Abstract. In this work, we present the results of an observing system simulation experiment (OSSE) in which we investigate the emergence of a surface-reflectance-dependent bias in retrieved column-averaged dry-air mole fractions of methane (XCH4). Our focus is on single-band retrievals in the shortwave infrared (SWIR) at 2.3 µm. This particular bias manifests as artificial gradients in XCH4 fields that relate to surface features on the ground and can, for example, cause erroneous estimates of methane source emission rates. We find that even for near-ideal conditions (that being a perfectly calibrated instrument, perfect knowledge of meteorology and trace gas vertical distributions, and an absence of clouds and aerosols) a surface-reflectance-related bias appears in the retrieved XCH4. While the magnitude of the bias is much lower than is observed in, for example, real data from the TROPOspheric Monitoring Instrument (TROPOMI), the overall qualitative shape is strikingly similar. When we study a more realistic scenario by considering synthetic measurements that are affected by aerosols, the surface bias increases in magnitude roughly by a factor of 10. We hold all other properties of the synthetic measurements fixed and thus can make the following statements about these surface biases from the 2.3 µm absorption band. First, the bias already appears in the near-perfect scenario, meaning that its origin is likely fundamental to XCH4 retrievals from this particular absorption band and using an optimal-estimation-type retrieval approach. Second, the magnitude of the bias increases significantly when aerosols are encountered. As aerosols give rise to a magnification of the bias, we have implemented a retrieval configuration in which the retrieval algorithm knows the true aerosol abundance profiles along with their optical properties. With this configuration, the surface bias returns mostly to the level first seen when synthetic measurements were not affected by aerosols. The results we present in this work should be considered for new missions where XCH4 is a target quantity and the design relies on the 2.3 µm absorption band. Since the surface bias will likely emerge, it is crucial that a validation approach is planned which sufficiently samples the needed range of surface reflectance in areas of near-uniform methane concentrations in order to capture the bias and thus correct for it.

Abstract. To improve the accuracy and precision of in situ water vapor measurements from aircraft, a new opto-mechanical design was implemented on the JPL Laser Hygrometer Mark2. The first JPL Laser Hygrometer (JLH Mark1), originally developed in mid-1990s, provided airborne in situ water vapor measurements for fifteen years from several platforms, including the NASA ER-2 and WB-57 aircraft. Due to heavy use over the years and aging of the instrument parts, many of the components in JLH Mark1 have been modified and replaced. This instrument paper reports on the redesigned opto-mechanical structure of the instrument, new data retrieval algorithms, and updated data analysis procedures, along with recent laboratory and field performance and a comparison with another water vapor instrument. Key changes in the redesigned instrument have significantly improved the performance, as demonstrated during the NASA Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) field mission and eight years of subsequent science flights on the Northrop Grumman Systems Corporation Flying Test Bed (FTB).

Abstract. Satellite observations of column-averaged carbon dioxide (XCO2) and methane (XCH4) mixing ratios provide essential data for monitoring greenhouse gas (GHG) emissions. However, the accuracy of emission estimates depends on the precision and bias of satellite retrievals, which require validation against ground-based reference measurements. This study presents a systematic validation of XCO2 and XCH4 data from GOSAT (Greenhouse gases Observing SATellite) and OCO-2 (Oribiting Carbon Observatory-2) satellites over South India using ground-based Fourier transform spectrometer (FTS) observations at Gadanki (13.5° N, 79.2° E) collected from October 2015 to July 2016. Satellite products from National Institute for Environmental Studies, Japan (NIES), NASA's Atmospheric CO2 Observations from Space (ACOS) project, USA (ACOS), and the University of Leicester, UK (UoL) were evaluated using a three-step spatial-temporal pairing method. Results show that the UoL's proxy XCH4 product meets the European Space Agency's Climate Change Initiative (ESA CCI) bias requirement (<10 ppb) across all spatial windows, while the NIES XCH4 product meets the requirement only for intermediate spatial scales. For XCO2, NASA ACOS and OCO-2 products meet the CCI bias requirement (<0.5 ppm), while NIES XCO2 exceeds this threshold. All products satisfy the precision requirement (<8 ppm for XCO2 and <34 ppb for XCH4) with substantial margins. In addition, FLEXPART model simulations using regional emission inventories revealed that agricultural activities dominate seasonal methane enhancements, contributing approximately 55 %, followed by waste and wetland emissions. The model captured seasonal trends but underestimated the amplitude of observed variations, highlighting the influence of changing background methane levels. These findings demonstrate the suitability of recent satellite products for regional GHG monitoring and emphasise the need for expanding ground-based FTS networks across South Asia to support improved emission assessments.

