The Copernicus Climate Change Service (C3S) includes estimates of Essential Climate Variables (ECVs) as a series of Climate Data Records (CDRs) derived from satellite data. The C3S Surface Albedo (SA) v1.0 CDR is composed of observations from National Oceanic and Atmospheric Administration (NOAA) Very High Resolution Radiometers (AVHRR) (1981–2005), and VEGETATION sensors onboard Satellites for the Observation of the Earth (SPOT/VGT) (1998–2014) and Project for Onboard Autonomy satellite (PROBA-V) (2014–2020), and will continue with Sentinel-3 (from 2020 onwards). The goal of this study is to assess the uncertainties associated with the C3S PROBA-V SA v1.0 product, with a focus on the transition from SPOT/VGT to PROBA-V. The methodology followed the good practices recommended by the Land Product Validation sub-group (LPV) of the Working Group on Calibration and Validation (WGCV) of the Committee on Earth Observing Satellites (CEOS) for the validation of satellite-derived global albedo products. Several performance criteria were evaluated, including an intercomparison with National Aeronautics and Space Agency (NASA) MCD43A3 C6 products. C3S PROBA-V SA v1.0 and MCD43A3 C6 showed similar completeness but had higher fractions of missing data than C3S SPOT/VGT SA v1.0. C3S PROBA-V SA v1.0 showed similar precision (~1%) to MCD43A3 C6, improving the results of SPOT/VGT SA v1.0 (2–3%), but C3S PROBA-V SA v1.0 provided residual noise in the near-infrared (NIR). Good spatio-temporal continuity between C3S PROBA-V and SPOT/VGT SA v1.0 products was found with a mean bias between ±2%. The comparison with MCD43A3 C6 showed positive mean biases (5%, 8%, and 12% for visible, NIR and total shortwave, respectively). The accuracy assessment with ground measurements showed a median error of 18.4% with systematic overestimation (positive bias of 11.5%). The percentage of PROBA-V retrievals complying with the C3S target requirements was 28.6%.
The Surface ALbedo VALidation (SALVAL) online platform is designed to allow producers of satellite-based albedo products to move to operational validation systems. The SALVAL tool integrates long-term satellite products, global in situ datasets, and community-agreed-upon validation protocols into an online and interactive platform. The SALVAL tool, available on the ESA Cal/Val portal, was developed by EOLAB under the framework outlined by the Committee on Earth Observation Satellites (CEOS) Working Group on Calibration and Validation (WGCV) Land Product Validation (LPV) subgroup, and provides transparency, consistency, and traceability to the validation process. In this demonstration, three satellite-based albedo climate data records from different operational services were validated and intercompared using the SALVAL platform: (1) the Climate Change Service (C3S) multi-sensor product, (2) the NASA MODIS MCD43A3 product (C6.1) and (3) Beijing Normal University’s Global LAnd Surface Satellites (GLASS) version 4 products. This work demonstrates that the three satellite albedo datasets enable long-term reliable and consistent retrievals at the global scale, with some discrepancies between them associated with the retrieval processing chain. The three satellite albedo products show similar uncertainties (RMSD = 0.03) when comparing the best quality retrievals with ground measurements. The SALVAL platform has proven to be a useful tool to validate and intercompare albedo datasets, allowing them to reach stage 4 of the CEOS LPV validation hierarchy.
Background: The relationship between serum lipid profile levels and cancer risk remained uncertain. Recently, it had been reported a significant inverse association between high-density lipoprotein cholesterol (HDL) and the risk of incident cancer that was independent of low-density lipoprotein cholesterol (LDL), age or sex. Objective: The aim of our study was to evaluate the behavior of HDL in cancer patients and in healthy population. Methods: We created a retrospectively search strategy in the laboratory information system (LIS). We filtered and searched those patients with CEA within 5-20 ng/ml and any value of HDL and corroborated the presence of cancer (Group 1 (G1)) or not (Group 2 (G2)). Moreover, we searched a control group (patients in outpatient oncology clinic) to observe the values of HDL. Comparisons and statistical logistic regression models were applied to link the levels of this biomarkers and cancer risk. Results: We examined 852 valid patients, median age 62 (50-73) years. Within the search strategy group, G2 showed highest levels of HDL (54 (43-67) mg/dl) and lowest CEA levels (6.7 (5.7-8.4) ng/ml) comparing with G1: HDL (47 (37-60) mg/dl) and the CEA (7.9 (6.2-10.9) ng/ml); p < 0.001. The values in control group were: the lowest HDL levels (41 (29-54) mg/dl) and CEA (25.2 (6.1-124.5) ng/ml); p < 0.001, between oncologic patients and the search strategy group. On logistic regression multivariate analysis, we obtained: r²: 0.092; p < 0.001; HDL [OR: 0.983 (0.974-0.993), (p = 0.001)] and CEA [OR: 1.115 (1.060-1.174), (p < 0.001)] and they were confirmed as independent predictors of cancer. Conclusion: Our findings confirmed the inverse association of HDL levels between healthy
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