From cc215eca7d914a0aa1d0757dc3782b30763a691d Mon Sep 17 00:00:00 2001 From: unuma Date: Mon, 1 Jan 2024 14:38:58 +0900 Subject: [PATCH] Site updated: 2024-01-01 14:38:57 --- .DS_Store | Bin 6148 -> 6148 bytes 2024/01/01/index.html | 301 +++++++++++++++++++++++++++ 2024/01/01/skewt.png | Bin 0 -> 81432 bytes archives/2024/01/index.html | 264 +++++++++++++++++++++++ archives/2024/index.html | 264 +++++++++++++++++++++++ archives/index.html | 32 +-- archives/page/2/index.html | 16 ++ atom.xml | 25 ++- index.html | 28 +-- page/2/index.html | 28 +-- page/3/index.html | 28 +-- page/4/index.html | 28 +-- rss2.xml | 27 ++- tags/Miscellaneous/index.html | 36 ++-- tags/Miscellaneous/page/2/index.html | 18 ++ 15 files changed, 1003 insertions(+), 92 deletions(-) create mode 100644 2024/01/01/index.html create mode 100644 2024/01/01/skewt.png create mode 100644 archives/2024/01/index.html create mode 100644 archives/2024/index.html diff --git a/.DS_Store b/.DS_Store index 65575f82fbd9b26cfae462833040081309ac042a..9073741549d92538a60d314c32931a9ec2be547d 100644 GIT binary patch delta 114 zcmZoMXfc?uH14!S69WSS3xgg*IzuKyNp8N2OHxjL5>Sl8adxJF=E9?nsPZXz+hqai#1 diff --git a/2024/01/01/index.html b/2024/01/01/index.html new file mode 100644 index 0000000..2998ec6 --- /dev/null +++ b/2024/01/01/index.html @@ -0,0 +1,301 @@ + + + + + + + + + + + + + Skew-T LogP plot by MetPy + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+

Skew-T LogP plot by MetPy

+ +

はじめに

+

大気の鉛直構造を把握するため、日々、高層気象観測が行われている。これらは観測としてのみならず、数値予報モデルの初期値・解析値としても用いられる重要な観測データである。本稿では、その観測データを地点毎に把握するための Skew-T LogP plot について述べる。ここでは、MetPy で提供されているものを利用し、特に理想化した数値実験の初期値に用いる一点鉛直プロファイルの可視化について紹介する。

+

データ

+

とある時刻・地点のデータを利用する。
+1行目は、地上の気圧 (hPa)・温位 (K)・水蒸気混合比 (g/kg)。
+2行目以降は、高度 (m)・温位 (K)・水蒸気混合比 (g/kg)・東西風 (m/s)・南北風 (m/s)。
+これを、input_sounding という名前で保存。

+
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 1000.00  298.81   18.08
  277.04  299.83   16.13   -5.29    1.54
  504.73  299.94   16.02   -5.96    1.84
  736.79  300.06   16.00   -6.08    2.16
  973.81  301.25   15.48   -5.57    3.11
 1465.20  303.95   14.38   -2.57    5.48
 1982.15  306.88   12.90    1.13    5.17
 3104.82  312.11    9.51    5.20    4.45
 4369.76  318.33    6.41    5.66    4.46
 5823.29  325.50    4.00   11.84   -0.45
 7542.22  334.36    1.78   15.32    3.46
 9655.91  343.85    0.65   15.49   -5.92
12000.00  351.53    0.11   15.49   -5.92
21000.00  500.53    0.01   15.49   -5.92
+

ソースコード

+

上記のデータを、下記で可視化する。

+
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import pandas as pd
import numpy as np

import matplotlib.pyplot as plt
import matplotlib
import matplotlib.dates as mdates

import metpy.calc as mpcalc
from metpy.calc import temperature_from_potential_temperature, height_to_pressure_std, dewpoint_from_specific_humidity
from metpy.plots import SkewT
from metpy.units import units, pandas_dataframe_to_unit_arrays
import pint_xarray

# ヘッダーを取り除いた上でカラムを指定、pandas dataframe として格納。
df = pd.read_fwf('input_sounding', header=0, usecols=[0, 1, 2, 3, 4], names=['height', 'theta', 'qv', 'uwnd', 'vwnd'])

# pandas dataframe を xarray dataset に変換
ds = df.to_xarray()

# MetPy.units を用い、単位を指定。
ds['height'] = ds['height'] * units.meter
ds['theta'] = ds['theta'] * units.kelvin
ds['qv'] = ds['qv'] * units('g/kg')
ds['uwnd'] = ds['uwnd'] * units.meter_per_second
ds['vwnd'] = ds['vwnd'] * units.meter_per_second

# SkewT logP plot の描画に必要な変数に変換。
ds['pressure'] = height_to_pressure_std(ds['height']).pint.to('hPa')
ds['temperature'] = temperature_from_potential_temperature(ds['pressure'], ds['theta']).pint.to('degC')
ds['dewpoint'] = dewpoint_from_specific_humidity(ds['pressure'], ds['temperature'], ds['qv']).pint.to('degC')

