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Abstract
The extraction of atmospheric motion vectors (AMVs) from cloud and moisture features from successive geostationary satellite images is an established and important data source for numerical weather prediction (NWP). So far the extraction of AMVs has been confined to the main synoptic times only, which grossly underutilizes the potential of these satellite-derived data. The advent of four-dimensional variational assimilation techniques provides the opportunity to utilize data derived at asynoptic times. This will enhance the capabilities of geostationary satellite systems that can provide continuous and near–real time observations. The new assimilation schemes are able to digest data representing various scales and with variable quality, which further enhances the usefulness of the satellite data. In order to fully exploit the AMVs derived with satellite data, it is imperative to accurately assess the quality and representativeness of individual wind vectors and to provide this information to the NWP centers as an integral part of the observations in near real time. The required high production and dissemination frequency cannot be met if manual intervention is required; hence, the emphasis has to be on fully automated schemes. This paper will describe the automatic quality control scheme implemented at EUMETSAT. It is based on the statistical properties of the derived AMVs and it provides a quality indicator (QI), describing the expected quality of every individual vector. The derived QIs are currently disseminated together with the derived vectors. The paper will also provide validation results based on collocated radiosonde statistics and report on first experiences by ECMWF in utilizing the QIs.
Abstract
The extraction of atmospheric motion vectors (AMVs) from cloud and moisture features from successive geostationary satellite images is an established and important data source for numerical weather prediction (NWP). So far the extraction of AMVs has been confined to the main synoptic times only, which grossly underutilizes the potential of these satellite-derived data. The advent of four-dimensional variational assimilation techniques provides the opportunity to utilize data derived at asynoptic times. This will enhance the capabilities of geostationary satellite systems that can provide continuous and near–real time observations. The new assimilation schemes are able to digest data representing various scales and with variable quality, which further enhances the usefulness of the satellite data. In order to fully exploit the AMVs derived with satellite data, it is imperative to accurately assess the quality and representativeness of individual wind vectors and to provide this information to the NWP centers as an integral part of the observations in near real time. The required high production and dissemination frequency cannot be met if manual intervention is required; hence, the emphasis has to be on fully automated schemes. This paper will describe the automatic quality control scheme implemented at EUMETSAT. It is based on the statistical properties of the derived AMVs and it provides a quality indicator (QI), describing the expected quality of every individual vector. The derived QIs are currently disseminated together with the derived vectors. The paper will also provide validation results based on collocated radiosonde statistics and report on first experiences by ECMWF in utilizing the QIs.
Abstract
The coverage and quality of atmospheric motion vectors (AMVs) derived from geostationary satellite imagery have improved considerably over the past few years. This is due not only to the deployment of the new generation of satellites, but is also a result of improved data processing and automated quality control (AQC) schemes. The postprocessing of the Geostationary Operational Environmental Satellite (GOES) derived displacement vectors at the National Oceanic and Atmospheric Administration/National Environmental Satellite, Data, and Information Service (NOAA/NESDIS) has been fully automated since early 1996. At the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) AQC was used as support to the manual quality control (MQC) for Meteosat vector fields until September 1998 when the MQC was discontinued and fully replaced with the automated procedure. The AQC schemes at the two organizations are quite diverse. Based on a method developed at the University of Wisconsin–Cooperative Institute for Meteorological Satellite Studies, the NOAA/NESDIS AQC involves an objective three-dimensional recursive filter analysis of the derived wind fields. The fit of each vector to that analysis yields a recursive filter flag (RFF). The AQC scheme employed at EUMETSAT derives a quality indicator (QI) for each individual vector based on the properties of the vector itself and its consistency with other AMVs in close proximity. Mainly relying on satellite data, this QI-based scheme has been proven to provide a good estimate of the reliability of the derived displacements, but it fails to identify fleets of winds that are consistently assigned to a wrong height. The RFF-based scheme is capable of readjusting the heights attributed to the wind vectors, which yields a better fit to the analysis and ancillary data. These quality estimates can be employed by the user community to select the part of the vector field that best suits their application, as well as in data assimilation schemes for optimizing the data selection procedures. Even though both schemes have already been successfully implemented into operational environments, the possibility exists to exploit the advantages of both approaches to create a superior combined methodology.
