Cross-track Infrared Sounder Research Articles

Ammonia emission estimates using CrIS satellite observations over Europe

Abstract. Over the past century, ammonia (NH3) emissions have increased with the growth of livestock and fertilizer usage. The abundant NH3 emissions lead to secondary fine particulate matter (PM2.5) pollution, climate change, and a reduction in biodiversity, and they affect human health. Up-to-date and spatially and temporally resolved information on NH3 emissions is essential to better quantify their impact. In this study we applied the existing Daily Emissions Constrained by Satellite Observations (DECSO) algorithm to NH3 observations from the Cross-track Infrared Sounder (CrIS) to estimate NH3 emissions. Because NH3 in the atmosphere is influenced by nitrogen oxides (NOx), we implemented DECSO to estimate NOx and NH3 emissions simultaneously. The emissions are derived over Europe for 2020 on a spatial resolution of 0.2°×0.2° using daily observations from both CrIS and the TROPOspheric Monitoring Instrument (TROPOMI; on the Sentinel-5 Precursor (S5P) satellite). Due to the limited number of daily satellite observations of NH3, monthly emissions of NH3 are reported. The total NH3 emissions derived from observations are about 8 Tg yr−1, with a precision of about 5 %–17 % per grid cell per year over the European domain (35–55° N, 10° W–30° E). The comparison of the satellite-derived NH3 emissions from DECSO with independent bottom-up inventories and in situ observations indicates a consistency in terms of magnitude on the country totals, with the results also being comparable regarding the temporal and spatial distributions. The validation of DECSO over Europe implies that we can use DECSO to quickly derive fairly accurate monthly emissions of NH3 over regions with limited local information on NH3 emissions.

Tropical tropospheric ozone distribution and trends from in situ and satellite data

Abstract. Tropical tropospheric ozone (TTO) is important for the global radiation budget because the longwave radiative effect of tropospheric ozone is higher in the tropics than midlatitudes. In recent decades the TTO burden has increased, partly due to the ongoing shift of ozone precursor emissions from midlatitude regions toward the Equator. In this study, we assess the distribution and trends of TTO using ozone profiles measured by high-quality in situ instruments from the IAGOS (In-Service Aircraft for a Global Observing System) commercial aircraft, the SHADOZ (Southern Hemisphere ADditional OZonesondes) network, and the ATom (Atmospheric Tomographic Mission) aircraft campaign, as well as six satellite records reporting tropical tropospheric column ozone (TTCO): TROPOspheric Monitoring Instrument (TROPOMI), Ozone Monitoring Instrument (OMI), OMI/Microwave Limb Sounder (MLS), Ozone Mapping Profiler Suite (OMPS)/Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2), Cross-track Infrared Sounder (CrIS), and Infrared Atmospheric Sounding Interferometer (IASI)/Global Ozone Monitoring Experiment 2 (GOME2). With greater availability of ozone profiles across the tropics we can now demonstrate that tropical India is among the most polluted regions (e.g., western Africa, tropical South Atlantic, Southeast Asia, Malaysia and Indonesia), with present-day 95th percentile ozone values reaching 80 nmol mol−1 in the lower free troposphere, comparable to midlatitude regions such as northeastern China and Korea. In situ observations show that TTO increased between 1994 and 2019, with the largest mid- and upper-tropospheric increases above India, Southeast Asia, and Malaysia and Indonesia (from 3.4 ± 0.8 to 6.8 ± 1.8 nmol mol−1 decade−1), reaching 11 ± 2.4 and 8 ± 0.8 nmol mol−1 decade−1 close to the surface (India and Malaysia–Indonesia, respectively). The longest continuous satellite records only span 2004–2019 but also show increasing ozone across the tropics when their full sampling is considered, with maximum trends over Southeast Asia of 2.31 ± 1.34 nmol mol−1 decade−1 (OMI) and 1.69 ± 0.89 nmol mol−1 decade−1 (OMI/MLS). In general, the sparsely sampled aircraft and ozonesonde records do not detect the 2004–2019 ozone increase, which could be due to the genuine trends on this timescale being masked by the additional uncertainty resulting from sparse sampling. The fact that the sign of the trends detected with satellite records changes above three IAGOS regions, when their sampling frequency is limited to that of the in situ observations, demonstrates the limitations of sparse in situ sampling strategies. This study exposes the need to maintain and develop high-frequency continuous observations (in situ and remote sensing) above the tropical Pacific Ocean, the Indian Ocean, western Africa, and South Asia in order to estimate accurate and precise ozone trends for these regions. In contrast, Southeast Asia and Malaysia–Indonesia are regions with such strong increases in ozone that the current in situ sampling frequency is adequate to detect the trends on a relatively short 15-year timescale.

