Use for earth, ocean, atmosphere and ecology data work: NetCDF/CF, GRIB, Zarr, HDF5, GeoTIFF and vector formats, coordinate reference systems, regridding and land/sea masks, non-standard calendars, anomalies and baselines, trends with autocorrelation, extremes and return periods, CMIP scenarios and ensemble spread, remote-sensing preprocessing, and field-ecology survey data.
Use for earth, ocean, atmosphere and ecology data work: NetCDF/CF, GRIB, Zarr, HDF5, GeoTIFF and vector formats, coordinate reference systems, regridding and land/sea masks, non-standard calendars, anomalies and baselines, trends with autocorrelation, extremes and return periods, CMIP scenarios and ensemble spread, remote-sensing preprocessing, and field-ecology survey data.
["\\b(NetCDF|\\.nc4?\\b|\\bGRIB2?\\b|CMIP[56]|ERA5|MODIS|Landsat|Sentinel-[12])\\b","\\b(reanalysis (?:data|product|dataset)|climate (?:model|projection|scenario)|RCP\\d(?:\\.\\d)?|SSP\\d-?\\d)\\b","\\b(detrend\\w+|seasonal (?:cycle|decomposition)|anomaly time series|teleconnection|\\bENSO\\b|\\bNAO\\b index)\\b","\\b(shapefile|geospatial (?:raster|vector)|EPSG:\\d+|remote sensing|zonal statistics|reprojection)\\b"]
Earth, climate and ecology data
Run skill_activate(skill="research-method") first and work from that spine —
question framing, evidence standards, citation honesty, what verification means.
Do not restate it. This pack covers what is different when the data is gridded,
projected, dated on a strange calendar and produced by somebody else.
Before any arithmetic
analysis_env(path). These stacks live in the project, not in this
process: xarray, netCDF4, cfgrib, rasterio, GDAL, cdo, nco, geopandas, R
terra/sf. Everything heavy runs through analysis_run in the project's own
interpreter. Missing tool -> say so and name the install; do not silently
substitute a worse method.
Record the product identity. Name, version/DOI, spatial and temporal
resolution, variable long_name and units, download date, and processing level
(L1/L2/L3). cite_add the dataset by its own DOI — datasets are citable
objects, and "ERA5" alone is not a citation.
Print the metadata before you trust the array.ncdump -h, gdalinfo,
xr.open_dataset(...).attrs. Read units, _FillValue/missing_value,
scale_factor/add_offset, calendar, the CRS, and whether latitude runs
north-to-south. references/data-formats-and-cf.md.
Say which kind of data it is and never mix silently. Observation,
reanalysis (a model constrained by observations — not an observation), and
free-running model output are three different epistemic objects. A figure
that overlays them must label each one.
Working rules
CRS. Every dataset carries or lacks a CRS; assuming EPSG:4326 because the
numbers look like degrees is the classic silent error. Reproject deliberately,
compute areas and distances in an equal-area or local projection, never in
degrees. references/crs-and-projections.md.
Area weighting. A global or regional mean of a lat/lon grid without
cos(latitude) weighting is wrong, and wrong in a direction that flatters
polar amplification. Use the grid cell areas the tool gives you.
Regridding. Bilinear for smooth fields you will look at; conservative for
anything that must integrate correctly (precipitation, fluxes, area totals).
Regrid once, at a documented step, and keep the source grid.
references/regridding-and-masks.md.
Land/sea masks differ between products; apply the
mask that belongs to the grid you are on. Model calendars can be 360-day or
no-leap: never compare a 360-day series to a Gregorian one by index.
Masks and calendars.
Anomalies. State the baseline period and the climatology construction
(which years, which smoothing, per-calendar-day or per-month). Two anomaly
series on different baselines are not comparable.
references/trends-and-autocorrelation.md.
Trends. Geophysical series are autocorrelated, so ordinary least-squares
standard errors are too small. Report the trend with an interval computed from
an effective sample size or a method that handles serial correlation, and say
which. Mann-Kendall with prewhitening or Sen slope where normality fails.
Check for breakpoints and instrument changes before calling a step a signal.
Extremes. Block maxima (GEV) or peaks-over-threshold (GPD), with the
threshold and declustering stated. A return period is a probability statement
about a stationary distribution; if the climate is changing, say so and use a
non-stationary fit or report the epoch.
references/extremes-and-return-periods.md.
Ensembles and scenarios. A CMIP number is meaningless without its scenario,
model, variant label and period. Report spread across models, not just the
mean, and never present a multi-model mean as a prediction.
references/cmip-ensembles-and-scenarios.md.
Remote sensing. Know the processing level, apply cloud/quality masks from
the product's own QA band, and do not compute indices across bands from
different atmospheric corrections. references/remote-sensing.md.
Field ecology. Detection is not perfect: transects, mark-recapture and
occupancy models estimate detection probability rather than assuming it.
references/ecology-field-data.md.
Read references/INDEX.md and pull what you need with file_read.
What counts as verified here
Units and magnitude check. Convert to physical units and compare against
an order-of-magnitude expectation (K vs degC, kg m-2 s-1 vs mm/day, m vs km).
A sign flip or 86400 factor is the most common real bug in this field.
Round-trip and conservation. After a conservative regrid, the
area-weighted global integral matches the source within tolerance — compute
it and report the residual.
Independent reproduction of a known quantity. Recompute a value the
product's own documentation publishes (a global mean, a documented trend) and
compare. If it does not match, the pipeline is wrong, not the product.
Provenance intact. Every figure came from an analysis_run;
analysis_ledger(action="verify", run_id=...) says INTACT before it goes in
the paper. data_diff between product versions before you blame the science
for a changed result.
Citations resolve.cite_check(manuscript, resolve=True) returns PASS,
and the dataset DOIs are in the bibliography alongside the papers.
Say the uncertainty out loud: observational coverage gaps, reanalysis
inhomogeneity across the satellite era, and model structural error are not
covered by the confidence interval you computed.
