Atmospheric Infrared Sounder Research Articles

Study of Methane Emission and Geological Sources in Northeast China Permafrost Area Related to Engineering Construction and Climate Disturbance Based on Ground Monitoring and AIRS

China’s largest high-latitude permafrost distribution zone is in Northeast China. With the intensification of global warming and engineering construction, the carbon stored in permafrost will gradually thaw and be released in the form of methane gas. However, research on the changes in methane concentration and emission sources in this area is still unclear. In this paper, the AIRS (Atmospheric Infrared Sounder) data carried by the Aqua satellite were used to analyze the distribution and change trends in the overall methane concentration in the near-surface troposphere in Northeast China from 2003 to 2022. These data, combined with national meteorological and on-site monitoring data, were used to study the methane emission characteristics and sources in the permafrost area in Northeast China. The results show that the methane concentration in the near-surface troposphere of Northeast China is mainly concentrated in the permafrost area of the Da and Xiao Xing’an Mountains. From 2003 to 2022, the methane concentration in the near-surface troposphere of the permafrost area in Northeast China showed a rapid growth trend, with an average linear trend growth rate of 4.787 ppbv/a. In addition, the methane concentration in the near-surface troposphere of the permafrost area shows a significant bimodal seasonal variation pattern. The first peak appears in summer (June–August), with its maximum value appearing in August, and the second peak appears in winter (December–February), with its maximum value appearing in December. Combined with ground surface methane concentration monitoring, it was found that the maximum annual ground surface methane concentration in degraded permafrost areas occurred in spring, causing the maximum average growth rate in methane concentration, also in spring, in the near-surface troposphere of permafrost areas in Northeast China (with an average value of 6.05 ppbv/a). The growth rate of methane concentration in the southern permafrost degradation zone is higher than that in the northern permafrost stable zone. In addition, with the degradation of permafrost, the geological methane stored deep underground (methane hydrate, coal seam, etc., mainly derived from the accumulation of ancient microbial origin) in the frozen layer will become an important source of near-surface troposphere methane in the permafrost degradation area. Due to the influence of high-permeability channels after permafrost degradation, the release rate of methane gas in spring is faster than predicted, and the growth rate of methane concentration in the near-surface troposphere of permafrost areas can be increased by more than twice. These conclusions can provide a data supplement for the study of the carbon cycle in permafrost areas in Northeast China.

Air Surface Temperature Variability and Trends from Satellite Homogenized Time Series Data Over Tunis 2003–2021

Abstract Monthly Air Surface Temperature (AST) data for Tunis were acquired by the Atmospheric InfraRed Sounder (AIRS) dataset (2003–2021), the enrolments of 77 grids, each located in Spatial resolution 1°x1°, were converted into monthly and annual data and analysed. The time series of AST in Tunis were investigated for temporal and spatial trends during study period over six climate stations each having sufficient data available were utilized for this purpose. The monthly time series showed similar variations and fluctuated of AST, minimum (decreases, January) and maximum (increases, July) in trend, with standard deviation (294.15 + 14.02 K°). Most stations, appeared positive trends in their annual AST series, only at Sfax negative, with higher trends for central stations than those closer to the desert and coast. The trends were lowest over coastal area. The Comparisons among selected six stations (Tunis, Tabarka, Thala, Sfax, Medenine, and EL-Borma) between the AST observed by AIRS and in-situ data showed close agreement range from 0.38 to 3.6 K°, and approximately the same in the north-to-south transect throughout the year. The validation results plainly evident a high correlation coefficient (R, 0.995, 0.997, 0.994, 0.94, 0.974 and 0.95), and the asset values of (R2) was 0.991, 0.995, 0.987, 0.93, 0.993, and 0.977 for Tunis, Sfax, Tabarka, Thala, Medenine, and EL-Borma respectively. The satellite observation (AIRS) is able to investigate the variations of the atmosphere AST over different zones.

