The recorded spectra are analysed using retrieval code PROFFAST v2.4 developed at KIT (KIT IMK-ASF, 2024a). PROFFAST software retrieves the gas amount by fitting solar absorption spectra and scaling a priori atmospheric profiles of the gases. It was run using a Python interface PROFFASTpylot v1.3 which also takes care of preprocessing of raw instrument data (Feld et al., 2024; KIT IMK-ASF, 2024b). The PROFFAST algorithm, which is a dedicated extension of PROFFIT Version 9.6 for processing of low-resolution spectra from spectrometers that are part of COCCON, is validated in several studies and used across all the COCCON sites to provide uniform and consistent data processing (Frey et al., 2019; Gisi et al., 2012; Hase et al., 2004; Sepúlveda et al., 2012; Sha et al., 2020). The spectral windows used for different species are listed in Table 2. The algorithm requires vertical profiles of temperature and pressure and a priori estimates of profiles of species to be estimated. Vertical profiles of temperature and pressure are obtained from National Center for Environmental Prediction (NCEP) reanalysis data corresponding to the dates of observations. The a priori estimates of species profiles are obtained from WACCM (Whole Atmosphere Community Climate Model) (Marsh et al., 2013) which is the average of 40-year monthly mean values for the site. The preprocessing step involves quality check of interferogram, DC correction, fast Fourier transform, phase correction and resampling of the spectra. Each record of raw data is a set of 10 spectra of which five are captured when the mirror is moving forward and five are captured when the mirror is moving backward. The interferogram is checked for signal level and source brightness fluctuations also known as DC variability and is removed from further analysis if threshold levels are not met. Other measurement and instrument specific corrections included in the processing are DC correction (correction for the sun brightness fluctuations) (Keppel-Aleks et al., 2007) and the application of ILS parameters (Abrams et al., 1994; Alberti et al., 2022a; Hase et al., 1999; Messerschmidt et al., 2010). As a first step, the columnar concentrations of CO₂, CH₄, O₂ and H₂O in terms of number of molecules per m² are retrieved. Then, the CO₂ and CH₄ concentrations are converted to column average mixing ratios by assuming O₂ mixing ratio as 20.95 % and normalising CO₂ and CH₄ concentrations with respect to O₂. This allows for compensating various systematic errors. XCO₂ measurement precision is 0.13 ppmv and XCH₄ measurement precision is 0.6 ppbv (Frey et al., 2019).

Observations were carried out from October 2015 to July 2016 in the Gadanki campus of NARL. Gadanki (Latitude: 13.45° N, Longitude: 79.18° E, 360 m above mean sea level) is a rural site in South India with a tropical wet climate. It experiences two monsoon seasons known as southwest and northeast monsoon seasons. Change in wind circulation from one season to the other season is known to have significant effect on trace gases and aerosol concentrations at the site (Renuka et al., 2014, 2020; Sai Suman et al., 2014). The site is surrounded by hilly terrain and the nearest city is approximately 35 km away. A major part of the terrain surrounding Gadanki is forest and farm lands. Though there is no farming of rice (paddy field) in the immediate vicinity, the region as a whole has a good number of paddy fields. More details about the site and various atmospheric observation facilities can be found in Pandit et al. (2015) and Jayaraman et al. (2010). The FTS observations were carried out from morning to evening at intervals of 1 min except during days with inclement weather and weekends. More than 39 000 spectra covering a period of 10 months were analysed to retrieve XCO₂ and XCH₄.

The GHG observing satellite (GOSAT) also known as IBUKI is a joint project of the Ministry of the Environment (MoE), Japan, the National Institute for Environmental Studies (NIES), Japan and the Japan Aerospace Exploration Agency (JAXA), Japan (Yokota et al., 2009). The main instrument onboard GOSAT is a Thermal and Near infrared Sensor for carbon Observations (TANSO) (Table 1). It is a FTS with two detectors, one for shortwave infrared (SWIR) wavelength range and the other for thermal infrared (TIR) wavelength range (Olsen et al., 2017). While the TIR sensor is used to retrieve CO₂ and CH₄ profiles, the SWIR sensor is used to retrieve column average dry mole fraction of CO₂ (XCO₂) and CH₄ (XCH₄). In the current study, only XCO₂ and XCH₄ values from SWIR sensor are used.

