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 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.

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;

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 A global long-term (1981–2000) land surface temperature product for NOAA AVHRR

2020 ◽  
Vol 12 (4) ◽  
pp. 3247-3268
Author(s):  
Jin Ma ◽  
Ji Zhou ◽  
Frank-Michael Göttsche ◽  
Shunlin Liang ◽  
Shaofei Wang ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Surface Radiation ◽  
Radiation Budget ◽  
Mean Bias Error ◽  
Noaa Avhrr ◽  
Orbital Drift

Abstract. Land surface temperature (LST) plays an important role in the research of climate change and various land surface processes. Before 2000, global LST products with relatively high temporal and spatial resolutions are scarce, despite a variety of operational satellite LST products. In this study, a global 0.05∘×0.05∘ historical LST product is generated from NOAA advanced very-high-resolution radiometer (AVHRR) data (1981–2000), which includes three data layers: (1) instantaneous LST, a product generated by integrating several split-window algorithms with a random forest (RF-SWA); (2) orbital-drift-corrected (ODC) LST, a drift-corrected version of RF-SWA LST; and (3) monthly averages of ODC LST. For an assumed maximum uncertainty in emissivity and column water vapor content of 0.04 and 1.0 g cm−2, respectively, evaluated against the simulation dataset, the RF-SWA method has a mean bias error (MBE) of less than 0.10 K and a standard deviation (SD) of 1.10 K. To compensate for the influence of orbital drift on LST, the retrieved RF-SWA LST was normalized with an improved ODC method. The RF-SWA LST were validated with in situ LST from Surface Radiation Budget (SURFRAD) sites and water temperatures obtained from the National Data Buoy Center (NDBC). Against the in situ LST, the RF-SWA LST has a MBE of 0.03 K with a range of −1.59–2.71 K, and SD is 1.18 K with a range of 0.84–2.76 K. Since water temperature only changes slowly, the validation of ODC LST was limited to SURFRAD sites, for which the MBE is 0.54 K with a range of −1.05 to 3.01 K and SD is 3.57 K with a range of 2.34 to 3.69 K, indicating good product accuracy. As global historical datasets, the new AVHRR LST products are useful for filling the gaps in long-term LST data. Furthermore, the new LST products can be used as input to related land surface models and environmental applications. Furthermore, in support of the scientific research community, the datasets are freely available at https://doi.org/10.5281/zenodo.3934354 for RF-SWA LST (Ma et al., 2020a), https://doi.org/10.5281/zenodo.3936627 for ODC LST (Ma et al., 2020c), and https://doi.org/10.5281/zenodo.3936641 for monthly averaged LST (Ma et al., 2020b).

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).

scholarly journals Cross-Comparison of Global Surface Albedo Operational Products—MODIS, GLASS, and CGLS

2021 ◽  
Vol 13 (23) ◽  
pp. 4869
Author(s):  
Congying Shao ◽  
Yanmin Shuai ◽  
Latipa Tuerhanjiang ◽  
Xuexi Ma ◽  
Weijie Hu ◽  
...  
Keyword(s):  
Energy Balance ◽  
Land Surface ◽  
Surface Albedo ◽  
Relative Difference ◽  
Surface Radiation ◽  
Radiation Budget ◽  
Dynamic Monitoring ◽  
Absolute Difference ◽  
Global Land ◽  
Global Mean

Surface albedo, as an important parameter for land surface geo-biophysical and geo-biochemical processes, has been widely used in the research communities involved in surface energy balance, weather forecasting, atmospheric circulation, and land surface process models. In recent years, operational products using satellite-based surface albedo have, from time to time, been rapidly developed, contributing significantly to the estimation of energy balance at regional or global scales. The increasing number of research topics on dynamic monitoring at a decades-long scale requires a combination of albedo products generated from various sensors or programs, while the quantitative assessment of agreement or divergence among different surface albedo products still needs further understanding. In this paper, we investigated the consistency of three classical operational surface albedo products that have been frequently used by researchers globally via the official issued datasets-MODIS, GLASS (Global LAnd Surface Satellite), and CGLS (Copernicus Global Land Service). The cross-comparison was performed on all the identical dates available during 2000–2017 to represent four season-phases. We investigated the pixel-based validity of each product, consistency of global annual mean, spatial distribution and different temporal dynamics among the discussed products in white-sky (WSA) and black-sky (BSA) albedo at visible (VIS), near-infrared (NIR), and shortwave (SW) regimes. Further, varying features along with the change of seasons was also examined. In addition, the variation in accuracy of shortwave albedo magnitude was explored using ground measurements collected by the Baseline Surface Radiation Network (BSRN) and the Surface Radiation Budget Network (SUFRAD). Results show that: (1) All three products can provide valid long-term albedo for dominant land surface, while GLASS can provide additional estimation over sea surfaces, with the highest percentage of valid land surface pixels, at up to 93% in October 24. The invalid pixels mainly existed in the 50°N–60°N latitude belt in December for GLASS, Central Africa in April and August for MODIS, and northern high latitudes for CGLS. (2) The global mean albedo of CGLS at the investigated bands has significantly higher values than those of MODIS and GLASS, with a relative difference of ~20% among the three products. The global mean albedo of MODIS and GLASS show a generally increasing trend from April to December, with an abrupt rise at NIR and SW of CGLS in June of 2014. Compared with SW and VIS bands, the linear temporal trend of the NIR global albedo mean in three products continues to increase, but the slope of CGLS is 10–100 times greater than that of the other two products. (3) The differences in albedo, which are higher in April, October, and December than in August, exhibit a small variation over the main global land surface regions, except for Central Eurasia, North Africa, and middle North America. The magnitude of global absolute difference among the three products usually varies within 0.02–0.06, but with the largest value occasionally exceeding 0.1. The relative difference is mainly within 10%–20%, and can deviate more than 40% away from the baseline. In addition, CGLS has a greater opportunity to achieve the largest difference compared with MODIS and GLASS. (4) The comparison with ground measurements indicates that MODIS generally performs better than GLASS and CGLS at the sites discussed. This study demonstrates that apparent differences exist among the three investigated albedo products due to the ingested source data, algorithm, atmosphere correction etc., and also points at caution regarding data fusion when multiple albedo products were organized to serve the following applications.

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