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- Author or Editor: David A. Rutan x
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Abstract
Effects of water mass imbalance and hydrometeor transport on the enthalpy flux and water phase on diabatic heating rate in computing the regional energy and water budget of the atmosphere over ocean are investigated. Equations of energy and water budget of the atmospheric column that explicitly consider the velocity of liquid and ice cloud particles, and rain and snow are formulated by separating water variables from dry air. Differences of energy budget equations formulated in this study from those used in earlier studies are that 1) diabatic heating rate depends on water phase, 2) diabatic heating due to net condensation of nonprecipitating hydrometeors is included, and 3) hydrometeors can be advected with a different velocity from the dry-air velocity. Convergence of water vapor associated with phase change and horizontal transport of hydrometeors is to increase diabatic heating in the atmospheric column where hydrometeors are formed and exported and to reduce energy where hydrometeors are imported and evaporated. The process can improve the regional energy and water mass balance when energy data products are integrated. Effects of enthalpy transport associated with water mass transport through the surface are cooling to the atmosphere and warming to the ocean when the enthalpy is averaged over the global ocean. There is no net effect to the atmosphere and ocean columns combined. While precipitation phase changes the regional diabatic heating rate up to 15 W m−2, the dependence of the global mean value on the temperature threshold of melting snow to form rain is less than 1 W m−2.
Abstract
Effects of water mass imbalance and hydrometeor transport on the enthalpy flux and water phase on diabatic heating rate in computing the regional energy and water budget of the atmosphere over ocean are investigated. Equations of energy and water budget of the atmospheric column that explicitly consider the velocity of liquid and ice cloud particles, and rain and snow are formulated by separating water variables from dry air. Differences of energy budget equations formulated in this study from those used in earlier studies are that 1) diabatic heating rate depends on water phase, 2) diabatic heating due to net condensation of nonprecipitating hydrometeors is included, and 3) hydrometeors can be advected with a different velocity from the dry-air velocity. Convergence of water vapor associated with phase change and horizontal transport of hydrometeors is to increase diabatic heating in the atmospheric column where hydrometeors are formed and exported and to reduce energy where hydrometeors are imported and evaporated. The process can improve the regional energy and water mass balance when energy data products are integrated. Effects of enthalpy transport associated with water mass transport through the surface are cooling to the atmosphere and warming to the ocean when the enthalpy is averaged over the global ocean. There is no net effect to the atmosphere and ocean columns combined. While precipitation phase changes the regional diabatic heating rate up to 15 W m−2, the dependence of the global mean value on the temperature threshold of melting snow to form rain is less than 1 W m−2.
Abstract
The algorithm to produce the Clouds and the Earth’s Radiant Energy System (CERES) Edition 4.0 (Ed4) Energy Balanced and Filled (EBAF)-surface data product is explained. The algorithm forces computed top-of-atmosphere (TOA) irradiances to match with Ed4 EBAF-TOA irradiances by adjusting surface, cloud, and atmospheric properties. Surface irradiances are subsequently adjusted using radiative kernels. The adjustment process is composed of two parts: bias correction and Lagrange multiplier. The bias in temperature and specific humidity between 200 and 500 hPa used for the irradiance computation is corrected based on observations by Atmospheric Infrared Sounder (AIRS). Similarly, the bias in the cloud fraction is corrected based on observations by Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and CloudSat. Remaining errors in surface, cloud, and atmospheric properties are corrected in the Lagrange multiplier process. Ed4 global annual mean (January 2005 through December 2014) surface net shortwave (SW) and longwave (LW) irradiances increase by 1.3 W m−2 and decrease by 0.2 W m−2, respectively, compared to EBAF Edition 2.8 (Ed2.8) counterparts (the previous version), resulting in an increase in net SW + LW surface irradiance of 1.1 W m−2. The uncertainty in surface irradiances over ocean, land, and polar regions at various spatial scales are estimated. The uncertainties in all-sky global annual mean upward and downward shortwave irradiance are 3 and 4 W m−2, respectively, and the uncertainties in upward and downward longwave irradiance are 3 and 6 W m−2, respectively. With an assumption of all errors being independent, the uncertainty in the global annual mean surface LW + SW net irradiance is 8 W m−2.
