LOCAL TEMPERATURE DIFFERENCES IN RELATION TO WEATHER PARAMETERS

INTERNATIONAL JOURNAL OF CLIMATOLOGY Int. J. Climatol. 20: 151 170 (2000) LOCAL TEMPERATURE DIFFERENCES IN RELATION TO WEATHER PARAMETERS J. BOGREN*, T. GUSTAVSSON and U. POSTGA RD Road Climate Centre, Physical Geography, Earth Sciences Centre, Uni ersity of Göteborg, Göteborg, Sweden Recei ed 17 December 1998 Re ised 21 April 1999 Accepted 9 May 1999 ABSTRACT The objective of this paper is to focus on the influence of clouds and wind on air and road surface temperature variations between different types of local climate environments. The study area covers 160 130 km 2 and includes 35 field stations in the Swedish Road Weather Information System (RWIS) and two synoptic weather stations. By combining data from the two sources, the spatial and temporal variations in air and road surface temperature have been analysed. In the first part of this paper the theoretical influence of different weather parameters is determined. In the empirical part of the study, a validation of the theoretical result is assessed using temperature and weather data from the study area. The results show that it is possible to calculate the temperature variations in relation to topographical siting and different weather factors. During day-time conditions, the effect of screening from the sun has a significant influence on the road surface temperature, even with cloudiness amounting to 4 6 octas, provided that the solar elevation is high. During night-time, the potential for pooling of cold air is determined by cloud cover and wind speed. When cloudy situations prevail during night-time, neutral stability is dominant resulting in a decrease with increasing altitude for both air and surface temperatures. Road surface temperatures, however, have a lower correlation with altitude than air temperature. The variation in surface temperature decreases with altitude is also larger and has a more even distribution than the air temperature decrease with altitude. Wind speed was not an important factor for the variation in surface temperature decrease with altitude, but insolation from the sun during day-time is one parameter to consider. Copyright 2000 Royal Meteorological Society. KEY WORDS: south-central Sweden; local climate; topoclimatology; Road Weather Information System; air temperature; road surface temperature; cold air pooling; screening effect; temperature decrease with altitude; weather 1. INTRODUCTION In the field of applied climatology, it is essential to have knowledge of how to predict temperature variations in relation to weather and topography. Several studies have documented the role of local topography in causing large temperature variations within a small area (e.g. Tabony, 1985; Mahrt, 1986; Bogren and Gustavsson, 1989; Toritani, 1990). From these studies, it is obvious that local topography is an important factor to consider when dealing with applications such as local frost risk or prediction of road slipperiness. In order to determine the influence of topography on local temperature variations it is very important to be able to rule out the influence of wind and clouds. It is a well-known fact that the temperature pattern is most clearly developed during clear, calm nights and for this reason most local climatological studies are carried out during this type of weather situation. However, in order to cover all situations, not just extreme ones, the influence of clouds and wind must be determined. The importance of prevailing wind speed and cloud cover for the development of large temperature differences, and the effect on road surface temperature, is dealt with in Gustavsson (1990, 1991). Others (e.g. Bootsma, 1976; Laughlin, 1982) have related temperature differences in complex terrain to radiation and wind speed by empirical formulae. * Correspondence to: Road Climate Centre, Physical Geography, Earth Sciences Centre, University of Göteborg, Box 460, SE-405 30 Göteborg, Sweden. Copyright 2000 Royal Meteorological Society

152 J. BOGREN ET AL. During the past 10 years, research concerning stretchwise temperature information has been conducted at the Laboratory of Climatology, University of Göteborg. Winter road information systems consist of field stations located in areas where an early warning of road icing can be achieved. As the risk of road slipperiness varies depending on weather situation and time of year and day, the stations are located in different area types, such as valleys, screened areas and on bridges. This results in only a limited number of stations supplying information about the most extreme conditions that can occur in a surveillance area during a given weather situation. During a clear sunny day, for example, large road surface temperature differences occur due to variation in screening potential. The screening potential depends on the height, density and orientation of the screening object. The effect can be determined from comparison of the temperatures measured at screened and sun-exposed sites. However, the information is only valid for that specific place, and no extrapolation is possible without detailed knowledge of the variation in screening from the sun along road stretches in an area. In order to deal with this problem a local climatological model (LCM) has been constructed, which uses meteorological data measured at the field stations and detailed information about topoclimatological factors such as variations in altitude, wind and solar exposure etc. along road stretches in order to predict temperature variations in-between field stations (Bogren and Gustavsson, 1989). The model has been developed using data from a large number of field stations located in different area types. Furthermore, recordings from mobile temperature surveys along different road stretches have also been used. Data from both the field stations and the mobile measurements are collected during different weather conditions, resulting in a very extensive data archive which can be used for analyses of topoclimatological relations. The present paper focuses on the influence of clouds and wind on air and road surface temperature variations between different field station locations. By combining data from the Swedish Road Weather Information System (RWIS) stations, as well as synoptic weather data, the spatial and temporal variations in temperatures have been analysed. The study emanates from the fact that there is a need for more detailed knowledge about the role of weather in controlling the potential for temperature variations to develop. The different type of situations which are included in the study cover day conditions where the screening effect is related to variation in cloudiness, night-time conditions where potential for cold air pooling is analysed in relation to cloudiness and wind, and finally the effect of altitude during cloudy nights. The study consists of two parts the first uses a theoretical approach in order to determine the influence of weather parameters on temperature, and the second is an empirical study which provides the possibility of validating the results from the theoretical part. 2. THEORETICAL APPROACH In predicting air and road surface temperature variations in relation to topography, the prevailing weather situation is a major factor to consider. Based on the radiation balance, a fundamental division can be made between (i) day-time situations and (ii) night-time situations. Furthermore, a division based on the prevailing weather can also be made. A theoretical approach is used to examine how to calculate the variation in temperature in relation to these divisions. 2.1. The influence by clouds during day-time During day-time, the amount of incoming solar radiation is the key factor determining the possibilities for temperature variations to develop. The incoming radiation can be calculated by S 0 =I sin +D, (1) where I is the intensity of the direct solar radiation, is the solar elevation and D is diffuse solar radiation from clear sky, clouds and other objects. This is valid for a flat area. However, when dealing with temperatures in undulating terrain the aspect is of pronounced importance. This is due to the fact that in a small area the incoming radiation (S) will

