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- Author or Editor: Johannes Schmetz x
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Abstract
A retrieval method is described for estimating a mean column value of the upper tropospheric relative humidity (UTH) from radiance measurements in the 6.3 μm channel of the geostationary satellite METEOSAT. The physical retrieval method is based on an efficient radiative transfer scheme which uses the temperature forecast profiles from the European Centre for Medium Range Weather Forecasts (ECMWF) as ancillary data. Theoretical radiances for the given temperature profile and a set of fixed upper tropospheric humidities are employed to- relate the observed radiance to a mean humidity for a layer between 600 and 300 hPa. The retrieval is confined to areas with neither medium-nor high-level clouds.
A calibration procedure of the 6.3 μm channel is described which uses the radiative transfer scheme with measured radiosonde profiles of temperature and humidity and collocated satellite measurements. An example of the UTH product and a comparison with radiosondes is presented. An estimate of the error of the UTH is obtained from a sensitivity test of the radiation scheme to errors in the input profiles. Both the sensitivity and the comparison with radiosondes yield absolute error estimates for the UTH of 10%–15%.
Abstract
A retrieval method is described for estimating a mean column value of the upper tropospheric relative humidity (UTH) from radiance measurements in the 6.3 μm channel of the geostationary satellite METEOSAT. The physical retrieval method is based on an efficient radiative transfer scheme which uses the temperature forecast profiles from the European Centre for Medium Range Weather Forecasts (ECMWF) as ancillary data. Theoretical radiances for the given temperature profile and a set of fixed upper tropospheric humidities are employed to- relate the observed radiance to a mean humidity for a layer between 600 and 300 hPa. The retrieval is confined to areas with neither medium-nor high-level clouds.
A calibration procedure of the 6.3 μm channel is described which uses the radiative transfer scheme with measured radiosonde profiles of temperature and humidity and collocated satellite measurements. An example of the UTH product and a comparison with radiosondes is presented. An estimate of the error of the UTH is obtained from a sensitivity test of the radiation scheme to errors in the input profiles. Both the sensitivity and the comparison with radiosondes yield absolute error estimates for the UTH of 10%–15%.
Abstract
Satellite-derived cloud-motion vector (CMV) production has been troubled by inaccurate height assignment of cloud tracers, especially in thin semitransparent clouds. This paper presents the results of an intercomparison of current operational height assignment techniques. Currently, heights are assigned by one of three techniques when the appropriate spectral radiance measurements are available. The infrared window (IRW) technique compares measured brightness temperatures to forecast temperature profiles and thus infers opaque cloud levels. In semitransparent or small subpixel clouds, the carbon dioxide (CO2) technique uses the ratio of radiances from different layers of the atmosphere to infer the correct cloud height. In the water vapor (H2O) technique, radiances influenced by upper-tropospheric moisture and IRW radiances are measured for several pixels viewing different cloud amounts, and their linear relationship is used to extrapolate the correct cloud height. The results presented in this paper suggest that the H2O technique is a viable alternative to the CO2 technique for inferring the heights of semitransparent cloud elements. This is important since future National Environmental Satellite, Data, and Information Service (NESDIS) operations will have to rely on H20-derived cloud-height assignments in the wind field determinations with the next operational geostationary satellite. On a given day, the heights from the two approaches compare to within 60–110 hPa rms; drier atmospheric conditions tend to reduce the effectiveness of the H2O technique. By inference one can conclude that the present height algorithms used operationally at NESDIS (with the C02 technique) and at the European Satellite Operations Center (ESOC) (with their version of the H20 technique) are providing similar results. Sample wind fields produced with the ESOC and NESDIS algorithms using Meteosat-4 data show good agreement.
Abstract
Satellite-derived cloud-motion vector (CMV) production has been troubled by inaccurate height assignment of cloud tracers, especially in thin semitransparent clouds. This paper presents the results of an intercomparison of current operational height assignment techniques. Currently, heights are assigned by one of three techniques when the appropriate spectral radiance measurements are available. The infrared window (IRW) technique compares measured brightness temperatures to forecast temperature profiles and thus infers opaque cloud levels. In semitransparent or small subpixel clouds, the carbon dioxide (CO2) technique uses the ratio of radiances from different layers of the atmosphere to infer the correct cloud height. In the water vapor (H2O) technique, radiances influenced by upper-tropospheric moisture and IRW radiances are measured for several pixels viewing different cloud amounts, and their linear relationship is used to extrapolate the correct cloud height. The results presented in this paper suggest that the H2O technique is a viable alternative to the CO2 technique for inferring the heights of semitransparent cloud elements. This is important since future National Environmental Satellite, Data, and Information Service (NESDIS) operations will have to rely on H20-derived cloud-height assignments in the wind field determinations with the next operational geostationary satellite. On a given day, the heights from the two approaches compare to within 60–110 hPa rms; drier atmospheric conditions tend to reduce the effectiveness of the H2O technique. By inference one can conclude that the present height algorithms used operationally at NESDIS (with the C02 technique) and at the European Satellite Operations Center (ESOC) (with their version of the H20 technique) are providing similar results. Sample wind fields produced with the ESOC and NESDIS algorithms using Meteosat-4 data show good agreement.
Abstract
A method and a passive microwave retrieval algorithm have been developed to retrieve upper-tropospheric water vapor (UTW) from Special Sensor Microwave Water Vapor Profiler (SSM/T-2) measurements taken at three discrete frequencies near the 183-GHz water vapor line. The algorithm is based on physical relaxation utilizing statistical covariance information to provide initial-guess profiles and to constrain the updating step in the relaxation process. The scheme incorporates a method to remove SSM/T-2 brightness temperature bias in comparison with collocated simulated brightness temperatures. Correction functions are designed for the three SSM/T-2 183-GHz channels. The algorithm is validated against radiosonde observations and collocated SSM/T-2 brightness temperatures. Under clear-sky and nonprecipitating-cloud conditions, the UTW retrievals exhibit an rms error of 0.68 kg m−2 with integrated water vapor biases below 5% for the upper-tropospheric layers of 700–500 and 500–200 hPa. The retrieval provides an independent source of satellite-derived water vapor information in the upper troposphere, distinct from upper-tropospheric humidity information retrieved from thermal infrared (IR) measurements around the 6.3-μm water vapor absorption band. The microwave retrievals can then be used to cross-check IR retrievals and/or to augment IR retrievals, dependent upon the problem at hand.
Abstract
A method and a passive microwave retrieval algorithm have been developed to retrieve upper-tropospheric water vapor (UTW) from Special Sensor Microwave Water Vapor Profiler (SSM/T-2) measurements taken at three discrete frequencies near the 183-GHz water vapor line. The algorithm is based on physical relaxation utilizing statistical covariance information to provide initial-guess profiles and to constrain the updating step in the relaxation process. The scheme incorporates a method to remove SSM/T-2 brightness temperature bias in comparison with collocated simulated brightness temperatures. Correction functions are designed for the three SSM/T-2 183-GHz channels. The algorithm is validated against radiosonde observations and collocated SSM/T-2 brightness temperatures. Under clear-sky and nonprecipitating-cloud conditions, the UTW retrievals exhibit an rms error of 0.68 kg m−2 with integrated water vapor biases below 5% for the upper-tropospheric layers of 700–500 and 500–200 hPa. The retrieval provides an independent source of satellite-derived water vapor information in the upper troposphere, distinct from upper-tropospheric humidity information retrieved from thermal infrared (IR) measurements around the 6.3-μm water vapor absorption band. The microwave retrievals can then be used to cross-check IR retrievals and/or to augment IR retrievals, dependent upon the problem at hand.
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.