A Gradient Boosting Decision Tree Based Correction Model for AIRS Infrared Water Vapor Product

AbstractHigh‐quality precipitable water vapor (PWV) measurements have an essential role in climate change and weather prediction studies. The Atmospheric Infrared Sounder (AIRS) instrument provides an opportunity to measure PWV at infrared (IR) bands twice daily with nearly global coverage. However, AIRS IR PWV products are easily affected by the presence of clouds. We propose a Gradient Boosting Decision Tree (GBDT) based correction model (GBCorM) to enhance the accuracy of PWV products from AIRS IR observations in both clear‐sky and cloudy‐sky conditions. The GBCorM considers many dependence factors that are in association with the AIRS IR PWV's performance. The results show that the GBCorM greatly improves the all‐weather quality of AIRS IR PWV products, especially in dry atmospheric conditions. The GBCorM‐estimated PWV result in the presence of clouds shows an accuracy comparable with that of official AIRS IR PWV products in clear‐sky conditions, demonstrating the capability of the GBCorM model.

Ground-based and AIRS carbon monoxide behavior at El Arenosillo observatory (Southwestern Europe)

Carbon monoxide (CO) observations registered at El Arenosillo observatory (Southwestern Europe), during three years (2019–2022) were used to explore levels, temporal variations and patterns. The seasonal mean hourly oscillated between 127 ± 21 and 97 ± 14 ppb, with extreme peaks measured under wildfire plume arrival. A remarkable monthly evolution was observed with peaks in cold months (135 ± 20 ppb in January) and minimum in summer (90 ± 11 ppb in June). Seasonal daily patterns showed a different nocturnal behavior depending on the season, with a clear maximum at 7:00–9:00 UTC, a decrease during diurnal time, and a minimum at 17:00–18:00 UTC. To determine the potential trends from 2002 to 2022, the observations of the AIRS (Atmospheric Infrared Sounder) instrument and the emissions from the global emission inventory of CAMS (Copernicus Atmosphere Monitoring Service) were used. A downward trend for emissions of −31% decade−1 was found, whereas the decrease from AIRS was −9.8% decade−1. The effort to reduce CO emissions is being countered by other processes. The CO behavior was investigated under weather conditions governed by the synoptic and mesoscale processes. Background levels (∼90 ppb) were obtained under Atlantic airflows, while an increase of ∼40% was observed with the impact of urban plumes from continental areas. CO even lower than 90 ppb was obtained under non-pure sea-land breezes, whereas, with pure breezes, reached peaks above 120 ppb, affecting the atmosphere at the surface and up to 1–1.2 km in the air. The extreme level was registered under wildfire plume arrival, with peaks exceeding 350 ppb. Climate change is modifying weather patterns and modulating breezes; in addition, it is also increasing the frequency and intensity of wildfires. These processes could be counteracting the efforts carried out to reduce emissions.

Observations of Typhoon Generated Gravity Waves From the CIPS and AIRS Instruments and Comparison to the High‐Resolution ECMWF Model

AbstractThe satellite‐based Cloud Imaging and Particle Size (CIPS) instrument and Atmospheric Infrared Sounder (AIRS) observed concentric gravity waves (GWs) generated by Typhoon Yutu in late October 2018. This work compares CIPS and AIRS nadir viewing observations of GWs at altitudes of 50–55 and 30–40 km, respectively, to simulations from the high‐resolution European Centre for Medium‐Range Weather Forecasting Integrated Forecasting System (ECMWF‐IFS) and ECMWF reanalysis v5 (ERA5). Both ECMWF‐IFS with 9 km and ERA5 with 31 km horizontal resolution show concentric GWs at similar locations and timing as the AIRS and CIPS observations. The GW wavelengths are ∼225–236 km in ECMWF‐IFS simulations, which compares well with the wavelength inferred from the observations. After validation of ECMWF GWs, five category five typhoon events during 2018 are analyzed using ECMWF to obtain characteristics of concentric GWs in the Western Pacific regions. The amplitudes of GWs in the stratosphere are not strongly correlated with the strength of typhoons, but are controlled by background wind conditions. Our results confirm that amplitudes and shapes of concentric GWs observed in the stratosphere and lowermost mesosphere are heavily influenced by the background wind conditions.

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.

