Surface radiation budget in the Clouds and the Earth's Radiant Energy System effort and in the Global Energy and Water Cycle Experiment

1990 ◽  
Author(s):  
Thomas P. Charlock ◽  
Fred G. Rose ◽  
G. Louis Smith
Keyword(s):  
Water Cycle ◽  
Radiant Energy ◽  
Energy System ◽  
Surface Radiation ◽  
Radiation Budget ◽  
Global Energy ◽  
Surface Radiation Budget

Validation of Clear-Sky Global LAnd Surface Satellite (GLASS) Longwave Radiation Product

2020 ◽  
Author(s):  
Qi Zeng ◽  
Jie Cheng ◽  
Feng Yang
Keyword(s):  
Land Surface ◽  
Water Cycle ◽  
Radiant Energy ◽  
Remote Sensing Data ◽  
Longwave Radiation ◽  
Energy System ◽  
Surface Radiation ◽  
Radiation Budget ◽  
Clear Sky ◽  
Global Land

<p>Surface longwave (LW) radiation plays an important rolein global climatic change, which is consist of surface longwave upward radiation (LWUP), surface longwave downward radiation (LWDN) and surface longwave net radiation (LWNR). Numerous studies have been carried out to estimate LWUP or LWDN from remote sensing data, and several satellite LW radiation products have been released, such as the International Satellite Cloud Climatology Project‐Flux Data (ISCCP‐FD), the Global Energy and Water cycle Experiment‐Surface Radiation Budget (GEWEX‐SRB) and the Clouds and the Earth’s Radiant Energy System‐Gridded Radiative Fluxes and Clouds (CERES‐FSW). But these products share the common features of coarse spatial resolutions (100-280 km) and lower validation accuracy.</p><p>Under such circumstance, we developed the methods of estimating long-term high spatial resolution all sky  instantaneous LW radiation, and produced the corresponding products from MODIS data from 2000 through 2018 (Terra and Aqua), named as Global LAnd Surface Satellite (GLASS) Longwave Radiation product, which can be free freely downloaded from the website (http://glass.umd.edu/Download.html).</p><p>In this article, ground measurements collected from 141 sites in six independent networks (AmerciFlux, AsiaFlux, BSRN, CEOP, HiWATER-MUSOEXE and TIPEX-III) are used to evaluate the clear-sky GLASS LW radiation products at global scale. The bias and RMSE is -4.33 W/m<sup>2 </sup>and 18.15 W/m<sup>2 </sup>for LWUP, -3.77 W/m<sup>2 </sup>and 26.94 W/m<sup>2</sup> for LWDN, and 0.70 W/m<sup>2 </sup>and 26.70 W/m<sup>2</sup> for LWNR, respectively. Compared with validation results of the above mentioned three LW radiation products, the overall accuracy of GLASS LW radiation product is much better. We will continue to improve the retrieval algorithms and update the products accordingly.</p>

scholarly journals Satellite measurements of solar radiation in the Yaqui Valley, northern Mexico

2001 ◽  
Vol 40 (3) ◽  
pp. 207-218
Author(s):  
Jaime Garatuza-Payán ◽  
W. James Shuttleworth ◽  
Rachel T. Pinker
Keyword(s):  
Solar Radiation ◽  
Water Cycle ◽  
Surface Radiation ◽  
Radiation Budget ◽  
Satellite Measurements ◽  
Global Energy ◽  
Yaqui Valley ◽  
Northern Mexico ◽  
Surface Radiation Budget

Se realizaron estimaciones cada media hora de radiación solar a partir de datos de satélite para el Valle del Yaqui en Sonora, México, para el período de noviembre de 1998 a marzo de 1999. Las estimaciones se hicieron en una malla de 50 km usando el algoritmo de "Global Energy and Water-Cycle Experiment Surface Radiation Budget (GEWEX/SRB)" aplicado con datos de GOES-East y, en una malla de 4 km, usando una versión de alta resolución del mismo algoritmo con datos de GOES-West. Los resultados se compararon con mediciones de terreno en dos sitios. En promedio, usando la actual calibración de los radiómetros de los satélites, los valores derivados de GOES-East son 18% mayores que los medidos en terreno, mientras que los de GOES-West son 9% menores. Asumiendo que estas diferencias sistemáticas reflejan la pobre calibración de los sensores en los satélites, las estimaciones fueron recalibradas por separado para ajustarse a los datos de campo. Después de la recalibración, existen aún diferencias entre las estimaciones y las observaciones porque el satélite da una estimación instantánea promedio de un área, mientras que la observación de campo representa una medición puntual promedio para un cierto intervalo de tiempo. Estas discrepancias se reducen cuando se comparan valores promedios diarios. El error entre el satélite y las mediciones es menor para la estimación de alta resolución, y existe mejoría en el detalle espacial con los datos de alta resolución, haciéndolos preferibles en aplicaciones hidrológicas. Investigaciones anteriores han demostrado la capacidad para estimar evaporación potencial a partir de radiación solar diaria usando una versión de la ecuación de Makkink calibrada localmente y los factores de cultivo relevantes han sido derivados para trigo y algodón. Finalmente, se demuestra la aplicación de las estimaciones de radiación solar de alta resolución para derivar estimaciones diarias de evapotranspiración en campos de trigo y algodón.

