ABSTRACT With hourly measurements available during daytime between local times of 09:00–16:00, ocean color data derived from the Geostationary Ocean Color Imager (GOCI) onboard the Korean Communication, Ocean, and Meteorological (COMS) satellite have been useful for research and surveillance of diurnal processes in the western Pacific Ocean region. However, in early morning and late afternoon measurements, there are significant errors in GOCI-derived ocean color products as the solar-zenith angle (θ 0 ) goes beyond 70°, especially in autumn and winter seasons. In this study, we employ a neural network (NN) model to make corrections on the GOCI-measured normalize water-leaving radiance spectra, nL w (λ), with high θ 0 (>70°) in open oceans. Results show that NN-corrected nL w (λ) are consistent with the previous-hour nL w (λ) and make the diurnal variations in the region much more stable and reasonable. Specifically, the GOCI-measured nL w (λ) with θ 0 ≤65° in earlier hours of the day (including some nL w (λ) diurnal variation) are considered accurate and used as the ground truth to train NN models for nL w (λ) correction with high θ 0 . Further analysis of the relationship of ratios (between the NN-corrected and original nL w (λ)) with θ 0 shows that the nL w (λ) ratios increase as θ 0 increase, which indicates that there are more significant corrections with larger θ 0 (>70°). The performance evaluation of the NN models is based on the comparison of NN-corrected nL w (λ) with the original previous hour nL w (λ) data. The ratios of NN-corrected nL w (λ) to the original previous hour nL w (λ) are 0.968–1.045 for the short blue/blue and green bands, and the performance of nL w (λ) correction at 14:00 and 15:00 is slightly better than that at 16:00, due to significantly large θ 0 at late afternoon hours. The NN-corrected nL w (λ) data are also used to derive chlorophyll-a (Chl-a) concentration, showing significantly improved Chl-a in GOCI’s late afternoon measurements with θ 0 >70°.

A unique End-of-Life (EOL) Deep Space View Test (DSVT) was performed on 27 November 2021 for the Advanced Microwave Sounding Unit-A (AMSU-A) and the Microwave Humidity Sounder (MHS) onboard the first EUMETSAT MetOp-A satellite in the deorbiting process. The purpose of this test is to recalibrate the antenna sidelobe, to derive antenna emission, and to quantify the in-orbit asymmetric scan biases of AMSU-A and MHS to, ultimately, improve Near Real-Time (NRT) products for MetOp-B and -C and the entire Fundamental Climate Data Records (FCDR). In this study, MetOp-A AMSU-A and MHS EOL DSVT data on 27 November 2021 have been analyzed. The deep space scene antenna temperatures were first applied for the antenna pattern correction; then, the antenna reflector channel emissivity values were derived from the corrected temperatures. For the MHS, the observed scan-angle-dependent brightness temperatures (BTs) for all channels were well behaved after the antenna pattern correction, except for channel 1. The derived antenna reflector emissivity values from this test are 0.0016, 0.0036, 0.0036, and 0.0019 for channels 1, 3, 4, and 5, respectively. For AMSU-A, the deep space view counts were not homogeneous during the test period, exhibiting large variations in the along-track and cross-track directions, mainly due to the instrument temperature’s rapid change during the test period. The large relative noise in the deep space view observations negatively impacted the data quality and limits the value of this test. The large relative noise may contribute to the different emissivity values derived from the same frequency for channels 9 to 14. We also found unexpected scan-angle-dependent BT after antenna pattern correction for quasi-vertical (QV) channels 1 and 2 when compared to the emission model. Further investigation using a simulation confirmed that channels 1 and 2 are QV channels, as designed.

Abstract Satellite observations are indispensable for weather forecasting, climate change monitoring, and environmental studies. Understanding and quantifying errors and uncertainties associated with satellite observations are essential for hardware calibration, data assimilation, and developing environmental and climate data records. Satellite observation errors can be classified into four categories: measurement, observation operator, representativeness, and preprocessing errors. Current methods for diagnosing observation errors still yield large uncertainties due to these complex errors. When simulating satellite errors, empirical errors are usually used, which do not always accurately represent the truth. We address these challenges by developing an error inventory simulator, the Satellite Error Representation and Realization (SatERR). SatERR can simulate a wide range of observation errors, from instrument measurement errors to model assimilation errors. Most of these errors are based on physical models, including existing and newly developed algorithms. SatERR takes a bottom-up approach: errors are generated from root sources and forward propagate through radiance and science products. This is different from, but complementary to, the top-down approach of current diagnostics, which inversely solves unknown errors. The impact of different errors can be quantified and partitioned, and a ground-truth testbed can be produced to test and refine diagnostic methods. SatERR is a community error inventory, open-source on GitHub, which can be expanded and refined with input from engineers, scientists, and modelers. This debut version of SatERR is centered on microwave sensors, covering traditional large satellites and small satellites operated by NOAA, NASA, and EUMETSAT.