Abstract. Shipping is an important source of nitrogen oxide (NOx) emissions worldwide, contributing to air pollution and negatively affecting marine environments, ecosystems, and biodiversity. TROPOMI (TROPOspheric Monitoring Instrument) on board the Sentinel-5 Precursor (S5P) has significantly enhanced the ability to detect ship emissions from space due to its low measurement noise levels and high spatial resolution of 5.5×3.5 km2 at nadir. This study uses the TROPOMI tropospheric NO2 slant column density (tSCD) to identify global shipping routes qualitatively. Preprocessing techniques, including iterative high-pass and Fourier filtering, markedly improve the detection of shipping lanes, revealing previously undetectable routes. Our analysis examines the impact of high-pass-filter box sizes, demonstrating that smaller sizes enhance the visibility of narrow shipping features, whereas larger box sizes increase overall NO2 signals. Additionally, we investigate various flagging criteria that affect NO2 signal distribution, highlighting the critical importance of careful selection for accurate emission monitoring. Filtered TROPOMI NO2 tSCDs over oceans show a strong correlation with shipping activities, as confirmed by comparison with the CAMS-GLOB-SHIP (Copernicus Atmospheric Monitoring Service for Global Shipping) inventory, and also reveal unknown shipping routes in regions such as the Bering Sea. Furthermore, TROPOMI effectively captures NO2 emissions from offshore oil and gas platforms, with NO2 hotspots in the TROPOMI data aligning well with locations of offshore installations listed in the OSPAR (Oslo and Paris Commission) and BOEM (Bureau of Ocean Energy Management) inventories. Lastly, the filtered TROPOMI NO2 tropospheric vertical column densities (tVCDs) are compared with the high-pass-filtered NO2 tVCDs from the CAMS (Copernicus Atmospheric Monitoring Service) model, which has a coarse spatial resolution of 0.4°. While both datasets effectively identify global shipping lanes, the high-pass-filtered CAMS NO2 tVCDs are significantly higher than the filtered TROPOMI NO2 tVCDs in the North Atlantic and strongly depend on the masking threshold in the high-pass filtering method in the South Atlantic Ocean.

Abstract. Airborne measurements of wind speed and direction, temperature, and relative humidity are critical due to their importance for atmospheric processes. Field campaigns with multiple coordinated aircraft present challenges when combining data from each platform due to atmospheric heterogeneity. To confront this issue, this work intercompares for the first time in situ measurements from the Turbulent Air Motion Measurement System (TAMMS) of horizontal winds and temperature and a diode laser hygrometer (relative humidity) deployed on an HU-25 Falcon flying mostly within the marine boundary layer to an independent set of measurements from dropsondes launched from a higher-flying King Air. Leveraging data from 162 joint flights over the northwest Atlantic from these two spatially coordinated aircraft during the NASA Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE) campaign in winter and summer seasons between 2020–2022, a total of 555 pairs of Falcon–dropsonde data points are identified within 30 km horizontal separation, with minimal vertical separation (usually < 1 m), and within 15 min. This analysis is based on the following range of conditions experienced: altitude = ∼ 0.1–5 km, temperature = −19–27 °C, relative humidity = 1 %–100 %, and wind speed = 0.2–42 m s−1. Based on scatterplots, correlation coefficients, and mean (in situ–dropsonde) error (ME), intercomparisons reveal good agreement for wind speed (r = 0.95, ME = 0.21 ± 1.68 m s−1), u/v wind components (r ∼ 0.96–0.97, ME ∼ 0.03–0.16 (± 1.62–1.67) m s−1), wind direction (r = 0.94, ME = 0.00 ± 0.22 based on cosine of direction angles), temperature (r = 0.99, ME = 0.00 ± 0.71 °C), and relative humidity (r = 0.91, ME = −3.86 ± 10.74 %). Sensitivity analysis shows that binning data into categories of horizontal separation distance, clear versus cloud, winter versus summer, altitude range, and terciles of the values for examined variables did not yield major changes except for relative humidity where there was more deviation, especially above 70 %. The effect of statistics was examined by relaxing the vertical separation distance criteria to expand the number of pairs to over 360 000, without much difference in intercomparison metrics. The effect of averaging more points for each instrument in the final 555 pairs was also shown to lead to minimal change in agreement. Overall, these results provide confidence in both the performance of the measurement techniques compared and combining dropsonde data with in situ data from a separate coordinated aircraft for ACTIVATE, which has relevance to other campaigns with multiple coordinated aircraft conducting similar types of measurements.