Space-based observations of tropospheric ethane map emissions from fossil fuel extraction

Ethane is the most abundant non-methane hydrocarbon in the troposphere, where it impacts ozone and reactive nitrogen and is a key tracer used for partitioning emitted methane between anthropogenic and natural sources. However, quantification has been challenged by sparse observations. Here, we present a satellite-based measurement of tropospheric ethane and demonstrate its utility for fossil-fuel source quantification. An ethane spectral signal is detectable from space in Cross-track Infrared Sounder (CrIS) radiances, revealing ethane signatures associated with fires and fossil fuel production. We use machine-learning to convert these signals to ethane abundances and validate the results against surface observations (R2 = 0.66, mean CrIS/surface ratio: 0.65). The CrIS data show that the Permian Basin in Texas and New Mexico exhibits the largest persistent ethane enhancements on the planet, with regional emissions underestimated by seven-fold. Correcting this underestimate reveals Permian ethane emissions that represent at least 4-7% of the global fossil-fuel ethane source.

Enabling the Assimilation of CrIS Shortwave Infrared Observations in Global NWP at NOAA. Part II: OSEs and Results

Abstract The assimilation of data from hyperspectral infrared sounders in global data assimilation systems has historically been focused on observations in the longwave infrared (LWIR) region of the spectrum (650-1100 cm−1), despite the often concurrent availability of measurements from the shortwave infrared (SWIR) region of the spectrum (2150-2550 cm−1), because issues (like solar effects) have generally prevented the assimilation of SWIR observations. Recent advances in radiative transfer models have worked to address some of the previous challenges in simulating SWIR observations, and the assimilation of SWIR data (e.g. from potential future small-satellites) is now a feasible prospect. Still, a better understanding of how these observations perform in a data assimilation system and impact resulting analyses and NWP forecasts is necessary. In this study, the value of SWIR observations in global NWP is assessed by assimilating SWIR observations from the Cross-track Infrared Sounder (CrIS) in NOAA’s Global Data Assimilation System (GDAS). The methodologies used to enable the assimilation of these observations, including the implementation of a scene-dependent observation error and the enhancement of quality control procedures, are discussed, as are the results of Observing System Experiments (OSEs) conducted to evaluate the impact of assimilating SWIR observations on forecast skill. The overall results show that SWIR assimilation produces similar forecast impacts to LWIR assimilation. The ability to demonstrate that the assimilation or SWIR observations in NWP is a realistic prospect may help to shape future constellations of small-satellites to serve as a beneficial complement to the current constellation if hyperspectral IR sounders.

Enabling the Assimilation of CrIS Shortwave Infrared Observations in Global NWP at NOAA. Part I: Background and Methods

Abstract Radiance observations from Earth-observing satellites have a significant positive impact on numerical weather prediction (NWP) forecasts, but some spectral regions are not fully exploited. Observations from hyperspectral infrared (IR) sounders in the longwave region (650-1100 cm−1), for instance, are routinely assimilated in many NWP models, but observations in the shortwave region (2155-2550 cm−1) are not. Each of these regions provides information on the temperature structure of the atmosphere, but the shortwave IR (SWIR) region is considered challenging to assimilate due to noise equivalent delta temperature (NEDT) that is highly variable depending on scene brightness temperature and to phenomena that are difficult to model, like non-Local Thermodynamic Equilibrium (NLTE) and solar reflectance. With recent advances in small-satellite technology, SWIR temperature sounders may provide an agile and cost-effective complement to the current constellation of IR sounders. Therefore, a better understanding of the use and impact of SWIR observations in data assimilation for NWP is warranted. In part one of this study, as presented here, the amount of unique information (as determined by Empirical Orthogonal Decomposition (EOD)) made available to a data assimilation system by Cross-track Infrared Sounder (CrIS) SWIR observations is reviewed, recent advancements to the Community Radiative Transfer Model (CRTM) for the simulation of CrIS shortwave radiances are tested, and enhancements to NOAA’s Global Data Assimilation System (GDAS) for the assimilation of CrIS SWIR observations are implemented and evaluated. Part two of this study, which seeks to assess the value of assimilating shortwave IR observations in global NWP, is also introduced.

