Instrument Trade Studies for Satellite Remote Sensing of Ammonia
Bibliographic record
Abstract
Atmospheric ammonia (NH3) has effects on air quality and water quality, but due to its short lifetime and high spatial variablility, obtaining global measurements is a challenge. Advances in radiative transfer modelling and radiometric instrument sensitivity have allowed for satellite NH3 observations to be performed by instruments such as NASA’s Tropospheric Emission Spectrometer and NASA’s Cross-track Infrared Sounder, but these instruments, and others like them, were not designed specifically to measure ammonia. This thesis pursues the development of an optimized instrument to measure ammonia with daily Canadian coverage, good ground spatial resolution (around 1 km), and a high signal-to-noise ratio. The work presented here shows an investigation into the optimized satellite remote sensing system by combining forward modelling, sensor design, and data retrieval algorithms. An imaging Fourier Transform Spectrometer (IFTS) is chosen for the instrument for its wide spectral range and high throughput. A software model of the instrument is used to generate interferograms for each pixel, average four neighbouring interferograms, and recover a spectrum. A secondary model convolves the theoretical instrument lineshape for an off-axis square pixel with the high-resolution radiance. The Rodgers-based retrieval algorithm takes in the spectrum from the full instrument model, and iterates (using the convolution model) to retrieve a surface temperature value and an ammonia profile. These three pieces working together allow for an exploration of different instrument configurations. The investigation of the instrument parameters resulted in two configurations that were selected for further study. These two configurations differed in the number of samples taken of the interferogram and the time required to obtain an interferogram, having undersampling factors of 2 and 3, respectively. In a large-scale run of microwindowed retrievals using high-resolution model fields of NH3, both sets of specifications were able to perform ammonia retrievals. Due to the use of microwindowing, the retrieved surface temperature values had errors large enough to impact the ammonia retrievals. Small tests using a full spectrum retrieval resulted in better performance for both the surface temperature and ammonia retrievals. Both sets of instrument specification meet the requirements for the project, though the case with an undersampling factor of 2 has a lower brightness noise level than the other case.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".