| # Forecast Methodology |
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| <!-- BEGIN:description --> |
| Meteomat shows probabilistic weather forecasts anywhere in the world. Instead of a single number, it shows a range of likely outcomes so you can see not just what is most likely, but how confident the forecast is. |
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| It is powered by ECMWF, the European Centre for Medium-Range Weather Forecasts, which produces what is widely considered the world's best operational weather model. |
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| ## How to read the charts |
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| Each chart shows a **central line** (the best estimate) surrounded by a **shaded band**. The band spans the 10th to 90th percentile of the forecast ensemble — meaning there is an 80% chance the true value falls within it. A narrow band means a confident forecast; a wide band means high uncertainty. |
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| - **Rainfall** uses a square-root y-axis so that small amounts (drizzle) remain visible without large amounts dominating the chart. |
| - **Wind and wave direction** is shown as arrows anchored to the top of each panel. |
| - **All panels share the same time axis** — zoom or pan one and the rest follow. |
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| ## Data Sources |
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| Weather forecasts combine two ECMWF land products, while a third is used for the wave and sea surface temperature data. They are both accessed via the [Open-Meteo](https://open-meteo.com) API at native resolution: |
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| <!-- BEGIN:sources --> |
| | Product | Resolution | Members | Horizon | |
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| | **ECMWF IFS Ensemble (EPS)** | ~18 km | 51 perturbed runs | 7 days | |
| | **ECMWF IFS HRES** | ~9 km | 1 deterministic run | 7 days | |
| | **ECMWF Wave Model** | ~28 km | — | 7 days | |
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| Both IFS products share the same model physics and initial conditions. The ensemble samples uncertainty by perturbing the initial atmospheric state 51 times; HRES is the single highest-resolution deterministic run from the same system. ECMF Wave model is only shown for coastal locations. |
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| ## Weather Variables |
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| ### Uncertainty bands (10th–90th percentile) |
| For each variable, the shaded area represents the range between the 10th and 90th percentile across the 51 ensemble members. This means there is an 80% probability that the true value falls within the shaded band. |
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| ### Temperature |
| - **Best estimate (line)**: ECMWF HRES value, shifted from the ensemble median. |
| - **Uncertainty bands**: Ensemble 10th/90th percentiles, shifted by the same offset as the median, so the best estimate always sits at the same relative position within the band. |
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| ### Wind speed & gusts |
| - **Best estimate (line)**: ECMWF HRES value, shifted from the ensemble median. |
| - **Uncertainty bands**: Ensemble 10th/90th percentiles, shifted by the same offset. |
| - **Gusts**: Ensemble 90th percentile shifted by the same HRES offset, representing the upper range of expected wind speed. |
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| ### Humidity |
| - **Best estimate (line)**: ECMWF HRES value, shifted from the ensemble median. |
| - **Uncertainty bands**: Ensemble 10th/90th percentiles, shifted by the same offset. |
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| ## Rainfall |
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| Rainfall is treated differently from other variables because its distribution is non-Gaussian (many ensemble members predict zero), and because HRES has a meaningful resolution advantage for detecting localised precipitation features (orographic, coastal, convective). |
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| ### Best estimate (line) |
| An asymmetric blend of the ensemble median and HRES, depending on whether HRES detects rain: |
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| - **When HRES detects rain (> 0.1 mm/h)**: 50% ensemble median + 50% HRES. |
| HRES gets equal weight because its higher resolution makes it more likely to be resolving a real precipitation feature. |
| - **When HRES detects no rain (≤ 0.1 mm/h)**: 75% ensemble median + 25% HRES. |
| The ensemble leads, but HRES pulls the estimate down — a displacement effect (where the ensemble places rain just over the location) is common, so no-rain from HRES is informative but not conclusive. |
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| ### Lower uncertainty band (10th percentile) |
| Taken directly from the ensemble, unchanged. This correctly reaches zero when a significant fraction of ensemble members predict no rain, giving an honest picture of the chance of dry conditions. |
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| ### Upper uncertainty band (90th percentile) |
| Taken from the ensemble, but raised when HRES predicts more rain than the ensemble median. The rationale: HRES's resolution advantage primarily helps detect precipitation-producing features that coarser ensemble members miss, so it should be able to raise the ceiling of possible rainfall. The upper band is never lowered by HRES. |
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| ### Probability of rain |
| The probability that rainfall exceeds 0.1 mm/h, blended using the same asymmetric weights as the best estimate: |
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| - **When HRES detects rain**: 50% × ensemble probability + 50% × 100% |
| - **When HRES detects no rain**: 75% × ensemble probability + 25% × 0% |
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| This keeps the displayed probability internally consistent with the displayed amount. |
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| Rainfall amounts are rounded to the nearest 0.1 mm, which reflects the true resolution of the forecast. |
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| ## Marine Variables |
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| ### Wave height, period, and direction |
| Sourced directly from the Open-Meteo Marine API. Total wave height is the combination of swell and wind-wave components. No post-processing is applied. |
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| ### Sea surface temperature (SST) |
| Sourced from the Open-Meteo Marine API. A 3-hour centred rolling average is applied for visual smoothness. Note that SST reflects the model's boundary condition (typically derived from satellite composites) and may not capture short-lived phenomena such as coastal upwelling, which can cause real surface temperatures to differ by 1–3°C from the forecast. |
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| ## Limitations |
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| <!-- BEGIN:limitations --> |
| - **Spatial resolution**: HRES at ~9 km and the ensemble at ~18 km cannot resolve sub-grid features such as valley-scale orography, urban heat islands, or small coastal inlets. Two nearby locations within the same grid cell will receive nearly identical forecasts. |
| - **Ensemble calibration**: The ECMWF ensemble is one of the best-calibrated probabilistic forecast systems in the world, but calibration is not perfect. Stated probabilities are skill estimates, not guarantees. |
| - **Rainfall uncertainty**: Precipitation is the hardest variable to forecast deterministically. The blending methodology is a pragmatic combination of two imperfect sources and should be interpreted with appropriate caution. |
| - **SST and upwelling**: The marine SST forecast does not account for wind-driven upwelling events, which are common along the Cantabrian coast of Spain and can cause surface temperatures significantly colder than the model suggests. |
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