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Emerging new-generation geostationary satellites have broadened the scope for studying the diurnal cycle of ecosystem functions. We exploit observations from the Geostationary Operational Environmental Satellite-R series to examine the effect of a severe U.S. heatwave in 2020 on the diurnal variations of ecosystem photosynthesis. We find divergent responses of photosynthesis to the heatwave across vegetation types and aridity gradients, with drylands exhibiting widespread midday and afternoon depression in photosynthesis. The diurnal centroid and peak time of dryland gross primary production (GPP) substantially shift toward earlier morning times, reflecting notable water and heat stress. Our geostationary satellite-based method outperforms traditional radiation-based upscaling methods from polar-orbiting satellite snapshots in estimating daily GPP and GPP loss during heatwaves. These findings underscore the potential of geostationary satellites for diurnal photosynthesis monitoring and highlight the necessity to consider the increased diurnal asymmetry in GPP under stress when evaluating carbon-climate interactions.
Gross primary productivity (GPP) is the largest flux in the global carbon cycle and satellite-based GPP estimates have long been used to study the trends and interannual variability of GPP. With recent updates to geostationary satellites, we can now explore the diurnal variability of GPP at a comparable spatial resolution to polar-orbiting satellites and at temporal frequencies comparable to eddy covariance (EC) tower sites. We used observations from the Advanced Baseline Imager on the Geostationary Operational Environmental Satellite-R series (GOES-R) to test the ability of subdaily satellite data to capture the shifts in the diurnal course of GPP at an oak savanna EC site in California, USA that is subject to seasonal soil moisture declines. We compared three methods to estimate GPP: (a) a light-use efficiency model, (b) a linear relationship between the product of near-infrared reflectance of vegetation and photosynthetically active radiation (LIN-NIR v P) and EC tower GPP, and (c) a light response curve (LRC-NIR v P) between NIR v P and EC GPP. The LRC-NIR v P achieved the lowest mean absolute error for winter (2 µmol CO 2 m –2 s –1 ), spring (2.51 µmol CO 2 m –2 s –1 ), summer (1.43 µmol CO 2 m –2 s –1 ), and fall (1.35 µmol CO 2 m –2 s –1 ). The ecosystem experienced the largest shift in daily peak GPP in relation to the peak of incoming solar radiation toward the morning hours during the dry summers. The LRC-NIRvP and the light-use efficiency model were in agreement with these patterns of a shift in peak daily GPP toward the morning hours during summer. Our results can help develop diurnal estimates of GPP from geostationary satellites that are sensitive to fluctuating environmental conditions during the day.
The success of future geostationary (GEO) satellite observation missions depends on our ability to design instruments that address their key scientific objectives. Here, an Observation System Simulation Experiment (OSSE) is performed to quantify the constraints on methane (CH 4 ) emissions in North America obtained from shortwave infrared (SWIR), thermal infrared (TIR), and multi-spectral (SWIR+TIR) measurements in geostationary orbit and from future SWIR low-Earth orbit (LEO) measurements. Furthermore, we used an efficient stochastic algorithm to compute the information content of the inverted emissions at high spatial resolution (0.5° × 0.7°) in a variational framework using the GEOS-Chem chemistry-transport model and its adjoint. Our results show that at sub-weekly timescales, SWIR measurements in GEO orbit can constrain about twice as many independent flux patterns than in LEO orbit, with a degree of freedom for signal (DOF) for the inversion of 266 and 115, respectively. Comparisons between TIR GEO and SWIR LEO configurations reveal that poor boundary layer sensitivities for the TIR measurements cannot be compensated for by the high spatiotemporal sampling of a GEO orbit. The benefit of a multi-spectral instrument compared to current SWIR products in a GEO context is shown for sub-weekly timescale constraints, with an increase in the DOF of about 50 % for a 3-day inversion. Our results further suggest that both the SWIR and multi-spectral measurements on GEO orbits could almost fully resolve CH 4 fluxes at a spatial resolution of at least 100 km × 100 km over source hotspots (emissions > 4 × 10 5 kg day -1 ). The sensitivity of the optimized emission scaling factors to typical errors in boundary and initial conditions can reach 30 and 50 % for the SWIR GEO or SWIR LEO configurations, respectively, while it is smaller than 5 % in the case of a multi-spectral GEO system. Our results demonstrate that multi-spectral measurements from a geostationary satellite platform would address the need for higher spatiotemporal constraints on CH 4 emissions while greatly mitigating the impact of inherent uncertainties in source inversion methods on the inferred fluxes.
