NIMCAM is a new EO satellite mission concept that has been designed to address the science requirements associated with identifying and monitoring changes in atmospheric methane due to emissions from large point sources. It has been under development at the University of Edinburgh since 2017.

The core NIMCAM instrument is a multispectral imager, optimised for detecting changes in atmospheric methane at high spatial resolution and with global coverage. NIMCAM employs a novel design using a minimal number of spectral channels located near 1640 nm. Each channel features a very narrow bandpass width of 1.0 nm Full Width Half Maximum, with the channels positioned in close proximity to one another. The spectral design consists of an “on-band” channel located at a methane absorption feature, bracketed by two “off-band” channels which provide a fit for background spectral variations and other confounding species, as well as a fourth channel specifically for targeting CO2 absorption. The channel specifications are shown in Table .

The NIMCAM retrieval algorithm combines the Beer–Lambert law with a linear fit to the background spectrum to retrieve methane slant column density. Modelling work based on this three-channel design has demonstrated good methane signal contrast and reliable rejection of confounding water and ground reflectance signals. Observing in the near infrared brings several benefits including a greater sensitivity to near-surface methane compared with thermal infrared observations and reduced requirements for detector and optical system cooling. Narrow bandpass interference filters define the spectral channels, manufactured by depositing thin layers of coatings over a substrate material. Further details of the spectral design and methane retrieval algorithm can be found in Appendix .

Other aspects of the NIMCAM flight instrument design include a telescope with primary aperture of 135 mm, imaging a cross-track field of view of 2.9°. We use a commercial Indium Gallium Arsenide (InGaAs) detector from Raptor Photonic, which provides a resolution of 640×512 pixels. Manufacturer-provided detector specifications have been used during our modelling work to assess the Signal to Noise Ratio of the system. NIMCAM operates with a swath width of approximately 30 km, and 60 m ground pixels. The narrow bandpass filters are arranged in strips across the field of view. As the scene moves under the instrument it sweeps across the filters, meaning a point on the ground is imaged through all channels during an overflight, providing the required channels of spectral data for each spatial pixel. Telecentric relay elements provide the necessary optical conditions to maintain filter spectral performance.

The instrument is nadir-viewing, and no additional tasking or targeting manoeuvres are required during flight, so that the orbital configuration delivers a weekly revisit interval for most land surface locations. NIMCAM's continuous observation approach means currently unknown or unsuspected leaks are more likely to be captured, driving a proactive rather than reactive approach to fugitive emission detection. The simplified spacecraft guidance and attitude control possible with a fixed nadir view also reduces cost and mission complexity.

For our calculations, we have assumed details about the NIMCAM methane retrieval and how these observations could be aggregated to infer diffuse methane sources. Point-source methane imagers, including the NIMCAM concept, are typically optimised to detect strong, localised plumes, where the methane enhancement signal is large compared to instrument noise and uncertainties in radiative transfer. In this regime, we can make relatively simple assumptions about atmospheric conditions, because the plume signal dominates over smaller variations in scattering and surface reflectance. However, when we apply the same system to more diffuse methane sources, the methane enhancement becomes much smaller and approaches the instrument noise floor. Under these conditions, effects such as clouds, aerosols, and spatial variability in surface albedo introduce changes in the observed radiance that are comparable in magnitude to the methane signal itself. These processes modify both the effective photon path length and the amount of reflected radiance, altering the apparent absorption that can propagate to erroneous methane variations that subsequently can be misinterpreted as real methane variability.

To address this challenge, we can adopt a CO2 proxy approach by incorporating an additional spectral channel sensitive to CO2 absorption (1610 nm) , located close in wavelength to the methane band used by NIMCAM (1637.7 nm), and normalise the methane retrieval with the corresponding CO2 retrieval. The idea is that because atmospheric CO2 is well mixed at the spatial scales relevant to these observations, we can assume its concentration to be nearly uniform across an individual scene. As a result, any observed spatial variability in CO2 absorption primarily reflects the same radiative transfer effects that also influence the methane measurement, such as scattering by aerosols , thin cloud, or variations in surface reflectance. By taking the ratio of methane and CO2 retrievals, we can largely cancel the common influence of these radiative transfer effects, although small residual effects may remain due to spectral and vertical sensitivity differences. As such, this approach helps us to extend the capability of a point-source imager such as NIMCAM to enable the detection and quantification of diffuse methane emissions.

