Grid length and Resolution in Weather Prediction

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Grid length and resolution in NWP (Numerical Weather Prediction) are not synonymous


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Introduction

My page on NWP talks about Global and meso-scale models but sidesteps the important issue of grid length and model resolution.


Grid length

NWP models have to calculate on a 3 - dimensional grid of points. The nearer the points are together, the better will the model represent the atmosphere and topography. A practical problem is that small horizontal grids need more levels in the vertical and shorter time steps. This requires more computing power.

A global model is needed to produce day to day forecasts with outlooks for the next few days. Available computing power limits grid lengths to between 13 and 20 km for most national weather services and ECMWF. For short period, local forecasting short grid lengths are used over small areas. These grids range from 1 km up to about 10 km.


Representing topography

Look at a 25 km (15 NM) grid around the Isle of Man. This is the grid used for output from the US Globa; Forecast System model. As will be seen here, there can be at most two grid points over the IoM, quite likely only one or even none.


It would need a grid length of about 2 km to give a general outline of the island and, even then, it would not represent the headlands and bays. With just one grid point over the island, the computer will think that it looks something like this -

Given that a 25 km grid length model will not be able to represent the IoM, imagine the same grid over Torbay or the Solent. It will become clear that to represent these areas, grid lengths of about 1.5 km would be needed for Torbay and under 1 km for the Solent. To model Start Point would need a grid length measured in 10s of metres, perhaps 100 metres at most.

The same applies to weather on these size scales.


Representing weather

NWP models cannot fully resolve weather occurring on the scale of their grid lengths. There has to be some filtering out of anything generated by the mathematics but which is below the model threshold.. For the same reason, topographic effects must also be smoothed. As a result, models filter out effects on scales below about 5 grid lengths. If this is not done, then computational noise will swamp the meteorological signal.

Thus a 0.1 degree (10 km) grid length model will provide forecasts only for features of scale 0.5 degree or more ie 50 km or more. Such features might have a lifetime, therefore a natural predictability of, perhaps, 6 to 12 hours at best.

Shorter grids, say around 2 km can model weather down to about 10 km size but such features will have lifetimes under 5 or 6 hours. Watch a large cumulus cloud grow and disappear within a matter of a couple of hours or so.


What short grid lengths do for us

Even if detail at, say, a 1 km (0.5 NM) scale could be observed (which it cannot), and we had a perfect prediction model (which we have not), it is unlikely that it (ie the detail) would still be there by the time a 6-hour forecast was produced and made available to users.

The biggest benefits from short grid length models will probably be in the prediction of:

  • Topographic effects determined by the large scale pattern.
  • Small disturbances, such as trough lines, in the general pattern.

Both up to about 24 hours ahead, and

  • Organised convective storms over the next 6 to 12 hours.

Observing small, locally generated weather requires observations that are truly on the very small scale eg satellite and radar imagery. These both give an effective observation spacing of around 5 km. Over the sea, of course radar data are only available to a fairly short distance from the coast.

In practice, there are, quite simply, not enough data to define the very small weather detail that sailors observe over the sea. Satellite (ASCAT) wind measurements look impressive but are only able to resolve detail on about a 25 km scale. Over land, data from radar can be used to help predict small scale and short lived detail.

Reducing grid lengths in global models will improve performance in predicting the larger scale evolution. For example, better analysis of detail around a developing low will improve prediction in the short, 24 hour term This will have a knock on effect in prediction of large scale patterns over the UK at 4-5 days ahead.

How much grid lengths can be reduced in global models is uncertain and will depend on observational data as well as computer power. The future is likely to be one of finer resolution global models but with not a great improvement on short term, small detail over the sea.


The future?

The major benefits of fine scale models are likely to be for prediction of heavy showers over land, clearing skies leading to fog or ice on roads. To focus on the specific needs of sailors. a NWP model with very short grid lengths will offer improvements in the following areas:

  • More realistic representation of the impact of topography on coastal winds and weather on such scales as the strong winds through the Dover Strait.
  • More reliable forecasts of small scale thundery troughs and the like..
  • More reliable forecasts of the development of organised convection systems, with associated severe weather.

Useful gains might be furthest ahead for the first area, less for the second, and perhaps only 12 hours for the third.


And a warning note

Short period, small scale prediction requires high resolution data. Without such observational data, claims for forecasts to be -

....exceptionally accurate

....high-resolution

....precise high quality,

Incredible detail....

....down to 1km resolution

Are meaningless.


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