LOCAL TEMPERATURES AND WEATHER PARAMETERS 153 not vary, given that the atmospheric conditions are the same. Hence, the slope and azimuth angle given by the local topography will determine the spatial variation in radiation loading. The variation between a flat and a sloping surface can be calculated from S=S 0 cos, (2) where is the angle between direct-beam and a normal to the slope surface. A factor which, in the case of road climatology, that has been shown to be of major importance for the surface temperature (T s ), is the screening of the surface from direct radiation (Gustavsson and Bogren, 1990; Bogren, 1991; Bogren et al., 1998). Orientation, geometry and type of screening object in relation to the surface give the degree of screening. The maximum temperature effect from screening can be calculated from the equation T s(sun) T s(screened) = 2.7+0.46( ), (3) where is the solar elevation at noon, and T s(sun) T s(screened) is the maximum difference in road surface temperature between a sun-exposed and a screened surface. This formula is valid for horizontal surfaces, i.e. road surfaces with a coating of black asphalt. However, by combining Equations (2) and (3), the temperature difference between a screened area and a sloping sun-exposed area can be calculated. The discussion above is valid for clear sky conditions. Clouds will cause a reduction in direct radiation and an increase in diffuse radiation, which will level out the variation in temperature compared with clear sky conditions. Using the formula given by Kasten and Czeplak (1980), the influence of clouds on short-wave radiation can be calculated: S=S 0 (1+b1(N) b2 ), (4) where b1= 0.75 and b2=3.4 are empirical constants, N is the cloud amount, and S 0 is the short-wave radiation which reaches the surface during clear conditions. The amount of incoming radiation for clear and overcast days from Equations (1) and (4), in relation to the sun angle for latitude 58 N between 1 November and 30 March, is shown in Figure 1. The difference is in the magnitude of a 50% reduction of the incoming radiation during cloudy conditions compared to clear sky conditions, slightly increasing with increasing solar elevation. This indicates potential for a screening effect, even during cloudy conditions. At the beginning of March incoming radiation during cloudy conditions is about the same as for clear conditions during the end of January, giving potential for a similar screening effect. However, to be able to calculate the influence on the T s(sun) T s(screened), the effects of diffuse radiation, which acts as a reducing component on the screening potential, must be considered. Figure 1. Amount of incoming radiation for clear and cloudy days, respectively, at 58 N for the period 1 November 30 March

154 J. BOGREN ET AL. Figure 2. Calculated road surface temperature differences between sun-exposed and screened areas for various cloud conditions Diffuse radiation increases with increasing cloud cover and results in a reduction of the temperature difference between a screened and a sun-exposed area, thus modifying the screening potential. Using Equation (4), the ratio between S and S 0 can be calculated in relation to the cloud amount. This, in combination with Equation (3), makes it possible to calculate the screening potential (Figure 2). From Figure 2 it can be seen that the screening potential, expressed as temperature difference between an open and a screened site, varies according to cloud cover and time of the year. For clear conditions, the T s(sun) T s(screened) ranges from 1.5 C in January to 13 C at the end of March. Almost the same screening potential is received for a cloud cover of 1 3 octas, which reduces the T s(sun) T s(screened) by less than 1 C. Partly cloudy conditions (4 6 octas) result in a marked reduction; the T s(sun) T s(screened) varies from 1 C to 9 C through the season. Cloudy conditions have a small potential for a screening effect ranging from 0.3 C to 3 C. The results show that for a cloud cover up to 3 octas, the screening potential can be treated as for clear conditions. A significant reduction of the T s(sun) T s(screened) is achieved during partly cloudy conditions, but the effect must be accounted for in practical use, at least from February where a calculated difference of 4.6 C is apparent for day-time conditions. During cloudy conditions, the road surface temperature differences between sun-exposed and screened stations are negligible for practical use. 2.2. The influence by clouds during night-time During night-time, long-wave radiation and prevailing wind speed have a large influence on the energy budget for the road surface. Long-wave cooling is controlled by the amount of clouds and the sky-view factor. Outgoing radiation (L (out) ) from a surface with the temperature (T s ) can be calculated from L (out) = s T s4 +(1 s )L (in), (5) where s is the surface emissivity and is the Stefan Boltzmann constant. The incoming radiation, L (in), can be calculated for cloudless sky conditions by 4 L (in) = (atm) T (atm), (6) where (atm) is the atmospheric emissivity. If the surface emissivity is taken as 1, the surface long-wave net radiation, L (net), can be calculated by 4 L (net) = T (atm) ( (atm) 1). (7) Clouds have a significant influence on the radiation budget and the modification compared with a cloud-free sky can be calculated from L (in-cloud) =L (in) (1+a(n) 2 ), (8)