NIMCAM is designed for deployment on a constellation of up to 9 small satellites in Sun Synchronous Low Earth Orbit at an altitude of approximately 600 km and orbital inclination of 97.8°. Each instrument provides a single-frame footprint of 38.4km×30.7km. In this study, we assess the capability of a single in-orbit demonstrator (IOD), compared to a constellation of three and five satellites. Each satellite follows an orbital ground track with a 10 d repeat cycle, giving close to full global coverage in 10 d with the five satellite configuration.

Our study area encompasses tropical Africa, spanning 10° S–20° N between the mainland coasts (Fig. ). Figure a and b display the NIMCAM IOD and constellation data coverage for a single day and a full 10 d cycle, respectively, while Fig. c provides the 10 d IOD coverage for comparison. We use these configurations to analyse clear-sky data availability for the IOD and the proposed three- and five-satellite constellations.

We evaluated cloud interference using the Sentinel-2 cloud probability feature. Data retrieval was performed via the Google Earth Engine Python API . To maintain spatial consistency with our NIMCAM model, the 10 m data was resampled to 60 m, reprojected to the local UTM coordinate system, and exported in GeoTIFF format. We synchronised the temporal resolution with NIMCAM's 10 d repeat cycle, capturing three distinct data periods per month.

Cloud Probability (CP) is a common metric for quality masking in greenhouse gas retrievals. However, the literature reveals significant variation in established cutoff thresholds. For instance, used a relaxed threshold for Sentinel-2 methane retrievals, considering an area cloud-free if the CP was below 65 % and filtering out scenes where more than 10 % of the region exceeded this limit. In contrast, studies of GOSAT-2 CO2 and CH4 observations adopted a more stringent filter, excluding all data with a CP>10 % . While reported limits in other studies span a broad range from 5 % to 70 % , we have opted for a conservative 10 % limit. This strict threshold reflects the high-quality control likely required for NIMCAM observations and allows us to evaluate the specific advantages of NIMCAM's high horizontal resolution when operating under challenging cloud conditions, as found over tropical ecosystems.

We sample cloud information at the Sentinel-2 local solar time of equatorial crossing (10:30 LST), which may introduce a diurnal bias. To estimate the impact of this bias, we performed an analysis on the GEOS-FP cloud fraction dataset, which provides cloud information at a coarser horizontal resolution of 0.25°×0.3125°, but at a higher temporal resolution of every 3 h (Appendix, Fig. ). Looking at cloud variability across the whole African region, cloud levels tend to be higher during the night, while during daylight hours they remain relatively stable. This suggests that cloud measurements taken at 10:30 LST do not exhibit a significant bias compared to other times of day. However, in the cloudiest region of tropical Africa, centred around the Congo Basin, there is a more distinct diurnal cycle during daylight hours. Cloud cover appears higher in the morning and evening, with a minimum occurring between 12:00 and 15:00 LST. In this region, cloud data extracted at 10:30 LST may slightly overestimate cloud levels at other times of day.

To characterise aerosol loading, we extract MODIS aerosol optical depth (AOD) in the green band (0.55 µm) at 1 km spatial resolution. Data were downloaded using the Earth Engine Python API in an analogous method to the cloud probability data extraction. MODIS has a 16 d repeat cycle, with additional data gaps due to quality screening. To maximise coverage, we aggregate the data over monthly periods. AOD is commonly used for quality masking in greenhouse gas retrieval studies, with thresholds typically ranging from 0.75 to 0.20 , and in some cases even more stringently with a threshold of 0.10 . For this study, we explore the more conservative thresholds, comparing the data availability for AOD cutoffs of 0.20 and 0.10. We downscale the 1 km MODIS AOD to the NIMCAM 60 m grid using a Gaussian probabilistic approach, described in Appendix .