# ある高度から持ち上げたパーセルの気温・露点温度のプロファイルを計算。
parcel_prof = mpcalc.parcel_profile(ds['pressure'], ds['temperature'][0], ds['dewpoint'][0]).pint.to('degC')

# 持ち上げ凝結高度の気圧・気温・露点温度を計算。
lcl_pressure, lcl_temperature = mpcalc.lcl(ds['pressure'][0], ds['temperature'][0], ds['dewpoint'][0])


# 描画
fig = plt.figure(figsize=(8, 6))
skew = SkewT(fig)
skew.ax.set_ylim(1050,100)
skew.ax.set_xlim(-30,35)
skew.ax.set_title("A sample sounding")
skew.plot(ds['pressure'], ds['temperature'], color="k", linestyle='-')
skew.plot(ds['pressure'], ds['dewpoint'], linestyle='-')
skew.shade_cape(ds['pressure'], ds['temperature'], parcel_prof)
skew.plot(ds['pressure'], parcel_prof, linestyle=':', linewidth=2)
skew.plot(lcl_pressure, lcl_temperature, 'ko', markerfacecolor='black')
skew.plot_moist_adiabats(linewidth=0.5)
fig.savefig('skewt.png', bbox_inches='tight')


# 各種指数・パラメーターの計算
p = ds['pressure']
T = ds['temperature']
Td = ds['dewpoint']
height = ds['height']
u = ds['uwnd']
v = ds['vwnd']

ctotals = mpcalc.cross_totals(p, T, Td)
kindex = mpcalc.k_index(p, T, Td)
showalter = mpcalc.showalter_index(p, T, Td)
total_totals = mpcalc.total_totals_index(p, T, Td)
vert_totals = mpcalc.vertical_totals(p, T)
lift_index = mpcalc.lifted_index(p, T, parcel_prof)
cape, cin = mpcalc.cape_cin(p, T, Td, parcel_prof)
lclp, lclt = mpcalc.lcl(p[0], T[0], Td[0])
lfcp, _ = mpcalc.lfc(p, T, Td)
el_pressure, _ = mpcalc.el(p, T, Td, parcel_prof)
ml_t, ml_td = mpcalc.mixed_layer(p, T, Td, depth=50 * units.hPa)
ml_p, _, _ = mpcalc.mixed_parcel(p, T, Td, depth=50 * units.hPa)
mlcape, mlcin = mpcalc.mixed_layer_cape_cin(p, T, parcel_prof, depth=50 * units.hPa)
mu_p, mu_t, mu_td, _ = mpcalc.most_unstable_parcel(p, T, Td, depth=50 * units.hPa)
mucape, mucin = mpcalc.most_unstable_cape_cin(p, T, Td, depth=50 * units.hPa)
(u_storm, v_storm), *_ = mpcalc.bunkers_storm_motion(p, u, v, height)
critical_angle = mpcalc.critical_angle(p, u, v, height, u_storm, v_storm)


# 各種指数・パラメーターの値を確認
print(f'        CAPE: {cape:.2f}')
print(f'         CIN: {cin:.2f}')
print(f'LCL Pressure: {lclp:.2f}')
print(f'LFC Pressure: {lfcp:.2f}')
print(f' EL Pressure: {el_pressure:.2f}')
print()
print('Mixed Layer - Lowest 50-hPa')
print(f'     ML Temp: {ml_t:.2f}')
print(f'     ML Dewp: {ml_td:.2f}')
print(f'     ML CAPE: {mlcape:.2f}')
print(f'      ML CIN: {mlcin:.2f}')
print()
print('Most Unstable - Lowest 50-hPa')
print(f'     MU Temp: {mu_t:.2f}')
print(f'     MU Dewp: {mu_td:.2f}')
print(f' MU Pressure: {mu_p:.2f}')
print(f'     MU CAPE: {mucape:.2f}')
print(f'      MU CIN: {mucin:.2f}')
print()
print(f'   Lifted Index: {lift_index:.2f}')
print(f'        K-Index: {kindex:.2f}')
print(f'Showalter Index: {showalter:.2f}')
print(f'   Cross Totals: {ctotals:.2f}')
print(f'   Total Totals: {total_totals:.2f}')
print(f'Vertical Totals: {vert_totals:.2f}')
print()
print('Bunkers Storm Motion Vector')
print(f'  u_storm: {u_storm:.2f}')
print(f'  v_storm: {v_storm:.2f}')
print(f'Critical Angle: {critical_angle:.2f}')
+

出力結果

+

図が Skew-T logP plot。横軸は気温または露点温度 (degree C)、縦軸は気圧 (hPa)。黒実線で気温、青実線で露点温度、橙点線で持ち上げたパーセルの気温をそれぞれ示す、赤色の陰影で示される面積は、持ち上げたパーセルと周辺大気との気温差、すなわち対流有効位置エネルギー (CAPE) である。黒丸印は持ち上げ凝結高度 (LCL) を示す。
+図の左下から右上に向かう灰色の線は等温線を、図の右下から左上に伸びる青色点線は等湿潤断熱線をそれぞれ示す。

