外文翻译---论准时定制的冬季天气信息系统与城市交通道路管理

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1、附录 (英文 ) Tailored and On-time Winter Weather Information for Road Traffic Management Thomas Gerz, Arnold Tafferner, Shinju Park, Felix Keis1 1 Institut fr Physik der Atmosphre, Deutsches Zentrum fr Luft- und Raumfahrt, Oberpfaffenhofen, Germany；2Hydrometeorological Innovate Solutions S.L., Barcelona

2、, Spain E-mail: thomas.gerzdlr.de ABSTRACT Recent developments are reported on techniques to determine the onset, duration, amount and type of precipitation as well as the snow and icing conditions at the surface. The algorithms, still under development, will be used to forecast the weather in short

3、 to medium lead times, i.e. for the next 30 minutes up to a few hours (“nowcasting”). An algorithm aims at detecting potential areas of snow fall by combining reflectivity data of precipitation and surface temperature data from a numerical model as well as surface stations in high spatial resolution

4、. Another approach combines profiling measurements with numerical weather forecast products. (e.g., meteo data measured by aircraft and polarimetric radar data) Keywords: type and amount of precipitation, nowcast, anticipating the weather 1 INTRODUCTION Weather phenomena contribute to congestions, a

5、ccidents and delays in all traffic modes. The road traffic in particular is derogate by adverse weather like snow, ice, fog, rain, strong wind and wind gusts. Increasing traffic makes transportation even more vulnerable to adverse weather conditions. Today stakeholders and participants in transporta

6、tion (be it air-borne or ground-based) most of the time only react on adverse weather when the disruption has already happened or is just about to happen. Future road management systems should proactively anticipate disruptive weather elements and their time scales of minutes to days well in advance

7、 to avoid or to mitigate the impact upon the traffic flow. But “weather” is not a technical problem that can be simply solved. Predicting the weather is a difficult and complex task and only possible within certain limits. It is therefore necessary to observe and forecast the changing state of the a

8、tmosphere as precisely and as rapidly as possible. Moreover, measures are required that translate “weather” to “impact” and minimise those impacts on traffic flow and its management. To inform traffic participants and traffic management centres in due time on (expected) adverse conditions, tailored

9、and accurate meteorological information is required on short notice. This information must be integrated in the process of information distribution and decision making to allow for tactical as well as strategic decisions. The Institute of Atmospheric Physics of the Deutsches Zentrum fr Luft- und Rau

10、mfahrt (DLR) in Oberpfaffenhofen, Germany, and the company Hydrometeorological Innovative Solutions S.L. (HYDS) in Barcelona, Spain, develop a meteorological decision support system for aviation (MEDUSA) within the EUs People Programme, Industry-Academia Partnerships and Pathways, and DLRs Research

11、Activity “Weather Optimised Air Transportation”. Its goal is to augment safety and efficiency of air transportation. Many of the developed methods focus on the ground level and, therefore, can well be adapted and applied for weather dictated issues in road transportation, too. We demonstrate our log

12、ic, first developed for the aviation transport sector, to combine various meteorological parameters to simple, self-explaining weather objects. Further we report on recent developments to determine the onset and duration of icing conditions at the surface. An algorithm aims at detecting potential ar

13、eas of snow fall by combining scanning reflectivity data of precipitation and surface temperature data from a numerical model as well as surface stations in high spatial resolution (1 km). Another approach combines profiling measurements (e.g., meteo data measured by aircraft and polarimetric radar

14、data) with numerical weather forecast products. For forecasting winter weather conditions up to about 24 hours or more, one can rely on operational numerical forecast models. Numerical models have made remarkable progress during the last few years in forecasting the overall weather state, e.g. the s

15、urface pressure distribution or whether it will rain or not. The forecast of winter weather phenomena, however, like freezing rain or drizzle, or light or heavy snow fall is still a demanding task. These phenomena result from the subtle interplay of various factors, like the vertical distribution of

16、 temperature and humidity, cloud cover and type, snow cover, soil moisture and the composition of the atmosphere with aerosols which again influence cloud and precipitation processes. The situation gets even more complicated as these processes result from instabilities which are triggered by small c

17、hanges in the atmospheric parameters, e.g. whether the temperature at the ground or through a certain depth of the atmosphere is slightly above or below 0 C. In order to better estimate the future atmospheric state ensemble models give better guidance than a single model run. Combined quantities lik

18、e ensemble mean, spread and others allow probabilities to be estimated which can be used for advanced planning. Here output of the KENDA ensemble model from the German Meteorological Service, DWD, can be used in future to provide this probability information. However, in order to mitigate the impact

19、 of wintry weather conditions on airport operations more efficiently, the focus should be laid on short-term forecasting (termed “nowcasting”) these conditions. This comprises the onset, duration and type of precipitation as rain, snow, freezing rain, or fog. DLR is developing a nowcasting system th

20、at provides users in aviation with 0 to 2 hour forecasts of these winter weather conditions8. We argue that a similar system imbedded in the process of information sharing for collaborative decision making would also be beneficial for operations on road networks. 2 WINTER WEATHER OBJECTS A certain w

21、inter weather phenomenon, like e.g. freezing precipitation, can be thought of a certain volume of air within which this phenomenon can be observed. Various observations are suited for describing one or the other attribute of that phenomenon, as e.g. the surface temperature, the precipitation type. W

22、ith no doubt the actual weather phenomenon can be determined more precisely when data from various sensors are combined 7. It is therefore advisable to think of such volumes as weather objects with certain inherent attributes. For our purposes, a winter weather object (WWO) in a certain limited area, e.g. an airport or a dense motorway network, can be defined through the following parameters: a vertical column of air consisting of several layers issued time valid time next update time layer description, e.g.: