Land Surface Radiation Budget Research Articles

Evaluation of nine machine learning methods for estimating daily land surface radiation budget from MODIS satellite data

ABSTRACT The all-wave net radiation (Rn) at the land surface represents surface radiation budget and plays an important role in the Earth's energy and water cycles. Many studies have been conducted to estimate from satellite top-of-atmosphere (TOA) data using various methods, particularly the application of machine learning (ML) and deep learning (DL). However, few studies have been conducted to provide a comprehensive evaluation about various ML and DL methods in retrieving. Based on extensive in situ measurements distributed at mid-low latitudes, the corresponding Moderate Resolution Imaging Spectroradiometer (MODIS) TOA observations, and the daily from the fifth generation of European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) used as a priori knowledge, this study assessed nine models for daily estimation, including six classic ML methods (random forest -RF, adaptive boosting - Adaboost, extreme gradient boosting -XGBoost, multilayer perceptron -MLP, radial basis function neural network -RBF, and support vector machine -SVM) and three DL methods (multilayer perceptron neural network with stacked autoencoders -SAE, deep belief network -DBN and residual neural network -ResNet). The validation results showed that the three DL methods were generally better than the six ML methods except XGBoost, although they all performed poorly in certain conditions such as winter days, rugged terrain, and high elevation. ResNet had the most robust performance across different land cover types, elevations, seasons, and latitude zones, but it has disadvantages in practice because of its highly configurable implementation environment and low computational efficiency. The estimated daily values from all nine models were more accurate than the corresponding Global LAnd Surface Satellite (GLASS) product.

Converted vegetation type regulates the vegetation greening effects on land surface albedo in arid regions of China

Land surface albedo (LSA) is a key parameter in the process of vegetation feedback to climate due to its decisive role in land surface radiation budget. However, our current knowledge on the relationship between LSA and vegetation changes is limited by one-sided attention to sole vegetation growth change or land use/land cover change (LULCC). How vegetation growth or LULCC respectively contributes to LSA change under their interactions remains poorly quantified. In this study, the arid and semi-arid areas of China (ASAC) with profound vegetation changes were selected to tackle this problem. The LSA showed a general downward trend (-0.00009 year−1) during 2000-2018 in response to ASAC's wide-range greening (0.0086 m2m−2year−1). Pairwise comparison analysis revealed that under the same unit of vegetation coverage change, the LSA change magnitude was contracted when grassland was converted to cultivated land or forest land, while the conversions of forest land to other vegetations led to an amplified change magnitude of LSA. Vegetation type directly leads to a difference in LSA change magnitude under greening due to their distinct canopy spectral characteristics. Grassland possesses lower LAI and its pixel-level LSA is more prone to be contaminated by background coverage, while LSA of forest land contains more vegetation canopy signal. In most areas where vegetation type converted, greening dominated LSA change with a contribution rate up to 98.14 %, except for the conversion of grassland to forest land, where LULCC accounted for about 66.38 % of LSA change. This study could improve the estimation of LSA from LAI, which is useful for optimizing vegetation-climate interaction models.

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

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), and daily mean 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 522 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, 26.77 W m−2 (31.54 %), and 1.16 W m−2 (1.37 %), 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 (MERRA-2), illustrate that our AVHRR Rn retrievals have the best accuracy under most of the considered surface and atmospheric conditions, especially thick-cloud or hazy conditions. However, the performance of the model needs to be further improved for the snow/ice cover surface. 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. The long-term record (1981–2019) of the AVHRR Rn product shows its value in climate change studies. This dataset is freely available at https://doi.org/10.5281/zenodo.5546316 for 1981–2019 (Xu et al., 2021).

