scholarly journals Evaluation of the Surface Radiation Budget in the Atmospheric Component of the Hadley Centre Global Environmental Model (HadGEM1)

2008 ◽  
Vol 21 (18) ◽  
pp. 4723-4748 ◽  
Author(s):  
A. Bodas-Salcedo ◽  
M. A. Ringer ◽  
A. Jones
Keyword(s):  
Land Surface ◽  
Surface Albedo ◽  
Longwave Radiation ◽  
Surface Radiation ◽  
Radiation Budget ◽  
Surface Radiation Budget ◽  
Global Environmental ◽  
Downward Longwave Radiation ◽  
Global Dimming ◽  
Hadley Centre

Abstract The partitioning of the earth radiation budget (ERB) between its atmosphere and surface components is of crucial interest in climate studies as it has a significant role in the oceanic and atmospheric general circulation. An analysis of the present-day climate simulation of the surface radiation budget in the atmospheric component of the new Hadley Centre Global Environmental Model version 1 (HadGEM1) is presented, and the simulations are assessed by comparing the results with fluxes derived from satellite data from the International Satellite Cloud Climatology Project (ISCCP) and ground measurements from the Baseline Surface Radiation Network (BSRN). Comparisons against radiative fluxes from satellite and ground observations show that the model tends to overestimate the surface incoming solar radiation (Ss,d). The model simulates Ss,d very well over the polar regions. Consistency in the comparisons against BSRN and ISCCP-FD suggests that the ISCCP-FD database is a good test for the performance of the surface downwelling solar radiation in climate model simulations. Overall, the simulation of downward longwave radiation is closer to observations than its shortwave counterpart. The model underestimates the downward longwave radiation with respect to BSRN measurements by 6.0 W m−2. Comparisons of land surface albedo from the model and estimates from the Moderate Resolution Imaging Spectroradiometer (MODIS) show that HadGEM1 overestimates the land surface albedo over deserts and over midlatitude landmasses in the Northern Hemisphere in January. Analysis of the seasonal cycle of the land surface albedo in different regions shows that the amplitude and phase of the seasonal cycle are not well represented in the model, although a more extensive validation needs to be carried out. Two decades of coupled model simulations of the twentieth-century climate are used to look into the model’s simulation of global dimming/brightening. The model results are in line with the conclusions of the studies that suggest that global dimming is far from being a uniform phenomenon across the globe.

scholarly journals Estimation of the earth's albedo over some selected area the Republic of Chad

2018 ◽  
Vol 6 (2) ◽  
pp. 64
Author(s):  
Zakaria Marouf BARKA ◽  
Théophile Lealea ◽  
Rene Tchinda
Keyword(s):  
Land Surface ◽  
Surface Albedo ◽  
Ground Surface ◽  
Absolute Error ◽  
Surface Radiation ◽  
Radiation Budget ◽  
Percentage Error ◽  
Surface Radiation Budget ◽  
Recorded Data ◽  
The Republic

Surface albedo is one parameter of the climate variables. It influences the surface radiation budget for a given site. The availability of surface albedo data at both temporally and spatially levels are needed. In the lack of ground recorded values of albedo, we have to estimate surface albedo from the climatic variables. The model generated in this study enables the continuous observation of land surface albedo through relative model established from the multivariate regression method. From satellite recorded data, we estimate the ground surface albedo for some selected sites. The result were satisfactory with the root mean square error (RMSE) is 0.035. The Mean Absolute Error (MAE) was computed and indicated to be as low as 0.027 and mean absolute percentage error (MAPE) is 7.58.  

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

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Chunlei Meng
Keyword(s):  
Solar Radiation ◽  
Land Surface ◽  
Surface Albedo ◽  
Near Infrared ◽  
Surface Radiation ◽  
Radiation Budget ◽  
Net Radiation ◽  
Annual Average ◽  
Data Set ◽  
Surface Radiation Budget

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.

scholarly journals Estimation of Long-Term Surface Downward Longwave Radiation over the Global Land from 2000 to 2018

2021 ◽  
Vol 13 (9) ◽  
pp. 1848
Author(s):  
Chunjie Feng ◽  
Xiaotong Zhang ◽  
Yu Wei ◽  
Weiyu Zhang ◽  
Ning Hou ◽  
...  
Keyword(s):  
Water Vapor ◽  
Spatial Resolution ◽  
Land Surface ◽  
Longwave Radiation ◽  
Surface Radiation ◽  
Radiation Budget ◽  
Mean Bias Error ◽  
Downward Longwave Radiation ◽  
Global Land

