Validation of a present weather observation method for driving rain. mapping

Transcription

1 Validation of a present weather observation method for driving rain mapping James P. Rydock a,b a Department of Civil and Transport Engineering, Norwegian University of Science and Technology (NTNU), Høgskoleringen 7A, NO-7491 Trondheim, Norway b Norwegian Building Research Institute (NBI), Høgskoleringen 7B, NO-7491 Trondheim, Norway Abstract Over the past decade, the method of choice for calculating driving rain loads from weather data has been to use long series of hourly wind and rainfall data, preferably 2 or years. In an earlier article we presented an alternative method for driving rain mapping in areas where long records of hourly weather data are not available, for example in Norway. In this method, annual directional driving rain loads are calculated based on present weather observations and knowledge of average annual rainfall. In this paper we compare the two methods using data from three weather stations in Great Britain from the 2-year period The results suggest that the present weather observation method yields comparable results to the hourly wind and rainfall method, and therefore can be used as a substitute in areas where sufficiently long series of hourly values of wind and rainfall amounts are not currently available. KEYWORDS Driving rain; Weather observations; Building enclosure performance 1

2 1. Introduction Over the past decade, the method of choice for building engineers to calculate driving rain loads from weather data has been to use long series of hourly wind and rainfall data [1] [2], preferably 2 or years. This is naturally because use of hourly wind and rainfall data is most logically consistent with the evolution over the past 5 years, starting with the work of Hoppestad in the mid 195 s [3] and Lacy in the 196 s [4] [5], of the semi-empirical derivation of a formula for calculating the impact of wind-driven rain on a vertical plane. This does not, however, preclude the possibility that similar, perhaps equally useful information about relative impacts of driving rain in different locations can be gleaned from other types of weather observations and statistics. In an earlier article [6], we presented an alternative method (referred to in this article as the present weather method ) for areas where long records of hourly weather data are not available, for example in Norway. In this method, annual directional driving rain loads are calculated based on present weather observations and knowledge of average annual rainfall. By using a multi-year data record, enough observations of wind-plus-rain events are obtained to yield a representative picture of the relative frequency of wind-driven rain from different directions at a weather observing station. Normalizing the frequency distribution by the annual rainfall amount at a station and summing directional driving rain contributions form directions with wind blowing against a wall having a specific orientation then allows for quantitative comparison of the annual directional driving rain load for walls near different stations. In this paper, the author compares the two methods using hourly weather data from three stations in Great Britain for the 2-year period

3 2. Methodology and results Synoptic observations from weather stations typically include the wind speed and direction at the time of observation as well as a numerical code (standardized by the World Meteorological Organization [7]) describing the state of the weather at the time of the observations. Each observation can therefore be viewed as a data point describing the instantaneous weather conditions at that location. A large number of instantaneous observations over time would be expected to yield a description of how the weather is, on average, at the location. For example, how often rain occurs with wind from a particular direction as opposed to another direction might be one relevant aspect in a description of the average weather at a station. Depending on the country and the station, synoptic observations can for example be made hourly, every three hours ( GMT, 3 GMT, 6 GMT, 9 GMT, etc.), four times daily (at GMT, 6 GMT, 12 GMT & 18 GMT) or three times daily (6 GMT, 12 GMT and 18 GMT). Records of synoptic observations go back to at least 196 for many stations. As the present-weather codes are numerical, records of observations from many years are easily analysed in spreadsheet programs. The strategy employed in the present weather method is quite simple. All observations in the analysis period that code for rain or rain showers are selected and grouped according to the wind direction (in increments) observed at the time of the rainfall event. The frequency distributions are converted into directional rainfall totals and then combined with average directional wind speeds into an annual driving rain value impacting a wall using the following formula. I =.26 v r cos( D θ ) θ D D D (1) 3