Observational evaluation of global climate model simulations of arctic sea ice and adjacent land pertaining to the radiative effects of frozen hydrometeors

This study explores the linkage of frozen hydrometeors (cloud ice and falling ice/snow) with sea ice and adjacent lands through surface energy budget using model-observation comparisons to quantify the roles of the falling ice radiative effects (FIREs) in determining the extent and thickness of Arctic sea ice and adjacent land surface radiation budget and land surface skin (Ts) and surface (SAT) temperatures. The Coupled Model Intercomparison Project Phase 5 (CMIP5) models without FIREs tend to produce underestimated downward longwave radiation, and overestimated shortwave downward radiation and surface albedo, resulting in too-cold skin temperature (TS) and surface air temperature (SAT) and overestimated sea ice concentration (SIC) and thickness (SIT). By comparing two simulations of late 20th Century climate from CESM1-CAM5 model with inclusion and exclusion of FIREs, it is found that TS, SAT, radiation, SIC, and SIT and their seasonal cycles are improved with the inclusion of FIREs. Exclusion of FIREs results in underestimated net downward longwave radiative flux, which is highly correlated with overestimated surface albedo, colder TS, and SAT with a confidence level at 99% (p < 1%). These biases in CESM1-CAM5 resemble those in CMIP5 models without FIREs. With the inclusion of FIREs, the SIC bias is reduced by 2%–15% in summer, while the SIT is improved up to 90% in winter despite little improvement in SIC. These findings suggest a potential link among the increased downward longwave flux, decreased downward shortwave flux, and decreased surface air and land surface temperatures locally, which then drives SIC melting and SIT thinning when FIREs are included relative to when FIREs are excluded. It is suggested that the roles played by FIREs over the Arctic regions are of the same order of importance as those over the Southern Oceans despite the differences in geography and impact of human activity.

Effects of Cropland Expansion on the Regional Land Surface Radiative Energy Balance and Heat Fluxes in Northern China

Land use change can impact the land surface radiation budget and energy balance by changing surface biophysical processes. Based on satellite remote sensing data and land use data from 2000 to 2015, we quantitatively estimated radiative forcing induced by cropland expansion during the early 21st century in northern China. The results showed that heat flux from the land surface to the atmosphere due to cropland expansion was quite variable in different climate zones. The heat flux increased in humid North China, whereas it decreased in arid Northwest China, semiarid Inner Mongolia, and humid Northeast China. Cropland expansion from woodland areas led to a general decline in the land surface heat flux to the atmosphere, which led to a cooling effect on the climate. The surface heat flux to the atmosphere due to cropland expansion in grassland areas displayed significant variations in different climate zones. The surface heat flux decreased only in humid Northeast China and arid Northwest China. The net surface radiation and latent heat flux both increased when grasslands were changed into cropland, but to different extents, which produced the differences in the surface heat flux to the atmosphere between different zones.

Surface Albedo Assimilation and Its Impact on Surface Radiation Budget in Beijing

Surface albedo is a crucial parameter in land surface radiation budget. As bias exists between the model simulated and observed surface albedo, data assimilation is an important method to improve the simulation results. Moreover, surface albedo is associated with the wavelength of the sunlight. So, solar radiation partitioning is important to parameterize the surface albedo. In this paper, the moderate resolution imaging spectroradiometer- (MODIS-) retrieved direct visible, direct near-infrared, diffuse visible, and diffuse near-infrared surface albedos were assimilated into the integrated urban land model (IUM). The solar radiation partitioning method was introduced to parameterize the surface albedo. Based on the albedo data from MODIS and the solar radiation partitioning method, the surface albedo data set for the Beijing municipal area was generated. Based on the surface albedo data set and the IUM, the impacts of the surface albedo on the surface radiation budget were discussed quantitatively. Surface albedo is inversely proportional to the net radiation. For urban areas, after assimilation, the annual average net radiation decreases about 5.6%. For cropland, grassland, and forest areas, after assimilation, the annual average net radiations increase about 20.2%, 24.3%, and 18.7%, respectively.

Spatial Downscaling of MSG Downward Shortwave Radiation Product Under Clear-Sky Condition

Downward shortwave radiation (DSR) plays a very important role in land surface radiation budget and land-surface processes modeling. Although there are several radiation products developed based on satellite observations, the coarse spatial resolution greatly limits their applications in regional or local scales. To get high-resolution and accuracy-reliable DSR data, a practical downscaling method for clear-sky condition was proposed by using the scale-invariant relationship of the radiative transfer process to decompose the global radiation into direct and diffuse components at horizontal level and conducting topographic correction finally. Based on this method, the time series of Meteosat Second Generation (MSG) DSR product covering part of Navarre province in the northern Spain was disaggregated into 30-m level with the use of the ALOS World 3D-30m digital elevation model (DEM) data. The downscaled results not only presented high spatial heterogeneity with respect to the changes in surface topography but also showed reasonable values at different times over different days in one year. The in situ validation indicated that the hourly downscaled DSR had quite high correlation with the surface measurements at each day with the coefficient of determination above 0.97 and the root-mean-squared error lower than 35 W/m2. Overall, the evaluation allows concluding on the proposed method that can be a good way to get reliable and high-resolution DSR data from coarse-resolution DSR product under clear-sky condition.

