Forecast Accuracy; Swift Transmission.

Metra Fast Trader delivers the most accurate temperature forecast with the current market moving weather forecast data.

FAST Trader plots the GFS and ECMWF deterministic and ensemble runs against Metra's more accurate deterministic forecast, the previous run(s) and a normal based on a ten year climatology.

All forecasts and the climatology are aggregated to produce a single number for the UK, based on demand weighted LDZ sites. This information is delivered to subscribers in real-time.

Why have we done this?

FAST Trader has been designed to offer you the ability to trade on the delta between Metra's best forecast and the raw, (less skillful) ECMWF and GFS, market moving data; as soon as the raw data is published.

How good is Metra's forecast?

The graph below shows the performance of Metra aggregated deterministic forecast for the UK LDZ sites between November 12 and December 11 2009.

Consider ECMWF's 2 degree cold bias at day 10 compared with Metra's negligible cold bias. Metra's Mean Absolute Error (MAE) is consistently 1 degree lower than the ECMWF ensemble mean throughout the entire forecast horizon.

FAST Trader comes with an ongoing skill score so you will know the skill of Metra's forecast in all weather regimes.

What makes the Metra Forecast so accurate?

Our temperature forecast combines medium range forecasts from global forecast systems, with short range mesoscale forecasts from high resolution models. We then apply our proprietary methodology to produce a seamless 15 day forecast with dependable accuracy through the step changes in forecast resolution.

Our system calibrates huge amounts of model data using a short time series of historical data and uses cutting edge statistical techniques, to extract, combine and place the correct emphasis on the different strengths of the best models.

Alongside this, a running skill score will instill confidence in Metra as the most accurate - as well as better understand the error and bias when compared to ECMWF and GFS.