6. 計算氣候場及距平值#
進行氣候分析時，最常從分析氣候場 (Climatology) 和距平值 (Anomaly) 開始。
利用groupby計算月氣候平均與距平#
先準備資料。
import numpy as np
import pandas as pd
import xarray as xr
from cartopy import crs as ccrs
from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
import matplotlib as mpl
from matplotlib import pyplot as plt
import cmaps
mpl.rcParams['figure.dpi'] = 100
# 選擇資料的時空範圍。
lats = -20
latn = 30
lon1 = 79
lon2 = 161
time1 = '2017-12-01'
time2 = '2017-12-31'
# 開啟檔案
olr_ds = xr.open_dataset("data/olr.nc")
olr = (olr_ds.sel(time=slice('1998-01-01','2016-12-31'),
lat=slice(lats,latn),
lon=slice(lon1,lon2)).olr)
olrrt = (olr_ds.sel(time=slice('2017-12-01','2017-12-31'),
lat=slice(lats,latn),
lon=slice(lon1,lon2)).olr)
/Users/waynetsai/.local/lib/python3.10/site-packages/ecmwflibs/__init__.py:83: UserWarning: dlopen(/Users/waynetsai/.local/lib/python3.10/site-packages/ecmwflibs/_ecmwflibs.cpython-310-darwin.so, 0x0002): tried: '/Users/waynetsai/.local/lib/python3.10/site-packages/ecmwflibs/_ecmwflibs.cpython-310-darwin.so' (mach-o file, but is an incompatible architecture (have 'x86_64', need 'arm64')), '/System/Volumes/Preboot/Cryptexes/OS/Users/waynetsai/.local/lib/python3.10/site-packages/ecmwflibs/_ecmwflibs.cpython-310-darwin.so' (no such file), '/Users/waynetsai/.local/lib/python3.10/site-packages/ecmwflibs/_ecmwflibs.cpython-310-darwin.so' (mach-o file, but is an incompatible architecture (have 'x86_64', need 'arm64'))
warnings.warn(str(e))
我們在第4單元中，已經了解過如何在xarray中操控datetime物件，因此在計算氣候場的時候，也可以將資料按照各月分類，這種聚集並分類的函數稱為groupby。按照月份分類後，將同類的平均，就可以達到計算月平均氣候場的效果。
olrGB = olr.groupby("time.month")
olrMonClim = olrGB.mean("time") # 這邊的mean指的是幫groupby的各組平均，和第3章學的mean意義稍有不同。
olrMonClim
<xarray.DataArray 'olr' (month: 12, lat: 50, lon: 82)>
array([[[270.46295, 270.73306, 270.65665, ..., 257.25116, 256.66913,
255.88489],
[269.24954, 268.8672 , 269.106 , ..., 253.44594, 253.16605,
252.61195],
[267.06573, 267.1016 , 267.57956, ..., 249.2561 , 248.4184 ,
247.00945],
...,
[257.9647 , 257.92267, 258.25693, ..., 256.99442, 256.7958 ,
257.13882],
[253.88763, 253.44121, 252.49252, ..., 252.67389, 252.67776,
252.66585],
[245.34576, 240.01846, 228.33456, ..., 248.6515 , 248.66846,
248.49454]],
[[266.90402, 267.24286, 267.5611 , ..., 255.47618, 254.07542,
253.18782],
[264.35098, 264.7394 , 264.88358, ..., 251.78017, 251.39395,
251.59004],
[261.4748 , 262.0324 , 262.23868, ..., 247.40343, 248.10788,
248.63504],
...