Characterizing fire and fire atmospheric states from space using collocated hyperspectral infrared sounding and narrow-band imagery

Under the influence of global climate change, wildfires are becoming increasingly significant within ecosystems. Satellite technology offers a critical perspective for studying fire. The synergy of high-spatial resolution narrow-band imagers and hyperspectral infrared (IR) sounders allow for comprehensive observation and long-term global monitoring of fire characteristics and associated atmospheric changes on the pixel scale. This study demonstrates methods to utilize the pixel-scale collocated fire observations from the Visible Infrared Imaging Radiometer Suite (VIIRS) and the IR radiance spectra from the Cross Track Infrared Sounder (CrIS), onboard NASA's Suomi National Polar-orbiting Partnership (SNPP) satellite, to analyze fire and the atmospheric conditions before, during, and after fires. Two months of satellite observations over the Southwest United States and the Amazonia regions when large fires occurred in the regions of interest (October 2017 and August 2020) are used. Our findings reveal that CrIS has high sensitivity to fire that affects as little as 1% of its field of view (FOV) with FOV total fire radiative power (FRP) larger than 200 MW (MW) at night and 1000 MW during the day. By employing spectral principal component analysis (PCA), the CrIS spectral signature to atmospheric temperature, humidity, and trace gases corresponding to fire characteristics are quantified. This approach highlights that collocated imager and IR sounder data, when paired with PCA, provide a powerful method to effectively identify and monitor wildfires. This technique also allows for the observation of subsequent atmospheric alterations while managing data volume efficiently, ensuring that crucial spectral information is preserved. This methodology advances our ability to understand and respond to the multifaceted impacts of wildfires on both local and global scales, reinforcing the value of integrated satellite observations in environmental science.

Improving All‐Sky Simulations of Typhoon Cloud/Rain Band Structures of NOAA‐20 CrIS Window Channel Observations

AbstractThe Cross‐track Infrared Sounder (CrIS) observations (O) contributed greatly to numerical weather prediction. Further contribution depends on the success of all‐sky data assimilation, which requires a method to produce realistic cloud/rain band structures from background fields (i.e., 6‐hr forecasts), and to remove large biases of all‐sky simulation of brightness temperature (TB) in the presence of clouds. In this study, CrIS all‐sky simulations of brightness temperatures at an arbitrarily selected window channel within Typhoon Hinnamnor (2022) are investigated. The 3‐km Weather Research and Forecasting model with three microphysics schemes were used to produce 6‐hr background forecasts (B). The O − B statistic deviate greatly from Gaussian distribution with large biases in either water clouds, or thin ice clouds, or thick ice clouds within Typhoon Hinnamnor. By developing a linear regression function of three all‐sky simulations of TB from 6‐hr forecasts with three microphysics schemes, the O − B statistics approximate a Gaussian normal distribution in water clouds, thin ice clouds and thick ice clouds. Taking the regression function that is established by a training data set to combine 6‐hr background forecasts at later times, the cloud/rain band structures compared much more favorably with CrIS observations than those from an individual microphysic. Furthermore, the regression coefficients derived from Typhoon Hinnamnor (2022) also work for Typhoon Khanun (2023). The work aims to quantify and remove biases in background fields of TB and generating realistic typhoon cloud/rain band structures in background fields will allow a better description of center position, intensity and size to improve typhoon forecasts.

A Comparison of Channel Selection for the Assimilation of CrIS Radiances for NWP

Abstract With hyperspectral instruments measuring radiation emitted by Earth and its atmosphere in the thermal infrared range in multiple channels, several studies were made to select a subset of channels in order to reduce the number of channels to be used in a data assimilation system. An optimal selection of channels based on the information content depends on several factors related to observation and background error statistics and the assimilation system itself. An optimal channel selection for the Cross-track Infrared Sounder (CrIS) was obtained and then compared to selections made for different NWP systems. For instance, the channel selection of Carminati has 224 channels also present in our optimal selection, which includes 455 channels. However, in terms of analysis error variance, the difference between the two selections is small. Integrated over the whole profile, the relative difference is equal to 15.3% and 4.5% for temperature and humidity, respectively. Also, different observation error covariance matrices were considered to evaluate the impact of this matrix on channel selection. Even though the channels selected optimally were different in terms of which channels were selected for the various matrices, the results in terms of analysis error are similar. Significance Statement Satellites measure radiation from Earth and its atmosphere in the thermal infrared. Those radiance data contain thousands of measurements, called channels, and thus, a selection needs to be done retaining most of the information content since the large number of individual pieces of information is not usable for numerical weather prediction systems. The goal of this paper is to find an optimal selection for the instrument CrIS and to compare this selection with selections made for different numerical weather prediction systems. It was found that even though the channels selected optimally were different in terms of which channels were selected compared to other selections, the results in terms of precision of the analysis are similar and the results in terms of analysis error are similar due to the nature of hyperspectral instruments, which have multiple Jacobians overlapping.

