外文翻譯---論準(zhǔn)時(shí)定制的冬季天氣信息系統(tǒng)與城市交通道路管理

1、<p><b>　　附錄 (英文)</b></p><p>　　Tailored and On-time Winter Weather Information for Road Traffic Management</p><p>　　Thomas Gerz, Arnold Tafferner, Shinju Park, Felix Keis1</p&

2、gt;<p>　　1 Institut für Physik der Atmosphäre, Deutsches Zentrum für Luft- und Raumfahrt, Oberpfaffenhofen, Germany;2Hydrometeorological Innovate Solutions S.L., Barcelona, Spain</p><p>

3、;　　E-mail: thomas.gerz@dlr.de</p><p><b>　　ABSTRACT</b></p><p>　　Recent developments are reported on techniques to determine the onset, duration, amount and type of precipitation as w

4、ell as the snow and icing conditions at the surface. The algorithms, still under development, will be used to forecast the weather in short to medium lead times, i.e. for the next 30 minutes up to a few hours (“nowcastin

5、g”). An algorithm aims at detecting potential areas of snow fall by combining reflectivity data of precipitation and surface temperature data from a numerical mod</p><p>　　Keywords: type and amount of precip

6、itation, nowcast, anticipating the weather </p><p>　　1 INTRODUCTION</p><p>　　Weather phenomena contribute to congestions, accidents and delays in all traffic modes. The road traffic in particula

7、r 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 t

8、ransportation (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.</p><p>　　But “weather” is not a technical p

9、roblem 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 atmosphere as

10、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. </p><p>　　To inform traffic participa

11、nts and traffic management centres in due time on (expected) adverse conditions, tailored and accurate meteorological information is required on short notice. This information must be integrated in the process of informa

12、tion distribution and decision making to allow for tactical as well as strategic decisions. The Institute of Atmospheric Physics of the Deutsches Zentrum für Luft- und Raumfahrt (DLR) in Oberpfaffenhofen, Germany, a

13、nd the company Hydrometeorological Inn</p><p>　　We demonstrate our logic, first developed for the aviation transport sector, to combine various meteorological parameters to simple, self-explaining weather ob

14、jects. Further we report on recent developments to determine the onset and duration of icing conditions at the surface. An algorithm aims at detecting potential areas of snow fall by combining scanning reflectivity data

15、of precipitation and surface temperature data from a numerical model as well as surface stations in high spatial resoluti</p><p>　　For forecasting winter weather conditions up to about 24 hours or more, one

16、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 surface pressure distribution or whether it w

17、ill 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 o</p><

18、p>　　However, in order to mitigate the impact of wintry weather conditions on airport operations more efficiently, the focus should be laid on short-term forecasting (termed “nowcasting”) these conditions. This compris

19、es the onset, duration and type of precipitation as rain, snow, freezing rain, or fog. DLR is developing a nowcasting system that provides users in aviation with 0 to 2 hour forecasts of these winter weather conditions[8

20、]. We argue that a similar system imbedded in the process of inform</p><p>　　2 WINTER WEATHER OBJECTS</p><p>　　A certain winter weather phenomenon, like e.g. freezing precipitation, can be thoug

21、ht 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

22、 type. With 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 ob</p><p>　　

23、? a vertical column of air consisting of several layers</p><p>　　? issued time</p><p>　　? valid time</p><p>　　? next update time</p><p>　　? layer description, e.g.: <

24、;/p><p>　　- Snow: upper and lower boundary with intensity: light, moderate, severe </p><p>　　- Rain: upper and lower boundary with intensity: light, moderate, severe</p><p>　　- Freezin

25、g rain: upper and lower boundary</p><p>　　- Freezing drizzle: upper and lower boundary</p><p>　　? surface conditions</p><p>　　? trends, e.g. intensity increasing, change to melting,

26、 etc.</p><p>　　Figure 1 sketches how weather parameters from various sources are combined by data fusion to a winter weather object, WWO (yellow cylinder), with different attributes in different layers. SYNO

27、P and automatic sensors (as from SWIS) allow determining surface conditions, in this example rain with temperature above zero. The temperature/humidity sounding can be provided from a numerical weather forecast model, ai

