The 16 Weather Observation and Forecasting Value Parameters, Explained

The How It’s Made series has revealed how the weather experts create an accurate, reliable weather forecast. It explores everything from Weather Observations,Meteorological Expertise, and everything in between. It’s shown the essential factors that turn a forecast from good to great.

 

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Accessing highly-accurate and reliable weather data is undeniable crucial when you rely on the weather forecast.

 

If your forecast isn’t reliable, you’ll not be able to access the insights you need to make informed business decisions. Inaccurate weather forecasts lead to offshore companies missing weather windows during projects, insurance companies that are insufficiently staffed during severe weather, and transmission system operators running power lines over or under capacity   - to name just a few examples.

 

But how do the weather experts know when good is actually good? And how can you recognize when the weather data you receive is held to the highest standard? Enter into the ring the weather observation and forecasting value parameters.

 

These value parameters are a framework, used by the weather experts, to ensure each and every step that they take adds value to the weather data. Quite simply, success comes down to measuring the result against the following value parameters.

 

What are the weather observations and forecasting value parameters?

 

There are 16 weather observation and forecasting value parameters, which are grouped into four areas:

 

  • Generic meteorological value parameters
  • Forecast specific value parameters
  • Meteorological quality value parameters
  • Technology quality value parameters

 

They are applied (as applicable) to the Five Categories that are essential to a high-quality professional forecasting service. Let’s explore the parameters in more detail and share how they add value to every category.

 

Generic meteorological value parameters

 

The generic meteorological value parameters apply to Category 1: Weather Observations, Category 2: Metocean models, and Category 3: Statistical post processing. 

 

Value Parameter 1: Frequency 

Definition: How regularly the observation data or model data is delivered

 

How the experts put it into practice: The weather experts invest in higher-frequency updates from weather stations for specific locations.

 

Value Parameter 2: Resolution, temporal

Definition: The time span between two shared values

How the experts put it into practice: The experts can use an algorithm to reduce the 5-minute interval between radar images to 1-minute.

 

Value Parameter 3: Resolution, spatial

Definition:  The density of the grid (radar, satellite, and model), as well as granularity of the weather station network

How the experts put it into practice:  Standard grid sizes are 10km, 25km or 50km but the density can be increased to hyper-local or downscaled for weather station using an algorithm. In-house metocean models can have a spatial resolution down to hundreds of meters.

 

Value Parameter 4: Coverage

Definition:  Areas where the experts can provide observations or forecasts

How the experts put it into practice: The experts provide global coverage through weather stations observations, with access to a network of 20,000 stations. Global coverage is also available for models including MOS, Nautical MeteoBase, and Road models. ScaDo complements this, by providing forecasts for locations with no observations.

 

Value Parameter 5: Completeness
Definition:
 To what extent the element is defined

How the experts put it into practice:  All MOS forecast locations have hourly forecasts for all elements, with downscaling algorithms used to compensate when a station is unable to deliver data on a particular element.

 

Value Parameter 6: Uniqueness
Definition:
The Availability of non-standard elements and derived elements

How the experts put it into practice:  The in-house metocean models provide the ability to access output parameters that are inaccessible in external data sources (i.e., spectral moments, effective cloud cover).

 

 

Forecast-specific value parameters

 

The forecast-specific value parameters typically apply to Category 2: Metocean models and Category 3: Statistical Post Processing.

 

Value Parameter 7: Accuracy
Definition:
Forecast is correct (within a margin) for deterministic values

How the experts put it into practice:  98% of the forecasts are within a 2-degree margin. The MOS provides high-quality forecasts for many stations, while ScaDo improves the temperature forecasts in valleys and mountains.

 

Value Parameter 8: Reliability

Definition: Forecast is consistent

How the experts put it into practice: The forecasts follow the correct pattern, even if there is a bias (structural over- or under-forecasting). Two years of training data ensure the MOS can be adjusted to local observation sites.

 

Value Parameter 9: Skill

Definition: Forecast is a hit, miss or false alarm

How the experts put it into practice: The experts measure whether the forecast is a hit (correct forecast), miss (exceeding the defined threshold but not forecasted), or false alarm (not exceeding the defined threshold, but forecasted). The MOS combines data from several models to improve the skill, as it takes out inconsistencies.

 

Value Parameter 10: Sharpness

Definition: Precision of the forecasts in time and space

How the experts put it into practice: The experts apply sharpness by ensuring the specificity of the forecast. It is the difference between it is going to rain versus it is going to rain at 10am in Amsterdam.

 

Value Parameter 11: Uncertainty

Definition: Spread of the probabilistic / ensemble forecast plume. Historic cases can sometimes be used to estimate the level of uncertainty

How the experts put it into practice: 25-50 ensemble forecast scenarios determine the outlines of the plume.

 

Meteorological quality value parameter

 

The meteorological quality value parameter is important for Category 2: Metocean models and Category 4: Quality Control & Data Management. It is, however, driven by the meteorological expertise, which is Category 5 in the weather forecasting methodology.

 

Value Parameter 12: Correct

Definition: Checked and errors are corrected where necessary

How the experts put it into practice: Forecasters edit the MOS forecast after comparing it with new observations and new model data. And, when it comes to data management, the experts carry out observation decoding, weather model changes, radar/satellite changes.

 

Technology quality value parameters      

 

The technology quality value parameters primarily apply to Category 4: Quality Control & Data Management.

 

Value Parameter 13: Trusts

Definition: Access to data always available, with no outages

How the experts put it into practice: The experts provide a redundant network, with 99.9% data availability. The migration to the AWS cloud, and continuous monitoring of servers and services by the MG Operations Center, further supports this value parameter.

 

Value Parameter 14: Availability

Definition: Data is accessible in standard formats

How the experts put it into practice: The formats include SI (Système international) / WMO-approved units (e.g. Celsius) and industry standard data formats.

 

Value Parameter 15: Speed

Definition: Data is accessible for customers within minutes

How the experts put it into practice: More than 90% of data is ingested, processed, and delivered within a few minutes of being provided by a third party.

 

 

Value Parameter 16: Visualization

Definition: Improve the usability of the data

How the experts put it into practice: The experts visualize the data and prepare it for online and on-screen presentations, which improves its usability.

 

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