The Distribution of Aircraft Icing Accretion in China—Preliminary Study

atmosphere

Article The Distribution of Aircraft Icing Accretion in China—Preliminary Study

Jinhu Wang 1,2,3,4,* , Binze Xie 1,* and Jiahan Cai 1 1 Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science and Technology (NUIST), Nanjing 210044, China; [email protected] 2 Key Laboratory of Middle Atmosphere and Global Environment Observation, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China 3 National Demonstration Center for Experimental Atmospheric Science and Environmental Meteorology Education, Nanjing University of Information Science and Technology, Nanjing 210044, China 4 Nanjing Xinda Institute of Safety and Emergency Management, Nanjing 210044, China * Correspondence: [email protected] (J.W.); [email protected] (B.X.); Tel.: +86-138-1451-2847 (J.W.) Received: 10 June 2020; Accepted: 11 August 2020; Published: 18 August 2020

Abstract: The icing environment is an important threat to aircraft flight safety. In this work, the icing index is calculated using linear interpolation and based on temperature and relative humidity (RH) curves obtained from radiosonde observations in China. The results show that: (1) there are obvious differences in icing index distribution in parameter over various climatic regions of China. The differences are reflected in duration, main altitude, and ice intensity. The reason for the differences is related to the temperature and humidity environment. (2) Before and after the summer rainfall process, there are obvious changes in the ice accretion index in the 4–6 km altitude area of Northeast China, and the areas with serious ice accretion are coincident with areas with large rainfall estimates. (3) In the process of snowfall in winter, the ground snow has an impact on the ice accumulation index in the east of China. When it is snowing, ice accumulation in low altitudes is serious. The results of this study offer a theoretical basis for prediction and early warning of aircraft icing.

Keywords: aviation ﬂight safety; icing index; interpolation; China’s icing climatic region; rainfall; snowfall

1. Introduction With the development of the civil aviation industry, a large number of aviation accidents have been caused by icing [1–3]. Aircraft icing has the following environmental characteristics: (1) the sky is mainly cloudy, and stratiform cloud easily induce icing or even serious icing [4]; (2) the main phase state of the condensate is liquid, and when an aircraft passes over an area containing super-cooled water droplets, the aircraft is prone to icing [5–13]; (3) a low temperature and high humidity environment is conducive to aircraft icing, with an external temperature generally between 15 C and 3 C, and the − ◦ − ◦ higher the humidity, the easier it is for. Approximately 75% of aircraft icing occurs when RH is greater than 70% [8,14,15]. Previous studies have shown that icing might change the shape of the wing surface and even affect its aerodynamic characteristics, leading to flight accidents [16–18]. Using the median volume diameter (MVD) to describe the distribution of liquid water content (LWC) at the cloud droplet scale can further describe the icing environment [19]. LWC can be obtained as a function of collection efficiency, MVD, integrated total droplet number concentration, true airspeed, and precipitation rate [20]. According to the “Part 25—Airworthiness Standards: Transport Category Airplanes” specified by the Federal Aviation Administration (FAA), appendix C specifies atmospheric

Atmosphere 2020, 11, 876; doi:10.3390/atmos11080876 www.mdpi.com/journal/atmosphere Atmosphere 2020, 11, 876 2 of 17 icing conditions and airframe ice accretions, and appendix O specifies super-cooled large droplets (SLD) icing conditions. In appendix C, the maximum continuous or intermittent intensity of atmospheric icing conditions are defined by the variables of the cloud LWC, the mean effective diameter of the cloud droplets, the ambient air temperature. In appendix O, SLD icing conditions are defined by the parameters of altitude, vertical and horizontal extent, temperature, liquid water content, and water mass distribution as a function of drop diameter distribution [21]. Civil Aviation Administration of China (CAAC) has made similar regulations. Review by Cao et al. [22] indicated that common causes of aircraft freezing include: the aircraft encounters clouds with super-cooled water droplets during flight, the surface of the aircraft has been contaminated before takeoff, or the aircraft encounters high concentrations of ice crystals during flight. The World Area Forecast Centers (WAFC) calculated an icing potential based on RH, where the cloud is present and the temperature is between 0 ◦C and 20 C, otherwise icing potential is set to 0. The forecasts from each WAFC are combined to produce − ◦ harmonized forecasts available to the global aviation community [23]. Although LWC and droplets MVD are directly related to icing in flight, however, the inversion of cloud microphysical characteristics is a complex problem, and the results of the algorithm are not easy to verify [20,24–30]. As far as current research is concerned, the research on icing numerical weather prediction for aviation operations is still immature [31]. The area with high humidity is more likely to appear super-cooled water, and RH is easy to be measured by remote sensing. Therefore, the ice accretion index is calculated according to temperature and RH, which represents the possibility of icing. Many researchers have proposed a variety of classic ice accretion prediction algorithms [6,32–34]. Seongmun et al. [35] put forward the ice detection model based on machine learning. Merino et al. [23] used a C-212 to measure the cloud microphysical characteristics of 37 areas containing super-cooled liquid water. Faisal et al. [36] investigate and understand weather conditions related to aircraft icing to improve ice prediction. Mei et al. [37] studied the application of the High-Resolution Rapid Refresh model in ice accretion prediction and improved it. Bowyer et al. [38] used satellite data to infer the icing potential in Europe, Asia, and Australia and compared it with the measured results. Many scholars have also researched the prediction of the type of condensate [39–41], hoping to find out the distribution range of particles that are easy to cause aircraft icing, such as SLD [22], to give early warning to pilots. Reisner et al. [42] studied the cloud microphysical characteristics in detail, which played an important role in weather modification and aircraft icing environment research. The National Transportation Safety Board (NTSB) has issued a large number of early warning reports on aircraft icing [43]. The premise of forecasting icing is that we need to have a clear judgment of the general distribution environment of aircraft icing. China has a vast territory, diverse climates, and large differences in temperature and humidity in time and space. Therefore, research on the distribution of areas prone to icing of Chinese aircraft is very complicated. Wang et al. [44] analyzed the climatic characteristics of aircraft icing accretion in the most recent 40 years using the power spectrum. Yang [45] used reanalysis data to study the validity and accuracy of ice accretion prediction. Wang [46] summarized the distribution characteristics of Chinese aircraft icing routes and established a prediction model of Chinese aircraft ice accumulation using multiple linear regression and neural networks. Huang constructed the climatic region of aircraft icing in China after a statistical analysis of high-altitude climate data, as shown in Figure1[ 47]. Area I is the most prone to icing and includes most of the northeast and the Qinghai-Tibet Plateau. Area II is more prone to icing and extends from the East Coast to the central region of the western region. It is difficult to accumulate ice in Area III, which is located mainly in the south of the middle and lower reaches of the Yangtze River. Area IV is the area where it is most difficult to accumulate ice and includes the main regions of North China, Xinjiang Province, and Liaoning Province. Atmosphere 2020, 11, 876 3 of 17 Atmosphere 2020, 11, x FOR PEER REVIEW 3 of 18