This approach follows the concept of “super observations”, in which multiple high-resolution retrievals are aggregated to the model grid scale . The nested GEOS-Chem atmospheric chemistry and transport model (ACTM), described below, has a horizontal resolution of 0.25° (latitude)×0.3125° (longitude), which is coarser than the NIMCAM 60 m resolution. To assess the theoretical ability of NIMCAM to infer methane fluxes over tropical Africa, we count all available NIMCAM 60 m clear-sky scenes into the GEOS-Chem model grid boxes along the NIMCAM ground track swath within 10 d windows. They form the NIMCAM super observations.

To determine clear-sky NIMCAM scenes, we use the 60 m Sentinel-2 cloud probability dataset, and the downscaled 60 m MODIS AOD dataset, described above. The available NIMCAM pixel count is determined for the condition: CP<10 %, AOD<0.1. This translates to a NIMCAM dataset described on the GEOS-Chem modelling coordinate system, with the data availability at a temporal resolution of 1 h. We use this approach for the NIMCAM IOD and NIMCAM constellations, as described above. Within each GEOS-Chem model grid, there are up to 270 000 NIMCAM 60 m pixels available. Individual retrievals are assigned to GEOS-Chem grid cells based on their centre location, such that each observation contributes to a single grid box. Data were prepared for GEOS-Chem model runs to perform methane flux inversions.

Methane inversion experiments are conducted for April and October 2019, corresponding to the long and short rain seasons over East Africa, respectively . Synthetic methane observations are generated for the NIMCAM IOD and the three- and five-satellite constellations, and consistently for the TROPOMI instrument.

We use the GEOS-Chem ACTM to describe the relationship between surface emissions of methane and observed changes in atmospheric methane. We use the nested version of the GEOS-Chem ACTM (version 13.4) to describe atmospheric methane within our tropical Africa domain (38° S–38° N, 20° W–55° E). The nested model uses a horizontal resolution of 0.25° (latitude) × 0.3125° (longitude) and is driven by the GEOS-FP meteorological analysis from the Global Modeling and Assimilation Office, based at NASA Goddard Space Flight Center (GSFC).

The prior simulations are forced by pre-defined anthropogenic and natural methane emission inventories, The natural methane fluxes include monthly wetland emissions covering rice paddies ; monthly fire emissions ; and termite emissions . We also account for emissions from geological macroseeps . For anthropogenic emissions, we use the EDGAR v4.32 global emission inventory , which includes a range of human-related sources such as the oil and gas sector, coal mining, livestock, and waste management. To represent the main oxidation sink of methane, we use monthly 3-D OH fields from the GEOS-Chem full-chemistry simulation , which are consistent with observed constraints on the lifetime of methyl chloroform.

The lateral boundary conditions are taken from a self-consistent global version of the ACTM but run on a coarser horizontal resolution of 2.0° (latitude) × 2.5° (longitude) using GEOS-FP meteorological reanalyses. This global model has been fitted to GOSAT proxy XCO₂ : XCH₄ retrievals . We use the posterior fluxes over Africa, inferred from these GOSAT data, as the truth in our OSSE to describe NIMCAM dry-air column methane mole fraction retrievals (XCH₄) in the GEOS-Chem ACTM. We assume that the prior flux estimates for the study region (10° S–20° N, 20° W–50° E) are the same as the prior inventories used by . Our nested model region is larger than our study region to minimise the influence of lateral boundary conditions.

