✦ PIXELOKAY · EXPLORATION STUDIO

Climate Visualization Studio

Reveal temperature patterns, rainfall variation and seasonal changes—using your own measurements.

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Illustrative data

OBSERVATIONS—Valid measurements

MEAN TEMPERATURE—Mean of available location-months

MEAN MONTHLY RAIN—Observed location-months

LOCATIONS—Years covered

02 / VISUAL INSIGHTS

Temperature over time

Findings & coverage

From observed data

Monthly summaries

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Period	Location	Temp	Rain	Records

How to read these charts: Temperature is an unweighted average of available observations in each month. Rainfall values are summed within each month; if the source already contains monthly totals, import one record per month. Missing months and missing readings are never assumed to be zero. Calculated baselines are *sample period averages*, not official 30-year climate normals or forecasts.

Visualize temperature and rainfall data locally

PixelOkay Climate Visualization Studio converts CSV, TSV, and JSON observations into timeline charts, seasonal comparisons, location summaries, and year-by-month heatmaps. All parsing and calculations happen inside your browser, without uploading datasets to external servers.

Understanding your climate dataset

Import readings with an ISO-style date or year-month, and map your temperature, rainfall, and location columns. Monthly temperatures are averaged over provided measurements, while rainfall amounts are summed. Missing observations stay missing. For statistically rigorous long-term climate normals, source data should be reviewed for sampling frequency, time coverage, quality, and appropriate reference periods.

Using Climate Visualization Studio

Map local CSV, TSV, or flat JSON climate observations, inspect date coverage and duplicates, and explore seasonal patterns or observed-period anomalies.

Workflow

Import a local observations file and map its date, station, temperature, and rainfall columns with the appropriate units.

Review daily/monthly coverage, duplicate diagnostics, and missing periods before choosing a seasonal chart or station comparison.

Interpret observed-period trends or anomalies with their coverage context, then export a chart, source/summary data, or report.

Worked example

Import a station CSV with date, temperature, and rainfall fields. Map the date and units, inspect the coverage view for missing months and duplicates, then compare seasonal summaries without treating the chart as an official normal or forecast.

Limits and data handling

This visualizes observations; it does not provide climate normals, attribution, or forecasts. Daily observations and monthly readings have different aggregation meanings. Temperature is an unweighted mean; rainfall sums available readings and may have incomplete coverage. Station comparisons do not equalize seasonal coverage, and missing months remain gaps. Limits: 50,000 rows, 12 MB input, and 28 MB backups. Synthetic examples stay labeled.

Frequently asked questions

Are these charts climate forecasts or official climate normals?

No. They summarize the imported observed period and are exploratory; they are not forecasts, attribution, or official climate normals.

Are missing months filled in during summaries?

No. Coverage gaps remain visible; review the daily/monthly period and completeness before comparing stations or seasons.