightness temperatures of the simulated images and the derived AMVs. The simulated AMVs show a considerable global bias in the height assignment (60-75 hPa) that is not observed in real AMVs. After removal of this bias, however, the statistics comparing the simulated AMVs with the true model wind show characteristics that are similar to statistics comparing real AMVs with short-range forecasts (speed bias and root-mean-square vector difference typically agree to within 1 m s−1). This result suggests that the error in the simulated AMVs is comparable to or larger than that in real AMVs. There is evidence for significant spatial, temporal, and vertical error correlations, with the scales for the spatial error correlations being consistent with estimates for real data. [ABSTRACT FROM AUTHOR]

After successful launch in November 2018 and successful commissioning of Metop-C, all three satellites of the EUMETSAT Polar System (EPS) are in orbit together and operational. EPS is part of the Initial Joint Polar System (IJPS) with the United States (NOAA) and provides the service in the midmorning orbit. The Metop satellites carry a mission payload of sounding and imaging instruments, which allow provision of support to operational meteorology and climate monitoring, which are the main mission objectives for EPS. Applications include numerical weather prediction, atmospheric composition monitoring, and marine meteorology. Climate monitoring is supported through the generation of long time series through the program duration of 20+ years. The payload was developed and contributed by partners, including NOAA, CNES, and ESA. EUMETSAT and ESA developed the space segment in cooperation. The system has proven its value since the first satellite Metop-A, with enhanced products at high reliability for atmospheric sounding, delivered a very strong positive impact on NWP and results beyond expectations for atmospheric composition and chemistry applications. Having multiple satellites in orbit—now three—has enabled enhanced and additional products with increased impact, like atmospheric motion vector products at latitudes not accessible to geostationary observations or increased probability of radio occultations and hence atmospheric soundings with the Global Navigation Satellite System (GNSS) Radio-Occultation Atmospheric Sounder (GRAS) instruments. The paper gives an overview of the system and the embarked payload and discusses the benefits of generated products for applications and services. The conclusions point to the follow-on system, currently under development and assuring continuity for another 20+ years. [ABSTRACT FROM AUTHOR]

Atmospheric Motion Vectors (AMVs) are an important input to many Numerical Weather Prediction (NWP) models. EUMETSAT derives AMVs from several of its orbiting satellites, including the geostationary satellites (Meteosat), and its Low-Earth Orbit (LEO) satellites. The algorithm extracting the AMVs uses pairs or triplets of images, and tracks the motion of clouds or water vapour features from one image to another. Currently, EUMETSAT LEO satellite AMVs are retrieved from georeferenced images from the Advanced Very-High-Resolution Radiometer (AVHRR) on board the Metop satellites. EUMETSAT is currently preparing the operational release of an AMV product from the Sea and Land Surface Temperature Radiometer (SLSTR) on board the Sentinel-3 satellites. The main innovation in the processing, compared with AVHRR AMVs, lies in the co-registration of pairs of images: the images are first projected on an equal-area grid, before applying the AMV extraction algorithm. This approach has multiple advantages. First, individual pixels represent areas of equal sizes, which is crucial to ensure that the tracking is consistent throughout the processed image, and from one image to another. Second, this allows features that would otherwise leave the frame of the reference image to be tracked, thereby allowing more AMVs to be derived. Third, the same framework could be used for every LEO satellite, allowing an overall consistency of EUMETSAT AMV products. In this work, we present the results of this method for SLSTR by comparing the AMVs to the forecast model. We validate our results against AMVs currently derived from AVHRR and the Spinning Enhanced Visible and InfraRed Imager (SEVIRI). The release of the operational SLSTR AMV product is expected in 2022. [ABSTRACT FROM AUTHOR]

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'. [ABSTRACT FROM AUTHOR]