In Europe, winter wind storms are the most damaging weather phenomenon. How such cyclones will react to climate change is uncertain; previous literature agrees that the zonal-mean storm track will generally shift polewards, but this may be the effect of low model resolution. Recent research has identified a tri-polar pattern in the change in the storm track over Europe (Zappa et al., 2013). The number of storms is generally expected to decrease, but locally numbers could increase (Mc- Donald, 2011). Discord arises when cyclone intensity is considered, due to the use of different models using different atmospheric physics, different initialisation and different resolutions. Fur- thermore, the lack of an agreed measure of cyclone intensity means different outcomes are reached using various methods (Ulbrich et al., 2009).
Chapter 2. Literature Review 47
Even when looking at trends in surface observations or reanalysis data, complications can arise. These are mainly due to internal variability on all timescales, but particularly decadal and multi- decadal, as the time period covered by both is insufficient to draw any conclusions about patterns in variation on such scales. Surface observations are susceptible to missing data and to changes in the observational network over time, such as the development of satellite data. Reanalyses are also prone to such changes, but they are also prone to model bias.
There are a variety of sources for this uncertainty, which are discussed in Section 2.5. Separating the different sources of uncertainty is key, which could be done using short model runs. The most readily available set of short model runs are those produced as weather forecasts, by dozens of NWP centres across the globe. Identifying the limitations of modelling in NWP forecasts will inform modelling on longer timescales. Furthermore, an analysis of NWP modelling will feed into the larger project within which this work fits, which will use short runs from climate models to identify sources of uncertainty. The present thesis will concentrate on the representation of historic severe European windstorms in NWP forecasts, and search for indications of where these models fall short. Improving short-term forecasts could facilitate mitigating action that could prevent human or economic losses.
Data and Methods
This Chapter discusses the data and methods used to obtain the results. Following the objectives in Section 1.3, the first step is to identify the necessary data sets (Section 3.1). Analysis data are needed for the work identifying and classifying the storms, to use as a yardstick for the forecast analysis, and for the work with storm-prone situations, the large-scale atmospheric situation in the lead up to storm development. Forecast data are needed for the forecast analysis.
The next step is to identify the criteria for selecting a set of severe, historical, winter storms (Sec- tion 3.2). The storms then need to be tracked in analysis data (Section 3.3), in order to allow assessment of the entire lifetime of the cyclone. Two methods are investigated in terms of cat- egorising the storms, based on the jet stream (Section 3.4) and the Pressure Tendency Equation (Section 3.4.2). As there are two methods of categorisation, it is important to assess whether there is any relationship between the different methods (Section 3.5.1).
After analysing the storms in analysis data only, the next steps involve the inclusion of forecast data (Section 3.6). The storms will be tracked in forecast data, and these forecast tracks matched automatically to the tracks in analysis data. Deviations between the forecast and analysis tracks will be calculated in terms of both storm location and intensity, and it is these that will be quan- titatively evaluated, in order to determine forecast quality and predictability. This evaluation will be done for all of the storms together, and in the different categories of storm already discussed, to allow assessment of whether storms of one category are more predictable than storms in another category.
Chapter 3. Data and Methods 49
The final steps examine storm-prone situations (Section 3.7). A storm-prone situation (SPS) de- scribes the large-scale atmospheric situation in the lead up to a storm’s development. The aim is to ascertain whether there are features or properties common to the development of severe cyclones. The first step is to identify one or more candidate(s) to act as an SPS, and then go on to assess whether it is (they are) a useful method of identifying a situation that is likely to cause a severe midlatitude cyclone to develop. Finally, links will be sought with the categorisation methods and the forecast quality and predictability.
3.1
Data
The data need to be at a minimum of 6-hourly temporal and T63 spatial resolution, for the tracker to successfully realise a storm track (Section 3.3). Additionally, mean sea level pressure and vorticity at 850hPa are both needed by the tracker. Covering the period 1990 to 2010 would be highly desirable, as storms would be selected from that period, for reasons are discussed in Section 3.2. Therefore, the data used are mainly from the European Centre for Medium-Range Weather Forecasting (ECMWF), because the data are readily available at the required temporal resolution and for the required time period, at sufficient resolution and of high quality, in both reanalysis and forecast data sets.
Where gridded data are needed, data sets are acquired on 0.5o resolution in both latitude and longitude, and so the native output is interpolated at the ECMWF before acquisition. For the storm tracking (Section 3.3), all of the data are acquired at native resolution, then interpolated and regridded using Climate Data Operators (CDO). Now, the reanalysis and forecast data sets used will be discussed, followed by the reasons for these choices.
