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- Author or Editor: Fuzhong Weng x
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Abstract
Development of fast and accurate radiative transfer models for clear atmospheric conditions has enabled direct assimilation of clear-sky radiances from satellites in numerical weather prediction models. In this article, fast radiative transfer schemes and their components critical for satellite data assimilation are summarized and discussed for their potential applications in operational global data assimilation systems. The major impediments to the fast radiative transfer schemes are highlighted and a call is made for broader community efforts to develop advanced radiative transfer components that can better handle the scattering from atmospheric constituents (e.g., aerosols, clouds, and precipitation) and surface materials (e.g., snow, sea ice, deserts).
Abstract
Development of fast and accurate radiative transfer models for clear atmospheric conditions has enabled direct assimilation of clear-sky radiances from satellites in numerical weather prediction models. In this article, fast radiative transfer schemes and their components critical for satellite data assimilation are summarized and discussed for their potential applications in operational global data assimilation systems. The major impediments to the fast radiative transfer schemes are highlighted and a call is made for broader community efforts to develop advanced radiative transfer components that can better handle the scattering from atmospheric constituents (e.g., aerosols, clouds, and precipitation) and surface materials (e.g., snow, sea ice, deserts).
Abstract
The Special Sensor Microwave Imager/Sounder (SSMIS) aboard the Defense Meteorological Satellite Program F-16 spacecraft measures the Earth-emitted radiation at frequencies from 19 to 183 GHz. From its high-frequency channels at 91 and 150 GHz, cloud microphysical parameters can be observed at a spatial resolution of 15 km. In this study, a simplified two-stream radiative transfer model is applied for microwave applications as a three-parameter equation and then used to retrieve the ice cloud water path (IWP) and ice particle effective diameter De . Since SSMIS is a conically scanning instrument, the retrieved IWP is less dependent on scan position and is a useful product for imaging atmospheric ice-phase clouds related to precipitation. Thus, IWP is also used to estimate surface rainfall rate through the same relationship derived previously and used in Advanced Microwave Sounding Unit (AMSU-B) and Microwave Humidity Sounder applications. The SSMIS-derived ice cloud products are compared with those from other microwave instruments on the MetOp-A satellite, and both agree well in their spatial distributions.
Abstract
The Special Sensor Microwave Imager/Sounder (SSMIS) aboard the Defense Meteorological Satellite Program F-16 spacecraft measures the Earth-emitted radiation at frequencies from 19 to 183 GHz. From its high-frequency channels at 91 and 150 GHz, cloud microphysical parameters can be observed at a spatial resolution of 15 km. In this study, a simplified two-stream radiative transfer model is applied for microwave applications as a three-parameter equation and then used to retrieve the ice cloud water path (IWP) and ice particle effective diameter De . Since SSMIS is a conically scanning instrument, the retrieved IWP is less dependent on scan position and is a useful product for imaging atmospheric ice-phase clouds related to precipitation. Thus, IWP is also used to estimate surface rainfall rate through the same relationship derived previously and used in Advanced Microwave Sounding Unit (AMSU-B) and Microwave Humidity Sounder applications. The SSMIS-derived ice cloud products are compared with those from other microwave instruments on the MetOp-A satellite, and both agree well in their spatial distributions.
Abstract
An algorithm is developed to derive cloud ice water path (IWP) and ice particle effective diameters D e from the advanced microwave sounding unit (AMSU) measurements. In the algorithm, both IWP and D e are related to the ice particle scattering parameters, which are determined from the AMSU 89- and 150-GHz measurements. The ratio of the scattering parameters measured at two frequencies provides a direct estimate of D e . IWP is then derived from the scattering parameter at 150 GHz with the derived D e and the constant bulk volume density. A screening procedure is developed to discriminate the scattering signatures between atmospheric clouds and surface materials. The major error sources affecting the retrievals are identified. The errors of retrieved effective diameter are primarily controlled by the errors in estimating cloud-base brightness temperatures at 89 and 150 GHz and the errors of the bulk volume density. It is shown that D e possibly contains an error of 5%–20%. For the retrieval of cloud ice water path, the errors are influenced by the uncertainties in estimated cloud-base brightness temperature, retrieved particle effective diameter, and particle volume density. A 30% error in bulk volume would alone result in a 25% error in retrieved IWP. The algorithm is applied for various weather events and can primarily detect the precipitating ice clouds as well as thick nonprecipitating clouds because of an increasing sensitivity of AMSU measurements at 150 GHz to smaller particle sizes. These results demonstrate the use of 89- and 150-GHz channels for studying the ice cloud properties and their spatial variability under various atmospheric environments.
