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NASA NTRSDate not supplied

Global Clear-Sky Downwelling Shortwave Flux Using CCCma and MODTRAN6 Radiative Transfer Models

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Baike Xi↗

NASA NTRSDate not supplied

CERES ERBE-like Instantaneous TOA Estimates (ES-8) in HDF (CER_ES8_Aqua-FM4_Edition1-CV)

The ES-8 archival data product contains a 24-hour, single-satellite, instantaneous view of scanner fluxes at the top-of-atmosphere (TOA) reduced from spacecraft altitude unfiltered radiances using Earth Radiation Budget Experiment (ERBE) scanner Inversion algorithms and the ERBE shortwave (SW) and longwave (LW) Angular Distribution Models (ADMs). The ES-8 also includes the total (TOT), SW, LW, and window (WN) channel radiometric data; SW, LW, and WN unfiltered radiance values; and the ERBE scene identification for each measurement. These data are organized according to the CERES 3.3-second scan into 6.6-second records. As long as there is one valid scanner measurement within a record, the ES-8 record will be generated. The following CERES ES8 data sets are currently available: CER_ES8_TRMM-PFM_Edition1 CER_ES8_TRMM-PFM_Edition2 CER_ES8_TRMM-PFM_Transient-Ops2 CER_ES8_Terra-FM1_Edition1 CER_ES8_Terra-FM2_Edition1 CER_ES8_Terra-FM1_Edition2 CER_ES8_Terra-FM2_Edition2 CER_ES8_Aqua-FM3_Edition1 CER_ES8_Aqua-FM4_Edition1 CER_ES8_Aqua-FM3_Edition2 CER_ES8_Aqua-FM4_Edition2 CER_ES8_Aqua-FM3_Edition1-CV CER_ES8_Aqua-FM4_Edition1-CV CER_ES8_Terra-FM1_Edition1-CV CER_ES8_Terra-FM1_Edition1-CV. [Location=GLOBAL] [Temporal_Coverage: Start_Date=1997-12-27; Stop_Date=2005-03-29] [Spatial_Coverage: Southernmost_Latitude=-90; Northernmost_Latitude=90; Westernmost_Longitude=-180; Easternmost_Longitude=180] [Data_Resolution: Temporal_Resolution=1 day; Temporal_Resolution_Range=Daily - < Weekly].

TOA FLUX↗

NASA NTRS1994

Nimbus-7 Earth radiation budget calibration history. Part 2: The Earth flux channels

Nine years (November 1978 to October 1987) of Nimbus-7 Earth radiation budget (ERB) products have shown that the global annual mean emitted longwave, absorbed shortwave, and net radiation were constant to within about + 0.5 W/sq m. Further, most of the small annual variations in the emitted longwave have been shown to be real. To obtain this measurement accuracy, the wide-field-of-view (WFOV) Earth-viewing channels 12 (0.2 to over 50 micrometers), 13 (0.2 to 3.8 micrometers), and 14 (0.7 to 2.8 micrometers) have been characterized in their satellite environment to account for signal variations not considered in the prelaunch calibration equations. Calibration adjustments have been derived for (1) extraterrestrial radiation incident on the detectors, (2) long-term degradation of the sensors, and (3) thermal perturbations within the ERB instrument. The first item is important in all the channels; the second, mainly in channels 13 and 14, and the third, only in channels 13 and 14. The Sun is used as a stable calibration source to monitor the long-term degradation of the various channels. Channel 12, which is reasonably stable to both thermal perturbations and sensor degradation, is used as a reference and calibration transfer agent for the drifting sensitivities of the filtered channels 13 and 14. Redundant calibration procedures were utilized. Laboratory studies complemented analyses of the satellite data. Two nearly independent models were derived to account for the thermal perturbations in channels 13 and 14. The global annual mean terrestrial shortwave and longwave signals proved stable enough to act as secondary calibration sources. Instantaneous measurements may still, at times, be in error by as much as a few Wm(exp -2), but the long-term averages are stable to within a fraction of a Wm(exp -2).