4 Where I is expressed in mm/yr,.26 is a conversion factor, v is the average annual wind θ D speed from direction D, r D is the average annual rainfall (in mm) with wind from direction D and D is the wind direction (angle from north). The summation is taken over all angles D representing a wind blowing against the wall (this includes the sector from θ - 8 to θ + 8 ). The corresponding annual driving rain loading for a wall at angle θ is given by the formula for the Airfield annual index in the ISO driving rain pre-standard [1]: vr cos( D θ ) 9 I A = N (2) Where I is also expressed in mm/yr, v is the hourly mean wind speed, r is the hourly A rainfall total, N is the number of years of data, where the summation is taken over all hours for which cos(d- θ) is positive. Though the formulas appear to be nearly identical, the two approaches are fundamentally different. Eqn. (1) uses angular distributions, determined by present weather observations, of annually averaged wind speeds and rainfall amounts, while Eqn. (2) uses hourly values of wind speed, wind direction and rainfall. Data from three observing stations with both hourly synoptic observations and hourly rainfall values over the 2-year period from are considered here. The stations are located at the airports of the cities of London (Heathrow airport), Manchester (Ringway airport, now Manchester International Airport) and Edinburgh (Turnhouse airport). Annual driving rain versus wall angle for the two methods at each of these stations are shown in Figure 1 a, b & c. The figure exhibits good agreement between the determination of driving rain from hourly wind and rain and the present weather method for London-Heathrow (Figure 1a), excellent agreement for Manchester-Ringway (Figure 1b) and fair agreement for 4

5 Edinburgh-Turnhouse (Figure 1c). For all stations, the locations of maxima and minima are nearly identical for the two methods. London-Heathrow and Manchester-Ringway both show a maximum at around 2 (from the southwest) and a minimum from the north (from about to for London and from about to 4 for Manchester). Edinburgh exhibits two maxima (at about 5-7 and ) and two minima (at about 14 and from ). Though the absolute maximum at Edinburgh-Turnhouse is centred at for both methods, the value at this angle is somewhat less for the hourly wind & rain method than predicted by the present weather method. Conversely, the secondary maximum centred at 6, determined from hourly wind and rain, is somewhat greater in amplitude than the value predicted by the present weather method. To explain this difference, we need to look at the relative distribution of moderate/heavy rainfall events versus the all rainfall events in the present weather observation data. Rain can be coded as light, moderate or heavy by a weather observer. For the present weather method, we give all rain observations equal weight in the summation of rainfall events that determines the angular distribution of wind plus rain events for calculating the annual driving rain amount versus wall angle at a station. This is in part because the moderate/heavy distribution is usually qualitatively similar to the distribution including all rain events. But it is also in part because in areas where only three or four observations per day are done (see below), there typically isn t a large enough sample of moderate or heavy observations to give a meaningful distribution. For example, the percentage of moderate or heavy observations is typically around 15% of the total number of observations coded as a rain event, and this is typically around 15% of the total number of observations. For 2 years of observations three times a day, we have would therefore expect approximately.15 x.15 x 2 x 365 x 3 = 493 observations of moderate or heavy rain distributed over 36 directions (36 in increments), or about 14 observations per direction 5

6 if the events were equally distributed around the compass. For stations that have a highly directional driving rain load (Bergen (in Norway), for example, in ref [6]), this can result in a distribution approximation suggesting some directions have a moderate or heavy driving rain frequency of zero. This is important particularly in areas like Bergen that also have a very high annual rainfall, as the present weather method is based on multiplying the directional frequency by the average annual rainfall to determine the driving rain load. Figures 2 a, b & c compare the angular distribution of moderate/heavy events versus all rainfall events for the three stations. For London-Heathrow and Manchester-Ringway, the Moderate/heavy distributions are qualitatively very similar to the distributions including all rainfall events. For Edinburgh-Turnhouse, on the other hand, there is an important qualitative difference: the Moderate/heavy distribution exhibits an absolute maximum at 5-7 while the all distribution has an absolute maximum at 24. What this means is that when rain is observed at Edinburgh-Turnhouse, the wind direction is most likely to be southwest, but when moderate or heavy rain is observed, the wind direction is most likely to be northeast. This implies that a driving rain estimation based solely on the angular distribution of present weather observations of all types of rain will underestimate the component from the northeast and overestimate the component form the southwest, exactly as is the case in Figure 1c. A question that could arise regarding the validity of the present weather method is to what extent observations taken as little as three or four times daily, always at the same time of day but over an interval of 2- years or more, give an accurate picture of the true angular distribution of wind plus rain events at a location. As long as the dominant precipitation events throughout the year do not arrive preferentially at particular times of day that are not sufficiently sampled by the method, for example between 18 GMT and 6 GMT for stations with 3 observations per day at 6 GMT, 12 GMT and 18 GMT, then the method should yield a reasonable picture of the annual driving rain distribution. Figure 3 a, b & c, which shows 6