Abstract. Observations of trace gases, such as O3, HCHO, and NO2, and their seasonal dependence can be made using satellite and ground-based data from the Ozone Monitoring Instrument (OMI) satellite and Pandora ground-based instruments. Both operate with spectrometers that have similar characteristics in wavelength range and spectral resolution that enable them to retrieve total column amounts of formaldehyde (TCHCHO) and nitrogen dioxide (TCNO2) and total column ozone (TCO). The polar orbiting OMI observes at 13:30 ± 0:25 LST (local solar time) plus an occasional second side-scan point 90 min later at mid-latitudes. The ground-based Pandora spectrometer system observes the direct sun all day, with a temporal resolution of 2 min. At most sites, the Pandora data show a strong seasonal dependence for TCO and TCHCHO and less seasonal dependence for TCNO2. Use of a low-pass filter LOWESS(3-month) can reveal the seasonal dependence of TCNO2 for both OMI and Pandora at mid-latitude sites usually correlated with seasonal heating using natural gas or oil. Compared to Pandora, OMI underestimates the amount of NO2 air pollution that occurs during most days, as the OMI TCNO2 retrieval occurs around 13:30 ± 0:25 LST, which tends to be near the frequent minimum of the daily TCNO2 time series. Even when the Pandora data are restricted to between 13:00 and 14:00 LST, OMI retrieves less TCNO2 than Pandora over urban sites because of OMI's large field of view. The seasonal behavior of TCHCHO is mostly caused by the release of HCHO precursors from plant growth and emissions from lakes that peak in the summer, as observed by Pandora and OMI. Long-term averages show that OMI TCHCHO usually has the same seasonal dependence but differs in magnitude from the amount measured by Pandora and is frequently larger. Comparisons of OMI total column NO2 and HCHO with Pandora daily time series show both agreement and disagreement at various sites and for different days, with the Pandora results frequently being larger. For ozone, daily time-dependent comparisons of OMI TCO with those retrieved by Pandora show good agreement in most cases. Additional diurnal comparisons are shown of Pandora TCO with hourly retrievals during a day from the EPIC (Earth Polychromatic Imaging Camera) spacecraft instrument orbiting the Earth–Sun Lagrange point L1.

Abstract. In this work, we describe the development of the Carbon monOxide Measurement from Ames (COMA) instrument for measurement of carbon monoxide (CO) and nitrous oxide (N2O) aboard NASA's WB-57 high-altitude research aircraft. While COMA has previously flown in the cabin of the NASA P3 platform, here the instrument was modified to operate in a significantly different environment – an unpressurized pallet flying primarily above 12 km (40 000 ft). Modifications were made to the laser to allow for detection of CO and N2O, ruggedization and thermal management were addressed, and a calibration system was designed to quantify the measurement stability in-flight. Testing was conducted in a thermal vacuum chamber to mimic anticipated ambient conditions experienced inside the WB-57 pallet bay and found that the electronic components remained within the thermal limits. COMA was operated successfully during 9 unattended transit flights to and from South Korea and 15 research flights during NASA's Asian summer monsoon Chemical & CLImate Project (ACCLIP) 2022 campaign, which was focused on studying the Asian summer monsoon anticyclone in the western Pacific. The CO measurement has an overall uncertainty ranging between 4.1 ppb (at 50 ppb CO) and 5.6 ppb (at 200 ppb CO). N2O has an overall uncertainty of 2.7 ppb (at 320 ppb N2O). In addition, COMA observations were compared with two other in situ CO instruments co-located on the WB-57: the Carbon Monoxide Laser Detector 2 (COLD2) and the Airborne Carbonic Oxides and Sulfide Spectrometer (ACOS). Comparisons for the 15 flights during the ACCLIP campaign indicate ranges in slope of 1.10–1.15 for COLD2 vs. COMA and 0.94–1.10 for ACOS vs. COMA.