LOCAL TEMPERATURES AND WEATHER PARAMETERS 155 and for the net radiation L (net-cloud) =L (net) (1 b(n) 2 ), (9) where n is the amount of cloud, expressed in tenths, and a and b are constants (Oke, 1990). In Figure 3, net radiation is plotted for different amounts and types of clouds. In this example, the air temperature is taken as 5 C and the atmospheric emissivity is calculated by use of Swinbank s formula (Swinbank, 1963): (atm) =0.937 10 5 T (atm). (10) For cloud-free conditions this gives a net radiation of 39 Wm 2. However, reduction due to increased cloud cover becomes important after 4 octas. Up to 4 octas, reduction is less than 25% for Stratus and Altocumulus/Cumulus. In Figure 3 it can also be seen that the effect from clouds differs significantly depending on cloud type. Cumulus and Stratus have a greater effect on net radiation than Cirrus; 8 octas of Cirrus gives a reduction of less than 20%. Determining cloud effect on the possible development of temperature differences is an important task in the modelling of local climate. Previous studies have shown that during clear nights, pooling of cold air occurs in valleys and low-points. This accumulation of cold air has a pronounced effect on both the air and the road surface temperature. Air temperature differences of 10 C have been shown to develop between valley locations and neutral reference areas, where no pooling of cool air can occur (Bogren and Gustavsson, 1991). This requires that the valley is rather large (width and depth of at least 10 km and Figure 3. The effect of different cloud types and cloud cover (octas) on the surface net radiation

156 J. BOGREN ET AL. 50 m, respectively), allowing intense cold air pools to develop. The temperature difference between the same two types of locations during cloudy conditions should be close to zero given that they are situated at the same altitude. In calculating the effect of clouds, it is important to stress that temperature differences are not caused by variations in radiation. Clear skies provide possibilities for other processes to act, such as pooling of cold air and stabilization of surface cold air due to sheltering. Given a temperature difference of 10 C during clear conditions, and equal temperatures during cloudy conditions, the counter radiation from clouds ought to affect the temperature difference in a similar way as shown in Figure 3. If the same type of function is applied, the variation between 0 and 8 octas should follow the curve given in Figure 4. A cloud cover of 4 octas will result in a reduction of the temperature difference by approximately 25% for low and medium high clouds. The dramatic effect occurring after 6 octas and in the interval 6 8 octas has a significant effect on the possible development of temperature variations. The shape of the curve presented in Figure 4 is determined by the maximum temperature difference occurring during clear, calm nights. In order to use the function, it is important to adjust the value in relation to area type. The calculation assumes a large valley where the temperature difference between valley bottom and top is not affected by variation in altitude. In the following section, the influence of topography for nights with a cloud amount in the interval 7 8 octas is discussed. 2.3. Cloudy situations According to temperature variation with height, the lapse rate can be classified as unstable, neutral and stable. Neutral and unstable conditions are the most common during cloudy, windy conditions. Counter radiation from clouds and mixing of the air by the wind ought to result in local temperature variations levelling out. Regional variations in altitude should be of greater importance during these conditions. An assessment of the frequency distribution of different night-time weather situations during January March 1996 at Jönköping Airport have been performed in order to analyse the frequency of different weather situations. A sum in percent of all night-time observations between sunset and sunrise during the period are presented in relation to different cloud cover and wind speed in Table I. Almost 69% of night-time conditions during January March 1996 are cloudy, which is in accordance with the frequencies during the last 10 years. Since cloudy conditions are so frequent, it is important to have proper knowledge of the surface temperature patterns. Figure 4. The calculated effect of cloud cover on the potential for development of maximum nocturnal air temperature differences as a result of cold air accumulation

LOCAL TEMPERATURES AND WEATHER PARAMETERS 157 Table I. Frequency (%) of different night-time weather situations during January March 1996 Wind speed (m/s) Cloud cover (octas) 0 2 3 4 5 6 7 8 0 2 14.2 2 3.0 18.6 3 5 4.4 2.3 2.4 40.2 5 1.6 0.4 10.0 Several studies have analysed the correlation between temperature variations and altitude during cloudy conditions (Laughlin, 1982; Thornes, 1989; Bogren et al., 1992). Laughlin (1982) used multiple regression to calculate the minimum temperature between two reference sites and showed that cloud cover and wind speed are the two most important parameters for the received temperature difference. He also calculated the lapse rate between two sites in a narrow valley by use of the lapse rate between two reference stations. Bogren et al. (1992) used the relationship between temperature and altitude during cloudy and windy conditions to develop equations which calculated the road surface temperature at one field station by use of the surface temperature at a reference station. If these two methods are combined, Equation (11) can be written. This formula calculates the road surface temperature at a selected field station (T s(calculated) ) with knowledge about the temperature decrease with altitude in the area (T s(d) ), the surface temperature at a reference station (T s(reference) ) and the altitude difference between the two stations ( h). The sign on h depends on whether the reference station is located above or below the altitude of the calculated station. T s(calculated) =T s(reference) h T s(d). (11) 3. DATA 3.1. Field stations For the empirical part of this study data from 35 different field stations within the Swedish RWIS have been used. The stations included in the study are located in the south-central part of Sweden, and data from different field stations were analysed in order to evaluate the role of topographical-induced temperature variations in relation to different weather conditions. The selection of stations is based on the topographical siting (see Table IIa, Figure 5). The field stations take measurements of air temperature and humidity at 2 m (Lambrecht 8091100), road surface temperature and precipitation at 5 m (OpticEye FFVGSH-5035), and stations located in open areas also measure wind speed and direction. For detailed specifications of measuring equipment and height see Table IIb. Station 1601, in the county of Skaraborg, was used for comparison of day-time temperature differences. The master station is located in an open field resulting in full sun exposure. Furthermore, the station is equipped with an extra surface temperature sensor located in a road rock cut, approximately 200 m south. The road is orientated south north with road rock cuts on both sides. The road cuts are described as solid, vertical walls and this results in the road section being screened during both the morning and afternoon. Three stations were used for analysis of night-time temperature differences: stations 1512, 1513 and 615 in the county of A lvsborg and Jönköping. These stations are located on the same road stretch (RV40) with a horizontal distance of 23 km. Station 1512 is situated on an open hilltop, station 1513 at the bottom of a large valley and station 615 in a small valley. For regional analysis of temperature variations during cloudy and windy situations, a larger area (120 100 km 2 ) including all 32 field stations within the county of Jönköping was used (see Table IIa).

