the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 20 Jan 2025
Towards a low-resolution infrared sounder for monitoring atmospheric ammonia (NH3) at high spatial resolution
Abstract. Over the past decade, hyperspectral infrared sounders on satellites have offered global measurements of atmospheric ammonia (NH3), providing valuable insights into its sources. However, due to their coarse spatial resolution and gaps in spatial coverage, inferring emissions from smaller sources or utilizing data from single overpasses remains very challenging. While a high spatial resolution imaging-sounder would greatly enhance monitoring capabilities, developing an instrument that combines high spatial and spectral resolution is technologically difficult and expensive. Here, we analyze the feasibility of measuring NH3 with instruments having a largely reduced spectral coverage and resolution compared to current operational sounders. We explore the performance trade-offs using simulated spectra, measurements from the Infrared Atmospheric Sounding Interferometer (IASI) satellite sounder, and spectra obtained from aircraft. The measured spectra are degraded spectrally, and their performance is evaluated using metrics such as NH3 measurement uncertainty, signal-to-noise ratio, and false alarm rate. Instruments that measure across a continuous spectral interval and instruments covering specific well-chosen spectral bands are both examined. We demonstrate that a future dedicated NH3 sounder with as few as three spectral bands of 1–5 cm-1 is feasible and would enable the detection of NH3 at both high spatial resolution and across continental scales. The advantage of choosing well-defined spectral bands is demonstrated, e.g. by showing that an instrument with five specific bands of 5 cm-1 performs similarly to one with 20 contiguous channels across 900–1000 cm-1. Additionally, we show that at high spectral resolutions (below 5 cm-1), the NH3 measurement capability is primarily driven by the instrumental noise. As the spectral resolution or number of measurement bands decreases, spectral interferences from other atmospheric constituents and the surface start to dominate the NH3 retrieval uncertainty budget, fundamentally limiting the unambiguous identification of NH3.
Competing interests: Michel Van Roozendael is a member of AMT's team of executive editors.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.- Preprint
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Status: open (until 05 Jun 2025)
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RC1: 'Comment on egusphere-2024-3455', Anonymous Referee #1, 28 Apr 2025
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Please see attached PDF for my review
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RC2: 'Comment on egusphere-2024-3455', Anonymous Referee #2, 07 May 2025
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The paper covers the potential design of a more ammonia specific sensor. Presently there are no satellites in orbit specifically designed to measure ammonia. Current satellites like IASI and CrIS are meteorological satellite sensors (designed to measure things like temperature and water vapor) that have been used to also monitor ammonia due to their large spectral range. Thus, these sensor design trade studies looking at specifically monitoring ammonia (high spatial resolution) are important and timely to help design future ammonia monitoring instruments. The paper is well written, and the results are provided in logical order. As with any trade study, the scope of what can be evaluated is large. The following are a few general comments for the authors to address.
- The authors select only one spring day of April 8th in 2020. IASI has a large range of observations over the years that span many atmospheric conditions. One would not want to design an instrument to monitor only very small amounts (like in the non-growing seasons), but it would be good to provide the reader with some idea of the conditions being covered on this day. For example, are they represented of what an instrument would on average see when monitoring, or are they under more ideal springtime conditions (e.g. histogram of the ammonia and atmospheric states from this day vs more typical IASI global observations, etc.). It is true when comparing sensor designs, they are all using the same inputs, so the relative comparison is fine, but it would be good to give the reader a better sense if the results are from more favourable or typical remote sensing conditions. Were any data filtered on this day?
- Related a bit to the first comment. Table 2 contains the resulting SNR values for a variety of band selections. What seems somewhat surprising is the magnitude of the overall SNR values are higher than expect, especially for the lower spectral resolutions. The SNR can be defined in many ways, so that might be a part of it, also the instrumental spectral noise level is not high. However, related to comment (1), it does make me wonder if the remote sensing conditions are more favourable with there not being on average as many conditions in the test dataset that would produce an ammonia radiance signal that is close or below the detection limit of the sensor.
- The spectral bands selected and results for a give sensor design will depend on the atmospheric conditions used in the trade study. Did the authors look at the changes in the results depending on the atmospheric conditions (e.g. ideal vs typical vs challenging remote sensing conditions)?
- It would be good for the authors to provide comments in the paper on difference between this approach over a traditional “microwindow” selection based on information content.
- In the spectral resolution trade study the authors appear to assume a constant instrument noise level. It has been shown in previous ammonia sensor design studies that often the instrument noise is the larger driver. The authors do mention in the conclusions that the analysis can be expanded in the future to consider different noise levels, which is good. When discussing overall results it is good to make it clear that they are for a specific noise level and that any specific sensor design will also depend on the instrument noise.
- The spectral selection will depend on cross-state errors (e.g. temperature, water vapor, spectroscopic parameters, etc.) as noted and accounted for by the authors in their analysis, which is great. Accounting for the impact of these interfering species is only as good as the specified error estimates. It would be good if the authors could provide more information on the generation of the estimates. Also, since the authors can easily produce simulated retrievals, did they perform any Monte Carlo type statistical tests to see if the estimates are robust. For example, put in errors in the temperature, water vapor, etc. (e.g. ECMWF) on a pixel-by-pixel basis and see the impact. This will be particularly important for any sensor design that is not on a more traditional meteorological sensor and does not have coincident water vapor and temperature sounding.
Citation: https://doi.org/10.5194/egusphere-2024-3455-RC2
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