Greenhouse gas emissions and aerosol distribution in brick kiln zones of Punjab, Pakistan: an appraisal using spatial information technology

Greenhouse Gases (GHGs) and climate-altering air pollutants are damaging the ecosystem of Earth. Hence, reducing gaseous emissions and air pollutants would help mitigate global warming and improve the low-carbon economy. Therefore, in this study, remote sensing datasets from the Goddard Earth Observing System, Atmospheric Infrared Sounder and Modern-Era Retrospective analysis for Research and Applications Version 2 are carried out to examine the seasonal trends of Carbon dioxide (CO2), Methane (CH4), Ozone (O3) and Aerosol Optical Depth (AOD) from 2016 to 2021 over brick kiln zones in Punjab, Pakistan. In this research, 2016 has been taken as the base year to compare the trends of GHGs and AOD in Punjab. Results of the study revealed that the highest concentration of AOD was exhibited in the winter seasons over Purple, Red and Orange Zones of brick kilns. Moreover, the highest extent of CO2, CH4 and AOD was observed in 2017. At the same time, O3 magnitude was evaluated as high during summer seasons which may be attributed to high temperature that leads to elevated formation of O3. Therefore, there is a dire need to undertake Nature Based Solutions (NBS) in the policymaking for the environmental development of Pakistan.

A Comparison of Stratospheric Gravity Waves in a High‐Resolution General Circulation Model With 3‐D Satellite Observations

AbstractAtmospheric gravity waves (GWs) play a key role in determining the thermodynamical structure of the Earth's middle atmosphere. Despite the small spatial and temporal scales of these waves, a few high‐top general circulation models (GCMs) that can resolve them explicitly have recently become available. This study compares global GW characteristics simulated in one such GCM, the Japanese Atmospheric GCM for Upper‐Atmosphere Research (JAGUAR), with those derived from three‐dimensional (3‐D) temperatures observed by the Atmospheric Infrared Sounder (AIRS) aboard NASA's Aqua satellite. The target period is from 15 December 2018 to 8 January 2019, including the onset of a major sudden stratospheric warming (SSW). The 3‐D Stockwell transform method is used for GW spectral analysis. The amplitudes and momentum fluxes of GWs in JAGUAR are generally in good quantitative agreement with those in the AIRS observations in both magnitude and distribution. As the SSW event progressed, the GW amplitudes and eastward momentum flux increased at low latitudes in the summer hemisphere in both the model and observation datasets. Case studies demonstrate that the model is able to reproduce comparable wave events to those in the AIRS observations with some differences, especially noticeable at low latitudes in the summer hemisphere. Through a comparison between the model results with and without the AIRS observational filter applied, it is suggested that the amplitudes of GWs near the entrance or exit of an eastward jet streak are underestimated in AIRS observations.

Analysis of aerosol optical depth and relation to covariates during pre-monsoon season (2002–2019) over Pakistan using ARIMAX model and cross-wavelet analysis

The pre-monsoon season heavily influences the precipitation amount in Pakistan. When hydrometeorological parameters interact with aerosols from multiple sources, a radiative climatic response is observed. In this study, aerosol optical depth (AOD) space-time dynamics were analyzed in relation to meteorological factors and surface parameters during the pre-monsoon season in the years 2002–2019 over Pakistan. Level-3 (L3) monthly datasets from Moderate Resolution Imaging Spectroradiometer (MODIS) and Multi-Angle Imaging Spectroradiometer (MISR) were used. Tropical Rainfall Measuring Mission (TRMM) derived monthly precipitation, Atmospheric Infrared Sounder (AIRS) derived air temperature, after moist relative humidity (RH) from Modern-Era Retrospective analysis for Research and Applications, Version-2 (MERRA-2), near-surface wind speed, and soil moisture data derived from Global Land Data Assimilation System (GLDAS) were also used on a monthly time scale. For AOD trend analysis, Mann-Kendall (MK) trend test was applied. Moreover, Autoregressive Integrated Moving Average with Explanatory variable (ARIMAX) technique was applied to observe the actual and predicted AOD trend, as well as test the multicollinearity of AOD with covariates. The periodicities of AOD were analyzed using continuous wavelet transformation (CWT) and the cross relationships of AOD with prevailing covariates on a time-frequency scale were analyzed by wavelet coherence analysis. A high variation of aerosols was observed in the spatiotemporal domain. The MK test showed a decreasing trend in AOD which was most significant in Baluchistan and Punjab, and the overall trend differs between MODIS and MISR datasets. ARIMAX model shows the correlation of AOD with varying meteorological and soil parameters. Wavelet analysis provides the abundance of periodicities in the 2–8 months periodic cycles. The coherency nature of the AOD time series along with other covariates manifests leading and lagging effects in the periodicities. Through this, a notable difference was concluded in space-time patterns between MODIS and MISR datasets. These findings may prove useful for short-term and long-term studies including oscillating features of AOD and covariates.