In order to substantiate the differences of the two methodologies, the two schemes were applied to the high-density AMV fields derived during the 1998 North Pacific Experiment from GOES and the Geostationary Meteorological Satellite (controlled by the Japan Meteorological Agency) multispectral imagery data. The performance of the two schemes was evaluated by examining departures from the analysis fields of the European Centre for Medium-Range Weather Forecasts (ECMWF) assimilation scheme, and a new combined methodology was further evaluated by looking at the impact on medium-range forecasts verified against the operational forecasts. It will be shown that the new combined AQC approach yields superior ECMWF forecast results.
Abstract
The coverage and quality of atmospheric motion vectors (AMVs) derived from geostationary satellite imagery have improved considerably over the past few years. This is due not only to the deployment of the new generation of satellites, but is also a result of improved data processing and automated quality control (AQC) schemes. The postprocessing of the Geostationary Operational Environmental Satellite (GOES) derived displacement vectors at the National Oceanic and Atmospheric Administration/National Environmental Satellite, Data, and Information Service (NOAA/NESDIS) has been fully automated since early 1996. At the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) AQC was used as support to the manual quality control (MQC) for Meteosat vector fields until September 1998 when the MQC was discontinued and fully replaced with the automated procedure. The AQC schemes at the two organizations are quite diverse. Based on a method developed at the University of Wisconsin–Cooperative Institute for Meteorological Satellite Studies, the NOAA/NESDIS AQC involves an objective three-dimensional recursive filter analysis of the derived wind fields. The fit of each vector to that analysis yields a recursive filter flag (RFF). The AQC scheme employed at EUMETSAT derives a quality indicator (QI) for each individual vector based on the properties of the vector itself and its consistency with other AMVs in close proximity. Mainly relying on satellite data, this QI-based scheme has been proven to provide a good estimate of the reliability of the derived displacements, but it fails to identify fleets of winds that are consistently assigned to a wrong height. The RFF-based scheme is capable of readjusting the heights attributed to the wind vectors, which yields a better fit to the analysis and ancillary data. These quality estimates can be employed by the user community to select the part of the vector field that best suits their application, as well as in data assimilation schemes for optimizing the data selection procedures. Even though both schemes have already been successfully implemented into operational environments, the possibility exists to exploit the advantages of both approaches to create a superior combined methodology.
In order to substantiate the differences of the two methodologies, the two schemes were applied to the high-density AMV fields derived during the 1998 North Pacific Experiment from GOES and the Geostationary Meteorological Satellite (controlled by the Japan Meteorological Agency) multispectral imagery data. The performance of the two schemes was evaluated by examining departures from the analysis fields of the European Centre for Medium-Range Weather Forecasts (ECMWF) assimilation scheme, and a new combined methodology was further evaluated by looking at the impact on medium-range forecasts verified against the operational forecasts. It will be shown that the new combined AQC approach yields superior ECMWF forecast results.
Abstract
Low-level wind fields over the Atlantic have been derived from clouds in Meteosat high-resolution visible images experimentally with one production cycle per day over a period of more than 1 yr. The cloud motion winds from VIS imagery (VIS-CMW) use a template size of 32 × 32 VIS pixels, corresponding to about 80 km × 80 km at the subsatellite point, which is four times better than for the corresponding IR (infrared window) winds (160 km × 160 km). The yield is increased through the better spatial resolution of the VIS images and a better contrast between cloud and ocean surface, which effectively leads to an increase in wind vectors by a factor of 6. This implies a much better description of the low-level atmospheric flow by the VIS-CMW as compared to IR winds. The impact of the new VIS-CMW has been tested with a data assimilation experiment at the European Centre for Medium-Range Weather Forecasts, and small positive improvements have been found. The mean vector rms difference versus the verifying analysis shows improvement by up to 15% over some areas of the Atlantic Ocean. Comparisons of the short-term forecast using VIS cloud motion winds with independent scatterometer surface winds confirm the small improvements.
