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Satellite‐based estimates of fossil fuel carbon dioxide (FFCO2) emissions are crucial for global stocktaking but are limited by sparse coverage. As nitrogen dioxide (NO2) observations can enhance CO2 emission estimates, we use NO2 observations from twin polar‐orbiting satellites (GaoFen‐5B and DaQi‐1) to constrain carbon emission estimates based on the OCO‐3 XCO2 measurements. Compared previous CO2 estimation constrained by polar‐orbiting satellites (e.g., Tropospheric Monitoring Instrument), this approach detects 40% more daily emission cases and reduces uncertainties by up to 12%, primarily due to reduced temporal differences and increased observation frequency. The improved estimates cover 69 global emission cases, mainly in East Asia, Europe, the Middle East, and North America. About 45% of the estimated emissions are lower than bottom‐up inventory values, potentially reflecting the impact of local emission control efforts. These findings highlight the importance of satellite monitoring for FFCO2 emissions and the value of future missions with high‐resolution collocated NO2/CO2 measurements.

Nitrogen dioxide (NO2) plays a crucial role in environmental processes and public health. In recent years, NO2 pollution has been monitored using a combination of in situ measurements and satellite remote sensing, supported by the development of advanced retrieval algorithms. With advancements in satellite technology, large-scale NO2 monitoring is now feasible through instruments such as GOME-2C and TROPOMI. However, the fixed local overpass times of polar-orbiting satellites limit their ability to capture the complete diurnal cycle of NO2, introducing uncertainties in emission estimation and pollution trend analysis. In this study, we evaluated differences in NO2 observations between GOME-2C (morning overpass at ~09:30 LT) and TROPOMI (afternoon overpass at ~13:30 LT) across three representative regions—East Asia, Central Africa, and Europe—that exhibit distinct emission sources and atmospheric conditions. By comparing satellite-derived tropospheric NO2 column densities with ground-based measurements from the Pandora network, we analyzed spatial distribution patterns and seasonal variability in NO2 concentrations. Our results show that East Asia experiences the highest NO2 concentrations in densely populated urban and industrial areas. During winter, lower boundary layer heights and weakened photolysis processes lead to stronger accumulation of NO2 in the morning. In Central Africa, where biomass burning is the dominant emission source, afternoon fire activity is significantly higher, resulting in a substantial difference (1.01 × 1016 molecules/cm2) between GOME-2C and TROPOMI observations. Over Europe, NO2 pollution is primarily concentrated in Western Europe and along the Mediterranean coast, with seasonal peaks in winter. In high-latitude regions, weaker solar radiation limits the photochemical removal of NO2, causing concentrations to continue rising into the afternoon. These findings demonstrate that differences in polar-orbiting satellite overpass times can significantly affect the interpretation of daily NO2 variability, especially in regions with strong diurnal emissions or meteorological patterns. This study highlights the observational limitations of fixed-time satellites and offers an important reference for the future development of geostationary satellite missions, contributing to improved strategies for NO2 pollution monitoring and control.

While geostationary satellites can provide continuous near-real-time observations, their low spatial resolution makes it difficult to detect small wildfires. Conversely, polar-orbiting satellites are capable of observing small wildfires at high spatial resolution, but can operate only within restricted observation periods. To improve wildfire spread prediction accuracy using polar-orbiting satellite observations, this paper proposes a novel methodology to accurately reproduce wildfire perimeters observed at specific time points by these satellites. The approach employs a wildfire spread simulator combined with a rate of spread (ROS) adjustment factor. The proposed algorithm derives ROS adjustment factors for each fuel model based on differential evolution, achieving up to a 0.4 increase in the Sørensen index when reproducing wildfire perimeter data at given observation times. Incorporating these factors into simulator-based predictions allows comprehensive consideration of external factors affecting wildfire propagation, which have not been sufficiently accounted for in previous methods. Moreover, considering the frequent occurrence of small wildfires in Korea, this study establishes a mapping between major species of trees in Korea and corresponding Fire Behavior Fuel Models (FBFMs). This serves as an example of appropriately matching major species of trees to FBFMs for wildfire spread prediction in countries where FBFMs have not been previously applied. The methodology’s effectiveness is demonstrated using wildfire perimeter data from polar-orbiting satellite observations and ignition points of recent wildfires in Korea. The proposed algorithm is expected to significantly enhance wildfire response by swiftly providing critical information for accurate wildfire spread prediction. This will facilitate prompt and precise countermeasures for small wildfires independent of external conditions such as weather.