+ +

図の 925 hPa から少なくとも 500 hPa までは、気温と露点温度の線が重なっている。これは、ほぼ相対湿度 100% であることを示す。加えて、800 hPa から 600 hPa までの層では、気温減率の傾きが湿潤断熱線の傾きよりも大きくなっており、湿潤絶対不安定 (Moist Absolutely Unstable Layer, Bryan and Fritsch 2000) の可能性がある。

+

下記は、一点鉛直プロファイルから得られる指数やパラメーターの出力である。一地点の鉛直プロファイルから、これだけ多様な診断パラメーターを算出することが可能となる。

+
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        CAPE: 729.54 joule / kilogram
         CIN: -3.21 joule / kilogram
LCL Pressure: 926.10 hectopascal
LFC Pressure: 885.54 hectopascal
 EL Pressure: 260.51 hectopascal

Mixed Layer - Lowest 50-hPa
     ML Temp: 22.89 degree_Celsius
     ML Dewp: 20.64 degree_Celsius
     ML CAPE: 1997.43 joule / kilogram
      ML CIN: -0.15 joule / kilogram

Most Unstable - Lowest 50-hPa
     MU Temp: 20.55 degree_Celsius
     MU Dewp: 20.17 degree_Celsius
 MU Pressure: 927.77 hectopascal
     MU CAPE: 754.89 joule / kilogram
      MU CIN: -1.08 joule / kilogram

   Lifted Index: -3.48 delta_degree_Celsius
        K-Index: 41.32 degree_Celsius
Showalter Index: -3.25 delta_degree_Celsius
   Cross Totals: 24.40 delta_degree_Celsius
   Total Totals: 48.64 delta_degree_Celsius
Vertical Totals: 24.24 delta_degree_Celsius

Bunkers Storm Motion Vector
  u_storm: 0.25 meter_per_second
  v_storm: -4.63 meter_per_second
Critical Angle: 179.85 degree
+

指数・診断パラメーターは、発案された地域や特性に依存するため、計算結果をそのまま異なる地域に当てはめることは得策ではない。上記で調べたように、可視化することで指数が意図した計算となっているか、愚直に確認することが重要である (時に時間を要するが)。ここでは、計算方法の紹介に留める。

+

おわりに

+

大気の状態を把握するための基礎的な観測の一つである、高層気象観測データの可視化と関連する指数・パラメータの計算について紹介した。

+

参考文献

+
    +
  • May, R. M., Goebbert, K. H., Thielen, J. E., Leeman, J. R., Camron, M. D., Bruick, Z., Bruning, E. C., Manser, R. P., Arms, S. C., and Marsh, P. T., 2022: MetPy: A Meteorological Python Library for Data Analysis and Visualization. Bull. Amer. Meteor. Soc., 103, E2273-E2284, doi:10.1175/BAMS-D-21-0125.1.
    • +
  • Bryan, G. H., and J. M. Fritsch, 2000: Moist Absolute Instability: The Sixth Static Stability State. Bull. Amer. Meteor. Soc., 81, 1207–1230, doi:10.1175/1520-0477(2000)081<1287:MAITSS>2.3.CO;2.
    • +
+

更新履歴

+
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  • 2024-01-01: 初稿
    • +
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— 2024年1月1日

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    diff --git a/atom.xml b/atom.xml index fda9fd9..3436dae 100644 --- a/atom.xml +++ b/atom.xml @@ -6,7 +6,7 @@ - 2022-11-20T09:33:35.000Z + 2024-01-01T05:37:46.502Z https://takashiunuma.github.io/blog/ @@ -16,6 +16,29 @@ Hexo + + Skew-T LogP plot by MetPy + + https://takashiunuma.github.io/blog/2024/01/01/ + 2024-01-01T04:15:24.000Z + 2024-01-01T05:37:46.502Z + + はじめに

    大気の鉛直構造を把握するため、日々、高層気象観測が行われている。これらは観測としてのみならず、数値予報モデルの初期値・解析値としても用いられる重要な観測データである。本稿では、その観測データを地点毎に把握するための Skew-T LogP plot について述べる。ここでは、MetPy で提供されているものを利用し、特に理想化した数値実験の初期値に用いる一点鉛直プロファイルの可視化について紹介する。

    データ

    とある時刻・地点のデータを利用する。
    1行目は、地上の気圧 (hPa)・温位 (K)・水蒸気混合比 (g/kg)。
    2行目以降は、高度 (m)・温位 (K)・水蒸気混合比 (g/kg)・東西風 (m/s)・南北風 (m/s)。
    これを、input_sounding という名前で保存。

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     1000.00  298.81   18.08
      277.04  299.83   16.13   -5.29    1.54
      504.73  299.94   16.02   -5.96    1.84
      736.79  300.06   16.00   -6.08    2.16
      973.81  301.25   15.48   -5.57    3.11
     1465.20  303.95   14.38   -2.57    5.48
     1982.15  306.88   12.90    1.13    5.17
     3104.82  312.11    9.51    5.20    4.45
     4369.76  318.33    6.41    5.66    4.46
     5823.29  325.50    4.00   11.84   -0.45
     7542.22  334.36    1.78   15.32    3.46
     9655.91  343.85    0.65   15.49   -5.92
    12000.00  351.53    0.11   15.49   -5.92
    21000.00  500.53    0.01   15.49   -5.92