28、rcraft measured data (AMDAR), or constructed from both depending on data availability. R</p><p>　　For nowcasting icing & snow conditions at the surface one has to consider weather changes due to advectio

29、n of air with different characteristics and, especially demanding, possible changes resulting from precipitation and cloud physics processes which can occur within short time spans at the observation site. For capturing

30、both of these effects an approach is followed where WWOs are determined at the various observation sites around an airport or along motorways where data from SYNOP, radar and </p><p>　　3 DIAGNOSIS AND PROGNO

31、SIS WITH ADWICE</p><p>　　ADWICE stands for Advanced Diagnosis and Warning system for aircraft Icing Environments. Based on the expert system IIDA (Integrated Icing Diagnostic Algorithm) by NCAR/RAP [4], to d

32、etect and predict clouds and precipitation with super cooled liquid water, it has been designed at DLR in Oberpfaffenhofen in 2003 [6] and further developed by the German Meteorological Service (DWD) [3]. Its purpose is

33、the detection and forecast of areas with super cooled large droplets (SLD) and possible three dime</p><p>　　ADWICE provides a diagnostic procedure to analyse the current icing situation of the investigated p

34、art of the atmosphere. Figure 2 schematically illustrates the proceeding of the diagnostic part of ADWICE. The most important information is ground measurements received from observation sites. The current version of ADW

35、ICE uses observations of the present weather as well as the cloud amount and an estimation of the cloud base height. In combination with radar reflectivity measurements, these data </p><p>　　In the prognosti

36、c part of ADWICE solely the output of a numerical weather prediction model is used to forecast three dimensional areas with the possible occurrence of super-cooled droplets. The current version of ADWICE, which is operat

37、ionally used by DWD, is operating with the output of the COSMO-EU model. Twice a day hourly icing predictions up to 21 forecast hours are created. Figure 3 illustrates the operational processing. The prognostic algorithm

38、 is started at 03UTC on the basis of the 00U</p><p>　　We are about to modify ADWICE by using the local area, high resolution numerical weather prediction model COSMO-DE. It covers the areas of Germany, Switz

39、erland, Austria and parts of their neighbouring countries and has a horizontal resolution of 2.8 km. In contrast to the regional model COSMO-EU, COSMO-DE is able to explicitly simulate (large) convective processes. A maj

40、or change in the prognostic part will be the use of some directly derived convection parameters of the COMSO-DE model. Also the</p><p>　　4 NOWCAST OF POTENTIALSNOW FALL AREAS</p><p>　　To nowcast

41、 potential areas of snow fall in a region we utilize </p><p>　　? Synoptic (large scale) maps of 1000-500 hPa and 1000-850 hPa thickness providing the region of cold air obtained from METAR (standard hourly o

42、bservation) or numerical weather prediction (NWP) models</p><p>　　? Surface temperature below/above 0~2°C or/and wet-bulb temp less than 0°C based on NWP model outputs and observation</p>&l

43、t;p>　　? Volumetric radar reflectivity observation</p><p>　　? Snow depth measurement </p><p>　　? Soundings from radiosonde and aircraft measurements or/and numerical weather prediction models.

44、SIRWEC 2012, Helsinki, 23-25 May 2012 5</p><p>　　The Potential Snow Fall Area (PSA) algorithm is based on real-time hourly operational data, like regional model surface temperature, precipitation composite e

45、stimated from low-level radar scans, and surface observations. This allows that the output, a warning in terms of PSA, can be generated in real-time and at low-cost. Also, the output can be used in building more complica

46、ted algorithms of winter weather warnings based on various other sources (e.g., the ADWICE introduced in the previous sect</p><p>　　4.1 Data sources</p><p>　　The algorithm is constructed with da

47、ta available over Cataluña including 1) terrain height from DEM, 2) temperature from model, surface station, soundings, and 3) radar reflectivity. The deployment of the observational sources is shown in Figure 4 ove

48、rlaid on orography. More detail on each source is provided in the following points.</p><p>　　Figure 4. Data available around Barcelona over orography: Crosses indicate the location of CDV-Radar and Airport B

49、arcelona. Similarly, large diamond for AEMET surface stations, small diamond for SMC surface stations, and triangles for soundings.</p><p>　　Digital elevation model (DEM) data used here are from ASTER GDEM (