158 J. BOGREN ET AL. Figure 5. Map of the studied area. The study was carried out in the shaded area close to Lake Vänren and Lake Vättern At the field stations, measurements are taken every 30 min and for this study the observations at 01:00 h were used in order to minimize influence from variations in sun exposure during the preceding day. For the frequency analysis of air and road surface temperature variations with altitude during cloudy situations, four stations in the county of Jönköping were used: stations 605, 612, 614 and 617. All four stations are situated close (varying between 4 and 9 km) to the meteorological weather station at Jönköping Airport and range from 90 m to 235 m above sea level. Station 605 and 617 are open and wind-exposed, and are situated in flat terrain. Station 612 is situated in a relatively open area and on the lower part of a large hill, while station 614 lies in a local low-point and is surrounded by high forest. 3.2. Meteorological data Data from two different synoptic weather stations were used in this study. Data from Såtenäs Airport (58 26 N, 12 42 E) during the period January March 1996, were used for analysis of day-time situations, while for night-time situations data from Jönköping Airport (57 46 N, 14 05 E) were used during the same period. The reason for using different weather stations was that synoptic data should be observed as close to the field stations as possible, varying from a few kilometres to a maximum of 80 km in the regional analysis. At both Såtenäs and Jönköping airports, cloud cover and wind speed are recorded every third hour. Cloud observation at 13:00 h, when the maximum temperature difference between sun-exposed and screened stations occurs, was used for the day-time study. The night-time cloud cover, on the other hand, is calculated as a mean value from the observations at 22:00 h, 01:00 h, 04:00 h and 07:00 h. In the study of cloudy situations, nights with a cloud cover of 7 and 8 octas were analysed. Nighttime synoptic weather observations at 01:00 h during the period January March 1996 and 1997 were taken from the synoptic station at Jönköping Airport. A criterion for selection of cloudy nights was that the cloud cover observation at 22:00 h and 01:00 h should not be less than 7 octas, in order to exclude weather changes during the studied period. For the regional study, cloud cover prognoses for

LOCAL TEMPERATURES AND WEATHER PARAMETERS 159 Table II. (a) Station description and (b) instrument specifications for the measuring equipment used at the RWIS stations (a) Station no. Wind sensor Extra surface temperature sensor Altitude Characteristics a (m.a.s.l.) The county of Jönköping 601 Y Y 190 Open and wind-exposed. Close to lake Vättern. 602 Y 195 Local low-point close to lake Vättern. High slope on the eastern side that screens the station during the morning. 603 130 Open and wind-exposed. Close to lake Vättern. Screened by topography during the morning. 604 255 Local low-point. Close to deciduous trees. 605 220 Open and wind-exposed. 607 200 Close to a small lake. Screened during the afternoon by terrain and trees. 609 220 Local low-point/bog surrounded by deciduous trees. 611 Y 250 Hilly site surrounded by road cuts and some trees. 612 Y 90 Relatively open terrain with scattered bushes and low deciduous trees. In the lower part of a large hill close to the bridge over the Taberg river. 613 230 On a hilltop surrounded by medium high coniferous trees. 614 235 Local low-point surrounded by high forest. 615 Y 225 Open on the southern side and medium high coniferous forest on the northern side of the station. Situated in a small valley. 616 Y 220 Hilly site. Open on the eastern side and medium high forest on the western side of the station. 617 Y 135 Open and wind-exposed. Arable fields on both sides of the station. 618 260 Height surrounded by road walls. Close to lake Vättern. 627 225 Hilly site surrounded by road cut and high coniferous forest. 628 182 Hilly site in forest. 629 Y 180 Hilly site in forest. 630 155 Local low-point in forest. 631 185 Open. 632 Y 145 Open, surrounded by arable land. 633 Y 165 Flat terrain in forest/bog. 634 Y 230 Wind-exposed and open at a hill crest. 635 183 Relatively open with scattered bushes and trees. Hill crest in medium high forest. 636 250 638 Y 225 Open and hilly site. 639 Y Y 225 Open in flat terrain/bog. 640 245 Close to hill crest surrounded by topography and forest. 641 305 Close to hill crest surrounded by topography and forest. 642 255 Hill crest surrounded by deciduous trees. 643 245 Close to hill crest surrounded by forest. 644 250 Local low-point in forest. The county of Skaraborg 1601 Y Y 95 Open and wind-exposed. The extra temperature probe located in a road rock cut. The county of A lvsborg 1512 Y 330 Open and hilly site. 1513 Y 295 Open and in the bottom of a large valley.