Figure 1. Climatic region of aircraft icing in China. Figure 1. Climatic region of aircraft icing in China. However, the temperature and humidity in the different seasons of China’s different regions show However, the temperature and humidity in the different seasons of China’s different regions great variations. Before and after the rainfall process and the snowfall process, the liquid water in the show great variations. Before and after the rainfall process and the snowfall process, the liquid water air may be significantly reduced and may have an impact on the aircraft icing environment. Therefore, in the air may be significantly reduced and may have an impact on the aircraft icing environment. based on Figure1, this paper discusses the changes in the ice index intensity and its main distribution Therefore, based on Figure 1, this paper discusses the changes in the ice index intensity and its main altitude in different seasons in different regions, including the changes of the icing index at middle and distribution altitude in different seasons in different regions, including the changes of the icing index low altitude before and after large-scale rainfall and snowfall. at middle and low altitude before and after large-scale rainfall and snowfall. 2. Materials and Methods 2. Materials and Methods 2.1. Materials 2.1. Materials The data used in this paper include FY-2 Satellite data and sound data. The data satellite used data in this are paper collected include from FY-2 the Satellite multichannel data and spin sound scan data. radiometer data of the FY-2 Satellite.The Thesatellite visible data infrared are collected spin scanning from radiometerthe multicha (VISSR)nnel spin is one scan of theradiometer main payloads data of of the the FY-2 FY-2 Satellite,Satellite. andThe visible every half infrared hour, spin a panoramic scanning original radiomet clouder (VISSR) image is covering one of the 1/3 main of the payloads global area of the can FY- be obtained.2 Satellite, VISSRand every has threehalf hour, channels a panoramic of visible original light, infrared, cloud image and water covering vapor. 1/3 With of the three global channels area can of data,be obtained. various VISSR satellite has meteorological three channels productsof visible canlight, be infrared, further obtained.and water The vapor. data With used three in this channels paper includeof data, thevarious snow satellite cover distribution meteorological of the products FY-2E satellite can be further and the obtained. precipitation The estimationdata used in of this the paper FY-2F satellite.include the Satellite snow datacover was distribution downloaded of the on FY-2E China satellite Meteorological and the Dataprecipitation Service Center estimation (CMDC). of the FY- 2F satellite.The sounding Satellite data was are downloaded taken from theon China Department Meteorological of Atmospheric Data Service Science, Center University (CMDC). of WyomingThe sounding (UW), which data suppliesare taken daily from 0 the h and Departme 12 h data.nt of TheAtmospheric sounding Science, data website University is http: of //Wyomingwww.weather.uwyo.edu (UW), which /upperairsupplies/ sounding.htmldaily 0 h and. The 12 physical h data. quantities The sounding used in thisdata paper website include is altitude,http://www.weather.uwyo.edu/upperair temperature, and RH. The second/sounding.html. component uses The the dataphysical of the qu currentantities month, used in and this the paper third componentinclude altitude, uses thetemperature, data of 0 h and of the RH. current The second day. It shouldcomponent be noted uses that,the data in Section of the2 ,current the website month, is missingand the third four dayscomponent (on 1, 2, uses 6, and the 7data January) of 0 h of of 0 the h data current and day. one dayIt should (on 24 be April) noted of that, 12 h in data. Section 2, the website is missing four days (on 1, 2, 6, and 7 January) of 0 h data and one day (on 24 April) of 12 2.2.h data. Icing Index

The International Civil Aviation Organization (ICAO) recommends using the icing index ‘IC’[48] 2.2. Icing Index to describe the icing environment:

The International Civil Aviation Organization (ICAO) recommends using the icing index ‘I’ T(T + 14) [48] to describe the icing environment:I = 2(RH 50) (1) C − · 49 TT+14− I =2RH − 50 ⋅ (1) In Equation (1), ‘RH’ is the relative humidity (%) and−49 ‘T’ is the temperature (◦C). When RH is lower than 50% or T is not in the range of 14 C to 0 C, it is considered that it is impossible to freeze. In Equation (1), ‘RH’ is the relative humidity− ◦ (%)◦ and ‘T’ is the temperature (°C). When RH is The I output range is from 0–100, and the larger the value, the stronger the icing possibility. When the lowerC than 50% or T is not in the range of −14 °C to 0 °C, it is considered that it is impossible to freeze. temperature is 7 ◦C and RH reaches 100%, the maximum value of IC is 100. The strength criterion The I output range− is from 0–100, and the larger the value, the stronger the icing possibility. When the temperature is −7 °C and RH reaches 100%, the maximum value of I is 100. The strength criterion of I is given as follows: if 0≤I <50, slight icing occurs; if 50 ≤ I <80, moderate icing occurs; if I ≥80, serious icing occurs.

Atmosphere 2020, 11, 876 4 of 17 of I is given as follows: if 0 I < 50, slight icing occurs; if 50 I < 80, moderate icing occurs; C ≤ C ≤ C ifAtmosphere I 80, 2020 serious, 11, x icingFOR PEER occurs. REVIEW 4 of 18 C ≥ 2.3. Interpolation Method and Software Implementation The interpolationinterpolation method method is ais basic a basic method method in numerical in numerical analysis analysis and is used and to is supply used ato continuous supply a functioncontinuous for function discrete sample for discrete points. sample This continuouspoints. This function continuous passes function through passes all existing through sample all existing points, sample points, i.e., for existing sample points: x,y, x,y,…,x,y , the constructor Px i.e., for existing sample points: x1, y1 , x2, y2 , ... , xN, yN , the constructor P(x) satisﬁes: satisfies:

P(xi) = yi, i = 1, 2, ... , N (2) Px = y,i=1,2,…,N (2) Thus, PP(xx) is is known known as as the the interpolation interpolation function.function. AccordingAccording toto thethe uniquenessuniqueness theorem,theorem, if the interpolationinterpolation function is a polynomialpolynomial function,function, the interpolationinterpolation result satisfyingsatisfying Equation (2) isis unique. The common interpolation methods include LagrangeLagrange interpolation, Newton interpolation, Hermite interpolation, cubic spline interpolation, andand others. Considering that the credibility of the region interpolationinterpolation outsideoutside the the sample sample is is low, low, all all of of the the conclusions conclusions in thisin this paper paper are are discussed discussed in the in interpolationthe interpolation region region within within the samplethe sample range. range. Software MATLAB R2015bR2015b containscontains manymany interpolationinterpolation functions,functions, which can be calculatedcalculated according toto demand.demand. Function Function ‘interp1’ ‘interp10 cancan interpolate one-di one-dimensionalmensional data, and functionfunction “scatteredinterpolant”“scatteredinterpolant” cancan interpolateinterpolate two-dimensionaltwo-dimensional oror three-dimensionalthree-dimensional data.data. In In this this paper, these twotwo functionsfunctions are are used used to to interpolate interpolate data. data. In In Section Section3, ‘interp1 3, ‘interp1’0 is used is used to interpolate to interpolate daily daily data alongdata along the altitude, the altitude, and the and interpolation the interpolation points poin are everyts are hundred-meterevery hundred-meter to 15 km to above15 km theabove station the altitude.station altitude. In Section In 4Section, “interp1” 4, “interp1” is used to is interpolateused to interpolate the data ofthe each data station of each to station each kilometer to each kilometer between 1between km and 1 6 km km. and At the6 km. same At altitude,the same “scatteredinterpolant” altitude, “scatteredinterpolant” is used to interpolateis used to interpolate to the entire to area, the formingentire area, grid forming point data grid with point a resolution data with ofa resolution0.1 0.1 . of The 0.1° interpolation × 0.1°. The methodinterpolation in this method paper is in linear this ◦ × ◦ interpolation.paper is linear Theinterpolation. “m_map” packageThe “m_map” is used package in plotting. is used in plotting.

3. Seasonal Distribution of Icing Index According to the four regions in Figure 11 thatthat areare divideddivided byby thethe didifficultyﬃculty of the aircraft icing, each region selects a near-oceannear-ocean site and an inlandinland site,site, asas shownshown inin FigureFigure2 2.. TheThe eighteight stationsstations areare Changchun station and Lhasa station in Area I, ZhengzhouZhengzhou station and Kashgar station in Area II, Shanghai station and Kunming Station in in Area Area III, and and Beijing Beijing station station and and Al Altaytay station station in in Area Area IV. IV. The selectedselected lengthslengths of of time time are are January, January, April, April, July, July, and Octoberand October 2019, 2019, which which represent represent winter, winter, spring, summer,spring, summer, and autumn, and autumn, respectively. respectively. The temperature The temperature and RH and are RH interpolated are interpolated to obtain to obtain the icing the index,icing index, and the and results the results are shown are inshown Figures in3 Figure–10. Thes 3–10. “station” The in“station” Figures 3in– 10Figures means 3–10 the altitudemeans ofthe thealtitude station. of the station.

Figure 2. Coordinates of 8 sounding stations.

Atmosphere 2020, 11, x FOR PEER REVIEW 5 of 18

Atmosphere 2020, 11, 876 5 of 17 Atmosphere 2020, 11, x FOR PEER REVIEW 5 of 18

(a) April (b) July

Figure 3. Icing index of Changchun Station (Area I). (a) April, (b). July, (c). October, (d). Janurary.

Atmosphere 2020, 11, x FOR PEER REVIEW 6 of 18 (a) April (b) July

(a) April (b) July

FigureFigure 4. 4.Icing Icing index index of of Lhasa Lhasa Station Station (Area (Area I). I). (a ()a April,) April, (b ().b). July, July, (c ).(c). October, October, (d ().d). Janurary. Janurary.

