Climatic study of the surface wind field and extreme winds over the Greek seas

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1 C O M E C A P e - b o o k o f p r o c e e d i n g s v o l. 3 P a g e 283 Climatic study of the surface wind field and extreme winds over the Greek seas Vagenas C., Anagnostopoulou C., Tolika K. The general characteristics (direction and speed) of the surface wind field are studied over the most important Greek seas, the Aegean Sea and the Ionian Sea. In the study, two data bases covering a period of 21 years ( ) are used. The data sets used are, wind components (U and V) of 15 island and coastal meteorological stations, and wind components (10m) of the ICTP-RegCM3 Regional Climate Model (RCM), after being dynamically downscaled to a horizontal resolution of 10x10 km. Different Principal Component Analyses (PCA s) are used in order to classify spatial patterns of the study region. Four distinct sub-regions are identified. The annual and seasonal wind characteristics for each PCA pattern, as well as their inter-annual variations, have been analyzed. Finally, different objective statistical methods - both parametric and non-parametric - have been applied to the stations and the climate model data in order to determine extreme wind speed thresholds. The results of the different methods have been evaluated and compared. Vagenas C. *, Anagnostopoulou C., Tolika K. Department of Meteorology and Climatology, School of Geology, Faculty of Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece. *corresponding author cvagenad@geo.auth.gr

2 C O M E C A P e - b o o k o f p r o c e e d i n g s v o l. 3 P a g e Introduction Winds just above the sea surface play an important role in many features of the Mediterranean Sea, for example, in coastal upwelling near the land-sea boundaries. Also, one of the main parameters used to assess the forcing fields for oceanic model runs is the surface wind (10m), which is directly used to derive stress and turbulent flux fields. Kara et al. (2009) compared satellite measured sea winds (QuikSCAT) with those estimated by numerical weather prediction model (NOGAPS) in the Mediterranean and explored their differences in coastal areas. Ruti et al. (2007) compared wind vector analyses derived from different routine assimilation systems and from blended products, to wind vectors obtained from QuikSCAT satellite sensor, as well as those directly measured by buoymounted anemometers. Klink and Willmott (1988) used separate vector and scalar Principal Component Analyses to study the USA wind field and compare their results. Kunz et al. (2010) investigated the ability of the Regional Climate Models (RCMs) to realistically simulate extreme wind speeds. The main goal of this study is to evaluate the ability of the RegCM3 to simulate the observed surface wind and utilize it to identify the major wind field patterns over the Greek seas. Moreover, an attempt is made to give wind speed thresholds employing statistical methods for extreme value, both parametric and non-parametric. 2 Data and Methodology 2.1 Data Two datasets are being utilized. The first one consists of observed meteorological data provided by NOAA NCDC s Integrated Surface database (ISD) and involves 15 seaside meteorological stations. Measurements are taken on 3 hour intervals and daily means for the wind speed (scalar average magnitude) and the wind direction (resultant vector direction) are calculated. For the second dataset, generated from the RegCM3 s run on a 10x10 km downscaled grid, a subset of 3142 grid points was selected. It comprises of 6 hour interval u and v wind components (10m), and the daily means are calculated as for the IDS. Both datasets cover the period between 1980 and The missing data of the ISD account for less than 0.5% on each of the 15 stations. 2.2 Methodology An evaluation of the RegCM3 s accordance with the observation data is conducted. Three Principal Component Analyses (PCAs) were applied to seasonal data in order to distinguish the prevailing patterns in both speed and direction and were conducted both for the RegCM3 and the ISD datasets. The most reliable results were obtained from the combination of u and v components of each grid point in a single variable. Two cross sections (space: 0.5 ο longitude/latitude, time: month) are being used to best summarize the inter-annual variability of the wind field, a north to south one and a west to east one. Three parametric and three nonparametric methods are being utilized in order to produce a set of extreme wind speed thresholds. The parametric methods include: a) The Mean Residual Life function (MRL), which, for a distribution with a finite mean, completely determines the distribution via an inversion formula. b) The Threshold Choice method (TC), which fits the Generalized Pareto Distribution (GPD) at a range of thresholds to look for the stability of the parameter estimates. c) The Dispersion Index method (DI), which is a normalized measure of the dispersion of a probability distribution. The non-parametric approach for determining the

C O M E C A P 2 0 1 4 e - b o o k o f p r o c e e d i n g s v o l. 3 P a g e 285 wind speed threshold consists of three percentile indices: the 90 th, the 95 th and the 99 th.

