Climate chart generator, temperature and precipitation on one honest scale
A Walter-Lieth diagram plots temperature and precipitation on the same graph, one degree Celsius against two millimetres of rain, so a physiologically dry month becomes visible at a glance instead of buried in two separate tables. Pick a built in Bulgarian station or enter your own twelve monthly figures, and read the wet and dry months alongside an automatic Koppen-Geiger classification.
Open the climate chart generator A free account opens this tool. The interface is in English and nothing you enter leaves your browser.How it works
Two ways to work. Pick a built in station for an instant chart, or type in your own twelve months of data.
Pick a station or enter your own data
Choose one of seven built in Bulgarian stations from the dropdown for an instant chart with an automatic classification, or enter your own twelve monthly temperatures, which may be negative, and twelve monthly precipitation totals, which may not. The tool checks that no month is missing and that no precipitation figure is negative before it draws anything.
Read the wet months against the dry ones
Temperature and precipitation share one scale, at the rate of one degree Celsius to two millimetres of rain. Wherever the precipitation curve sits above the temperature curve, the month is wet and plants have enough water. Wherever it drops below, the month is physiologically dry, because evaporation is outpacing what the rain supplies. Above one hundred millimetres of monthly rain the scale compresses tenfold, marked with a distinct dark blue band, so a very wet month does not stretch the whole chart off the page.
Check the Koppen code and the annual amplitude
The tool derives a Koppen-Geiger code automatically from the twelve months you entered, alongside the annual temperature amplitude, the gap between the warmest and coldest month, and the annual precipitation total. The code and the short explanation that comes with it describe the climate in the same shorthand used across the published classification literature.
Seven Bulgarian stations, from the coast to the summit
Real annual figures for temperature, precipitation and the resulting Koppen code, so a class can compare climates without leaving the country.
Built to show the physics, not just draw a graph
A chart is only as useful as the honesty of what it claims to know, and this one is explicit about the difference between a measured monthly average and a statistical estimate built around it.
The scale is the whole trick, not decoration
The one degree to two millimetre ratio was chosen specifically because that relationship is what exposes physiological drought, the point where soil moisture stops being enough for plant growth. Before this kind of chart existed, climatologists described drought in prose, with no way to show precisely which months a plant on a given site actually suffers in.
What Bulgaria does not have is shown honestly
Bulgaria has no true cold steppe climate under the modern classification, because rainfall is sufficient even at the driest sites in the country. The tool includes synthetic test data for a cold steppe, a hot desert, a tropical rainforest and permanent ice specifically so a class can see the contrast, clearly labelled as data that does not correspond to any real Bulgarian station, rather than inventing a fictional local example.
Export matches what the classroom actually needs
Every chart generates as an SVG for on screen use and as a PNG at double resolution for print, so a handout stays sharp. Everything is generated locally in the browser, with no request sent to an external weather server, which also means a teacher can build a live demonstration or hand students figures from a real station in their own town without needing a live connection.
The 3D climate tower only shows what the data actually supports
The tower's axis is month, not hour of day, even though an early design sketch described a month by hour grid. The reason is stated plainly: the tool only has twelve monthly climate normals per station, no hourly resolution measured or simulated anywhere in the underlying data, and an hour axis would have meant showing an invented curve dressed up as data. The axis was changed to a selected month instead, a slider that shows exactly what the tool actually knows.
Frequently asked questions
Why does a Walter-Lieth diagram put temperature and precipitation on the same axis?
Because that is what makes physiological drought visible on a single chart. Heinrich Walter and Helmut Lieth, the German botanists behind the diagram, needed a way to show teachers and biologists whether plants at a given place get enough water through the year, and plotting temperature in degrees Celsius against precipitation in millimetres at a one to two ratio does exactly that. Before the diagram, climatologists could describe drought in words but had no simple graphic way to show precisely when it happens at a given site.
How is a physiologically dry month actually defined?
A month is physiologically dry when its precipitation curve falls below its temperature curve on the shared scale, which in numeric terms means the monthly rainfall in millimetres is less than twice the monthly temperature in degrees Celsius. It is a simplified model, not a direct soil moisture measurement, but it is consistent enough across climates to compare regions on the same footing, which is exactly what the diagram was built for.
How does the Koppen-Geiger code get built from three letters?
The first letter is the climate group: A for tropical, B for arid, C for temperate, D for continental snow climates, and E for polar. The second letter describes the precipitation regime, f for no dry season, s for a dry summer, w for a dry winter. The third letter describes the summer, a for hot, b for warm, c for short and cool. Sofia, for instance, comes out as Cfb, temperate, no dry season, warm summer, directly from its twelve monthly figures.
Why does Bulgaria include synthetic climates that do not exist in the country?
Because showing contrast is part of teaching the classification properly, and Bulgaria genuinely does not contain every climate type on the map. There is no true cold steppe in the country, because rainfall is adequate everywhere within its borders even at the driest stations, so the tool includes clearly labelled synthetic data for a cold steppe, a hot desert, a tropical rainforest and a permanent ice climate, letting a class see the full range of the classification without a lesson depending on a station that does not exist.
If twelve monthly averages are all the tool has, how does it estimate hot and frost days?
By building a statistical day count around each monthly average rather than pretending to have daily records it does not possess. The extremes mode constructs a normal distribution centred on the monthly mean, with a spread the user sets, and counts how many of many simulated days cross a chosen threshold. The random generator is seeded, meaning the same seed always produces the same result, so the exercise is reproducible in front of a class rather than different every time it runs, and the result is shown with a confidence interval, clearly marked as a statistical estimate rather than a measured fact from a thermometer.
Does the tool do anything beyond drawing the climate chart itself?
Yes, it links out to several related physics modes built on the same station data. A psychrometric mode works out humidity, enthalpy and dew point from temperature and one other humid air property, validated against the WMO standard Magnus-Teten formula. A thermal comfort mode runs the classical Fanger PMV and PPD model from ISO 7730 for air conditioned spaces, alongside a separate adaptive comfort model from ASHRAE 55 for naturally ventilated buildings. A degree day mode scales a building's seasonal heating and cooling load against the station's real annual temperature curve, and a water balance mode runs the Thornthwaite method with a simple soil moisture reservoir. Every mode is built from the same twelve monthly figures, so nothing is invented that the underlying station data does not support.
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