Abstract
Low-level wind fields over the Atlantic have been derived from clouds in Meteosat high-resolution visible images experimentally with one production cycle per day over a period of more than 1 yr. The cloud motion winds from VIS imagery (VIS-CMW) use a template size of 32 × 32 VIS pixels, corresponding to about 80 km × 80 km at the subsatellite point, which is four times better than for the corresponding IR (infrared window) winds (160 km × 160 km). The yield is increased through the better spatial resolution of the VIS images and a better contrast between cloud and ocean surface, which effectively leads to an increase in wind vectors by a factor of 6. This implies a much better description of the low-level atmospheric flow by the VIS-CMW as compared to IR winds. The impact of the new VIS-CMW has been tested with a data assimilation experiment at the European Centre for Medium-Range Weather Forecasts, and small positive improvements have been found. The mean vector rms difference versus the verifying analysis shows improvement by up to 15% over some areas of the Atlantic Ocean. Comparisons of the short-term forecast using VIS cloud motion winds with independent scatterometer surface winds confirm the small improvements.
Abstract
The displacement of clouds in successive satellite images reflects the atmospheric circulation at various scales. The main application of the satellite-derived cloud-motion vectors is their use as winds in the data analysis for numerical weather prediction. At low latitudes in particular they constitute an indispensible data source for numerical weather prediction.
This paper describes the operational method of deriving cloud-motion winds (CMW) from the IR image (10.5–12.5 µm) of the European geostationary Meteostat satellites. The method is automatic, that is, the cloud tracking uses cross correlation and the height assignment is based on satellite observed brightness temperature and a forecast temperature profile. Semitransparent clouds undergo a height correction based on radiative forward calculations and simultaneous radiance observations in both the IR and water vapor (5.7–7.1 µm) channel. Cloud-motion winds are subject to various quality checks that include manual quality control as the last step. Typically about 3000 wind vectors are produced per day over four production cycles.
This paper documents algorithm changes and improvements made to the operational CMWs over the last five years. The improvements are shown by long-term comparisons with both collocated radiosondes and the first guess of the forecast model of the European Centre for Medium-Range Weather Forecasts. In particular, the height assignment of a wind vector and radiance filtering techniques preceding the cloud tracking have ameliorated the errors in Meteostat winds. The slow speed bias of high-level CMWs (<400 hPa) in comparison to radiosonde winds have been reduced from about 4 to 1.3 m s−1 for a mean wind speed of 24 m s−1. Correspondingly, the rms vectors error of Meteosat high-level CMWs decreased from about 7.8 to 5 m s−1. Medium- and low-level CMWs were also significantly improved.
Abstract
The displacement of clouds in successive satellite images reflects the atmospheric circulation at various scales. The main application of the satellite-derived cloud-motion vectors is their use as winds in the data analysis for numerical weather prediction. At low latitudes in particular they constitute an indispensible data source for numerical weather prediction.
This paper describes the operational method of deriving cloud-motion winds (CMW) from the IR image (10.5–12.5 µm) of the European geostationary Meteostat satellites. The method is automatic, that is, the cloud tracking uses cross correlation and the height assignment is based on satellite observed brightness temperature and a forecast temperature profile. Semitransparent clouds undergo a height correction based on radiative forward calculations and simultaneous radiance observations in both the IR and water vapor (5.7–7.1 µm) channel. Cloud-motion winds are subject to various quality checks that include manual quality control as the last step. Typically about 3000 wind vectors are produced per day over four production cycles.
This paper documents algorithm changes and improvements made to the operational CMWs over the last five years. The improvements are shown by long-term comparisons with both collocated radiosondes and the first guess of the forecast model of the European Centre for Medium-Range Weather Forecasts. In particular, the height assignment of a wind vector and radiance filtering techniques preceding the cloud tracking have ameliorated the errors in Meteostat winds. The slow speed bias of high-level CMWs (<400 hPa) in comparison to radiosonde winds have been reduced from about 4 to 1.3 m s−1 for a mean wind speed of 24 m s−1. Correspondingly, the rms vectors error of Meteosat high-level CMWs decreased from about 7.8 to 5 m s−1. Medium- and low-level CMWs were also significantly improved.
This paper describes the results from a collaborative study between the European Space Operations Center, the European Organization for the Exploitation of Meteorological Satellites, the National Oceanic and Atmospheric Administration, and the Cooperative Institute for Meteorological Satellite Studies investigating the relationship between satellite-derived monthly mean fields of wind and humidity in the upper troposphere for March 1994. Three geostationary meteorological satellites GOES-7, Meteosat-3, and Meteosat-5 are used to cover an area from roughly 160°W to 50°E. The wind fields are derived from tracking features in successive images of upper-tropospheric water vapor (WV) as depicted in the 6.5-μ absorption band. The upper-tropospheric relative humidity (UTH) is inferred from measured water vapor radiances with a physical retrieval scheme based on radiative forward calculations.