Satellite Sensed Data-Dose Response Functions: A Totally New Approach for Estimating Materials’ Deterioration from Space

When construction materials are exposed to the atmospheric environment, they are subject to deterioration, which varies according to the time period of exposure and the location. A tool named Dose–Response Functions (DRFs) has been developed to estimate this deterioration. DRFs use specific air pollutants and climatic parameters as input data. Existing DRFs in the literature use only ground-based measurements as input data. This fact constitutes a limitation for the application of this tool because it is too expensive to establish and maintain such a large network of ground-based stations for pollution monitoring. In this study, we present the development of new DRFs using only satellite data as an input named Satellite Sensed Data Dose-Response Functions (SSD-DRFs). Due to the global coverage provided by satellites, this new tool for monitoring the corrosion/soiling of materials overcomes the previous limitation because it can be applied to any area of interest. To develop SSD-DRFs, we used measurements from MODIS (Moderate Resolution Imaging Spectroradiometer) and AIRS (Atmospheric Infrared Sounder) on board Aqua and OMI (Ozone Monitoring Instrument) on Aura. According to the obtained results, SSD-DRFs were developed for the case of carbon steel, zinc, limestone and modern glass materials. SSD-DRFs are shown to produce more reliable corrosion/soiling estimates than “traditional” DRFs using ground-based data. Furthermore, research into the development of the SSD-DRFs revealed that the different corrosion mechanisms taking place on the surface of a material do not act additively with each other but rather synergistically.

New Data on the Patterns of Methane Distribution over the Arctic Shelf of Eurasia

New results on the analysis of methane distribution in the troposphere over the Arctic shelf of Eurasia using the Atmospheric InfraRed Sounder (AIRS) are reported. The main trends in variability of the methane content (increase), temperature (AIRS, fluctuations without a distinctly manifested trend), and ice coverage for the period 2010–2022 have been revealed. Conclusions were made about the difference in the distributions of atmospheric methane over the western and eastern Eurasian Arctic. These parts were divided according to the zone of permeability, which was formed by a seismically active lineament of Gakkel Ridge. It has been shown that hydrometeorological parameters, including temperature and ice formation, are probably not the main factors for increasing the methane content in the atmosphere of the Eurasian Arctic shelf. The increase in the methane content and the methane distribution are associated with natural regional factors that are widespread over vast areas. These factors include, first of all, geological structures: oil and gas basins and tectonically fragmentated zones of the lithosphere with a different type of degassing.

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.

Observations of 4.26 μm CO2 Auroral Emissions From AIRS Nadir Sounder Measurements

AbstractThe Atmospheric Infrared Sounder (AIRS) instrument onboard the NASA Aqua satellite is used to observe aurora associated with the CO2 4.26 μm emission. These observations are due to non‐local thermodynamic equilibrium (NLTE) resulting from the vibrational excitation of CO2, which arises in the process of auroral energetic particle precipitation, as opposed to the dayside NLTE occurring due to solar radiation. The observations are confirmed to be associated with aurora using the Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) limb measurements and the SuperMAG Electrojet (SME) index. The high spectral resolution and low noise associated with the AIRS instrument allows for the emission spectrum to be calculated and confirmed to arise from CO2. Our new NLTE index values derived from AIRS provide the ability to globally measure auroral events associated with CO2 with a spatial resolution on the order of ∼13.5 km.