[281.75525, 281.8884 , 282.38974, ..., 263.41367, 263.98563,
264.8334 ],
[279.72992, 279.606 , 276.21063, ..., 257.44904, 258.60928,
259.01102],
[270.86935, 264.80884, 252.717 , ..., 252.37294, 253.24257,
254.25145]],
[[277.6884 , 277.96237, 278.4138 , ..., 268.97833, 268.33752,
267.38034],
[276.88388, 276.72455, 277.11646, ..., 267.85056, 267.2011 ,
266.11264],
[274.72495, 274.7343 , 275.10522, ..., 266.41324, 264.9135 ,
264.2871 ],
...,
[269.02094, 269.55533, 270.47006, ..., 258.97546, 259.5016 ,
260.03702],
[266.66284, 267.0835 , 265.3553 , ..., 253.46599, 253.83995,
254.14189],
[258.7794 , 254.24706, 243.16267, ..., 248.40625, 248.42702,
248.41463]]], dtype=float32)
Coordinates:
* lon (lon) float32 79.5 80.5 81.5 82.5 83.5 ... 157.5 158.5 159.5 160.5
* lat (lat) float32 -19.5 -18.5 -17.5 -16.5 -15.5 ... 26.5 27.5 28.5 29.5
* month (month) int64 1 2 3 4 5 6 7 8 9 10 11 12
Attributes:
standard_name: toa_outgoing_longwave_flux
long_name: NOAA Climate Data Record of Daily Mean Upward Longwave Fl...
units: W m-2
cell_methods: time: mean area: mean計算距平時，將即時(real time)的資料也對月份進行groupby，再直接減去氣候場即可，xarray會根據氣候場中的groupby分類進行計算。
olra = olrrt.groupby('time.month').mean() - olrMonClim
olra
<xarray.DataArray 'olr' (month: 1, lat: 50, lon: 82)>
array([[[ 9.78241 , 9.909576 , 8.493408 , ..., 2.497589 ,
1.3457642 , -0.74990845],
[ 8.7960205 , 9.101654 , 9.233459 , ..., 0.71640015,
-0.6616211 , -2.1523438 ],
[ 7.6419373 , 9.022461 , 10.015717 , ..., 1.4698181 ,
-0.83166504, -3.696045 ],
...,
[-6.5422363 , -5.305298 , -6.9757385 , ..., 10.722809 ,
12.269043 , 13.60672 ],
[-5.522644 , -5.337219 , -5.0463257 , ..., 7.494705 ,
8.71553 , 9.534042 ],
[-5.712723 , -4.6646423 , -4.2814636 , ..., 4.3125763 ,
5.6015167 , 6.79834 ]]], dtype=float32)
Coordinates:
* lon (lon) float32 79.5 80.5 81.5 82.5 83.5 ... 157.5 158.5 159.5 160.5
* lat (lat) float32 -19.5 -18.5 -17.5 -16.5 -15.5 ... 26.5 27.5 28.5 29.5
* month (month) int64 12我們把資料畫出來看看。
from matplotlib import pyplot as plt
from cartopy import crs as ccrs
from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
import cmaps
olr_anml_cmap=cmaps.sunshine_diff_12lev
proj = ccrs.PlateCarree()
fig,ax = plt.subplots(1,1,subplot_kw={'projection':proj})
clevs = [-50,-40,-30,-20,-10,-5,5,10,20,30,40,50,60]
olrPlot = (olra[0,:,:]
.plot.contourf("lon", "lat",
transform=proj,
ax=ax,
levels=clevs,
cmap=olr_anml_cmap,
add_colorbar=True,
extend='both',
cbar_kwargs={'orientation': 'horizontal', 'aspect': 30, 'label': ' ', 'ticks':clevs})
)
ax.set_extent([lon1,lon2,lats,latn],crs=proj)
ax.set_xticks(np.arange(80,180,20), crs=proj)
ax.set_yticks(np.arange(-20,40,10), crs=proj) # 設定x, y座標的範圍，以及多少經緯度繪製刻度。
lon_formatter = LONGITUDE_FORMATTER
lat_formatter = LATITUDE_FORMATTER
