scholarly journals A new benchmark for surface radiation products over the East Asia-Pacific region retrieved from the Himawari-8/AHI next-generation geostationary satellite

2021 ◽  
pp. 1-40
Author(s):  
Husi Letu ◽  
Takashi Y. Nakajima ◽  
Tianxing Wang ◽  
Huazhe Shang ◽  
Run Ma ◽  
...  
Keyword(s):  
East Asia ◽  
Land Surface ◽  
Radiant Energy ◽  
Geostationary Satellite ◽  
Surface Radiation ◽  
Asia Pacific ◽  
Next Generation ◽  
Spatiotemporal Resolution ◽  
Downward Radiation ◽  
Mean Square Errors

AbstractSurface downward radiation (SDR), including shortwave downward radiation (SWDR) and longwave downward radiation (LWDR), is of great importance to energy and climate studies. Considering the lack of reliable SDR data with a high spatiotemporal resolution in the East Asia-Pacific (EAP) region, we derived SWDR and LWDR at 10-min and 0.05° resolutions for this region from 2016-2020 based on the next-generation geostationary satellite Himawari-8 (H-8). The SDR product is unique in terms of its all-sky features, high accuracy and high resolution levels. The cloud effect is fully considered in the SDR product, and the influence of high aerosol loadings and topography on the SWDR are considered. Compared to benchmark products of the radiation, such as Clouds and the Earth’s Radiant Energy System (CERES) and the European Centre for Medium-Range Weather Forecasts (ECMWF) next-generation reanalysis (ERA5), and the Global Land Surface Satellite (GLASS), not only is the resolution of the new SDR product notably much higher but the product accuracy is also higher than that of those products. In particular, hourly and daily root mean square errors of the new SWDR are 104.9 and 31.5 Wm−2, respectively, which are much smaller than those of CERES (at 121.6 and 38.6 Wm−2, respectively), ERA5 (at 176.6 and 39.5 Wm−2, respectively) and GLASS (daily of 36.5 Wm−2). Meanwhile, RMSEs of hourly and daily values of the new LWDR are 19.6 and 14.4 Wm−2, respectively, which are comparable to that of CERES and ERA5, and even better over high altitude regions.

scholarly journals Impact of Insolation Data Source on Remote Sensing Retrievals of Evapotranspiration over the California Delta

2019 ◽  
Vol 11 (3) ◽  
pp. 216 ◽  
Author(s):  
Martha Anderson ◽  
George Diak ◽  
Feng Gao ◽  
Kyle Knipper ◽  
Christopher Hain ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Real Time ◽  
Land Surface ◽  
Water Resource Management ◽  
Radiant Energy ◽  
Data Sources ◽  
Delta Region ◽  
Spatiotemporal Resolution ◽  
Sky Conditions ◽  
Mean Square Errors

The energy delivered to the land surface via insolation is a primary driver of evapotranspiration (ET)—the exchange of water vapor between the land and atmosphere. Spatially distributed ET products are in great demand in the water resource management community for real-time operations and sustainable water use planning. The accuracy and deliverability of these products are determined in part by the characteristics and quality of the insolation data sources used as input to the ET models. This paper investigates the practical utility of three different insolation datasets within the context of a satellite-based remote sensing framework for mapping ET at high spatiotemporal resolution, in an application over the Sacramento–San Joaquin Delta region in California. The datasets tested included one reanalysis product: The Climate System Forecast Reanalysis (CFSR) at 0.25° spatial resolution, and two remote sensing insolation products generated with geostationary satellite imagery: a product for the continental United States at 0.2°, developed by the University of Wisconsin Space Sciences and Engineering Center (SSEC) and a coarser resolution (1°) global Clouds and the Earth’s Radiant Energy System (CERES) product. The three insolation data sources were compared to pyranometer data collected at flux towers within the Delta region to establish relative accuracy. The satellite products significantly outperformed CFSR, with root-mean square errors (RMSE) of 2.7, 1.5, and 1.4 MJ·m−2·d−1 for CFSR, CERES, and SSEC, respectively, at daily timesteps. The satellite-based products provided more accurate estimates of cloud occurrence and radiation transmission, while the reanalysis tended to underestimate solar radiation under cloudy-sky conditions. However, this difference in insolation performance did not translate into comparable improvement in the ET retrieval accuracy, where the RMSE in daily ET was 0.98 and 0.94 mm d−1 using the CFSR and SSEC insolation data sources, respectively, for all the flux sites combined. The lack of a notable impact on the aggregate ET performance may be due in part to the predominantly clear-sky conditions prevalent in central California, under which the reanalysis and satellite-based insolation data sources have comparable accuracy. While satellite-based insolation data could improve ET retrieval in more humid regions with greater cloud-cover frequency, over the California Delta and climatologically similar regions in the western U.S., the CFSR data may suffice for real-time ET modeling efforts.