The column-averaged dry-air mole fractions of methane (XCH₄) and carbon dioxide (XCO₂) retrieved from GOSAT are available from three different sources: (1) National Institute for Environmental Studies (NIES), Japan, (2) UK National Centre for Earth Observation at University of Leicester (UoL), UK and (3) the Goddard Earth Science Data Information and Services Center (GES DISC) of National Aeronautics and Space Administration (NASA, USA).

NIES Data Products: NIES provides operational XCH₄ and XCO₂ products using a full physics algorithm, which minimises the difference between observed and simulated spectra generated by a radiative transfer model (Someya et al., 2023). In the current study, we use bias-corrected FTS SWIR Level 2 v3.05 data products from NIES, hereafter referred to as NIES XCH₄ or NIES XCO₂.

The Orbiting Carbon Observatory-2 (OCO-2) is NASA's Earth remote sensing satellite to study atmospheric carbon dioxide from space (Crisp et al., 2004). In the current work, we have used processed and bias-corrected data version 11.1r downloaded from the website of GES DISC (http://disc.gsfc.nasa.gov/, last access: 26 August 2025). Version 11.1r is the latest version of data which were released in May 2023. The version 11.1r data contain retrospectively retrieved XCO₂ values using a full physics algorithm with several improvements with respect to its predecessor algorithms (Jacobs et al., 2024; Payne et al., 2025). The OCO-2 was launched on 2 July 2014 in sun-synchronous orbit with equatorial crossing time at 13:30 on an ascending node with a 16 d repeat cycle (Table 1). The OCO-2 instrument consists of three boresight high-resolution imaging grating spectrometers which provide high-resolution spectra of reflected sun light in oxygen A band (0.765 µm) and in two CO₂ bands at 1.61 and 2.06 µm. The instruments can be operated in three modes viz., target, glint and nadir. The ground resolution varies depending on the mode of operation. In the current study, data from the nadir mode are used which has a spatial resolution of 1.29 km × 2.25 km (Crisp et al., 2017). The spectra are corrected for various artefacts such as bad pixels, cosmic ray artefacts and converted to radiometric values. Using a full physics radiative transfer model, synthetic spectra are produced and compared with observed spectra. An inverse model iteratively modifies the assumed atmospheric state to improve the fit. The number densities of CO₂ and O₂ thus retrieved are used to get XCO₂ by taking their ratio and multiplying it by 0.2095. The retrieval is further enhanced with applied bias correction obtained from collocated TCCON data, models and small area analysis (O'Dell et al., 2018). More details of the retrieval process are available in Crisp et al. (2021). The OCO-2 data are distributed in two formats known as standard files and Lite files. The standard files contain CO₂ mixing ratios without bias correction, whereas mixing ratios in the Lite files are bias corrected (Payne et al., 2025). The data files contain a quality flag for each retrieval. The quality flag value “0” corresponds to good data, whereas the quality flag value “1” suggests the presence of any of the 24 algorithmically identified quality issues in the retrieved value. In the present work, we used bias corrected data with quality flag “0” only.