A U-Net algorithm was used to retrieve surface pressure and wind speed over the ocean within tropical cyclones (TCs) and their neighboring areas using NOAA-20 Advanced Technology Microwave Sounder (ATMS) reprocessed Sensor Data Record (SDR) brightness temperatures (TBs) and geolocation information. For TC locations, International Best Track Archive for Climate Stewardship (IBTrACS) data have been used over the North Atlantic Ocean and West Pacific Ocean between 2018 and 2021. The European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) surface pressure and wind speed were employed as reference labels. Preliminary results demonstrated that the visualizations for wind speed and pressure matched the prediction and ERA5 location. The residual biases and standard deviations between the predicted and reference labels were about 0.15 m/s and 1.95 m/s, respectively, for wind speed and 0.48 hPa and 2.67 hPa, respectively, for surface pressure, after applying cloud screening for each ATMS pixel. This indicates that the U-Net model is effective for surface wind speed and surface pressure estimates over general ocean conditions.

Chlorophyll-a (Chl-a) is one of the optically active constituents in waters, and its concentration is frequently utilized as a proxy for lake trophic levels. However, generating a large-scale, long-term, and consistent data record of Chl-a in lakes from satellite images has been a challenging undertaking due to the limitations of conventional algorithms in monitoring inland waters spanning various optical properties. Here, we develop a practical deep neural network (DNN) model to generate a long-term Chl-a series (2012−2021) in 217 large lakes (> 50 km2) across China from the Visible Infrared Imaging Radiometer Suite (VIIRS) imagery. The assessment showed that the NOAA operational VIIRS remote sensing reflectance (Rrs(λ)) products were reliable over 28 of China's examined lakes (N = 340, bias = −12%, mean absolute percentage error [MAPE] = 38%), particularly at bands ranging from the green to near-infrared domain. The DNN model performed satisfactorily on Chl-a retrievals (bias = 5%, MAPE = 32%) in 79 lakes over three orders of magnitude (0.1–300 μg L−1) spanning clear/deep to turbid/shallow waters, with significant improvements compared with the existing algorithms and other machine learning algorithms. The algorithm was applied to VIIRS images to produce a data record of spatial and temporal variations in Chl-a for China's large lakes over the past decade. The VIIRS-derived data record showed that China's lakes have an average Chl-a of 9.5 μg L−1 and are to 45.5% eutrophic. The results revealed a spatial trend of lower Chl-a in the western deep lakes than that in the eastern shallow lakes. In addition, we observed a significant increase in Chl-a in the lakes of the China East Plain but a decreasing trend of Chl-a in the Tibetan Plateau. This study highlights the feasibility of a machine learning approach based on synchronous matchups to derive Chl-a data in various lakes from satellite images. Our results provide a comprehensive understanding of overall changes to the optical conditions of China's lakes and enable scientists to elucidate the roles of climate and human activities in regulating lake productivity.