Abstract
An algorithm is developed to derive cloud ice water path (IWP) and ice particle effective diameters D e from the advanced microwave sounding unit (AMSU) measurements. In the algorithm, both IWP and D e are related to the ice particle scattering parameters, which are determined from the AMSU 89- and 150-GHz measurements. The ratio of the scattering parameters measured at two frequencies provides a direct estimate of D e . IWP is then derived from the scattering parameter at 150 GHz with the derived D e and the constant bulk volume density. A screening procedure is developed to discriminate the scattering signatures between atmospheric clouds and surface materials. The major error sources affecting the retrievals are identified. The errors of retrieved effective diameter are primarily controlled by the errors in estimating cloud-base brightness temperatures at 89 and 150 GHz and the errors of the bulk volume density. It is shown that D e possibly contains an error of 5%–20%. For the retrieval of cloud ice water path, the errors are influenced by the uncertainties in estimated cloud-base brightness temperature, retrieved particle effective diameter, and particle volume density. A 30% error in bulk volume would alone result in a 25% error in retrieved IWP. The algorithm is applied for various weather events and can primarily detect the precipitating ice clouds as well as thick nonprecipitating clouds because of an increasing sensitivity of AMSU measurements at 150 GHz to smaller particle sizes. These results demonstrate the use of 89- and 150-GHz channels for studying the ice cloud properties and their spatial variability under various atmospheric environments.
Abstract
Satellite data assimilation requires rapid and accurate radiative transfer and radiance gradient models. For a vertically stratified scattering and emitting atmosphere, the vector discrete-ordinate radiative transfer model (VDISORT) was developed to derive all Stokes radiance components at the top of the atmosphere. This study further enhances the VDISORT to compute the radiance gradients or Jacobians. The band matrix used in the VDISORT is simplified and confined along the diagonal direction so that the Jacobians relative to atmospheric and surface parameters are directly derived from its analytic solutions. The radiances and Jacobians at various wavelengths from the VDISORT are compared against those from other techniques that have been benchmarked before. It is shown that the present method is accurate and computationally efficient.
In the VDISORT, both emissivity vector and reflectivity matrix are integrated as part of the radiance and Jacobian calculations. In this study, only the emissivity models at microwave frequencies are tested and implemented for VDISORT applications. Over oceans, a full polarimetric emissivity model is utilized. The cutoff wavenumber separating the large-scale waves from the small-scale waves is derived from an ocean wave spectrum model. Over land, a microwave emissivity model previously developed is used to compute various emissivity spectra.
Abstract
Satellite data assimilation requires rapid and accurate radiative transfer and radiance gradient models. For a vertically stratified scattering and emitting atmosphere, the vector discrete-ordinate radiative transfer model (VDISORT) was developed to derive all Stokes radiance components at the top of the atmosphere. This study further enhances the VDISORT to compute the radiance gradients or Jacobians. The band matrix used in the VDISORT is simplified and confined along the diagonal direction so that the Jacobians relative to atmospheric and surface parameters are directly derived from its analytic solutions. The radiances and Jacobians at various wavelengths from the VDISORT are compared against those from other techniques that have been benchmarked before. It is shown that the present method is accurate and computationally efficient.
In the VDISORT, both emissivity vector and reflectivity matrix are integrated as part of the radiance and Jacobian calculations. In this study, only the emissivity models at microwave frequencies are tested and implemented for VDISORT applications. Over oceans, a full polarimetric emissivity model is utilized. The cutoff wavenumber separating the large-scale waves from the small-scale waves is derived from an ocean wave spectrum model. Over land, a microwave emissivity model previously developed is used to compute various emissivity spectra.
Abstract
The Advanced Microwave Sounding Unit (AMSU) images display strong dependence on the scanning angle because of the temperature gradient of the atmosphere and the change in the optical pathlength between Earth and the satellite. Using a limb-adjustment algorithm, the temperature gradients can be restored from the images. Various limb-correction algorithms have been developed for infrared and microwave sounders by aid of radiative transfer simulations. Together with the National Oceanic and Atmospheric Administration (NOAA)-16 AMSU, the NOAA-18 satellite with AMSU (launched on 20 May 2005) provides the best opportunity to collocate observations from two satellites. The collocated measurement pairs from NOAA-16 and NOAA-18 contain data for which both observations have the same scanning angle and various scanning angles—in particular, off-nadir observations from NOAA-16 and nadir observations from NOAA-18. The coincident data pair having the same scan position from NOAA-16 and NOAA-18 can be used for intercalibration of the sensors of the two satellites. The coincident data pair having nadir measurement from NOAA-18 and off-nadir measurement from NOAA-16 can be used for testing the limb-adjustment algorithm using pure satellite measurements. This study applies collocated measurements to evaluate the performance of the current NOAA microwave limb-correction algorithm for brightness temperatures at AMSU-A channels 5, 6, and 7 for the first time. With the limb correction, the warm core of Hurricane Katrina in 2005 can also be detected using a cross-scan sensor such as AMSU-A.
