A comparison of carbon monoxide retrievals between the MOPITT satellite and Canadian high-Arctic ground-based NDACC and TCCON FTIR measurements
Bibliographic record
Abstract
Abstract. Measurements of Pollution In The Troposphere (MOPITT) is an instrument on NASA's Terra satellite that has measured tropospheric carbon monoxide (CO) from early 2000 to the present day. Validation of data from satellite instruments like MOPITT is often conducted using ground-based measurements to ensure the continued accuracy of the space-based instrument's measurements and its scientific results. Previous MOPITT validation studies generally found a larger bias in the MOPITT data poleward of 60∘ N. In this study, we use data from 2006 to 2019 from the Bruker IFS 125HR Fourier Transform Infrared spectrometer (FTIR) located at the Polar Environment Atmospheric Research Laboratory (PEARL) in Eureka, Nunavut, Canada, to validate the MOPITT version 8 (V8) retrievals. These comparisons utilize mid- and near-infrared FTIR measurements made as part of the Network for the Detection for Atmospheric Composition Change (NDACC) and the Total Carbon Column Observing Network (TCCON), respectively. All MOPITT version 8 retrievals within a radius of 110 km (1∘) from the PEARL Ridge Laboratory and within a 24 h time interval are used in this validation study. MOPITT retrieval products include those from the near-infrared (NIR) channel, the thermal infrared (TIR) channel, and a joint product from the thermal and near-infrared (TIR–NIR) channels. Each channel's detector has 4 pixels. We calculated the MOPITT pixel-to-pixel biases for each pixel, which were found to vary based on the season and surface type (land or water). The systematic bias for pixel 1 over land is larger than that for other pixels, which can reach up to 20 ppb. We use a small-region approximation method to find filtering criteria. We then apply the filters to the MOPITT dataset to minimize the MOPITT pixel bias and the number of outliers in the dataset. The sensitivity of each MOPITT pixel and each product is examined over the Canadian high Arctic. We then follow the methodologies recommended by NDACC and TCCON for the comparison between the FTIR and satellite total column retrievals. MOPITT averaging kernels are used to weight the NDACC and TCCON retrievals and take into account the different vertical sensitivities between the satellite and PEARL FTIR measurements. We use a modified Taylor diagram to present the comparison results from each pixel for each product over land and water with NDACC and TCCON measurements. Our results show overall consistency between MOPITT and the NDACC and TCCON measurements. When compared to the FTIR, the NIR MOPITT retrievals have a positive bias of 3 %–10 % depending on the pixel. The bias values are negative for the TIR product, with values between −5 % and 0 %. The joint TIR–NIR products show differences of −4 % to 7 %. The drift in MOPITT biases (in units of % yr−1) relative to NDACC and TCCON varies by MOPITT data product. In the NIR, drifts vs. TCCON are smaller than those vs. NDACC; however, this scenario is reversed for the MOPITT TIR and joint TIR–NIR products. Overall, this study aims to provide detailed validation for MOPITT version 8 measurements in the Canadian high Arctic.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".