In the absence of real NIMCAM data, we use an OSSE configuration in which we define a “true” methane emission distribution that is used to determine the “true” 4-D distribution of atmospheric methane. We then sample that 4-D field as it would be observed by NIMCAM, considering its orbit, across-track measurement strategy, and the instrument averaging kernel that describe the instrument vertical sensitivity to changes in atmospheric methane (Appendix ), and remove observed scenes according to CP<10 % and AOD<0.1. We add realistic sources of instrument noise to these NIMCAM “observations”, with a simple error model of 40 ppb for solar zenith angles (SZA) below 40° and 80 ppb for SZA above 40°. These values were derived from a detailed NIMCAM instrument performance and uncertainty analysis, including Monte Carlo propagation of instrumental, calibration, and systematic uncertainty sources through the retrieval measurement equation (Appendix ). To account for the reduced sensitivity of SWIR methane retrievals over water surfaces, the observation error is increased by a factor of two for grid cells where the GEOS-Chem FRLAND value is less than 50 %, which are treated as water bodies.

We then use these “observations” to infer posterior methane emission estimates using the GEOS-Chem ACTM, driven by a different methane emission distribution and an EnKF. For comparison, we use a similar approach to generate synthetic TROPOMI observations. The locations for the quality-filtered (qa>0.75) TROPOMI data are taken from an operational L2 XCH4 product . This OSSE follows an “identical twin” configuration, in which the same atmospheric transport model (GEOS-Chem) is used to generate synthetic observations and to perform the inversion. As a result, structural transport errors are not represented in this framework. This configuration is intentionally designed to isolate the role of observational sampling and measurement uncertainty; however, it represents an idealised scenario compared to real-world inversions.

We anticipate it is likely there will be many 60 m NIMCAM observations that fall within each GEOS-Chem nested model grid. In our OSSEs, we assimilate only one “super observation” for each model grid. To achieve this, we sample the GEOS-Chem model fields for each grid containing clear-sky NIMCAM observations (Sect. ) and calculate individual values of XCH4. This is done by convolving the resulting model methane profiles with an averaging kernel. The averaging kernels for NIMCAM are not fully characterised, so we assume values from GOSAT that vary with solar zenith angle and surface type. We assume the observation uncertainty δ for the NIMCAM super observations is described by: 1δ=δl+δGOSATN, where δl is the uncertainty lower limit of 5 ppb, which describes sub-grid variations and scene-dependent biases, and N is the number of clean observations over land. The value of N is calculated as the number of 60 m NIMCAM clear-sky observations that fall within a grid's land fraction. This formulation assumes that individual retrieval errors are independent, such that the random component of the uncertainty decreases as 1/N. In practice, this represents an upper bound on the achievable precision, as spatially correlated and scene-dependent errors (e.g. arising from surface reflectance, aerosols, and viewing geometry) will reduce the effective number of independent observations (Neff<N). As a result, the uncertainty reduction achieved through aggregation may be weaker than implied by idealised 1/N scaling.

To infer posterior methane emission estimates from these atmospheric data, we use a modified version of Local Ensemble Transform Kalman Filter (LETKF) . We use an ensemble of 91 flux perturbations at the model grid to represent the methane flux uncertainty over our study region. The prior uncertainty of methane fluxes is still challenging to quantify, with natural emissions often misallocated or even missing at the model grid resolution. Here, for simplicity, we define the prior uncertainty of the methane emission at each grid box by: 2σ=0.2(Sprior+[Sprior-Spost]), where Sprior and Spost denote the prior posterior emission estimate, respectively, from the corresponding global inversion . We assume the error correlation between grid boxes is 100 km, following previous studies . The posterior error covariance is given by: 3Spost=Sprior-1+KSobs-1KT-1, where Sobs denotes the observational error covariance matrix, K denotes the Jacobian that describes the sensitivity of the observation (in this case, NIMCAM methane columns) to changes in surface emissions of methane.

We calculate the uncertainty reduction, to assess the theoretical potential of NIMCAM data: 4η=1-SpostiiSpriorii, where Sxii denotes the ith diagonal elements of the error covariance matrix.

We evaluate the data availability of NIMCAM methane observations (60 m resolution, 10 d repeat cycle) under three scenarios: as an IOD and as part of three- and five-satellite constellations. To provide context, these results were compared against the all-sky distribution of 7 km resolution data (equivalent to TROPOMI) and a 1 km resolution retrieval with a 5 d repeat cycle (representing a notional mid-resolution observing system.).