160 J. BOGREN ET AL. Table II. (continued) (b) Variable Level Range and distant constant Accuracy Sampling Instrument frequency (min) Air temperature Road surface temperature 2.0 m Lowered 2 mm in the asphalt top layer Range 60 to 70 C Range 60 to 70 C 0.3 C 0.3 C (sensor) 30 Lambrecht 8091100 30 Pt100, DIN 43760 K1A Wind speed 5.0 m Distance constant 1.5 m, 10 m/s 0.1 m/s, 30 Vaisala, WAA 15 threshold 0.4 m/s and 10 75 m/s 2% range 0.4 75 m/s Wind direction 5.0 m Distance constant 1.0 m, 2.8 30 Vaisala, WAV 15 threshold 0.3 m/s and range 0 360 (wind speed 0.3 75 m/s) a The immediate vicinity of the field stations effecting the local climate were determined from observations in field and from topographical maps. Y=yes, there is an existing sensor.

LOCAL TEMPERATURES AND WEATHER PARAMETERS 161 each field station were also used to ensure that the cloud cover was the same in the entire county. The cloud cover prognoses are developed by The Swedish Meteorological Office through analysis of satellite images. The homogeneity of the cloud cover was also verified with the use of synoptic weather charts. The wind speed records were taken from Jönköping Airport at a height of 10 m. 4. EMPIRICAL APPROACH 4.1. Day-time temperature ariations During day-time, incoming solar radiation is the most important component in determining the potential for temperature variations. Occurrence of clouds means that direct radiation is reduced and diffuse radiation increases, thus leading to a reduction of the temperature difference between sun-exposed and screened areas. As previously discussed, the influence of clouds on the short-wave incoming radiation can be calculated using Equation (4). Using this formula with the constants (b1= 0.75 and b2=3.4) means that for day conditions the cloud amount can be divided into three classes for practical use: (i) 1 3 octas; (ii) 4 6 octas; and (iii) 7 8 octas according to the size of the T s(sun) T s(screened). To perform a verification of the theoretical approach, an empirical study was carried out with observations of the road surface temperature differences during various cloud conditions. From the empirical study of station 1601 during different cloud conditions and various solar elevations, the calculated temperature variations can be compared with the observations (Table III). As can be seen from Table III, it is obvious that there are large variations in the screening effect due to variations in cloud amount as well as alteration of solar elevation. This study shows that the screening effect can be very pronounced even if the amount of cloud is 4 6 octas. During the end of February, the screening effect is larger with 4 6 octas than during the cloudless period in January. From the observations the T s(sun) T s(screened) amounts to 3.2 C on 15 February compared with 1.9 C in January for clear conditions. At the end of the winter season, at 58 N the observed effect of screening on the T s amounts to 11 C in cloud class (i), 9.6 C for class (ii) and 4.2 C for class (iii). The absolute difference between observed and calculated T s(sun) T s(screened) is less than 1.0 C for the mid winter period. The magnitude for all cloudiness classes ranges between 0.3 and 0.8 C. In the middle of February the divergence between calculated and observed T s(sun) T s(screened) increases markedly, with an absolute difference amounting to 2.6 C for 1 3 octas and 2.5 C for 4 6 octas at the beginning of March. The cloudy conditions (7 8 octas), on the other hand, have a difference of 1.2 C in mid February but well below 1 C in mid winter and in March. One possible explanation for the divergence at the end of the winter season for classes (i) and (ii) is that during that time of the year, the solar elevation reaches a height where a small variation in cloudiness has a large effect on the T s(sun) T s(screened). The distance between the synoptic station where the cloud observations are made and field station 1601 is approximately 30 km, which means cloudiness can differ slightly between the locations. When considering broken cloud cover, the way in which the different cloud elements are distributed in the sky, as well as the cloud type present, is of great importance. 4.2. Night-time temperature ariations Regarding night-time temperature differences, two major factors can be distinguished: topography and weather. These factors, to a high degree, control the possible development of temperature differences. During clear, calm nights the differences in air and surface temperature between valley bottoms and summits can be related to such factors as width and depth of valleys and catchment area for cold air drainage (Bogren and Gustavsson, 1991). These factors have a large influence on the intensity of cold air pools. However, Bogren and Gustavsson (1991) also clearly showed that wind speed was a very important factor for controlling the establishment of possible cold air pools. It was concluded that the prevailing wind speed must be below 2 3 m/s before any large variations could be developed in a weakly undulating terrain.