Long-term improvements in air quality and public health in the continental USA were disrupted over the past decade by increased fire emissions that potentially offset the decrease in anthropogenic emissions. This study aims to estimate trends in black carbon and PM2·5 concentrations and their attributable mortality burden across the USA. In this study, we derived daily concentrations of PM2·5 and its highly toxic black carbon component at a 1-km resolution in the USA from 2000 to 2020 via deep learning that integrated big data from satellites, models, and surface observations. We estimated the annual PM2·5-attributable and black carbon-attributable mortality burden at each 1-km2 grid using concentration-response functions collected from a national cohort study and a meta-analysis study, respectively. We investigated the spatiotemporal linear-regressed trends in PM2·5 and black carbon pollution and their associated premature deaths from 2000 to 2020, and the impact of wildfires on air quality and public health. Our results showed that PM2·5 and black carbon estimates are reliable, with sample-based cross-validated coefficients of determination of 0·82 and 0·80, respectively, for daily estimates (0·97 and 0·95 for monthly estimates). Both PM2·5 and black carbon in the USA showed significantly decreasing trends overall during 2000 to 2020 (22% decrease for PM2·5 and 11% decrease for black carbon), leading to a reduction of around 4200 premature deaths per year (95% CI 2960-5050). However, since 2010, the decreasing trends of fine particles and premature deaths have reversed to increase in the western USA (55% increase in PM2·5, 86% increase in black carbon, and increase of 670 premature deaths [460-810]), while remaining mostly unchanged in the eastern USA. The western USA showed large interannual fluctuations that were attributable to the increasing incidence of wildfires. Furthermore, the black carbon-to-PM2·5 mass ratio increased annually by 2·4% across the USA, mainly due to increasing wildfire emissions in the western USA and more rapid reductions of other components in the eastern USA, suggesting a potential increase in the relative toxicity of PM2·5. 100% of populated areas in the USA have experienced at least one day of PM2·5 pollution exceeding the daily air quality guideline level of 15 μg/m3 during 2000-2020, with 99% experiencing at least 7 days and 85% experiencing at least 30 days. The recent widespread wildfires have greatly increased the daily exposure risks in the western USA, and have also impacted the midwestern USA due to the long-range transport of smoke. Wildfires have become increasingly intensive and frequent in the western USA, resulting in a significant increase in smoke-related emissions in populated areas. This increase is likely to have contributed to a decline in air quality and an increase in attributable mortality. Reducing fire risk via effective policies besides mitigation of climate warming, such as wildfire prevention and management, forest restoration, and new revenue generation, could substantially improve air quality and public health in the coming decades. National Aeronautics and Space Administration (NASA) Applied Science programme, NASA MODIS maintenance programme, NASA MAIA satellite mission programme, NASA GMAO core fund, National Oceanic and Atmospheric Administration (NOAA) GEO-XO project, NOAA Atmospheric Chemistry, Carbon Cycle, and Climate (AC4) programme, and NOAA Educational Partnership Program with Minority Serving Institutions.

Abstract The radius of maximum wind Rmax, an important parameter in tropical cyclone (TC) ocean surface wind structure, is currently resolved by only a few sensors so that, in most cases, it is estimated subjectively or via crude statistical models. Recently, a semiempirical model relying on an outer wind radius, intensity, and latitude was fit to best-track data. In this study we revise this semiempirical model and discuss its physical basis. While intensity and latitude are taken from best-track data, Rmax observations from high-resolution (3 km) spaceborne synthetic aperture radar (SAR) and wind radii from an intercalibrated dataset of medium-resolution radiometers and scatterometers are considered to revise the model coefficients. The new version of the model is then applied to the period 2010–20 and yields Rmax reanalyses and trends that are more accurate than best-track data. SAR measurements corroborate that fundamental conservation principles constrain the radial wind structure on average, endorsing the physical basis of the model. Observations highlight that departures from the average conservation situation are mainly explained by wind profile shape variations, confirming the model’s physical basis, which further shows that radial inflow, boundary layer depth, and drag coefficient also play roles. Physical understanding will benefit from improved observations of the near-core region from accumulated SAR observations and future missions. In the meantime, the revised model offers an efficient tool to provide guidance on Rmax when a radiometer or scatterometer observation is available, for either operations or reanalysis purposes.

Abstract. Since 2011 the Global Energy and Water cycle Exchanges (GEWEX) Water Vapor Assessment (G-VAP) has provided performance analyses for state-of-the-art reanalysis and satellite water vapour products to the GEWEX Data and Analysis Panel (GDAP) and the user community in general. A significant component of the work undertaken by G-VAP is to characterise the quality and uncertainty of these water vapour records to; i) ensure full exploitation and ii) avoid incorrect use or interpretation of results. This study presents results from the second phase of G-VAP, where we have extended and expanded our analysis of Total Column Water Vapour (TCWV) from phase 1, in conjunction with updating the G-VAP archive. For version 2 of the archive, we consider 28 freely available and mature satellite and reanalysis data products, remapped to a regular longitude-latitude grid of 2°× 2°, and on monthly time steps between January 1979 and December 2019. We first analysed all records for a 'common' short period of five years (2005–2009), focusing on variability (spatial & seasonal) and deviation from the ensemble mean. We observed that clear-sky daytime-only satellite products were generally drier than the ensemble mean, and seasonal variability/disparity in several regions up to 12 kg/m2 related to original spatial resolution and temporal sampling. For 11 of the 28 data records, further analysis was undertaken between 1988–2014. Within this 'long period', key results show i) trends between -1.18±0.68 to 3.82±3.94 kg/m2/decade and -0.39±0.27 to 1.24±0.85 kg/m2/decade were found over ice-free global oceans and land surfaces respectively, and ii) regression coefficients of TWCV against surface temperatures of 6.17±0.24 to 27.02±0.51 %/K over oceans (using sea surface temperature) and 3.00±0.17 to 7.77±0.16 %/K over land (using surface air temperature). It is important to note that trends estimated within G-VAP are used to identify issues in the data records rather than analyse climate change. Additionally, breakpoints have been identified and characterised for both land and ocean surfaces within this period. Finally, we present a spatial analysis of correlations to six climate indices within the “long period”, highlighting regional areas of significant positive and negative correlation and the level of agreement among records.