First, we consider all-sky methane retrievals aggregated onto 7 km grid cells with a temporal resolution of 10 d, corresponding to the approximate repeat time of the NIMCAM IOD. The number of potential NIMCAM retrievals is affected by the image resolution and the temporal resolution. For NIMCAM, the IOD only provides coverage of 19 % of the region over the 10 d period, while the three and five satellite constellations achieve 54 % and 89 % coverage, respectively (Fig. ). In comparison, TROPOMI provides 100 % coverage daily due to its significantly larger across-track swath of 2600 km. In other words, daily TROPOMI all-sky coverage is comparable to 10 d of NIMCAM data collected by the five-constellation mission concept. At the 1 km resolution, we implement a 5 d repeat cycle, which translates to all-sky coverage every 5 d, and therefore 2 observations within each 10 d cycle.

Within each 7 km grid cell, there are 13 611 NIMCAM pixels at 60 m resolution, 49 pixels at 1 km resolution, and a single pixel at 7 km resolution (nominally TROPOMI). However, the more frequent revisit times of the lower resolution data means that the number of retrievals per 10 d cycle increases to 98 and 10 for the 1 and 7 km resolution scenarios, respectively.

We now consider how cloud and aerosols affect the data availability at the different instrument retrieval spatial resolutions. Figure  shows the proportion of 7 km grid cells within our tropical African study region that are affected by cloud cover and elevated AOD (values of 0.1 and 0.2, as described above) for the four months of our study.

October is the cloudiest month corresponding to the short rains season, with an average cloud coverage of 77 %. Most of the cloud cover is over the southern and mid-latitude areas including the tropical rainforest regions (Congo Basin, Guinean forests of West Africa, and eastern Afromontane). The cloud coverage is slightly lower for March, April, and November with an average coverage of 57 %, 62 %, and 60 %, respectively. Distributions of AOD loading over the regions are primarily influenced by seasonal biomass burning. Using an AOD threshold of 0.2, March exhibits the highest fraction of affected pixels (55 %), followed by April (44 %), October (37 %), and November (19 %). Using a stricter AOD threshold of 0.1, the corresponding values increase substantially, with March at 94 %, April at 90 %, October at 87 %, and November at 80 %. These results reflect the known seasonal cycle of biomass burning over Central and West Africa, which peaks during the late dry season in March–April and is lowest in November.

Figure  shows the spatial distribution of data availability following cloud masking (CP>10 %) alone, and combined cloud and aerosol masking using AOD thresholds of 0.2 and 0.1. All scenarios are evaluated on a common 7 km analysis grid, and data availability is quantified as the total number of clear-sky retrieval pixels accumulated within each 7 km cell over a 10 d observation window. We present results for April and October 2019 (with March and November shown in Appendix ) for the NIMCAM IOD, and the 3- and 5-satellite NIMCAM constellations. NIMCAM observations are simulated at their native 60 m resolution and aggregated to the 7 km grid. For comparison, we also include a 1 km resolution, 5 d repeat scenario and a 7 km resolution, daily repeat scenario (nominally TROPOMI).

Looking at cloud masking alone, the NIMCAM IOD (single satellite) achieves the lowest fractional coverage, ranging from 10 %–15 % of the domain, as expected, reflecting its limited swath – in a clear-sky scenario the IOD only cover 18 % of the region. Three NIMCAM satellites improve coverage, while five NIMCAM satellites consistently surpass the TROPOMI scenario, and generally match or slightly underperform the 1 km scenario. Across all months, NIMCAM's high native spatial resolution of 60 m leads to 2–3 orders of magnitude more clear-sky pixels per 7 km cell compared to the TROPOMI case and 1 km mid-resolution scenario, as shown by the colour bar of monthly counts.

The benefits of NIMCAM's finer resolution are most evident in October, the cloudier of the two months studied. Here, three NIMCAMs outperform the TROPOMI scenario, and five NIMCAMs achieve comparable coverage to the 1 km scenario, despite the overall lower fractional coverage of the domain. Elevated cloud cover at TROPOMI resolution obscures almost the entirety of the African tropical rainforest regions, while NIMCAM's finer sampling spreads observations across a wider area of cells, reducing contiguous gaps and providing more spatially distributed information.