162 J. BOGREN ET AL. Table III. Observed and calculated temperature differences between sun-exposed and screened sites during different cloud conditions and various solar elevations Date Solar Calculated Class (i) Class (ii) Class (iii) elevation ( ) T s(sun) T s(screened) clear Observed Calculated Absolute Observed Calculated Absolute Observed Calculated Absolute T s(sun) T s(sun) difference T s(sun) T s(sun) difference T s(sun) T s(sun) difference T s(screened) T s(screened) T s(screened) T s(screened) T s(screened) T s(screened) 1 3 octas 1 3 octas 4 6 octas 4 6 octas 7 8 octas 7 8 octas 1 January 9 1.4 0.5 1.3 0.8 1.0 0 0.3 0.3 15 January 11 2.4 1.9 2.2 0.3 1.7 0.1 0.6 0.5 1 February 15 4.5 3.9 4.3 0.4 1.6 3.2 1.6 0 1.1 1.1 15 February 20 6.5 4.5 6.2 1.7 3.2 4.6 1.4 0.4 1.6 1.2 1 March 25 8.8 5.8 8.4 2.6 3.8 6.3 2.5 1.7 2.3 0.6 15 March 30 11.1 8.7 10.6 1.9 5.8 7.9 2.1 2.2 2.7 0.5 1 April 35 13.4 11 12.8 1.8 9.6 9.6 0 4.2 3.3 0.9

LOCAL TEMPERATURES AND WEATHER PARAMETERS 163 The difference in minimum night-time air temperature between stations 1512 (hilltop) and 1513 (valley) for 88 different nights have been plotted versus the mean night-time wind speed at station 1512 (Figure 6). The data clearly show that wind speed is an important factor in the development of air temperature differences. Plotted differences in air temperature (T a(top) T a(bottom) ) support the conclusion of Bogren and Gustavsson (1991) that a wind speed above 2 m/s prevents the development of large air temperature differences. However, this relation is only valid for open, wind-exposed valleys. Studies by Gustavsson (1995) and Gustavsson et al. (1998) have demonstrated the importance of wind shelter for the development of a diversified temperature pattern. Gustavsson (1995) demonstrated that air temperature differences of more than 8 C could be established during clear nights with a wind speed in the interval 1 3 m/s between wind-exposed and wind-sheltered locations. Such a weak wind can prevent a large temperature drop during night-time in wind-exposed locations by vertical mixing of the surface cooled air with the warmer air above. Wind shelters, in the form of valley sides and trees, have both been shown to have the same effect. The influence of clouds on temperature differences has been examined using the night-time mean cloud cover data from the synoptic weather station at Jönköping, together with calculated air temperature differences between stations 1512 (hilltop), 1513 (large valley) and 615 (small valley). Only nights with a wind speed below 0.5 m/s have been used in order to exclude the influence from that factor. The differences in minimum night-time air temperature between the wind-exposed hilltop station versus the valley stations in relation to cloud cover have been plotted in Figure 7. Station 1513 is represented by a circle and station 615 by a triangle. Polynomial functions have been fitted to the data, which give a correlation of 0.72 for station 1513, and 0.79 for station 615. This indicates that cloud cover has a significant influence on the temperature difference between the examined stations. In Figure 7 the theoretical curves from Figure 4 have also been included (solid line), based on the mean temperature difference between the valley stations and the hilltop during clear nights, i.e. station 1513: C=9.7 C; station 615: C=7.3 C. There is a large resemblance between the theoretical and empirical curves in Figure 7. This result shows that it is possible to calculate the reduction of air temperature variation between different topographical sitings by the use of the maximum air temperature difference developed during clear and calm nights. 4.3. Temperature ariations during cloudy nights The frequency analysis of different weather situations in the theoretical part of the study showed that cloudy weather is the most frequent weather condition in the studied area. According to theory, air temperature decreases with increasing altitude during neutral stability, which is most frequent during Figure 6. Observed difference in minimum night-time air temperature between hilltop (1512) and valley (1513) for 88 nights versus mean night-time wind speed

164 J. BOGREN ET AL. Figure 7. The difference in minimum night-time air temperature between the wind-exposed hilltop and two valley stations (large and medium size valleys) in relation to cloud cover (octas). The solid curves represent the theoretical values and the dashed curves the empirical values cloudy, windy weather. The surface temperature is, to a large degree, influenced by air temperature but can also be affected by other factors. The importance of these factors can be analysed from the differences in temperature decrease with altitude between air and surface. In order to study the air and surface temperature decrease with altitude during cloudy, windy situations on a regional scale, all 32 stations in the county of Jönköping have been analysed in relation to altitude above sea level. Situations with cloud cover of 8 octas in the entire county, and a regional wind speed above 5 m/s atjönköping Airport, have been selected. The criterion of wind speeds above 5 m/s has been chosen as good mixing of air is essential for reducing local effects. This is necessary for isolating the effect of altitude on air and road surface temperature. To assure the homogeneity of the cloud cover in the county, the cloud cover prognoses at each station have also been used together with weather charts. Twelve occasions met the selection criterion and were used in the study. Temperature decrease with altitude and the correlation between temperature and altitude for both air and road surface were calculated for these days, and the results are presented in Table IV. Table IV. Correlation coefficient and temperature decrease with altitude for the linear regression between temperature and altitude during the 12 studied nights Date Correlation coefficient, R 2 for air Correlation Air temperature Surface temperature coefficient, decrease with decrease with R 2 for surface altitude ( C/100 m) altitude ( C/100 m) 11 February 1996 0.84 0.43 1.00 0.70 17 February 1996 0.62 0.48 0.79 0.91 25 February 1996 0.58 0.65 1.07 1.27 13 January 1997 0.39 0.38 0.77 1.17 14 January 1997 0.18 0.20 0.65 0.76 4 February 1997 0.04 0.48 0.22 0.89 7 February 1997 0.69 0.42 0.75 0.89 21 February 1997 0.61 0.49 0.87 1.11 24 February 1997 0.35 0.41 0.77 1.06 25 February 1997 0.63 0.32 0.94 0.59 26 February 1997 0.67 0.41 0.98 0.69 1 March 1997 0.38 0.33 0.67 0.33