Spatial, temporal, and spectral variations in albedo due to vegetation changes in China’s grasslands

Changes in Earth’s albedo due to vegetation dynamics, snow cover, and land cover change have attracted much attention. However, the effects of vegetation dynamics on albedo have not been comprehensively documented according to its spatial (regional), temporal (within growing season), and spectral (visible, near-infrared, and shortwave) characteristics. This study examined the effects of vegetation greenness on albedo from 2000 to 2014 in China’s grasslands, which have considerable intra- and inter-annual variations, using remote sensing-based albedo and two-band Enhanced Vegetation Index (EVI2) data. Generally, we found an insignificant negative correlation between the shortwave (SW) albedo and EVI2 for grasslands in China. However, the visible (VIS) albedo was more sensitive to changes in vegetation greenness than near-infrared (NIR) albedo in China’s grasslands. The relationship between the NIR albedo and EVI2 was more complicated, especially in the Tibetan Plateau (TP), where the correlation was negative in the early growing season and positive in the late growing season; while the correlation between the NIR albedo and EVI2 was always negative in main part of Inner Mongolia (IM). The different albedo-EVI2 relationships in IM and TP may be related to differences in soil albedos. The higher sensitivity of the SW albedo to vegetation greenness change in IM, the stronger effect on land surface radiation budget. Our finding about vegetation-induced changes in albedo differ in space, time and spectral bands is expected to contribute to the improvement of land surface models.

Estimation of all-sky instantaneous surface incident shortwave radiation from Moderate Resolution Imaging Spectroradiometer data using optimization method

Surface incident shortwave radiation (ISR) is a crucial parameter in the land surface radiation budget. Many reanalysis, observation-based, and satellite-derived global radiation products have been developed but often have insufficient accuracy and spatial resolution for many applications. In this paper, we propose a method based on a radiative transfer model for estimating surface ISR from Moderate Resolution Imaging Spectroradiometer (MODIS) Top of Atmosphere (TOA) observations by optimizing the surface and atmospheric variables with a cost function. This approach consisted of two steps: retrieving surface bidirectional reflectance distribution function parameters, aerosol optical depth (AOD), and cloud optical depth (COD); and subsequently calculating surface ISR. Validation against measurements at seven Surface Radiation Budget Network (SURFRAD) sites resulted in an R2 of 0.91, a bias of −6.47 W/m2, and a root mean square error (RMSE) of 84.17 W/m2 (15.12%) for the instantaneous results. Validation at eight high-latitude snow-covered Greenland Climate Network (GC-Net) sites resulted in an R2 of 0.86, a bias of −21.40 W/m2, and an RMSE of 84.77 W/m2 (20.96%). These validation results show that the proposed method is much more accurate than the previous studies (usually with RMSEs of 80-150 W/m2). We further investigated whether incorporating additional satellite products, such as the MODIS surface broadband albedo (MCD43), aerosol (MOD/MYD04), and cloud products (MOD/MYD06), as constraints in the cost function would improve the accuracy. When the AOD and COD estimates were constrained, RMSEs were reduced to 62.19 W/m2 (12.12%) and 71.70 W/m2 (17.74%) at the SURFRAD and GC-Net sites, respectively. This algorithm could estimate surface ISR with MODIS TOA observations over both snow-free and seasonal/permanent snow-covered surfaces. The algorithm performed well at high-latitude sites, which is very useful for radiation budget research in the polar regions.