Abstract
The algorithm to produce the Clouds and the Earth’s Radiant Energy System (CERES) Edition 4.0 (Ed4) Energy Balanced and Filled (EBAF)-surface data product is explained. The algorithm forces computed top-of-atmosphere (TOA) irradiances to match with Ed4 EBAF-TOA irradiances by adjusting surface, cloud, and atmospheric properties. Surface irradiances are subsequently adjusted using radiative kernels. The adjustment process is composed of two parts: bias correction and Lagrange multiplier. The bias in temperature and specific humidity between 200 and 500 hPa used for the irradiance computation is corrected based on observations by Atmospheric Infrared Sounder (AIRS). Similarly, the bias in the cloud fraction is corrected based on observations by Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and CloudSat. Remaining errors in surface, cloud, and atmospheric properties are corrected in the Lagrange multiplier process. Ed4 global annual mean (January 2005 through December 2014) surface net shortwave (SW) and longwave (LW) irradiances increase by 1.3 W m−2 and decrease by 0.2 W m−2, respectively, compared to EBAF Edition 2.8 (Ed2.8) counterparts (the previous version), resulting in an increase in net SW + LW surface irradiance of 1.1 W m−2. The uncertainty in surface irradiances over ocean, land, and polar regions at various spatial scales are estimated. The uncertainties in all-sky global annual mean upward and downward shortwave irradiance are 3 and 4 W m−2, respectively, and the uncertainties in upward and downward longwave irradiance are 3 and 6 W m−2, respectively. With an assumption of all errors being independent, the uncertainty in the global annual mean surface LW + SW net irradiance is 8 W m−2.
Clouds and the Earth’s Radiant Energy System (CERES) Cloud Radiative Swath (CRS) Edition 4 Data ProductAbstract
Satellite observations from Clouds and the Earth’s Radiant Energy System (CERES) radiometers have produced over two decades of world-class data documenting time–space variations in Earth’s top-of-atmosphere (TOA) radiation budget. In addition to energy exchanges among Earth and space, climate studies require accurate information on radiant energy exchanges at the surface and within the atmosphere. The CERES Cloud Radiative Swath (CRS) data product extends the standard Single Scanner Footprint (SSF) data product by calculating a suite of radiative fluxes from the surface to TOA at the instantaneous CERES footprint scale using the NASA Langley Fu–Liou radiative transfer model. Here, we describe the CRS flux algorithm and evaluate its performance against a network of ground-based measurements and CERES TOA observations. CRS all-sky downwelling broadband fluxes show significant improvements in surface validation statistics relative to the parameterized fluxes on the SSF product, including a ∼30%–40% (∼20%) reduction in SW↓ (LW↓) root-mean-square error (RMSΔ), improved correlation coefficients, and the lowest SW↓ bias over most surface types. RMSΔ and correlation statistics improve over five different surface types under both overcast and clear-sky conditions. The global mean computed TOA outgoing LW radiation (OLR) remains within <1% (2–3 W m−2) of CERES observations, while the global mean reflected SW radiation (RSW) is excessive by ∼3.5% (∼9 W m−2) owing to cloudy-sky computation errors. As we highlight using data from two remote field campaigns, the CRS data product provides many benefits for studies requiring advanced surface radiative fluxes.
Abstract
Satellite observations from Clouds and the Earth’s Radiant Energy System (CERES) radiometers have produced over two decades of world-class data documenting time–space variations in Earth’s top-of-atmosphere (TOA) radiation budget. In addition to energy exchanges among Earth and space, climate studies require accurate information on radiant energy exchanges at the surface and within the atmosphere. The CERES Cloud Radiative Swath (CRS) data product extends the standard Single Scanner Footprint (SSF) data product by calculating a suite of radiative fluxes from the surface to TOA at the instantaneous CERES footprint scale using the NASA Langley Fu–Liou radiative transfer model. Here, we describe the CRS flux algorithm and evaluate its performance against a network of ground-based measurements and CERES TOA observations. CRS all-sky downwelling broadband fluxes show significant improvements in surface validation statistics relative to the parameterized fluxes on the SSF product, including a ∼30%–40% (∼20%) reduction in SW↓ (LW↓) root-mean-square error (RMSΔ), improved correlation coefficients, and the lowest SW↓ bias over most surface types. RMSΔ and correlation statistics improve over five different surface types under both overcast and clear-sky conditions. The global mean computed TOA outgoing LW radiation (OLR) remains within <1% (2–3 W m−2) of CERES observations, while the global mean reflected SW radiation (RSW) is excessive by ∼3.5% (∼9 W m−2) owing to cloudy-sky computation errors. As we highlight using data from two remote field campaigns, the CRS data product provides many benefits for studies requiring advanced surface radiative fluxes.