It is of great importance for climate change studies to construct a worldwide, long-term surface downward longwave radiation (Ld, 4–100 μm) dataset. Although a number of global Ld datasets are available, their low accuracies and coarse spatial resolutions limit their applications. This study generated a daily Ld dataset with a 5-km spatial resolution over the global land surface from 2000 to 2018 using atmospheric parameters, which include 2-m air temperature (Ta), relative humidity (RH) at 1000 hPa, total column water vapor (TCWV), surface downward shortwave radiation (Sd), and elevation, based on the gradient boosting regression tree (GBRT) method. The generated Ld dataset was evaluated using ground measurements collected from AmeriFlux, AsiaFlux, baseline surface radiation network (BSRN), surface radiation budget network (SURFRAD), and FLUXNET networks. The validation results showed that the root mean square error (RMSE), mean bias error (MBE), and correlation coefficient (R) values of the generated daily Ld dataset were 17.78 W m−2, 0.99 W m−2, and 0.96 (p < 0.01). Comparisons with other global land surface radiation products indicated that the generated Ld dataset performed better than the clouds and earth’s radiant energy system synoptic (CERES-SYN) edition 4.1 dataset and ERA5 reanalysis product at the selected sites. In addition, the analysis of the spatiotemporal characteristics for the generated Ld dataset showed an increasing trend of 1.8 W m−2 per decade (p < 0.01) from 2003 to 2018, which was closely related to Ta and water vapor pressure. In general, the generated Ld dataset has a higher spatial resolution and accuracy, which can contribute to perfect the existing radiation products.

scholarly journals CLARA-A2: the second edition of the CM SAF cloud and radiation data record from 34 years of global AVHRR data

2017 ◽  
Vol 17 (9) ◽  
pp. 5809-5828 ◽  
Author(s):  
Karl-Göran Karlsson ◽  
Kati Anttila ◽  
Jörg Trentmann ◽  
Martin Stengel ◽  
Jan Fokke Meirink ◽  
...  
Keyword(s):  
Surface Albedo ◽  
Surface Radiation ◽  
Radiation Budget ◽  
Climate Data ◽  
Ground Segment ◽  
Surface Radiation Budget ◽  
Avhrr Data ◽  
Data Record ◽  
Development And Validation ◽  
Climate Data Record

Abstract. The second edition of the satellite-derived climate data record CLARA (The CM SAF Cloud, Albedo And Surface Radiation dataset from AVHRR data – second edition denoted as CLARA-A2) is described. The data record covers the 34-year period from 1982 until 2015 and consists of cloud, surface albedo and surface radiation budget products derived from the AVHRR (Advanced Very High Resolution Radiometer) sensor carried by polar-orbiting, operational meteorological satellites. The data record is produced by the EUMETSAT Climate Monitoring Satellite Application Facility (CM SAF) project as part of the operational ground segment. Its upgraded content and methodology improvements since edition 1 are described in detail, as are some major validation results. Some of the main improvements to the data record come from a major effort in cleaning and homogenizing the basic AVHRR level-1 radiance record and a systematic use of CALIPSO-CALIOP cloud information for development and validation purposes. Examples of applications studying decadal changes in Arctic summer surface albedo and cloud conditions are provided.

scholarly journals Evaluation of Six High-Spatial Resolution Clear-Sky Surface Upward Longwave Radiation Estimation Methods with MODIS

2020 ◽  
Vol 12 (11) ◽  
pp. 1834
Author(s):  
Boxiong Qin ◽  
Biao Cao ◽  
Hua Li ◽  
Zunjian Bian ◽  
Tian Hu ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Hybrid Methods ◽  
Longwave Radiation ◽  
Surface Radiation ◽  
Radiation Budget ◽  
Estimation Methods ◽  
Mean Bias Error ◽  
Clear Sky ◽  
Surface Radiation Budget

Surface upward longwave radiation (SULR) is a critical component in the calculation of the Earth’s surface radiation budget. Multiple clear-sky SULR estimation methods have been developed for high-spatial resolution satellite observations. Here, we comprehensively evaluated six SULR estimation methods, including the temperature-emissivity physical methods with the input of the MYD11/MYD21 (TE-MYD11/TE-MYD21), the hybrid methods with top-of-atmosphere (TOA) linear/nonlinear/artificial neural network regressions (TOA-LIN/TOA-NLIN/TOA-ANN), and the hybrid method with bottom-of-atmosphere (BOA) linear regression (BOA-LIN). The recently released MYD21 product and the BOA-LIN—a newly developed method that considers the spatial heterogeneity of the atmosphere—is used initially to estimate SULR. In addition, the four hybrid methods were compared with simulated datasets. All the six methods were evaluated using the Moderate Resolution Imaging Spectroradiometer (MODIS) products and the Surface Radiation Budget Network (SURFRAD) in situ measurements. Simulation analysis shows that the BOA-LIN is the best one among four hybrid methods with accurate atmospheric profiles as input. Comparison of all the six methods shows that the TE-MYD21 performed the best, with a root mean square error (RMSE) and mean bias error (MBE) of 14.0 and −0.2 W/m2, respectively. The RMSE of BOA-LIN, TOA-NLIN, TOA-LIN, TOA-ANN, and TE-MYD11 are equal to 15.2, 16.1, 17.2, 21.2, and 18.5 W/m2, respectively. TE-MYD21 has a much better accuracy than the TE-MYD11 over barren surfaces (NDVI < 0.3) and a similar accuracy over non-barren surfaces (NDVI ≥ 0.3). BOA-LIN is more stable over varying water vapor conditions, compared to other hybrid methods. We conclude that this study provides a valuable reference for choosing the suitable estimation method in the SULR product generation.