To calculate the concentrations resulting from recent regional emissions (emissions within past 10 d of a given observation), we used ECLIPSEv6b (Evaluating the CLimate and air quality ImPacts of Short-livEd pollutants version 6b) emission inventory (Amann et al., 2011, 2012; Klimont et al., 2017; Höglund-Isaksson, 2012; Stohl et al., 2015). The inventory is prepared following the IPCC (2006) recommended method and using the Greenhouse Gas – Air Pollution Interactions and Synergies (GAINS) model (Amann et al., 2011). It provides sector-specific anthropogenic emission estimates for 11 species, including CH₄, across eight economic sectors. The data are provided as 0.5° × 0.5° gridded values for the years from 1990 to 2050 at an interval of 5 years for two scenarios – the current legislation for air pollution – which is also the reference scenario and maximum technically feasible reductions scenario. The latest version (Version 6b) was released in August 2019 and incorporates updates for historical data, new waste sectors, soil NO_X emissions, international shipping emissions and energy-macroeconomic data. The inventory includes only anthropogenic emission fluxes from sectors; viz. energy, industry, solvent use, transport, domestic combustion, agriculture, open biomass and agricultural waste burning and waste treatment. Natural emissions from wetlands, forest fires, biogenic emissions, etc. are not included in the inventory. The total Global, South Asia (members of SAARC – South Asian Association for Regional Cooperation), and India's emissions of methane for the year 2015 were 336.2, 44.2 and 31.5 Tg, respectively.

The emissions from wetlands can contribute significant atmospheric load of methane at the observation site and hence in addition to anthropogenic emissions from ECLIPSEv6 inventory, we used Wetland CH₄ emissions and uncertainty dataset for atmospheric chemical transport models (WetCHARTs) version 1.0 inventory (Bloom et al., 2017a, b) for calculating methane concentrations at Gadanki from recent emissions. The inventory contains global monthly emission fluxes of methane at 0.5° by 0.5° resolution for ensemble of multiple terrestrial biosphere models, wetland extent scenarios and temperature dependencies. The emission fluxes from 2001–2015 are provided for three choices of global scaling, two choices of wetland spatial extent, two choices for temporal variability of wetland extent, nine choices of heterotrophic respiration schemes and three choices of parameterisation scheme for temperature dependency. In the current work, we used data corresponding to the scaling factor with global emissions 166 TgCH₄ yr⁻¹, CARDAMOM (CARbon DAta MOdel fraMework) terrestrial C cycle analysis for heterotrophic respiration (Bloom et al., 2016), mid-range temperature sensitivity and, spatial and temporal extent of wetlands constrained with SWAMPS (Surface WAter Microwave Product Series) multi-satellite surface water product (Schroeder et al., 2015). These choices were made based on following consideration. Choice of scaling factor represents the mid-point global emissions among the three choices available, viz. 124.5, 166 and 207.5 Tg CH₄ yr⁻¹. While there are nine choices for heterotrophic respiration, there is only one choice available for emission fluxes after 2010 which is CARDAMOM and used here. Between the two choices of spatial extent and two choices of temporal variability, the SWAMPS multi-satellite surface water product is used because it represents observationally constrained inundated areas including lakes and other water bodies.

The GOSAT satellite revisits the same point on Earth every 3 d, with retrievals performed only under cloud-free sky conditions. This limits the number of concurrent satellite and ground-based FTIR measurements. To address this limitation and ensure sufficient data pairs for comparison, we followed an approach similar to Buchwitz et al. (2017). This approach relies on the fact that CO₂ and CH₄ have long atmospheric residence times, allowing the history of air parcels to be used to pair data for comparison.

In this approach, the first step is to identify all satellite data within a certain distance of the ground station. Buchwitz et al. (2017) used satellite data within ±30° longitude and ±10° latitude of TCCON sites to evaluate GOSAT and OCO-2 data products. Wunch et al. (2017) used box of ±5° longitude and ±2.5° latitude around the TCCON sites in the Northern Hemisphere and ±60° longitude and ±10° latitude around the TCCON sites in the Southern Hemisphere to evaluate XCO₂ estimates from the OCO-2 satellite. In the second step, ground-based observations taken within 3 d of the satellite overpass and during same time of the day (within 2 h) are paired with the satellite data. In the third step, the data pairs obtained in step 2 are further filtered using the criterion that the CAMS model output of XCH₄ and XCO₂ values, interpolated to the satellite location and ground station, cannot differ by more than 0.25 ppm for XCO₂ and 5 ppb for XCH₄, respectively. This third step is based on the premise that the CAMS model is capable of simulating transport accurately, meaning that while the absolute values may not always be correct, the spatial variability in the model is reliable. The criteria in step 3 ensures that satellite and ground values are only compared when they share the same air mass history. Note that the absolute value of the model simulation and its differences with observations are not relevant in this step. More detailed discussions on the need and the rationale behind this complex approach for data pairing can be found in Nguyen et al. (2014) and Wunch et al. (2011). A sensitivity test, described in Table S1 of the Supplement, shows omitting the model-based air mass filtering (step 3) increases the number of matched pairs by factors of 2–3 across species and datasets. While the effect on bias is mixed, the scatter generally increases slightly when step 3 is not applied. For consistency with previous studies, we report results based on the full three-step pairing procedure.