Quantitative information on large-scale circulation patterns in the upper troposphere is possible with the dense spatial coverage of the WV wind vectors. The monthly mean wind field is used to estimate the large-scale divergence; values range between about −5 × 10−6 and 5 × 10−6 sec−1 when averaged over a scale length of about 1000–2000 km. The spatial patterns of the UTH field and the divergence of the wind field closely resemble one another, suggesting that UTH patterns are principally determined by the large-scale circulation.
Since the upper-tropospheric humidity absorbs upwelling radiation from lower-tropospheric levels and therefore contributes significantly to the atmospheric greenhouse effect, this work implies that studies on the climate relevance of water vapor should include threedimensional modeling of the atmospheric dynamics. The fields of UTH and WV winds are useful parameters for a climate-monitoring system based on satellite data. The results from this 1-month analysis suggest the desirability of further GOES and Meteosat studies to characterize the changes in the upper-tropospheric moisture sources and sinks over the past decade.
This paper describes the results from a collaborative study between the European Space Operations Center, the European Organization for the Exploitation of Meteorological Satellites, the National Oceanic and Atmospheric Administration, and the Cooperative Institute for Meteorological Satellite Studies investigating the relationship between satellite-derived monthly mean fields of wind and humidity in the upper troposphere for March 1994. Three geostationary meteorological satellites GOES-7, Meteosat-3, and Meteosat-5 are used to cover an area from roughly 160°W to 50°E. The wind fields are derived from tracking features in successive images of upper-tropospheric water vapor (WV) as depicted in the 6.5-μ absorption band. The upper-tropospheric relative humidity (UTH) is inferred from measured water vapor radiances with a physical retrieval scheme based on radiative forward calculations.
Quantitative information on large-scale circulation patterns in the upper troposphere is possible with the dense spatial coverage of the WV wind vectors. The monthly mean wind field is used to estimate the large-scale divergence; values range between about −5 × 10−6 and 5 × 10−6 sec−1 when averaged over a scale length of about 1000–2000 km. The spatial patterns of the UTH field and the divergence of the wind field closely resemble one another, suggesting that UTH patterns are principally determined by the large-scale circulation.
Since the upper-tropospheric humidity absorbs upwelling radiation from lower-tropospheric levels and therefore contributes significantly to the atmospheric greenhouse effect, this work implies that studies on the climate relevance of water vapor should include threedimensional modeling of the atmospheric dynamics. The fields of UTH and WV winds are useful parameters for a climate-monitoring system based on satellite data. The results from this 1-month analysis suggest the desirability of further GOES and Meteosat studies to characterize the changes in the upper-tropospheric moisture sources and sinks over the past decade.
The evolving constellation of environmental/meteorological satellites and their associated sensor technology is rapidly advancing. This is providing opportunities for creatively improving satellite-derived products used in weather analysis and forecasting. For example, the retrieval methods for deriving atmospheric motion vectors (AMVs) from satellites have been expanding and evolving since the early 1970s. Contemporary AMV processing methods are continuously being updated and advanced through the exploitation of new sensor technologies and innovative new approaches. It is incumbent upon the research community working in AMV extraction techniques to ensure that the quality of the current operational products meets or exceeds the needs of the user community. In particular, the advances in data assimilation and numerical weather prediction in recent years have placed an increasing demand on data quality.
To keep pace with these demands, innovative research toward improving methods of deriving winds from satellites has been a focus of the World Meteorological Organization and Coordination Group for Meteorological Satellites (CGMS) cosponsored International Winds Workshops (IWWs). The IWWs are held every 2 yr, and bring together AMV researchers from around the world to present new ideas on AMV extraction techniques, interpretation, and applications. The NWP community is always well represented at these workshops, which provide an important exchange of information on the latest in data assimilation issues. This article draws from recent IWWs, and describes several new advances in satellite-produced wind technologies, derivation methodologies, and products. Examples include AMVs derived from Geostationary Operational Environmental Satellite (GOES) rapid scans and the shortwave IR channel, AMVs over the polar regions from the Moderate Resolution Imaging Spectroradiometer (MODIS), improved AMV products from the new Meteosat Second Generation satellite, and new processing approaches for deriving AMVs. The article also provides a glimpse into the pending opportunities that will be afforded with emerging/anticipated new sensor technologies.