Effect of Solar-Cloud-Satellite Geometry on Land Surface Shortwave Radiation Derived from Remotely Sensed Data

Clouds and their associated shadows are major obstacles to most land surface remote sensing applications. Meanwhile, solar-cloud-satellite geometry (SCSG) makes the effect of clouds and shadows on derived land surface biophysical parameters more complicated. However, in most existing studies, the SCSG effect has been frequently neglected although it is pointed out by many works that SCSG effect is a noticeable problem, especially in the field of land surface radiation budget. Taking shortwave downward radiation (SWDR) as a testing variable, this study quantified the SCSG effect on the derived SWDR, and proposed an operational scheme to correct the big effect. The results demonstrate that the proposed correcting scheme is very effective and works very well. It is revealed that a significant under- or overestimation is detected in retrieved SWDR if the SCSG effect is ignored. Typically, the induced error in SWDR can reach up to 80%. The scheme and findings of this study are expected to be inspirational for the land surface remote sensing community, wherein solar-cloud-satellite geometry is an unavoidable issue.

Generating Global LAnd Surface Satellite incident shortwave radiation and photosynthetically active radiation products from multiple satellite data

Incident shortwave radiation (ISR) at the surface is an essential parameter in the land surface radiation budget and in many land surface process models. Incident photosynthetically active radiation (PAR) is required by nearly all terrestrial ecosystem models. Although several global radiation products from numeric models and satellite observations have been released, their coarse spatial resolutions and low accuracy, especially at high altitude regions, limit their applications in land community. In this study, the Global LAnd Surface Satellite (GLASS) ISR and PAR products are developed based on an improved look-up table method from 2008 to 2010 at a 5km spatial resolution and a 3hour temporal resolution, the first global radiation products at such high resolutions, from multiple polar-orbiting and geostationary satellite data, including the Moderate Resolution Imaging Spectroradiometer (MODIS), the Meteosat Second Generation (MSG2) SEVIRI, the Multi-functional Transport Satellite (MTSAT)-1R, and the Geostationary Operational Environmental Satellite (GOES) Imager. The look-up table algorithm primarily considers the influences of surface elevation, atmospheric water vapor because sensitivity experiments indicate that ISR and PAR are more sensitive to surface elevation, and less sensitive to atmospheric profiles and ozone amount. The water vapor absorption has strong impact on ISR but weak on PAR. Moreover, the surface bi-directional reflectance distribution function (BRDF) is taken into account to retrieve spatially and temporally continuous surface reflectances from the geostationary satellite observations, which is an input parameter in the revised look-up table method. Ground-based measurement data from 34 sites are used to validate the improved algorithm and the GLASS products. The validation results of the instantaneous ISR and PAR products at all validation sites are notably good with coefficient of determination values of 0.83 and 0.84, respectively, and root mean square error values of 115.0Wm−2 and 49.0Wm−2, respectively. We also aggregate the GLASS ISR product for comparing with the corresponding ISCCP and CERES data at 7 SURFRAD sites and demonstrate that the GLASS ISR product is more accurate. The GLASS ISR and PAR products have been made publicly available.

Validation of the moderate-resolution imaging spectroradiometer land surface emissivity products over the Taklimakan Desert

Land surface emissivity is a key parameter in estimating the land surface radiation budget. The validation of the moderate-resolution imaging spectroradiometer (MODIS) land sur- face emissivity with field measurements is rarely performed.In this study, a field measurement was performed over the central part of the Taklimakan Desert for the validation of the MODIS land surface emissivity products (MOD11B1) Version 4 (V4.1) and Version 5 (V5). The homogeneity of two validation sites was verified using the advanced spaceborne thermal emission and reflection radiometer (ASTER) land surface temperature and emissivity acquired closely before and after the overpass of MODIS. MOD11B1 V4.1 and V5 emissivity data for bands 29, 31, and 32 were compared to the emissivity calculated from the field measured emissivity spectra convolved with the filter function of the MODIS bands 29 (8.52 μm), 31 (11.03 μm), and 32 (12.04 μm). The comparison results indicate that the V4.1 emissivity data agree well with the fieldmeasurements, with meanabsolute differences of 0.017 and 0.007 for site 1 and site 2,respec- tively, and the mean absolute differences of the V5 emissivity data were 0.034 and 0.033 for site 1 and site 2, respectively. The data version used must be considered when MOD11B1 is used in real applications, especially for time series analysis. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI. (DOI: 10.1117/1.JRS.8.083675)

Scenario Simulation of the Influence of Land Use Change on the Regional Temperature in a Rapidly Urbanizing Region: A Case Study in Southern-Jiangsu, China