Daily global surface solar irradiance (SSI) is of great importance parameter in the surface energy balance, climate modelling, and solar energy utilization. However, it is still challenging to extend the estimated instantaneous solar radiation to the daily global SSI from either geostationary satellites or polar-orbiting satellites. In this study, a method for estimating the daily global SSI is proposed based on the Himawari-8 hourly SSI products using pixel-by-pixel Gaussian fitting to simulate the diurnal SSI variation. Compared with ground-based observations, the daily global SSI estimated by Gaussian fitting have higher accuracy under various weather conditions than by the simple accumulation or quadratic polynomials, and the coefficient of determination (R2) between the estimated and observed values exceeds 0.86. The verification results also show different estimation accuracies under different weather conditions. The root-mean-square errors (RMSEs) under clear-sky and all-sky conditions are 2.36 MJ/m2 and 3.06 MJ/m2, respectively. In addition, experimental results show that the daily global SSI in China has high spatial heterogeneity. For higherelevation areas with low cloud cover, such as the Qinghai-Tibet Plateau, Inner Mongolia, and Northwest China, the daily global SSI is higher than other areas. In contrast, the values of daily global SSI is relatively low in the eastern regions of Southwest and Northeast China.

The Orbiting Carbon Observatory-3 (OCO-3) is NASA's next instrument dedicated to extending the record of the dry-air mole fraction of column carbon dioxide (XCO2) and solar-induced fluorescence (SIF) measurements from space. The current schedule calls for a launch from the Kennedy Space Center no earlier than April 2019 via a Space-X Falcon 9 and Dragon capsule. The instrument will be installed as an external payload on the Japanese Experimental Module Exposed Facility (JEM-EF) of the International Space Station (ISS) with a nominal mission lifetime of 3 years. The precessing orbit of the ISS will allow for viewing of the Earth at all latitudes less than approximately 52∘, with a ground repeat cycle that is much more complicated than the polar-orbiting satellites that so far have carried all of the instruments capable of measuring carbon dioxide from space. The grating spectrometer at the core of OCO-3 is a direct copy of the OCO-2 spectrometer, which was launched into a polar orbit in July 2014. As such, OCO-3 is expected to have similar instrument sensitivity and performance characteristics to OCO-2, which provides measurements of XCO2 with precision better than 1 ppm at 3 Hz, with each viewing frame containing eight footprints approximately 1.6 km by 2.2 km in size. However, the physical configuration of the instrument aboard the ISS, as well as the use of a new pointing mirror assembly (PMA), will alter some of the characteristics of the OCO-3 data compared to OCO-2. Specifically, there will be significant differences from day to day in the sampling locations and time of day. In addition, the flexible PMA system allows for a much more dynamic observation-mode schedule. This paper outlines the science objectives of the OCO-3 mission and, using a simulation of 1 year of global observations, characterizes the spatial sampling, time-of-day coverage, and anticipated data quality of the simulated L1b. After application of cloud and aerosol prescreening, the L1b radiances are run through the operational L2 full physics retrieval algorithm, as well as post-retrieval filtering and bias correction, to examine the expected coverage and quality of the retrieved XCO2 and to show how the measurement objectives are met. In addition, results of the SIF from the IMAP–DOAS algorithm are analyzed. This paper focuses only on the nominal nadir–land and glint–water observation modes, although on-orbit measurements will also be made in transition and target modes, similar to OCO-2, as well as the new snapshot area mapping (SAM) mode.