LOCAL TEMPERATURES AND WEATHER PARAMETERS 165 Air temperature has a high correlation with altitude during 7 of the selected 12 days. Surface temperature on the contrary has a lower correlation with altitude during 6 of these 7 days (Table IV). The lower correlation for the surface temperature is a result of the larger scatter around the calculated straight line. To investigate why surface temperatures vary more than air temperatures, the residuals from the linear regression between temperature and altitude for each of the 32 stations were analysed during the selected 12 days. In Figure 8, the mean residual from the linear regression between temperature and altitude for the 12 days versus station number are plotted (there are no stations between 619 and 626, which explains why there are no points in this interval). The dashed line represents the accuracy of the instruments. As seen in Figure 8, several stations have surface temperatures that differ from the linear regression line. These differences are also larger than the accuracy of the instruments. For stations 602 and 607, both air and surface temperature differ in the same direction but for the other stations only surface temperature differs. These deviations influence the correlation between surface temperature and altitude. Since the correlation is a useful criterion for climate modelling during cloudy conditions, it is important to understand why some stations differ from the theoretical value. In order to study the differences between air and surface temperature decrease with altitude, four field stations close to the synoptic station at Jönköping Airport were selected to provide a more comprehensive set of data. In the analysis, 66 cloudy nights were included, representing the period January March 1996 and 1997. The cloud cover during these nights was 7 and 8 octas and the wind speed varied between 0 and 16 m/s. Frequency analyses of the calculated temperature decrease with altitude, divided into 0.2 C/100 m intervals, were performed in order to identify the most frequent stability during cloudy conditions for both air and surface. The results show that in 39.4% of the occasions had air temperature decrease with altitude in the interval 0.8 to 1.0 C/100 m. The second most frequent temperature decrease interval is 0.8 to 0.6 C/100 m, with 25.8% (see Figure 9(a)). This means that neutral stability is the most frequent for air temperature. The same temperature decrease with altitude is also frequently found for surface temperature, with a frequency of 28.8%. However, the frequencies are more evenly distributed between the classes than for air temperature (see Figure 9(b)). A greater part of the road surface has a higher temperature decrease with altitude than the air temperature. Two weather parameters which can affect the wide range in surface temperature decrease with altitude during cloudy situations are (i) wind speed and (ii) day-time heating. Wind speed can influence the degree of mixing of the air in the atmosphere. In Figure 10 the surface temperature decrease with altitude versus wind speed at Jönköping Airport is plotted, and it can be Figure 8. Mean residuals from the linear regression between temperature and altitude versus station number. The mean is calculated with the selected 12 days. The dashed line represents the accuracy of the instrument

166 J. BOGREN ET AL. Figure 9. Frequency distribution (%) of (a) air and (b) road surface temperature decrease with altitude (T a(d) and T s(d), respectively) during cloudy conditions concluded that wind speed is not a very important factor. The surface temperature decrease with altitude does not show any strong correlation with variation in wind speed. Wind speed data from Jönköping Airport has been used in the analysis, and this can vary from the wind speed at the field stations. The same result is, however, also found when wind speed from the exposed station 617 is used. Day-time heating is one factor that can explain the higher temperature decrease with altitude for the road surface. The road surface has a greater thermal inertia compared to the air, and the surface temperature is affected by the type of weather preceding the time of measurement (Gustavsson, 1991). Wood et al. (1998) propose that the delay in cooling of the road surface compared to the air after cloud clearance and cold advection is due to a balance between the effects of cloud cover, surface wetness, wind speed and local topography. This time lag of the road surface means that day-time heating, i.e. insolation received during day-time, can influence the night-time surface temperature. This ought to be most important effect at the end of the winter season, where large surface temperature differences can be established between sun-exposed and screened stations (Bogren, 1991; Bogren et al., 1998). In order to investigate whether short-wave radiation during day-time influences the night-time surface temperature, air and road surface temperature variations at 01:00 h were compared for 2 nights in

LOCAL TEMPERATURES AND WEATHER PARAMETERS 167 Figure 10. Road surface temperature decrease with altitude (T s(d) ) versus wind speed during 66 cloudy nights, January March 1996 and 1997 Figure 11. Air and road surface temperature differences (T a(difference) and T s(difference) ) versus altitude above sea level: (a b) 17 March 1996 at 01:00 h; (c d) 23 March 1996 at 01:00 h. The differences are calculated between each field station and the reference station (605) March at 31 field stations. From 16 to 17 March the cloud cover was 8 octas during the entire day and the wind speed varied between 3 and 5 m/s. On the contrary, from 22 to 23 March the cloud cover was 1 octa during the observations at 13:00 h and 16:00 h, and after that the cloud cover increased to 8 octas