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 DSCOVR/EPIC-derived global hourly and daily downward shortwave and photosynthetically active radiation data at 0.1° × 0.1° resolution

2020 ◽  
Vol 12 (3) ◽  
pp. 2209-2221
Author(s):  
Dalei Hao ◽  
Ghassem R. Asrar ◽  
Yelu Zeng ◽  
Qing Zhu ◽  
Jianguang Wen ◽  
...  
Keyword(s):  
Temporal Resolution ◽  
Photosynthetically Active Radiation ◽  
Spatial Scales ◽  
Radiant Energy ◽  
Surface Radiation ◽  
Radiation Budget ◽  
High Temporal Resolution ◽  
Active Radiation ◽  
Global Coverage ◽  
Mean Square Errors

Abstract. Downward shortwave radiation (SW) and photosynthetically active radiation (PAR) play crucial roles in Earth system dynamics. Spaceborne remote sensing techniques provide a unique means for mapping accurate spatiotemporally continuous SW–PAR, globally. However, any individual polar-orbiting or geostationary satellite cannot satisfy the desired high temporal resolution (sub-daily) and global coverage simultaneously, while integrating and fusing multisource data from complementary satellites/sensors is challenging because of co-registration, intercalibration, near real-time data delivery and the effects of discrepancies in orbital geometry. The Earth Polychromatic Imaging Camera (EPIC) on board the Deep Space Climate Observatory (DSCOVR), launched in February 2015, offers an unprecedented possibility to bridge the gap between high temporal resolution and global coverage and characterize the diurnal cycles of SW–PAR globally. In this study, we adopted a suite of well-validated data-driven machine-learning models to generate the first global land products of SW–PAR, from June 2015 to June 2019, based on DSCOVR/EPIC data. The derived products have high temporal resolution (hourly) and medium spatial resolution (0.1∘×0.1∘), and they include estimates of the direct and diffuse components of SW–PAR. We used independently widely distributed ground station data from the Baseline Surface Radiation Network (BSRN), the Surface Radiation Budget Network (SURFRAD), NOAA's Global Monitoring Division and the U.S. Department of Energy's Atmospheric System Research (ASR) program to evaluate the performance of our products, and we further analyzed and compared the spatiotemporal characteristics of the derived products with the benchmarking Clouds and the Earth's Radiant Energy System Synoptic (CERES) data. We found both the hourly and daily products to be consistent with ground-based observations (e.g., hourly and daily total SWs have low biases of −3.96 and −0.71 W m−2 and root-mean-square errors (RMSEs) of 103.50 and 35.40 W m−2, respectively). The developed products capture the complex spatiotemporal patterns well and accurately track substantial diurnal, monthly, and seasonal variations in SW–PAR when compared to CERES data. They provide a reliable and valuable alternative for solar photovoltaic applications worldwide and can be used to improve our understanding of the diurnal and seasonal variabilities of the terrestrial water, carbon and energy fluxes at various spatial scales. The products are freely available at https://doi.org/10.25584/1595069 (Hao et al., 2020).

