A Numerical Study on Intraseasonal Variations in Planetary Waves and Ozone Distribution in the Southern Hemisphere Stratosphere

名古屋大学Nagoya University博士(理学)南半球のオゾン全量はsubpolar maximumと呼ばれる中緯度に極大を持つ緯度分布をしており､その極大の位置は冬から春先への季節進行にともなって､しだいに高緯度へ移動する｡また､冬期の南半球では､波数1のプラネタリー波が準周期的に増幅することが､その特徴的な現象として知られている｡また､この波数1の増幅が東進する波数2のプラネタリー波との相互作用によって引き起こされてていることが､衛星データ解析によって示唆されている｡しかし､プラネタリー波がsubpolar maximumの形成にどの程度の影響を及ぼしているのか､また､波数2の東進波との相互作用による波数1の増幅機構どのようになっているのか､などについては未解明のままである｡本論文ではまず最初に､subpolar maximumの形成と春先における極方向への移動に対して､プラネタリー波の果たす基本的な役割を､簡単化した条件での数値実験の結果から議論する｡次に､波数1の停滞波と波数2の東進波が共存する条件下で､現実的なオゾン全量の水平分布の変動がシミュレートされることを示し､最後に､波数2との相互作用で波数1のプラネタリー波が増幅するメカニズムについて明らかにすることを目的とする｡過渡的な輸送過程および波と平均流の相互作用をシミュレートするために､経度方向に低次切断したセミスペクトルモデルが用いられる｡プラネタリー波の増幅にともなうオゾン輸送の計算には､全球モデルを使用し､波数1の増幅のメカニズムの解析には半球モデルを使用する｡プラネタリー波の増幅時のオゾン輸送を各月毎に計算した一連の数値実験で､プラネタリー波はsubpolar maximumの形成に対して次の様な役割を果たしていること明らかにされた｡北半球では誘起された子午面循環は極域まで達し､冬期全体をとおして極域でのオゾンの増加に寄与しているのに対し､南半球のブラネタリー波の増幅は中緯度下部成層圏に下降流が集中するような子午面循環を誘起し､その結果として中緯度のオゾン量の増加に寄与している｡冬から春先にかけて､中緯度の下降流域の極方向への移動にともなって､オゾンの増加域も高緯度へとシフトする｡TEM理論および波動伝播論に基づく診断的な解析から､南半球と北半球の非対称な子午面循環は､波の伝播場の違いに起因していることが示された｡南半球のプラネタリー波は､屈折率の極大線に沿って赤道に向かって伝播し､中緯度成層圏でEP fluxの収束場を形成するため､残差循環は極域まで達することができない｡波数1の停滞波と波数2の東進波を共存させることで､観測される波数1の増幅とオゾン全量の水平分布の変動パターンがシミュレートされる｡エネルギー論的な解析から､波数1の増幅は､波数1から平均流への運動エネルギー輸送の急激な減少によって引き起こされていることが示された｡この減少は､波数1の低緯度向きの伝播が抑制されることに対応しており､波数1の増幅時には､ジェットの低緯度側の南北シアーの強まりによって､中緯度に屈折率の負の領域が出現し､高緯度に導波管が形成される｡波数1と波数2の相互作用は､増幅時には波数1の南北位相勾配を減少させ低緯度伝播を抑える役割を果たし､減衰時には逆に､位相勾配を強め波数1の低緯度への伝播を促進する｡これらの波と平均流､波と波の相互作用の効果が複合して､波数1の周期的な増幅を引き起こしている｡この力学的メカニズムにより､オゾン輸送がより現実的にシミュレートできた｡The column ozone distribution in southern hemisphere (SH) have a maximum in midlatutede called subpolar maximum and the location of the maximum gradually shifts poleward as the seasonal progress from late winter to early spring. From the dynamical aspect, a quasi-periodic amplification of planetary wave with wavenumber 1 (wave 1) is observed in the stratosphere in SH. Some resent analyses of the satellite data suggested that the amplification is caused by the interaction with an eastward traveling wave with wavenumber 2 (wave 2). However, it is not well understood that mechanisms of the subpolar maximum formation and wave 1 amplification through the interaction with the eastward traveling wave. First part of this thesis explores fundamental roles of planetary waves on the formation of the subpolar maximum and its poleward shift in early spring under simplified conditions. Second part shows that horizontal variation of the total ozone content can be realized more realistically in the context of the wave 1-wave 2 interaction. The mechanism of the wave 1 amplification interacting with wave 2 is explored in the third part. Semi-spectral models with low-order longitudinal truncation are used to express the transient transport process and the interaction among waves and mean flow. The hemispheric version of the model is used to simulate the wave 1 amplification. The spherical version of the model is used to reveal roles of planetary wave amplification on the ozone transport and formation of the subpolar maximum. A series of experiment calculating ozone transports during planetary wave amplification for each month reveals the roles of planetary waves on the formation of subpolar maximum. Amplification of planetary waves in SH winter induces a meridional circulation whose downward motion is concentrated into the midlatitude lower stratosphere, and results in the ozone increase there. On the other hand, meridional circulation in the northern hemisphere(NH) extends to the polar stratosphere and contributes to the formation of ozone maximum in the polar region throughout winter. Diagnoses based on TEM theory and wave-propagation theory show that the asymmetric circulation between SH and NH is arisen from the difference of propagation field of waves. Planetary waves in SH propagates equatorward through a maximum line of refractive index which makes the convergence region of EP flux in the midlatitude stratosphere. Therefore, the residual circulation cannot reach the SH polar region. It is shown that the amplification of wave 1 and associated temporal variation of horizontal ozone distribution can be realized in the framework of the interaction between stationary wave with wavenumber 1 and eastward traveling wave with wavenumber 2. From the analyses of energetic, it was found that the amplification of wave 1 is attributed to the abrupt decrease of kinetic energy conversion to the zonal-mean flow which is caused by suppression of equatorward propagation of the wave 1. The wave-wave interaction decreases the phase gradient during the amplification stage and increases it during the post-amplification stage. The combined system of the wave-mean flow and wave-wave interactions causes the periodic amplification of wave 1, which leads to realistic representation of ozone distibusion in SH.名古屋大学博士学位論文 学位の種類:博士(理学) (論文) 学位授与年月日:平成8年6月4日doctoral thesi

