Climatologies from satellite measurements: the impact of orbital sampling on the standard error of the mean
Bibliographic record
Abstract
Abstract. Climatologies of atmospheric observations are often produced by binning measurements according to latitude and calculating zonal means. The uncertainty in these climatological means is characterised by the standard error of the mean (SEM). However, the usual estimator of the SEM, i.e., the sample standard deviation divided by the square root of the sample size, holds only for uncorrelated randomly sampled measurements. Measurements of the atmospheric state along a satellite orbit cannot always be considered as independent because (a) the time-space interval between two nearest observations is often smaller than the typical scale of variations in the atmospheric state, and (b) the regular time-space sampling pattern of a satellite instrument strongly deviates from random sampling. We have developed a numerical experiment where global chemical fields from a chemistry climate model are sampled according to real sampling patterns of satellite-borne instruments. As case studies, the model fields are sampled using sampling patterns of the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) and Atmospheric Chemistry Experiment Fourier-Transform Spectrometer (ACE-FTS) satellite instruments. Through an iterative subsampling technique, and by incorporating information on the random errors of the MIPAS and ACE-FTS measurements, we produce empirical estimates of the standard error of monthly mean zonal mean model O3 in 5° latitude bins. We find that generally the classic SEM estimator is a conservative estimate of the SEM, i.e., the empirical SEM is often less than or approximately equal to the classic estimate. Exceptions occur only when natural variability is larger than the random measurement error, and specifically in instances where the zonal sampling distribution shows non-uniformity with a similar zonal structure as variations in the sampled field, leading to maximum sensitivity to arbitrary phase shifts between the sample distribution and sampled field. The occurrence of such instances is thus very sensitive to slight changes in the sampling distribution, and to the variations in the measured field. This study highlights the need for caution in the interpretation of the oft-used classically computed SEM, and outlines a relatively simple methodology that can be used to assess one component of the uncertainty in monthly mean zonal mean climatologies produced from measurements from satellite-borne instruments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".