Kyle, H. Lee↗

NASA NTRS2020

Radiative Transfer Models for Deriving Geostationary Broadband Shortwave Radiances Directly from Visible Channels for the Ceres SYN1DEG Product

The Clouds and the Earth's Radiant Energy System(CERES)project was designed to observe the Earth’s radiation budget at the 1° regional and global scale. The CERES instruments on the Terra (10:30 AM local equator crossing time) and Aqua (1:30 PM) satellite may not accurately capture the 24-hour mean flux over regions with systematic diurnal cycles. The Terra-only and Aqua-only derived regional monthly mean SW flux can differ by 25 Wm-2 over maritime stratus and land afternoon convective regions. The CERES project utilizes hourly Geostationary imager (GEO) derived broadband fluxes to more accurately estimate the regional diurnal flux in between CERES measurements. We propose an approach that converts the GEO visible radiance to broadband radiance using RTM radiances that are specific to the individual GEO visible channel spectral response functions. The RTM is first validated using the very spectrally narrow MODIS visible channel and CERES broadband radiances.

geostationary derived broadband radiances↗

NASA NTRSDate not supplied

CERES Monthly TOA and SRB Averages (SRBAVG) data in HDF-EOS Grid (CER_SRBAVG_Terra-FM1-MODIS_Edition2D)

The Monthly TOA/Surface Averages (SRBAVG) product contains a month of space and time averaged Clouds and the Earth's Radiant Energy System (CERES) data for a single scanner instrument. The SRBAVG is also produced for combinations of scanner instruments. The monthly average regional flux is estimated using diurnal models and the 1-degree regional fluxes at the hour of observation from the CERES SFC product. A second set of monthly average fluxes are estimated using concurrent diurnal information from geostationary satellites. These fluxes are given for both clear-sky and total-sky scenes and are spatially averaged from 1-degree regions to 1-degree zonal averages and a global average. For each region, the SRBAVG also contains hourly average fluxes for the month and an overall monthly average. The cloud properties from SFC are column averaged and are included on the SRBAVG. [Location=GLOBAL] [Temporal_Coverage: Start_Date=1998-02-01; Stop_Date=2004-05-31] [Spatial_Coverage: Southernmost_Latitude=-90; Northernmost_Latitude=90; Westernmost_Longitude=-180; Easternmost_Longitude=180] [Data_Resolution: Latitude_Resolution=1 degree; Longitude_Resolution=1 degree; Horizontal_Resolution_Range=100 km - < 250 km or approximately 1 degree - < 2.5 degrees; Temporal_Resolution=1 month; Temporal_Resolution_Range=Monthly - < Annual].

EOSDIS↗

NASA NTRS1990

Time dependence of the earth's radiation fields determined from ERBS and NOAA-9 satellites

Satellite measurements from the Earth Radiation Budget Experiment (ERBE) are providing important quantitative data on the diurnal variability of broadband shortwave and longwave radiation. The results derived from the combination of the Earth Radiation Budget Satellite (ERBS) and NOAA-9 indicate that the largest diurnal variations in longwave radiation occur typically over deserts and over land areas which experience intense convective activity. Maximum values of the albedo diurnal amplitude factor are over oceans. Seasonal and cloud cover variations have important effects on the diurnal cycles of earth's radiation budget. ERBE results derived for individual regions are in substantial agreement with the diurnal results derived from the Geostationary Operational Environmental Satellite (GOES) measurements.

Harrison, E. F.↗

NASA NTRSDate not supplied

CERES ERBE-like Instantaneous TOA Estimates (ES-8) in HDF (CER_ES8_Aqua-FM4_Edition1)

SHORTWAVE FILTERED RADIANCE↗

NASA NTRS2012

Changes in Extratropical Storm Track Cloudiness 1983-2008: Observational Support for a Poleward Shift

