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Surface downward shortwave radiation (DSR) and photosynthetically active radiation (PAR) play critical roles in the Earth's surface processes. As the main inputs of various ecological, hydrological, carbon, and solar photovoltaic models, increasing requirements for high-spatiotemporal-resolution DSR and PAR estimation with high accuracy have been observed in recent years. However, few existing products satisfy all of these requirements. This study employed a well-established physical-based lookup table (LUT) approach to the GeoNEX gridded top-of-atmosphere bidirectional reflectance factor data acquired by the Advanced Himawari Imager (AHI) and Advanced Baseline Imager (ABI) sensors. It produced a data product of DSR and PAR over both AHI and ABI coverage at an hourly temporal step with a 1 km spatial resolution. GeoNEX DSR data were validated over 63 stations, and GeoNEX PAR data were validated over 27 stations. The validation showed that the new GeoNEX DSR and PAR products have accuracy higher than other existing products, with root mean square error (RMSE) of hourly GeoNEX DSR achieving 74.3 W m -2 (18.0 %), daily DSR estimation achieving 18.0 W m -2 (9.2 %), hourly GeoNEX PAR achieving 34.9 W m -2 (19.6 %), and daily PAR achieving 9.5 W m -2 (10.5 %). The study also demonstrated the application of the high-spatiotemporal-resolution GeoNEX DSR product in investigating the spatial heterogeneity and temporal variability of surface solar radiation. The data product can be freely accessed through the NASA Advanced Supercomputing Division GeoNEX data portal: https://data.nas.nasa.gov/geonex/geonexdata/GOES16/GEONEX-L2/DSR-PAR/ (last access: 12 March 2023) and https://data.nas.nasa.gov/geonex/geonexdata/HIMAWARI8/GEONEX-L2/DSR-PAR/ (last access: 12 March 2023) (10.5281/zenodo.7023863; Wang and Li, 2022). [ABSTRACT FROM AUTHOR]

Evidence indicates that glucose variation (GV) plays an important role in mortality of critically ill patients. We aimed to investigate the relationship between the coefficient of variation of 24-h venous blood glucose (24-hVBGCV) and mortality among patients with acute respiratory failure. The records of 1625 patients in the Multiparameter Intelligent Monitoring in Intensive Care II (MIMIC II) database were extracted. The 24-hVBGCV was calculated as the ratio of the standard deviation (SD) to the mean venous blood glucose level, expressed as a percentage. The outcomes included ICU mortality and in-hospital mortality. Participants were divided into three subgroups based on tertiles of 24-hVBGCV. Multivariable logistic regression models were used to evaluate the relationship between 24-hVBGCV and mortality. Sensitivity analyses were also performed in groups of patients with and without diabetes mellitus. Taking the lowest tertile as a reference, after adjustment for all the covariates, the highest tertile was significantly associated with ICU mortality [odds ratio (OR), 1.353; 95% confidence interval (CI), 1.018–1.797] and in-hospital mortality (OR, 1.319; 95% CI, 1.003–1.735), especially in the population without diabetes. The 24-hVBGCV may be associated with ICU and in-hospital mortality in patients with acute respiratory failure in the ICU, especially in those without diabetes. [ABSTRACT FROM AUTHOR]

The retrieval of downward shortwave radiation (DSR) with high spatiotemporal resolution and short latency is critical. It is the fundamental driving force of surface energy, carbon, and hydrological circulations, and a key energy source for photovoltaic electricity. However, existing methods face significant challenges owing to cloud heterogeneity and their reliance on other satellite-derived products, which hinder the retrieval of accurate and timely DSR with high spatiotemporal resolution. In addition to the spectral features used in traditional approaches, deep learning (DL) can incorporate the spatial and temporal features of satellite data. This study developed and compared three DL methods, namely the DenseNet , the bidirectional gated recurrent unit without surface albedo as inputs (BiGRU nor), and the convolutional neural network with gated recurrent unit without surface albedo as inputs (CNNGRU nor). These methods were used to estimate DSR at 1 km and 10/15 min resolutions directly from top-of-atmosphere reflectance over the Advanced Himawari Imager (AHI) onboard Himawari-8 and the Advanced Baseline Imager (ABI) onboard GOES-16 coverage, achieving high accuracies. The instantaneous root mean square error (RMSE) and relative RMSE for the three models were 68.4 (16.1%), 69.4 (16.3%), and 67.1 (15.7%) W / m 2 , respectively, which are lower than the baseline machine learning method, the multilayer perceptron model (MLP), with RMSE at 76.8 W / m 2 (18.0%). Hourly accuracies for the three DL methods were 58.6 (14.1%), 57.8 (14.0%), and 57.3 (13.8%) W / m 2 , which are within the DSR RMSEs that we estimated for existing datasets of the Earth's Radiant Energy System (CERES) (88.8 W / m 2 , 21.4%) and GeoNEX (77.8 W / m 2 , 18.8%). The study illustrates that DL models that incorporate temporal information can eliminate the need for surface albedo as an input, which is crucial for timely monitoring and nowcasting of DSR. Incorporating spatial information can enhance retrieval accuracy in overcast conditions, and incorporating infrared bands can further improve the accuracy of DSR estimation. • 1 km and 10/15 min DSR estimated using deep learning from AHI and ABI. • Method can utilize spatial and temporal information in addition to spectral data. • Spatial structures improve retrieval accuracy under overcast conditions. • Temporal structures eliminate the dependency on the input data of surface albedo. • Thermal infrared bands improve accuracy particularly over surfaces with high albedo. [ABSTRACT FROM AUTHOR]

