Evaluation of satellite land surface temperatures using ground measurements from surface radiation budget network

2008 ◽  
Author(s):  
Yunyue Yu ◽  
Dan Tarpley ◽  
M. K. Rama Varma Raja ◽  
Konstantin Vinnikov ◽  
Mitch D. Goldberg
Keyword(s):  
Land Surface ◽  
Surface Radiation ◽  
Radiation Budget ◽  
Surface Radiation Budget ◽  
Surface Temperatures ◽  
Land Surface Temperatures

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 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 Estimation of Land Surface Incident and Net Shortwave Radiation from Visible Infrared Imaging Radiometer Suite (VIIRS) Using an Optimization Method

2020 ◽  
Vol 12 (24) ◽  
pp. 4153
Author(s):  
Yi Zhang ◽  
Shunlin Liang ◽  
Tao He ◽  
Dongdong Wang ◽  
Yunyue Yu
Keyword(s):  
Land Surface ◽  
Infrared Imaging ◽  
Optimization Method ◽  
Surface Radiation ◽  
Radiation Budget ◽  
Shortwave Radiation ◽  
Modis Data ◽  
Surface Radiation Budget ◽  
Mean Square Errors ◽  
Key Parameter

Incident surface shortwave radiation (ISR) is a key parameter in Earth’s surface radiation budget. Many reanalysis and satellite-based ISR products have been developed, but they often have insufficient accuracy and resolution for many applications. In this study, we extended our optimization method developed earlier for the MODIS data with several major improvements for estimating instantaneous and daily ISR and net shortwave radiation (NSR) from Visible Infrared Imaging Radiometer Suite observations (VIIRS), including (1) an integrated framework that combines look-up table and parameter optimization; (2) enabling the calculation of net shortwave radiation (NSR) as well as daily values; and (3) extensive global validation. We validated the estimated ISR values using measurements at seven Surface Radiation Budget Network (SURFRAD) sites and 33 Baseline Surface Radiation Network (BSRN) sites during 2013. The root mean square errors (RMSE) over SURFRAD sites for instantaneous ISR and NSR were 83.76 W/m2 and 66.80 W/m2, respectively. The corresponding daily RMSE values were 27.78 W/m2 and 23.51 W/m2. The RMSE at BSRN sites was 105.87 W/m2 for instantaneous ISR and 32.76 W/m2 for daily ISR. The accuracy is similar to the estimation from MODIS data at SURFRAD sites but the computational efficiency has improved by approximately 50%. We also produced global maps that demonstrate the potential of this algorithms to generate global ISR and NSR products from the VIIRS data.

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 Toward an Operational Land Surface Temperature Algorithm for GOES

2013 ◽  
Vol 52 (9) ◽  
pp. 1974-1986 ◽  
Author(s):  
Donglian Sun ◽  
Yunyue Yu ◽  
Li Fang ◽  
Yuling Liu
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Regression Tree ◽  
Surface Radiation ◽  
Radiation Budget ◽  
Surface Radiation Budget ◽  
Shortwave Infrared ◽  
Dual Window

AbstractFor most land surface temperature (LST) regression algorithms, a set of optimized coefficients is determined by manual separation of the different subdivisions of atmospheric and surface conditions. In this study, a machine-learning technique, the regression tree (RT) technique, is introduced with the aim of automatically finding these subranges and the thresholds for the stratification of regression coefficients. The use of RT techniques in LST retrieval has the potential to contribute to the determination of optimal regression relationships under different conditions. Because of the lack of split-window channels for the Geostationary Operational Environmental Satellite (GOES) M–Q series (GOES-12–GOES-15, plus GOES-Q), a dual-window LST algorithm was developed by combining the infrared 11-μm channel with the shortwave-infrared (SWIR) 3.9-μm channel, which presents lower atmospheric absorption than does the infrared split-window channels (11 and 12 μm). The RT technique was introduced to derive the regression models under different conditions. The algorithms were used to derive the LST product from GOES observations and were evaluated against the 2004 Surface Radiation budget network. The results indicate that the RT technique outperforms the traditional regression method.

Remote Sensing of the Land Surface Radiation Budget

2013 ◽  
pp. 121-162 ◽  
Author(s):  
Shunlin Liang ◽  
Xiaotong Zhang ◽  
Tao He ◽  
Jie Cheng ◽  
Dongdong Wang
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Surface Radiation ◽  
Radiation Budget ◽  
Surface Radiation Budget
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