As shown in Figure 4, the icing distribution at Lhasa station shows that the icing time accounts for approximately 1/2 of spring and that the icing altitude above and below 6 km consists mainly of

light icing, with a small amount of moderate icing. In summer, daily icing occurs, which is stable between 6 km and 8 km, and the intensity is a large amount of moderate icing and even serious icing. The icing deposition in autumn is similar to that in spring, but it lasts longer. In winter, icing is notably weak and rarely occurs.

(a) April (b) July

Figure 5. Icing index of Zhengzhou Station (Area Ⅱ). (a) April, (b). July, (c). October, (d). Janurary.

The icing index of Zhengzhou station is presented in Figure 5. The duration of icing at Zhengzhou station is approximately 1/3, and the altitude is between 3 km and 6 km, mainly light icing with a small amount of moderate icing. During summer, the duration of icing is approximately 2/3, and the altitude of icing is between 5 km and 7 km. The distribution of icing in autumn is similar to that in spring but is more continuous. The most serious icing occurs in winter, is located from the ground to 5 km, and is mainly moderate and severe.

Atmosphere 2020, 11, x FOR PEER REVIEW 6 of 18 Atmosphere 2020, 11, 876 6 of 17

As can be seen in Figure3, the distribution of the icing index at Changchun Station shows that there is an icing area for about 1/3 in spring, which is below 6 km with mild icing mainly. In summer, icing has the longest time and is highly stable in the range of 4 km to 7 km. The duration of icing in autumn accounts for about 1/2, located below 6 km, mainly mild icing, but also severe icing for several days, basically similar to spring. In winter, icing is below 3 km, which gradually weakened from near the surface to low altitude. As shown in Figure4, the icing distribution at Lhasa station shows that the icing time accounts for approximately 1/2 of spring and that the icing altitude above and below 6 km consists mainly of light icing, with a small amount of moderate icing. In summer, daily icing occurs, which is stable between 6 km and 8 km, and the( intensityc) October is a large amount of moderate icing and (d) even Janurary serious icing. The icing deposition in autumn is similar to that in spring, but it lasts longer. In winter, icing is notably weak Figure 4. Icing index of Lhasa Station (Area I). (a) April, (b). July, (c). October, (d). Janurary. and rarely occurs. TheAs icingshown index in Figure of Zhengzhou 4, the icing station distribution is presented at Lhasa in Figure station5. The shows duration that ofthe icing icing at time Zhengzhou accounts stationfor approximately is approximately 1/2 of 1/3, spring and the and altitude that the is betweenicing altitude 3 km andabove 6km, and mainly below light6 km icing consists with mainly a small of amountlight icing, of moderate with a icing.small Duringamount summer, of moderate the duration icing. In of summer, icing is approximately daily icing occurs, 2/3, and which the altitude is stable ofbetween icing is between 6 km and 5 km8 km, and and 7 km. the intensity The distribution is a large of amount icing in autumnof moderate is similar icing toand that even in spring serious but icing. is moreThe continuous.icing deposition The mostin autumn serious is icing similar occurs to inthat winter, in spring, is located but it from lasts the longer. ground In towinter, 5 km, andicing is is mainlynotably moderate weak and and rarely severe. occurs.

(a) April (b) July

FigureFigure 5. 5.Icing Icing index index of of Zhengzhou Zhengzhou Station Station (Area (Area II). Ⅱ). (a ()a April,) April, (b ().b). July, July, (c ).(c). October, October, (d ().d). Janurary. Janurary.

TheThe icing icing index index of Kashgarof Zhengzhou station station is presented is presented in Figure in6. Figure Icing at 5. Kashgar The duration station occursof icing in at spring,Zhengzhou at altitude station primarily is approximately between 4 km1/3, and and 6 the km, altitude with mainly is between light icing 3 km and and little 6 km, moderate mainly icing. light Inicing summer, with a icing small occurs amount with of moderate an altitude icing. between During 5 km summer, and 7 the km duration and is mainly of icing light is approximately icing and a certain2/3, and amount the altitude of moderate of icing icing. is between The icing 5 km distribution and 7 km. in The autumn distribution is similar of toicing that in in autumn spring, is and similar the durationto that in is spring almost but 2/3. is In more winter, continuous. icing occurs The from most the serious ground icing to 3occurs km and in winter, is mainly is located light icing from and the selectedground moderate to 5 km, and icing, is withmainly little moderate icing above and 3severe. km with a weak icing intensity.

Atmosphere 2020, 11, 876 7 of 17 Atmosphere 2020, 11, x FOR PEER REVIEW 7 of 18

(a) April (b) July

FigureFigure 6. 6.Icing Icing index index of of Kashgar Kashgar Station Station (Area (Area II). II). (a ()a April,) April, (b ().b). July, July, (c ).(c). October, October, (d ().d). Janurary. Janurary.

AsThe shown icing in index Figure of7 ,Kashgar in spring, station the icing is presented distribution in Figure of Shanghai 6. Icing station at Kashgar is relatively station discrete, occurs in withspring, an altitude at altitude between primarily 3 km between and 7 km, 4 km and and is mainly6 km, with light mainly and moderate light icing icing and and little a moderate small amount icing. ofIn serious summer, icing. icing The occurs duration with icing an accountsaltitude forbetween almost 5 1km/2 of and summer, 7 km withand is an mainly altitude light between icing 5and km a andcertain 8 km, amount and is mainly of moderate light and icing. moderate The icing icing. distribu The distributiontion in autumn of icing is similar in autumn to that is similar in spring, to that and inthe spring. duration The icingis almost in winter 2/3. In is winter, the most icing serious, occurs from from the the ground ground to 6 to km, 3 km and and is mainly is mainly medium light andicing grievousand selected icing. moderate icing, with little icing above 3 km with a weak icing intensity. As shown in Figure8, the duration of icing at Kunming station is longer than 2 /3 of spring, with an altitude of 5 km to 6 km, and is mainly light icing and a small amount of moderate icing. The icing in summer is generally continuous, with an altitude of 6 km to 8 km, and is mainly moderate icing. The duration of icing is approximately 2/3 of autumn, the altitude is stable at 5 km to 7 km, and the icing intensity is mainly light with little moderate. The duration of icing is relatively short, and the altitude is maintained at a low altitude of 3 km to 4 km in winter. As displayed in Figure9, the icing at Beijing station lasts for approximately 1 /2 of spring, and the altitude changes greatly, but the range is generally below 6 km. The intensity of icing is moderate or serious most of the time. In summer, the icing altitude is between 4 km and 8 km, and the intensity is mainly light icing. The distribution of icing in autumn and spring is nearly identical and diﬀerent from other seasons, there is almost no icing in winter. The icing index distribution of Altay station (Figure 10) shows that the icing time in spring (a) April (b) July accounts for approximately 1/2, the altitude range is generally below 5 km, and mainly there is mild icing and a small amount of moderate icing. The icing time in summer accounts for approximately 1/3, with an altitude between 4 km and 6 km, and light icing is the main component. The icing takes about 1/3 of the time in autumn, and the altitude is widely distributed, and icing may also occur near the ground, with a small amount of moderate icing. In winter, icing occurs, extending from the surface to 3 km.

Atmosphere 2020, 11, x FOR PEER REVIEW 7 of 18

(a) April (b) July

Figure 6. Icing index of Kashgar Station (Area II). (a) April, (b). July, (c). October, (d). Janurary.

The icing index of Kashgar station is presented in Figure 6. Icing at Kashgar station occurs in spring, at altitude primarily between 4 km and 6 km, with mainly light icing and little moderate icing. In summer, icing occurs with an altitude between 5 km and 7 km and is mainly light icing and a certain amount of moderate icing. The icing distribution in autumn is similar to that in spring, and Atmospherethe duration2020, 11 is, 876 almost 2/3. In winter, icing occurs from the ground to 3 km and is mainly light8 oficing 17 and selected moderate icing, with little icing above 3 km with a weak icing intensity.