Multi-altitude observations of the lower atmospheric disturbances and its ionospheric signatures over the Indian region

Tropospheric disturbances like tropical cyclones produce a spectrum of gravity waves which can propagate to higher altitudes and can even perturb the ionosphere-thermosphere system. The observations of a severe cyclonic storm Hudhud during 7–14 October 2014 using vertical total electron content (VTEC) from 11 stations and the brightness temperature from AIRS (atmospheric infrared sounder) imagery are analysed. Main results show ring-arc structures resembling concentric gravity waves (CGWs) in the brightness temperature and widespread, synchronized and episodic perturbations in VTEC during 10, 11 and 12 October. The stratospheric perturbations are found to spread in a range of radial distance between ∼ 730 and 2500 km with horizontal wavelengths between 307 and 541 km. A wavelet analysis of the daytime band-pass (5–60 min) filtered dVTEC perturbations shows enhancements in the spectral power associated with periods of ∼ 30–50 min on different days following the cyclone movement from east to west. The radial phase speed of the dVTEC for different temporal bands is found to vary between 114 and 320 m/s at different intervals during 10–11 October. Additionally, a comparative analysis of the past and present results is performed using the established dispersion relations which shows a compatible range of variability of wave parameters among all the studies including the present results. The results substantiate strong signatures of the Hudhud cyclone at multiple altitudes and depict vertical coupling of the atmosphere–ionosphere system through a theoretical framework.

Retrieving Atmospheric Gas Profiles Using FY-3E/HIRAS-II Infrared Hyperspectral Data by Neural Network Approach

The observed radiation data from the second-generation Hyperspectral Infrared Atmospheric Sounder (HIRAS-II) on the Fengyun-3E (FY-3E) satellite contain useful vertical atmosphere information which can distinguish and retrieve vertical profiles of atmospheric gas components including ozone (O3), carbon monoxide (CO), and methane (CH4). This paper utilizes FY-3E/HIRAS-II observational data to optimize each gas channel using the improved Optimal Sensitivity Profile method (OSP) channel algorithm and establishes a typical convolutional neural network model (CNN) and a representative U-shaped network model (UNET) with deep features and shallow feature links to perform atmospheric profile retrieval calculations of O3, CO, and CH4. We chose the clear sky data of the Indian and its southern seas in December 2021 and January 2022, with reanalysis data from European Center for Medium-Range Weather Forecasts Reanalysis v5 (ERA5) and European Center for Medium-Range Weather Forecasts Atmospheric Composition Reanalysis v4 (EAC4) serving as the reference values. The retrieval outcomes were then compared against advanced numerical forecast models including the Whole Atmosphere Community Climate Model (WACCM), Global Forecast System (GFS), and satellite products from an Atmospheric Infrared Sounder (AIRS) and Infrared Atmospheric Sounding Interferometer (IASI). Experimental results show that the generalization ability and retrieval accuracy of CNN are slightly higher compared with UNET. For O3 profile retrieval, the mean percentage error (MPE) of the whole layers for CNN and UNET data in relation to ERA5 data was less than 8%, while the root-mean-square error (RMSE) was below 1.5 × 10−7 kg/kg; for CH4 profile retrieval, the MPE of the whole layers for CNN and UNET data in relation to EAC4 data was less than 0.7%, while the RMSE was below 1.5 × 10−8 kg/kg. The retrieval of O3 and CH4 are resulted in a significant improvement compared to the forecast data and satellite products in most pressure levels; for CO profile retrieval, the MPE of the whole layers for CNN and UNET data in relation to EAC4 data was less than 11%, while the RMSE was below 4 × 10−8 kg/kg. The error of the CO retrieval results was higher than that of the forecast data at the pressure level of 200~500 hPa and lower than that of similar satellite products with most pressure levels. The experiments indicated that the neural network method effectively determines the atmospheric gas profiles using infrared hyperspectral data, exhibiting a positive performance in accuracy and retrieval speed.