TROPESS-CrIS CO single-pixel vertical profiles: intercomparisons with MOPITT and model simulations for 2020 western US wildfires

Abstract. The new TROPESS (TRopospheric Ozone and its Precursors from Earth System Sounding) profile retrievals of carbon monoxide (CO) from the Cross-track Infrared Sounder (CrIS) are evaluated against Measurement of Pollution in the Troposphere (MOPITT) CO version 9 data. Comparison results that were adjusted to common a priori constraints in the retrieval processes have improved agreement between the two data sets over direct comparisons. TROPESS-CrIS CO profiles are within 5 % of MOPITT but have higher concentrations in the lower troposphere and lower concentrations in the upper troposphere. For the intense western US wildfire events in September 2020, we compare CO fields simulated by the GISS climate model to the two satellite CO observations. We show intermediate steps of the comparison process to illustrate the evaluation of model simulations by deriving the “retrieved” model CO profiles as they would be observed by the satellite. This includes the application of satellite level-2 data along with their corresponding diagnostic operators provided in the TROPESS-CrIS and MOPITT products. The process allows a diagnosis of potential model improvements in modeling fire emissions and pollution transport.

TOLNet validation of satellite ozone profiles in the troposphere: impact of retrieval wavelengths

Abstract. The Tropospheric Ozone Lidar Network (TOLNet) was used to validate retrievals of ozone (O3) profiles in the troposphere from the TROPOspheric Monitoring Instrument (TROPOMI) ultraviolet (UV), Cross-track Infrared Sounder (CrIS) infrared (IR), and a combined UV + IR wavelength retrieval from TROPOMI/CrIS. Observations from six separate ground-based lidar systems and various locations of ozonesondes distributed throughout North America and in the Netherlands were used to quantify systematic bias and random errors for each satellite retrieval. Furthermore, TOLNet data were used to intercompare idealized UV, IR, and UV + IR convolved lidar profiles of O3 in the troposphere during case studies representative of high-O3 events. This study shows that the improved sensitivity and vertical resolution in UV + IR retrievals in the middle- and upper-troposphere resulted in tropospheric degree of freedom (DOF) values ∼ 33 % higher compared to UV- and IR-only retrievals. The increased DOFs in the UV + IR retrievals allowed for improved reproduction of mid- and upper-tropospheric O3 enhancements and, to a lesser degree, near-surface pollution enhancements compared to single-wavelength satellite products. The validation of O3 profiles in the troposphere retrieved with the UV-only, IR-only, and UV + IR Tikhonov regularised Ozone Profile retrievAl with SCIATRAN (TOPAS) algorithm developed at the Institute for Environmental Physics, University of Bremen, demonstrated the utility of using TOLNet as a satellite evaluation data set. TOPAS UV-only, IR-only, and UV + IR wavelength retrievals had systematic biases, quantified with normalized mean bias, throughout the troposphere of 11.2 ppb (22.1 %), −1.7 ppb (−0.3 %), and 3.5 ppb (7.8 %), respectively, which meet the tropospheric systematic bias requirements defined by the science teams for the TROPOMI and CrIS sensors. The primary drivers of systematic bias were determined to be solar zenith angle, surface albedo, and cloud fraction. Random errors, representative of uncertainty in the retrievals and quantified by root mean squared errors (RMSEs), were large for all three retrievals, with UV-only, IR-only, and UV + IR wavelength retrievals having RMSEs throughout the troposphere of 17.4 ppb (19.8 % of mean tropospheric column values), 10.5 ppb (12.6 % of mean tropospheric column values), and 14.0 ppb (14.6 % of mean tropospheric column values), respectively. TOPAS UV-only profiles did not meet the uncertainty requirements defined for TROPOMI for the troposphere; however, CrIS IR-only retrievals did meet the uncertainty requirements defined by this mission. The larger random errors reflect the challenge of retrieving daily O3 profiles due to the limited sensitivity and vertical resolution of these retrievals in the troposphere. Tropospheric systematic biases and random error were lower in IR-only and combined UV + IR retrievals compared to UV-only products due to the increased sensitivity in the troposphere allowing the retrievals to deviate further from the a priori profiles. Observations from TOLNet demonstrated that the performance of the three satellite products varied by season and altitude in the troposphere. TOLNet was shown to result in similar validation statistics compared to ozonesonde data, which are a commonly used satellite evaluation data source, demonstrating that TOLNet is a sufficient source of satellite O3 profile validation data in the troposphere, which is critical as this data source is the primary product identified for the tropospheric O3 validation of the recently launched Tropospheric Emissions: Monitoring of Pollution (TEMPO) mission.

