Submitted as: methods for assessment of models
08 Nov 2022
Submitted as: methods for assessment of models | 08 Nov 2022
Status: this preprint is currently under review for the journal GMD.

Improved objective identification of meteorological fronts: a case study with ERA-Interim

Philip G. Sansom1,2 and Jennifer L. Catto1 Philip G. Sansom and Jennifer L. Catto
  • 1Faculty of Environment, Science and Economy, University of Exeter, North Park Road, Exeter, EX4 4QE, UK
  • 2Met Office, FitzRoy Road, Exeter, EX1 3PB, UK

Abstract. Meteorological fronts are important for their associated surface impacts, including extreme precipitation and extreme winds. Objective identification of fronts is therefore of interest in both operational and research settings. We have implemented a number of changes in a widely used objective front identification algorithm, and present the improvements associated with these changes. First, we show that a change to the order of operations from applying a mask then joining frontal points to contouring the thermal field then applying the mask, yields smoother fronts with fewer breaks. Next we address the selection of the identification parameters, including the thresholds and number of smoothing passes. This allows a comparison between datasets of differing resolutions. Finally, we have made a number of numerical improvements in the implementation of the algorithm, such as more accurate finite differencing, direct calculation of the wet-bulb potential temperature, and better handling of short fronts, which yield further benefits in smoothness and number of breaks. This updated version of the algorithm has been made fully portable and scalable to different datasets in order to enable future climatological studies of fronts and their impacts.

Philip G. Sansom and Jennifer L. Catto

Status: open (until 03 Jan 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Philip G. Sansom and Jennifer L. Catto

Philip G. Sansom and Jennifer L. Catto


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Short summary
Weather fronts bring a lot of rain and strong winds to many regions of the midlatitudes. We have developed an updated method of identifying these fronts in gridded data that can be used on new datasets with small grid spacing. The method can be easily applied to different datasets due to the use of open source software for its development and shows improvements over similar previous methods. We present an updated estimate of the average frequency of fronts over the past 40 years.