AIRS Data Research Articles

Northward Shift and Narrowing of the ITCZ in 20 Years of AIRS Data

AbstractA time series analysis of Deep Convective Clouds (DCC) in Atmospheric InfraRed Sounder data between 2002 and 2023 shows that the mean latitude of the Inter Tropical Convergence Zone (ITCZ) has shifted northward for land at a rate of about 30 km/decade. The shift for ocean is north as well at about half of the rate for land. Comparisons of our results with the existing literature are complicated by the use of different methodologies and data sources: We used the daily observations of DCCs, which by their association with extreme rain rates are indicators of locally extreme vertical velocity on 15 km horizontal scales, while most of the literature used model or reanalysis gridded data with typically 100 km bins. The southern boundary of the ITCZ shifts north faster than the northern boundary, as qualitatively stated in the literature. The resulting contraction of the ITCZ width with global warming was qualitatively predicted based on theoretical consideration. Our observation of the rate of decrease in the width for ocean is statistically consistent with the contraction deduced from European Centre for Medium Range Forecast Reanalysis and Modern‐Era Retrospective analysis for Research and Applications data. The observed northward shift of the ITCZ was not predicted and is not seen in climate models in the 21st century.

Investigation of the LAIC mechanism of the Haiti earthquake (August 14, 2021) using CSES-01 satellite observations and other earthquake precursors

A powerful Mw = 7.2 earthquake occurred in Haiti on August 14, 2021 at 12:29:08 UTC (18.434° N, 73.482° W, depth ∼10 km). In this study, in a period of three months from June 1 to August 31, 2021, the variations in electron density and temperature parameters obtained from CSES-01 satellite measurements in the Dobrovolsky’s area the Haiti earthquake are considered. With the aim of investigating the proposed mechanisms for interactions between different layers of the Earth in the process of earthquakes (LAIC-Lithospheric Atmospheric Ionospheric Coupling), the behavior of 48 other precursors obtained from the data of other satellites in the same location and time period were analyzed. These precursors are 1) TEC obtained from GPS satellite data, 2) scalar and vector magnetic field data as well as electron density and temperature measured by the related sensors of three Swarm A, B and C satellites during day and night and 3) Variations in lithospheric and atmospheric parameters NO2 and SO2 obtained from OMI satellite measurements and also Ozone, carbon monoxide and surface temperature parameters obtained from AIRS data. By analyzing the above mentioned precursors, abnormal behaviors are observed around 30 and 50 days before the earthquake, and especially 1 to 15 days before the earthquake and by comparative study of the observed anomalies, possible LAIC mechanisms are discussed. The findings of this study can acknowledge the sequence of seismic anomalies observed in the lithosphere, then the atmosphere and finally the ionosphere. Therefore, this study once again emphasizes the necessity of performing multi-precursor analysis with the aim of justifying the LAIC mechanism and also observing seismically prone anomalies in the time period of about 10 days before powerful earthquakes.

Comparison of Radiosonde Measurements of Meteorological Variables with Drone, Satellite Products, and WRF Simulations in the Tropical Andes: The Case of Quito, Ecuador

Radiosondes are the most widely used method for studies of vertical atmospheric behavior, but the high costs associated, and the logistic limitations have forced researchers to look for alternative methods for atmospheric profiling, such as lidar and satellite measurements, or modeling. However, the assessment of the accuracy of alternative methods is recommended, especially in complex terrain, such as the tropical Andes. In this research, the atmospheric profiling of satellite data from AIRS and MODIS products, simulations of the Weather Research and Forecasting model, WRF, and drone measurements are evaluated for a campaign of 10 radio soundings, between August 2021 and January 2022. Additionally, the capability to capture the planetary boundary layer height, hPBL, is studied. The measurements were conducted at Izobamba station near Quito, Ecuador. Temperature, T, Dew Point Temperature, TD, Mixing Ratio, Q, and Potential Temperature, PT, were evaluated from 0 to 300 m above ground level (magl.) for satellite, WRF, and drone data, and from 0 km to 15 km for satellite and WRF data. Additionally, the capability to capture the planetary boundary layer height, HPBL, was assessed. The results show that drone profiles best represented the magnitude of the analyzed variables showing mean RMSE of 0.79 for T, but the noise of the measurements caused a low correlation with radio sounding profiles, which was partially corrected with a quadratic fit on the profile. The WRF results achieved a positive representation in terms of correlation, but error metrics show that there are remarkable differences in magnitude in the first 300 magl., up to the tropopause height, which surpasses satellite representations for all variables. The MODIS profiles do not generally perform well due to their low vertical resolution and limitations with cloud coverage. However, AIRS data, despite its low resolution, show a better representation of vertical profiles than MODIS, for T and TD, surpassing WRF simulations in some dates. For the HPBL, the WRF results show that physical and atmospheric conditions limit its determination, and the methods and conditioning factors should be further analyzed.