scholarly journals A global long term (1981–2019) daily land surface radiation budget product from AVHRR satellite data using a residual convolutional neural network

2021 ◽  
Author(s):  
Jianglei Xu ◽  
Shunlin Liang ◽  
Bo Jiang
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Convolutional Neural Network ◽  
Satellite Data ◽  
Land Surface ◽  
Radiant Energy ◽  
Surface Radiation ◽  
Radiation Budget ◽  
Surface Radiation Budget

Abstract. The surface radiation budget, also known as all-wave net radiation (Rn), is a key parameter for various land surface processes including hydrological, ecological, agricultural, and biogeochemical processes. Satellite data can be effectively used to estimate Rn, but existing satellite products have coarse spatial resolutions and limited temporal coverage. In this study, a point-surface matching estimation (PSME) method is proposed to estimate surface Rn using a residual convolutional neural network (RCNN) integrating spatially adjacent information to improve the accuracy of retrievals. A global high-resolution (0.05°) long-term (1981–2019) Rn product was subsequently generated from Advanced Very High-Resolution Radiometer (AVHRR) data. Specifically, the RCNN was employed to establish a nonlinear relationship between globally distributed ground measurements from 537 sites and AVHRR top of atmosphere (TOA) observations. Extended triplet collocation (ETC) technology was applied to address the spatial scale mismatch issue resulting from the low spatial support of ground measurements within the AVHRR footprint by selecting reliable sites for model training. The overall independent validation results show that the generated AVHRR Rn product is highly accurate, with R2, root-mean-square error (RMSE), and bias of 0.84, 26.66 Wm−2 (31.66 %), and 1.59 Wm−2 (1.89 %), respectively. Inter-comparisons with three other Rn products, i.e., the 5 km Global Land Surface Satellite (GLASS), the 1° Clouds and the Earth's Radiant Energy System (CERES), and the 0.5° × 0.625° Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA2), illustrate that our AVHRR Rn retrievals have the best accuracy under all of the considered surface and atmospheric conditions, especially thick cloud or hazy conditions. The spatiotemporal analyses of these four Rn datasets indicate that the AVHRR Rn product reasonably replicates the spatial pattern and temporal evolution trends of Rn observations. This dataset is freely available at https://doi.org/10.5281/zenodo.5509854 for 1981–2019 (Xu et al., 2021).

scholarly journals Uncertainty in Satellite-Derived Surface Irradiances and Challenges in Producing Surface Radiation Budget Climate Data Record

2020 ◽  
Vol 12 (12) ◽  
pp. 1950
Author(s):  
Seiji Kato ◽  
David A. Rutan ◽  
Fred G. Rose ◽  
Thomas E. Caldwell ◽  
Seung-Hee Ham ◽  
...  
Keyword(s):  
Sensible Heat Flux ◽  
Radiant Energy ◽  
Energy System ◽  
Surface Radiation ◽  
Radiation Budget ◽  
Climate Data ◽  
Surface Irradiance ◽  
Uncertainty Estimates ◽  
The Difference ◽  
Global Mean

The Clouds and the Earth’s Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) Edition 4.1 data product provides global surface irradiances. Uncertainties in the global and regional monthly and annual mean all-sky net shortwave, longwave, and shortwave plus longwave (total) irradiances are estimated using ground-based observations. Error covariance is derived from surface irradiance sensitivity to surface, atmospheric, cloud and aerosol property perturbations. Uncertainties in global annual mean net shortwave, longwave, and total irradiances at the surface are, respectively, 5.7 Wm−2, 6.7 Wm−2, and 9.7 Wm−2. In addition, the uncertainty in surface downward irradiance monthly anomalies and their trends are estimated based on the difference derived from EBAF surface irradiances and observations. The uncertainty in the decadal trend suggests that when differences of decadal global mean downward shortwave and longwave irradiances are, respectively, greater than 0.45 Wm−2 and 0.52 Wm−2, the difference is larger than 1σ uncertainties. However, surface irradiance observation sites are located predominately over tropical oceans and the northern hemisphere mid-latitude. As a consequence, the effect of a discontinuity introduced by using multiple geostationary satellites in deriving cloud properties is likely to be excluded from these trend and decadal change uncertainty estimates. Nevertheless, the monthly anomaly timeseries of radiative cooling in the atmosphere (multiplied by −1) agrees reasonably well with the anomaly time series of diabatic heating derived from global mean precipitation and sensible heat flux with a correlation coefficient of 0.46.

