@phdthesis{oai:mie-u.repo.nii.ac.jp:00012317,
 author = {荒木, 健太郎},
 month = {Dec},
 note = {application/pdf, 冬季首都圏では，本州南岸を進む南岸低気圧と呼ばれる低気圧に伴って降雪がもたらされる．首都圏では少しの雪でも交通等に大きな影響があり，ひとたび大雪となると雪崩や集落の孤立，農業被害など多岐にわたる雪氷災害が発生するが，現状ではこの降雪現象の正確な予測は難しい．首都圏降雪現象を高精度に予測するためには，まずは現象の実態解明が必要不可欠である．そこで，本研究では，南岸低気圧による降雪現象の実態解明のために以下の研究に取り組んだ．
まず，これまで南岸低気圧が八丈島の北を通る場合は関東平野への暖気流入が強くなるために雨，南を通る場合は雪が降るといわれてきた．この経験則を確かめるため，1958～2015 年冬季の東京都心における降雪・降雨事例について，気象庁55 年長期再解析を用いて東京の雨と雪をわける要因を統計的に調べた．その結果，南岸低気圧の進路，発達率，平均移動速度の各特性は，それぞれが単独で東京の雨雪に関係していないことが明らかとなった．また，東京における雨と雪の事例では，特に総観スケールの気温場が大きく異なり，大陸から吹き出す下層寒気や上層寒気は東京で降水が始まる2 日間ほど前から有意な差が見られた．さらに，南岸低気圧の進路が八丈島の北で陸の近くを通過する降雪事例では，同様な降雨事例と比べて暖気流入に大きな違いはなく，総観スケールで下層が低温であることに加え，低気圧の中心気圧が低く北からの下層寒気移流が強かった．このため，低気圧中心付近でも降雪に適した低温な環境となっていた．これらのことから，東京都心の雨雪は南岸低気圧の進路のみでは決まらず，総観スケールの環境場が重要であるといえる．
また，2017 年3 月27 日に南岸低気圧に伴う大雪により，栃木県那須町で表層雪崩による災害が発生した．表層雪崩発生には短時間での多量の降雪が重要と言われているが，山岳域での大雪時の降雪強化メカニズムやその水平分布等の特性は理解が不足している．そこで，この大雪の事例解析を行うとともに，1989～2017 年の那須における降雪事例について統計解析を行い，降雪・気象場の諸特性を調べた．事例解析の結果，3 月27 日の大雪事例では低気圧接近に伴い，湿潤な北～東風の強まりとともに形成された地形性上昇流が過冷却の水雲を下層で発生させていた．この下層雲と低気圧に伴う雲からの降雪が，Seeder-Feeder メカニズムを通して那須岳の北～東斜面で降雪を強化し，局地的な短時間大雪をもたらしていたことが示唆された．統計解析の結果，この事例と同規模の大雪は3 年に1 度，3 月としては約20 年に1 度発生していた．那須で大雪iiとなる気圧配置は西高東低の冬型が63％，低気圧が30％であり，いずれも日降雪時間が長いほど日降雪深が大きかった．しかし，低気圧による降雪の場合には例外的に短時間で大雪になることがあり，これらの事例の多くは閉塞段階の低気圧が関東付近を通過していたことがわかった．
さらに，降雪現象の高精度予測のためには，降雪雲の物理特性の実態解明が必要不可欠である．そこで，関東甲信地方で降雪時に市民から雪結晶画像を募集する「#関東雪結晶 プロジェクト」を実施し，2016～2017 年冬季観測結果により，シチズンサイエンスによる雪結晶観測の有効性を確かめ，降雪特性の実態把握を試みた．雪結晶の撮影にはスマートフォンのカメラを採用し，ソーシャル・ネットワーキング・サービスを用いた画像収集を行った．これにより，ごく簡易な雪結晶観測手法を確立し，シチズンサイエンスとして効率的な観測データ収集を実現した．この結果，ひと冬を通して1 万枚以上の雪結晶画像が集まり，そのうち解析可能なものは73％だった．この取り組みによって首都圏での時空間的に超高密度な雪結晶観測が実現できた．観測結果は，現象の実態解明だけでなく，数値予報モデルの検証・改良や偏波レーダーを用いた降水種別判別手法の高精度化などにも応用可能である．一方，シチズンサイエンスデータの特性として，人口の多い都心部での現象では観測数が増えるものの，内陸部のみでの降雪の場合は観測数が少ない傾向が見られた．今後，シチズンサイエンスによる雪結晶観測のネットワークを拡充するために，自治体や教育機関との連携，効果的な広報・普及活動が必要である．
このように，本研究は南岸低気圧による首都圏降雪現象について，都心部での雨と雪をわける要因，山岳域での短時間大雪時の大気場の特徴や雲の構造を明らかにした．さらに，首都圏降雪現象のさらなる実態解明や監視・予測技術の高度化のために重要である新たな降雪観測手法を確立することができた．本研究で得られた知見は，予報担当者の診断的予測技術や降雪監視技術の向上を通し，気象庁の発表する雪氷災害に関わる防災気象情報の高精度化に貢献できる．
In winter seasons, extratropical cyclones moving along with the south coast of Japan, so-called South-Coast Cyclones (SCCs), sometimes bring snowfall in the metropolitan area in Japan. Even a small amount of snowfall has a great influence on transportation in the metropolitan area. Once heavy snowfall occurs, various snow and ice disasters such as avalanches and isolation of settlements, agricultural and construction damages are brought by the snowfall. At present, however, it is difficult to predict such snowfall phenomena accurately. In order to accurately predict these snowfall phenomena in the metropolitan area in Japan, it is essential to elucidate the actual state and structure of the phenomena. In this paper, we studied following topics to elucidate the snowfall phenomena in the metropolitan area in Japan due to the SCCs.