We note that no averaging kernel (AK) correction were applied in this analysis. While applying AK corrections is ideal to account for vertical sensitivity differences between satellite and ground-based retrievals, the effect of their omission is expected to be small for our study location. Sha et al. (2021) demonstrated that at low-latitude sites, the effect of smoothing and a priori profile differences on XCH₄ biases is minor, typically below -0.25 %, with an average effect of -0.14 %. Given that Gadanki (13.5° N) is a low-latitude station, the lack of AK correction is unlikely to significantly affect our conclusions.

We performed calculations for three different box sizes around the observation site at Gadanki (13.45° N, 79.18° E): (±5° longitude, ±2.5° latitude), (±10° longitude, ±5° latitude), and (±30° longitude, ±10° latitude). By the end of the third step, we obtained 55 pairs of XCH₄ from GOSAT NIES v3.05, 81 pairs of XCH₄ from GOSAT UoL v9, 117 pairs of XCO₂ from GOSAT v3.05, 117 pairs of XCO₂ from ACOS v9.2 and 120 pairs of XCO₂ from OCO-2 v11.1 for the biggest box size in step 1 (see Table 4, Fig. 2). The number of data pairs for XCO₂ is more than double that of XCH₄ for all box sizes. This difference reflects the fact that carbon dioxide has a much longer atmospheric lifetime (>100 years) compared to methane (∼ 12 years).

With the paired dataset in place, we evaluated the bias, scatter and correlation between satellite and ground-based measurements, as listed in Table 4. Here, bias is defined as the mean of difference between satellite- and the ground-based dry-air mole fractions; scatter as the standard deviation of these differences; correlation as the Pearson correlation coefficient (R) between the paired values. The European Space Agency's Climate Change Initiative (ESA CCI) specifies performance targets of <34 ppb for scatter (precision) and <10 ppb for bias (systematic error) for XCH₄, and <8 ppm for scatter and <0.5 ppm for bias for XCO₂ (GHG-CCI, 2020).

Figure 4 shows methane mixing ratio enhancements calculated using the FLEXPART model and the ECLIPSEv6 + Wetland inventory. As previously mentioned, the model is configured such that the values represent daytime mean mixing ratios in the altitude range of 0 to 15 km over Gadanki, contributed by emissions from the preceding 10 d. The altitude range of 0 to 15 km is selected because the tropopause altitude in the tropics is typically between 15 and 18 km (Pandit et al., 2014), and emissions from the past 10 d are generally confined within this range.

The 10 d back trajectory is chosen based on earlier work by Gadhavi et al. (2015), which demonstrated that, for the Gadanki location, a 10 d back trajectory captures emissions from almost the entire South Asia. The averaging period is selected as daytime (9 am to 6 pm local time) to ensure a one-to-one correspondence with observed mixing ratios, which are measured using solar radiation through FTS and are therefore only available during daylight hours.

Hereafter, these values will be referred to as model values, However, it is important to note that the model values do not account for the columnar CH₄ mixing ratio resulting from emissions prior to the 10 d period and, therefore, do not represent the total columnar mixing ratio as seen in FTS or satellite data.