The latest generation of geostationary satellites provide Earth observations similar to widely used polar-orbiting sensors but at intervals as frequently as every 5–10 min, making them ideal for studying the diurnal dynamics of land–atmosphere interactions. The NASA Earth Exchange (NEX) group created the GeoNEX datasets by collating data from several geostationary platforms, including GOES-16/17/18, Himawari-8/9, and GK-2A, and placing them on a common grid to facilitate use by the Earth science community. Here, we document the GeoNEX Coincident Ground Observations (GeCGO) dataset for terrestrial ecosystem studies and provide examples for its use. Currently, GeCGO provides GOES-16 Advanced Baseline Imager (ABI) data over a 10 km × 10 km area surrounding 1586 network sites across the Americas. GeCGO makes it easy to compare the time series of geostationary data with the diurnal ground observations, including carbon/water fluxes and aerosol optical depth, and is extensible to other regions. We also develop GeoNEXTools to facilitate analyses that require both GeoNEX data and other NASA satellite data. The objectives of this paper are to introduce GeCGO and GeoNEXTools and demonstrate their applications. First, we describe the details of GeCGO and GeoNEXTools. Second, we explain how GeCGO can be integrated with other satellite data. Finally, we showcase comparisons between GeCGO and observations from three ground-based networks. GeCGO is available at https://doi.org/10.25966/y5pe-xp41 (Hashimoto et al., 2025).
Accurately simulating a geostationary hyperspectral infrared sounder is critical for quantitative applications. Traditional radiation simulations of such instruments often overlook the influence of slant observation geometry by using vertical profile assumption, leading to inadequate simulation accuracy. By using global atmospheric profiles with 1 km spatial resolution, the slant-path effects on brightness temperature simulations are quantified. Experiments indicate that the slant geometry has less impact on longwave brightness temperature simulations and has a substantial impact on middle-wave brightness temperature simulations. It may introduce 0.5 K (or more) uncertainty to brightness temperatures of water vapor absorption channels when the satellite zenith angle is greater than 45°. Considering the slant profile is recommended for quantitative applications of geostationary hyperspectral sounder data, such as sounding retrieval and data assimilation.
In this work, we present the first observations of gigantic jets (GJs) by the Geostationary Lightning Mapper (GLM) on board the Geostationary Operational Environmental Satellite-R series. Fourteen GJs produced by Tropical Storm Harvey on 19 August 2017 were observed by both GLM and a ground-based low-light-level camera system. The majority of the GJs produced distinguishable signatures in the GLM data, which include long continuous emissions, large peak flash optical energies, and small lateral propagation distances in comparison with other flashes observed by GLM. For two GJs with the best ground-based images, each have a single pixel that contains the largest optical energy throughout the duration of the GJ and also coincides with the azimuth of the GJ from the video images. The optical energy of the pixel increases as the GJ propagates upward, reaches its peak when the GJ connects to the ionosphere, and then fades away.
This report captures the outputs from the AE9 for the electron environment at geostationary orbit. There is no new work, simply a collection of model outputs to make them easier to use and tailored to quick data gathering for geostationary orbit studies. Other models do exist, such as POLE, but a comprehensive look at each is beyond the scope of this work. Other resources are contained in the references.
In this paper, we describe the second phase of an Observing System Simulation Experiment (OSSE) that utilizes the synthetic measurements from a constellation of satellites measuring atmospheric composition from geostationary (GEO) Earth orbit presented in part I of the study. Our OSSE is focused on carbon monoxide observations over North America, East Asia and Europe where most of the anthropogenic sources are located. Here we assess the impact of a potential GEO constellation on constraining northern hemisphere (NH) carbon monoxide (CO) using data assimilation. We show how cloud cover affects the GEO constellation data density with the largest cloud cover (i.e., lowest data density) occurring during Asian summer. We compare the modeled state of the atmosphere (Control Run), before CO data assimilation, with the known “true” state of the atmosphere (Nature Run) and show that our setup provides realistic atmospheric CO fields and emission budgets. Overall, the Control Run underestimates CO concentrations in the northern hemisphere, especially in areas close to CO sources. Assimilation experiments show that constraining CO close to the main anthropogenic sources significantly reduces errors in NH CO compared to the Control Run. We assess the changes in error reduction when only single satellite instruments are available as compared to the full constellation. We find large differences in how measurements for each continental scale observation system affect the hemispherical improvement in long-range transport patterns, especially due to seasonal cloud cover. Finally, a GEO constellation will provide the most efficient constraint on NH CO during winter when CO lifetime is longer and increments from data assimilation associated with source regions are advected further around the globe.