Constraints on Climate Sensitivity from Radiation Patterns in Climate Models

2011 ◽  
Vol 24 (4) ◽  
pp. 1034-1052 ◽  
Author(s):  
Markus Huber ◽  
Irina Mahlstein ◽  
Martin Wild ◽  
John Fasullo ◽  
Reto Knutti
Keyword(s):  
Climate Sensitivity ◽  
Radiative Forcing ◽  
Climate Models ◽  
Radiant Energy ◽  
Carbon Dioxide Concentration ◽  
Energy System ◽  
Surface Radiation ◽  
Radiation Budget ◽  
Radiation Patterns ◽  
Strong Constraint

Abstract The estimated range of climate sensitivity, the equilibrium warming resulting from a doubling of the atmospheric carbon dioxide concentration, has not decreased substantially in past decades. New statistical methods for estimating the climate sensitivity have been proposed and provide a better quantification of relative probabilities of climate sensitivity within the almost canonical range of 2–4.5 K; however, large uncertainties remain, in particular for the upper bound. Simple indices of spatial radiation patterns are used here to establish a relationship between an observable radiative quantity and the equilibrium climate sensitivity. The indices are computed for the Coupled Model Intercomparison Project phase 3 (CMIP3) multimodel dataset and offer a possibility to constrain climate sensitivity by considering radiation patterns in the climate system. High correlations between the indices and climate sensitivity are found, for example, in the cloud radiative forcing of the incoming longwave surface radiation and in the clear-sky component of the incoming surface shortwave flux, the net shortwave surface budget, and the atmospheric shortwave attenuation variable β. The climate sensitivity was estimated from the mean of the indices during the years 1990–99 for the CMIP3 models. The surface radiative flux dataset from the Clouds and the Earth’s Radiant Energy System (CERES) together with its top-of-atmosphere Energy Balanced and Filled equivalent (CERES EBAF) are used as a reference observational dataset, resulting in a best estimate for climate sensitivity of 3.3 K with a likely range of 2.7–4.0 K. A comparison with other satellite and reanalysis datasets show similar likely ranges and best estimates of 1.7–3.8 (3.3 K) [Earth Radiation Budget Experiment (ERBE)], 2.9–3.7 (3.3 K) [International Satellite Cloud Climatology Project radiative surface flux data (ISCCP-FD)], 2.8–4.1 (3.5 K) [NASA’s Modern Era Retrospective-Analysis for Research and Application (MERRA)], 3.0–4.2 (3.6 K) [Japanese 25-yr Reanalysis (JRA-25)], 2.7–3.9 (3.4 K) [European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-Interim)], 3.0–4.0 (3.5 K) [ERA-40], and 3.1–4.7 (3.6 K) for the NCEP reanalysis. For each individual reference dataset, the results suggest that values for the sensitivity below 1.7 K are not likely to be consistent with observed radiation patterns given the structure of current climate models. For the aggregation of the reference datasets, the climate sensitivity is not likely to be below 2.9 K within the framework of this study, whereas values exceeding 4.5 K cannot be excluded from this analysis. While these ranges cannot be interpreted properly in terms of probability, they are consistent with other estimates of climate sensitivity and reaffirm that the current climatology provides a strong constraint on the lower bound of climate sensitivity even in a set of structurally different models.

scholarly journals Validation of the CERES Edition-4A Surface-Only Flux Algorithms

2020 ◽  
Vol 59 (2) ◽  
pp. 281-295 ◽  
Author(s):  
David P. Kratz ◽  
Shashi K. Gupta ◽  
Anne C. Wilber ◽  
Victor E. Sothcott
Keyword(s):  
Radiant Energy ◽  
Energy System ◽  
Surface Fluxes ◽  
Surface Radiation ◽  
Radiation Budget ◽  
Random Errors ◽  
High Quality ◽  
Radiative Fluxes ◽  
Accuracy Requirement ◽  
Future Work