Anthropogenic aerosols are hypothesized to enhance planetary albedo and offset some of the warming due to the buildup of greenhouse gases in Earth’s atmosphere. Aerosols can enhance the coverage, reflectance, and lifetime of warm low-level clouds. However, the relationship between cloud lifetime and aerosol concentration has been challenging to measure from polar orbiting satellites. We estimate two timescales relating to the formation and persistence of low-level clouds over 1° × 1° spatial domains using multiple years of geostationary satellite observations provided by the Clouds and Earth’s Radiant Energy System (CERES) Synoptic (SYN) product. Lagrangian trajectories spanning several days along the classic stratus-to-cumulus transition zone are stratified by aerosol optical depth and meteorology. Clouds forming in relatively polluted trajectories tend to have lighter precipitation rates, longer average lifetime, and higher cloud albedo and cloud fraction compared with unpolluted trajectories. While liquid water path differences are found to be negligible, we find direct evidence of increased planetary albedo primarily through increased drop concentration (Nd ) and cloud fraction, with the caveat that the aerosol influence on cloud fraction is positive only for stable atmospheric conditions. While the increase in cloud fraction can be large typically in the beginning of trajectories, the Twomey effect accounts for the bulk (roughly 3/4) of the total aerosol indirect radiative forcing estimate.

Fine particulate matter with a diameter of less than 2.5 µm (PM2.5) has been used as an important atmospheric environmental parameter mainly because of its impact on human health. PM2.5 is affected by both natural and anthropogenic factors that usually have strong diurnal variations. Such information helps toward understanding the causes of air pollution, as well as our adaptation to it. Most existing PM2.5 products have been derived from polar-orbiting satellites. This study exploits the use of the next-generation geostationary meteorological satellite Himawari-8/AHI (Advanced Himawari Imager) to document the diurnal variation in PM2.5. Given the huge volume of satellite data, based on the idea of gradient boosting, a highly efficient tree-based Light Gradient Boosting Machine (LightGBM) method by involving the spatiotemporal characteristics of air pollution, namely the space-time LightGBM (STLG) model, is developed. An hourly PM2.5 dataset for China (i.e., ChinaHighPM2.5) at a 5 km spatial resolution is derived based on Himawari-8/AHI aerosol products with additional environmental variables. Hourly PM2.5 estimates (number of data samples = 1 415 188) are well correlated with ground measurements in China (cross-validation coefficient of determination, CV-R2 = 0.85), with a root-mean-square error (RMSE) and mean absolute error (MAE) of 13.62 and 8.49 µgm-3, respectively. Our model captures well the PM2.5 diurnal variations showing that pollution increases gradually in the morning, reaching a peak at about 10:00 LT (GMT+8), then decreases steadily until sunset. The proposed approach outperforms most traditional statistical regression and tree-based machine-learning models with a much lower computational burden in terms of speed and memory, making it most suitable for routine pollution monitoring.

NOAA’s National Centers for Environmental Prediction (NCEP) Production Suite (NPS) currently includes over 20 operational weather forecast systems, providing forecasts from the mesoscale to global seasonal outlooks. In an effort to optimize resources, the NPS is being simplified to far fewer systems within the Unified Forecast System (UFS) framework that nevertheless span NOAA’s weather prediction mission: short-range regional and atmospheric composition (RRFS, WoF), medium-range subseasonal (GEFS) to seasonal (SFS), marine and coastal (GFS, GEFS, NWPS, GLWU), hurricanes (HAFS), on-demand atmospheric dispersion (HySPLIT), hydrology (NWM), and space weather (WAM/IPE) (see appendix for a full list of abbreviation definitions). An international constellation of low-Earth-orbit and geosynchronous-equatorial-orbit satellites are expanding our Earth intelligence; for example, polar-orbiting satellites now provide 85% of the data used in global weather models. In addition to the advancement of DA research, the Joint Effort for Data assimilation Integration (JEDI), operated by the UCAR Joint Center for Satellite Data Assimilation (JCSDA), provides a common software infrastructure for full community engagement in the testing, research, and development of new observations and DA methods.