3 C O M E C A P e - b o o k o f p r o c e e d i n g s v o l. 3 P a g e 285 wind speed threshold consists of three percentile indices: the 90 th, the 95 th and the 99 th. Parametric and nonparametric thresholds were estimated by adapting the same procedure used by Anagnostopoulou and Tolika (2011). 3 Results 3.1 Surface wind field The loadings of the rotated PCs were plotted and analyzed (Fig. 1). These loading maps represent the relationship between PC scores and station data and, therefore, the closely related or unrelated regions can be identified. The spatial patterns account for about 90% of the variance for the RegCM3 and 76% for the ISD. There seems to be a very good agreement between the wind patterns of the RegCM3 and those of the ISD. Fig. 1. Winter s first four rotated PC s of the surface wind field calculated of the model (left) and station (right) datasets, with the blue contour representing loading value of 0.5. More specifically, the first pattern (Fig. 1) provides a strong correlation center to the southeast Aegean Sea, including eastern Crete and Dodecanese. In the second pattern, the positive loading center is located at the north Aegean. In the third pattern, the positive phase is shifted towards the west, at the Ionian Sea. Finally, the last pattern shows the highest correlation in the area to the south of Peloponnesus and to the west of Crete.

C O M E C A P 2 0 1 4 e - b o o k o f p r o c e e d i n g s v o l.

4 C O M E C A P e - b o o k o f p r o c e e d i n g s v o l. 3 P a g e 286 The plotted PC loadings for the other three seasons (not shown) reveal small differences, such as slight shifts of the correlation centers and the extent of the area covered by the 0.5 loading contours. The most notable difference is observed for the RegCM3 during the summer, where PC4 extends about 1 ο, both to the north and the east, while its center shifts north, in the area among Peloponnesus, Cyclades and Crete. As a result, the area covered by PC1 is compressed to the northern Aegean Sea. The other two seasons represent transitional phases between winter and summer, with spring s spatial distribution of the wind field being more like winter s and autumn s being more like the summer one. Fig. 2. a) North-south cross section showing monthly averaged wind speed (colored) and monthly prevailed wind direction for the RecCM3 data. b) Same as in a) but for the west-east cross section. c) RegCM grid (grey and red), grid points included I the cross sections (red), and stations locations (blue). The cross section plots (Fig. 2) show that the seasonal variability of the wind field is becoming quite apparent. In the north-south cross section the winter wind speed maxima are easily distinguished from the following spring and early summer minima. Generally, the maximum wind speeds are located in northern Aegean Sea, followed by the southern Aegean winds between the Cyclades and Crete. The most prominent characteristic of the prevailing wind direction is the dominance of north section winds throughout the year, with a shift to south section winds occurring during January. Winds blowing from the west section during the spring in the south Aegean are also observed. In the west-east cross section some of the previous features such as the winter maxima and spring minima in wind speed can also be seen. However, it is interesting that in the south-east Aegean Sea there is a wind speed maximum during the summer (July-August) related to the Etesians which blow from the northwest in that region. The variability of the wind direction seems more intense in this cross section both in time and space with the latter probably related to the effect of Crete. Throughout the year, the prevailing wind direction in the southwest Aegean seems to gradually shift from WSW in January to NW in late July, while from August till December prevailing wind are steadily blowing from the NE. In the southeast