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 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 Retrieval of Global Orbit Drift Corrected Land Surface Temperature from Long-term AVHRR Data

2019 ◽  
Vol 11 (23) ◽  
pp. 2843 ◽  
Author(s):  
Liu ◽  
Tang ◽  
Yan ◽  
Li ◽  
Liang
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Surface Radiation ◽  
Bayesian Optimization ◽  
Radiation Budget ◽  
Temperature Cycle ◽  
Avhrr Data ◽  
Mean Square Errors

Advanced Very High Resolution Radiometer (AVHRR) sensors provide a valuable data source for generating long-term global land surface temperature (LST). However, changes in the observation time that are caused by satellite orbit drift restrict their wide application. Here, a generalized split-window (GSW) algorithm was implemented to retrieve the LST from the time series AVHRR data. Afterwards, a novel orbit drift correction (ODC) algorithm, which was based on the diurnal temperature cycle (DTC) model and Bayesian optimization algorithm, was also proposed for normalizing the estimated LST to the same local time. This ODC algorithm is pixel-based and it only needs one observation every day. The resulting LSTs from the six-year National Oceanic and Atmospheric Administration (NOAA)-14 satellite data were validated while using Surface Radiation Budget Network (SURFRAD) in-situ measurements. The average accuracies for LST retrieval varied from −0.4 K to 2.0 K over six stations and they also depended on the viewing zenith angle and season. The simulated data illustrate that the proposed ODC method can improve the LST estimate at a similar magnitude to the accuracy of the LST retrieval, i.e., the root-mean-square errors (RMSEs) of the corrected LSTs were 1.3 K, 2.2 K, and 3.1 K for the LST with a retrieval RMSE of 1 K, 2 K, and 3 K, respectively. This method was less sensitive to the fractional vegetation cover (FVC), including the FVC retrieval error, size, and degree of change within a neighboring area, which suggested that it could be easily updated by applying other LST expression models. In addition, ground validation also showed an encouraging correction effect. The RMSE variations of LST estimation that were introduced by ODC were within ±0.5 K, and the correlation coefficients between the corrected LST errors and original LST errors could approach 0.91.

scholarly journals Bay of Bengal‐East Asia‐Pacific Teleconnection in Boreal Summer

2019 ◽  
Vol 124 (8) ◽  
pp. 4395-4412 ◽  
Author(s):  
Ruowen Yang ◽  
Shu Gui ◽  
Jie Cao
Keyword(s):  
East Asia ◽  
Bay Of Bengal ◽  
Asia Pacific ◽  
Boreal Summer

scholarly journals Optimization of the Local Split-Window Algorithm for FY-4A Land Surface Temperature Retrieval

2019 ◽  
Vol 11 (17) ◽  
pp. 2016
Author(s):  
Lijuan Wang ◽  
Ni Guo ◽  
Wei Wang ◽  
Hongchao Zuo
Keyword(s):  
Surface Temperature ◽  
Diurnal Variation ◽  
Land Surface Temperature ◽  
Satellite Data ◽  
Land Surface ◽  
Regional Scale ◽  
Pso Algorithm ◽  
Variation Characteristics ◽  
Split Window Algorithm ◽  
Mean Square Errors

FY-4A is a second generation of geostationary orbiting meteorological satellite, and the successful launch of FY-4A satellite provides a new opportunity to obtain diurnal variation of land surface temperature (LST). In this paper, different underlying surfaces-observed data were applied to evaluate the applicability of the local split-window algorithm for FY-4A, and the local split-window algorithm parameters were optimized by the artificial intelligent particle swarm optimization (PSO) algorithm to improve the accuracy of retrieved LST. Results show that the retrieved LST can efficiently reproduce the diurnal variation characteristics of LST. However, the estimated values deviate hugely from the observed values when the local split-window algorithms are directly used to process the FY-4A satellite data, and the root mean square errors (RMSEs) are approximately 6K. The accuracy of the retrieved LST cannot be effectively improved by merely modifying the emissivity-estimated model or optimizing the algorithm. Based on the measured emissivity, the RMSE of LST retrieved by the optimized local split-window algorithm is reduced to 3.45 K. The local split-window algorithm is a simple and easy retrieval approach that can quickly retrieve LST on a regional scale and promote the application of FY-4A satellite data in related fields.

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