scholarly journals New Vegetation Albedo Parameters and Global Fields of Soil Background Albedo Derived from MODIS for Use in a Climate Model

2009 ◽  
Vol 10 (1) ◽  
pp. 183-198 ◽  
Author(s):  
Caroline J. Houldcroft ◽  
William M. F. Grey ◽  
Mike Barnsley ◽  
Christopher M. Taylor ◽  
Sietse O. Los ◽  
...  
Keyword(s):  
Land Surface ◽  
Surface Albedo ◽  
Climate Model ◽  
Global Fields ◽  
Global Environmental ◽  
Land Cover Map ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Surface Scheme ◽  
Hadley Centre

Abstract New values are derived for snow-free albedo of five plant functional types (PFTs) and the soil/litter substrate from data acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors on board Terra and Aqua. The derived albedo values are used to provide and test an improved specification of surface albedo for the land surface scheme known as the Joint U.K. Land Environment Simulator (JULES) that forms part of the Hadley Centre Global Environmental Model (HadGEM) climate model. The International Geosphere–Biosphere Programme (IGBP) global land cover map is used in combination with the MODIS albedo to estimate the albedo of each cover type in the IGBP classification scheme, from which the albedo values of the JULES PFTs are computed. The albedo of the soil/litter substrate, referred to as the soil background albedo, is derived from partially vegetated regions using a method that separates the vegetation contribution to the albedo signal from that of the soil/litter substrate. The global fields of soil background albedo produced using this method exhibit more realistic spatial variations than the soil albedo map usually employed in conjunction with the JULES model. The revised total shortwave albedo values of the PFTs are up to 8% higher than those in the existing HadGEM scheme. To evaluate the influence of these differences upon surface albedo in the climate model, differences are computed globally between mean monthly land surface albedo, modeled using the existing and revised albedo values, and MODIS data. Incorporating the revised albedo values into the model reduces the global rmse for snow-free July land surface albedo from 0.051 to 0.024, representing a marked improvement on the existing parameterization.

scholarly journals Predicting Downward Longwave Radiation for Various Land Use in All-Sky Condition: Northeast Florida

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Chi-Han Cheng ◽  
Fidelia Nnadi
Keyword(s):  
Land Use ◽  
Cloud Cover ◽  
Water Vapor Pressure ◽  
Longwave Radiation ◽  
Heat Fluxes ◽  
Surface Radiation ◽  
Radiation Budget ◽  
Seasonal Effects ◽  
Land Use Patterns ◽  
Downward Longwave Radiation

Accurate estimate of the surface longwave radiation is important for the surface radiation budget, which in turn controls evaporation and sensible heat fluxes. Regional land use changes can impact local weather conditions; for example, heterogeneous land use patterns and temporal changes in atmospheric circulation patterns would affect air temperature and water vapor pressure, which are more commonly used as inputs in existing models for estimating downward longwave radiation (LWd). In this study, first, we analyzed the cloud cover and land use covers impacts onLWd. Next,LWdon all-sky conditions were developed by using the existing land use-adapted model and cloud cover data from the region of Saint Johns River Water Management District (SJRWMD), FL. The results show that factors, such as, seasonal effects, cloud cover, and land use, are of importance in the estimation ofLWdand they cannot be ignored when developing a model forLWdprediction. The all-sky land use-adapted model with all factors taken into account performs better than other existing models statistically. The results of the statistical analyses indicated that the BIAS, RMSE, MAE, and PMRE are −0.18 Wm−2, 10.81 Wm−2, 8.00 Wm−2, and 2.30%; −2.61 Wm−2, 14.45 Wm−2, 10.64 Wm−2, and 3.19%; −0.07 Wm−2, 10.53 Wm−2, 8.03 Wm−2, and 2.27%; and −0.62 Wm−2, 13.97 Wm−2, 9.76 Wm−2, and 2.87% for urban, rangeland, agricultural, and wetland areas, respectively.

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).

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

&lt;p&gt;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&amp;#8208;Flux Data (ISCCP&amp;#8208;FD), the Global Energy and Water cycle Experiment&amp;#8208;Surface Radiation Budget (GEWEX&amp;#8208;SRB) and the Clouds and the Earth&amp;#8217;s Radiant Energy System&amp;#8208;Gridded Radiative Fluxes and Clouds (CERES&amp;#8208;FSW). But these products share the common features of coarse spatial resolutions (100-280 km) and lower validation accuracy.&lt;/p&gt;&lt;p&gt;Under such circumstance, we developed the methods of estimating long-term high spatial resolution all sky&amp;#160; 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).&lt;/p&gt;&lt;p&gt;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&lt;sup&gt;2 &lt;/sup&gt;and 18.15 W/m&lt;sup&gt;2 &lt;/sup&gt;for LWUP, -3.77 W/m&lt;sup&gt;2 &lt;/sup&gt;and 26.94 W/m&lt;sup&gt;2&lt;/sup&gt; for LWDN, and 0.70 W/m&lt;sup&gt;2 &lt;/sup&gt;and 26.70 W/m&lt;sup&gt;2&lt;/sup&gt; 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.&lt;/p&gt;

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