Climate model simulations suggest that the extratropical storm tracks will shift poleward as a consequence of global warming. In this study the northern and southern hemisphere storm tracks over the Pacific and Atlantic ocean basins are studied using observational data, primarily from the International Satellite Cloud Climatology Project, ISCCP. Potential shifts in the storm tracks are examined using the observed cloud structures as proxies for cyclone activity. Different data analysis methods are employed, with the objective to address difficulties and uncertainties in using ISCCP data for regional trend analysis. In particular, three data filtering techniques are explored; excluding specific problematic regions from the analysis, regressing out a spurious viewing geometry effect, and excluding specific cloud types from the analysis. These adjustments all, to varying degree, moderate the cloud trends in the original data but leave the qualitative aspects of those trends largely unaffected. Therefore, our analysis suggests that ISCCP data can be used to interpret regional trends in cloudiness, provided that data and instrumental artefacts are recognized and accounted for. The variation in magnitude between trends emerging from application of different data correction methods, allows us to estimate possible ranges for the observational changes. It is found that the storm tracks, here represented by the extent of the midlatitude-centered band of maximum cloud cover over the studied ocean basins, experience a poleward shift as well as a narrowing over the 25 year period covered by ISCCP. The observed magnitudes of these effects are larger than in current generation climate models (CMIP3). The magnitude of the shift is particularly large in the northern hemisphere Atlantic. This is also the one of the four regions in which imperfect data primarily prevents us from drawing firm conclusions. The shifted path and reduced extent of the storm track cloudiness is accompanied by a regional reduction in total cloud cover. This decrease in cloudiness can primarily be ascribed to low level clouds, whereas the upper level cloud fraction actually increases, according to ISCCP. Independent satellite observations of radiative fluxes at the top of the atmosphere are consistent with the changes in total cloud cover. The shift in cloudiness is also supported by a shift in central position of the mid-troposphere meridional temperature gradient. We do not find support for aerosols playing a significant role in the satellite observed changes in cloudiness. The observed changes in storm track cloudiness can be related to local cloud-induced changes in radiative forcing, using ERBE and CERES radiative fluxes. The shortwave and the longwave components are found to act together, leading to a positive (warming) net radiative effect in response to the cloud changes in the storm track regions, indicative of positive cloud feedback. Among the CMIP3 models that simulate poleward shifts in all four storm track areas, all but one show decreasing cloud amount on a global mean scale in response to increased CO2 forcing, further consistent with positive cloud feedback. Models with low equilibrium climate sensitivity to a lesser extent than higher-sensitivity models simulate a poleward shift of the storm tracks.

Bender, Frida A-M.↗

NASA NTRS1991

Determining surface solar absorption from broadband satellite measurements for clear skies - Comparison with surface measurements

Using data from collocated satellite pixel measurements obtained during the Earth Radiation Budget Experiement and near-surface measurements carried out at the Boulder Atmospheric Observatory Tower, the shortwave (SW) surface radiation from broadband satellite measurements for clear-sky conditions was compared with surface measurements. Results demonstrate that the surface-SW absorption is a more meaningful quantity for climate studies than is surface insolation. It is also shown that a direct evaluation of the surface-SW absorption can be more accurately obtained from satellite measurements than can be surface insolation. An algorithm is presented for transferring satellite SW measurements to surface-SW absorption.

Cess, Robert D.↗

NASA NTRSDate not supplied

CERES ERBE-like Instantaneous TOA Estimates (ES-8) in HDF (CER_ES8_Terra-FM2_Edition1-CV)

The ES-8 archival data product contains a 24-hour, single-satellite, instantaneous view of scanner fluxes at the top-of-atmosphere (TOA) reduced from spacecraft altitude unfiltered radiances using Earth Radiation Budget Experiment (ERBE) scanner Inversion algorithms and the ERBE shortwave (SW) and longwave (LW) Angular Distribution Models (ADMs). The ES-8 also includes the total (TOT), SW, LW, and window (WN) channel radiometric data; SW, LW, and WN unfiltered radiance values; and the ERBE scene identification for each measurement. These data are organized according to the CERES 3.3-second scan into 6.6-second records. As long as there is one valid scanner measurement within a record, the ES-8 record will be generated. The following CERES ES8 data sets are currently available: CER_ES8_TRMM-PFM_Edition1 CER_ES8_TRMM-PFM_Edition2 CER_ES8_TRMM-PFM_Transient-Ops2 CER_ES8_Terra-FM1_Edition1 CER_ES8_Terra-FM2_Edition1 CER_ES8_Terra-FM1_Edition2 CER_ES8_Terra-FM2_Edition2 CER_ES8_Aqua-FM3_Edition1 CER_ES8_Aqua-FM4_Edition1 CER_ES8_Aqua-FM3_Edition2 CER_ES8_Aqua-FM4_Edition2 CER_ES8_Aqua-FM3_Edition1-CV CER_ES8_Aqua-FM4_Edition1-CV CER_ES8_Terra-FM1_Edition1-CV CER_ES8_Terra-FM1_Edition1-CV. [Location=GLOBAL] [Temporal_Coverage: Start_Date=1997-12-27; Stop_Date=2006-10-31] [Spatial_Coverage: Southernmost_Latitude=-90; Northernmost_Latitude=90; Westernmost_Longitude=-180; Easternmost_Longitude=180] [Data_Resolution: Temporal_Resolution=1 day; Temporal_Resolution_Range=Daily - < Weekly].