A Workshop on Using NASA AIRS Data to Monitor Drought for the U.S. Drought Monitor

A Workshop on Using NASA AIRS Data to Monitor Drought for the U.S. Drought Monitor

Data Fusion of AIRS and CrIMSS Near Surface Air Temperature

AbstractWe present a near surface air temperature (NSAT) fused data product over the contiguous United States using Level 2 data from the Atmospheric Infrared Sounder, on the Aqua satellite, and the Cross‐track Infrared Microwave Sounding Suite (CrIMSS), on the Suomi National Polar‐orbiting Partnership satellite. We create the fused product using Spatial Statistical Data Fusion, a procedure for fusing multiple data sets by modeling spatial dependence in the data, along with ground station data from NOAA's Integrated Surface Database (ISD) which is used to estimate bias and variance in the input satellite data sets. Our fused NSAT product is produced twice daily and on a 0.25° latitude‐longitude grid. We provide detailed validation using withheld ISD data and comparison with ERA5‐Land reanalysis. The fused gridded product has no missing data; has improved accuracy and precision relative to the input satellite data sets, and comparable accuracy and precision to ERA5‐Land; and includes improved uncertainty estimates. Over the domain of our study, the fused product decreases daytime bias magnitude by 1.7 and 0.5 K, nighttime bias magnitude by 1.5 and 0.2 K, and overall RMSE by 35% and 15% relative to the AIRS and CrIMSS input data sets, respectively. Our method is computationally fast and generalizable, capable of data fusion from multiple data sets estimating the same quantity. Finally, because our product reduces bias, it produces long‐term data sets across multi‐instrument remote sensing records with improved bias stationarity, even as individual missions and their data records begin and end.

Application of Orthogonal Polynomial Fitting Method to Extract Gravity Wave Signals From AIRS Data Related to Typhoon Deep Convection

AbstractGravity waves can influence weather and climate patterns on various temporal and spatial scales in the atmosphere. Despite their recognized importance, there are clearly a lack of sufficient and accurate observations from currently available satellite observing systems to satisfy the requirements of many satellite users. Common method to detect gravity waves is to measure brightness temperature (BT) anomalies, which rely on an initial efficient background removal method. Before gravity waves can be extracted from Atmospheric Infrared Sounder raw radiances, Hoffmann and Alexander (2010, http://doi.org/10.1029/2010jd014401) used a fourth‐order polynomial fitting (4PF) method to remove the background variations. In this study, we propose a new strategy, an optimal orthogonal polynomial fitting (OPF) method using Chebyshev Polynomials as basis functions, to remove the background variations and estimate BT perturbations. By extending the classic 4PF method to the fifth‐order polynomial fitting (5PF) method and combining the Cressman interpolation method, some experiments are designed to validate the feasibility and superiority of the OPF method. The results show that OPF is the optimal method to remove the limb‐brightening effect in the extraction of gravity wave signals generated by typhoons. In addition, what we noticed is that an appropriate fitting orders have to be selected to get more accurate BT anomalies signals in the experiments to extract gravity wave signals.