It has been shown that land use change in urbanized region, especially urban land expansion, will influence regional climate. However, there has been very little research on the climate effects of the future land use change in a rapidly urbanizing region. Taking the southern part of Jiangsu province in China as the study area and through a scenario analysis, the influence of land use change on the regional temperature was analyzed from the perspective of land surface radiation budget and energy balance. The results indicated that (1) the monthly average temperature is significantly higher under the Rapid Economic Growth (REG) scenario than under the Cooperate Environmental Sustainability (CES) scenario in 2050, especially in the hottest month (July). (2) The range of high-temperature regions is much wider under the REG scenario than it is under the CES scenario in 2050. (3) The land surface net radiation and latent heat flux are two key factors through which land use change influences the regional temperature in the study area, and the latent heat flux plays a dominant role. (4) Land use change mainly influences the land surface net radiation by altering the land surface albedo and emissivity. These results are helpful to mitigate regional climate change effects caused by land use change.

Dynamic Emulations of Surface Radiation Components During Day and Night Under all Sky and Surface Conditions Using Temporal Neural Networks

Modeling of land surface radiation budget and its components is essential to a better understanding of soil-vegetation-atmosphere interactions. Time Delay Neural Network (TDNN) and Time Lag Recurrent Network (TLRN) models were used to emulate all the hourly surface radiation components for a temperate peatland during day and night under all-sky and -surface conditions. Sensitivity analyses of full versus reduced models, daytime versus nighttime periods, and TDNN versus TLRN models were carried out using training-, cross-validation, and testing-derived metrics of root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R2) for each of the components. The full daytime temporal neural network models performed best based on RMSE of 2.3 W m−2 in downwelling longwave radiation to 112.2 W m−2 in upwelling shortwave radiation; R2 of 0.52 in downwelling longwave radiation to 0.88 in net shortwave radiation and net radiation; and MAE of 1.73 W m−2 in downwelling longwave radiation to 90.57 W m−2 in upwelling shortwave radiation. The best nighttime TDNN models led to RMSE values that ranged from 4.4 W m−2 in downwelling longwave radiation to 9.3 W m−2 in upwelling longwave radiation; R2 values that ranged from 0.38 in net longwave radiation to 0.60 in downwelling longwave radiation; and MAE values that ranged from 4.1 W m−2 in downwelling longwave radiation to 8.1 W m−2 in upwelling longwave radiation. Temporal neural networks used in this study appear to be a promising approach to predict nonlinear behaviors of the daytime and nighttime surface radiation components.

Validation of global land surface satellite (GLASS) downward shortwave radiation product in the rugged surface

The downward shortwave radiation (DSR) is an essential parameter of land surface radiation budget and many land surface models that characterize hydrological, ecological and biogeochemical processes. The new Global LAnd Surface Satellite (GLASS) DSR datasets have been generated recently using multiple satellite data in China. This study investigates the performances of direct comparison approach, which is mostly used for validation of surface insolation retrieved from satellite data over the plain area, and indirect comparison approach, which needs a fine resolution map of DSR as reference, for validation of GLASS DSR product in time-steps of 1 and 3 hours over three Chinese Ecosystem Research Network sites located in the rugged surface. Results suggest that it probably has a large uncertainty to assess GLASS DSR product using the direct comparison method between GLASS surface insolation and field measurements over complex terrain, especially at Mt. Gongga 3,000 m station with root mean square error of 279.04 and 229.06 W/m(2) in time-steps of 1 and 3 hours, respectively. Further improvement for validation of GLASS DSR product in the rugged surface is suggested by generation of a fine resolution map of surface insolation and comparison of the aggregated fine resolution map with GLASS product in the rugged surface. The validation experience demonstrates that the GLASS DSR algorithm is satisfactory with determination coefficient of 0.83 and root mean square error of 81.91W/m(2) over three Chinese Ecosystem Research Network sites, although GLASS product overestimates DSR compared to the aggregated fine resolution map of surface insolation.

Discussion on the Problems on Land SurfaceAlbedo Retrieval by Remote Sensing Data

The effects of albedo on land surface radiation budget and regional and global climate were analyzed in this paper. Retrieving accurately land surface albedo by remote sensing data is the precondition of other land surface parameters retrieval, so albedo is the first important parameter in land surface parameters retrieval by remote sensing data. On the basis of the concept of albedo and other terms often confused were demonstrated, the steps and solutions to main difficulties of retrieving land surface albedo by remote sensing data were given and discussed briefly. At last, we analyzed the process of calculating albedo in present land surface process models and compared the results with the products related to MODIS. The integration of remote sensing and land surface process models and global climate models was emphasized.