When additionally removing scenes with elevated AOD, data availability declines further, with the largest impacts observed in March and April and primarily affecting the northwest of the domain. At the moderate AOD threshold of 0.2, the NIMCAM IOD still shows the lowest coverage across all months, while three NIMCAM satellites surpass the TROPOMI scenario in every month except November. Additionally, the five-satellite NIMCAM constellation consistently outperforms the 1 km 5 d repeat scenario in all months except November.

Applying the stricter AOD threshold of 0.1 leads to a dramatic reduction in data availability for the TROPOMI scenario, with clear-sky coverage dropping to 4 % of the domain in March, April, and October, and 14 % in November. Under these conditions, NIMCAM's high-resolution observations become increasingly valuable. The single NIMCAM satellite now outperforms the TROPOMI scenario in all months except November, although its valid 7 km cells are more widely distributed across the domain, whereas the TROPOMI retains coverage primarily in the most northern areas. Three NIMCAM satellites exceed both the 1 km mid-resolution and TROPOMI scenarios in all months, including November, while the full five-satellite constellation achieves the highest coverage overall: April, 39 %; October, 42 %; March, 27 %; and November, 58 %.

These results demonstrate that the finer 60 m resolution of NIMCAM leads to a substantially more spatially extensive and distributed dataset, especially when clouds and aerosols remove large fractions of coarser-grid pixels. As data masking becomes stricter, the advantages of NIMCAM's high resolution and multi-satellite configurations become increasingly pronounced.

To evaluate the use of these satellite data for methane flux inversions (e.g. using GEOS-Chem and the ensemble Kalman filter, Sect. ), we calculate the number of GEOS-Chem nested model grid boxes (0.25°×0.3125°) containing clear-sky observations for each 10 d study period. We compared the data density of TROPOMI against the NIMCAM IOD and the three-satellite and five-satellite constellations (Fig. ). We find there are more observations for TROPOMI than the single NIMCAM IOD when we mask for clouds and use a AOD threshold of 0.2. However, when we adopt a stricter AOD threshold of 0.1 with the cloud masking, the NIMCAM IOD coverage exceeds TROPOMI across every 10 d period. The three-satellite and five-satellite NIMCAM constellations consistently outperform TROPOMI in grid-box availability. This relative advantage becomes more pronounced as the AOD masking criteria become more stringent. Using the 5-satellite constellation and our strict 0.1 AOD/cloud mask, NIMCAM provides up to 6 times more valid grid boxes than TROPOMI. These results demonstrate that NIMCAM's higher spatial resolution significantly enhances data availability, particularly in regions where strict quality filtering would otherwise limit observational coverage.

Here, we investigate how higher spatial resolution data influences random errors in accumulated methane super-observations on the GEOS-Chem nested model resolution of 0.25°×0.3125°. By comparing TROPOMI precision with estimated NIMCAM retrieval errors, mapped to the nested GEOS-Chem model grid over 10 d intervals, we quantify the impact of data co-addition. Under the assumption of uncorrelated noise, the random error scales by 1/N, where N is the number of pixels contributing to each super-observation.

Analysis of TROPOMI CH4 column data across our study region reveals an average single-pixel precision of 1.1 ppb (1.0–1.4 depending on the month). The NIMCAM single-pixel random error is estimated to be 40 ppb, based on radiative transfer simulations and retrieval algorithm modelling (Appendix ). Although this is more than an order of magnitude worse than TROPOMI at the single-pixel level, commensurate with the different science requirements, it is achieved at a substantially finer spatial resolution. The native NIMCAM pixel size is approximately 117 times smaller in linear dimension than TROPOMI, resulting in approximately 13 689 NIMCAM pixels per TROPOMI footprint. Conversely, TROPOMI provides approximately ten times higher temporal sampling than NIMCAM.