Long-term gridded precipitation products are crucial for several applications in hydrology, agriculture and climate sciences. Currently available precipitation products suffer from space and time inconsistency due to the non-uniform density of ground networks and the difficulties in merging multiple satellite sensors. The recent “bottom-up” approach that exploits satellite soil moisture observations for estimating rainfall through the SM2RAIN (Soil Moisture to Rain) algorithm is suited to build a consistent rainfall data record as a single polar orbiting satellite sensor is used. Here we exploit the Advanced SCATterometer (ASCAT) on board three Meteorological Operational (MetOp) satellites, launched in 2006, 2012, and 2018, as part of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Polar System programme. The continuity of the scatterometer sensor is ensured until the mid-2040s through the MetOp Second Generation Programme. Therefore, by applying the SM2RAIN algorithm to ASCAT soil moisture observations, a long-term rainfall data record will be obtained, starting in 2007 and lasting until the mid-2040s. The paper describes the recent improvements in data pre-processing, SM2RAIN algorithm formulation, and data post-processing for obtaining the SM2RAIN–ASCAT quasi-global (only over land) daily rainfall data record at a 12.5 km spatial sampling from 2007 to 2018. The quality of the SM2RAIN–ASCAT data record is assessed on a regional scale through comparison with high-quality ground networks in Europe, the United States, India, and Australia. Moreover, an assessment on a global scale is provided by using the triple-collocation (TC) technique allowing us also to compare these data with the latest, fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA5), the Early Run version of the Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG), and the gauge-based Global Precipitation Climatology Centre (GPCC) products. Results show that the SM2RAIN–ASCAT rainfall data record performs relatively well at both a regional and global scale, mainly in terms of root mean square error (RMSE) when compared to other products. Specifically, the SM2RAIN–ASCAT data record provides performance better than IMERG and GPCC in data-scarce regions of the world, such as Africa and South America. In these areas, we expect larger benefits in using SM2RAIN–ASCAT for hydrological and agricultural applications. The limitations of the SM2RAIN–ASCAT data record consist of the underestimation of peak rainfall events and the presence of spurious rainfall events due to high-frequency soil moisture fluctuations that might be corrected in the future with more advanced bias correction techniques. The SM2RAIN–ASCAT data record is freely available at https://doi.org/10.5281/zenodo.3405563 (Brocca et al., 2019) (recently extended to the end of August 2019).

Assessing the seasonal patterns of the Amazon rainforests has been difficult because of the paucity of ground observations and persistent cloud cover over these forests obscuring optical remote sensing observations. Here, we use data from a new generation of geostationary satellites that carry the Advanced Baseline Imager (ABI) to study the Amazon canopy. ABI is similar to the widely used polar orbiting sensor, the Moderate Resolution Imaging Spectroradiometer (MODIS), but provides observations every 10–15 min. Our analysis of NDVI data collected over the Amazon during 2018–19 shows that ABI provides 21–35 times more cloud-free observations in a month than MODIS. The analyses show statistically significant changes in seasonality over 85% of Amazon forest pixels, an area about three times greater than previously reported using MODIS data. Though additional work is needed in converting the observed changes in seasonality into meaningful changes in canopy dynamics, our results highlight the potential of the new generation geostationary satellites to help us better understand tropical ecosystems, which has been a challenge with only polar orbiting satellites. Cloud cover and scarcity of ground-based validation hinder remote sensing of forest dynamics in the Amazon basin. Here, the authors analyse imagery from a high-frequency geostationary satellite sensor to study monthly NDVI patterns in the Amazon forest, finding support for spatially extensive seasonality.