Atmosphere 2020, 11, x FOR PEER REVIEW 8 of 18

Atmosphere 2020, 11, x FOR PEER REVIEW 8 of 18 (a) April (b) July

Figure 7. Icing index of Shanghai Station (Area Ⅲ). (a) April, (b). July, (c). October, (d). Janurary.

As shown in Figure 7, in spring, the icing distribution of Shanghai station is relatively discrete, with an altitude between 3 km and 7 km, and is mainly light and moderate icing and a small amount of serious icing. The duration icing accounts for almost 1/2 of summer, with an altitude between 5 km and 8 km, and is mainly light and moderate icing. The distribution of icing in autumn is similar (c) October (d) Janurary to that in spring. The icing in winter is the most serious, from the ground to 6 km, and is mainly mediumFigureFigure and 7. 7.grievousIcing Icing index index icing. of of Shanghai Shanghai Station Station (Area (Area III). Ⅲ). (a ()a April,) April, (b ().b). July, July, (c ).(c). October, October, (d ().d Janurary.). Janurary.

(a) April (b) July

FigureFigure 8. 8Icing Icing index index of of Kunming Kunming Station Station (Area (Area III). Ⅲ). (a ()a April,) April, (b ().b). July, July, (c ().c). October, October, (d ().d). Janurary. Janurary.

As shown in Figure 8, the duration of icing at Kunming station is longer than 2/3 of spring, with an altitude of 5 km to 6 km, and is mainly light icing and a small amount of moderate icing. The icing in summer is generally continuous, with an altitude of 6 km to 8 km, and is mainly moderate icing. The duration of icing is approximately 2/3 of autumn, the altitude is stable at 5 km to 7 km, and the (c) October (d) Janurary icing intensity is mainly light with little moderate. The duration of icing is relatively short, and the altitudeFigure is maintained 8 Icing index at a of low Kunming altitude Station of 3 km(Area to Ⅲ4 ).km (a) in April, winter. (b). July, (c). October, (d). Janurary.

As shown in Figure 8, the duration of icing at Kunming station is longer than 2/3 of spring, with an altitude of 5 km to 6 km, and is mainly light icing and a small amount of moderate icing. The icing in summer is generally continuous, with an altitude of 6 km to 8 km, and is mainly moderate icing. The duration of icing is approximately 2/3 of autumn, the altitude is stable at 5 km to 7 km, and the icing intensity is mainly light with little moderate. The duration of icing is relatively short, and the altitude is maintained at a low altitude of 3 km to 4 km in winter.

Atmosphere 2020, 11, x FOR PEER REVIEW 9 of 18

Atmosphere 2020, 11, 876 9 of 17 Atmosphere 2020, 11, x FOR PEER REVIEW 9 of 18

(a) April (b) July

Figure 9. Icing index of Beijing Station (Area Ⅳ). (a) April, (b). July, (c). October, (d). Janurary.

As displayed in Figure 9, the icing at Beijing station lasts for approximately 1/2 of spring, and the altitude changes greatly, but the range is generally below 6 km. The intensity of icing is moderate or serious most of the time. In summer, the icing altitude is between 4 km and 8 km, and the intensity (c) October (d) Janurary is mainly light icing. The distribution of icing in autumn and spring is nearly identical and different from otherFigureFigure seasons, 9. 9.Icing Icing indexthere index ofis of Beijingalmost Beijing Station no Station icing (Area (Area in winter. IV). Ⅳ). (a ()a April,) April, (b ().b). July, July, (c ).(c). October, October, (d ().d). Janurary. Janurary.

Atmosphere 2020, 11, x FOR PEER REVIEW 10 of 18 (a) April (b) July

(a) April (b) July

FigureFigure 10. 10.Icing Icing index index of of Altay Altay Station Station (Area (Area IV). Ⅳ). (a ()a April,) April, (b ().b). July, July, (c ).(c). October, October, (d ().d Janurary.). Janurary.

The icing index distribution of Altay station (Figure 10) shows that the icing time in spring accounts for approximately 1/2, the altitude range is generally below 5 km, and mainly there is mild

icing and a small amount of moderate icing. The icing time in summer accounts for approximately 1/3, with an altitude between 4 km and 6 km, and light icing is the main component. The icing takes about 1/3 of the time in autumn, and the altitude is widely distributed, and icing may also occur near the ground, with a small amount of moderate icing. In winter, icing occurs, extending from the surface to 3 km.

4. Impact of Rainfall and Snowfall on Icing Index When certain weather processes occur, the temperature and humidity environment change significantly in a short period of time, which is difficult to clearly show in monthly scale statistics and must be discussed separately. Based on the conclusion of the second section, icing occurs primarily in the near-ocean region, in Area I when in summer, in the Area II and Area III when in winter. In this section, we analyze the influence of two different weather processes on the icing index of different altitude before and after the summer rainfall in Northeast China and the winter snowfall in East China. It should be noted that the results for 1 km, 2 km, and 3 km are omitted in Section 4.1 and those for 6 km are omitted in Section 4.2 because there is no icing in the entire area at the above altitude.

4.1. Rainfall Process In August 2019, high-intensity rainfall occurred frequently in Northeast China, with the number of precipitation days generally ranging from 12 to 20 days, and in certain special areas, the precipitation days exceeded 20 days. The cumulative precipitation in Heilongjiang Province and Jilin Province was the highest since 1961, and a total of 198 stations in Northeast China experienced extreme continuous precipitation events, which led to the water level rise in Songhua River, Nenjiang River and other sections of the river, and certain areas suffered from rainstorms and floods. Figure 11 shows the precipitation estimation from FY-2F on 11th and 19th August 2019. It can be observed from the figure that rainfall occurred in the entire region of Northeast China (120° E–140° E, 38° N– 50° N) on 11th August, and the rainfall mostly ended on 19th August.

Atmosphere 2020, 11, x FOR PEER REVIEW 10 of 18

Figure 10. Icing index of Altay Station (Area Ⅳ). (a) April, (b). July, (c). October, (d). Janurary.

The icing index distribution of Altay station (Figure 10) shows that the icing time in spring accounts for approximately 1/2, the altitude range is generally below 5 km, and mainly there is mild icing and a small amount of moderate icing. The icing time in summer accounts for approximately 1/3, with an altitude between 4 km and 6 km, and light icing is the main component. The icing takes about 1/3 of the time in autumn, and the altitude is widely distributed, and icing may also occur near the ground, with a small amount of moderate icing. In winter, icing occurs, extending from the surfaceAtmosphere to2020 3 km., 11, 876 10 of 17

4. Impact of Rainfall and Snowfall on Icing Index 4. Impact of Rainfall and Snowfall on Icing Index When certain weather processes occur, the temperature and humidity environment change significantlyWhen certain in a short weather period processes of time, which occur, is thedifficult temperature to clearly andshow humidity in monthly environment scale statistics change and mustsignificantly be discussed in a short separately. period ofBased time, on which the conclusi is difficulton toof clearlythe second show section, in monthly icing scale occurs statistics primarily and inmust the benear-ocean discussed region, separately. in Area Based I when on the in conclusion summer, in of the the Area second II section,and Area icing III when occurs in primarily winter. In in thisthe near-oceansection, we region, analyze in Areathe influence I when in of summer, two different in the Areaweather II and processes Area III whenon the in icing winter. index In this of differentsection, wealtitude analyze before the and influence after the of twosummer different rainfall weather in Northeast processes China on theand icing the winter index ofsnowfall different in Eastaltitude China. before It should and after be noted the summer that the rainfall results in for Northeast 1 km, 2 km, China and and 3 km the are winter omitted snowfall in Section in East 4.1 China. and thoseIt should for 6 be km noted are thatomitted the resultsin Section for 14.2 km, because 2 km, andthere 3 kmis no are icing omitted in the in Sectionentire area 4.1 andat the those above for altitude.6 km are omitted in Section 4.2 because there is no icing in the entire area at the above altitude. 4.1. Rainfall Process 4.1. Rainfall Process In August 2019, high-intensity rainfall occurred frequently in Northeast China, with the number In August 2019, high-intensity rainfall occurred frequently in Northeast China, with the number of precipitation days generally ranging from 12 to 20 days, and in certain special areas, the precipitation of precipitation days generally ranging from 12 to 20 days, and in certain special areas, the days exceeded 20 days. The cumulative precipitation in Heilongjiang Province and Jilin Province was precipitation days exceeded 20 days. The cumulative precipitation in Heilongjiang Province and Jilin the highest since 1961, and a total of 198 stations in Northeast China experienced extreme continuous Province was the highest since 1961, and a total of 198 stations in Northeast China experienced precipitation events, which led to the water level rise in Songhua River, Nenjiang River and other extreme continuous precipitation events, which led to the water level rise in Songhua River, Nenjiang sections of the river, and certain areas suffered from rainstorms and floods. Figure 11 shows the River and other sections of the river, and certain areas suffered from rainstorms and floods. Figure precipitation estimation from FY-2F on 11 and 19 August 2019. It can be observed from the figure that 11 shows the precipitation estimation from FY-2F on 11th and 19th August 2019. It can be observed rainfall occurred in the entire region of Northeast China (120 E–140 E, 38 N–50 N) on 11 August, from the figure that rainfall occurred in the entire region of Northeast◦ ◦ China◦ (120°◦ E–140° E, 38° N– and the rainfall mostly ended on 19 August. 50° N) on 11th August, and the rainfall mostly ended on 19th August.