50、Advanced Space-borne Thermal Emission and Reflection Radiometer Global Digital Elevation Model1) which has a horizontal resolution of 1 arc-second, both in longitude and in latitude. This high-resolution terrain height i

51、s re-mapped with a grid spacing of 0.01°in the horizontal as shown in Figure 4 and used not only as the background of the output but also for the adjustment of atmospheric temperature in the vertical. </p>&l

52、t;p>　　Surface Stations: In real-time, surface station data (e.g., temperature [°C], relative humidity [%], and precipitation [mm/h]) at 2 m height were available every 10 minutes from La Agencia Estatal de Meteor

53、ología (AEMET) over the Iberian Peninsula as well as from the additional denser network of surface measurements of Servei Meteorològic de Cataluña (SMC) over Cataluña. However, for the selected case,

54、these were provided in hourly updated values. </p><p>　　Radar reflectivity: Real-time and quality checked radar reflectivity fields at 0.5° elevation angle are generated with SMC’s CDV operational C-ban

55、d radar. The algorithm takes instantaneous scans corresponding to the forecast time (e.g., 1 hour forecast) with 0.01° grid spacing.</p><p>　　Numerical weather prediction model: The model output for the

56、 surface is generated over the Iberian Peninsula by meteoblue AG, a private company that runs NMM (Non-hydrostatic Mesoscale Model [2]) in 13-km spatial resolution twice a day with 1-hour forecast up to 72 hours. </p&

57、gt;<p>　　4.2 Analysis of potential snow area</p><p>　　The PSA is determined mainly based on temperature (obtained from both model output and surface stations) and precipitation (obtained from radar re

58、flectivity and/or model output) at near-ground level. The algorithm interpolates each input field onto the common grid with 0.01° by 0.01° resolution and modifies the model temperature field by height adjustmen

59、ts and taking into account the measured temperatures. The spatial variability (or the structure) of the temperature is obtained from the model o</p><p>　　Figure 5: Potential Snow Areas (in yellowish filled-c

60、ontour) at 14:00 UTC 08 Mar 2010: a) model temperature only, b) after modification. Grey background is orography, diamonds are surface stations, and their colors correspond to temperature [°C] shown in the color Bar

61、. Bluish filled contour indicates reflectivity larger than 15 dBZ. </p><p>　　4.3 Forecast of potential snow area </p><p>　　The forecast update frequency depends on the update time of observation

62、al data, and the forecast lead-time depends on those of the model and the radar precipitation nowcasting. Four forecast strategies are proposed:</p><p>　　A. Model: It is initialized at 00 and 12 UTC and actu

63、alization is between 4 and 6 hour after initialization. In other words, for verification time at 00 UTC, a model run is chosen at lead-time 12 hour of the runs initialized previously at -12 UTC. On the other hand for ver

64、ification time at 06 UTC, the model run is chosen from the runs initialized at 00 UTC. </p><p>　　B. Model tendency (Mtendency): Tendency is referred to as the forward changes of model temperature in time (&#

65、176;C /hour). Although the model values may be wrong, their changes in time still represent a part of reality. Hence, extrapolation of a corrected initial condition can be performed using the computed tendency from initi

66、al time to each lead-time. Here, the station temperature is used as the initial value (forecast lead time zero; FLT0 hereafter).</p><p>　　C. Conditional merging (CM): This strategy assumes that the initial o

67、bservation persists in the future and only the forecasted spatial variability of the temperature field is used. This frozen reality assumption may work for short lead times (2 to 3 hours) because it reflects the reality

68、better than the model initialized 12~6 hours earlier. Of course, quick changes as in frontal passages would reduce the useable lead-time.</p><p>　　D. Relaxation: A weighting function is applied [1], where th

69、e observation data have a higher weight than the model data for short forecast lead times and vice versa for longer lead times.</p><p>　　Figure 6 shows an example of a temperature-forecast verification in te

70、rms of mean absolute error for the different model strategies. For this particular case, the conditional merging strategy C seems to be more accurate than using model only strategy A or model tendency strategy B. Up to n

71、ow, the algorithm has been tested with one event only. A long-term verification of temperature, precipitation and potential snow fall area nowcasts will be performed in the future.</p><p>　　5 CONCLUSIONS AND