AbstractSurface radiative fluxes have been derived with the objective of supplementing top-of-atmosphere (TOA) radiative fluxes being measured under NASA’s Clouds and the Earth’s Radiant Energy System (CERES) project. This has been accomplished by using combinations of CERES TOA measurements, parameterized radiative transfer algorithms, and high-quality meteorological datasets available from reanalysis projects. Current CERES footprint-level products include surface fluxes derived from two shortwave (SW) and three longwave (LW) algorithms designated as SW models A and B and LW models A, B, and C. The SW and LW models A work for clear conditions only; the other models work for both clear and cloudy conditions. The current CERES Edition-4A computed surface fluxes from all models are validated against ground-based flux measurements from high-quality surface networks like the Baseline Surface Radiation Network and NOAA’s Surface Radiation Budget Network (SURFRAD). Validation results as systematic and random errors are provided for all models, separately for five different surface types and combined for all surface types as tables and scatterplots. Validation of surface fluxes is now a part of CERES processing and is used to continually improve the above algorithms. Since both models B work for clear and cloudy conditions alike and meet the accuracy requirement, their results are considered to be the most reliable and most likely to be retained for future work. Both models A have limited use given that they work for clear skies only. Models B will continue to undergo further improvement as more validation results become available.

Linking Cloud Variability with Surface Radiation Budget over the Continental United States Using NASA CERES Satellite Observations and CMIP6 Model Simulations

2020 ◽  
Author(s):  
Danning Fu
Keyword(s):  
United States ◽  
21St Century ◽  
Radiant Energy ◽  
Cloud Amount ◽  
Surface Energy Budget ◽  
Surface Radiation ◽  
Radiation Budget ◽  
Model Simulations ◽  
Surface Radiation Budget ◽  
Cloud Water Content

<p>The surface radiation budget is defined by the difference between the downward and upward components of shortwave and thermal infrared longwave radiation at the surface. The instability of the surface radiation budget plays a significant role in climate change and variability through the modulation of temperature, precipitation, atmospheric circulation, etc. Clouds are believed to be a key factor to regulate such energy imbalance at the surface, as they generally reflect shortwave radiation from the sun and emit infrared radiation. Specifically, we are going to focus on the continental United States and answer the following questions: How is the surface radiation budget varied with time and space in the observations? How do clouds impact variations of surface radiation budget? How do state-of-the-art global climate models capture these observed features? What can they tell us about future changes in the surface radiation budget?</p><p>To investigate these questions, the NASA Clouds and the Earth's Radiant Energy System (CERES) observations will be used, along with model simulations from Phase 6 of the Coupled Model Intercomparison Project (CMIP6). We will first focus on the surface radiation budget from CERES observations in the 21st century, and examine their seasonal cycles, spatial patterns, long-term trends, and interannual variations over the continental United States. More importantly, we are going to investigate how cloud variability, including cloud types, cloud amount and cloud water content, influences the surface radiation budget. Then the CMIP6 historical simulations will be compared with CERES observations over the same time period. In addition, the CMIP6 future scenario simulations will be used to investigate how the surface radiation budget changes from the middle and late 21st century to the early 21st century. Overall, this study will help us to better understand the cloud and radiation variations in the past, as well as build credibility in the hindcast and future projections of surface energy budget over the continental United States.</p>

Evaluation of net shortwave radiation over China with a regional climate model

2020 ◽  
Vol 80 (2) ◽  
pp. 147-163
Author(s):  
X Liu ◽  
Y Kang ◽  
Q Liu ◽  
Z Guo ◽  
Y Chen ◽  
...  
Keyword(s):  
Regional Climate Model ◽  
Climate Model ◽  
Regional Climate ◽  
Radiant Energy ◽  
Energy System ◽  
Radiation Budget ◽  
Shortwave Radiation ◽  
Monsoon Region ◽  
Clear Sky ◽  
Sky Conditions

The regional climate model RegCM version 4.6, developed by the European Centre for Medium-Range Weather Forecasts Reanalysis, was used to simulate the radiation budget over China. Clouds and the Earth’s Radiant Energy System (CERES) satellite data were utilized to evaluate the simulation results based on 4 radiative components: net shortwave (NSW) radiation at the surface of the earth and top of the atmosphere (TOA) under all-sky and clear-sky conditions. The performance of the model for low-value areas of NSW was superior to that for high-value areas. NSW at the surface and TOA under all-sky conditions was significantly underestimated; the spatial distribution of the bias was negative in the north and positive in the south, bounded by 25°N for the annual and seasonal averaged difference maps. Compared with the all-sky condition, the simulation effect under clear-sky conditions was significantly better, which indicates that the cloud fraction is the key factor affecting the accuracy of the simulation. In particular, the bias of the TOA NSW under the clear-sky condition was <±10 W m-2 in the eastern areas. The performance of the model was better over the eastern monsoon region in winter and autumn for surface NSW under clear-sky conditions, which may be related to different levels of air pollution during each season. Among the 3 areas, the regional average biases overall were largest (negative) over the Qinghai-Tibet alpine region and smallest over the eastern monsoon region.

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