    ソースコード

    上記のデータを、下記で可視化する。

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    import pandas as pd
    import numpy as np

    import matplotlib.pyplot as plt
    import matplotlib
    import matplotlib.dates as mdates

    import metpy.calc as mpcalc
    from metpy.calc import temperature_from_potential_temperature, height_to_pressure_std, dewpoint_from_specific_humidity
    from metpy.plots import SkewT
    from metpy.units import units, pandas_dataframe_to_unit_arrays
    import pint_xarray

    # ヘッダーを取り除いた上でカラムを指定、pandas dataframe として格納。
    df = pd.read_fwf('input_sounding', header=0, usecols=[0, 1, 2, 3, 4], names=['height', 'theta', 'qv', 'uwnd', 'vwnd'])

    # pandas dataframe を xarray dataset に変換
    ds = df.to_xarray()

    # MetPy.units を用い、単位を指定。
    ds['height'] = ds['height'] * units.meter
    ds['theta'] = ds['theta'] * units.kelvin
    ds['qv'] = ds['qv'] * units('g/kg')
    ds['uwnd'] = ds['uwnd'] * units.meter_per_second
    ds['vwnd'] = ds['vwnd'] * units.meter_per_second

    # SkewT logP plot の描画に必要な変数に変換。
    ds['pressure'] = height_to_pressure_std(ds['height']).pint.to('hPa')
    ds['temperature'] = temperature_from_potential_temperature(ds['pressure'], ds['theta']).pint.to('degC')
    ds['dewpoint'] = dewpoint_from_specific_humidity(ds['pressure'], ds['temperature'], ds['qv']).pint.to('degC')

    # ある高度から持ち上げたパーセルの気温・露点温度のプロファイルを計算。
    parcel_prof = mpcalc.parcel_profile(ds['pressure'], ds['temperature'][0], ds['dewpoint'][0]).pint.to('degC')

    # 持ち上げ凝結高度の気圧・気温・露点温度を計算。
    lcl_pressure, lcl_temperature = mpcalc.lcl(ds['pressure'][0], ds['temperature'][0], ds['dewpoint'][0])


    # 描画
    fig = plt.figure(figsize=(8, 6))
    skew = SkewT(fig)
    skew.ax.set_ylim(1050,100)
    skew.ax.set_xlim(-30,35)
    skew.ax.set_title("A sample sounding")
    skew.plot(ds['pressure'], ds['temperature'], color="k", linestyle='-')
    skew.plot(ds['pressure'], ds['dewpoint'], linestyle='-')
    skew.shade_cape(ds['pressure'], ds['temperature'], parcel_prof)
    skew.plot(ds['pressure'], parcel_prof, linestyle=':', linewidth=2)
    skew.plot(lcl_pressure, lcl_temperature, 'ko', markerfacecolor='black')
    skew.plot_moist_adiabats(linewidth=0.5)
    fig.savefig('skewt.png', bbox_inches='tight')


    # 各種指数・パラメーターの計算
    p = ds['pressure']
    T = ds['temperature']
    Td = ds['dewpoint']
    height = ds['height']
    u = ds['uwnd']
    v = ds['vwnd']

    ctotals = mpcalc.cross_totals(p, T, Td)
    kindex = mpcalc.k_index(p, T, Td)
    showalter = mpcalc.showalter_index(p, T, Td)
    total_totals = mpcalc.total_totals_index(p, T, Td)
    vert_totals = mpcalc.vertical_totals(p, T)
    lift_index = mpcalc.lifted_index(p, T, parcel_prof)
    cape, cin = mpcalc.cape_cin(p, T, Td, parcel_prof)
    lclp, lclt = mpcalc.lcl(p[0], T[0], Td[0])
    lfcp, _ = mpcalc.lfc(p, T, Td)
    el_pressure, _ = mpcalc.el(p, T, Td, parcel_prof)
    ml_t, ml_td = mpcalc.mixed_layer(p, T, Td, depth=50 * units.hPa)
    ml_p, _, _ = mpcalc.mixed_parcel(p, T, Td, depth=50 * units.hPa)
    mlcape, mlcin = mpcalc.mixed_layer_cape_cin(p, T, parcel_prof, depth=50 * units.hPa)
    mu_p, mu_t, mu_td, _ = mpcalc.most_unstable_parcel(p, T, Td, depth=50 * units.hPa)
    mucape, mucin = mpcalc.most_unstable_cape_cin(p, T, Td, depth=50 * units.hPa)
    (u_storm, v_storm), *_ = mpcalc.bunkers_storm_motion(p, u, v, height)
    critical_angle = mpcalc.critical_angle(p, u, v, height, u_storm, v_storm)