72、 OUTLOOK</p><p>　　Algorithms to diagnose and nowcast snow fall and icing conditions in limited areas have been described. Providing end-users adequate and easy to understand ground level warnings for winter

73、precipitation at a local position or area is not an easy task. Besides the problem of understanding and modelling complex physical processes like icing, snow formation and precipitation to the ground, also the combinatio

74、n of data from different sources such as radar, satellite, and surface stations with model </p><p>　　6 REFERENCES</p><p>　　[1] Haiden, T., A. Kann, G. Pistotnik, K. Stadlbacher, and C. Wittmann,

75、 2009: Integrated Nowcasting through Comprehensive Analysis (INCA)—System description. ZAMG Rep., Zentralanstalt für Meteorologie und Geodynamik, Vienna, Austria, 60 pp. [Available online at http://www.zamg.ac.at/fi

76、x/INCA_system.pdf.]</p><p>　　[2] Janjic, Z. I. and J. P. Gerrtty, 2001: An alternative approach to nonhydrostatic modelling. Mon. Wea. Rev., 129, 1164-1178.</p><p>　　[3] Leifeld, C., 2004: Weite

77、rentwicklung des Nowcastingsystems ADWICE zur Erkennung vereisungsgefährdeter Lufträume. Berichte des Dt. Wetterdienstes, 224 </p><p>　　[4] McDonough, F., B. Bernstein, 1999: Combining satellite, r

78、adar and surface observations with model data to create a better aircraft icing diagnosis. 8th Conference on Aviation, Range and Aerospace Meteorology,10 – 15 Jan. 1999, AMS, Dallas, Texas, 467 – 471</p><p>

79、　　[5] Schraff, C., H. Reich, A. Rhodin, R. Potthast, U. Blahak, K.Stephan, Y. Zeng, D. Epperlein, D. Leuenberger, T. Weusthoff, M. Tsyrulnikov, V. Gorin, A. Iriza, M. Lazanowicz, L, Torrisi: 2011 COSMO Priority Project K

80、ENDA for Km-Scale Ensemble-Based Data Assimilation, 9th SRNWP Workshop on Nonhydrostatic Modelling, Bad Orb, 16 – 18 May 2011</p><p>　　[6] Tafferner, A., T. Hauf, C. Leifeld, T. Hafner, H. Leykauf, U. Voigt,

81、 2003: ADWICE – Advanced Diagnosis and Warning System for Aircraft Icing Environments. Wea. Forecasting, 18, 184 – 203</p><p>　　[7] Tafferner, A., Hagen, M., Keil, C., Zinner, T. and Volkert, H., 2008, Devel

82、opment and propagation of severe thunderstorms in the upper Danube catchment area: Towards an integrated nowcasting and forecasting system using real-time data and high-resolution simulations, Meteorology and Atmospheric

83、 Physics, 101, 211-227, DOI 10.1007/s00703-008-0322-7</p><p>　　[8] Tafferner A., Keis F. 2012: Nowcasting winter weather at Munich airport. In: The DLR Project Wetter & Fliegen, eds. T. Gerz & C. Sch

84、warz, Final Research Report DLR-FB 2012-02, 46-57.</p><p><b>　　附錄（翻譯）</b></p><p>　　論準(zhǔn)時(shí)定制的冬季天氣信息系統(tǒng)與城市交通道路管理</p><p><b>　　摘 要</b></p><p>　　最近，在技術(shù)上，

85、確定冬季開(kāi)始、持續(xù)時(shí)間、降水量、積雪和結(jié)冰條件等研究有了新的發(fā)展報(bào)告和進(jìn)程，而這個(gè)技術(shù)，還仍然處于開(kāi)發(fā)階段，將來(lái)能在短短的30分鐘到幾日小時(shí)內(nèi)用來(lái)預(yù)測(cè)在短期至中期的天氣情況。（即實(shí)時(shí)天氣報(bào)告）這種技術(shù)旨在從一個(gè)數(shù)值模型以及在高空間分辨率地面氣象站，檢測(cè)地區(qū)潛在的降雪反射率數(shù)據(jù)相結(jié)合的降水和地表溫度數(shù)據(jù)。另一方面用此技術(shù)結(jié)合分析測(cè)量結(jié)果從而預(yù)測(cè)天氣預(yù)報(bào)。（如：衡量飛機(jī)數(shù)據(jù)和偏振雷達(dá)數(shù)據(jù)）</p><p>　　關(guān) 鍵

