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Unit
Unit value
Time steps
Hourly
3 Hourly
6 Hourly
Daily
Time interpretation
Time interpretation value
Bits per value

The bitsPerValue determines the relative size of the data download required for each parameter. The bitsPerValue ranges from 12 to 20 bits for our current parameters.

Note the data download size is not a direct multiplier of the bitsPerValue as there are standard overheads within the generated GRIB files which are common for all parameters. However, the bitsPerValue is a good guide to the relative cost of each parameter.
BitsPerValue value
Level Type
Level type value
Parameter ID
Parameter ID value
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Application name

Your application name is used as a link to our gateway through which your API requests are processed. On subscription we automatically create an application for you with a default name. The application name has no bearing on how things are processed.

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Should you wish to discuss licensing terms, then please call 0033 678 3454 or email licensing@metoffice.gov.uk

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About monthly data usage

Data plans

All data plans are managed in GBs worth of data but note we use 1,024 conversion for all our plans. So 1,024 GB is equivalent to 1 TB (Terabyte). Strictly speaking this is TiB and GiB i.e. Tebibyte and Gigibyte

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About monthly data usage

Estimate monthly usage

Estimated order size if all selected data for all runs is taken based on an expected file size per parameter. Note this is a guide only and actual sizes for each parameter will vary.

Whilst every effort has been made to make the estimated file download sizes accurate customers should check the projected figures which are based on actual downloads as well as the estimated file sizes.

Usage this month

This figure is the actual data transferred for this calendar month so far.

Projected Monthly Usage

The projected monthly usage is calculated by:

  • retrieving the amount of data downloaded in the last 30 days
  • divide by the number of active days (>0 bytes downloaded) to get a daily average
  • multiply by the number of days in the current month for maximum monthly usage
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Order Naming

Note when used in the API the order name/ id is transformed by substituting spaces with hyphens and all letters are lowercase e.g. 'GLOBAL10k 12amRun' becomes 'global10k-12amrun'

For the avoidance of doubt the transformed order id to use in the API reproduced below in the Delivery section

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Taxpayer Identification Number (TIN)

The taxpayer identification number (TIN) is the unique identifier assigned to the Account Holder by the tax administration in the Account Holder’s jurisdiction of tax residence. It is a unique combination of letters and/or numbers used to identify an individual or entity for the purposes of administering the tax laws of that jurisdiction.

For more information, please see https://www.gov.uk/hmrc-internal-manuals/international-exchange-of-information/ieim402040

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Notes on Projection

Introduction

The UK model forecast data are being made available on a 'Latitude-Longitude' (Lat-Long) grid, as well as the standard Lambert Azimuthal Equal Area (LAEA) grid. This has the advantage the users are more familiar with Lat-Long than LAEA, but the re-gridding process effects the quality of the data. The information below is to ensure that users are aware of the effects of the re-gridding process, potential weakness or strengths with the re-gridded dataset, to help ensure that the data are being used appropriately.

General points

Smoothing

The application of re-gridding involves some form of interpolation which always reduces the information, or the quality of that information, in the dataset. As bi-linear (or a variant on this) is used for almost all parameters, the effect is to smooth the values, reducing the extremes.

This will have the most significant effects where there is a lot of spatial variation on a field. For example:

  • Depending on their scale, urban heat islands may not be so well captured. Although an attempt to preserve the temperature variations at the coast is made, using a modified form of bi-linear interpolation that does not spread temperature values form land to sea or visa versa;
  • It is less likely that extreme maximum or minimum temperatures are captured as well, or extreme precipitation rates.
    In some cases this may slightly improve some aspects of the forecast, where the smoothing reduces an unjustifiable level of detail. For example:
  • In a showery situation where the model is unlikely to get the exact position of the showers, the 'smearing' out of the precipitation, might provide slightly better guidance on the chance of getting wet, but not on the intensity of the precipitation (although this is quite a small effect).

Distortion

A Lambert Azimuthal Equal Area (LAEA) grid was chosen as this is area-preserving - the size of the grid squares are the same over the whole domain. For a latitude-longitude (or Equirectangular) grid, although the latitudinal (north-south) grid point spacing remains constant, the longitudinal (east-west) grid point spacing varies significantly between the north and south of the grid; the spacing at the extreme south is nearly 1.5 times that of the spacing at the extreme north.

This can introduce inaccuracies in some calculations, such as calculating area averages.

Non-rectangular domain

The grid of data is rectangular in the LAEA projection, but the projection difference means that the Lat-long data do not appear to be rectangular. This means that:

  • If all the data are required, they will be returned as a large grid, with quite a bit of missing data in the east and west of the grid, and some in the north and south;
  • If a dataset with no missing data is required, only a much smaller cut-out of the full grid is available.

Choice of dataset

Given the impacts of re-gridding from LAEA to Lat-long, if the points raised above are a concern, it is worth using the standard LAEA data, if possible. This is particularly true if further processing (e.g. interpolation to a set of points) will be carried out.