Changes to Peroxyacyl Nitrates (PANs) Over Megacities in Response to COVID‐19 Tropospheric NO2 Reductions Observed by the Cross‐Track Infrared Sounder (CrIS)

AbstractThe COVID‐19 pandemic perturbed air pollutant emissions as cities shut down worldwide. Peroxyacyl nitrates (PANs) are important tracers of photochemistry that are formed through the oxidation of non‐methane volatile organic compounds in the presence of nitrogen oxide radicals (NOx = NO + NO2). We use satellite measurements of free tropospheric PANs from the Suomi‐National Polar‐orbiting Partnership Cross‐track Infrared Sounder (CrIS) over eight of the world's megacities. We quantify the seasonal cycle of PANs over these megacities and find seasonal maxima in PANs correspond to seasonal peaks in local photochemistry. CrIS is used to explore changes in PANs in response to the COVID‐19 lockdowns. Statistically significant changes to PANs occurred over four megacities: with decreases over Los Angeles and Delhi, and increases over Mexico City and Beijing in the winter. Our analysis suggests that large perturbations in NOx may not result in significant declines in NOx export potential of megacities.

Bias correction of OMI HCHO columns based on FTIR and aircraft measurements and impact on top-down emission estimates

Abstract. Spaceborne formaldehyde (HCHO) measurements constitute an excellent proxy for the sources of non-methane volatile organic compounds (NMVOCs). Past studies suggested substantial overestimations of NMVOC emissions in state-of-the-art inventories over major source regions. Here, the QA4ECV (Quality Assurance for Essential Climate Variables) retrieval of HCHO columns from OMI (Ozone Monitoring Instrument) is evaluated against (1) FTIR (Fourier-transform infrared) column observations at 26 stations worldwide and (2) aircraft in situ HCHO concentration measurements from campaigns conducted over the USA during 2012–2013. Both validation exercises show that OMI underestimates high columns and overestimates low columns. The linear regression of OMI and aircraft-based columns gives ΩOMI=0.651Ωairc+2.95×1015 molec.cm-2, with ΩOMI and Ωairc the OMI and aircraft-derived vertical columns, whereas the regression of OMI and FTIR data gives ΩOMI=0.659ΩFTIR+2.02×1015 molec.cm-2. Inverse modelling of NMVOC emissions with a global model based on OMI columns corrected for biases based on those relationships leads to much-improved agreement against FTIR data and HCHO concentrations from 11 aircraft campaigns. The optimized global isoprene emissions (∼445Tgyr-1) are 25 % higher than those obtained without bias correction. The optimized isoprene emissions bear both striking similarities and differences with recently published emissions based on spaceborne isoprene columns from the CrIS (Cross-track Infrared Sounder) sensor. Although the interannual variability of OMI HCHO columns is well understood over regions where biogenic emissions are dominant, and the HCHO trends over China and India clearly reflect anthropogenic emission changes, the observed HCHO decline over the southeastern USA remains imperfectly elucidated.

Spectral Fingerprinting of Methane from Hyper-Spectral Sounder Measurements Using Machine Learning and Radiative Kernel-Based Inversion