Air sensors are being used more frequently to measure hyper-local air quality. The PurpleAir sensor is among one of the most popular air sensors used worldwide to measure fine particulate matter (PM2.5). However, there is a need to understand PurpleAir data quality especially under different environmental conditions with varying particulate matter (PM) sources and size distributions. Several correction factors have been developed to make the PurpleAir sensor data more comparable to reference monitor data. The goal of this work was to determine the performance of a remote calibration tool called MOment MAtching (MOMA) for PM2.5 sensors monitoring near temporally varying pollution sources of PM2.5. MOMA performs calibrations using reference site data within 0 - 15 km from the sensor. Data from 20 PurpleAir sensors deployed across a network in Phoenix, Arizona from July 2019 to April 2021. Results showed that the MOMA calibration tool made the PurpleAir PM2.5 data more comparable to the co-located reference data (calibrated Mean Absolute Error (MAE): , Mean Bias Error (MBE): ). The improvements were comparable to the Environmental Protection Agency (EPA) correction factor (MAE: , MBE: ). However, MOMA provided a better estimate of daily average concentrations than the EPA correction factor when compared to the reference data under smoke conditions. Using the MOMA gain, representative of the sensor-proxy relationship, MOMA was able to distinguish between PM sources such as winter wood burning, wildfires, and dust events in the summer.

Abstract. The orientation of ice crystals plays a significant role in determining their radiative and precipitating effects; horizontally oriented ice crystals (HOICs) reflect up to ∼40 % more shortwave radiation back to space than randomly oriented ice crystals (ROICs). This study introduces an automatic range-resolved algorithm for HOIC identification using a combination of ground-based zenith-pointing and 15° off-zenith-pointing polarization lidars. The lidar observations provided high-resolution cloud-phase information. The data were collected in Beijing over 354 d in 2022. A case study from 13 October 2022 is presented to demonstrate the effectiveness and the feasibility of the detection method. The synergy of lidars and collocated Ka-band cloud radar, radiosonde, and ERA5 data provides phenomenological insights into HOIC events. While cloud radar Doppler velocity data allowed the estimation of ice crystal size, Reynolds numbers, and turbulent eddy dissipation rates, corresponding environmental and radar-detected variables are also provided. HOICs were present, accompanied by weak horizontal wind of 0–20 m s−1 and relatively high temperature between −8 and −22 °C. Compared to the ROICs, HOICs exhibited larger reflectivity, larger spectral width, a larger turbulent eddy dissipation rate, and a median Doppler velocity of about 0.8 m s−1. Ice crystal diameters (1029 to 1756 µm for 5th and 95th percentiles) and Reynolds numbers (28 to 88 for 5th and 95th percentiles) are also estimated with the help of cloud radar Doppler velocity using an aerodynamic model. One interesting finding is that the previously found switch-off region of the specular reflection in the region of cloud base shows a higher turbulence eddy dissipation rate, probably caused by the latent heat released due to the sublimation of ice crystals in the cloud-base region. The newly derived properties of HOICs have the potential to aid the derivation of the likelihood of their occurrence in output from general circulation models (GCMs) of the atmosphere.

Abstract. Volatile organic compounds (VOCs) are important atmospheric components that contribute to air pollution, but their accurate quantification by proton-transfer-reaction mass spectrometry (PTR-MS) remains challenging. In this work, we coupled a gas chromatograph (GC) prior to PTR-MS and analyzed complex ambient air in urban Shanghai to speciate the PTR signal to identify the VOC species that were responsible for the generation of the ions detected by PTR. We classified 176 individual PTR signals with associated compounds resolved by the GC based on whether they could be used to quantify a VOC species without pre-separation. In this classification, Category I includes 45 decent signal ions that were produced from a single VOC species and thus can be used for reliable quantification, although some of the Category I ions are not the conventionally used protonated quasi-molecular ions (MH+). Category II includes 39 signal ions that were produced from a group of isomers and can be used to quantify the isomeric sum, but with an increased uncertainty if a single calibration factor for one specific isomer is used to represent all structures. Category III includes 92 signal ions that were generated from more than one non-isomeric species (e.g., through protonation, fragmentation, cluster formation) and thus merely gave an upper limit of VOC concentrations. In addition, we propose taking aromatic compounds, for instance, the quantification of selected VOCs, utilizing either non-MH+ or non-Category I ions. Our results help to achieve more comprehensive species identification and reliable VOC quantification in PTR measurements.