7 present weather observation annual driving rain distributions for London, Manchester and Edinburgh based on one observation per hour, four observations per day and three observations per day for the 2 year period , suggests that number of daily observations matters little. For the 24/day results, all observations from the data set were included. For the 4/day results, only the wind plus rain observations from GMT, 6 GMT, 12 GMT and 18 GMT were included and for the 3/day results, only the observations form 6 GMT, 12 GMT and 18 GMT were included in the annual driving rain distribution calculations. The results at the three stations are virtually indistinguishable for the different synoptic observation frequencies. 3. Discussion Perhaps most surprisingly, the results presented above suggest that the present weather method can yield values that are not only qualitatively similar in terms of angular distributions of relative maxima and minima at and between stations, but also nearly quantitatively identical in some cases, and directly comparable to annual driving rain numbers obtained by summing hourly wind driven rain quantities impacting a wall. Where available, hourly rainfall totals and hourly mean wind speed and direction should probably be used to calculate driving rain loads, as this type of data gives the greatest flexibility for looking at both long and short term driving rain impacts, as well as average and extreme occurrences. Where hourly data are not available, however, it is likely that long records of synoptic observations of wind and weather can be obtained. In this case, the present weather method for determining average annual driving rain can be used to yield information about driving rain loads that appears to be comparable to average annual driving rain as determined from hourly wind and rain data, provided that the angular moderate/heavy distribution is qualitatively similar to the angular distribution from summing all rain with wind events at a station. In cases where the distributions are qualitatively different, the driving 7

8 rain profile from the present weather method could be adjusted to reflect this. As sufficiently long time series of hourly rainfall totals and hourly mean wind speed and direction become available from automatic weather stations installed at different times and locations in the years to come, driving rain mapping with the present weather observation method could be replaced, station-by-station, with the traditional method. In this way, driving rain maps can be created at present in areas, such as Norway, that do not currently have sufficient coverage in time or in space of hourly measurement data. Average annual driving rain yields a representation of the average moisture content of a wall from wind driven rain. Mapping of average annual driving rain would yield information about how the average moisture content of a wall facing a particular direction compares with the average wetness of walls facing other directions in the same area and walls facing all directions in other mapped areas. Average moisture content alone isn t the only quantity of interest with regard to driving rain. Maximum short-term intensity may be more important for risk of rain penetration through doors, windows and other openings in a façade. The amount of driving rain received in a spell of rain, defined as a period in which the risk of penetration through masonry increases upon passage of a weather system or a series of weather systems [1], may be more important for evaluating the risk of driving rain penetration through a masonry wall. Average moisture content, on the other hand, is likely to be a factor associated with risk of mould and algae growth and microbiological degradation and rot in vulnerable facades and façade materials. Availability of maps of average annual driving rain would therefore be useful for informing studies of moisture- related damage of building facades. In general, standardized maps across international borders will become increasingly important for ensuring quality in an open European market, where firms from other countries can compete on equal footing for building contracts with local firms. While local builders are likely to have knowledge about what is necessary to engineer a façade that can tolerate local 8