    # 各種指数・パラメーターの値を確認
    print(f'        CAPE: {cape:.2f}')
    print(f'         CIN: {cin:.2f}')
    print(f'LCL Pressure: {lclp:.2f}')
    print(f'LFC Pressure: {lfcp:.2f}')
    print(f' EL Pressure: {el_pressure:.2f}')
    print()
    print('Mixed Layer - Lowest 50-hPa')
    print(f'     ML Temp: {ml_t:.2f}')
    print(f'     ML Dewp: {ml_td:.2f}')
    print(f'     ML CAPE: {mlcape:.2f}')
    print(f'      ML CIN: {mlcin:.2f}')
    print()
    print('Most Unstable - Lowest 50-hPa')
    print(f'     MU Temp: {mu_t:.2f}')
    print(f'     MU Dewp: {mu_td:.2f}')
    print(f' MU Pressure: {mu_p:.2f}')
    print(f'     MU CAPE: {mucape:.2f}')
    print(f'      MU CIN: {mucin:.2f}')
    print()
    print(f'   Lifted Index: {lift_index:.2f}')
    print(f'        K-Index: {kindex:.2f}')
    print(f'Showalter Index: {showalter:.2f}')
    print(f'   Cross Totals: {ctotals:.2f}')
    print(f'   Total Totals: {total_totals:.2f}')
    print(f'Vertical Totals: {vert_totals:.2f}')
    print()
    print('Bunkers Storm Motion Vector')
    print(f'  u_storm: {u_storm:.2f}')
    print(f'  v_storm: {v_storm:.2f}')
    print(f'Critical Angle: {critical_angle:.2f}')

    出力結果

    図が Skew-T logP plot。横軸は気温または露点温度 (degree C)、縦軸は気圧 (hPa)。黒実線で気温、青実線で露点温度、橙点線で持ち上げたパーセルの気温をそれぞれ示す、赤色の陰影で示される面積は、持ち上げたパーセルと周辺大気との気温差、すなわち対流有効位置エネルギー (CAPE) である。黒丸印は持ち上げ凝結高度 (LCL) を示す。
    図の左下から右上に向かう灰色の線は等温線を、図の右下から左上に伸びる青色点線は等湿潤断熱線をそれぞれ示す。

    図の 925 hPa から少なくとも 500 hPa までは、気温と露点温度の線が重なっている。これは、ほぼ相対湿度 100% であることを示す。加えて、800 hPa から 600 hPa までの層では、気温減率の傾きが湿潤断熱線の傾きよりも大きくなっており、湿潤絶対不安定 (Moist Absolutely Unstable Layer, Bryan and Fritsch 2000) の可能性がある。

    下記は、一点鉛直プロファイルから得られる指数やパラメーターの出力である。一地点の鉛直プロファイルから、これだけ多様な診断パラメーターを算出することが可能となる。

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            CAPE: 729.54 joule / kilogram
             CIN: -3.21 joule / kilogram
    LCL Pressure: 926.10 hectopascal
    LFC Pressure: 885.54 hectopascal
     EL Pressure: 260.51 hectopascal

    Mixed Layer - Lowest 50-hPa
         ML Temp: 22.89 degree_Celsius
         ML Dewp: 20.64 degree_Celsius
         ML CAPE: 1997.43 joule / kilogram
          ML CIN: -0.15 joule / kilogram

    Most Unstable - Lowest 50-hPa
         MU Temp: 20.55 degree_Celsius
         MU Dewp: 20.17 degree_Celsius
     MU Pressure: 927.77 hectopascal
         MU CAPE: 754.89 joule / kilogram
          MU CIN: -1.08 joule / kilogram

       Lifted Index: -3.48 delta_degree_Celsius
            K-Index: 41.32 degree_Celsius
    Showalter Index: -3.25 delta_degree_Celsius
       Cross Totals: 24.40 delta_degree_Celsius
       Total Totals: 48.64 delta_degree_Celsius
    Vertical Totals: 24.24 delta_degree_Celsius

    Bunkers Storm Motion Vector
      u_storm: 0.25 meter_per_second
      v_storm: -4.63 meter_per_second
    Critical Angle: 179.85 degree

    指数・診断パラメーターは、発案された地域や特性に依存するため、計算結果をそのまま異なる地域に当てはめることは得策ではない。上記で調べたように、可視化することで指数が意図した計算となっているか、愚直に確認することが重要である (時に時間を要するが)。ここでは、計算方法の紹介に留める。

    おわりに

    大気の状態を把握するための基礎的な観測の一つである、高層気象観測データの可視化と関連する指数・パラメータの計算について紹介した。

    参考文献

    • May, R. M., Goebbert, K. H., Thielen, J. E., Leeman, J. R., Camron, M. D., Bruick, Z., Bruning, E. C., Manser, R. P., Arms, S. C., and Marsh, P. T., 2022: MetPy: A Meteorological Python Library for Data Analysis and Visualization. Bull. Amer. Meteor. Soc., 103, E2273-E2284, doi:10.1175/BAMS-D-21-0125.1.
    • Bryan, G. H., and J. M. Fritsch, 2000: Moist Absolute Instability: The Sixth Static Stability State. Bull. Amer. Meteor. Soc., 81, 1207–1230, doi:10.1175/1520-0477(2000)081<1287:MAITSS>2.3.CO;2.

