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A Model for Road Icing Forecast and Control

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A Model for Road Icing Forecast and Control IWAIS XIII, Andermatt, September 8 to 11, 2009 A Model for Road Icing Forecast and Control Dr. N. Bezrukova1, E. Stulov1 and Dr. M. Khalili2 1Central Aerological Observatory, Pervomayskaya 3, Dolgoprudny, Moscow Region, 141700 Russia Email: [email protected] , [email protected] 2 Moscow State University, Email:[email protected] Abstract— A technique is developed for diagnosing different meteorological forecasting and diagnosing block and the types of ice, frost and rime phenomena based on the classification development by M. Khalili of a chemical block governing de- of processes causing ice generation on the road. Based on this icing agents dispensing [3]. Additional introduction of the technique, an algorithm to forecast and diagnose glazed frost on chemical block extended the scope of application of the new the road has been worked out, which is realized as a model © program demonstrating the potentials of the technique involved. program complex ICE_2003 to include both conventional According to this technique, glazed frost on the road is forecasted road maintenance and use in automated systems of road icing in two steps. As a first step, the road temperature is forecasted forecast and control. with sufficient lead-time. Secondly, based on the forecasted road temperature values and some other parameters, a diagnostic analysis and forecast of glazed frost events are performed. II. ICE LOADING IN MOSCOW REGION The model program is run with a certain set of sensors available at an automated road weather station (RWS) and meant The cold season of Russia’s European part is characterized for automated information systems to prevent glazed frost by by atmospheric circulation processes leading to variable using de-icing liquid. weather, frequent air mass changes accompanied by A technical problem has been solved, enabling data scanning precipitation phase changes close to and below 0ºC. All this in an interface suitable for examination, and software has been makes ice loading and rime formation typical of cold season’s developed for personal computer to distinguish the corresponding weather in this area [1]. types of events in the course of road weather station measurements. Moscow area is characterized by the following types of ice The verification of the model during two winter seasons at two and rime phenomena, when slipperiness is generated: ice, automated road weather stations has demonstrated satisfactory glazed frost, atmospheric glazed ice, granular and crystalline results. The relatively faultless diagnoses of the events concerned rime, radiation rime frost, hoar-frost, packed snow. For motor amounted to 83% for the winter of 1996/1997 and 79% for the transport, such phenomena as glazed frost, atmospheric glazed winter of 1997/1998. ice, hoar-frost, and packed snow are the most dangerous ones. Ice coating (“black ice“) includes such events as freezing forecast model, diagnose of ice, de-icing liquid, Keywords: ‘wet’, freezing water covering (including water from melting road icing control snow), freezing rain or freezing drizzle, rainwater freezing on a cold surface, and freezing dew. Frost includes events such as frost due to radiative cooling, I. INTRODUCTION frost during periods with increasing air temperature, but still Current approaches to developing automated systems of cold road surface, frost due to warm advection, supercooled prophylactic road de-icing maintenance imply the availability fog water (granular rime), fog water, and freezing on a cold of an efficient model of slipperiness forecast optimized for a surface. particular area. The effort to reduce de-icing agents Snow includes wet snow and snow packed by traffic. consumption and thus to decrease the adverse effects on the The authors have fulfilled climatic zoning for the road environment induces the upgrading of the model employed. network of Moscow Region and the neighboring ones [1]. This paper presents a model for diagnosing and predicting road icing and packed snow formation based on measurements Based on the classification by types of ice, frost and rime of automated road weather stations (RWS). The first version phenomena, typical combinations of air temperature, humidity, of the model BEST-98 by N. Bezrukova and E. Stulov [2] was dew-point temperature, type and amount of precipitation, all of validated using measurements from automated RWS of the them responsible for the above diversity of ice formations and Swedish company Telub in the two winter seasons of slipperiness on the road, have been formalized. Model graphs 1996/1997 and 1997/1998 and upgraded in 1999 by the of the variation of parameters for basic types of ice/snow/hoar- © authors in the new version of BEST-2000 . In 2002 and frost deposition are plotted in Fig. 1, using long-term 2003, the model was modified and