Satellite-based hyper-spectral infrared (IR) sensors such as the Atmospheric Infrared Sounder (AIRS), the Cross-track Infrared Sounder (CrIS), and the Infrared Atmospheric Sounding Interferometer (IASI) cover many methane (CH4) spectral features, including the ν1 vibrational band near 1300 cm−1 (7.7 μm); therefore, they can be used to monitor CH4 concentrations in the atmosphere. However, retrieving CH4 remains a challenge due to the limited spectral information provided by IR sounder measurements. The information required to resolve the weak absorption lines of CH4 is often obscured by interferences from signals originating from other trace gases, clouds, and surface emissions within the overlapping spectral region. Consequently, currently available CH4 data product derived from IR sounder measurements still have large errors and uncertainties that limit their application scope for high-accuracy climate and environment monitoring applications. In this paper, we describe the retrieval of atmospheric CH4 profiles using a novel spectral fingerprinting methodology and our evaluation of performance using measurements from the CrIS sensor aboard the Suomi National Polar-orbiting Partnership (SNPP) satellite. The spectral fingerprinting methodology uses optimized CrIS radiances to enhance the CH4 signal and a machine learning classifier to constrain the physical inversion scheme. We validated our results using the atmospheric composition reanalysis results and data from airborne in situ measurements. An inter-comparison study revealed that the spectral fingerprinting results can capture the vertical variation characteristics of CH4 profiles that operational sounder products may not provide. The latitudinal variations in CH4 concentration in these results appear more realistic than those shown in existing sounder products. The methodology presented herein could enhance the utilization of satellite data to comprehend methane’s role as a greenhouse gas and facilitate the tracking of methane sources and sinks with increased reliability.

Thermal infrared observations of a western United States biomass burning aerosol plume

Abstract. Biomass burning smoke particles, due to their submicron particle size in relation to the average thermal infrared (TIR) wavelength, theoretically have negligible signals at the TIR channels. However, nearly instantaneous longwave (LW) signatures of thick smoke plumes can be frequently observed at the TIR channels from remotely sensed data, including at 10.6 µm (IR window), as well as in water-vapor-sensitive wavelengths at 7.3, 6.8, and 6.3 µm (e.g., lower, middle, and upper troposphere). We systematically evaluated multiple hypotheses as to causal factors of these IR signatures of biomass burning smoke using a combination of data from the Aqua MODerate resolution Imaging Spectroradiometer (MODIS), Aqua Cloud and the Earth Radiant Energy System (CERES), Geostationary Operational Environmental Satellite 16/17 (GOES-16/17) Advanced Baseline Imager, and Suomi-NPP Visible Infrared Imaging Radiometer Suite (VIIRS) and Cross-track Infrared Sounder (CrIS). The largely clear transmission of light through wildfire smoke in the near infrared indicates that coarse or giant ash particles are unlikely to be the dominant cause. Rather, clear signals in water vapor and TIR channels suggest that both co-transported water vapor injected to the middle to upper troposphere and surface cooling by the reduction of surface radiation by the plume are more significant, with the surface cooling effect of smoke aloft being the most dominant. Giving consideration of the smoke impacts on TIR and longwave, CERES indicates that large wildfire aerosol plumes are more radiatively neutral. Further, this smoke-induced TIR signal may be used to map very optically thick smoke plumes, where traditional aerosol retrieval methods have difficulties.

Estimation of the Concentration of XCO2 from Thermal Infrared Satellite Data Based on Ensemble Learning

Global temperatures are continuing to rise as atmospheric carbon dioxide (CO2) concentrations increase, and climate warming has become a major challenge to global sustainable development. The Cross-Track Infrared Sounder (CrIS) instrument is a Fourier transform spectrometer with 0.625 cm−1 spectral resolution covering a 15 μm CO2-absorbing band, providing a way of monitoring CO2 with on a large scale twice a day. This paper proposes a method to predict the concentration of column-averaged CO2 (XCO2) from thermal infrared satellite data using ensemble learning to avoid the iterative computations of radiative transfer models, which are necessary for optimization estimation (OE). The training data set is constructed with CrIS satellite data, European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) meteorological parameters, and ground-based observations. The training set was processed using two methods: correlation significance analysis (abbreviated as CSA) and principal component analysis (PCA). Extreme Gradient Boosters (XGBoost), Extreme Random Trees (ERT), and Gradient Boost Regression Tree (GBRT) are used for training and learning to develop the new retrieval model. The results showed that the R2 of XCO2 prediction built from the PCA dataset was bigger than that from the CSA dataset. These three learning models were verified by validation sets, and the ERT model showed the best agreement between model predictions and the truth (R2 = 0.9006, RMSE = 0.7994 ppmv, MAE = 0.5804 ppmv). The ERT model was finally selected to estimate the concentrations of XCO2. The deviation of XCO2 predictions of 12 TCCON sites in 2019 was within ±1 ppm. The monthly averages of XCO2 concentrations in close agreement with TCCON ground observations were grouped into four regions: Asia (R2 = 0.9671, RMSE = 0.7072 ppmv), Europe (R2 = 0.9703, RMSE = 0.8733 ppmv), North America (R2 = 0.9800, RMSE = 0.6187 ppmv), and Oceania (R2 = 0.9558, RMSE = 0.4614 ppmv).

Cross-Calibration of HY-1D/COCTS Thermal Emissive Bands in the South China Sea

Haiyang-1D (HY-1D) is the second operational satellite in China’s Haiyang-1 series of satellites, carrying the Chinese Ocean Color and Temperature Scanner (COCTS) to provide ocean color and temperature observations. The radiometric calibration is a prerequisite to guarantee the quality of the satellite observations and the derived products, and the radiometric calibration of the thermal emissive bands of HY-1D/COCTS can effectively improve the accuracy of sea surface temperature (SST) derived from the thermal infrared data. In this paper, a study on the regional cross-calibration of the COCTS thermal emissive bands is conducted for high-accuracy SST observations in the South China Sea. The Visible Infrared Imaging Radiometer Suite (VIIRS) on board the NOAA-20 satellite launched by the National Oceanic and Atmospheric Administration (NOAA) is selected as the calibration reference sensor, and a double-difference cross-calibration method is used for HY-1D/COCTS thermal infrared brightness temperature (BT) evaluation. The results show that the bias of the 11 µm and 12 µm thermal emissive bands of COCTS and VIIRS in the South China Sea are 0.101 K and 0.892 K, respectively, and the differences in BTs between the two sensors show temperature dependence. The cross-calibration coefficients are obtained and used to correct the BT of the COCTS thermal emissive bands. The bias of the BT of the 11 µm and 12 µm bands of COCTS are about 0.01 K after cross-calibration. To further validate the results, COCTS post-calibration data were examined using the NOAA-20 Cross-track Infrared Sounder (CrIS) data as a third-party source. The BT is calculated with the spectral response functions of the COCTS thermal emissive bands using the convolution calculation of the CrIS hyperspectral region observations. The comparison shows a small bias between the post-calibration COCTS thermal emissive band observations and CrIS, which is consistent with the comparison between VIIRS and CrIS. The accuracy of the post-calibration COCTS thermal emissive band BT data in the South China Sea has been significantly improved.

Validation of MUSES NH3 observations from AIRS and CrIS against aircraft measurements from DISCOVER-AQ and a surface network in the Magic Valley

Abstract. Ammonia is a significant precursor of PM2.5 particles and thus contributes to poor air quality in many regions. Furthermore, ammonia concentrations are rising due to the increase of large-scale, intensive agricultural activities, which are often accompanied by greater use of fertilizers and concentrated animal feedlots. Ammonia is highly reactive and thus highly variable and difficult to measure. Satellite-based instruments, such as the Atmospheric Infrared Sounder (AIRS) and the Cross-Track Infrared Sounder (CrIS), have been shown to provide much greater temporal and spatial coverage of ammonia distribution and variability than is possible with in situ networks or aircraft campaigns, but the validation of these data is limited. Here we evaluate MUSES (multi-spectra, multi-species, multi-sensors) ammonia retrievals from AIRS and CrIS against ammonia measurements from aircraft in the California Central Valley and in the Colorado Front Range. These are small datasets taken over high-source regions under very different conditions: winter in California and summer in Colorado. Direct comparisons of the surface values of the retrieved profiles are biased very low in California (∼ 40 ppbv) and slightly high in Colorado (∼ 4 ppbv). This bias appears to be primarily due to smoothing error, since applying the instrument operator effectively reduces the bias to zero; even after the smoothing error is accounted for, the average uncertainty at the surface is in the 20 %–30 % range. We also compare 3 years of CrIS ammonia against an in situ network in the Magic Valley in Idaho We show that CrIS ammonia captures both the seasonal signal and the spatial variability in the Magic Valley, although it is biased low here also. In summary, this analysis substantially adds to the validation record but also points to the need for more validation under many different conditions and at higher altitudes.

Single field-of-view sounder atmospheric product retrieval algorithm: establishing radiometric consistency for hyper-spectral sounder retrievals

Abstract. The single field-of-view (SFOV) sounder atmospheric product (SiFSAP) retrieval algorithm has been developed to address the need to retrieve high-spatial-resolution atmospheric data products from hyper-spectral sounders and ensure the radiometric consistency between the retrieved properties and measured spectral radiances. It is based on an integrated optimal-estimation inversion scheme that processes data from the satellite-based synergistic microwave (MW) and infrared (IR) spectral measurements from advanced sounders. The retrieval system utilizes the principal component radiative transfer model (PCRTM), which performs radiative transfer calculations monochromatically and includes accurate cloud-scattering simulations. SiFSAP includes temperature, water vapor, surface skin temperature and emissivity, cloud height and microphysical properties, and concentrations of essential trace gases for each SFOV at a native instrument spatial resolution. Error estimations are provided based on a rigorous analysis for uncertainty propagation from the top-of-atmosphere (TOA) spectral radiances to the retrieved geophysical properties. As a comparison, the spatial resolution for the traditional hyper-spectral sounder retrieval products is much coarser than the native resolution of the instruments due to the common use of the “cloud-clearing” technique to compensate for the lack of cloud-scattering simulation in the forward model. The degraded spatial resolution in traditional cloud-clearing sounder retrieval products limits their applications for capturing meteorological or climate signals at finer spatial scales. Moreover, a rigorous uncertainty propagation estimation needed for long-term climate trend studies cannot be given due to the lack of direct radiative transfer relationships between the observed TOA radiances and the retrieved geophysical properties. With the advantages of the higher spatial resolution; the simultaneous retrieval of atmospheric, cloud, and surface properties using all available spectral information; and the establishment of “radiance closure” in the sounder spectral measurements, the SiFSAP provides additional information needed for various weather and climate studies and applications using sounding observations. This paper gives an overview of the SiFSAP retrieval algorithm and assessment of SiFSAP atmospheric temperature, water vapor, clouds, and surface products derived from the Cross-track Infrared Sounder (CrIS) and Advanced Technology Microwave Sounder (ATMS) data.

Comparison of the AIRS, IASI, and CrIS Infrared Sounders Using Simultaneous Nadir Overpasses: Novel Methods Applied to Data From 1 October 2019 to 1 October 2020

AbstractHigh spectral resolution infrared sounders are an integral component of the global observing system and are used in a broad range of applications. This is enabled by their high accuracies which are ensured by rigorous calibration/validation activities. One of these activities is the post‐launch intercomparison with other high spectral resolution infrared sounders using simultaneous nadir overpasses (SNOs). This paper introduces a novel application of the previously developed SNO methodology by including time difference histogram symmetrization and a spatial sampling uncertainty. Where possible, radiometric measurement uncertainties are included and propagated through the statistics. Comparisons of Atmospheric Infrared Sounder (AIRS), METOP‐A/B/C Infrared Atmospheric Sounding Interferometer (IASI), and Cross‐track Infrared Sounder (CrIS) from 1 October 2019, to 1 October 2020, are analyzed. Results show AIRS and IASI differences as well as CrIS and AIRS differences are generally less than 0.4 K across the spectrum, and CrIS and IASI differences are generally less than 0.25 K. Comparison of the Suomi National Polar‐Orbiting Partnership and NOAA‐20 CrIS instruments via IASI and AIRS shows differences are generally less than 0.1 K across all bands and that the two CrIS instruments statistically agree within their radiometric uncertainties except for the narrow 2,370 cm−1 region where artifacts due to brightness temperature conversion are prone to occur.

Spatiotemporal Variability of Global Atmospheric Methane Observed from Two Decades of Satellite Hyperspectral Infrared Sounders

Methane (CH4) is the second most significant contributor to climate change after carbon dioxide (CO2), accounting for approximately 20% of the contributions from all well-mixed greenhouse gases. Understanding the spatiotemporal distributions and the relevant long-term trends is crucial to identifying the sources, sinks, and impacts on climate. Hyperspectral thermal infrared (TIR) sounders, including the Atmospheric Infrared Sounder (AIRS), the Cross-track Infrared Sounder (CrIS), and the Infrared Atmospheric Sounding Interferometer (IASI), have been used to measure global CH4 concentrations since 2002. This study analyzed nearly 20 years of data from AIRS and CrIS and confirmed a significant increase in CH4 concentrations in the mid-upper troposphere (around 400 hPa) from 2003 to 2020, with a total increase of approximately 85 ppb, representing a +4.8% increase in 18 years. The rate of increase was derived using global satellite TIR measurements, which are consistent with in situ measurements, indicating a steady increase starting in 2007 and becoming stronger in 2014. The study also compared CH4 concentrations derived from the AIRS and CrIS against ground-based measurements from NOAA Global Monitoring Laboratory (GML) and found phase shifts in the seasonal cycles in the middle to high latitudes of the northern hemisphere, which is attributed to the influence of stratospheric CH4 that varies at different latitudes. These findings provide insights into the global budget of atmospheric composition and the understanding of satellite measurement sensitivity to CH4.