86、 詞:類型和降水量 實(shí)時(shí)預(yù)測(cè) 預(yù)測(cè)天氣</p><p><b>　　一. 介紹</b></p><p>　　不好的天氣會(huì)導(dǎo)致交通堵塞，交通事故和交通延誤。道路交通尤其被不利的天氣如雪、冰、霧、雨、大風(fēng)所耽誤和影響。交通的發(fā)展使得交通更加容易受到不利天氣條件影響。如今，當(dāng)惡劣天氣時(shí)的反應(yīng)已經(jīng)發(fā)生或即將發(fā)生時(shí)，與交通運(yùn)輸有關(guān)的人和參與運(yùn)輸者(包括空運(yùn)的或地面)大部分的

87、時(shí)間才有受到影響。而未來(lái)的道路管理系統(tǒng)應(yīng)該主動(dòng)預(yù)見(jiàn)破壞性天氣元素和大大提前預(yù)報(bào)的尺度，以避免或減輕對(duì)交通流的影響。</p><p>　　然而“天氣”本身不是一個(gè)可以簡(jiǎn)單輕松地解決的技術(shù)問(wèn)題。天氣的預(yù)報(bào)是一個(gè)困難和復(fù)雜的任務(wù)，某些程度上會(huì)受到相當(dāng)大的限制。因此非常有必要盡快觀察和預(yù)測(cè)大氣的變化狀況，提高天氣預(yù)報(bào)的準(zhǔn)確程度。此外，措施的要求是能預(yù)測(cè)到“天氣”及其影響和減少這些影響對(duì)交通流及其管理的管理。</p&

88、gt;<p>　　交通參與者和交通管理中心在合適的時(shí)間預(yù)計(jì)不利條件通知大眾，為之作出明確的政策和準(zhǔn)確的氣象信息是必要的。這個(gè)信息必須整合過(guò)程中的信息分布和決策，也允許戰(zhàn)術(shù)和戰(zhàn)略決策。它的目標(biāo)是增加航空運(yùn)輸?shù)男屎桶踩?。許多發(fā)達(dá)的方法集中在地面交通方面，因此能很好地適應(yīng)其政策的實(shí)行，從而完善道路交通的管理。</p><p>　　我們首先建立其程序和系統(tǒng)的邏輯關(guān)系，對(duì)于最為發(fā)展完善的航空運(yùn)輸領(lǐng)域，結(jié)合不

89、同氣象的簡(jiǎn)單參數(shù)，使得天氣氣象不解自明。再進(jìn)一步，我們根據(jù)最近的報(bào)告進(jìn)展來(lái)確定結(jié)冰條件的發(fā)生和預(yù)計(jì)其持續(xù)時(shí)間。這個(gè)技術(shù)和算法通過(guò)結(jié)合掃描反射率數(shù)據(jù)的降水和地表溫度數(shù)據(jù)，從一個(gè)數(shù)值模型以及在高空分辨率(1公里)，旨在檢測(cè)潛在的地區(qū)的降雪水平。這技術(shù)結(jié)合分析測(cè)量(如流星數(shù)據(jù)衡量飛機(jī)和偏振雷達(dá)數(shù)據(jù))與預(yù)報(bào)天氣數(shù)值。</p><p>　　冬天的天氣條件下一個(gè)人可以依靠操作數(shù)值預(yù)報(bào)模型，預(yù)測(cè)未來(lái)24小時(shí)或以上的天氣環(huán)境。數(shù)