168 J. BOGREN ET AL. for the observations at 22:00 h and 01:00 h. Wind speed varied between 2 and 5 m/s. In Figure 11(a d), the air and surface temperature differences (T a(difference) and T s(difference) ) between the reference station (605) and each station versus altitude above sea level are plotted for the 2 nights. During 17 March 1996, air temperature differences show a strong correlation with altitude (R 2 =0.84) and the temperature decrease with altitude is 1.2 C/100 m (Figure 11(a)). Surface temperature differences are more scattered around the line but still have a high correlation with altitude (R 2 =0.70), and the decrease with altitude is 1.2 C/100 m (Figure 11(b)). During 23 March 1996, after it had been clear during the preceding day, the air temperature differences also show high correlation with altitude (Figure 11(c)), but surface temperature differences have low correlation with altitude (Figure 11(d)). Ten of the 13 stations that are colder than the linear regression line of 23 March are screened during midday and the afternoon. This clearly indicates that the day-time insolation is a factor that influences the night-time surface temperature. Future studies are planned to further investigate this matter. Variations in the roadbed material can also lead to differences in surface temperature. This is caused by the thermal properties of the material. Several studies (Gustafson, 1981; Gustavsson, 1991; Gustavsson and Bogren, 1991) discuss the importance of thermal conductivity and the thermal capacity of the construction material for road surface temperature. These properties influence the reaction time of the drop in temperature between different sites and may be one of the reasons why some of the stations in the county differ from the linear regression between temperature and altitude during cloudy and windy situations (Figure 8). Stations 607 and 639, which are colder than the linear regression line when all stations in the county are analysed (Figure 8), are both situated above a water-filled culvert in the road, which could explain the deviation. Placement of the surface temperature sensors in the road is also an important factor for the quality of the surface temperatures recorded at the stations. Since there is a large temperature gradient in both air and soil near the surface (Araya, 1988), the exact location of the sensor is important. A sensor that is located too deep will, for example, be warmer than a sensor at the surface during night-time. Stations 601, 602, 618, 631 and 634 are all significantly warmer than the linear regression line (see Figure 8), and field investigations have shown that these surface temperature sensors are all covered by asphalt or other material, i.e. not situated exactly at the surface as they ought to be. If the mentioned stations are excluded from the analysis, the correlation coefficient between surface temperature and altitude increases. In order to verify Equation (11) for the 66 selected cloudy nights, stations 612 and 616 have been used since they are situated relatively close to each other (20 km) and to the synoptic station at Jönköping Airport and they also have a large difference in altitude. Station 612 is open and station 616 has medium high forest on its western side. There is a difference of 130 m in altitude between the two stations. Station 612 has been used as a reference and the surface temperatures at station 616 have been calculated for the 66 nights; see Equation (12). T s(616) =T s(612) + h T s(d). (12) The sign of h is positive in this case since station 616 is located 130 m higher than the reference. The temperature decrease with altitude, T s(d), is calculated using stations 605, 612, 614 and 617 since they represent a range of different altitudes and are situated close to each other and to the synoptic weather station. When the measured surface temperatures are compared to the calculated surface temperatures at station 616, the result shows that it is almost a 1:1 relation and R 2 =0.97 (see Figure 12). Even if the correlation between surface temperature and altitude is lower when data from the entire county are studied, a verification of Equation (11) for the 12 analysed nights gave almost the same result as for the 66 studied days above, although the temperature decrease with altitude differs. When the calculated surface temperatures at station 616 were compared to measured surface temperatures, the resulting correlation coefficient was also high (R 2 =0.98).

LOCAL TEMPERATURES AND WEATHER PARAMETERS 169 Figure 12. Measured road surface temperature at station 616 versus calculated surface temperature for the analysed cloudy nights. Equation (12) is used to calculate the road surface temperature and station 612 is selected as the reference 5. CONCLUSIONS The results for clear day conditions showed that screening from the sun has a significant influence on the road surface temperature, even with cloudiness amounting to 4 6 octas, provided the solar elevation is high (end of February 20 ). The theoretical calculations compared with the observations show that it is possible and necessary to include a cloud-modified screening effect when modelling the road surface temperature along roads. The differences in temperature between exposed and screened road surfaces was as much as 5 C in the middle of March (4 6 octas). This result is very important since until now the screening effect has only been calculated for clear conditions. It is also seen that a more comprehensive set of data is needed to be able to use the empirical approach in assessing a model for practical use. Type of cloud and also the way in which the cloudiness is distributed could be a useful input in such a study. For night-time temperature differences, both the theoretical and empirical approaches showed that cloud cover and prevailing wind speed have a significant influence on the potential for temperature differences to develop. The two approaches also showed good resemblance. The conclusion is that topographical siting together with data on wind and clouds forms a very good base for calculation of temperature differences. The study of night-time temperature variations during cloudy conditions showed that neutral stability was the most frequent during cloudy situations, but that the variation in surface temperature decrease with altitude is larger and has a more even distribution than the air temperature decrease with altitude. Wind speed was not an important factor for the variation in surface temperature decrease with altitude. Insolation from the sun during day-time is, however, one parameter to consider. The location of surface temperature sensors in the road can also play a significant role in recording surface temperature. Another important factor is the type of road construction material. These are some factors which influence the relationship between surface temperature and altitude during cloudy and windy situations. If the calculated correlation coefficient for the area is used as a selection criterion in the model during cloudy conditions, it is necessary to use selected key stations in the county to be able to exclude the described effects. ACKNOWLEDGEMENTS The study presented was carried out with the financial support from the Swedish National Road Administration, Carl Trygger Foundation, Hierta-Retzius Foundation and Adlerbertska Foundation. We acknowledge Prof. S. Lindqvist for comments on a draft of the paper, S. Svensson for the drafting of figures and S. Cornell for the linguistic revision.