INVESTIGATION FOR A POSSIBLE INFLUENCE OF IOANNINA AND METSOVO LAKES (EPIRUS, NW GREECE), ON PRECIPITATION, DURING THE WARM PERIOD OF THE YEAR

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1 Proceedings of the 13 th International Conference of Environmental Science and Technology Athens, Greece, 5-7 September 2013 INVESTIGATION FOR A POSSIBLE INFLUENCE OF IOANNINA AND METSOVO LAKES (EPIRUS, NW GREECE), ON PRECIPITATION, DURING THE WARM PERIOD OF THE YEAR O.A. SINDOSI 1, A. BARTZOKAS 1, V. KOTRONI 2 and K. LAGOUVARDOS 2 1 Laboratory of Meteorology, Physics Department, University of Ioannina, Ioannina, Greece 2 Institute for Environmental Research and Sustainable Development, National Observatory of Athens, Athens, Greece ABSTRACT The aim of this work is to investigate if and to what extend the most important lakes of Epirus, northwestern Greece, namely, Ioannina and Metsovo Lakes, affect the precipitation regime (height and distribution) in the area during the warm period of the year. For this reason, an afternoon thermal precipitation event is considered and simulated by applying the numerical meteorological model MM5. Sequential sensitivity tests showed that the precipitation event is satisfactorily predicted by the model by applying Kain-Fritsch-2 convective parameterization scheme even in the finest domain of 2 Km. The comparison of simulations with unmodified and modified land use data showed that the humidity over and around the lakes, in the afternoon, is mainly controlled by air masses coming from the Ionian Sea. In other words, the lakes do not affect the events via their contribution in humidity and this is one of the reasons that thermal precipitation in Epirus does not occur exclusively in the areas of Ioannina and Metsovo but also in many other inland places. The role of the lakes is found to be indirect, as they influence the air flows above them and thus affect the updrafts which cause precipitation. KEYWORDS: Lake, NW Greece, summer precipitation, meteorological model MM5 1. INTRODUCTION The impact of inland water bodies on air flow is a very complex topic, extremely difficult to be evaluated and investigated in detail. Lake waters affect air temperature above them, as they have a different thermal capacity from the neighbouring land areas, humidity, as they act as water vapour sources, wind velocity, as they present lower friction, etc. All these factors affect turbulence and thus thermal convection and humidity transmission to higher layers in the troposphere, and eventually they may modify local precipitation. Because of these difficulties, the problem of the assessment of lakes contribution to local precipitation amount and distribution is usually investigated theoretically, by using numerical meteorological models. The aim of this work is to investigate whether and to what extend the two largest lakes of Epirus, northwestern (NW) Greece, namely Ioannina Lake and Metsovo Lake, influence local precipitation during the warm period of the year. Ioannina Lake is located on the central plateau of Epirus (480m) covering an area of about 19 Km 2 while Metsovo Lake is located at the eastern part of Epirus, on Pindus mountain range, at an altitude of 1300m, covering approximately 9 Km 2 (Figure 1a). A precipitation case during the warm period of the year is selected and studied in detail, by using the mesoscale meteorological nonhydrostatic model MM5. At first, various sensitivity tests, regarding the initialization time and the convective parameterization scheme (CPS), are performed in order to reveal the

combination leading to the best simulation of the event.

CASE DESCRIPTION, MODEL SET UP AND METHODOLOGY The below case study is the precipitation event of 31 May 2011.

