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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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Message

By ordering products from the Met Office Weather DataHub and clicking “confirm”, you are agreeing to be bound by the Met Office Weather DataHub Terms and Conditions which apply to the order.

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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The button below will take you to the Billing page on Weather Data Hub, which will open in a new tab.

Please update your details there and then return to this tab to proceed with your subscription.

If you have been logged out, you can find the Billing page by clicking on your email address in the top-right corner and then going to 'My Organisation', after logging back in.

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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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Soil Moisture Guidance

Soil moisture data are made available on Weather Data Hub as a Beta product for development and testing ONLY due to some known quality issues with the data. Soil moisture data should not be used for operational or safety-critical applications until these issues have been resolved and the data fully approved for operations. The issues with the quality of the data are not due to the underlying model but to the way in which the data are interpolated to the standard gridded form presented through WDH. More details on the issue are provided below for those interested.

The way that soil moisture is represented in the forecast model depends on the properties of the soil type. If the soil properties in the model are changed, then the soil moisture would change in order to be in equilibrium with these properties. The issue, though, is that the soil properties are very non-linear,so if you interpolate them then the resultant parameters would not be physically consistent. Moreover, these non-linearities mean that interpolating soil moisture and interpolating the soil parameters would not give you the soil moisture that is in equilibrium with the parameters. In the worst case scenario it is possible that at some grid-points, after interpolation, the soil moisture could be greater than saturation, which is physically impossible. Users should therefore be aware that soil moisture values could be misleading in some locations, particularly near soil type boundaries, and that in a few locations invalid values could be present. The solution will involve an alternative approach to interpolation specifically for soil moisture, but this is complex and will take some time to resolve. We do not yet have a date for when a solution will be implemented.