Data transparency
Weather data sources for degree days and M&V.
Degree days and weather normalization are only as trustworthy as the weather behind them. Here is where Energy-Data.io gets historical observations, reanalysis fills, and typical-year / long-term reference data — and how each is used.
Overview
Two jobs, two kinds of weather
Energy analysis needs both what actually happened (Actual Meteorological Year / AMY) and what is typical (TMY or a long-term average). We use observed and reanalysis weather for actual periods, and separate TMY / long-term inputs for normalization, simulation, and benchmarking.
Historical (AMY)
Station networks plus reanalysis, interpolated to your site coordinates for baseline and reporting periods.
Typical / long-term
EPW TMY files from climate.onebuilding.org for simulation, and coordinate-precise long-term averages (up to 30 years) for degree-day normalization.
Historical weather
Station networks behind actual-year series
Historical weather for degree days and M&V periods is built on government and national observation databases, quality-checked and interpolated to the requested location.
ISD (NOAA)
Integrated Surface Database — global sub-hourly and hourly observations from airports and surface stations.
MADIS (NOAA)
METAR/ASOS/AWOS and mesonet feeds, including maritime and specialty networks, for denser near-surface coverage.
DWD
Deutscher Wetterdienst station observations across Germany and Central Europe.
GHCN-Daily / GHCNh (NOAA)
NOAA Global Historical Climate Network daily summaries and quality-controlled hourly station records.
Reanalysis
ERA5 and MERRA-2 fill the gaps
Where station density is low or records have gaps, reanalysis provides a continuous global hourly series. We combine ECMWF ERA5 (Copernicus Climate Change Service) and NASA MERRA-2 — the same streams cited in our API documentation when describing continuous hourly or daily weather for any location.
ERA5 (ECMWF / Copernicus)
Hourly global reanalysis from 1940 to near-present — used to complete and extend series where station coverage is thin.
MERRA-2 (NASA)
Global reanalysis from 1980 onward — a second independent stream for continuity and cross-checks.
TMY and long-term averages
Where typical-year data comes from
A Typical Meteorological Year is a synthetic year assembled from multi-year history (Sandia / ISO 15927-4 month selection). Energy-Data.io uses two complementary approaches:
Simulation EPW files
Primary source: the Repository of Building Simulation Climate Data (climate.onebuilding.org) — curated EPW files used widely with EnergyPlus and OpenStudio.
Coordinate-precise long-term averages
For degree-day normalization we do not snap to a fixed station TMY. We calculate long-term mean temperature series at your exact coordinates for a user-defined window of up to 30 years — auditable for contracts, ISO 50001, and WMO-style climate normals.
In strict simulation language, “TMY” means a month-selected EPW file. In normalization workflows we often say “long-term average” for the coordinate-precise reference series — same role, clearer terminology.
In practice
How the pieces fit together
- Actual periods use historical station + reanalysis weather at your coordinates (AMY).
- Reference weather for normalization uses your chosen long-term window, or an EPW TMY when the workflow is simulation-led.
- Degree days are computed from those series at your base temperature, then used in regression and M&V reporting.
Why this matters Transparent sources let auditors and clients reproduce the weather basis — not just accept a black-box degree-day number.
FAQ
Common questions
-
Historical (actual-year) weather combines NOAA ISD and MADIS station networks, DWD observations, and GHCN daily and hourly records with ERA5 and MERRA-2 reanalysis. Observations are quality-checked and interpolated to the coordinates you request.
-
Station data are measured observations from airports and surface networks. Reanalysis (ERA5, MERRA-2) fills gaps by combining observations with a weather model, so remote or station-sparse locations still get a continuous hourly series.
-
For building-simulation EPW files we use the Repository of Building Simulation Climate Data at climate.onebuilding.org. For degree-day normalization we calculate coordinate-precise long-term averages over a user-defined reference period of up to 30 years — not a fixed nearest-station TMY file.
-
Nearest-station snaps can misrepresent coastal gradients, terrain, and urban heat islands. Coordinate-precise series reduce that proximity error and make the reference period auditable for M&V and ISO 50001 reporting.
Next step
Try the weather on your site.
Run heating and cooling degree days for any location, base temperature, and date range — free, no account required.