2 combination leading to the best simulation of the event. Then, the results are used as a basis for a new simulation with modified land use data (without lakes) in order to reveal the influence of lake waters on the height and the distribution of precipitation. In this way, the differentiations in the fields of some meteorological parameters, which are related to precipitation, as for example humidity, air temperature and wind velocity, are also studied. 2. CASE DESCRIPTION, MODEL SET UP AND METHODOLOGY The below case study is the precipitation event of 31 May This case was selected because afternoon precipitation was observed in many inland areas of Epirus and moreover the prevailing synoptic conditions, which present a very low pressure gradient over the Balkans and the Mediterranean (Figure 1b), indicate that precipitation was of local thermal origin. Considerable amounts of convective precipitation fell in a few hours (13-16 UTC, Greek local time) in the area of Ioannina while lower amounts were observed around Metsovo. During the same hours, intense lightning activity took place as recorded by ZEUS lightning detection network (Figure 1a). Because of the lack of radar data over NW Greece, the maxima of lightning activity may give an insight to the event and indirectly indicate areas of maximum convective precipitation inasmuch as, in this area of the country, rain gauges are rather sparse. Figure May 2011: a) The lightning activity (red dots) over Epirus for UTC (black dots indicate the sites of the three stations), b). The synoptic conditions at 12 UTC: sea level pressure (black contours) 500 hpa height (colours). For the simulation of the event, MM5 is applied by using as initial and boundary conditions, 6-hourly analysis data, provided by the European Centre for Medium-Range Weather Forecasts (ECMWF). These data are interpolated in a coarse grid with 24 Km horizontal grid increment, which covers most of Europe and the Mediterranean using 40 σ-levels in the vertical. For the simulation the microphysical scheme described by Schultz (1995) and the boundary layer scheme proposed by Hong and Pan (1996) have been selected. The output of this simulation are used on a subsequent run, with a finer grid spacing, covering Greece (8x8 Km), following the one-way nesting technique. Finally, using the output of the latter, a third simulation is carried out for the region of Epirus using a 2x2 Km grid. The topography fields are derived from a terrain data file with 30 arc sec resolution provided by the United States Geological Survey (USGS). In order to achieve the best possible simulation in terms of precipitation, various sensitivity experiments are performed with different model initialization times and different CPSs. Specifically, the model is initialized at 00 and at 06 UTC, 31 May 2011, i.e. 13 and 7 hours before the beginning of the rain, respectively, by activating either Grell (GR)

3 (Grell, 1993) or Kain-Fritsch-2 (KF2) CPS (Kain, 2004) in all the domains. The role of a CPS in a simulation, especially in cases of strong convection, is very important as such a scheme redistributes in the vertical, heat and humidity in order to achieve stability in a grid column. Some researchers suggest that in simulations with grid of high horizontal resolution (e.g. < 4 Km), no CPS is required (see e.g. Wang and Seaman, 1997; Wilson and Roberts, 2006) but according to some others there is evidence that the application of CPSs in such simulations leads to improved forecasts (Kotroni and Lagouvardos, 2004; Yu and Lee, 2011). For Epirus, Sindosi et al. (2012), who verified intense precipitation forecasts during the cold period of the year, based on 22 precipitation events, showed that, the activation of a CPS in the finest grid of 2x2 km is necessary as the results are considerably improved. In the present work, the necessity of the use of a CPS in the finest domain (2 Km) is investigated also for the warm period of the year. For the verification of the results, the required interpolation of forecast rain at the sites of the three stations around the lakes, namely, Ioannina University (UOI), Lake island (ISL) and Metsovo (MET) (Fig. 1a), is achieved applying the Cressman method (4 grid points), which uses an inverse distance formula (Cressman, 1959). The most accurate simulation found is set as a reference and then a new experimental simulation is carried out with modified land use data. In the new land use data set, the water body of the grids corresponding to the lakes is replaced by the land use of the neighbouring grid rectangles. Then, the differentiations, from the reference simulation, in the fields of the most important meteorological parameters, which are directly related to precipitation (i.e. water vapour mixing ratio, temperature, wind flow), are investigated in order to reveal how the lakes affect these parameters and thus the precipitation regime in the area. 3. RESULTS According to the results, the 00 UTC initialization time and the use of Kain-Fritsch-2 CPS, for all the domains, gives, for the lake areas, the best simulation (Table 1). Also, the distribution of forecasted rain over Epirus (Figure 2) looks similar to the lightning activity recorded by ZEUS system (Figure 1a). Thus, this simulation (UL- unmodified land use data) was set as a reference one. Table UTC precipitation forecasts and observations (mm) for the sites of the three meteorological stations. CPS used and model initialization time are indicated. Stations Forecast Forecast Forecast Forecast KF2 00 UTC KF2 06 UTC GR 00 UTC GR 06 UTC Observation UOI ISL MET The non activation of CPS in the finest domain leads to excessively heavy rainfall in some areas and no rainfall at all in many others, in which rain was observed (Figure 3). This finding is in agreement with the ones of Sindosi et al. (2012) for winter rainfall. Then, the experimental simulation was carried out with modified land use data (ML) keeping initialization time and CPS the same as in the simulation with unmodified land use (UL). The differentiation in the field of surface temperature between the two simulations at 04 UTC, i.e. around sunrise, indicates that the lakes of Ioannina and Metsovo, act as warm surfaces increasing air temperature above them at about 0.3 C and 1.7 C respectively (not shown). This is due to the large thermal capacity of water, which during the night does not cool as much as the neighbouring land surfaces. Figure 4 presents the differentiations, in the field of water vapour mixing ratio, between the two simulations at the same time at σ-level (approximately 2m above ground). As it can be seen, water vapour mixing ratio near the ground is slightly higher (0.27 g/kg near