Atmosphere 2020, 11, x FOR PEER REVIEW 11 of 18

Figure 11. Precipitation estimation data based on FY-2F (The left figure is 11 August, and the right Figure 11. Precipitation estimation data based on FY-2F (The left figure is 11th August, and the right figure is 19 August). figure is 19th August). We selected 8 sounding stations, such as Harbin station in Northeast China, the station number is We selected 8 sounding stations, such as Harbin station in Northeast China, the station number shown in Figure 12, and the icing index of different altitude is shown in Figure 13. is shown in Figure 12, and the icing index of different altitude is shown in Figure 13.

FigureFigure 12.12. CoordinatesCoordinates ofof 88 soundingsounding stationsstations inin NortheastNortheast China.China.

(a) Icing index before rainfall (11th August)

(b) Icing index after rainfall (19th August)

Figure 13. Icing index before and after rainfall (In (a) and (b), the altitude from first to third is 4 km to 6 km, respectively).

Figure 13 shows that there is no possibility of ice accretion at a low altitude below 3 km before and after the rainfall process, whereas for the airspace of 4 km to 6 km, the range of rainfall estimation is notably close to the range of ice accretion distribution, which is also consistent with the results

Atmosphere 2020, 11, x FOR PEER REVIEW 11 of 18

Figure 11. Precipitation estimation data based on FY-2F (The left figure is 11th August, and the right figure is 19th August).

We selected 8 sounding stations, such as Harbin station in Northeast China, the station number is shown in Figure 12, and the icing index of different altitude is shown in Figure 13.

Atmosphere 2020, 11, 876 11 of 17 Figure 12. Coordinates of 8 sounding stations in Northeast China.

(a) Icing index before rainfall (11th August)

(b) Icing index after rainfall (19th August)

Figure 13. Icing index beforebefore andand afterafter rainfallrainfall (In (In ( a(a,b) ),and the ( altitudeb), the altitude from ﬁrst from to thirdfirst to is 4third km tois 64 km,km torespectively). 6 km, respectively).

Figure 13 shows that there is no possibility of ice accretion at a low altitude below 3 km before and after the rainfall process, whereas for the airspace of 4 km to 6 km, the rang rangee of of rainfall rainfall estimation estimation is notably close to the range of iceice accretionaccretion distribution,distribution, which is also consistent with the results presented in Figure3. The distribution of the icing index at a 5 km altitude on 11 August (Figure 13a) and the index at a 4 km altitude on 19 August (Figure 13b) are most consistent with the rainfall estimation in Figure 11, and the larger rainfall estimation also corresponds to the stronger icing. A possible reason for this result is that a certain proportion of super-cooled large water droplets occur in the rainfall area. The above analysis shows that before the summer rainfall process in Northeast China, ice accretion easily occurs at the altitude of 4–6 km, and the intensity of icing is roughly related to precipitation estimation.

4.2. Snowfall Process In January 2018, three large-scale rain and snow events occurred in China’s central and eastern regions (3rd–4th, 5th–7th and 24th–28th), of which the 24th–28th event had the most extensive, long-term and severe impact process during this winter. The accumulated snowfall in the southwest of the Yellow River, the Huaihe River, and the north of regions south of the Yangtze River ranged from 10 mm to 25 mm. The accumulated snowfall reached more than 25 mm, such as in the northeast of the Hunan Province, the north and east of Hubei Province, the central and south of Anhui Province, the southwest of Jiangsu Province, and the north of Zhejiang Province. The snow distribution data from FY-2E on 28 January and 4 February 2018, are shown in Figure 14. The ﬁgure shows that the central and eastern regions of China (110◦ E–123◦ E, 25◦ N–40◦ N) are generally covered with snow Atmosphere 2020, 11, x FOR PEER REVIEW 12 of 18 Atmosphere 2020, 11, x FOR PEER REVIEW 12 of 18 presented in Figure 3. The distribution of the icing index at a 5 km altitude on August 11th (Figure presented13a) and the in indexFigure at 3. a The4 km distribution altitude on Augustof the icin 19thg index (Figure at 13b)a 5 km are altitudemost consistent on August with 11th the (Figurerainfall 13a)estimation and the in index Figure at a11, 4 kmand altitude the larger on Augustrainfall 19thestimation (Figure also 13b) corresponds are most consistent to the stronger with the icing. rainfall A estimationpossible reason in Figure for this 11, resultand the is thatlarger a certain rainfall proportion estimation of also super- correspondscooled large to thewater stronger droplets icing. occur A possiblein the rainfall reason area. for thisThe result above is analysis that a certain shows proportion that before of the super- summercooled rainfall large waterprocess droplets in Northeast occur inChina, the rainfall ice accretion area. Theeasily above occurs analysis at the altitude shows thatof 4–6 before km, andthe thesummer intensity rainfall of icing process is roughly in Northeast related China,to precipitation ice accretion estimation. easily occurs at the altitude of 4–6 km, and the intensity of icing is roughly related to precipitation estimation. 4.2. Snowfall Process 4.2. Snowfall Process In January 2018, three large-scale rain and snow events occurred in China’s central and eastern regionsIn January (3rd–4th, 2018, 5th–7th three and large-scale 24th–28th), rain of and which snow the events 24th–28th occurred event in had China’s the most central extensive, and eastern long- regionsterm and (3rd–4th, severe impact 5th–7th process and 24th–28th), during this of winter which. theThe 24th–28th accumulated event snowfall had the in most the southwestextensive, oflong- the termYellow and River, severe the impact Huaihe process River, during and the this north winter of re. Thegions accumulated south of the snowfall Yangtze in River the southwest ranged from of the 10 Yellowmm to 25River, mm. the The Huaihe accumulated River, and snowfall the north reached of re moregions thansouth 25 of mm, the Yangtzesuch as inRiver the northeastranged from of the 10 mmHunan to 25 Province, mm. The the accumulated north and east snowfall of Hubei reached Province, more the than central 25 mm, and such south as ofin Anhuithe northeast Province, of the AtmosphereHunansouthwest Province,2020 of, 11Jiangsu, 876 the northProvince, and andeast theof Hubei north Province,of Zhejiang the Province. central and The south snow of distribution Anhui Province, data12 from ofthe 17 southwestFY-2E on 28 of JanuaryJiangsu andProvince, 4 February and the 2018, north are of shown Zhejiang in Figure Province. 14. TheThe figuresnow distributionshows that thedata central from FY-2Eand eastern on 28 Januaryregions ofand China 4 February (110° E–123° 2018, are E, 25°shown N–40° in Figure N) are 14. generally The figure covered shows with that snow the central on 28 on 28 January, among which Shandong Province, most of Jiangsu Province and the eastern portion andJanuary, eastern among regions which of China Shandong (110° Province,E–123° E, most25° N–40° of Jiangsu N) are Province generally and covered the eastern with snow portion on 28of of Anhui Province have the thickest snow. By 4 February, the snow in this region has mostly melted, January,Anhui Province among havewhich the Shandong thickest snow. Province, By February most of 4th, Jiangsu the snow Province in this and region the haseastern mostly portion melted, of with less snow on the ground. Anhuiwith less Province snow on have the the ground. thickest snow. By February 4th, the snow in this region has mostly melted, with less snow on the ground.

