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  • Author or Editor: Norman G. Loeb x
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Konstantin Loukachine
and
Norman G. Loeb

Abstract

The Clouds and the Earth's Radiant Energy System (CERES) provides top-of-atmosphere (TOA) radiative flux estimates from shortwave (SW) and longwave (LW) radiance measurements by applying empirical angular distribution models (ADMs) for scene types defined by coincident high-resolution imager-based cloud retrievals. In this study, CERES ADMs are simulated using a feed-forward error back-propagation (FFEB) artificial neural network (ANN) simulation to provide a means of estimating TOA SW and LW radiative fluxes for different scene types in the absence of imager radiance measurements. In all cases, the ANN-derived TOA fluxes deviate from CERES TOA fluxes by less than 0.3 W m−2, on average, and show a smaller dependence on viewing geometry than TOA fluxes derived using ADMs from the Earth Radiation Budget Experiment (ERBE). The ANN-derived TOA SW and LW fluxes show a significant improvement in accuracy over the CERES ERBE-like fluxes when compared regionally.

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Xianglei Huang
,
Norman G. Loeb
, and
Huiwen Chuang

Abstract

Clouds and the Earth’s Radiant Energy System (CERES) daytime longwave (LW) radiances are determined from the difference between a total (TOT) channel (0.3–200 μm) measurement and a shortwave (SW) channel (0.3–5 μm) measurement, while nighttime LW radiances are obtained directly from the TOT channel. This means that a drift in the SW channel or the SW portion of the TOT channel could impact the daytime longwave radiances, but not the nighttime ones. This study evaluates daytime and nighttime CERES LW radiances for a possible secular drift in CERES LW observations using spectral radiances observed by Atmospheric Infrared Sounder (AIRS). By examining the coincidental AIRS and CERES Flight Model 3 (FM3) measurements over the tropical clear-sky oceans for all of January and July months since 2005, a secular drift of about −0.11% yr−1 in the daytime CERES-FM3 longwave unfiltered radiance can be identified in the CERES Single Scanner Footprint (SSF) Edition 2 product. This provides an upper-bound estimation for the drift in daytime outgoing longwave radiation, which is approximately −0.323 W m−2 yr−1. This estimation is consistent with the independent assessment concluded by the CERES calibration team. Such secular drift has been greatly reduced in the latest CERES SSF Edition 3 product. Comparisons are conducted for the CERES window channel as well, and it shows essentially no drift. This study serves as a practical example illustrating how the measurements of spectrally resolved radiances can be used to help evaluate data products from other narrowband or broadband measurements.

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Bijoy Vengasseril Thampi
,
Takmeng Wong
,
Constantin Lukashin
, and
Norman G. Loeb

Abstract

Continuous monitoring of the earth radiation budget (ERB) is critical to the understanding of Earth’s climate and its variability with time. The Clouds and the Earth’s Radiant Energy System (CERES) instrument is able to provide a long record of ERB for such scientific studies. This manuscript, which is the first of a two-part paper, describes the new CERES algorithm for improving the clear/cloudy scene classification without the use of coincident cloud imager data. This new CERES algorithm is based on a subset of the modern artificial intelligence (AI) paradigm called machine learning (ML) algorithms. This paper describes the development and application of the ML algorithm known as random forests (RF), which is used to classify CERES broadband footprint measurements into clear and cloudy scenes. Results from the RF analysis carried using the CERES Single Scanner Footprint (SSF) data for January and July are presented in the manuscript. The daytime RF misclassification rate (MCR) shows relatively large values (>30%) for snow, sea ice, and bright desert surface types, while lower values (<10%) for the forest surface type. MCR values observed for the nighttime data in general show relatively larger values for most of the surface types compared to the daytime MCR values. The modified MCR values show lower values (<4%) for most surface types after thin cloud data are excluded from the analysis. Sensitivity analysis shows that the number of input variables and decision trees used in the RF analysis has a substantial influence on determining the classification error.