Here, we compare the performance of three different filters that identify solar artifacts in the Geostationary Lightning Mapper (GLM) data. These filters differ in their complexity and intended use. We use the most appropriate filters to document the severity of GLM solar contamination and to compute the frequency of extreme Americas lightning. Solar artifacts occur at an average rate of one every ~8 min. Due to their continuous emissions across a portion of the GLM CCD array, these events disproportionately contribute to the GLM event and group rates. Up to 40% of all events recorded between 10:00 and 12:00 local time during 2018 were solar artifacts. The frequency-domain solar filter also reveals a carrier wave at 50 to 60 Hz in some lightning flashes and solar episodes that appears to be anthropogenic in origin. Removing the relatively frequent solar artifact cases reveals rare cases of extreme lightning flashes. Exceptional flashes on the order of 500+ km in length and 10+ s in duration only occur a few times per year and are produced by storms that are predisposed for exceptional lightning rather than as single random occurrences.
The daily maximum relativistic electron flux at geostationary orbit can be predicted well with a set of daily averaged predictor variables including previous day's flux, seed electron flux, solar wind velocity and number density, AE index, IMF B z , Dst , and ULF and VLF wave power. As predictor variables are intercorrelated, we used multiple regression analyses to determine which are the most predictive of flux when other variables are controlled. Empirical models produced from regressions of flux on measured predictors from 1 day previous were reasonably effective at predicting novel observations. Adding previous flux to the parameter set improves the prediction of the peak of the increases but delays its anticipation of an event. Previous day's solar wind number density and velocity, AE index, and ULF wave activity are the most significant explanatory variables; however, the AE index, measuring substorm processes, shows a negative correlation with flux when other parameters are controlled. This may be due to the triggering of electromagnetic ion cyclotron waves by substorms that cause electron precipitation. VLF waves show lower, but significant, influence. The combined effect of ULF and VLF waves shows a synergistic interaction, where each increases the influence of the other on flux enhancement. Correlations between observations and predictions for this 1 day lag model ranged from 0.71 to 0.89 (average: 0.78). Furthermore, a path analysis of correlations between predictors suggests that solar wind and IMF parameters affect flux through intermediate processes such as ring current ( Dst ), AE , and wave activity.
The mean diurnal cycle of cloud entrainment rate (w e ) over the northeast Pacific region is for the first time computed by combining, in a mixed-layer model framework, the hourly composited GOES-15 satellite-based cloud top height (H T ) tendency, advection, and large-scale vertical velocity (w) during May to September 2013, with horizontal winds and w taken from the European Centre for Medium-Range Weather Forecasts (ECMWF) model. The tendency term dominates the magnitude and phase of the w e diurnal cycle, with a secondary role of w, and a modest advective contribution. The peak and minimum in w e occur between 20:00–22:00 LT and 9:00–11:00 LT, respectively, in close agreement with the diurnal cycle of turbulence driven by cloud top longwave cooling. Uncertainties in H T and ECMWF fields are assessed with in situ observations and three meteorological reanalysis data sets. This study provides the basis for constructing nearly global climatologies of w e by combining a suite of well-calibrated geostationary satellites.
The composition of the inner magnetosphere is of great importance for determining the plasma pressure, and thus the currents and magnetic field configuration. In this study, we perform a statistical survey of equatorial plasma pressure distributions and investigate the relative contributions of ions and electron with different energies inside of geostationary orbit under two AE levels based on over sixty months of observations from the HOPE and RBSPICE mass spectrometers on board Van Allen Probes. We find that the total and partial pressures of different species increase significantly at high AE levels with Hydrogen (H + ) pressure being dominant in the plasmasphere. The pressures of the heavy ions and electrons increase outside the plasmapause and develop a strong dawn-dusk asymmetry with ion pressures peaking at dusk and electron pressure peaking at dawn. In addition, ring current H + with energies ranging from 50 keV up to several hundred keV is the dominant component of plasma pressure during both quiet (> 90%) and active times (> 60%), while Oxygen (O + ) with 10 < E < 50 keV and electrons with 0.1 < E < 40 keV become important during active times contributing more than 25% and 20% on the nightside, respectively, while the Helium (He + ) contribution is generally small. The results presented in this study provide a global picture of the equatorial plasma pressure distributions and the associated contributions from different species with different energy ranges, which advance our knowledge of wave generation and provide models with a systematic baseline of plasma composition.