In recent years, machine learning (ML) has been successfully used in estimating downward shortwave radiation (DSR). To achieve global estimations, traditional ML models need sufficient ground measurements covering various atmospheric and surface conditions globally, which is difficult to accomplish. Training on the simulated data of a radiative transfer model (RTM) is a possible solution, but widely used RTMs ignore some complex cloud conditions which brings bias to simulations. In this study, a neural network applied with the transfer-learning (TL) concept is introduced to utilize both radiative transfer simulations and ground measurement data, achieving global DSR estimation with only top-of-atmosphere and surface albedo at local solar noon as inputs. The proposed method estimates both instantaneous and daily DSR from Visible Infrared Imaging Radiometer Suite (VIIRS) data at 750-m resolution, and both the estimates are validated by 40 independent stations globally. The root mean-square error and relative root mean square error of instantaneous DSR validation over 25 Baseline Surface Radiation Network, seven Surface Radiation Network, and eight Greenland Climate Network stations in 2013 were 91.2 (16.1%), 106.3 (18.3%), 75.0 (24.2%) W / m 2, respectively, and the daily validation achieved 30.8 (15.5%), 33.5 (17.6%), and 31.3 (14.4) W / m 2, respectively. The proposed method presents significant high accuracy over polar regions and similar performances over other areas compared with traditional ML models, physics models (e.g., look-up tables and direct estimations), and existing DSR products. The algorithm is also applied to VIIRS swath data to test its global efficacy. Instantaneous mapping captures the spatial pattern of the cloud-mask product, and daily mapping shows spatial patterns similar to the Clouds and the Earth's Radiant Energy System Synoptic TOA and surface fluxes and clouds product, but with more detail. Further analysis indicates that model performance is less sensitive to the quantity of training data after TL has been incorporated. This study demonstrates the advantages of TL on boosting both the generality and accuracy of DSR estimation, which can potentially be applied to other variable retrievals. • Transfer-learning used to estimate surface downward shortwave radiation from VIIRS data. • The method leverages the advantage of radiative transfer simulation and ground-based measurement. • Estimation of downward shortwave radiation over polar area was significantly improved. [ABSTRACT FROM AUTHOR]

Surface downward shortwave radiation (DSR) is a fundamental variable in determining the Earth's radiation balance and is essential in many applications. Considerable efforts have been devoted to algorithm development, product generation, and validation. However, few studies have focused on comparing retrieval approaches, examining their strengths and weaknesses, and identifying the most suitable scenarios for each approach. In this study, we implemented and evaluated five representative DSR retrieval algorithms, including the forward parameterization approach, two physical inversion methods (look-up table (LUT) and optimization), and two statistical inversion methods (direct estimation and neural networks). We then proposed an algorithm-integration framework that combined the results of these DSR retrieval methods to further improve DSR estimation accuracy and consistency. To validate the DSR retrievals, we used in-situ data collected at 25 stations of the Baseline Surface Radiation Network (BSRN) over one year. Validation revealed that forward parameterization consistently performed best, with an overall root mean square error (RMSE) of 91.7 W/m2 or a relative RMSE of 16.9%, although it generated the fewest valid retrievals. For an identical data set, the LUT approach generated results comparable to those of parameterization. The neural network-based algorithm-integration approach reduced the RMSE by 11.0 W/m2 or the relative RMSE by 2.0%, compared to the best individual retrieval algorithm. Our analysis demonstrates that algorithm integration is a promising way to obtain DSR data that are superior to estimates from any individual retrieval algorithm. • Five downward shortwave radiation retrieval approaches were compared. • Parameterization performed the best, but it generated the fewest valid retrievals. • An algorithm-integration framework was proposed to combine results of the five algorithms. • Algorithm integration produced DSR superior to any individual retrieval algorithm. [ABSTRACT FROM AUTHOR]