    更新履歴

    • 2024-01-01: 初稿
    ]]>     + + + + + <h1 id="はじめに"><a class="markdownIt-Anchor" href="#はじめに"></a> はじめに</h1> +<p>大気の鉛直構造を把握するため、日々、高層気象観測が行われている。これらは観測としてのみならず、数値予報モデルの初期値・解析値としても + + + + + + + +     + 粒径分布パラメータのレーダー変数による推定 diff --git a/index.html b/index.html index a670e4b..814d4a0 100644 --- a/index.html +++ b/index.html @@ -180,6 +180,20 @@

    Welcome to Takashi Unuma's blog.

    Recent Posts

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    2024年1月1日

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    Recent Posts

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    2022年1月26日

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    diff --git a/page/2/index.html b/page/2/index.html index 24e130e..c3c36d4 100644 --- a/page/2/index.html +++ b/page/2/index.html @@ -180,6 +180,20 @@

    Welcome to Takashi Unuma's blog.

    Recent Posts

    +
    + +
    +

    2024年1月1日

    +
    +
    + + + Skew-T LogP plot by MetPy + + +
    +
    +
    @@ -236,20 +250,6 @@

    Recent Posts

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    -

    2022年1月26日

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    - - - 反射強度・反射因子差のバイアス補正 - - -
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    diff --git a/page/3/index.html b/page/3/index.html index d54dd44..fec1e95 100644 --- a/page/3/index.html +++ b/page/3/index.html @@ -180,6 +180,20 @@

    Welcome to Takashi Unuma's blog.

    Recent Posts

    +
    + +
    +

    2024年1月1日

    +
    +
    + + + Skew-T LogP plot by MetPy + + +
    +
    +
    @@ -236,20 +250,6 @@

    Recent Posts

    -
    - -
    -

    2022年1月26日

    -
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    - - - 反射強度・反射因子差のバイアス補正 - - -
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    All Posts

    diff --git a/page/4/index.html b/page/4/index.html index 7661153..04bb9fc 100644 --- a/page/4/index.html +++ b/page/4/index.html @@ -180,6 +180,20 @@

    Welcome to Takashi Unuma's blog.

    Recent Posts

    +
    + +
    +

    2024年1月1日

    +
    +
    + + + Skew-T LogP plot by MetPy + + +
    +
    +
    @@ -236,20 +250,6 @@

    Recent Posts

    -
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    -

    2022年1月26日

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    - - - 反射強度・反射因子差のバイアス補正 - - -
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    diff --git a/rss2.xml b/rss2.xml index c7e8fd2..e919448 100644 --- a/rss2.xml +++ b/rss2.xml @@ -15,9 +15,34 @@ - Sun, 20 Nov 2022 09:33:35 GMT + Mon, 01 Jan 2024 05:37:46 GMT http://hexo.io/ + + Skew-T LogP plot by MetPy + https://takashiunuma.github.io/blog/2024/01/01/ + https://takashiunuma.github.io/blog/2024/01/01/ + Mon, 01 Jan 2024 04:15:24 GMT + + + + <h1 id="はじめに"><a class="markdownIt-Anchor" href="#はじめに"></a> はじめに</h1> +<p>大気の鉛直構造を把握するため、日々、高層気象観測が行われている。これらは観測としてのみならず、数値予報モデルの初期値・解析値としても + + + + + はじめに

    大気の鉛直構造を把握するため、日々、高層気象観測が行われている。これらは観測としてのみならず、数値予報モデルの初期値・解析値としても用いられる重要な観測データである。本稿では、その観測データを地点毎に把握するための Skew-T LogP plot について述べる。ここでは、MetPy で提供されているものを利用し、特に理想化した数値実験の初期値に用いる一点鉛直プロファイルの可視化について紹介する。

    データ

    とある時刻・地点のデータを利用する。
    1行目は、地上の気圧 (hPa)・温位 (K)・水蒸気混合比 (g/kg)。
    2行目以降は、高度 (m)・温位 (K)・水蒸気混合比 (g/kg)・東西風 (m/s)・南北風 (m/s)。
    これを、input_sounding という名前で保存。

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     1000.00  298.81   18.08
      277.04  299.83   16.13   -5.29    1.54
      504.73  299.94   16.02   -5.96    1.84
      736.79  300.06   16.00   -6.08    2.16
      973.81  301.25   15.48   -5.57    3.11
     1465.20  303.95   14.38   -2.57    5.48
     1982.15  306.88   12.90    1.13    5.17
     3104.82  312.11    9.51    5.20    4.45
     4369.76  318.33    6.41    5.66    4.46
     5823.29  325.50    4.00   11.84   -0.45
     7542.22  334.36    1.78   15.32    3.46
     9655.91  343.85    0.65   15.49   -5.92
    12000.00  351.53    0.11   15.49   -5.92
    21000.00  500.53    0.01   15.49   -5.92

