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Estimation of Near-Real-Time Outgoing Longwave Radiation from Cross-Track Infrared Sounder (CrIS) Radiance Measurementsand Arkin 1992 ; Chiodi and Harrison 2010 ; Xie and Arkin 1996 ). The trends of OLR have been used to study climate feedbacks and processes (e.g., Chu and Wang 1997 ; Susskind et al. 2012 ). Clouds and the Earth’s Radiant Energy System (CERES) ( Wielicki et al. 1996 ) was designed to extend the Earth Radiation Budget Experiment (ERBE) data record of TOA longwave (LW) and shortwave (SW) fluxes. Since the infrared (IR) radiance measured in space by radiometers and spectrometers is part of the
and Arkin 1992 ; Chiodi and Harrison 2010 ; Xie and Arkin 1996 ). The trends of OLR have been used to study climate feedbacks and processes (e.g., Chu and Wang 1997 ; Susskind et al. 2012 ). Clouds and the Earth’s Radiant Energy System (CERES) ( Wielicki et al. 1996 ) was designed to extend the Earth Radiation Budget Experiment (ERBE) data record of TOA longwave (LW) and shortwave (SW) fluxes. Since the infrared (IR) radiance measured in space by radiometers and spectrometers is part of the
). Radiometric measurements of AIRS have been applied to numerical weather prediction (NWP) in Chahine et al. (2006) . At the same time, the climatologies of the radiances from these sensors are envisaged to be critical in climate monitoring and benchmarking of climate models ( Anderson et al. 2004 ). Cross-track scanning sensors such as AIRS detect, for the most part, radiation from off-nadir viewing angles where “limb effect” on observed radiance is significant. The limb effect refers to the change in the
). Radiometric measurements of AIRS have been applied to numerical weather prediction (NWP) in Chahine et al. (2006) . At the same time, the climatologies of the radiances from these sensors are envisaged to be critical in climate monitoring and benchmarking of climate models ( Anderson et al. 2004 ). Cross-track scanning sensors such as AIRS detect, for the most part, radiation from off-nadir viewing angles where “limb effect” on observed radiance is significant. The limb effect refers to the change in the
localization, typically implemented as a Schur (elementwise) product of the raw ensemble covariance matrix and some positive definite localization matrix, works well both in the horizontal and for conventional observations. Vertical covariance localization for satellite radiances is becoming more important as the number and type of satellite observations increases much more rapidly than conventional observations. Radiance space localization is already being used in the operational data assimilation
localization, typically implemented as a Schur (elementwise) product of the raw ensemble covariance matrix and some positive definite localization matrix, works well both in the horizontal and for conventional observations. Vertical covariance localization for satellite radiances is becoming more important as the number and type of satellite observations increases much more rapidly than conventional observations. Radiance space localization is already being used in the operational data assimilation
summer of 1993 ( McKenzie et al. 1993 ). The purpose of this study is twofold. The first aim is to cross-correlate spectral UV measurements of the NIWA instruments with those from an independent instrument with calibrations traceable to a different standards laboratory. The second aim is to measure the distribution of sky radiance to verify model calculations under pristine conditions at a relatively low altitude and to better quantify the magnitude of possible errors in cosine corrections that arise
summer of 1993 ( McKenzie et al. 1993 ). The purpose of this study is twofold. The first aim is to cross-correlate spectral UV measurements of the NIWA instruments with those from an independent instrument with calibrations traceable to a different standards laboratory. The second aim is to measure the distribution of sky radiance to verify model calculations under pristine conditions at a relatively low altitude and to better quantify the magnitude of possible errors in cosine corrections that arise
(SW) channel measures between 0.3 and 5 μ m. The narrowband channel is the thermal IR window (WN; 8.1–11.8 μ m) channel. The digital count recorded by each channel is first converted to calibrated “filtered” radiances, which is the convolution of the actual radiances intercepted by the optics and the spectral response function of each channel. An “unfiltering” algorithm is then applied to obtain the “unfiltered” radiances, that is, the actual radiances prior to entering the optics ( Loeb et al
(SW) channel measures between 0.3 and 5 μ m. The narrowband channel is the thermal IR window (WN; 8.1–11.8 μ m) channel. The digital count recorded by each channel is first converted to calibrated “filtered” radiances, which is the convolution of the actual radiances intercepted by the optics and the spectral response function of each channel. An “unfiltering” algorithm is then applied to obtain the “unfiltered” radiances, that is, the actual radiances prior to entering the optics ( Loeb et al