Changes in air quality during and after large-scale social restriction periods in Jakarta city, Indonesia

COVID-19 outbreak has constrained human activities in Jakarta, Indonesia during the large-scale social restriction (LSSR) period. The objective of this study was to evaluate the changes in the spatial variation of air pollutants over Jakarta during and after the LSSR periods. This study used satellite retrievals such as OMI, AIRS, and MERRA-2 satellite data to assess spatial variations of NO2, CO, O3, SO2, and PM2.5 from May to June 2020 (during the LSSR period) and from July to August 2020 (after the LSSR period) over Jakarta. The satellite images were processed using GIS software to increase the clarity of the images. The relationship between air pollutants and meteorological data was analyzed using Pearson correlation. The results showed the levels of NO2, PM2.5, O3, and CO increased by 59.4%, 21.2%, 16.2%, and 1.0%, respectively, while SO2 decreased by 19.1% after the LSSR period. The temperature value was inversely correlated with PM2.5, NO2, and SO2 concentrations. Furthermore, the backward trajectory analysis revealed that air pollutants from outland areas such as the east and southeast carried more particulate matter and gases pollutants, which contributed to the air pollution during and after the LSSR periods. As a whole, the COVID-19 outbreak had bad impacts on human health, but the increase in air pollutants levels after loosening the LSSR policy could also lead to a higher risk of severe respiratory diseases. This study provides new insight into air pollutant distribution during and after LSSR periods and recommends an effective method of mitigating the air pollution issues in Jakarta.

Numerical experiments with real data for estimating greenhouse gas fluxes in a region

This work is devoted to the problem of obtaining an estimate of methane emissions using satellite data and the results of mathematical modeling. To implement the algorithm, a variant of the local Kalman ensemble filter (LETKF) is used, which represents an optimal estimate of the desired parameter based on observational data and a forecast based on a metematic model in a given time interval. This algorithm has properties that allow it to be used locally, i.e., to assimilate data by subdomains. The paper presents the implementation of the algorithm for real observational data and the results of mathematical modeling (calculation of the forecast of the state of the system). The results of the three-dimensional model of transport and diffusion (MOZART-4) are taken as the results of mathematical modeling, and satellite observations (AIRS data) are used as observational data. As a result of the algorithm, an average estimate of methane fluxes in the subdomain was obtained at specified time intervals.

Then and Now: COVID-19 Pandemic Lockdown Misfire Atmospheric Methane over India

COVID-19 lockdown resulted in the revival of the environment due to reduced emissions of various pollutants globally. In particular, aerosols, NOx and SO2 showed significant reductions at most places. However, the greenhouse gases are not necessarily following this reduction everywhere. In most areas, a decrease in NOx increases methane (CH4) concentration by enhancing the lifetime, but also results in decreased concentrations with reduced emissions. Analyzing the atmospheric CH4 variations during the COVID-19 lockdown over India is crucial as India is one of the regions with high seasonal variability of CH4. The present study has analyzed the tropospheric CH4 trends over India during the pre-monsoon season (March–May) for 2003–2021 using AIRS data. The study analyzed the lockdown variations (24 March–31 May) of tropospheric CH4 over India with the same period of 2019 and 2021 using TROPOMI to find the changes in CH4 concentrations over different regions of India due to lockdown. Our results capture the undeviating north (low)–south (high) gradient in the CH4 concentration with anticipated regional intensifications, likely, in the eastern and western coastal regions, with more comprehensive details than it ever has been presented before.

Spatial-time variations in the distribution of atmospheric methane at high latitudes of the northern hemisphere

A preliminary study of spatio-temporal variations of methane distribution at high latitudes of the northern hemisphere was conducted based on AIRS and JR-STATION data. The analysis showed a noticeable increase (~4%) in the methane concentration in the lower layers of the atmosphere and upper layers of the troposphere. According to the AIRS data, winter (February) and summer (July- August) peaks of the methane concentration were reported in the seasonal cycle of 2004-2018. Seasonal methane variations in the upper troposphere obtained from AIRS data have similarities to ground observations, but with a smaller amplitude

