Count Jakarta wet days with an explicit 1 mm threshold

The direct answer

Define a wet day before counting. This example uses daily precipitation of at least 1 mm, then counts qualifying days in each local month. Counting all positive values would also include lighter precipitation and produce a different metric.

Prepare the observation table

For every valid 2023 daily precipitation value, assign wet = 1 when precipitation_mm ≥ 1, otherwise 0. Keep missing values separate. Sum wet by month, retain valid_days, and map month to X and wet_days_ge_1mm to Y.

Public dataset and a reproducible example

The download is a prepared extract, not a live data connection. The source links below include the original query, observation period and reuse policy. Keep an original copy when you update the data; revised source values can change the result.

Weather data by Open-Meteo.com, using ERA5 reanalysis; attribution under CC BY 4.0. These are grid-cell estimates with local calendar dates, not station observations or forecasts. Review API terms before automating commercial retrieval.

Snapshot retrieved: 2026-10-26. Prepared rows: 12.

Download the prepared CSVDownload the reference SVG

Build it in Datamon

Sign in with Google to create an editable visualization. Public articles and examples can be read without an account. The prepared CSV already contains the joins, filters or aggregations described above; do those steps in a spreadsheet first when using your own data.

1. Open Data and choose a file, or paste the table with its header row. Check the detected numeric columns, decimal separator and dates. The current importer accepts files below 20 MB; these examples are smaller. Keep blanks distinct from recorded zeros.
2. In Visualize, select Column chart. Connect the roles exactly as listed below. Review the preview against at least two rows in the example table rather than relying only on automatic mapping.
3. In Refine, add a title that states the question, the unit, observation period and source. Check labels and contrast at the size your reader will see. Keep a table when exact values matter.
4. Use the export controls for SVG, PNG, PDF or PowerPoint, then inspect the actual file. Save an editable project backup. Publish only after checking the underlying values: a public link makes the published data available to anyone possessing the URL.

Calculation and column mapping

The rule below is pseudocode. The downloadable CSV already contains the prepared values; spreadsheet functions and cell references depend on your sheet layout.

wet_days_ge_1mm = COUNT(valid precipitation_mm >= 1) per local month

X

month

Y

wet_days_ge_1mm

Read the result

In this extract, wet_days_ge_1mm ranges from 2 (2023-08) to 31 (2023-03).

Use the preview to check the structure and the CSV to check exact values. Its styling is a reference; your Datamon rendering can look different. Follow the mapping and preparation above to reproduce the analytical meaning.

Mistakes that change the meaning

Wet-day frequency and rainfall volume measure different things. A month can have fewer wet days and a larger rainfall total. The 1 mm cutoff is an analytical definition for this tutorial, not a claim about local official climate classifications.

Questions before sharing

What is free and what needs an account?

Reading this guide and downloading its CSV and chart images are free. Datamon currently has no paid checkout or subscription plans in this deployment. Creating and saving an editable visualization requires Google sign-in. Provider-backed AI, cloud analysis, publishing and video depend on configured services; this guide does not promise unlimited free connected services.

Sources and further reading

• Open-Meteo / ERA5 · Jakarta
• Open-Meteo / ERA5 · Jakarta · API / CSV
• Open-Meteo / ERA5 · Jakarta · License / policy
• Data sources, queries and reuse notes
• Datamon · Blog
• Visualize column chart: easy guide, free example