Abstract
The NOAA-9 Earth Radiation Budget Experiment (ERBE) scanner measured broadband shortwave, longwave, and total radiances from February 1985 through January 1987. These scanner radiances are reprocessed using the more recent Clouds and the Earth’s Radiant Energy System (CERES) unfiltering algorithm. The scene information, including cloud properties, required for reprocessing is derived using Advanced Very High Resolution Radiometer (AVHRR) data on board NOAA-9, while no imager data were used in the original ERBE unfiltering. The reprocessing increases the NOAA-9 ERBE scanner unfiltered longwave radiances by 1.4%–2.0% during daytime and 0.2%–0.3% during nighttime relative to those derived from the ERBE unfiltering algorithm. Similarly, the scanner unfiltered shortwave radiances increase by ~1% for clear ocean and land and decrease for all-sky ocean, land, and snow/ice by ~1%. The resulting NOAA-9 ERBE scanner unfiltered radiances are then compared with NOAA-9 nonscanner irradiances by integrating the ERBE scanner radiance over the nonscanner field of view. The comparison indicates that the integrated scanner radiances are larger by 0.9% for shortwave and 0.7% smaller for longwave. A sensitivity study shows that the one-standard-deviation uncertainties in the agreement are ±2.5%, ±1.2%, and ±1.8% for the shortwave, nighttime longwave, and daytime longwave irradiances, respectively. The NOAA-9 and ERBS nonscanner irradiances are also compared using 2 years of data. The comparison indicates that the NOAA-9 nonscanner shortwave, nighttime longwave, and daytime longwave irradiances are 0.3% larger, 0.6% smaller, and 0.4% larger, respectively. The longer observational record provided by the ERBS nonscanner plays a critical role in tying the CERES-like NOAA-9 ERBE scanner dataset from the mid-1980s to the present-day CERES scanner data record.
Abstract
The NOAA-9 Earth Radiation Budget Experiment (ERBE) scanner measured broadband shortwave, longwave, and total radiances from February 1985 through January 1987. These scanner radiances are reprocessed using the more recent Clouds and the Earth’s Radiant Energy System (CERES) unfiltering algorithm. The scene information, including cloud properties, required for reprocessing is derived using Advanced Very High Resolution Radiometer (AVHRR) data on board NOAA-9, while no imager data were used in the original ERBE unfiltering. The reprocessing increases the NOAA-9 ERBE scanner unfiltered longwave radiances by 1.4%–2.0% during daytime and 0.2%–0.3% during nighttime relative to those derived from the ERBE unfiltering algorithm. Similarly, the scanner unfiltered shortwave radiances increase by ~1% for clear ocean and land and decrease for all-sky ocean, land, and snow/ice by ~1%. The resulting NOAA-9 ERBE scanner unfiltered radiances are then compared with NOAA-9 nonscanner irradiances by integrating the ERBE scanner radiance over the nonscanner field of view. The comparison indicates that the integrated scanner radiances are larger by 0.9% for shortwave and 0.7% smaller for longwave. A sensitivity study shows that the one-standard-deviation uncertainties in the agreement are ±2.5%, ±1.2%, and ±1.8% for the shortwave, nighttime longwave, and daytime longwave irradiances, respectively. The NOAA-9 and ERBS nonscanner irradiances are also compared using 2 years of data. The comparison indicates that the NOAA-9 nonscanner shortwave, nighttime longwave, and daytime longwave irradiances are 0.3% larger, 0.6% smaller, and 0.4% larger, respectively. The longer observational record provided by the ERBS nonscanner plays a critical role in tying the CERES-like NOAA-9 ERBE scanner dataset from the mid-1980s to the present-day CERES scanner data record.