90、值模型已經(jīng)取得了令人矚目的進(jìn)步，在過(guò)去的幾年里，能夠在預(yù)測(cè)整體的天氣狀態(tài),如表面壓力分布或是否會(huì)下雨。然而對(duì)于冬天天氣現(xiàn)象的預(yù)測(cè)，如冰雨或細(xì)雨，或輕或重的降雪仍是一個(gè)要求技術(shù)進(jìn)一步提高的任務(wù)。這些現(xiàn)象造成微妙的關(guān)系的各種因素，如垂直分布的溫度和濕度、云量和類型、積雪、土壤水分和大氣氣溶膠的組成與再次影響云和降水過(guò)程。情況就變得更加復(fù)雜的時(shí)候，這些不穩(wěn)定的過(guò)程，會(huì)引發(fā)了大氣參數(shù)的微小變化，如是否在地面的溫度或通過(guò)一定深度的大氣層測(cè)量其溫度

91、略高于或低于0°C。為了更好地估計(jì)未來(lái)大氣狀態(tài)，總體模型比單一模型的運(yùn)行會(huì)給出更好的結(jié)果，結(jié)合到量的估計(jì)，如系統(tǒng)平均、傳播，其他人對(duì)于概率的估計(jì)可以用于先進(jìn)的技術(shù)應(yīng)用當(dāng)中。這里的輸出模型從德國(guó)一個(gè)整體興建大氣氣象服務(wù)，DWD，它在未來(lái)可以用于提供這個(gè)概率的預(yù)測(cè)信息。</p><p>　　然而，為了緩解寒冷的天氣條件的影響，使得機(jī)場(chǎng)運(yùn)作更有效率,其系統(tǒng)建設(shè)的重點(diǎn)應(yīng)該放在短期預(yù)測(cè)(稱為“短時(shí)預(yù)測(cè)”)這個(gè)方面

92、。這包括天氣現(xiàn)象的發(fā)生，持續(xù)時(shí)間和降水的類型，或雨、或雪、或凍雨、或霧。DLR系統(tǒng)是開(kāi)發(fā)一個(gè)短時(shí)預(yù)報(bào)系統(tǒng)，提供用戶在航空領(lǐng)域?qū)τ谖磥?lái)0到2小時(shí)，冬天的天氣條件的預(yù)測(cè)。我們認(rèn)為，一個(gè)類似的系統(tǒng)嵌入過(guò)程中的信息共享的協(xié)同決策也將有利于公路網(wǎng)絡(luò)的管理發(fā)展。</p><p>　　二. 關(guān)于冬季的天氣</p><p>　　一個(gè)特定的冬天的天氣現(xiàn)象，像如冷凍沉淀，可以認(rèn)為一定體積的空氣中觀察到這一現(xiàn)象

93、。不同的是，觀測(cè)適合于描述一個(gè)或其他屬性的現(xiàn)象，如表面溫度、降水類型。無(wú)疑與實(shí)際天氣現(xiàn)象可以更精確地確定從數(shù)據(jù)和各種傳感器相結(jié)合。因此，作為觀察對(duì)象，我們可以明智地認(rèn)為與某些天氣的固有屬性有密切的關(guān)系。我們的目的，一個(gè)冬天的天氣對(duì)象(WWO)在一個(gè)特定的有限區(qū)域，如機(jī)場(chǎng)或一個(gè)密集的高速公路網(wǎng)絡(luò)，可以定義通過(guò)以下參數(shù)來(lái)觀察:</p><p>　　一個(gè)由幾層組成的垂直的大氣層</p><p>

94、<b>　　發(fā)生時(shí)間</b></p><p><b>　　持續(xù)時(shí)間</b></p><p><b>　　下一次發(fā)生時(shí)間</b></p><p><b>　　級(jí)別的描述，如：</b></p><p>　　雪：上、下限邊界和強(qiáng)度的定義:輕、中度,嚴(yán)重</

95、p><p>　　雨：上、下限邊界和強(qiáng)度的定義:輕、中度,嚴(yán)重</p><p><b>　　冰雹：上、下限邊界</b></p><p>　　凍毛毛雨：上、下限邊界</p><p><b>　　地表狀況</b></p><p>　　趨勢(shì),如強(qiáng)度增加,改變?nèi)诨?等等</p>

