How Baseline computes every number

Every number Baseline returns — a percentile rank, a "wetter than normal" label, a water year total — comes from a specific, fixed process described here. Nothing in Baseline's output is generated or estimated by a language model. This document exists so anyone using Baseline, or building on top of it, can check that claim rather than take it on faith.

The data: ERA5-Land reanalysis

Baseline's historical numbers come from ERA5-Land, a reanalysis dataset produced by the European Centre for Medium-Range Weather Forecasts (ECMWF). Reanalysis is not a network of weather stations — it's a physically consistent, gridded reconstruction of the atmosphere built by combining decades of observations (stations, satellites, weather balloons, ships, aircraft) with a fixed numerical weather model, run once, over the whole historical period.

That matters for a specific reason: station records are uneven. Stations open, close, move, get new instruments, or simply don't exist in a lot of the world's more sparsely monitored places. Comparing "this week vs. 1995" at a single station can mean comparing against a different instrument, a different location, or a gap in the record. A reanalysis grid doesn't have that problem — every grid cell has a complete, consistently-produced record for the full period, computed the same way in 2026 as it was for 1996. That consistency, more than raw accuracy at any single point, is why Baseline uses it as its historical baseline.

Coverage: 1991–2025, 0.1° resolution (roughly 11 km at the equator), land areas only. ERA5-Land doesn't produce values for ocean grid cells. Within the regions Baseline has generated tile data for, a location that lands on an ocean or otherwise invalid grid cell resolves to the nearest valid land cell in the same tile — this is how most coastlines and larger islands are handled. It does not search beyond the tile the location falls in: a location outside Baseline's generated tile coverage altogether (some small or remote islands, for instance) doesn't get snapped to a distant tile's land data. Baseline reports plainly that no historical baseline is available there rather than substitute a real, but geographically unrepresentative, answer.

Climatology normals vs. historical ranking — two different periods, on purpose

Baseline uses two different windows of the ERA5-Land record, for two different jobs:

• "Normal" (the expected value for a given place and date) is computed over 1991–2020 — the 30-year period the World Meteorological Organization (WMO) designates as the current standard climatological normal. This is the same convention national weather services use, so a Baseline "normal" means the same thing a meteorologist means by it.
• Historical ranking ("3rd driest since 1991," "wetter than 91% of years") is computed over the full 1991–2025 record — all years currently available, not just the 30-year normals window. A 35-year ranking window gives a more meaningful answer to "how unusual is this" than a 30-year one would, and it lets the most recent years be ranked against history at all, which a fixed 1991–2020 window couldn't do.

In short: Baseline tells you what's normal using the WMO standard, and tells you how unusual something is using the fullest record available. Both numbers are labeled with their source period in Baseline's response provenance.

How rank and percentile are computed

For a given location, date, and variable (precipitation or temperature), Baseline pulls the matching value for every year in the ranking window and compares the current value against that full set:

• Rank ("3rd driest," "7th wettest") counts how many years in the record had a more extreme value, plus one. If two other years were wetter than this one, this one ranks 3rd wettest.
• Percentile ("wetter than 83% of years") is the share of years in the record at or below the current value. It answers "where does this year fall in the distribution," independent of how many years are in the record.

Both numbers describe the same underlying comparison from two different angles — rank is easier to say in a sentence, percentile is easier to compare across locations with different record lengths.

Water year vs. calendar year

Baseline frames cumulative precipitation context two ways, depending on the user's location:

• Water year (Oct 1 – Sep 30), used for North American users. This is the standard US hydrological accounting year — it starts in the fall so a full winter snowpack season falls inside a single year, rather than being split across two calendar years.
• Calendar year (Jan 1 – Dec 31), used everywhere else.

This is a real limitation worth being upfront about: the Oct 1 water year start is a US-specific convention, not a global hydrological standard — other countries define their own water years differently, or don't use the concept at all. Baseline currently applies the US convention to North American locations and calendar year everywhere else; the rankings themselves are valid globally, but the Oct 1 start date for North America is a convention choice, not a universal one.

Forecast data

Baseline's forward-looking numbers (the next 10 days) come from a separate source — Open-Meteo — and are never blended with, or used to adjust, the historical ranking. Forecast and historical context are always computed and reported independently.

Precipitation vs. snowfall — not the same variable

When Baseline reports "precipitation," that number is always liquid-equivalent precipitation — rain, or the melted-water equivalent of any frozen precipitation — from the same ERA5-Land source described above. It is not a measurement of snow depth or snowfall amount.

Snowfall is a separate, more limited variable, sourced differently: it comes from Open-Meteo's own ERA5 archive at 0.25° resolution (roughly 25 km, coarser than the 0.1° grid used for precipitation and temperature), because ERA5-Land itself does not produce a usable snowfall field. Snowfall is currently only available for comparing named locations against each other (for example, ranking ski resorts by seasonal snowfall) — it is not available as an answer to a question about a specific location on a specific date.

Practical effect: asking Baseline something like "was there a lot of snow in [place] on [date]" will be answered using precipitation, not snowfall — and, if the question uses a snow-specific word, the response says so explicitly rather than silently substituting one variable for the other. If you need an actual snow-depth or snowfall-amount answer for a specific date, Baseline does not currently provide that; the location-comparison snowfall figures are the only real snowfall numbers it computes.

Known limitations

• Land-only, 0.1° grid. No ocean cells; locations near coastlines or on larger islands resolve to the nearest valid land grid point within the same tile, which may be a few kilometers away. Locations outside Baseline's generated tile coverage entirely — including some small or remote islands — aren't snapped to a distant tile's data; Baseline reports no historical baseline available for those instead.
• Archive lag. The most recent 1–2 months of ERA5-Land data can arrive with precipitation before temperature is finalized. When that gap occurs, Baseline falls back to Open-Meteo's historical archive for temperature and flags the response accordingly — it does not leave the gap unfilled or guess.
• Reanalysis vs. a specific station. ERA5-Land is a model-assimilated reconstruction, not a direct instrument reading. It's built to be highly accurate and, critically, consistent across the full record — but a nearby station could show a somewhat different number for any single day.
• Oct 1 water year is a US convention, applied to North American locations by default (see above).

Provenance line

Every Baseline response ends with a line like:

Source: Baseline v0.1.0 | ERA5-Land reanalysis 1991-2025 (35-yr daily climatology,
WMO 1991-2020 normals), 0.1-degree resolution, land-only | Forecast: Open-Meteo

Read left to right: the Baseline version that produced this answer (methodology changes bump the version), the historical dataset and the two windows described above, the spatial resolution, and the separate forecast source. If you're relaying a Baseline answer to someone else, this line is the citation.

About this document

Baseline is built by someone with a background in operational climate services, including work with NOAA. That background is why the reanalysis-vs-station distinction and the "normal" vs. "ranking" period split above are treated as first-class product decisions rather than implementation details — they're the same distinctions a working climate scientist has to get right. Baseline's audience isn't limited to any one field; this methodology holds the same whether the question comes from a ski resort operator, an insurance analyst, a journalist, or a rancher.