adjusted for the automated observational data on atmospheric ice deposition presented in road weather stations of the German Lufft Company. This the [7] and [9]. modification consisted in considerable improvement of the IWAIS XIII, Andermatt, September 8 to 11, 2009 4 3 Icing 2 1 Air temperature 0 Road temperature C t -1 Dew point -2 -3 -4 Wet Snow Rain -5 0 2 4 6 8 10 12 14 16 18 20 22 Time a 8 Icing by 6 frozen water 4 2 Air temperature C Road temperature t 0 Dew point -2 -4 -6 -8 0 2 4 6 8 10 12 14 16 18 20 22 Time b Fig. 1 a, b. Model graphs of the variation of parameters for basic types of ice when slipperiness is generated: snow deposition (a), frozen water (b) III. FORECAST MODEL OF DESCRIPTION For the 1st class road maintenance, a forecast must be issued 1 Russia is lacking a regular network of road weather to 1,5-2 hours in advance [8]. stations, although a certain progress has been made in its In lots of models, the moment of ice formation on the road development: the ring motor road around Moscow has been is predicted by road temperature (Tr) approaching dew-point equipped with seven stationary automated road weather (Td) within a hazardous range close to 0ºC. The critical stations. However, there is still no regular data exchange temperature difference T = Tr – Td is commonly taken to be between the stations of different subordination and prognostic 0.25 – 0.5ºC. This method, straightforward, but crude enough, divisions of the Weather Service. As a first step in developing leads to numerous false alerts and misses of slipperiness events the model, the authors had to solve the problem of road that are not associated with the above temperature difference, icing/slipperiness forecast by automated RWS measurements as in case of snowfalls. Apart from ice, the traction coefficient at a single point. This required forecasts timely enough for on road slopes is reduced on snow compacted by motor road treatment with de-icing agents using motor vehicles. vehicles at temperatures close to 0ºC. Note that the IWAIS XIII, Andermatt, September 8 to 11, 2009 contribution of snow to road slipperiness is especially The 1st step: road surface temperature is predicted by the pronounced on the European territory of Russia. A more developed schemes of road temperature forecast, only using reliable prediction of road state may build upon identification data from automated RWS sensors. The most important is the of typical ice loading characteristics of a given area, using prediction of road surface temperature change as the RWS measurement. Each type of ice and rime phenomena in temperature approaches the freezing point. Slipperiness forecast is produced in two steps. c 0 -2 -4 -6 Air temperature C Icing by freezing Road temperature t -8 Dew point -10 -12 Snow Freezing rain -14 0 2 4 6 8 10 12 14 16 18 20 22 Time d 4 2 0 -2 Air temperature -4 Road temperature Dew point -6 -8 Hoar frost -10 -12 0 2 4 6 8 10 12 14 16 18 20 22 Time Fig. 1 c, d. Model graphs of the variation of parameters for basic types of ice: freezing rain (c), frost (d) the model is considered as a complex of weather elements with Inertial forecast for a period from 30 min. to 2 hour is only certain values and a certain dynamics of the values variation. applied at a smooth temperature change resulting from warm The model ICE_2003 includes: /cold advection. With an abrupt change in this process when - a classification of typical models of ice events; the role of diurnal temperature variation increases, the - road surface temperature forecast; probability of errors within ranges close to extremes rises - a scheme of automated search and identification of such events sharply, the changes in the prognostic curve then falling in analyzing measurement data and their prognostic values; behind real measurements. The model allows for diurnal - a system of predicting road ice formation. temperature variation by distinguishing its two basic types IWAIS XIII, Andermatt, September 8 to 11, 2009 such as: advection type where time variation of approximate solution of the heat balance equation. However, meteorological elements is due to the air mass changing; air- additional sensors are required to measure wind speed and mass type where changes in meteorological elements occur direction, temperature at depths of 5 and 30 cm, radiation due to diurnal temperature variation. The advection type balance in the range of 0.3 – 40 , and atmospheric pressure. model is applied for inertial forecast. The data on RWS measurements presenting input data are For inertial road icing prediction 30 to 120 min. in advance, given in Table 1. RWS must have sensors to measure road surface and air nd temperatures, relative humidity, precipitation type and The 2 step: Based on prognostic road temperature intensity, and road surface state. To predict more reliably for values, a diagnosis of ice formation on road surface is over 1 hour in advance, the model allows the use of an produced.
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