4 Ioannina Lake and 0.22 g/kg near Metsovo Lake - about 2%) with the lakes than without them. Figure 2. Accumulated precipitation (mm) for UTC as forecasted by using Kain-Fritsch-2 CPS and 00 UTC initialization time. The red dots indicate the positions of the 3 meteorological stations and the blue areas the model grid rectangles that represent the two lakes. Figure 3. Accumulated precipitation (mm) for UTC as forecasted by using Kain-Fritsch-2 CPS in the coarser domains and no CPS in the finest one (initialization time: 00 UTC ). The red dots indicate the positions of the 3 meteorological stations. After sunrise, ground temperature increases more than lake surface temperature and as a result, at 10 UTC the air above the lakes of Ioannina and Metsovo in ML is higher than in UL, by about 4.3 and 3.6 C respectively. The 10UTC time stamp was selected because water-air temperature difference is maximum in as much as later clouds were developed. Thus, at this time, in the areas of the lakes, the lower layers of the atmosphere are more unstable in ML than UL (not shown). In any case, the turbulence at this time is intense, growing the boundary layer and making the role of the lakes, as

998 (about 2m above ground) at 04 UTC. Red colours indicate areas where water vapour mixing ratio is higher with the lakes than without them.

5 sources of water vapour, not obvious, as they are transferred and dispersed high into the atmosphere. Thus, the distribution of water vapour mixing ratio does not differ between ML and UL in any atmospheric layer. Figure 4. UL-ML water vapor mixing ratio difference (g/kg) at σ-level (about 2m above ground) at 04 UTC. Red colours indicate areas where water vapour mixing ratio is higher with the lakes than without them.. The prevailing wind in NW Greece after 08 UTC is, in general, westerly, transferring humid air masses from the Ionian Sea. Figure 5 presents the wind field and the humidity mixing ratio near the surface (σ=0.998) at 08 and 14 UTC for UL simulation. The humidity in Ioannina plateau at about 12 UTC (not shown) and in Metsovo at about 16 UTC is mainly controlled by these masses constraining the role of the lakes as water vapour sources. Figure 5. Water vapour mixing ratio (g/kg) over Epirus at σ-level (about 2m above ground) at: a) 08 and b) 14 UTC. At about 16 UTC, according to all the forecasts of Table 1 a very strong updraft is formed northeast of Ioannina Lake, because of air flow convergence at hPa (not shown). The strong updraft transfers water vapour at high levels, where it is condensed and produces heavy rain (Figure 2). Figure 6 presents the distribution of vertical velocities at 550hPa as they are forecasted by: a) UL and b) ML simulation. The comparison between

the two distributions indicates that the modification of land use data affects the intensity as well as the location of the maximum velocity of the updraft.

6 the two distributions indicates that the modification of land use data affects the intensity as well as the location of the maximum velocity of the updraft. Figure 7 presents the cross section of vertical velocities on a vertical plane near Ioannina Lake (Line 1 in Figure 6), for UL and ML simulations. The updraft is extended high to the atmosphere (up to 350hPa), while the maximum vertical velocity is found to be at slightly higher in ML simulation. Figure 6. Vertical velocities (colours) and wind field (arrows) at 550hPa at 16 UTC for: a) UL and b) ML simulation. Line 1 indicates the cross section area of Figure 7. Figure 7. Cross section of the vertical velocity (colours) and wind projection on the vertical plane (arrows) of Line 1 (Figure 6) at 16 UTC for: a) UL and b) ML simulation. Now, the question is how the modification of land use in a four neighbouring grids (16 km 2 ) causes changes in the upper layers of troposphere. Figure 8 presents the wind and the convergence-divergence fields near the surface (σ-level 0.998) for UL and ML simulations at 16 UTC, in an area around Ioannina Lake. As it can be seen, the removal of Ioannina Lake (ML) modifies the flow near the surface and, as a result, displaces and changes the area of convergence west of the lake. The convergence near the ground creates a rather moderate updraft in this area which extends up to 650hPa and its maximum is at about 850hPa (see Figure 7). The change of air flow near the ground

influences the moderate updraft modifying, eventually, atmospheric circulation in the upper layers (see Figure 6) and as a result the strong updraft in the northeast of the Lake.

controlled by the structure and the strength of the updrafts which, as mentioned above, are different for the two simulations.

