Spatial analysis of 2m relative humidity over Scandinavia

We improve the representation of relative humidity fields over Scandinavia by combining outputs from our numerical weather model (MEPS) with real-time weather observations from citizen-managed stations. The method has to work operationally as a part of the automatic forecast at Yr.no, so both computational speed and accuracy are important evaluation metrics.  

 

The method of choice is Optimal Interpolation (OI), a Bayesian statistical analysis technique. In this approach, the model output serves as the prior information, which is updated by incorporating the more accurate and precise observations from real-time data. OI operates under the assumption that both model predictions and observations have associated uncertainties. By weighing these uncertainties, OI adjusts the model output towards the observations, aiming to minimise the overall error in the final data product. 

 

A challenge with real-time observations from citizen-managed stations is that they cannot always be trusted, for various reasons. Therefore, we need a method of quality assurance of the observations. A second challenge is to incorporate OI in a practical way in the codebase. For the first challenge we use the titanlib library, developed at the Norwegian Meteorological Institute (MET Norway). This library deals with automatic quality control of weather observations. For the second challenge we use the library gridpp, also developed at MET Norway. 

 

The quality of the reconstructed relative humidity fields is then evaluated by comparing them against a set of independent observations, which are known to be trusted and accurate. An example would be the weather station at Blindern, outside the headquarters of MET.

 

The software are openly available on:

 

Titanlib: https://github.com/metno/titanlib

GridPP: https://github.com/metno/gridpp