Ground‐Validation and Error Attribution of Near‐Surface Air Temperature From AIRS in North America

AbstractAccurate estimation of near‐surface air temperature (TsurfAir) at fine spatiotemporal resolution is crucial for environmental applications, data assimilation into land surface models, and climate change studies. The Atmospheric Infrared Sounder (AIRS) on board the Aqua satellite measures TsurfAir twice a day at the global scale. In this study, the AIRS TsurfAir is validated using ground‐based weather stations in North America. Owing to the high density of weather stations, spatial heterogeneity (<1°) of TsurfAir is modeled using semi‐variograms and further used to calculate the validation reference via ordinary kriging at AIRS retrieval footprints. This analysis intends to reveal error characteristics in cold and hot seasons by selecting six months of data in January and July of three years, along with a case study on two extreme events. Furthermore, MODIS‐enabled land surface temperature and land cover are used to attribute the observed error in TsurfAir. Results show an overall warm bias of 0.11 K from AIRS TsurfAir and conditional bias over‐ and underestimating cold and hot temperatures, respectively. Spatial heterogeneity of TsurfAir is found to influence the variance of AIRS TsurfAir error. As a possible source of TsurfAir error, land surface temperatures (Tskin) from AIRS and MODIS are compared. Close match between the two Tskin products indicates a lesser influence of the Tskin difference on TsurfAir error. Overall, the ground validation of AIRS TsurfAir along with the error attribution can facilitate post‐processing of AIRS products and uncertainty analysis for the wide range of applications of AIRS TsurfAir.

Practical Implications of CLIMCAPS Cloud Clearing and Derived Quality Metrics

AbstractClouds present one of the largest sources of uncertainty in infrared (IR) measurements of the vertical atmospheric state. Hyperspectral instruments onboard Earth orbiting satellites, such as the Atmospheric Infrared Sounder (AIRS) on Aqua (2002–present), measure the top of atmosphere (TOA) radiances emitted by Earth surface and atmosphere. IR measurements are limited in their ability to distinguish different cloud structures. Lack of prior knowledge about clouds at the time of measurement can therefore significantly reduce the quality of retrieved soundings. The Community Long‐term Infrared Microwave Atmospheric Product System (CLIMCAPS) uses the AIRS Science Team (AST) cloud clearing methodology to remove the radiative effect of clouds from TOA IR measurements to improve the quality of its sounding retrievals and enable vertical observations in clear and partly cloudy conditions. In this paper we discuss how AST cloud clearing affects CLIMCAPS soundings and enable a global product for long‐term analysis of the atmospheric state.

Study of Time-Frequency Domain Characteristics of the Total Column Ozone in China Based on Wavelet Analysis

Ozone is a very important trace gas in the atmosphere, it is like a “double-edged sword”. Because the ozone in the stratosphere can effectively help the earth’s organisms to avoid the sun’s ultraviolet radiation damage, the ozone near the ground causes pollution. Therefore, it is essential to explore the time-frequency domain variation characteristics of total column ozone and have a better understanding of its cyclic variation. In this paper, based on the monthly scale dataset of total column ozone (TCO) (September 2002 to February 2023) from Atmospheric Infrared Sounder (AIRS) carried by NASA’s Aqua satellite, linear regression, coefficient of variation, Mann-Kendall (M-K) mutation tests, wavelet analysis, and empirical orthogonal function decomposition (EOF) analysis were used to analyze the variation characteristics of the TCO in China from the perspectives of time domain, frequency domain, and spatial characteristics. Finally, this study predicted the future of TCO data based on the seasonal autoregressive integrated moving average (SARIMA) model in the time series algorithm. The results showed the following: (1) From 2003 to 2022, the TCO in China showed a slight downward trend, with an average annual change rate of −0.29 DU/a; the coefficient of variation analysis found that TCO had the smallest intra-year fluctuations in 2008 and the largest intra-year fluctuations in 2005. (2) Using the M-K mutation test, it was found that there was a mutation point in the total amount of column ozone in 2016. (3) Using wavelet analysis to analyze the frequency domain characteristics of the TCO, it was observed that TCO variation in China had a combination of 14-year, 6-year, and 4-year main cycles, where 14 years is the first main cycle with a 10-year cycle and 6 years is the second main cycle with a 4-year cycle. (4) The spatial distribution characteristics of the TCO in China were significantly different in each region, showing a distribution characteristic of being high in the northeast and low in the southwest. (5) Based on the EOF analysis of the TCO in China, it was found that the variance contribution rate of the first mode was as high as 52.85%, and its spatial distribution of eigenvectors showed a “-” distribution. Combined with the trend analysis of the time coefficient, this showed that the TCO in China has declined in the past 20 years. (6) The SARIMA model with the best parameters of (1, 1, 2) × (0, 1, 2, 12) based on the training on the TCO data was used for prediction, and the final model error rate was calculated as 1.34% using the mean absolute percentage error (MAPE) index, indicating a good model fit.