Figure 14. Snow cover distribution data based on FY-2E (The left figure is 28 January, and the right Figure 14. Snow cover distribution data based on FY-2E (The left figure is 28 January, and the right Figurefigure is14. 4 February).Snow cover distribution data based on FY-2E (The left figure is 28 January, and the right figure is 4 February). figure is 4 February). As shownshown in in Figure Figure 16, 16, we we obtained obtained the icingthe icing index index of diff erentof different altitudes altitudes by selecting by 21selecting sounding 21 As shown in Figure 16, we obtained the icing index of different altitudes by selecting 21 stationssounding such stations as Beijing such stationas Beijing in thestation eastern in the portion eastern of portion China, andof China, the station and the number station is number shown inis sounding stations such as Beijing station in the eastern portion of China, and the station number is Figureshown 15in. Figure 15. shown in Figure 15.

Figure 15. Coordinates of 21 sounding stations in the eastern portion of China. Figure 15. CoordinatesCoordinates of 21 sounding stations in the eastern portion of China.

We can conclude that ice accretion is nonexistent in the higher airspace over 5 km, but for the lower airspace below 4 km, when snow is on the ground, an obvious ice accretion area exists. On 28 January (Figure 16a), from 4 km down to 1 km, the ice accretion increases, and in Area II to the north of the Yangtze River, serious ice accretion occurs, which is also consistent with the conclusions in Figures5 and7. At the same time, the low-level ice area is similar to the snow area in Figure 14. With the increase in altitude, the diﬀerence between the ice area and the snow area gradually increases, and on 4 February, after the snow melts (Figure 16b), the risk of ice accretion at low altitudes is reduced, and only a small amount of area has light ice accretion. The above analysis shows that the winter snow in the east region of China easily causes low-level ice accretion. Atmosphere 2020, 11, x FOR PEER REVIEW 13 of 18

We can conclude that ice accretion is nonexistent in the higher airspace over 5 km, but for the lower airspace below 4 km, when snow is on the ground, an obvious ice accretion area exists. On January 28th (Figure 16a), from 4 km down to 1 km, the ice accretion increases, and in Area II to the north of the Yangtze River, serious ice accretion occurs, which is also consistent with the conclusions in Figures 5 and 7. At the same time, the low-level ice area is similar to the snow area in Figure 14. With the increase in altitude, the difference between the ice area and the snow area gradually increases, and on February 4th, after the snow melts (Figure 16b), the risk of ice accretion at low Atmospherealtitudes 2020is reduced,, 11, 876 and only a small amount of area has light ice accretion. The above analysis shows13 of 17 that the winter snow in the east region of China easily causes low-level ice accretion.

(a) Icing index with snow on the ground (28 January)

(b) Icing index without snow on the ground (4 February)

Figure 16.16. Icing index withwith andand withoutwithout snowsnow onon thethe groundground (In(In ((aa,b) ),and the (b altitude), the altitude from ﬁrst from to first ﬁfth to is 1fifth km is to 1 5 km km, to respectively). 5 km, respectively).

5. Discussion

5.1. Icing Index Distribution After comparing thethe resultsresults ofof thethe eighteight sitessites inin SectionSection3 3,, the the main main results results are are as as follows: follows:

(1)(1) InIn general, according toto thethe distributiondistribution of of icing icing in in the the four four seasons, seasons, the the threat threat of of icing icing in in spring spring is isthe the weakest, weakest, mostly mostly distributed distributed almost almost 3–6 3–6 km, km, with with slight slight icing icing mainly. mainly. TheThe heightheight ofof icingicing inin summer is increased and relatively stable. The situation is similar in autumn and spring. The icing summer is increased and relatively stable. The situation is similar in autumn and spring. The altitude in winter is mostly close to the ground, and severe icing may occur in Southeast China. (2) icingFor the altitude same icingin winter climatic is mostly region close in Figure to the1, the ground, icing altitudeand severe range icing and may intensity occur at in near-ocean Southeast China.stations are larger and stronger than those at inland stations. A possible reason for this result is (2) Forthat the a large same amount icing climatic of liquid region water in Figure might be1, the present icing inaltitude the low range and and hollow intensity altitude at near-ocean due to the stationshigh humidity are larger in the and near-ocean stronger than area. those At the at sameinland time, stations. the existence A possible of thereason sea-land for this breeze result and is the influence of multi-scale weather processes make the duration of icing unstable. In relative that a large amount of liquid water might be present in the low and hollow altitude due to the terms, the higher altitude of stations in the inland corresponds to a more stable temperature and highLWC humidity environment. in the near-ocean area. At the same time, the existence of the sea-land breeze and (3) theFor influence the same site, of multi-scale great differences weather occur processes in the distribution make the ofduration icing in of di fficingerent unstable. seasons. TheIn relative reason terms,for this the result higher is that altitude most areasof stations in East in China the in areland aff correspondsected by the monsoon.to a more stable Therefore, temperature the moisture and content contained in the monsoon inevitably leads to different LWC. In addition, China has a vast territory, complex terrain, and temperature difference between winter and summer is large in most areas. Different temperatures and vapor environments in the four seasons create various icing distributions. Atmosphere 2020, 11, x FOR PEER REVIEW 14 of 18

LWC environment. (3) For the same site, great differences occur in the distribution of icing in different seasons. The reason for this result is that most areas in East China are affected by the monsoon. Therefore, the moisture content contained in the monsoon inevitably leads to different LWC. In addition, China has a vast territory, complex terrain, and temperature difference between winter and summer is large in most areas. Different temperatures and vapor environments in the four seasons create Atmosphere 2020, 11, 876 14 of 17 various icing distributions.

5.2.5.2. The Impact ofof WeatherWeather ProcessesProcesses InIn thethe processprocess ofof rainfallrainfall andand snowfall,snowfall, wewe selectedselected twotwo stationsstations withwith seriousserious iceice accumulation:accumulation: NenjiangNenjiang StationStation (No.(No. 50557)50557) andand NanjingNanjing StationStation (No.(No. 58238) to analyze the main inﬂuencinginfluencing factorsfactors ofof iceice accumulation.accumulation. AsAs cancan bebe seenseen inin FigureFigure 1717a,a, thethe temperaturetemperature diddid notnot changechange signiﬁcantlysignificantly beforebefore andand afterafter thethe rainfallrainfall process,process, butbut atat 4–54–5 kmkm altitude,altitude, RHRH decreaseddecreased fromfrom nearnear saturationsaturation toto nearlynearly 50%.50%. AsAs cancan bebe seenseen fromfrom FigureFigure 1717b,b, comparedcompared duringduring andand afterafter snowfall,snowfall, thethe airair temperaturetemperature belowbelow 22 kmkm droppeddropped aboutabout 55 ◦°C,C, andand thethe temperaturetemperature dropdrop atat 2–52–5 kmkm waswas greater,greater, andand RHRH fellfell fromfrom nearnear saturationsaturation toto lessless thanthan 30%.30%. OtherOther stationsstations withwith thethe possibilitypossibility ofof icingicing accumulationaccumulation havehave similarsimilar characteristics.characteristics.

(a) Rainfall (b) Snowfall

FigureFigure 17.17. Height proﬁleprofile ofof temperaturetemperature andand relativerelative humidityhumidity (RH).(RH). ((aa)) Rainfall,Rainfall, ((bb)) Snowfall.Snowfall.