Validation of estimated cloud fraction from MERRA-2 and AIRS data using ground based observation over IAO, Hanle

We report for validation of cloud fraction estimated from high-resolution (hourly) data from MERRA-2 and AIRS instrument aboard NASA’s Aqua satellite during its ascending and descending orbits with hourly ground-based visual observation taken at Indian Astronomical Observatory (IAO), Hanle during 1997–2018. The study reveals 70% useful nights, equivalent to 254 spectroscopic nights in a year. A comparative study of cloud fraction was performed using homogeneous and continuous data spanning six years (2013–2018) from MERRA-2 and AIRS over IAO, Hanle and Merak sites in the Himalayan region, and several astronomical observatories located in Atacama desert in Chile, Canary islands in Spain, National Astronomical Observatory (NAO), Mexico, and Mauna Loa in Hawaii. The study reveals cloud fractions of 0.07–0.11 at 25% (1st Quartile) and 0.32–0.40 at 50% (median) at the Himalayan sites. On the other hand, the median cloud fractions are 0.06–0.13 at the Chilean sites, 0.16 at NAO, and 0.29 at Canary islands. The observed 25% of the cloud fraction at the Himalayan sites during the lowest three cloud-fractional months (October-December) are comparable with other sites located in Chile and Canary islands. The median cloud fraction at Hanle is slightly lower than Mauna Loa. The study further concluded that the percentage of useful nights obtained from the visual observation (70%) at IAO-Hanle is very close to the percentage of clear sky (68%) estimated from the MERRA-2 data.

Variations of methane in the Antarctic atmosphere in 2009–2017 by ground-based and satellite data

The article presents the results of systematic measurements (2009−2017) of the total column abundances of methane (TC_CH4 ) and the column-averaged concentration (X_CH4 ) at the Antarctic station Novolazarevskaya. Solar radiation is recorded in the range 2990 – 3006 cm–1 using a diffraction spectrometer with 0.2 cm–1 resolution. The inverse task CH4 total column determining is achieved using the SFIT4 v0.9.4.4 code. The analysis shows that during the measurement period the average TC_CH4 was (3.4±0.8).1019 molecules/cm2 during the measurement period, and the average X_CH4 is (1663±34) ppbv. TC_CH4 trend is (4.5±2.2).1015 molecules/cm2 /month, and X_CH4 trend is (0.28 ± 0.11) ppbv/month. The average TC_CH4 values and trend at Novolazarevskaya are in good agreement with the measurements by the Brucker120HR instrument at the Arrival Heights station. Seasonal variations of atmospheric methane have the maximum in October–November and the minimum in May–July. The trends of surface methane concentrations at Sywa, the Halley station and the Amundsen-Scott South Pole station are 0.59 – 0.61 ppbv/month and exceed the trend of the column-averaged concentration at Novolazarevskaya and AIRS trends for the troposphere (0.24 – 0.32) ppbv/month, due to a decrease in the maximal values of TC_CH4 in the period after 2014. The closest agreement of X_CH4 variations at Novolazarevskaya with AIRS data is observed at the levels of 150–200 hPa. Significant semiannual harmonics varied with height are characteristic of CH4 variations according to the AIRS data. The interference of annual and semiannual harmonics leads to the appearance of two maxima in the seasonal variations of methane with relative position to each other varies with height. The statistical model is developed for all the series considered. It approximates the trend, annual and semi-annual components of CH4 oscillations.The authors have no competing interests.

Optical properties of dust and crop burning emissions over India using ground and satellite data

