A flexible approach to defining weather patterns and their application in weather forecasting over Europe

Abstract

A method is presented for deriving weather patterns objectively over an area of interest, in this case the UK and surrounding European area. A set of 30 and eight patterns are derived through k-means clustering of daily mean sea level pressure (MSLP) data . These patterns have been designed for the purpose of post-processing forecast output from ensemble prediction systems and understanding how forecast models perform under different circulation types. The 30 weather patterns are designed for use in the medium-range and the eight weather patterns are designed for use in the monthly and seasonal timescales, or when there is low forecast confidence in the medium-range. Weather patterns are numbered according to their annual historic occurrences, with lower numbered patterns occurring most often. Lower numbered patterns occur more in summer (with weak MSLP anomalies) and higher numbered patterns occur more in winter (with strong MSLP anomalies). Weather patterns have been applied in a weather forecasting context, whereby ensemble members are assigned to the closest matching pattern definition. This provides a probabilistic insight into which patterns are most likely within the forecast range and summarises key aspects from the large volumes of data which ensembles provide. Verification of European Centre for Medium-Range Weather Forecasts medium-range ensemble forecasts for the set of eight weather patterns shows small forecast biases annually with some large variations seasonally. The most prominent seasonal variation shows the westerly (NAO+) pattern to over-forecast in summer and under-forecast in winter. Forecast skill was found to be better in winter than summer for most patterns.

Use of probabilistic medium‐ to long‐range weather‐pattern forecasts for identifying periods with an increased likelihood of coastal flooding around the UK

“…If poor distances coincide with a negative MSLP trend, then the assumption can be made that ensemble members have deeper areas of low pressure than their assigned weather patterns, which is potentially a sign of severe weather. Neal et al (, table 2) provide more examples for the interpretation of distance and correlation.…”

“…The underlying probabilistic weather pattern forecasts behind Coastal Decider are objectively verified for all models, with early results presented in Neal et al () for medium‐range ECMWF forecasts. Forecasts verified on a day‐by‐day basis and collated over all seasons were shown to have forecast skill out to at least 10 days for all weather patterns.…”

“…A weather pattern can be described as one of many circulation types over a defined region, which differs in its characteristics from other weather patterns over the same region and can vary on a daily basis. The Met Office recently derived a set of 30 weather patterns (MO30; Figure ) over the UK and surrounding European area (Neal et al, ) by clustering 154 years’ worth (1850–2003) of daily mean sea level pressure (MSLP) data from the European and North Atlantic daily to MULtidecadal climATE variability (EMULATE)‐gridded 5° resolution observation data set (Ansell et al, ). The MO30 are defined in terms of their MSLP anomalies and can be used to examine variations within a broad scale type, such as variations within a cyclonic westerly.…”

“…Mean sea level pressure (MSLP) anomalies plotted as filled contours (hPa); and MSLP mean values are plotted in the foreground (2 hPa intervals). Source: Neal et al ()…”

New approaches to postprocessing of multi‐model ensemble forecasts

“…Since synoptic weather conditions in the surrounding regions also affect the local weather (Neal et al . ), the MSLP fields used in this study cover the North Atlantic European region and central Europe (35 to 70°N, 30°W to 20°E; shown in Figure ); other studies have found this to be the optimal domain size for reconstructing surface temperatures from MSLP‐derived regime classifications (Beck et al . ).…”