When aggregated to our GEOS-Chem nested model resolution of 0.25°×0.3125° over a 10 d window, the maximum potential sample size for NIMCAM reaches 273 780 pixels (468×585), whereas TROPOMI yields at most ∼200 measurements (10d×∼20 pixels per day). The effective number of contributing pixels is further reduced by orbital coverage, such that the IOD, three-satellite, and five-satellite constellations sample approximately 18 %, 54 %, and 89 % of the grid, respectively.

These results are summarized in Table . Under ideal clear-sky conditions, the ratio of random error in NIMCAM super-observations to that of TROPOMI super-observations (σNIM/σTROP) is 2.31, 1.33, and 1.04 for the IOD, three-satellite, and five-satellite constellations, respectively. This ratio metric is used in the following sections to assess how relative performance evolves under cloud and aerosol masking.

Figure  illustrates how the ratio between NIMCAM and TROPOMI super-observation random errors changes when progressively masking for clouds and aerosols. Across all scenarios, data masking leads to a proportionally larger increase in TROPOMI error relative to NIMCAM, such that the majority of grid cells exhibit error ratios lower than the corresponding clear-sky value (dashed line). This behaviour is particularly evident for the single-satellite IOD configuration, where the upper quartile of the distribution lies below the clear-sky reference across all time periods. This indicates that, despite its more limited spatial coverage, a single NIMCAM platform can still generate methane super-observations with random errors that remain competitive with those achieved by the three- and five-satellite constellations under realistic filtering conditions.

Including AOD filtering in addition to cloud masking further enhances the relative advantage of NIMCAM, with the error ratio decreasing under AOD masking and showing additional reductions when applying the stricter AOD threshold of 0.1. Both the magnitude of the ratios and their overall trends are consistent between April and October, indicating that the behaviour is robust across seasons. Overall, these results demonstrate that high-resolution NIMCAM measurements are more resilient to data loss than TROPOMI. As cloud and aerosol filtering become increasingly stringent, NIMCAM exhibits a smaller relative degradation in effective sampling, leading to a slower growth in super-observation random error. The greatest overall benefit is seen for the 5-satellite constellation, where approximately 75 % of NIMCAM super-observations have smaller random errors than the corresponding TROPOMI super-observations (σNIM/σTROP<1) under the combined cloud and AOD filtering scenarios.

Figure  show emission inversion estimates using simulated clear-sky observations of the NIMCAM IOD, the three and five NIMCAM constellation mission concepts, and TROPOMI for April 2019, respectively.

Compared to TROPOMI, the NIMCAM IOD shows data coverage across the study region instead of being focused over the broader Sahel region and Western Africa. The NIMCAM constellation fills in the observational gaps left by the IOD, with progressively more coverage over key methane-emitting regions, e.g. Congo Basin and the Sudd wetland regions in South Sudan. The biggest differences between the true values of XCH₄ and values corresponding to prior estimates are typically over the Congo Basin for the NIMCAM IOD and the constellations.

While XCH₄ differences for NIMCAM mission concepts are generally larger than those for TROPOMI, reflecting the larger retrieval random errors for NIMCAM, the density of NIMCAM super-observations provides a distinct advantage. In key regions where TROPOMI clear-sky data are sparse, the high volume of NIMCAM data significantly reduces the discrepancy between the posterior and the true emission estimates. Consequently, NIMCAM demonstrates significant theoretical potential to constrain methane emission estimates over tropical Africa during the rainy season, effectively filling critical observational gaps left by TROPOMI.

Figure b shows the corresponding improvements in the methane emission estimates in April. As expected, the posterior emission estimates are closer to the true values than the prior values. The NIMCAM IOD improves the emission estimates across many of the major methane-emitting regions, with performance improving progressively as the number of satellites in the constellation increases. The NIMCAM IOD outperforms TROPOMI for this month due to its ability to observe methane between clouds and between regions of elevated aerosol across the study domain. As a result, the uncertainty reduction, η, is generally larger for NIMCAM than for TROPOMI. We find that values of η begin to plateau after the three-satellite NIMCAM constellation, with only modest additional uncertainty reduction achieved by increasing the constellation size from three to five satellites. Although additional constellation members provide valuable data, the measurement uncertainty becomes small relative to errors associated with the atmospheric transport model. In other words, our ability to interpret these data is limited by the atmospheric transport model and not the data themselves.