Abstract
The Advanced Microwave Sounding Unit (AMSU) images display strong dependence on the scanning angle because of the temperature gradient of the atmosphere and the change in the optical pathlength between Earth and the satellite. Using a limb-adjustment algorithm, the temperature gradients can be restored from the images. Various limb-correction algorithms have been developed for infrared and microwave sounders by aid of radiative transfer simulations. Together with the National Oceanic and Atmospheric Administration (NOAA)-16 AMSU, the NOAA-18 satellite with AMSU (launched on 20 May 2005) provides the best opportunity to collocate observations from two satellites. The collocated measurement pairs from NOAA-16 and NOAA-18 contain data for which both observations have the same scanning angle and various scanning angles—in particular, off-nadir observations from NOAA-16 and nadir observations from NOAA-18. The coincident data pair having the same scan position from NOAA-16 and NOAA-18 can be used for intercalibration of the sensors of the two satellites. The coincident data pair having nadir measurement from NOAA-18 and off-nadir measurement from NOAA-16 can be used for testing the limb-adjustment algorithm using pure satellite measurements. This study applies collocated measurements to evaluate the performance of the current NOAA microwave limb-correction algorithm for brightness temperatures at AMSU-A channels 5, 6, and 7 for the first time. With the limb correction, the warm core of Hurricane Katrina in 2005 can also be detected using a cross-scan sensor such as AMSU-A.
Abstract
The Special Sensor Microwave Imager/Sounder (SSMIS) on board the Defense Meteorological Satellite Program (DMSP) F-16 satellite is the first conically scanning sounding instrument that provides information on atmospheric temperature and water vapor profiles. The SSMIS data were preprocessed by the Naval Research Laboratory (NRL) using its Unified Preprocessor Package (UPP) and then distributed to the numerical weather prediction centers by the Fleet Numerical Meteorology and Oceanography Center (FNMOC). This dataset was assimilated into the Global Forecast System (GFS) using gridpoint statistical interpolation (GSI). The initial assimilation of the SSMIS data into the GFS did not improve the medium-range (5–7 days) forecast skill. The SSMIS bias (O-B) still changes with location and time after the GSI bias-correction scheme is implemented. This bias characteristic is related to residual calibration errors in the correction of the SSMIS antenna emission and warm target contamination. The large O-B standard deviation is probably due to the large instrument noise in the SSMIS UPP data. The large O-B and its standard deviation for several surface sensitive channels are also caused by uncertainty in surface emissivity. In this study, a new scheme is developed to remove regionally dependent bias using a weekly composite O-B. The SSMIS noise is reduced through a Gaussian function filter. A new emissivity database for snow and sea ice is developed for the SSMIS surface sensitive channels. After applying these algorithms, the quality of the SSMIS low-atmospheric sounding (LAS) data is improved; the surface-sensitive channels can be effectively assimilated, and the impacts of SSMIS LAS data on the medium-range forecast in the GFS are positive and similar to those from Advanced Microwave Sounding Unit-A (AMSU-A) data.