    ソースコード

    上記のデータを、下記で可視化する。

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    import pandas as pd
    import numpy as np

    import matplotlib.pyplot as plt
    import matplotlib
    import matplotlib.dates as mdates

    import metpy.calc as mpcalc
    from metpy.calc import temperature_from_potential_temperature, height_to_pressure_std, dewpoint_from_specific_humidity
    from metpy.plots import SkewT
    from metpy.units import units, pandas_dataframe_to_unit_arrays
    import pint_xarray

    # ヘッダーを取り除いた上でカラムを指定、pandas dataframe として格納。
    df = pd.read_fwf('input_sounding', header=0, usecols=[0, 1, 2, 3, 4], names=['height', 'theta', 'qv', 'uwnd', 'vwnd'])

    # pandas dataframe を xarray dataset に変換
    ds = df.to_xarray()

    # MetPy.units を用い、単位を指定。
    ds['height'] = ds['height'] * units.meter
    ds['theta'] = ds['theta'] * units.kelvin
    ds['qv'] = ds['qv'] * units('g/kg')
    ds['uwnd'] = ds['uwnd'] * units.meter_per_second
    ds['vwnd'] = ds['vwnd'] * units.meter_per_second

    # SkewT logP plot の描画に必要な変数に変換。
    ds['pressure'] = height_to_pressure_std(ds['height']).pint.to('hPa')
    ds['temperature'] = temperature_from_potential_temperature(ds['pressure'], ds['theta']).pint.to('degC')
    ds['dewpoint'] = dewpoint_from_specific_humidity(ds['pressure'], ds['temperature'], ds['qv']).pint.to('degC')

    # ある高度から持ち上げたパーセルの気温・露点温度のプロファイルを計算。
    parcel_prof = mpcalc.parcel_profile(ds['pressure'], ds['temperature'][0], ds['dewpoint'][0]).pint.to('degC')

    # 持ち上げ凝結高度の気圧・気温・露点温度を計算。
    lcl_pressure, lcl_temperature = mpcalc.lcl(ds['pressure'][0], ds['temperature'][0], ds['dewpoint'][0])


    # 描画
    fig = plt.figure(figsize=(8, 6))
    skew = SkewT(fig)
    skew.ax.set_ylim(1050,100)
    skew.ax.set_xlim(-30,35)
    skew.ax.set_title("A sample sounding")
    skew.plot(ds['pressure'], ds['temperature'], color="k", linestyle='-')
    skew.plot(ds['pressure'], ds['dewpoint'], linestyle='-')
    skew.shade_cape(ds['pressure'], ds['temperature'], parcel_prof)
    skew.plot(ds['pressure'], parcel_prof, linestyle=':', linewidth=2)
    skew.plot(lcl_pressure, lcl_temperature, 'ko', markerfacecolor='black')
    skew.plot_moist_adiabats(linewidth=0.5)
    fig.savefig('skewt.png', bbox_inches='tight')


    # 各種指数・パラメーターの計算
    p = ds['pressure']
    T = ds['temperature']
    Td = ds['dewpoint']
    height = ds['height']
    u = ds['uwnd']
    v = ds['vwnd']

    ctotals = mpcalc.cross_totals(p, T, Td)
    kindex = mpcalc.k_index(p, T, Td)
    showalter = mpcalc.showalter_index(p, T, Td)
    total_totals = mpcalc.total_totals_index(p, T, Td)
    vert_totals = mpcalc.vertical_totals(p, T)
    lift_index = mpcalc.lifted_index(p, T, parcel_prof)
    cape, cin = mpcalc.cape_cin(p, T, Td, parcel_prof)
    lclp, lclt = mpcalc.lcl(p[0], T[0], Td[0])
    lfcp, _ = mpcalc.lfc(p, T, Td)
    el_pressure, _ = mpcalc.el(p, T, Td, parcel_prof)
    ml_t, ml_td = mpcalc.mixed_layer(p, T, Td, depth=50 * units.hPa)
    ml_p, _, _ = mpcalc.mixed_parcel(p, T, Td, depth=50 * units.hPa)
    mlcape, mlcin = mpcalc.mixed_layer_cape_cin(p, T, parcel_prof, depth=50 * units.hPa)
    mu_p, mu_t, mu_td, _ = mpcalc.most_unstable_parcel(p, T, Td, depth=50 * units.hPa)
    mucape, mucin = mpcalc.most_unstable_cape_cin(p, T, Td, depth=50 * units.hPa)
    (u_storm, v_storm), *_ = mpcalc.bunkers_storm_motion(p, u, v, height)
    critical_angle = mpcalc.critical_angle(p, u, v, height, u_storm, v_storm)