The Lightning Cluster Filter Algorithm in the Geostationary Lightning Mapper (GLM) ground system identifies lightning flashes from the stream of event detections. It excels at clustering simple flashes but experiences anomalies with complex flashes that last longer than 3 s or contain more than 100 groups, leading to flashes being artificially split. In this work, we develop a technique that corrects these anomalies and apply it to the 2018 GLM data to document all lightning across the Americas. We produce statistics describing the characteristics and frequencies of reclustered GLM flashes and thunderstorm area features. The average GLM Americas flash rate in 2018 was 11.7 flashes per second with the greatest flash rate densities occurring over Lake Maracaibo (157 flashes per km 2 /year). Lloró, Chocó, Colombia had the most thunderstorm activity with 256 thunder days. The longest GLM flash spanned 673 km, the largest flash covered 114,997 km 2 , and the longest-lasting flash had a 13.496-s duration. The first case occurred over Rio Grande do Sul in Brazil, while the other two cases occurred in the central United States. All three extreme flashes are located in the stratiform regions of Mesoscale Convective Systems. The highest flash rate for a thunderstorm area feature was 17.6 flashes per second, while the largest thunderstorm was 216,865 km 2 in size. Both storms occurred in South America. In conclusion, these initial results demonstrate the value that the development of a reprocessed GLM science product would offer and how such a product might be created at a reduced computational cost.
Abstract The Radio Frequency Sensor (RFS), a new radio frequency lightning detector, was launched into geosynchronous orbit in December 2021, and first collected data in January 2022. RFS is a specialized software‐defined radio receiver that detects, records, and reports impulsive broadband radio‐frequency (RF) signatures from lightning in the very high frequency (VHF; 30–300 MHz) range. Its vantage point from a Western hemisphere geosynchronous orbit provides unique opportunities to study evolution of RF lightning signatures over the durations of thunderstorms over the Americas and Pacific Ocean. Its overlapping view with the Geostationary Lightning Mappers (GOES‐16 & 17) enables additional comparisons between the sources of optical emissions and associated VHF emissions that were not possible with previous sensors. We find that RFS preferentially detects bright VHF signals called transionospheric pulse pairs (trans‐ionospheric pulse pairs (TIPPs)). It is estimated that more than 85% of the RFS‐detected lightning events are TIPPs. This paper presents initial results from the first year and a half of on‐orbit operation.
Commercial-Off-The-Shelf (COTS) electronics offer cutting-edge capability at lower prices compared to their space-grade counterparts. However, their use in space missions has been limited due to concerns around survivability in a space environment; COTS devices are not designed to survive the harsh radiation environment of space. Nonetheless, for space missions with short durations it may be possible to use COTS electronics. This study evaluates the use of several families of COTS electronics for a specific short-term mission. An assembled database including selected space grade and COTS components is discussed. High confidence FPGAs, microprocessors, and optocouplers COTS are identified. Medium confidence Memory, ADCs, DACs, power electronics, and RFMMICs COTS are also included, as well as testing to improve confidence in medium confidence parts. An experimental approach for evaluating tin whisker susceptibility for tin-leaded COTS components is described. Using COTS electronics in Short-Term Geostationary Satellites is feasible; this report includes enabling tools.
The Geostationary Operational Environmental Satellite (GOES) records sky brightness temperatures and irradiances at several wavelengths. From these the cloud microphysical and optical properties can be routinely retrieved. These properties are derived using the technique proposed by Minnis et al. 2020, and is similar to the technique used for deriving cloud properties from Moderate Resolution Imaging Spectroradiometer (MODIS) data. The derived GOES properties for the duration of the second Wind Forecasting Improvement Project (WFIP2) within the domain are reported here. Nominally the retrieved dataset has a 30- minute temporal and a 9 km spatial resolution.
Automated methods for the detection and tracking of deep convective clouds in geostationary satellite imagery have a vital role in both the forecasting of severe storms and research into their behaviour. Studying the interactions and feedbacks between multiple deep convective clouds (DCC), however, poses a challenge for existing algorithms due to the necessary compromise between false detection and missed detection errors. We utilise an optical flow method to determine the motion of deep convective clouds in GOES-16 ABI imagery in order to construct a semi-Lagrangian framework for the motion of the cloud field, independently of the detection and tracking of cloud objects. The semi-Lagrangian framework allows severe storms to be simultaneously detected and tracked in both spatial and temporal dimensions. For the purpose of this framework we have developed a novel Lagrangian convolution method and a number of novel implementations of morphological image operations that account for the motion of observed objects. These novel methods allow the accurate extension of computer vision techniques to the temporal domain for moving objects such as DCCs. By combining this framework with existing methods for detecting DCCs (including detection of growing cores through cloud top cooling and detection of anvil clouds using brightness temperature), we show that the novel framework enables reductions in errors due to both false and missed detections compared to any of the individual methods, reducing the need to compromise when compared with existing frameworks. The novel framework enables the continuous tracking of anvil clouds associated with detected deep convection after convective activity has stopped, enabling the study of the entire life cycle of DCCs and their associated anvils. Furthermore, we expect this framework to be applicable to a wide range of cases including the detection and tracking of low-level clouds and other atmospheric phenomena. In addition, this framework may be used to combine observations from multiple sources, including satellite observations, weather radar and reanalysis model data.