Model runs, cycles and lead time

Global numerical weather prediction (NWP) models, such as the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS) and the US Global Forecast System (GFS), are typically run four times a day. These cycles are synchronised with the international standard time, Coordinated Universal Time (UTC), at 00 UTC, 06 UTC, 12 UTC, and 18 UTC.

Each cycle begins with a fresh set of observations, assimilated into the model's initial state. This initialisation phase is critical; it sets the starting conditions for the atmospheric simulation. The model then integrates forward in time, calculating the state of the atmosphere at future timesteps.

For example, a 00 UTC run will incorporate observations collected around midnight UTC. It then produces forecasts valid for 01 UTC, 02 UTC, and so on, extending out to several days or even weeks. The choice of these four main cycles balances the need for timely updates with the computational resources required to run complex global models. Regional models may run more frequently or at different times, often nested within global model outputs to provide higher resolution over specific areas.

While a model run is notionally tied to its initialisation time (e.g., 00 UTC), there is an unavoidable delay between the collection of observations and their processing and assimilation into the model. This is known as the data cut-off time. Observations from around the globe are gathered, quality-controlled, and then fed into the model's data assimilation system. This process takes several hours.

Consequently, a model run initiated at, for example, 00 UTC, will typically not become available to forecasters and end-users until 3 to 5 hours later. For the ECMWF IFS, the 00 UTC run usually becomes available around 03:30 to 04:30 UTC. The GFS model often has a slightly faster turnaround, with its 00 UTC run available around 03:00 UTC.

This delay means that when you access a forecast, you are always looking at information that is a few hours old, even for the 'latest' run. For example, if you check a forecast at 08:00 UTC, the most recent 06 UTC model run will likely still be processing, and the 'latest' available data will be from the 00 UTC run, which was initialised 8 hours prior.

It is crucial to distinguish between lead time and valid time when interpreting forecasts. The valid time is the specific future moment for which the forecast is made (e.g., 14:00 UTC on Tuesday).

The lead time is the duration between the model's initialisation time and the valid time of the forecast. For instance, if a model run starts at 00 UTC on Monday and produces a forecast for 14:00 UTC on Tuesday, the lead time for that specific forecast point is 38 hours (24 hours for Monday + 14 hours for Tuesday).

Consider a forecast for 12:00 UTC on Wednesday:

• From the 00 UTC Monday run: lead time = 60 hours (24 hours Mon + 24 hours Tue + 12 hours Wed).
• From the 12 UTC Tuesday run: lead time = 24 hours (12 hours Tue + 12 hours Wed).
• From the 06 UTC Wednesday run (if available): lead time = 6 hours.

Generally, forecasts with shorter lead times are more reliable because the model has less time for errors to accumulate and has incorporated more recent observations. The Wind Agent's charts, such as the meteogram, clearly show the valid time on the x-axis, allowing users to assess the lead time for any point in the forecast.

The accuracy, or skill, of a numerical weather prediction model generally decreases as the lead time increases. This is an inherent characteristic of forecasting complex, chaotic systems like the atmosphere. Small errors in the initial conditions or imperfections in the model's physics tend to amplify over time, leading to greater divergence from reality.

For wind forecasts, this decay in skill is particularly noticeable for specific values like gust speeds or precise timings of weather phenomena. While the general pattern of a large-scale weather system might be predictable several days out, the exact timing of a frontal passage or the peak wind speed in a squall line becomes increasingly uncertain with longer lead times.

Typical skill decay rates vary by model and atmospheric variable. For surface wind speed, global models often show good skill out to 3-5 days, with significant degradation beyond 7-10 days. The ensemble plume chart illustrates this decay by showing the increasing spread between individual ensemble members at longer lead times, reflecting the growing uncertainty. This spread is a direct indicator of the model's confidence, or lack thereof, in the forecast.

Users should approach long-range forecasts (beyond 3-5 days) with increasing caution, focusing on general trends and probabilities rather than precise values.

When successive model runs produce significantly different forecasts for the same valid time, the forecast is said to be jumpy or inconsistent. This is a common challenge in NWP and can be a source of frustration for users.

Jumpiness often occurs when:

1. New observations lead to a significant re-analysis of the initial state. If a critical observation (e.g., from a weather balloon or satellite) reveals a different atmospheric configuration than previously assumed, the model will adjust its forecast accordingly.
2. The atmosphere is in a sensitive or chaotic state. Small perturbations can lead to large changes in the forecast, particularly when a weather system is on the cusp of two possible evolutions.
3. A model run struggles to correctly initialise a developing feature. For example, a rapidly deepening low-pressure system might be missed or misplaced in an earlier run, only to be picked up more accurately in a later run with more recent data.

While jumpiness can be unsettling, it is often a sign that the model is responding to new information and trying to converge on the most likely outcome. However, persistent jumpiness for a specific event can indicate high uncertainty in the forecast. The Wind Agent's model compare chart allows direct comparison of different model runs, highlighting inconsistencies.

Deciding which model run to 'trust' is not about picking a favourite, but rather about understanding the information each run provides. Generally, the most recent run is preferred, as it incorporates the latest available observations and has the shortest lead time for the immediate future. However, this is not an absolute rule.

If the most recent run shows a significant departure from previous runs, especially for a high-impact event, it is prudent to exercise caution. Consider:

• Consistency: How has the forecast evolved over the last 2-3 runs? If a new run is an outlier, it might be an 'unstable' run that will correct itself in the next cycle.
• Ensemble spread: Check the ensemble output. If the new run's forecast falls within the spread of the previous ensemble, it might be a plausible outcome. If it is outside, it warrants closer scrutiny.
• Model type: Different models (e.g., ECMWF, GFS, UKMO) have varying strengths and weaknesses. Comparing across models can provide a broader perspective (see icon-gfs-ecmwf-ukmo).

For critical decisions, it is often best to consult multiple runs and models, looking for convergence or divergence. The Wind Agent's model_compare chart is designed for this exact purpose, allowing users to visually assess agreement between different model outputs and runs.

For the very near-term forecast, typically the next 0-6 hours, rapid-update cycle (RUC) models and nowcasting techniques offer higher frequency and resolution than global models. These systems are designed to ingest observations almost continuously, providing updates every 1-3 hours.

Examples include regional models like Met Éireann's HARMONIE-AROME, which runs at a very high resolution (2.5 km) and is updated more frequently than global models. These models are particularly adept at resolving local phenomena such as sea breezes, convective showers, and terrain-influenced wind patterns.

Nowcasting systems, which blend observations (e.g., radar, satellite, surface stations) with short-range model output, can provide even more immediate, highly localised forecasts. These are crucial for operations sensitive to sudden changes, such as drone flights or crane lifts, where a 6-hour old global model run might miss rapidly developing conditions.

While The Wind Agent primarily uses global model data for its core forecasts, the underlying data sources are continuously updated as new runs become available, ensuring the most recent information is always presented.