無線LANにおける周辺端末の通信状況が位置特定性能に与える影響について

本稿では, 無線LANにおける周辺端末の通信状況が自端末の位置特定性能に与える影響について実験による評価を行っている. まず事前実験では, 時間帯によりアクセスポイント(AP) 探索結果が変化すること, 周辺端末の通信がAP探索結果に影響を及ぼすことを確認している. 事前実験をふまえて, 周辺端末の通信状況が異なる場合の無線LANを用いた位置特定実験を行っている．これから，無線LAN位置特定システムのデータベース(DB) を構築する際は，通信が行われていない状態のデータのみで構成したほうが，通信が行われている状態のデータのみで構成した場合よりも位置特定距離誤差が小さくなることを示す．さらに，通信が行われていない状態のデータのみで構成したDBに対して，通信が行われている状態のデータを数割含ませることで，位置特定誤差距離は更に小さくなることを示している． In this paper, we evaluate the communication of peripheral terminals affects positioning performance in wireless LAN by experiment. In the preliminary experiments, it is confirmed that the scan of the access points(AP) varies depending on the time period, and the communication of peripheral terminals affects the scan of the AP. The based on preliminary experiments, we evaluate positioning performance of wireless LAN in cases where the communication status of peripheral terminals is different. From the positioning experiment, when constructing the database(DB) of the wireless LAN positioning system, it is better the situation only when there is no communication than the case of configuring only the situation in which communication is being conducted. It shows that the distance error decreases. In addition, it is suggested that including a few percent of the data only the situation in which communication is being conducted for the DB composed only of the data not in the situation of communication, the distance error becomes further smaller There.Copyright ©2018 by IEICEtextapplication/pdftechnical repor

Upper Bound on the Decay τ→μγ from the Belle Detector

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ON SOME FUNDAMENTAL TRAFFIC PROBLEMS.

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Striatal network parameters.

<p>Striatal network parameters.</p

Effect of spatial scale of correlations in <i>ϕ</i> on the emergence and velocity of STAS in I-networks.