9 climatic conditions, foreign firms will clearly benefit from maps that yield detailed information about climatic stresses in areas or countries where they may not have worked previously. 4. Conclusion The results presented here suggest that the present weather method can yield driving rain information that is not only qualitatively similar in terms of angular distributions of relative annual average driving rain maxima and minima at and between stations, but also nearly quantitatively identical in some cases to annual driving rain numbers obtained by summing hourly wind driven rain quantities impacting a wall. This opens up the opportunity for creating driving rain maps in areas where hourly rain and wind data are not available that are directly comparable to driving rain maps determined from hourly wind and rain data. Acknowledgements The author would like to thank Chris Sanders of the Centre for Research on Indoor Climate and Health, Glasgow Caledonian University, for providing the hourly met. station data, and the UK Meteorological Office for allowing use of Crown copyright data in this paper. The author gratefully acknowledges financial support from the Norwegian University of Science and Technology, as well as partial funding from the Research Council of Norway within the ongoing NBI research & development programme Climate 2 Building constructions in a more severe climate (2 26), Strategic Institute Project Impact of climate change on the built environment. The author would also like to thank Kim Robert Lisø for comments on the text. 9

10 References [1] ISO/CD : Hygrothermal performance of buildings, Part 3: Calculation of a driving rain index for vertical surfaces from hourly wind and rain data. European Committee for Standardization 25 (under approval). [2] Fazio P., Mallidi S.R., Zhu D. A quantitative study for the measurement of driving rain exposure in the Montréal region. Building and Environment 1995;(1):1-11. [3] Hoppestad S. Slagregn i Norge (Driving rain in Norway, in Norwegian). NBI Report 13. Norwegian Building Research Institute, Oslo [4] Lacy R.E., Shellard H.C. An index of driving rain. The Meteorological Magazine 1962;91(8) July: [5] Lacy R.E. Driving rain maps and the onslaught of rain on buildings. Proceedings of RILEM/CIB symposium on moisture problems in buildings. Helsinki [6] Rydock J.P., Lisø, K.R., Førland E.J., Nore, K., Thue, J.V. A driving rain exposure index for Norway. In Press in Building and Environment. [7] WMO Publication 6, Manual on Codes, Volume I, International Codes. World Meteorological Organization, Geneva.

11 *Complete address of Corresponding Author: Dr. James P. Rydock Norwegian Building Research Institute Høgskoleringen 7B NO-7491 Trondheim Norway Fax: james.rydock@byggforsk.no 11

12 Figure captions Figure 1 Comparison of average annual driving rain on a wall versus wall angle using hourly wind and rain data and present weather observation method for the period for (a) London-Heathrow, (b) Manchester-Ringway and (c) Edinburgh-Turnhouse. Figure 2 Relative frequency of observations coded as moderate or heavy and observations coded as some form of rain vs. wind direction for the period at (a) London- Heathrow, (b) Manchester-Ringway and (c) Edinburgh-Turnhouse. Figure 3 Comparison of average annual driving rain on a wall versus wall angle from the present weather observation method using 24 observations per day, 4 observations per day and 3 observations per day for the period for (a) London-Heathrow, (b) Manchester-Ringway and (c) Edinburgh-Turnhouse. 12

13 London-Heathrow Hourly wind & rain Present weather method Drving rain (mm/yr) Manchester-Ringway Hourly wind & rain Present weather method Driving rain (mm/year) Edinburgh-Turnhouse Hourly wind & rain Present weather method Driving rain (mm/yr)

14 London-Heathrow Frequency (percent) Moderate/heavy all Manchester-Ringway Frequency (percent) Moderate/heavy all Edinburgh-Turnhouse Frequency (percent) Moderate/heavy all 14

15 London-Heathrow 24/day 4/day 3/day 4 Driving rain (mm/year) Manchester-Ringway 24/day 4/day 3/day 6 Driving rain (mm/year) Edinburgh-Turnhouse /day 4/day 3/day Driving rain (mm/yr)