Convective organization and 3D structure of tropical cloud systems deduced from synergistic A-Train observations and machine learning

Abstract. We are building a 3D description of upper tropospheric (UT) cloud systems in order to study the relation between convection and cirrus anvils. For this purpose we used cloud data from the Atmospheric InfraRed Sounder and the Infrared Atmospheric Sounding Interferometer and atmospheric and surface properties from the meteorological reanalyses ERA-Interim and machine learning techniques. The different artificial neural network models were trained on collocated radar–lidar data from the A-Train in order to add cloud top height, cloud vertical extent and cloud layering, as well as a rain intensity classification to describe the UT cloud systems. The latter has an accuracy of about 65 % to 70 % and allows us to build objects of strong precipitation, used to identify convective organization. This rain intensity classification is more efficient to detect large latent heating than cold cloud temperature. In combination with a cloud system analysis, we found that deeper convection leads to larger heavy rain areas and a larger detrainment, with a slightly smaller thick anvil emissivity. This kind of analysis can be used for a process-oriented evaluation of convective precipitation parameterizations in climate models. Furthermore, we have shown the usefulness of our data to investigate tropical convective organization metrics. A comparison of different tropical convective organization indices and proxies to define convective areas has revealed that all indices show a similar annual cycle in convective organization, in phase with convective core height and anvil detrainment. The geographical patterns and magnitudes in radiative heating rate interannual changes with respect to one specific convective organization index (Iorg) for the period 2008 to 2018 are similar to the ones related to the El Niño–Southern Oscillation. However, since the interannual anomalies of the convective organization indices are very small and noisy, it was impossible to find a coherent relationship with those of other tropical mean variables such as surface temperature, thin cirrus area or subsidence area.

Spatial variability of trace gases (NO2, O3 and CO) over Indian region during 2020 and 2021 COVID-19 lockdowns.

COVID-19 lockdown has given us an opportunity to investigate the pollutant concentrations in response to the restricted anthropogenic activities. The atmospheric concentration levels of nitrogen dioxide (NO2), carbon monoxide (CO) and ozone (O3) have been analysed for the periods during the first wave of COVID-19 lockdown in 2020 (25th March-31st May 2020) and during the partial lockdowns due to second wave in 2021 (25th March-15th June 2021) across India. The trace gas measurements from Ozone Monitoring Instrument (OMI) and Atmosphere InfraRed Sounder (AIRS) satellites have been used. An overall decrease in the concentration of O3 (5-10%) and NO2 (20-40%) have been observed during the 2020 lockdown when compared with business as usual (BAU) period in 2019, 2018 and 2017. However, the CO concentration increased up to 10-25% especially in the central-west region. O3 and NO2 slightly increased or had no change in 2021 lockdown when compared with the BAU period, but CO showed a mixed variation prominently influenced by the biomass burning/forest fire activities. The changes in trace gas levels during 2020 lockdown have been predominantly due to the reduction in the anthropogenic activities, whereas in 2021, the changes have been mostly due to natural factors like meteorology and long-range transport, as the emission levels have been similar to that of BAU. Later phases of 2021 lockdown saw the dominant effect of rainfall events resulting in washout of pollutants. This study reveals that partial or local lockdowns have very less impact on reducing pollution levels on a regional scale as natural factors like atmospheric long-range transport and meteorology play deciding roles on their concentration levels.