Abstract
The Special Sensor Microwave Imager/Sounder (SSMIS) on board the Defense Meteorological Satellite Program (DMSP) F-16 satellite is the first conically scanning sounding instrument that provides information on atmospheric temperature and water vapor profiles. The SSMIS data were preprocessed by the Naval Research Laboratory (NRL) using its Unified Preprocessor Package (UPP) and then distributed to the numerical weather prediction centers by the Fleet Numerical Meteorology and Oceanography Center (FNMOC). This dataset was assimilated into the Global Forecast System (GFS) using gridpoint statistical interpolation (GSI). The initial assimilation of the SSMIS data into the GFS did not improve the medium-range (5–7 days) forecast skill. The SSMIS bias (O-B) still changes with location and time after the GSI bias-correction scheme is implemented. This bias characteristic is related to residual calibration errors in the correction of the SSMIS antenna emission and warm target contamination. The large O-B standard deviation is probably due to the large instrument noise in the SSMIS UPP data. The large O-B and its standard deviation for several surface sensitive channels are also caused by uncertainty in surface emissivity. In this study, a new scheme is developed to remove regionally dependent bias using a weekly composite O-B. The SSMIS noise is reduced through a Gaussian function filter. A new emissivity database for snow and sea ice is developed for the SSMIS surface sensitive channels. After applying these algorithms, the quality of the SSMIS low-atmospheric sounding (LAS) data is improved; the surface-sensitive channels can be effectively assimilated, and the impacts of SSMIS LAS data on the medium-range forecast in the GFS are positive and similar to those from Advanced Microwave Sounding Unit-A (AMSU-A) data.
Abstract
The assimilation of satellite microwave measurements and the retrieval of geophysical parameters require a fast and accurate radiative transfer model. In this study, a scheme is developed to solve the vector radiative transfer equation using a polarimetric two-stream approximation. In the scheme, the integration of the phase matrix over azimuth angle is derived as an analytic form that can also be directly utilized for the general radiative transfer scheme. Each Stokes radiance component is expressed as an analytical function of atmospheric and surface optical parameters. The model is applicable for spherical and randomly oriented nonspherical scatters. The differences of brightness temperatures between the polarimetric two-stream model and the matrix operator method are less than 2 K for various frequencies.
Abstract
The assimilation of satellite microwave measurements and the retrieval of geophysical parameters require a fast and accurate radiative transfer model. In this study, a scheme is developed to solve the vector radiative transfer equation using a polarimetric two-stream approximation. In the scheme, the integration of the phase matrix over azimuth angle is derived as an analytic form that can also be directly utilized for the general radiative transfer scheme. Each Stokes radiance component is expressed as an analytical function of atmospheric and surface optical parameters. The model is applicable for spherical and randomly oriented nonspherical scatters. The differences of brightness temperatures between the polarimetric two-stream model and the matrix operator method are less than 2 K for various frequencies.
Abstract
The doubling–adding method (DA) is one of the most accurate tools for detailed multiple-scattering calculations. The principle of the method goes back to the nineteenth century in a problem dealing with reflection and transmission by glass plates. Since then the doubling–adding method has been widely used as a reference tool for other radiative transfer models. The method has never been used in operational applications owing to tremendous demand on computational resources from the model. This study derives an analytical expression replacing the most complicated thermal source terms in the doubling–adding method. The new development is called the advanced doubling–adding (ADA) method. Thanks also to the efficiency of matrix and vector manipulations in FORTRAN 90/95, the advanced doubling–adding method is about 60 times faster than the doubling–adding method. The radiance (i.e., forward) computation code of ADA is easily translated into tangent linear and adjoint codes for radiance gradient calculations. The simplicity in forward and Jacobian computation codes is very useful for operational applications and for the consistency between the forward and adjoint calculations in satellite data assimilation.
ADA is implemented into the Community Radiative Transfer Model (CRTM) developed at the U.S. Joint Center for Satellite Data Assimilation.
Abstract
The doubling–adding method (DA) is one of the most accurate tools for detailed multiple-scattering calculations. The principle of the method goes back to the nineteenth century in a problem dealing with reflection and transmission by glass plates. Since then the doubling–adding method has been widely used as a reference tool for other radiative transfer models. The method has never been used in operational applications owing to tremendous demand on computational resources from the model. This study derives an analytical expression replacing the most complicated thermal source terms in the doubling–adding method. The new development is called the advanced doubling–adding (ADA) method. Thanks also to the efficiency of matrix and vector manipulations in FORTRAN 90/95, the advanced doubling–adding method is about 60 times faster than the doubling–adding method. The radiance (i.e., forward) computation code of ADA is easily translated into tangent linear and adjoint codes for radiance gradient calculations. The simplicity in forward and Jacobian computation codes is very useful for operational applications and for the consistency between the forward and adjoint calculations in satellite data assimilation.
ADA is implemented into the Community Radiative Transfer Model (CRTM) developed at the U.S. Joint Center for Satellite Data Assimilation.