WINDOW UNFILTERED RADIANCE↗

NASA NTRSDate not supplied

CERES Single Scanner Satellite Footprint, TOA, Surface Fluxes and Clouds (SSF) data in HDF (CER_SSF_TRMM-PFM-VIRS_Edition2A-TransOps)

The Single Scanner Footprint TOA/Surface Fluxes and Clouds (SSF) product contains one hour of instantaneous Clouds and the Earth's Radiant Energy System (CERES) data for a single scanner instrument. The SSF combines instantaneous CERES data with scene information from a higher-resolution imager such as Visible/Infrared Scanner (VIRS) on TRMM or Moderate-Resolution Imaging Spectroradiometer (MODIS) on Terra and Aqua. Scene identification and cloud properties are defined at the higher imager resolution and these data are averaged over the larger CERES footprint. For each CERES footprint, the SSF contains the number of cloud layers and for each layer the cloud amount, height, temperature, pressure, optical depth, emissivity, ice and liquid water path, and water particle size. The SSF also contains the CERES filtered radiances for the total, shortwave (SW), and window (WN) channels and the unfiltered SW, longwave (LW), and WN radiances. The SW, LW, and WN radiances at spacecraft altitude are converted to Top-of-the-Atmosphere (TOA) fluxes based on the imager defined scene. These TOA fluxes are used to estimate surface fluxes. Only footprints with adequate imager coverage are included on CER_SSF_TRMM-PFM-VIRS_Subset_Edition1the SSF which is much less than the full set of footprints on the CERES ES-8 product. The following CERES SSF data sets are currently available: CER_SSF_TRMM-PFM-VIRS_Edition1 CER_SSF_TRMM-PFM-VIRS_Subset_Edition1 CER_SSF_TRMM-PFM-VIRS_Edition2A CER_SSF_TRMM-SIM-VIRS_Edition2_VIRSonly CER_SSF_TRMM-PFM-VIRS_Edition2A-TransOps CER_SSF_TRMM-PFM-VIRS_Edition2B-TransOps CER_SSF_TRMM-PFM-VIRS_Edition2B CER_SSF_Terra-FM1-MODIS_Edition1A CER_SSF_Terra-FM1-MODIS_Edition1A CER_SSF_Terra-FM1-MODIS_Edition2A CER_SSF_Terra-FM2-MODIS_Edition2A CER_SSF_Terra-FM1-MODIS_Edition2B CER_SSF_Terra-FM2-MODIS_Edition2B CER_SSF_Aqua-FM4-MODIS_Beta1 CER_SSF_Aqua-FM3-MODIS_Beta2 CER_SSF_Aqua-FM4-MODIS_Beta2. [Location=GLOBAL] [Temporal_Coverage: Start_Date=1998-01-01; Stop_Date=1999-07-31] [Spatial_Coverage: Southernmost_Latitude=-90; Northernmost_Latitude=90; Westernmost_Longitude=-180; Easternmost_Longitude=180] [Data_Resolution: Temporal_Resolution=1 hour; Temporal_Resolution_Range=Hourly - < Daily].

CLOUD PARTICLE PHASE↗

NASA NTRS2023

The Historical Greenland Climate Network (GC-Net) Curated and Augmented Level-1 Dataset