96、<p>　　圖1表示天氣參數(shù)如何從各種來(lái)源相結(jié)合的數(shù)據(jù)融合到一個(gè)冬天不同的屬性在不同的層天氣對(duì)象的描述，WWO(黃色缸)。SYNOP和自動(dòng)傳感器(如從SWIS)允許確定表面條件，在這個(gè)例子中，雨水與溫度高于零。溫度/濕度探測(cè)可以提供一個(gè)數(shù)值天氣預(yù)報(bào)模型、飛機(jī)實(shí)測(cè)數(shù)據(jù)為它)，或由兩個(gè)取決于數(shù)據(jù)的可用性。雷達(dá)觀察降水高度和也能夠穿過(guò)云層確定水文氣象(旋光能力和相關(guān)技術(shù))。ADWICE -對(duì)于結(jié)冰環(huán)境先進(jìn)的診斷和預(yù)警系統(tǒng)-[6、

97、4)，使在地面連同試探的溫度和濕度和雷達(dá)測(cè)量，用天氣信息的報(bào)道來(lái)確定飛機(jī)飛行過(guò)程中飛機(jī)表面積冰威脅程度。ADWICE系統(tǒng)現(xiàn)在進(jìn)一步發(fā)展、擴(kuò)大到診斷和預(yù)測(cè)雪和結(jié)冰條件下。綜上所述，其派生的分析可以被壓縮到WWO示意圖，在圖1的右邊顯示作為一個(gè)黃色條狀。很明顯，這個(gè)對(duì)象有幾個(gè)不同的危害威脅。對(duì)于給定的情況下在一個(gè)沉淀云層之上，會(huì)有一個(gè)接近表層氣溫零上到高度H1，其中包含雨滴，第二個(gè)層從H1到H2,包含超冷卻水滴和相應(yīng)的潛在威脅。</p

98、><p>　　對(duì)于短時(shí)預(yù)測(cè)結(jié)冰&雪條件必須考慮天氣的變化，由于平流的空氣具有不同特點(diǎn)，可能造成改變降水和云層物理性質(zhì)，可在短的時(shí)間間隔時(shí)發(fā)生。這兩種效應(yīng)的方法用于采集天氣信息，WWOs決心在各種觀測(cè)地點(diǎn)，從SYNOP、雷達(dá)和SWIS站，應(yīng)用在機(jī)場(chǎng)或沿高速公路數(shù)據(jù)系統(tǒng)中，提供指導(dǎo)預(yù)期變化的位置信息。</p><p>　　三. ADWICE 的診斷和預(yù)測(cè)</p><p&

99、gt;　　ADWICE系統(tǒng)技術(shù)代表著它為飛機(jī)結(jié)冰環(huán)境先進(jìn)的診斷和預(yù)警?；趯ｉT系統(tǒng)IIDA (集成診斷算法)通過(guò)NCAR /RAP[4]，檢測(cè)和預(yù)測(cè)云層和具有超強(qiáng)的冷卻液體的降水，在2003年它在DLR 拜仁區(qū)被設(shè)計(jì)成功[6]并進(jìn)一步發(fā)展成熟，為德國(guó)氣象所服務(wù)(DWD)。它的目的是檢測(cè)和預(yù)測(cè)該地區(qū)的超級(jí)冷卻液體(SLD)和可能的結(jié)冰地區(qū)分別對(duì)飛機(jī)構(gòu)成的威脅。</p><p>　　ADWICE系統(tǒng)提供診斷程序來(lái)分析

100、當(dāng)前現(xiàn)狀，調(diào)查部分結(jié)冰的大氣層。圖2示意說(shuō)明ADWICE進(jìn)行診斷和預(yù)測(cè)的一部分。最重要的信息是地面測(cè)量觀測(cè)站點(diǎn)能夠接受到其相關(guān)信息。當(dāng)前狀態(tài)下的ADWICE系統(tǒng)，使用觀察現(xiàn)在的天氣以及云的價(jià)值估計(jì)其數(shù)值和云底高度。結(jié)合雷達(dá)反射率測(cè)量數(shù)據(jù)，這些數(shù)據(jù)是一定比觀測(cè)站點(diǎn)準(zhǔn)確的第一個(gè)預(yù)測(cè)和估計(jì)的信息。這些數(shù)據(jù)表明一個(gè)結(jié)冰的天氣情況，ADWICE系統(tǒng)搜索垂直擴(kuò)展可能的危險(xiǎn)地帶。根據(jù)觀察到的或模型預(yù)報(bào)資料的濕度和溫度以及一些派生的對(duì)流參數(shù)，像云底高度