Accurate thermal infrared (TIR) fast-forward models are critical for weather forecasting via numerical weather prediction (NWP) satellite radiance assimilation and operational environmental data record (EDR) retrieval algorithms. The thermodynamic and compositional data about the surface and lower troposphere are derived from semi-transparent TIR window bands (i.e., surface-sensitive channels) that can span into the far-infrared (FIR) region under dry polar conditions. To model the satellite observed radiance within these bands, an accurate a priori emissivity is necessary for the surface in question, usually provided in the form of a physical or empirical model. To address the needs of hyperspectral TIR satellite radiance assimilation, this paper discusses the research, development, and preliminary validation of a physically based snow/ice emissivity model designed for practical implementation within operational fast-forward models such as the U.S. National Oceanic and Atmospheric Administration (NOAA) Community Radiative Transfer Model (CRTM). To accommodate the range of snow grain sizes, a hybrid modeling approach is adopted, combining a layer scattering model based on the Mie theory (viz., the Wiscombe–Warren 1980 snow albedo model, its complete derivation provided in the Appendices) with a specular facet model. The Mie-scattering model is valid for the smallest snow grain sizes typical of fresh snow and frost, whereas the specular facet model is better suited for the larger sizes and welded snow surfaces typical of aged snow. Comparisons of the model against the previously published spectral emissivity measurements show reasonable agreement across zenith observing angles and snow grain sizes, and preliminary observing system experiments (OSEs) have revealed notable improvements in snow/ice surface window channel calculations versus hyperspectral TIR satellite observations within the NOAA NWP radiance assimilation system.

Abstract. Atmospheric water vapor plays a crucial role in the global energy balance, hydrological cycle, and climate system. High-quality and consistent water vapor data from different sources are vital for weather prediction and climate research. This study assesses the consistency between the Formosa Satellite Mission 3–Constellation Observing System for Meteorology, Ionosphere, and Climate (FORMOSAT-3/COSMIC) radio occultation (RO) and European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis Model 5 (ERA5) water vapor datasets. Comparisons are made across different atmospheric pressure levels (300, 500, and 850 hPa) from 2007 to 2018. Generally, the two datasets show good spatial and temporal agreement. COSMIC's global water vapor retrieval is slightly lower than ERA5's at 500 and 850 hPa, with distinct latitudinal differences between hemispheres. COSMIC exhibits global water vapor increasing trends of 3.47 ± 1.77 % per decade, 3.25 ± 1.25 % per decade, and 2.03 ± 0.65 % per decade at 300, 500, and 850 hPa, respectively. Significant regional variability in water vapor trends, encompassing notable increasing and decreasing patterns, is observable in tropical and subtropical regions. At 500 and 850 hPa, strong water vapor increasing trends are noted in the equatorial Pacific Ocean and the Laccadive Sea, while decreasing trends are evident in the Indo-Pacific Ocean region and the Arabian Sea. Over land, substantial increasing trends at 850 hPa are observed in the southern United States, contrasting with decreasing trends in southern Africa and Australia. The differences between the water vapor trends of COSMIC and ERA5 are primarily negative in the tropical regions at 850 hPa. However, the water vapor increasing trends at 850 hPa estimated from COSMIC are significantly higher than the ones derived from ERA5 data for two low-height stratocumulus-cloud-rich ocean regions west of Africa and South America. These regions with notable water vapor trend differences are located in the Intertropical Convergence Zone (ITCZ) area with frequent occurrences of convection, such as deep clouds. The difference in characterizing water vapor distribution between RO and ERA5 in deep cloud regions may cause such trend differences. The assessment of spatiotemporal variability in RO-derived water vapor and reanalysis of atmospheric water vapor data helps ensure the quality of these datasets for climate studies.