intersatellite calibration were also carried out on HIRS-derived outgoing longwave radiation to produce a long-term climate dataset ( Lee et al. 2007 ). Cao et al. (2005) used simultaneous nadir overpass (SNO) observations to intercompare radiances measured by HIRS on board N15 , N16 , and N17 . The SNO observations were collocated at the satellite nadir within a few seconds. The method was developed to quantify the observed radiance differences measured by HIRS on different satellites with little
intersatellite calibration were also carried out on HIRS-derived outgoing longwave radiation to produce a long-term climate dataset ( Lee et al. 2007 ). Cao et al. (2005) used simultaneous nadir overpass (SNO) observations to intercompare radiances measured by HIRS on board N15 , N16 , and N17 . The SNO observations were collocated at the satellite nadir within a few seconds. The method was developed to quantify the observed radiance differences measured by HIRS on different satellites with little
1. Introduction The new generation of atmospheric spectral radiance sensors has much higher radiometric sensitivity and spatial and spectral resolution than previous sensors and has a much larger number of channels. These enhancements and the growth in the number of high-resolution sensors place increasing demands on the computational aspects of remote sensing. Atmospheric radiative transfer (RT) models are an integral part of the modeling and simulation of scene radiances for use in sensor
1. Introduction The new generation of atmospheric spectral radiance sensors has much higher radiometric sensitivity and spatial and spectral resolution than previous sensors and has a much larger number of channels. These enhancements and the growth in the number of high-resolution sensors place increasing demands on the computational aspects of remote sensing. Atmospheric radiative transfer (RT) models are an integral part of the modeling and simulation of scene radiances for use in sensor
1. Introduction Satellite radiance data are currently assimilated in many operational numerical weather prediction (NWP) centers primarily using the variational data assimilation approach ( Derber and Wu 1998 ; Andersson et al. 1994 ). The radiance data are one of the most important observations for global forecast performance, especially over areas where conventional observations are limited (e.g., the Southern Hemisphere). Theoretically, direct radiance assimilation is superior to retrieval
1. Introduction Satellite radiance data are currently assimilated in many operational numerical weather prediction (NWP) centers primarily using the variational data assimilation approach ( Derber and Wu 1998 ; Andersson et al. 1994 ). The radiance data are one of the most important observations for global forecast performance, especially over areas where conventional observations are limited (e.g., the Southern Hemisphere). Theoretically, direct radiance assimilation is superior to retrieval
these channels, AMSR-E has four low-frequency channels in the microwave region (6.925 and 10.65 GHz, dual polarization) that are sensitive to the sea surface wind speed and the sea surface temperature, and are less affected by the atmosphere. Therefore, these measurements provide useful information on the sea surface wind speed and sea surface temperature under almost all weather conditions. The Japan Meteorological Agency (JMA) has been using AMSR-E radiance data in their global data assimilation
these channels, AMSR-E has four low-frequency channels in the microwave region (6.925 and 10.65 GHz, dual polarization) that are sensitive to the sea surface wind speed and the sea surface temperature, and are less affected by the atmosphere. Therefore, these measurements provide useful information on the sea surface wind speed and sea surface temperature under almost all weather conditions. The Japan Meteorological Agency (JMA) has been using AMSR-E radiance data in their global data assimilation
atmospheric (and surface) retrievals from a combined multisensor, multiwavelength retrieval approach. Previous investigations have used combinations of MODIS and AIRS, either together or with other instruments, for the intercalibration of radiances ( Tobin et al. 2006 , hereafter T06 ), to characterize AIRS subpixel variability in atmospheric and surface properties and retrievals of cloud parameters ( Li et al. 2004a , b ), to investigate optimal cloud-clearing approaches that exploit the high spatial
atmospheric (and surface) retrievals from a combined multisensor, multiwavelength retrieval approach. Previous investigations have used combinations of MODIS and AIRS, either together or with other instruments, for the intercalibration of radiances ( Tobin et al. 2006 , hereafter T06 ), to characterize AIRS subpixel variability in atmospheric and surface properties and retrievals of cloud parameters ( Li et al. 2004a , b ), to investigate optimal cloud-clearing approaches that exploit the high spatial