The Greenland Climate Network (GC-Net) consists of 31 automatic weather stations (AWSs) at 30 sites across the Greenland Ice Sheet. The first site was initiated in 1990, and the project has operated almost continuously since 1995 under the leadership of the late Konrad Steffen. The GC-Net AWS measured air temperature, relative humidity, wind speed, atmospheric pressure, downward and reflected shortwave irradiance, net radiation, and ice and firn temperatures. The majority of the GC-Net sites were located in the ice sheet accumulation area (17 AWSs), while 11 AWSs were located in the ablation area, and two sites (three AWSs) were located close to the equilibrium line altitude. Additionally, three AWSs of similar design to the GC-Net AWS were installed by Konrad Steffen's team on the Larsen C ice shelf, Antarctica. After more than 3 decades of operation, the GC-Net AWSs are being decommissioned and replaced by new AWSs operated by the Geological Survey of Denmark and Greenland (GEUS). Therefore, making a reassessment of the historical GC-Net AWS data is necessary. We present a full reprocessing of the historical GC-Net AWS dataset with increased attention to the filtering of erroneous measurements, data correction and derivation of additional variables: continuous surface height, instrument heights, surface albedo, turbulent heat fluxes, and 10 m ice and firn temperatures. This new augmented GC-Net level-1 (L1) AWS dataset is now available at https://doi.org/10.22008/FK2/VVXGUT (Steffen et al., 2023) and will continue to be refined. The processing scripts, latest data and a data user forum are available at https://github.com/GEUS-Glaciology-and-Climate/GC-Net-level-1-data-processing (last access: 30 November 2023). In addition to the AWS data, a comprehensive compilation of valuable metadata is provided: maintenance reports, yearly pictures of the stations and the station positions through time. This unique dataset provides more than 320 station years of high-quality atmospheric data and is available following FAIR (findable, accessible, interoperable, reusable) data and code practices.

Greenland Climate Network↗

NASA NTRSDate not supplied

CERES ERBE-like Instantaneous TOA Estimates (ES-8) in HDF (CER_ES8_Aqua-FM4_Edition2)

LONGWAVE UNFILTERED RADIANCE↗

NASA NTRS2012

Using ISCCP Weather States to Decompose Cloud Radiative Effects

The presentation will examine the shortwave (SW) and longwave (LW) cloud radiative effect CRE (aka "cloud radiative forcing") at the top-of-the-atmosphere and surface of ISCCP weather states (aka "cloud regimes") in three distinct geographical zones, one tropical and two mid-latitude. Our goal is to understand and quantify the contribution of the different cloud regimes to the planetary radiation budget. In the tropics we find that the three most convectively active states are the ones with largest SW, LW and net TOA CRE contributions to the overall daytime tropical CRE budget. They account for 59%, 71% and 55% of the total CRE, respectively. The boundary layer-dominated weather states account for only 34% of the total SW CRE and 41% of the total net CRE, so to focus only on them in cloud feedback studies may be imprudent. We also find that in both the northern and southern midlatitude zones only two weather states, the first and third most convectively active with large amounts of nimbostratus-type clouds, contribute ",40% to both the SW and net TOA CRE budgets, highlighting the fact that cloud regimes associated with frontal systems are not only important for weather (precipitation) but also for climate (radiation budget). While all cloud regimes in all geographical zones have a slightly larger SFC than TOA SW CRE, implying cooling of the surface and slight warming of the atmosphere, their LW radiative effects are more subtle: in the tropics the weather states with plentiful high clouds warm the atmosphere while those with copious amounts of low clouds cool the atmosphere. In both midlatitude zones only the weather states with peak cloud fractions at levels above 440 mbar warm the atmosphere while all the rest cool it. These results make the connection of the contrasting CRE effects to the atmospheric dynamics more explicit - "storms" tend to warm the atmosphere whereas fair weather clouds cool it, suggesting a positive feedback of clouds on weather systems. The breakdown of CRE by cloud regime are however not entirely similar between the two midlatitude zones. Despite the existence of an additional state in the nort!lern midlatitudes, only four weather states have net daytime CREs with absolute values above 100 Watts per square meter compared to six in the south. This reminds us that the environment where clouds occur also has a crucial role in determining their radiative effects. All the above make evident that reproducing grand averages of current CRE by climate models in only part of the challenge. If existing cloud regimes and shifts in their distributions and frequency of occurrence in a changed climate are not properly simulated, the radiative role of clouds will not be adequately predicted.

Oreopoulos, L.↗

NASA NTRS2002

Comparison of ScaRaB, GOES 8, Aircraft, and Surface Observations of the Absorption of Solar Radiation by Clouds

Data obtained by the Scanner for Radiation Budget (ScaRaB) instrument on the Meteor 3 satellite have been analyzed and compared to satellite (GOES 8), aircraft (Radiation Measurement System, RAMS), and surface (Baseline Solar Radiation Network (BSRN), Solar and Infrared Observations System (SIROS), and RAMS) measurements of irradiance obtained during the Atmospheric Radiation Measurements Enhanced Shortwave Experiment (ARESE). It is found that the ScaRaB data covering the period from March 1994 to February 1995 (the instrument's operational lifetime) indicate excess absorption of solar radiation by the cloudy atmosphere in agreement with previous aircraft, surface, and GOES 8 results. The full ScaRaB data set combined with BSRN and SIROS surface observations gives an average all-sky absorptance of 0.28. The GOES 8 data set combined with RAMS surface observations gives an average all-sky absorptance of 0.26. The aircraft data set (RAMS) gives a mean all-sky absorptance of 0.24 (for the column between 0.5 and 13 km).

