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Abstract
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
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.
Derivation of Wind Vectors from AVHRR/MetOp at EUMETSATAbstract
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
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
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
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.
EUMETSAT Global AVHRR Wind ProductAbstract
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
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
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
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
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
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
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
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
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
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
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
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.