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Norman G. Loeb
,
Seiji Kato
,
Konstantin Loukachine
, and
Natividad Manalo-Smith

Abstract

The Clouds and Earth’s Radiant Energy System (CERES) provides coincident global cloud and aerosol properties together with reflected solar, emitted terrestrial longwave, and infrared window radiative fluxes. These data are needed to improve the understanding and modeling of the interaction between clouds, aerosols, and radiation at the top of the atmosphere, surface, and within the atmosphere. This paper describes the approach used to estimate top-of-atmosphere (TOA) radiative fluxes from instantaneous CERES radiance measurements on the Terra satellite. A key component involves the development of empirical angular distribution models (ADMs) that account for the angular dependence of the earth’s radiation field at the TOA. The CERES Terra ADMs are developed using 24 months of CERES radiances, coincident cloud and aerosol retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS), and meteorological parameters from the Global Modeling and Assimilation Office (GMAO)’s Goddard Earth Observing System (GEOS) Data Assimilation System (DAS) V4.0.3 product. Scene information for the ADMs is from MODIS retrievals and GEOS DAS V4.0.3 properties over the ocean, land, desert, and snow for both clear and cloudy conditions. Because the CERES Terra ADMs are global, and far more CERES data are available on Terra than were available from CERES on the Tropical Rainfall Measuring Mission (TRMM), the methodology used to define CERES Terra ADMs is different in many respects from that used to develop CERES TRMM ADMs, particularly over snow/sea ice, under cloudy conditions, and for clear scenes over land and desert.

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Norman G. Loeb
,
Seiji Kato
,
Konstantin Loukachine
,
Natividad Manalo-Smith
, and
David R. Doelling

Abstract

Errors in top-of-atmosphere (TOA) radiative fluxes from the Clouds and the Earth’s Radiant Energy System (CERES) instrument due to uncertainties in radiance-to-flux conversion from CERES Terra angular distribution models (ADMs) are evaluated through a series of consistency tests. These tests show that the overall bias in regional monthly mean shortwave (SW) TOA flux is less than 0.2 W m−2 and the regional RMS error ranges from 0.70 to 1.4 W m−2. In contrast, SW TOA fluxes inferred using theoretical ADMs that assume clouds are plane parallel are overestimated by 3–4 W m−2 and exhibit a strong latitudinal dependence. In the longwave (LW), the bias error ranges from 0.2 to 0.4 W m−2 and regional RMS errors remain smaller than 0.7 W m−2. Global mean albedos derived from ADMs developed during the Earth Radiation Budget Experiment (ERBE) and applied to CERES measurements show a systematic increase with viewing zenith angle of 4%–8%, while albedos from the CERES Terra ADMs show a smaller increase of 1%–2%. The LW fluxes from the ERBE ADMs show a systematic decrease with viewing zenith angle of 2%–2.4%, whereas fluxes from the CERES Terra ADMs remain within 0.7%–0.8% at all angles. Based on several months of multiangle CERES along-track data, the SW TOA flux consistency between nadir- and oblique-viewing zenith angles is generally 5% (<17 W m−2) over land and ocean and 9% (26 W m−2) in polar regions, and LW TOA flux consistency is approximate 3% (7 W m−2) over all surfaces. Based on these results and a theoretically derived conversion between TOA flux consistency and TOA flux error, the best estimate of the error in CERES TOA flux due to the radiance-to-flux conversion is 3% (10 W m−2) in the SW and 1.8% (3–5 W m−2) in the LW. Monthly mean TOA fluxes based on ERBE ADMs are larger than monthly mean TOA fluxes based on CERES Terra ADMs by 1.8 and 1.3 W m−2 in the SW and LW, respectively.

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David A. Rutan
,
Seiji Kato
,
David R. Doelling
,
Fred G. Rose
,
Le Trang Nguyen
,
Thomas E. Caldwell
, and
Norman G. Loeb