(a) The top row shows the spatial distribution of ϕ for different scales of Perlin noise. The Perlin scales decreased from left to right as reflected in the size of single color blobs. The Perlin scale is indicated in terms of grid points in the network. The bottom row shows the spatial distribution of average firing rates in each of the seven configurations. (b) The number of STAS observed in 1 sec. for different Perlin scales. The box plot shows that statistics of STAS estimated over 90 epochs of 1 sec. each. Different colors indicate the scale of the corresponding Perlin noise. (c) The distribution of the velocity of STAS. Different colors indicate the scale of the corresponding Perlin noise. (d) The distribution of STAS directions in polar plots. In a homogeneous configuration, most sequences moved in a single direction (blue curve). As the Perlin scale decreased, the distribution of movement direction became more widely distributed, indicating an increase in the number of sequences that moved in different directions.</p

Spatial clustering of <i>ϕ</i> results in feedforward pathways in otherwise locally connected random networks.

(a) The eigenvalue spectrum of the connectivity matrix of 1,000 inhibitory neurons randomly selected from symmetric (blue dots), random (orange dots), Perlin (green dots) and homogeneous (red dots) I-networks. (b) Number of unique target neurons participating in a feedforward path (y-axis) as a function of the effective length of the feedforward path (Euclidean distance between the centroids of F1 and F50 (see Methods). Feedforward path in I-network (dots), feedforward path in EI-network (crosses). The four colors indicate the network configurations. Note that distinctly more unique neurons with longer path length of the sequential activity movement were observed in Perlin and homogeneous configurations. (c) Effective feedforward pathways in an I-network model with the four configurations (see Methods). Feedforward paths starting from four different locations are shown. The starting neuron set F1 is shown in yellow, the final set F50 is shown in orange. Effective feedforward pathways were visible as trails changing color from yellow to orange. The starting neuron set F1 consisted of 64 neurons located in an 8×8 region of the network. (d) Same as in c, but for an EI-network model with nine different starting set locations.</p

Network structure and spiking activity in I-networks.

(a) Spatial distribution of connection asymmetries. The square represents the 2-D space of the network. The four panels (a1-a4) show the four different configurations of asymmetric connectivity: symmetric, random, Perlin and homogeneous. The panel a5 shows the distribution of ϕ, measured for each neuron from the locations of its post-synaptic neurons. (b) Spatial distribution of in-degrees of individual neurons in the four configurations (b1-b4). The in-degree distribution was similar for all four configurations (b5). Note that in the Perlin configuration, neurons with high and low in-degree were spatially clustered (b3). (c) Spatial distribution of average firing rates of individual neurons in the four network configurations (c1-c4). (c5)The distribution of firing rate of all the neurons. (d1-d4) Spatially distributed direction of neuronal activity flow in the four configurations. (d5) Distribution of the direction of neuronal activity flow independent of space. In symmetric, random and Perlin configurations, activity could move in all possible directions (blue, orange, green), whereas in the homogeneous configuration, activity flowed in a single direction (red). Note that in symmetric and random configurations, despite the presence of all possible directions of projection, the network activity remained locked at certain specific locations (d1,d2), unlike in the Perlin configuration, in which a clear and spatially diverse flow of activity emerged (d3).</p

Parameter values for the networks (top), for the connections (middle) and for an external input (bottom) in EI-network model.

Parameter values for the networks (top), for the connections (middle) and for an external input (bottom) in EI-network model.</p

Schematics of the asymmetric network models.

(a:left) Neurons were arranged on a regular 2-D grid, folded to form a torus. The colored circles indicate the symmetric (blue) and asymmetric (green) spatial connectivity schemes. The pre-synaptic neuron is marked by the orange dot. (a:center) Locations of post-synaptic neurons chosen according to the asymmetric (green) or symmetric (blue) connectivity. In this case the distance-dependent connectivity profile varied non-monotonically, according to a Γ distribution. This connectivity profile was used for purely inhibitory network models. (a:right) Same as in the center panel, but here the distance-dependent connectivity profile varied monotonically according to a Gaussian distribution. This connectivity profile was used in the present study for network models with both excitatory and inhibitory neurons. (b) Schematic of spatial distribution of connection asymmetries. Each arrow shows the direction in which the neuron makes preferentially most connections (ϕ). Here we show examples for random, Perlin and homogeneous configurations.</p