Pope, Shelly K.↗

NASA NTRS1992

In-flight shortwave calibrations of the active cavity radiometers using tungsten lamps

The Earth Radiation Budget Experiment (ERBE) active cavity radiometers are used to measure the incoming solar, reflected shortwave solar, and emitted longwave radiations from the Earth and atmosphere. The radiometers are located on the NASA's Earth Radiation Budget Satellite (ERBS) and the NOAA-9 and NOAA-10 spacecraft platforms. Two of the radiometers, one wide field of view (WFOV) and one medium field of view (MFOV), measure the total radiation in the spectral region of 0.2 to 50 microns and the other two radiometers (WFOV and MFOV) measure the shortwave radiation in the spectral region of 0.2 to 5.0 microns. For the in-flight calibrations, tungsten lamp and the sun are used as calibration sources for shortwave radiometers. Descriptions of the tungsten lamp and solar calibration procedures and mechanisms are presented. The tungsten lamp calibration measurements are compared with the measurements of solar calibration for ERBS and NOAA-9 instruments.

Thomas, Susan↗

NASA NTRSDate not supplied

Artificial Neural Network (ANN) Surface Longwave and Shortwave Fluxes Trained on CERES Observations

The Clouds and Earth’s Radiant Energy System (CERES) project provides satellite-based observations of the radiative fluxes and clouds systems. CERES climate quality data products typically take several months of calibration and validation before release to the public. The Fast Longwave and Shortwave Radiative Flux (FLASHFlux) data product was developed to provide key data for the applied sciences and educational users within a week of observation. FLASHFlux achieves this by using simplified calibration, an operational meteorological product from Global Modeling and Assimilation Office (GMAO), and its own surface parameterizations model. The CERES FLASHFlux provides two data products: 1) an hourly Level 2 Single Scanner Footprint (SSF) data separately for Terra and NOAA-20 observations, and 2) a daily Level 3 Time Interpolated and Spatially Averaged (TISA) 1o x 1o gridded data that combines Terra and NOAA-20 observations. Currently, FLASHFlux uses the Langley Parameterized Shortwave Algorithm (LPSA) and Langley Parameterized Longwave Algorithm (LPLA) to derive its surface fluxes (Kratz et al., 2010; Gupta et al, 2001). A new Machine Learning (ML) based approach using Artificial Neural Networks to derive Surface Longwave (LW) & Shortwave (SW) fluxes based on training data from the CERES Clouds Radiative Swath (CRS) product is being investigated to replace LPSA and LPLA in the SSF surface flux products. One of the biggest hurdles in training ML model is model fitting. To overcome the problem of overfitting we use feature engineering that helps in finding the important feature and remove features that are irrelevant to the model. In our training we employed the Leave-One-Feature-Out Importance (LOFO) to evaluate the significance of each feature in our training. We intercompare ANN fluxes against surface fluxes produced from the Fu-Liou model in CRS and the LPSA/LPLA in FLASHFlux SSF. Furthermore, we validated ANN derived fluxes to the Baseline Surface Radiation Network (BSRN).

P C Sawaengphokhai↗

NASA NTRS1981

Simulation of a numerical filter for enhancing earth radiation budget measurements

The Earth Radiation Budget Experiment has the objective to collect the radiation budget data which are needed to determine the radiation budget at the top of the atmosphere (TOA) on a regional scale. A second objective is to determine the accuracy of the results. Three satellites will carry wide and medium field of view radiometers which measure the longwave and shortwave components of radiation. Scanning radiometers will be included to detect small spatial features. A proposal has been made to employ for the nonscanning radiometers a one-dimensional numerical filter which reduces satellite measurements to TOA radiant excitances. The numerical filter was initially formulated by House (1980). It enhances the resolution of the radiation budget along the satellite groundtrack. The accuracy of the numerical filter estimate is studied by simulating the data gathering and measurement inversion process. The results of the study are discussed, taking into account two error sources.

Green, R. N.↗