5 C O M E C A P e - b o o k o f p r o c e e d i n g s v o l. 3 P a g e 287 Aegean winds seem to be generally of N-NNW direction during October-March and shifted a little more to the west in the warm period of the year. 3.2 Extreme winds The comparison of the results of the threshold estimation parametric methods with those of the nonparametric ones, applied to the ISD dataset, shows that the method producing the lowest extreme values is the TC. The majority of TC thresholds vary from 5 to 10 m/s and are similar to the 90 th percentile ones. The highest thresholds are produced by the MRL, as they reach gale speeds m/s in some stations, and are constantly higher than those of the 99 th percentile. The thresholds of the DI method are also high, although there are less cases where they fall into the gale winds category than the MRL results. Thus they are more comparable to the 99 th percentile. Table 1. Wind speed thresholds calculated by parametric and nonparametric statistical methods. ISD RegCM3 name mrl tc di pq90 pq95 pq99 mrl tc di pq90 pq95 pq99 Aktio Alex/poli Andravida Heraklion Kerkyra Kythira Lemnos Methoni Milos Mytilene Naxos Rhodes Samos Skyros Souda For the RegCM3 dataset the MRL method produces the highest thresholds up to near gale speeds, and their values are higher than those of the 99 th percentile. The lowest threshold speeds are produced by the TC method, between the 90 th and the 95 th percentile. The threshold values of the DI method fall between those of the 95 th and the 99 th percentiles and they fall in the categories of the strongest breezes. The threshold values of the RegCM3 seem to be higher than those of the ISD in the regions of Dodecanese and Crete in the Aegean Sea, and in the Ionian Sea. On the other hand, the ISD produces higher thresholds in the southwest, central and north Aegean Sea. 4 Conclusions Overall, the RegCM3 can be considered that it is in good agreement with the observed data regarding the surface wind field. The PCA results showed that the model identifies four regions with distinct wind characteristics, three regions in Aegean Sea (north, southeastern and southwestern) and one in Ionian Sea, These results are consistent with the ISD ones. The two cross sections, plotted from the RegCM3 dataset, capture the core elements of the wind field in the Aegean Sea, depicting wind speed maxima during winter accompanied with increased southern sector winds. It also simulates satisfactorily the Etesian wind pattern during mid-summer to early-autumn with a secondary maximum in wind speeds blowing steadily from north sector directions. The application of extreme values techniques on the RegCM3 dataset results in high wind speed thresholds in the regions of central and southwest Aegean Sea (13-16 m/s) regarding

6 C O M E C A P e - b o o k o f p r o c e e d i n g s v o l. 3 P a g e 288 the MRL, DI and pq99 methods. The highest thresholds of the TC and pq95 methods are, also, located in the central Aegean Sea, even though their values range from 10 to 13 m/s. Acknowledgments. This research has been co-financed by the European Union (European Social fund - ESF) and Greek national funds through the Operational Program Education and Lifelong Learning of the National Strategic Reference Framework (NSRF) - Research Funding Program: Thales. Investing in knowledge society through the European Social Fund. ( Data provided by the National Climatic Data Center (NCDC) Integrated Surface Database (ISD). References Anagnostopoulou C and Tolika K (2011). Extreme precipitation in Europe: statistical threshold selection based on climatological criteria. Springer, doi: /s Kara AB, Wallcraft AJ, Martin PJ, Pauley RL (2009). Optimizing surface winds using QuikSCAT measurements in the Mediterranean Sea during Journal of Marine Systems 78: Klink K and Willmott CJ (1989). Principal components of the surface wind field in the United States: a comparison of analysis based upon wind velocity, direction, and speed. Int. J. Climatol. 9: Kunz M, Mohr S, Rauthe M, Lux R, Kottmeier C (2010). Assessment of extreme wind speeds from Regional Climate Models Part1: Estimation of return values and their evaluation. Nat. Hazards Earth Syst. Sci. 10: Ruti PM, Marullo S, D Ortenzio F, Tremant M (2007) Comparison of analyzed and measured wind speeds in the perspective of oceanic simulations over the Mediterranean basin: Analyses, QuikSCAT and buoy data. Journal of Marine Systems 70:33-48

Keywords: lightning climatology; lightning flashes; Macedonia Greece.

Keywords: lightning climatology; lightning flashes; Macedonia Greece. International Scientific Conference GEOBALCANICA 2018 A 10-YEAR CLIMATOLOGY OF LIGHTNING FOR MACEDONIA, GREECE Paraskevi Roupa 1 Theodore Karacostas 2 1 Hellenic National Meteorological Service, Greece