3.1.1 Reanalyses
Reanalysis data are used extensively in this work. Alternatives include operational analysis or raw observational data. Reanalysis has limitations, including that it is affected by the quality and quantity of observations that are fed in, the data assimilation scheme used to process them, and intrinsic features of the model such as its resolution. However, analysis data have the same problems with observations, with the added complication that the model and data assimilation scheme are regularly upgraded. The tracks of different storms from different phases of model
development would be difficult to compare in analysis data, as differences could be due to an upgrade to the model, rather than being due to dynamical differences in the storms themselves. Raw observations are sparse, particularly over the Atlantic where the storms’ tracks begin, so are not suitable either because the resolution would be insufficient. Although the observations could be interpolated, the distances between each observation are sufficiently large that this would not be accurate and would miss details. Therefore, neither analysis nor raw observations are suitable, and using reanalysis data is the best option.
The first data that are used are from the most up-to-date ECMWF Reanalysis project: ERA-Interim (Dee et al., 2011). There is an older reanalysis project, called ERA-40 (Uppala et al., 2005), but it was only run until mid-2002 and so does not cover the entire time period of interest. The native resolution also differs, with ERA-40 running at T159 (1.125o) and ERA-Interim at T255 (0.75o). Where possible, the storms were also tracked in ERA-40 data, in order to ascertain whether there are any systematic differences, but it quickly becomes clear that it would not be suitable in this context (not shown). Therefore, given its superior spatial resolution and coverage of all selected storms, ERA-Interim was used for the analysis discussed in Chapters 4 and 6, and as the yardstick for comparison in Chapter 5.
3.1.2 Forecasts
Both ECMWF deterministic and ensemble operational forecasts are used, and how their resolu- tions evolve with time is shown in Table 3.1. The Ensemble Prediction System (EPS) consists of two different types of forecast: a control run, initialised with operational analysis data but at a lower resolution than the deterministic operational forecast; and ensemble members, which are initialised with perturbed versions of the operational analysis data. These perturbations are not random, but instead are targeted to those where perturbations grow fastest (Palmer et al., 1997). In addition to the changes described below, there was an important change to the ensemble data in October 1999, when the number of members was increased from 30 to 50. As Buizza and Palmer (1998) found that there is a strong dependence between how well an EPS performs and the number of members it contains, this work only examines the storms after October 1999 in ensemble member data.
Chapter 3. Data and Methods 51
Date Deterministic Ensemble
Horizontal Vertical Horizontal Vertical
May 1985 T 106 N/A 95km Sept 1991 T 213 31 47km Dec 1992 T 63 150km Dec 1996 TL159 31 72km Apr 1998 TL319 31km Oct 1999 60 40 Nov 2000 TL511 TL255 19km 42km Feb 2006 TL799 91 TL399 62 13km 25km Jan 2010 TL1279 TL639 8km 16km
T 3.1: Resolution of the ECMWF forecast models. The values in italics are the approximate
conversion from spectral resolution to kilometres, at 60oN.
3.1.3 Discussion
Although ECMWF data are selected for investigation due to its availability for the entire period under investigation, two alternative sources of data were considered: the National Centres for Environmental Prediction (NCEP) and the UK’s Met Office (UKMO). NCEP’s Climate Forecast System Reanalysis (CFSR) is an reanalysis, so could be compared with ERA-Interim; however, this is not possible in the current work. Although mean sea level pressure is readily available is CFSR, vorticity or winds aloft are not, so the tracking algorithm would need to be changed significantly in order to cope with the lack of vorticity data. NCEP also have operational models in the Global Forecasting System (GFS) and its ensemble companion (GEFS). However, both analysis and forecast data are only archived for approximately the last ten years, so are unsuitable
as they cover less than half of the required time period. Therefore, no NCEP data are used in this analysis.
UKMO data also have limited temporal coverage, as they are only archived back to 1999, so covers more than half of the selected storms and could be suitable for some limited analysis. However, although the forecast output is at sufficient temporal resolution, the analysis is only available at 12 hourly resolution for the older storms and so is not suitable for tracking. Though UKMO forecasts could be compared to ERA-Interim reanalysis, it would prove difficult to unpick the reasons why they differ. The difference could be due to the effects of the different spatial or temporal resolutions, or the different parametrisation schemes in the models. Furthermore, vorticity or winds at 850hPa are not available for the entire period since 1999, but only for the most recent portion. For these two reasons, no UKMO data are used in the present study.
Once the ECMWF data were acquired, a set of historic, intense, European windstorms needed to be selected.