Abstract
Based on the radiative transfer theory, the microwave radiance emanating from ice clouds at arbitrary viewing angles is expressed as an analytic function of the cloud ice water path (IWP), the particle effective diameter (D e ), and the particle bulk density (ρ i ). Thus, for a given particle density, the earth-viewing measurements at two frequencies (e.g., 340 and 89 GHz) can provide an estimate of D e and IWP for submillimeter-size particles. This physical retrieval is tested using data from the Millimeter-wave Imaging Radiometer (MIR). A comparison among MIR, radar, and infrared sensor measurements shows that the MIR frequencies are affected primarily by thick ice clouds such as cirrus anvil and convection. Over highly convective areas, the measurements from 89 to 220 GHz are nearly identical since the scattering by large ice particles aloft approaches the geometric optics limit, which is independent of wavelength. Under these conditions, only the lower MIR frequencies (89 and 150 GHz) are used to retrieve D e and IWP. In general, the MIR-derived D e displays a reasonable spatial distribution comparable to the radar and infrared measurements. However, the magnitude of the IWP remains highly uncertain because of insufficient information on the ice particle bulk density.
Abstract
Based on the radiative transfer theory, the microwave radiance emanating from ice clouds at arbitrary viewing angles is expressed as an analytic function of the cloud ice water path (IWP), the particle effective diameter (D e ), and the particle bulk density (ρ i ). Thus, for a given particle density, the earth-viewing measurements at two frequencies (e.g., 340 and 89 GHz) can provide an estimate of D e and IWP for submillimeter-size particles. This physical retrieval is tested using data from the Millimeter-wave Imaging Radiometer (MIR). A comparison among MIR, radar, and infrared sensor measurements shows that the MIR frequencies are affected primarily by thick ice clouds such as cirrus anvil and convection. Over highly convective areas, the measurements from 89 to 220 GHz are nearly identical since the scattering by large ice particles aloft approaches the geometric optics limit, which is independent of wavelength. Under these conditions, only the lower MIR frequencies (89 and 150 GHz) are used to retrieve D e and IWP. In general, the MIR-derived D e displays a reasonable spatial distribution comparable to the radar and infrared measurements. However, the magnitude of the IWP remains highly uncertain because of insufficient information on the ice particle bulk density.
Abstract
The Geostationary Operational Environmental Satellites (GOES) provide high-resolution, temporally continuous imager radiance data over the West Coast (GOES-West currently known as GOES-11) and East Coast (GOES-East currently GOES-12) of the United States. Through a real case study, benefits of adding GOES-11/12 imager radiances to the satellite data streams in NWP systems for improved coastal precipitation forecasts are examined. The Community Radiative Transfer Model (CRTM) is employed for GOES imager radiance simulations in the National Centers for Environmental Prediction (NCEP) gridpoint statistical interpolation (GSI) analysis system. The GOES imager radiances are added to conventional data for coastal quantitative precipitation forecast (QPF) experiments near the northern Gulf of Mexico and the derived precipitation threat score was compared with those from six other satellite instruments. It is found that the GOES imager radiance produced better precipitation forecasts than those from any other satellite instrument. However, when GOES imager radiance and six different types of satellite instruments are all assimilated, the score becomes much lower than the individual combination of GOES and any other instrument. Our analysis shows that an elimination of Advance Microwave Sounding Unit-B (AMSU-B)/Microwave Humidity Sounder (MHS) data over areas where GOES detects clouds significantly improved the forecast scores from AMSU-B/MHS data assimilation.
Abstract
The Geostationary Operational Environmental Satellites (GOES) provide high-resolution, temporally continuous imager radiance data over the West Coast (GOES-West currently known as GOES-11) and East Coast (GOES-East currently GOES-12) of the United States. Through a real case study, benefits of adding GOES-11/12 imager radiances to the satellite data streams in NWP systems for improved coastal precipitation forecasts are examined. The Community Radiative Transfer Model (CRTM) is employed for GOES imager radiance simulations in the National Centers for Environmental Prediction (NCEP) gridpoint statistical interpolation (GSI) analysis system. The GOES imager radiances are added to conventional data for coastal quantitative precipitation forecast (QPF) experiments near the northern Gulf of Mexico and the derived precipitation threat score was compared with those from six other satellite instruments. It is found that the GOES imager radiance produced better precipitation forecasts than those from any other satellite instrument. However, when GOES imager radiance and six different types of satellite instruments are all assimilated, the score becomes much lower than the individual combination of GOES and any other instrument. Our analysis shows that an elimination of Advance Microwave Sounding Unit-B (AMSU-B)/Microwave Humidity Sounder (MHS) data over areas where GOES detects clouds significantly improved the forecast scores from AMSU-B/MHS data assimilation.