Overall, the model estimates methane mixing ratio enhancements ranging from 2 to 26 ppb during 2015 and 2016. While there is significant day-to-day variability, a seasonal pattern is still discernible in the model-calculated values. Typically, the mixing ratio enhancements are high in November, decrease slightly in December, and rise again during January and February. They decrease in March and April, briefly rise in the second half of May, and then decrease again, remaining low from June to September. The mixing ratios rise again in October, peaking in November. Sector-wise, wetlands do not show large seasonal variations. Wetland contributions are low from December to March. In other seasons, wetland contributions occasionally reach as high as 40 % of total mixing ratios, but for most part of the year, they remain around 10 %. The highest contribution comes from the agriculture sector, accounting for nearly 55 % of the total mixing ratio enhancements, followed by the waste sector, which contributes approximately 17 % to the model values at Gadanki. The domestic and energy sectors contribute approximately 5 % each. The domestic sector's contribution is lower in July and August, mainly due to the air masses originating from the west of Gadanki in peninsular India, where the population is smaller and contributes less to methane emissions. Flaring contributes negligibly for most part of the year, but during June to July, its contribution can reach up to 40 %, primarily due to low emissions from other sectors during this period and the winds from the Arabian Sea bringing emissions from oil rigs off the west coast of India, the eastern Arabian Peninsula, and northeastern Africa. Industry, transport, shipping and agricultural waste burning activities contribute less than 1 % of atmospheric load of methane at Gadanki.

Figure 5 shows model-calculated methane mixing ratio (ΔXCH₄; solid blue line; left y axis) and the methane mixing ratios (XCH₄) observed using FTS (red filled circles; right y axis) in a single plot. The left-hand side y axis represents the model mixing ratio, which only accounts for emissions from the preceding 10 d. For lack of a better term, we refer to it as ΔXCH₄. The right-hand side y axis shows the observed values in ppm. As mentioned earlier, the model was configured to reflect incremental variability caused by regional emissions. If the background CH₄ mixing ratios were constant, the day-to-day variability relative to background values should be the same in both the model and the observations. However, we observe differences in both the absolute values of variability and their seasonal patterns. Several sudden increases in the model values, which appear as spikes in Fig. 5 (e.g. 5 October and 27 December 2015), correspond to variations in the observations. While the observations are not as continuous as model values and cannot capture all the variability seen in the model, some degree of day-to-day variability is correlated between the model and observations (R2=0.35). However, the magnitude of variability between the model and observed values is quite different. For instance, the observed mixing ratios from 5 to 8 October 2015 decreased by 49 ppb, whereas the model values during the same period decreased only by 16 ppb. Over the entire observation period, total column methane mixing ratios varied by 100 ppb, while the model values which excludes background mixing ratios varied only by 20 ppb. This discrepancy may be due to two main factors: either the emission fluxes in the emission inventory are underestimated, or the background mixing ratios are not constant. The latter factor could explain the mismatch on a monthly scale. Starting in October 2015, both model and observed values are high and decrease toward June–July 2016. While the model values are already low by March 2016, the observed values decrease gradually from November 2015 to January 2016, remain nearly constant from January 2016 to April 2016, and then decrease rapidly in May, reaching a minimum during the last week of June and the first week of July.

Chandra et al. (2017) analysed methane variations over different parts of India using the Japan Agency for Marine-earth Science and TEChnology (JAMSTEC) Atmospheric Chemical Transport Model. They found that, over South India, although 60 % of the columnar concentration is attributed to CH₄ in the lower troposphere, there is very little correlation between regional emissions and columnar methane variations. This was attributed to changes in atmospheric chemistry and transport. According to Chandra et al. (2017), the methane loss rate increases from 6 ppb d⁻¹ in January to 12 ppb d⁻¹ from April to September. In addition, anticyclonic winds in the upper troposphere confine uplifted methane molecules over broader South Asia during the monsoon season, contributing significantly to methane over Western India, but not significantly over South India. Since FLEXPART does not include chemistry other than the reaction with OH radical, a lower decrease in model values from March to July could be due to absence of chemistry as well as transport of background methane.