Evaluation of ASTER and MODIS land surface temperature and emissivity products using long-term surface longwave radiation observations at SURFRAD sites

Land surface temperature (LST) and emissivity are key parameters in estimating the land surface radiation budget, a major controlling factor of global climate and environmental change. In this study, Terra Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) and Aqua MODerate resolution Imaging Spectroradiometer (MODIS) Collection 5 LST and emissivity products are evaluated using long-term ground-based longwave radiation observations collected at six Surface Radiation Budget Network (SURFRAD) sites from 2000 to 2007. LSTs at a spatial resolution of 90 m from 197 ASTER images during 2000–2007 are directly compared to ground observations at the six SURFRAD sites. For nighttime data, ASTER LST has an average bias of 0.1 °C and the average bias is 0.3 °C during daytime. Aqua MODIS LST at 1 km resolution during nighttime retrieved from a split-window algorithm is evaluated from 2002 to 2007. MODIS LST has an average bias of − 0.2 °C. LST heterogeneity (defined as the Standard Deviation, STD, of ASTER LSTs in 1 × 1 km 2 region, 11 × 11 pixel in total) and instrument calibration error of pyrgeometer are key factors impacting the ASTER and MODIS LST evaluation using ground-based radiation measurements. The heterogeneity of nighttime ASTER LST is 1.2 °C, which accounts for 71% of the STD of the comparison, while the heterogeneity of the daytime LST is 2.4 °C, which accounts for 60% of the STD. Collection 5 broadband emissivity is 0.01 larger than that of MODIS Collection 4 products and ASTER emissivity. It is essential to filter out the abnormal low values of ASTER daily emissivity data in summer time before its application.

Five years of shortwave radiation budget measurements at a continental land site in southeastern Australia

A new data set of near‐continuous, high‐quality measurements of the components of the land surface radiation budget (SRB) at a uniform site in Australia is described. Its intended uses include the validation of SRB parameters predicted by general circulation models (GCMs), the validation of SRB parameters remotely sensed by satellites, and the study of surface processes. The site at Uardry (145.30°E, 34.39°S) near the town of Hay is the first in Australia's Continental Integrated Ground‐truth Site Network. It is located in extremely flat and homogeneous natural terrain, with grassland cover that is characteristic of much of the Australian continent, beneath an atmosphere that is characterized by low amounts of water vapor and aerosol. The measurements include the downwelling and upwelling shortwave flux densities. The upwelling measurements are made at several points across the site in order to sample the variability on a scale of 1 km, which is approximately the resolution of many current and planned satellite‐based sensors. The paper describes the shortwave component of the data set, shows some internal quality checks, presents some statistical summaries, and makes comparisons with the albedos used in GCMs. It then discusses features of the temporal variation of the albedos derived from the data set in terms of their impact on the remote sensing of land albedo. In particular, significant morning‐afternoon asymmetry is present in the diurnal variation on some days, and changes of 20% in albedo occur over a few days. Both of these characteristics violate common assumptions of algorithms used to retrieve land reflective properties from satellite measurements. Consideration of their effect is merited.

Downwelling Longwave Fluxes at Continental Surfaces-A Comparison of Observations with GCM Simulations and Implications for the Global Land-Surface Radiation Budget

Abstract Previous work suggests that general circulation (global climate) models have excess net radiation at land surfaces, apparently due to overestimates in downwelling shortwave flux and underestimates in upwelling long-wave flux. Part of this excess, however, may be compensated for by an underestimate in downwelling longwave flux. Long term observations of the downwelling longwave component at several land stations in Europe, the United States, Australia, and Antarctica suggest that climate models (four are used, as in previous studies) underestimate this flux component on an annual basis by up to 10 W m−2, yet with low statistical significance. It is probable that the known underestimate in boundary-layer air temperature contributes to this, as would low model cloudiness and neglect of minor gases such as methane, nitrogen oxide, and the freons. The bias in downwelling longwave flux, together with those found earlier for downwelling shortwave and upwlling long-wave fluxes, are consistent with the mo...