Abstract

The Clouds and the Earth’s Radiant Energy System Synoptic (SYN1deg), edition 3, product provides climate-quality global 3-hourly 1° × 1°gridded top of atmosphere, in-atmosphere, and surface radiant fluxes. The in-atmosphere surface fluxes are computed hourly using a radiative transfer code based upon inputs from Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS), 3-hourly geostationary (GEO) data, and meteorological assimilation data from the Goddard Earth Observing System. The GEO visible and infrared imager calibration is tied to MODIS to ensure uniform MODIS-like cloud properties across all satellite cloud datasets. Computed surface radiant fluxes are compared to surface observations at 85 globally distributed land (37) and ocean buoy (48) sites as well as several other publicly available global surface radiant flux data products. Computed monthly mean downward fluxes from SYN1deg have a bias (standard deviation) of 3.0 W m−2 (5.7%) for shortwave and −4.0 W m−2 (2.9%) for longwave compared to surface observations. The standard deviation between surface downward shortwave flux calculations and observations at the 3-hourly time scale is reduced when the diurnal cycle of cloud changes is explicitly accounted for. The improvement is smaller for surface downward longwave flux owing to an additional sensitivity to boundary layer temperature/humidity, which has a weaker diurnal cycle compared to clouds.

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Moguo Sun
,
David R. Doelling
,
Norman G. Loeb
,
Ryan C. Scott
,
Joshua Wilkins
,
Le Trang Nguyen
, and
Pamela Mlynczak

Abstract

The Clouds and the Earth’s Radiant Energy System (CERES) project has provided the climate community 20 years of globally observed top of the atmosphere (TOA) fluxes critical for climate and cloud feedback studies. The CERES Flux By Cloud Type (FBCT) product contains radiative fluxes by cloud type, which can provide more stringent constraints when validating models and also reveal more insight into the interactions between clouds and climate. The FBCT product provides 1° regional daily and monthly shortwave (SW) and longwave (LW) cloud-type fluxes and cloud properties sorted by seven pressure layers and six optical depth bins. Historically, cloud-type fluxes have been computed using radiative transfer models based on observed cloud properties. Instead of relying on radiative transfer models, the FBCT product utilizes Moderate Resolution Imaging Spectroradiometer (MODIS) radiances partitioned by cloud type within a CERES footprint to estimate the cloud-type broadband fluxes. The MODIS multichannel derived broadband fluxes were compared with the CERES observed footprint fluxes and were found to be within 1% and 2.5% for LW and SW, respectively, as well as being mostly free of cloud property dependencies. These biases are mitigated by constraining the cloud-type fluxes within each footprint with the CERES Single Scanner Footprint (SSF) observed flux. The FBCT all-sky and clear-sky monthly averaged fluxes were found to be consistent with the CERES SSF1deg product. Several examples of FBCT data are presented to highlight its utility for scientific applications.

Open access
David R. Doelling
,
Norman G. Loeb
,
Dennis F. Keyes
,
Michele L. Nordeen
,
Daniel Morstad
,
Cathy Nguyen
,
Bruce A. Wielicki
,
David F. Young
, and
Moguo Sun

Abstract

The Clouds and the Earth’s Radiant Energy System (CERES) instruments on board the Terra and Aqua spacecraft continue to provide an unprecedented global climate record of the earth’s top-of-atmosphere (TOA) energy budget since March 2000. A critical step in determining accurate daily averaged flux involves estimating the flux between CERES Terra or Aqua overpass times. CERES employs the CERES-only (CO) and the CERES geostationary (CG) temporal interpolation methods. The CO method assumes that the cloud properties at the time of the CERES observation remain constant and that it only accounts for changes in albedo with solar zenith angle and diurnal land heating, by assuming a shape for unresolved changes in the diurnal cycle. The CG method enhances the CERES data by explicitly accounting for changes in cloud and radiation between CERES observation times using 3-hourly imager data from five geostationary (GEO) satellites. To maintain calibration traceability, GEO radiances are calibrated against Moderate Resolution Imaging Spectroradiometer (MODIS) and the derived GEO fluxes are normalized to the CERES measurements. While the regional (1° latitude × 1° longitude) monthly-mean difference between the CG and CO methods can exceed 25 W m−2 over marine stratus and land convection, these regional biases nearly cancel in the global mean. The regional monthly CG shortwave (SW) and longwave (LW) flux uncertainty is reduced by 20%, whereas the daily uncertainty is reduced by 50% and 20%, respectively, over the CO method, based on comparisons with 15-min Geostationary Earth Radiation Budget (GERB) data.

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Ryan C. Scott
,
Fred G. Rose
,
Paul W. Stackhouse Jr.
,
Norman G. Loeb
,
Seiji Kato
,
David R. Doelling
,
David A. Rutan
,
Patrick C. Taylor
, and
William L. Smith Jr.

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.

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