Firstly, it has been empirically thought that snowfall and rainfall in the metropolitan area respectively occur when a SCC passes on the south and north sides of Hachijo-jima Island, because warm and cold advections surrounding the cyclones determine the surface temperature in the metropolitan area. In order to confirm this empirical rule, factors distinguishing between snowfall and rainfall in Tokyo urban area for the cases from 1958 to 2015 were statistically investigated using the Japan Meteorological Agency 55-year reanalysis data. As the result, it's found that cyclone characteristics of the traveling path, maximum and averaged developing rates, and averaged moving velocity were independent of the precipitation phase in Tokyo urban area. On the other hand, there were significant differences in synoptic-scale upper and low-level temperature fields between snowfall and rainfall cases about 2 days before the time of precipitation starting in Tokyo. In addition, in snowfall cases with a SCC located on the north side of Hachijo-jima Island, the low-level cold air flow blowing into the cyclone from the north was stronger than that in rainfall cases with the same cyclone position, where the cyclone developed in snowfall cases more than in rainfall cases in addition to the low-level colder atmospheric condition. These results indicate that only the traveling path of SCCs never determines the precipitation phase in Tokyo urban area, and that synoptic-scale conditions play a key role on the determination.
Secondly, a heavy snowfall associated with cyclones caused a surface avalanche in Nasu, Tochigi Prefecture, Japan on 27 March 2017. Although it is known that large amounts of snowfall in a short time are important for surface avalanches, understanding of snowfall characteristics in mountainous regions during heavy snowfall events is lacking. We conducted a case study of this event and also performed a statistical analysis of snowfall events in Nasu from 1989 to 2017, where we investigated the snowfall characteristics and meteorological conditions of each event. In the March 2017 event, low-level supercooled water clouds were formed by orographically forced updrafts in mountainous regions in Nasu as moist northerly and easterly flows intensified due to the cyclone's approach. Localized snowfall intensification and short-duration heavy snowfalls were produced by the Seeder-Feeder mechanism associated with the low-level clouds and snow from the upper clouds of the cyclone. The statistical analysis revealed that similar heavy snowfall events occur about once every 3 years, but only once every20 years in March. The surface pressure patterns in heavy snowfall cases in Nasu were about 63% in the typical winter monsoon pattern and about 30% in cyclones. Although snowfall amounts became larger as snowfall duration increased in both patterns, some short-duration heavy snowfalls exceptionally occurred in cases where occluded cyclones passed near the Kanto region.
Thirdly, to improve forecasts of snowfall events, a better understanding of the microphysical properties of snow clouds is needed. The Meteorological Research Institute conducted the "#KantoSnowCrystal Project" to collect images of snow crystals from citizens during snowfall events in the Kanto and Koshin regions in Japan. Smartphone cameras were used to capture the images, which were mainly collected through social networking services. Through the campaign in the 2016-2017 winter season, we confirmed the availability of snow crystal observations by citizen science, and tried understanding the snowfall characteristics in the metropolitan area. Through the project, we were able to establish an easy method for snow crystal observation and data collection. More than 10,000 snow crystal images were gathered throughout the 2016-2017 winter, of which 73% were analyzable. The #KantoSnowCrystal Project thereby realized spatiotemporally ultra-dense observations of snow crystals in the metropolitan area, and the observation dataset should contribute to investigations of snowfall mechanisms in these areas and the verification and improvement of numerical weather models, etc. The amount of data, however, varied considerably between heavily populated central urban areas and less-populated inland areas. Collaboration with local autonomous bodies and educational organizations, and effective outreach and dissemination activities are needed to expand the network of snow crystal observation by citizen science.
In these ways, this study focused on the snowfall phenomenon in the metropolitan area due to the SCCs, and revealed the synoptic-scale factors distinguishing between rainfall and snowfall in the center of the metropolitan area and also the characteristics of the atmospheric field and the structure of the snow clouds at the time of short-duration heavy snowfall causing surface avalanches in the mountainous region. Moreover, we could establish a new observation method which is important for elucidating the snowfall phenomena in the metropolitan area and for improving the monitoring and prediction technology. Through the improvement of diagnostic forecasting techniques and snowfall monitoring techniques of forecasters in the Japan Meteorological Agency, these findings obtained in this study can contribute to the improvement of weather information for disaster prevention associated with snowfall phenomena in the metropolitan area in Japan., 本文, 110p},
 school = {三重大学},
 title = {南岸低気圧による首都圏降雪現象の実態解明のための研究},
 year = {2018},
 yomi = {アラキ, ケンタロウ}
}