    # 各種指数・パラメーターの値を確認
    print(f'        CAPE: {cape:.2f}')
    print(f'         CIN: {cin:.2f}')
    print(f'LCL Pressure: {lclp:.2f}')
    print(f'LFC Pressure: {lfcp:.2f}')
    print(f' EL Pressure: {el_pressure:.2f}')
    print()
    print('Mixed Layer - Lowest 50-hPa')
    print(f'     ML Temp: {ml_t:.2f}')
    print(f'     ML Dewp: {ml_td:.2f}')
    print(f'     ML CAPE: {mlcape:.2f}')
    print(f'      ML CIN: {mlcin:.2f}')
    print()
    print('Most Unstable - Lowest 50-hPa')
    print(f'     MU Temp: {mu_t:.2f}')
    print(f'     MU Dewp: {mu_td:.2f}')
    print(f' MU Pressure: {mu_p:.2f}')
    print(f'     MU CAPE: {mucape:.2f}')
    print(f'      MU CIN: {mucin:.2f}')
    print()
    print(f'   Lifted Index: {lift_index:.2f}')
    print(f'        K-Index: {kindex:.2f}')
    print(f'Showalter Index: {showalter:.2f}')
    print(f'   Cross Totals: {ctotals:.2f}')
    print(f'   Total Totals: {total_totals:.2f}')
    print(f'Vertical Totals: {vert_totals:.2f}')
    print()
    print('Bunkers Storm Motion Vector')
    print(f'  u_storm: {u_storm:.2f}')
    print(f'  v_storm: {v_storm:.2f}')
    print(f'Critical Angle: {critical_angle:.2f}')

    出力結果

    図が Skew-T logP plot。横軸は気温または露点温度 (degree C)、縦軸は気圧 (hPa)。黒実線で気温、青実線で露点温度、橙点線で持ち上げたパーセルの気温をそれぞれ示す、赤色の陰影で示される面積は、持ち上げたパーセルと周辺大気との気温差、すなわち対流有効位置エネルギー (CAPE) である。黒丸印は持ち上げ凝結高度 (LCL) を示す。
    図の左下から右上に向かう灰色の線は等温線を、図の右下から左上に伸びる青色点線は等湿潤断熱線をそれぞれ示す。

    図の 925 hPa から少なくとも 500 hPa までは、気温と露点温度の線が重なっている。これは、ほぼ相対湿度 100% であることを示す。加えて、800 hPa から 600 hPa までの層では、気温減率の傾きが湿潤断熱線の傾きよりも大きくなっており、湿潤絶対不安定 (Moist Absolutely Unstable Layer, Bryan and Fritsch 2000) の可能性がある。

    下記は、一点鉛直プロファイルから得られる指数やパラメーターの出力である。一地点の鉛直プロファイルから、これだけ多様な診断パラメーターを算出することが可能となる。

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            CAPE: 729.54 joule / kilogram
             CIN: -3.21 joule / kilogram
    LCL Pressure: 926.10 hectopascal
    LFC Pressure: 885.54 hectopascal
     EL Pressure: 260.51 hectopascal

    Mixed Layer - Lowest 50-hPa
         ML Temp: 22.89 degree_Celsius
         ML Dewp: 20.64 degree_Celsius
         ML CAPE: 1997.43 joule / kilogram
          ML CIN: -0.15 joule / kilogram

    Most Unstable - Lowest 50-hPa
         MU Temp: 20.55 degree_Celsius
         MU Dewp: 20.17 degree_Celsius
     MU Pressure: 927.77 hectopascal
         MU CAPE: 754.89 joule / kilogram
          MU CIN: -1.08 joule / kilogram

       Lifted Index: -3.48 delta_degree_Celsius
            K-Index: 41.32 degree_Celsius
    Showalter Index: -3.25 delta_degree_Celsius
       Cross Totals: 24.40 delta_degree_Celsius
       Total Totals: 48.64 delta_degree_Celsius
    Vertical Totals: 24.24 delta_degree_Celsius

    Bunkers Storm Motion Vector
      u_storm: 0.25 meter_per_second
      v_storm: -4.63 meter_per_second
    Critical Angle: 179.85 degree

    指数・診断パラメーターは、発案された地域や特性に依存するため、計算結果をそのまま異なる地域に当てはめることは得策ではない。上記で調べたように、可視化することで指数が意図した計算となっているか、愚直に確認することが重要である (時に時間を要するが)。ここでは、計算方法の紹介に留める。

    おわりに

    大気の状態を把握するための基礎的な観測の一つである、高層気象観測データの可視化と関連する指数・パラメータの計算について紹介した。

    参考文献

    • May, R. M., Goebbert, K. H., Thielen, J. E., Leeman, J. R., Camron, M. D., Bruick, Z., Bruning, E. C., Manser, R. P., Arms, S. C., and Marsh, P. T., 2022: MetPy: A Meteorological Python Library for Data Analysis and Visualization. Bull. Amer. Meteor. Soc., 103, E2273-E2284, doi:10.1175/BAMS-D-21-0125.1.
    • Bryan, G. H., and J. M. Fritsch, 2000: Moist Absolute Instability: The Sixth Static Stability State. Bull. Amer. Meteor. Soc., 81, 1207–1230, doi:10.1175/1520-0477(2000)081<1287:MAITSS>2.3.CO;2.

    更新履歴

    • 2024-01-01: 初稿
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