The evolving constellation of environmental/meteorological satellites and their associated sensor technology is rapidly advancing. This is providing opportunities for creatively improving satellite-derived products used in weather analysis and forecasting. For example, the retrieval methods for deriving atmospheric motion vectors (AMVs) from satellites have been expanding and evolving since the early 1970s. Contemporary AMV processing methods are continuously being updated and advanced through the exploitation of new sensor technologies and innovative new approaches. It is incumbent upon the research community working in AMV extraction techniques to ensure that the quality of the current operational products meets or exceeds the needs of the user community. In particular, the advances in data assimilation and numerical weather prediction in recent years have placed an increasing demand on data quality.
To keep pace with these demands, innovative research toward improving methods of deriving winds from satellites has been a focus of the World Meteorological Organization and Coordination Group for Meteorological Satellites (CGMS) cosponsored International Winds Workshops (IWWs). The IWWs are held every 2 yr, and bring together AMV researchers from around the world to present new ideas on AMV extraction techniques, interpretation, and applications. The NWP community is always well represented at these workshops, which provide an important exchange of information on the latest in data assimilation issues. This article draws from recent IWWs, and describes several new advances in satellite-produced wind technologies, derivation methodologies, and products. Examples include AMVs derived from Geostationary Operational Environmental Satellite (GOES) rapid scans and the shortwave IR channel, AMVs over the polar regions from the Moderate Resolution Imaging Spectroradiometer (MODIS), improved AMV products from the new Meteosat Second Generation satellite, and new processing approaches for deriving AMVs. The article also provides a glimpse into the pending opportunities that will be afforded with emerging/anticipated new sensor technologies.
Abstract
To better understand the impacts of climate change, environmental monitoring capabilities must be enhanced by deploying additional and more accurate satellite- and ground-based (including in situ) sensors. In addition, reanalysis of observations collected decades ago but long forgotten can unlock precious information about the recent past. Historical, in situ observations mainly cover densely inhabited areas and frequently traveled routes. In contrast, large selections of early meteorological satellite data, waiting to be exploited today, provide information about remote areas unavailable from any other source. When initially collected, these satellite data posed great challenges to transmission and archiving facilities. As a result, data access was limited to the main teams of scientific investigators associated with the instruments. As archive media have aged, so have the mission scientists and other pioneers of satellite meteorology, who sometimes retired in possession of unique and unpublished information.
This paper presents examples of recently recovered satellite data records, including satellite imagery, early infrared hyperspectral soundings, and early microwave humidity soundings. Their value for climate applications today can be realized using methods and techniques that were not yet available when the data were first collected, including efficient and accurate observation simulators and data assimilation into reanalyses. Modern technical infrastructure allows serving entire mission datasets online, enabling easy access and exploration by a broad range of users, including new and old generations of climate scientists.
Abstract
To better understand the impacts of climate change, environmental monitoring capabilities must be enhanced by deploying additional and more accurate satellite- and ground-based (including in situ) sensors. In addition, reanalysis of observations collected decades ago but long forgotten can unlock precious information about the recent past. Historical, in situ observations mainly cover densely inhabited areas and frequently traveled routes. In contrast, large selections of early meteorological satellite data, waiting to be exploited today, provide information about remote areas unavailable from any other source. When initially collected, these satellite data posed great challenges to transmission and archiving facilities. As a result, data access was limited to the main teams of scientific investigators associated with the instruments. As archive media have aged, so have the mission scientists and other pioneers of satellite meteorology, who sometimes retired in possession of unique and unpublished information.
This paper presents examples of recently recovered satellite data records, including satellite imagery, early infrared hyperspectral soundings, and early microwave humidity soundings. Their value for climate applications today can be realized using methods and techniques that were not yet available when the data were first collected, including efficient and accurate observation simulators and data assimilation into reanalyses. Modern technical infrastructure allows serving entire mission datasets online, enabling easy access and exploration by a broad range of users, including new and old generations of climate scientists.