Over the whole study region, the RMSE between the prior flux and the assumed true flux, calculated for all grid boxes with true emissions greater than 0.1 gm-2yr-1, is 5.35 gm-2yr-1. After assimilation, it decreases to 4.70, 3.78, 3.50, and 3.41 gm-2yr-1 for the TROPOMI, one-, three-, and five-satellite NIMCAM observation systems, respectively. These correspond to overall reductions of 12.1 %, 29.3 %, 34.3 %, and 36.9 %, demonstrating substantial improvements from the NIMCAM observations but only modest additional benefit when increasing the constellation size from three to five satellites. The remaining errors are dominated by persistent overestimations in the Congo Basin, which are not fully constrained even by the five-satellite NIMCAM configuration. This highlights the challenges faced by space-based observations in regions where observational coverage is severely limited by persistently high cloud cover.

In addition, mismatches between the spatial patterns of the prior and assumed true fluxes limit the ability of neighbouring observations to correct emissions in these key regions. The assumed true fluxes are derived from a top-down inversion at coarse spatial resolution (hundreds of kilometres), resulting in spatially smoothed emission fields that can be displaced from their true locations and, in some cases, allocated over water bodies that cannot be observed by NIMCAM. Such discrepancies give rise to well-known challenges in LETKF inversions, including emission over- and underestimation and spatial misallocation. Consequently, part of the limited RMSE reduction reported here reflects the particularly demanding nature of the assumed truth scenario rather than limitations of the observing system alone. Nevertheless, mismatches between prior and true emission patterns are unavoidable in real-world LETKF applications, especially as inversion resolution increases and prior knowledge of local emissions becomes more uncertain. These limitations could be reduced through improved specification of prior uncertainties, particularly their spatial correlation structures, aided by the expanded observational constraints provided by future missions such as NIMCAM.

Figure  shows the same analysis but for October 2019 during the short rains across East Africa. Performance of NIMCAM is worse for this month due to more extensive cloud cover across the study region compared to April (Fig. ). Similarly, the performance of TROPOMI is also worse for this month. After the NIMCAM data are ingested by the EnKF, we find that the posterior estimate is much closer to the truth, with substantial reductions over the Congo Basin. Unlike April, we find less evidence of the performance of the NIMCAM constellation plateauing after three satellites. This is because there are generally fewer clear-sky scenes, so the additional satellites are still providing valuable information to refine the methane emission estimates. This point is less obvious when we examine the posterior methane emission estimates. For this month the RMSE between the prior flux and the assumed truth, is 5.82 gm-2yr-1. After assimilation, it decreases to 5.02, 3.66, 3.52, and 3.48 gm-2yr-1 for the TROPOMI, one-, three-, and five-satellite NIMCAM observation systems, respectively. Again we see substantial improvements with high resolution NIMCAM observations but modest additional agreement from increased constellation size.

The results shown here assume idealised random errors in which individual retrievals are assumed to be independent and transport model error is not directly accounted for. These assumptions are used to isolate the role of observational sampling but represent a simplified inversion framework. A more realistic assessment could be achieved using a “fraternal twin” OSSE, in which different transport models are used to generate and invert the synthetic observations, thereby introducing structural model error. In practice, both transport errors and spatially correlated retrieval errors would reduce the effective information content of the observations and weaken the rate at which uncertainty decreases with increasing sampling density. The uncertainty reductions presented here should therefore be interpreted as a best-case (upper bound) estimate of achievable performance under realistic conditions. Nevertheless, the increased spatial coverage provided by high-resolution instruments such as NIMCAM remains a key advantage, particularly in regions where coarser-resolution observations provide limited valid data.