The final result is a differentiation in the distribution of condensations and thus of precipitation, not only close to the lakes but also in a distance from them (Figure 9).

UL-ML precipitation differences (mm) for 12-18 UTC.

7 influences the moderate updraft modifying, eventually, atmospheric circulation in the upper layers (see Figure 6) and as a result the strong updraft in the northeast of the Lake. Furthermore, when the convective activity begins northeast of Ioannina Lake the water vapour mixing ratio distribution in the horizontal as well as in the vertical is mainly controlled by the structure and the strength of the updrafts which, as mentioned above, are different for the two simulations. The modification of land use data for Metsovo Lake also modifies the local air flow (not shown). The final result is a differentiation in the distribution of condensations and thus of precipitation, not only close to the lakes but also in a distance from them (Figure 9). Figure 8. Convergence (blue) - divergence (red) and wind field at σ-level around Ioannina Lake at 16 UTC for: a) UL and b) ML simulation. Figure 9. UL-ML precipitation differences (mm) for UTC. Red colours indicate areas where precipitation is higher with the lakes while blue colours indicate areas where precipitation is higher without them. 4. CONCLUSIONS The aim of this work was to investigate if and to what extend the most important lakes of Epirus, NW Greece, namely, Ioannina and Metsovo Lakes, affect the precipitation in the area during the warm period of the year. For this reason, an afternoon thermal precipitation event was considered and simulated by applying the numerical

8 meteorological model MM5 in a high resolution horizontal grid of 2x2 Km. Various sensitivity tests were performed in order to reveal the combination of convective parameterization scheme and initialization time of the simulation leading to the best reproduction of precipitation in the study areas. The optimum simulation was set as a reference and then another experimental simulation was carried out with modified land use data. In the new data basis, the water body of the grids corresponding to the lakes was replaced by the land use of the neighbouring grid rectangles. Then, the differentiations, from the reference simulation, in the fields of the most important meteorological parameters, which are directly related to precipitation (i.e. water vapour mixing ratio, temperature), were investigated in order to reveal how the presence of the lakes affects the fields of these parameters and thus the precipitation regime around the area. The precipitation event was satisfactorily predicted by the model and the experimental simulations also revealed the important role and the necessity of the use of a convective parameterization scheme even in the finest grid of 2 Km. Furthermore, the results of the study showed that the water vapour field, which is a crucial factor for the afternoon precipitation events over and around the lakes, is mainly controlled by air masses coming from the Ionian Sea. In other words, the lakes do not affect the events via their contribution in humidity and this is one of the reasons that afternoon precipitation in Epirus does not occur exclusively in the areas of Ioannina and Metsovo but also in many other inland places. The role of the lakes is found to be indirect, as they influence the air flow above them and thus affect the updrafts which cause precipitation. 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: Heracleitus II. Investing in knowledge society through the European Social Fund. REFERENCES 1. Cressman, G. (1959), An operational objective analysis system, Mon. Wea. Rev., 87, Grell G.A (1993), Prognostic evaluation of assumptions used by cumulus parameterizations, Mon. Wea. Rev., 121, Hong, S. and Pan, H. (1996), Nonlocal boundary layer vertical diffusion in a medium-range forecast model, Mon. Wea. Rev., 124, Kain, J.S. (2004), The Kain-Fritsch convective parametrization. An update. J. Appl. Meteor. 43, Kotroni, V. and Lagouvardos, K.: Evaluation of MM5 highresolution real-time forecasts over the urban area of Athens, Greece, J. Appl. Meteorol., 43, , Schultz, P. (1995), An explicit cloud physics parameterization for operational numerical weather prediction, Mon. Wea. Rev., 123, Sindosi, O.A., Bartzokas, A., Kotroni, V., Lagouvardos, K. (2012): Verification of precipitation forecasts of MM5 model over Epirus, NW Greece, for various convective parameterization schemes. Natural Hazards and Earth System Sciences, 12, Wang, W. and Seaman, N.L. (1997), A comparison study of convective parameterization schemes in a mesoscale model, Mon. Wea. Rev., 125, Wislson, J.W. and Roberts, R.D. (2006), Summary of Convective storm initiation and during IHOP : observational and modeling perspective, Mon. Wea. Rev., 134, Yu, X. and Lee, T.-Y. (2011), Role of convective parameterization in simulations of heavy precipitation systems at grey-zone resolutions case studies. Asia-Pacific J. Atm. Sci.,47,

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