ItIt cancan bebe seenseen thatthat inin thethe large-scalelarge-scale summersummer rainfallrainfall inin NortheastNortheast ChinaChina andand winterwinter snowfallsnowfall inin EastEast China:China:

(1)(1) During thethe summersummer rainfallrainfall in in Northeast Northeast China, China, the the temperature temperature is relativelyis relatively high high and and there there is no is nochange change before before and and after after the the rainfall. rainfall. However, However, the the obvious obvious drop drop in RH in RH reduces reduces LWC LWC in the in air,the causes the possibility of icing is weakened or even disappeared. The data volume in this area has air, causes the possibility of icing is weakened or even disappeared. The data volume in this area a small number of altitude grid points, so it is difficult to judge the lifting condensation level has a small number of altitude grid points, so it is difficult to judge the lifting condensation level based on the inversion layer, to accurately judge the cloud height. However, combined with the previousbased on analysis,the inversion because layer, the to icingaccurately area is judge similar the to cloud the rainfall height. estimationHowever, combined area calculated with the by previoussatellite data, analysis, it is possiblebecause thatthe icing the icing area hereis similar is related to the to rainfall the cloud, estimation but this area conclusion calculated needs by satellitefurther verification.data, it is possible that the icing here is related to the cloud, but this conclusion needs (2) furtherThe change verification. of temperature and humidity in the snowfall process in East China in winter has obvious cold front transit characteristics. The confluence of warm and cold flow in front of the (2) The change of temperature and humidity in the snowfall process in East China in winter has cold front caused snowfall. The ground temperature was slightly below 0 ◦C, the humidity was obvioushigh, and cold there front was transit a lot ofcharacteristics. liquid water atThe low conf altitude,luence whichof warm was and likely cold to flow cause in thefront aircraft of the coldto freeze. front Whencaused thesnowfall. snow hasThe melted,ground thetemperat cold fronture was has slightly completely below passed, 0 °C, the and humidity the dry andwas high,cold airand occupies there was the a heightlot of liquid of the water original at low warm altitude, and humidwhich was air. likely Therefore, to cause there the has aircraft been to a significant cooling, and the humidity has dropped significantly. The liquid water in the air has freeze. When the snow has melted, the cold front has completely passed, and the dry and cold been consumed and the aircraft is not easy to freeze. (3) After the process of rainfall and snowfall, the temperature and RH decreased. A decrease in RH will inevitably cause a decrease in the icing index, and even icing may not occur. However, a decrease in temperature does not necessarily bring about a decrease in icing index: (1) if it just drops below 0 ◦C, it may be more conducive to the appearance of super-cooled water and increase LWC, to increase icing possibility. (2) If the temperature drops below 20 C or even − ◦ lower, LWC will decrease and icing possibility will decrease. (3) The temperature drops stably at each level will bring the 0 ◦C level downward, which will reduce the altitude of the icing area. Atmosphere 2020, 11, 876 15 of 17

6. Conclusions Based on the climatic region of aircraft icing in China and the icing index, we investigated data from sounding sites in different icing zones in different seasons using linear interpolation and analyzed the impact of summer rainfall in the Northeast China and winter snowfall in East China on the icing index on 1–6 km altitude. The conclusions are given as follows: (1) The distribution of icing varies greatly in different regions, seasons, and altitude. This phenomenon comes from differences in temperature and cloud microphysical characteristics such as LWC and MVD by different environments. The icing index calculated by temperature and relative humidity can effectively reflect this. (2) Before the summer rainfall in Northeast China, icing is prone to occur at altitudes of 4 km to 6 km, and the intensity of icing may be related to precipitation estimation. Snow on the ground in winter in East China is likely to cause low-altitude icing and its intensity may be serious. However, when the rainfall and snowfall process is over, the LWC in the air is decreased, and the threat of icing is significantly reduced. This feature is helpful for early warning of icing for the take-off and landing of transport aircraft and the flight of general aviation. (3) Ideas for further research: the above conclusions can qualitative analysis and a theoretical basis for the prediction of aircraft icing and improvement in flight safety. Considering the limitations of the interpolation method, additional station information and detailed meteorological data are required for further verification of the conclusion of this paper, as well as actual flight testing, if necessary.

Author Contributions: B.X. conceived and designed the experiments, performed the experiments, analyzed the data, and wrote the paper; J.W. and J.C. helped in the discussion and revision; J.W. reviewed the manuscript. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by National Natural Science Foundation of China (41905026); the Natural Science Foundation of Jiangsu Province (BK20170945); the 63rd Batch of China Postdoctoral Science Foundation in General (2018M631554); Open Fund by Key Laboratory of Aerosol-Cloud-Precipitation of CMA-NUIST (KDW1703); Key Laboratory of Middle Atmosphere and Global Environment Observation (LAGEO-2019-05). Acknowledgments: The author B.X. is particularly grateful to the questions from the defense group in the opening report of the master’s thesis, which reminded me of the need to study the icing altitude distribution in detail. The authors would like to acknowledge the CMDC and the UW for providing the observation data and precise products. Conﬂicts of Interest: The authors declare no conﬂict of interest.

References

1. Cole, J.; Sand, W. Statistical study of aircraft icing accidents. In Proceedings of the 29th Aerospace Science Meeting, Reno, NV, USA, 7–10 January 1991; American Institute of Aeronautics: Washington, DC, USA, 1991. [CrossRef] 2. Lilly, J.; Fabry, F. Detection of in-flight icing conditions through the analysis of hydrometeors with a vertically pointing radar, In Proceedings of the 11th Conference on Aviation, Range and Aerospace Meteorology, Hyannis, MA, USA, 4–8 October 2004. 3. Reehorst, A.L.; Brinker, D.J.; Ratvasky, T.P. The NASA Icing Remote Sensing System; NASA/TM 2005-213591; NASA Glenn Research Center: Cleveland, OH, USA, 2005. 4. Hauf, T.; Schroder, F. Aircraft icing research flights in embedded convention. Meteorol. Atmos. Phys. 2006, 91, 247–265. [CrossRef] 5. Bernstein, B.C.; Omeron, T.A.; McDonough, F.; Politovich, M.K. The relationship between aircraft icing and synoptic-scale weather conditions. Weather Forecast. 1997, 12, 742–762. [CrossRef] 6. Thompson, G.; Bruintjes, R.T.; Brown, B.G.; Hage, F. Intercomparison of In-Flight Icing Algorithms. Part I: WISP94 Real-Time Icing Prediction and Evaluation Program. Weather Forecast. 1997, 12, 878–889. [CrossRef] 7. Pazmany, A.L.; Mead, S.J.B.; McLaughlin, D.J. Multi-Frequency Radar Estimation of Cloud and Precipitation Properties Using an Artificial Neural Network. In Proceedings of the 30th International Conference on Radar Meteorology, Munich, Germany, 19–24 July 2001; pp. 154–156. Atmosphere 2020, 11, 876 16 of 17