Dust storms during the pre-monsoon season (April–June) and crop residual burning events during the post-monsoon season (mid-October–mid-November) are common every year over the Indo-Gangetic plains (IGP). In this paper, we have compared dust storm and crop burning aerosols characteristics for the years 2016, 2017 and 2018 using ground and satellite data. CALIPSO data show that dust layers extended from the ground upto an altitude of about 5 km and the smoke from crop burning upto the height of 2 km. Characteristics of dust and crop burning aerosols show pronounced difference based on Kanpur AERONET data. Dominance of coarse particles (0.6–15 μm) during dust storms (pre-monsoon season), while fine particles (0.05–0.6 μm) dominate during crop residual burning. The spectral variations of single scattering albedo (SSA) during dusty days and crop burning days show low and high fractions of anthropogenic aerosols. We have also observed the impact of dust particles on Himalayan snow (cover, albedo and reflectance) and meteorological parameters (relative humidity and water vapor) on the surface and lower atmosphere using MODIS data and AIRS data at different pressure levels. Pronounced aerosols behaviors of the crop residual burning event coincided with Diwali festival on 30–31 October 2016 were observed. Our detailed analysis combining ground and satellite observations provides better understanding of aerosol optical and microphysical properties of dust storms and crop residual burnings. The results will be valuable in monitoring surrounding environment, identifying the emission source and dynamics of dust storms and crop burning emissions over India, especially in the IGP.

Analysis of long-term (2003–2015) spatial-temporal distribution of atmospheric methane in the troposphere over the Qinghai-Xizang Plateau based on AIRS data

This paper has conducted a contrastive study on the AIRS retrieval results and the observational data of methane concentration at Mt. Waliguan atmospheric background station, and has analyzed the distribution characteristics of atmospheric methane concentration over the Qinghai-Xizang Plateau from 2003 to 2015. The results show that the AIRS retrieval data display the same monthly, annual, and seasonal variation trend, as well as segmental variation characteristics. The methane concentration features high in the north and low in the south along with the geographical line of Kunlun Mountains–Tanggula Mountains–Hengduan Mountains, and decreases significantly as the altitude rises, with the highest and the most sensitive variability in the south central region of the Qinghai-Xizang Plateau. From 2003 to 2015, the methane concentration in the Qinghai-Xizang Plateau continued to rise, with the fastest growth in autumn, the slowest in winter, and an annual growth rate of 5.2 nmol mol−1 a−1, while the growth rate from 2013 to 2015 was lower than the global average. The seasonal variation showed a unimodal curve, with the highest value in summer and the lowest value in spring, and with the altitude rising, the seasonal variation was more significant.

Generalised Variational Assimilation of Cloud-affected Brightness Temperature of AIRS Data based on the Constraint Terms

Generalised Variational Assimilation of Cloud-affected Brightness Temperature of AIRS Data based on the Constraint Terms

Sensitivity of Landsat 8 Surface Temperature Estimates to Atmospheric Profile Data: A Study Using MODTRAN in Dryland Irrigated Systems

The land surface temperature (LST) represents a critical element in efforts to characterize global surface energy and water fluxes, as well as being an essential climate variable in its own right. Current satellite platforms provide a range of spatial and temporal resolution radiance data from which LST can be determined. One of the most complete records of data comes via the Landsat series of satellites, which provide a continuous sequence that extends back to 1982. However, for much of this time, Landsat thermal data were provided through a single broadband thermal channel, making surface temperature retrieval challenging. To fully exploit the valuable time-series of thermal information that is available from these satellites requires efforts to better describe and understand the accuracy of temperature retrievals. Here, we contribute to these efforts by examining the impact of atmospheric correction on the estimation of LST, using atmospheric profiles derived from a range of in-situ, reanalysis, and satellite data. Radiance data from the thermal infrared (TIR) sensor onboard Landsat 8 was converted to LST by using the MODTRAN version 5.2 radiative transfer model, allowing the production of an LST time series based upon 28 Landsat overpasses. LST retrievals were then evaluated against in-situ thermal measurements collected over an arid zone farmland comprising both bare soil and vegetated surface types. Atmospheric profiles derived from AIRS, MOD07, ECMWF, NCEP, and balloon-based radiosonde data were used to drive the MODTRAN simulations. In addition to examining the direct impact of using various profile data on LST retrievals, randomly distributed errors were introduced into a range of forcing variables to better understand retrieval uncertainty. Results indicated differences in LST of up to 1 K for perturbations in emissivity and profile measurements, with the analysis also highlighting the challenges in modeling aerosol optical depth (AOD) over arid lands and its impact on the TIR bands. Days with high AOD content (AOD > 0.5) in the evaluation study seem to consistently underestimate in-situ LSTs by 1–2 K, suggesting that MODTRAN is unable to accurately simulate the aerosol conditions for the TIR bands. Comparisons between available in-situ and Landsat 8 derived LST illustrate a range of seasonal and land surface dynamics and provide an assessment of retrieval accuracy throughout the nine-month long study period. In terms of the choice of atmospheric profile, when excluding the in-situ data, results show a mean absolute range of between 1.2 K to 1.8 K over bare soil and 3.3 K to 3.8 K over alfalfa for the different meteorological forcing, with the AIRS profile providing the best reproduction over the studied arid land irrigation region.