8. Bernstein, B.C.; McDonough, F.; Politovich, M.K.; Brown, B.G.; Ratvasky, T.P.; Miller, D.R.; Wolff, C.A.; Cunning, G. Current Icing Potential: Algorithm Description and Comparison with Aircraft Observations. J. Appl. Meteorol. 2005, 44, 969–986. [CrossRef] 9. Broeren, A.P.; La Marre, C.M.; Bragg, M.B. Characteristics of SLD Ice Accretions on Airfoils and Their Aerodynamic Effects; AIAA: Reston, VA, USA, 2005. 10. Tsao, J.C.; David, N.A. Latest Developments in SLD Scaling; AIAA: Reston, VA, USA, 2005. 11. Eugene, G.H. Overview of Federal Aviation Administration Aviation Safety Research for Aircraft Icing; AIAA: Reston, VA, USA, 2006. 12. Bernstein, B.C.; Le Bot, C. An Inferred Climatology of Icing Conditions Aloft, Including Supercooled Large Drops. Part II: Europe, Asia, and the Globe. J. Appl. Meteorol. Clim. 2009, 48, 1503–1526. [CrossRef] 13. Pierre, B. Experimental Study of Supercooled Lagre Droplets Impact in an Icing Wind Tunnel; AIAA: Reston, VA, USA, 2012. 14. Schults, P.; Politovich, M.K. Toward the improvement of aircraft-icing forecasts for the continental United States. Weather Forecast. 1992, 7, 491–500. [CrossRef] 15. Bernstein, B.C.; Wolff, C.A.; McDonough, F. An Inferred Climatology of Icing Conditions Aloft, Including Supercooled Large Drops. Part I: Canada and the Continental United States. J. Appl. Meteorol. Clim. 2007, 46, 1857–1878. [CrossRef] 16. Cooper, W.A.; Sand, W.R.; Politovich, M.K.; Veal, D.L. Effects of icing on performance of research aircraft. J. Aircr. 1984, 21, 708–715. [CrossRef] 17. Sand, W.R.; Cooper, W.A.; Politovich, M.K.; Veal, D.L. Icing conditions encountered by a research aircraft. J. Clim. Appl. Meteorol. 1984, 23, 1427–1440. [CrossRef] 18. Politovich, M.K. Aircraft icing caused by larger supercooled droplets. J. Appl. Meteorol. 1989, 28, 856–868. [CrossRef] 19. Cober, S.G.; Isszc, G.A. Estimating maximum aircraft icing environment using a large database of in-situ observation. In Proceedings of the 44th AIAA Aerospace Science Meeting and Exhibit, Reno, NV, USA, 9–12 January 2006; AIAA: Reston, VA, USA, 2006. 20. Gultepe, I.; Agelin-Chaab, M.; Komar, J.; Elfström, G.; Boudala, F.; Zhou, B. A Meteorological Supersite for Aviation and Cold Weather Applications. Pure Appl. Geophys. 2018, 176, 1977–2015. [CrossRef] 21. Federal Aviation Administration. Part 25—Airworthiness Standards: Transport Category Airplanes. Available online: https://www.ecfr.gov/cgi-bin/ECFR?page=browse (accessed on 9 June 2020). 22. Cao, Y.; Tan, W.; Wu, Z. Aircraft icing: An ongoing threat to aviation safety. Aerosp. Sci. Technol. 2018, 75, 353–385. [CrossRef] 23. Merino, A.; García-Ortega, E.; Fernández-González, S.; Díaz-Fernández, J.; Quitián-Hernández, L.; Martín, M.L.; López, L.; Marcos-Menéndez, J.L.; Valero, F.; Sánchez, J.L. Aircraft Icing: In-Cloud Measurements and Sensitivity to Physical Parameterizations. Geophys. Res. Lett. 2019, 46, 11559–11567. [CrossRef] 24. Hai, L.; Miaoxin, X.; Chong, W.; Yaokui, H.; Shouxiang, Z. Ground-based Remote Sensing of LWC in Cloud and Rainfall by a Combined Dual-wavelength Radar-Rsdiometer System. Adv. Atmos. Sci. 1985, 2, 93–103. [CrossRef] 25. Maier, F.; Bendix, J.; Thies, B. Simulating Z–LWC Relations in Natural Fogs with Radiative Transfer Calculations for Future Application to a Cloud Radar Profiler. Pure Appl. Geophys. 2011, 169, 793–807. [CrossRef] 26. Tang, F.; Zou, X. Liquid water path retrieval using the lowest frequency channels of Fengyun-3C Microwave Radiation Imager (MWRI). J. Meteorol. Res. 2017, 31, 1109–1122. [CrossRef] 27. Wang, J.; Ge, J.; Zhang, Q.; Fan, P.; Wei, M.; Li, X. Study of aircraft icing warning algorithm based on millimeter wave radar. J. Meteorol. Res. 2017, 31, 1034–1044. [CrossRef] 28. Gultepe, I.; Sharman, R.; Williams, P.D.; Zhou, B.; Ellrod, G.; Minnis, P.; Trier, S.; Griffin, S.; Yum, S.S.; Gharabaghi, B.; et al. A Review of High Impact Weather for Aviation Meteorology. Pure Appl. Geophys. 2019, 176, 1869–1921. [CrossRef] 29. Zhu, Z.; Lamer, K.; Kollias, P.; Clothiaux, E.E. The Vertical Structure of Liquid Water Content in Shallow Clouds as Retrieved From Dual-Wavelength Radar Observations. J. Geophys. Res. Atmos. 2019, 124, 14184–14197. [CrossRef] Atmosphere 2020, 11, 876 17 of 17

30. Xin-dong, C.; Zhi-gang, Y.; Zeng-liang, Z.; Min-wei, W.; Chun-hui, F.; Tao, S. Comparison of Liquid Water Content Retrievals for Airborne Millimeter-Wave Radar with Different Particle Parameter Schemes. J. Trop. Meteorol. 2020, 26, 188–198. [CrossRef] 31. Thompson, G.; Politovich, M.K.; Rasmussen, R.M. A Numerical Weather Model’s Ability to Predict Characteristics of Aircraft Icing Environments. Weather Forecast. 2017, 32, 207–221. [CrossRef] 32. Forbes, G.S.; Hu, Y.; Brown, B.G.; Bernstein, B.C.; Politovich, M.K. Examination of Conditions in the Proximity of Pilotreports of Icing during STORM-FEST. In Proceedings of the Fifth International Conference on Aviation Weather Systems, Vienna, Virginia, 2–6 August 1993; AMS: Vienna, Virginia, 1993; pp. 282–286. 33. Kelsch, M.; Wharton, L. Comparing PIREPs with NAWAU turbulence and icing forecast: Issues and results. Weather Forecast. 1996, 11, 385–390. [CrossRef] 34. McDonough, F.; Bernstein, B.; Politovich, M.; Wolff, C. The Forecast Icing Potential Algorithm. In Proceedings of the 51st AIAA Aerospace Sciences Meeting and Exhibit, Grapevine, TX, USA, 7–10 January 2013. 35. Sim, S.; Im, J.; Park, S.; Park, H.; Ahn, M.-H.; Chan, P.W. Icing Detection over East Asia from Geostationary Satellite Data Using Machine Learning Approaches. Remote Sens. 2018, 10, 631. [CrossRef] 36. Boudala, F.; Isaac, G.A.; Wu, D. Aircraft Icing Study Using Integrated Observations and Model Data. Weather Forecast. 2019, 34, 485–506. [CrossRef] 37. Xu, M.; Thompson, G.; Adriaansen, D.R.; Landolt, S.D. On the Value of Time-Lag-Ensemble Averaging to Improve Numerical Model Predictions of Aircraft Icing Conditions. Weather Forecast. 2019, 34, 507–519. [CrossRef] 38. Bowyer, R.L.; Gill, P.G. Objective verification of global in-flight icing forecasts using satellite observations: Verification of WAFS icing forecasts using satellite observations. Meteorol. Appl. 2019, 26, 610–619. [CrossRef] 39. Hu, Y.X.; Winker, D.; Yang, P.; Baum, B.; Poole, L.; Vann, L. Identification of cloud phase from PICASSO CENA lidar depolarization: A multiple scattering sensitivity study. J. Quant. Spectrosc. Radiat. Transf. 2001, 70, 569–579. [CrossRef] 40. Wang, Z.E.; Sassen, K. Cloud type and macrophysical property retrieval using multiple remote sensors. J. Appl. Meteorol. 2001, 40, 1665–1682. [CrossRef] 41. Turner, D.D.; Clough, S.A.; Liljegren, J.C.; Clothiaux, E.E.; Cady-Pereira, K.E.; Gaustad, K.L. Retrieving liquid water path and precipitable water vapor from the atmospheric radiation measurement (ARM) microwave radiometers. IEEE Trans. Geosci. Remote Sens. 2007, 45, 3680–3690. [CrossRef] 42. Reisner, J.; Rasmussen, R.M.; Bruintjes, R.T. Explicit forecasting of supercooled liquid water storms using the MM5 mesoscale model. Q. J. R. Meteor. Soc. 1998, 124, 1071–1107. [CrossRef] 43. Dillingham, G. Aviation Safety: Icing and Winter Weather-Related Recommendations that NTSB Has Issued Since 1996 (GAO-10-679SP), an E-supplement to (GAO-10-678); Government Accountability Office: Washington DC, USA, 2010. 44. Wang, X.W.; Zhang, J.; Wang, S. Climatic characteristics of aircraft icing in China. Meteorol. Sci. 2002, 3, 93–100. (In Chinese) 45. Yang, C. Prediction of Aircraft Icing Based on Reanalysis Data. Master’s Thesis, Civil Aviation Flight University of China, Guanghan, China, 2017. (In Chinese) 46. Wang, H.D. Prediction Method of Aircraft Icing and Characteristics of Route Distribution. Master’s Thesis, Civil Aviation Flight University of China, Guanghan, China, 2018. (In Chinese) 47. Huang, Y.F. Aerometeorology, 2nd ed.; Southwest Jiaotong University Press: Chengdu, China, 2011. (In Chinese) 48. Liu, F.L.; Sun, L.T.; Li, S.J. Study on diagnostic prediction method of aircraft icing. Meteorol. Environ. Sci. 2011, 34, 26–30. (In Chinese)

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).