Research Progress of Quality Control for AIRS Data

Research Progress of Quality Control for AIRS Data

Variations of carbon monoxide in the atmosphere during the atmospheric blocking event over European Russia in summer of 2010 (according to AIRS data)

Variations of carbon monoxide in the atmosphere during the atmospheric blocking event over European Russia in summer of 2010 (according to AIRS data)

The relationship between boundary layer stability and cloud cover in the post-cold frontal region.

Using NASA-Aqua MODIS and AIRS data, the relationship between low-level cloud cover (cloud top below the 700 hPa level) and boundary layer stability is explored in post-cold frontal conditions. A linear relationship is found between seasonal cloud cover and two separate measures of inversion strength, the lower tropospheric stability (LTS) and the estimated inversion strength (EIS), for two specific regions in the north Atlantic and Pacific in quiescent and weakly subsiding conditions. The relationship barely changes when considering dynamically active and subsiding post-cold frontal conditions for the same regions. To explore the generality of this result and increase sample size, cold front centered composites of cloud cover and stability are constructed. The northern and southern hemisphere seasonal cloud cover and stability distributions in the post-cold frontal regions are then compared. A fairly good correlation between cloud cover and EIS is found in both hemispheres across all seasons, suggesting that a linear relationship between cloud cover and inversion strength proposed for quiescent conditions exists also in more dynamically active subsiding post-cold frontal conditions. However, for a given season and hemisphere, the correlation between cloud cover and EIS degrades in post-cold frontal regions, especially in the northern hemisphere. At these scales, other large scale factors tend to correlate better with cloud cover.

Plume Tracker: Interactive mapping of volcanic sulfur dioxide emissions with high-performance radiative transfer modeling

We describe the development of Plume Tracker, an interactive toolkit for the analysis of multispectral thermal infrared observations of volcanic plumes and clouds. Plume Tracker is the successor to MAP_SO2, and together these flexible and comprehensive tools have enabled investigators to map sulfur dioxide (SO2) emissions from a number of volcanoes with TIR data from a variety of airborne and satellite instruments. Our objective for the development of Plume Tracker was to improve the computational performance of the retrieval procedures while retaining the accuracy of the retrievals. We have achieved a 300× improvement in the benchmark performance of the retrieval procedures through the introduction of innovative data binning and signal reconstruction strategies, and improved the accuracy of the retrievals with a new method for evaluating the misfit between model and observed radiance spectra. We evaluated the accuracy of Plume Tracker retrievals with case studies based on MODIS and AIRS data acquired over Sarychev Peak Volcano, and ASTER data acquired over Kilauea and Turrialba Volcanoes. In the Sarychev Peak study, the AIRS-based estimate of total SO2 mass was 40% lower than the MODIS-based estimate. This result was consistent with a 45% reduction in the AIRS-based estimate of plume area relative to the corresponding MODIS-based estimate. In addition, we found that our AIRS-based estimate agreed with an independent estimate, based on a competing retrieval technique, within a margin of ±20%. In the Kilauea study, the ASTER-based concentration estimates from 21 May 2012 were within ±50% of concurrent ground-level concentration measurements. In the Turrialba study, the ASTER-based concentration estimates on 21 January 2012 were in exact agreement with SO2 concentrations measured at plume altitude on 1 February 2012.