ax.xaxis.set_major_formatter(lon_formatter)
ax.yaxis.set_major_formatter(lat_formatter) # 將經緯度以degN, degE的方式表示。
ax.coastlines() # 繪製地圖海岸線。
ax.set_ylabel(' ') # 設定坐標軸名稱。
ax.set_xlabel(' ')
plt.title(' ')
plt.title("Dec. 2017 mean OLR anml. (W m$^{-2}$)", loc='left')
plt.show()
/Users/waynetsai/micromamba/envs/p3/lib/python3.10/site-packages/shapely/predicates.py:798: RuntimeWarning: invalid value encountered in intersects
return lib.intersects(a, b, **kwargs)
計算日氣候平均與距平#
和計算月氣候場類似，datetime物件有dayofyear的attribute，因此利用groupby('time.dayofyear')就可以計算氣候場。
olrGB = olr.groupby("time.dayofyear")
olrDayClim = olrGB.mean("time")
olrDayClim
olra = olrrt.groupby('time.dayofyear') - olrDayClim
計算「平滑」氣候場#
平滑氣候場是保留氣候場的前n個諧函數 (harmonics)，必須運用傅立葉變換 (fast Fourier transform, FFT) 計算。計算FFT需要用到scipy函式庫。然而由於scipy目前尚不支援xarray.DataArray，因此必須先轉換成numpy.ndarray才能進行FFT運算，或是利用xrft (Fourier transforms for xarray data)模組。下列程式碼smthClmDay函數和NCL完全一樣。
from scipy.fft import rfft, irfft
def smthClmDay(clmDay, nHarm):
nt, ny, nx = clmDay.shape
cf = rfft(clmDay.values, axis=0) # xarray.DataArray.values 可將DataArray 轉換成numpy.ndarray。
cf[nHarm,:,:] = 0.5*cf[nHarm,:,:] # mini-taper.
cf[nHarm+1:,:,:] = 0.0 # set all higher coef to 0.0
icf = irfft(cf, n=nt, axis=0) # reconstructed series
clmDaySmth = clmDay.copy(data=icf, deep=False)
return(clmDaySmth)
olrDayClim_sm = smthClmDay(olrDayClim, 3)
olrDayClim_sm
<xarray.DataArray 'olr' (dayofyear: 366, lat: 50, lon: 82)>
array([[[273.80515, 274.10938, 274.2879 , ..., 261.79453, 260.9497 ,
259.96237],
[272.50845, 272.31113, 272.69553, ..., 259.65186, 259.0594 ,
258.18594],
[270.22046, 270.31717, 270.83304, ..., 256.96295, 255.90952,
254.99857],
...,
[260.15997, 260.5604 , 261.29028, ..., 257.65775, 257.77252,
258.23935],
[256.91174, 257.18832, 256.1388 , ..., 252.47044, 252.64952,
252.72314],
[249.13116, 244.76387, 233.80005, ..., 247.78957, 247.84798,
247.76076]],
[[273.66003, 273.96432, 274.14062, ..., 261.48926, 260.66068,
259.6891 ],
[272.35568, 272.1524 , 272.53165, ..., 259.30475, 258.74103,
257.90274],
[270.0584 , 270.1501 , 270.6593 , ..., 256.57834, 255.56288,
254.67546],
...
[260.96295, 261.39435, 262.15237, ..., 257.77438, 257.91214,
258.39255],
[257.76877, 258.06982, 256.98972, ..., 252.51714, 252.72371,
252.81175],
[249.98839, 245.6104 , 234.62175, ..., 247.77896, 247.85403,
247.79373]],
[[273.94824, 274.25226, 274.4333 , ..., 262.10028, 261.2389 ,
260.23553],
[272.6587 , 272.46793, 272.85754, ..., 259.9982 , 259.37653,
258.46808],
[270.37982, 270.48196, 271.00443, ..., 257.34586, 256.2545 ,
255.32027],
...,
[260.5534 , 260.96948, 261.71365, ..., 257.7139 , 257.84018,
258.31378],
[257.33243, 257.6214 , 256.55756, ..., 252.49161, 252.68434,
252.7651 ],
[249.55333, 245.18178, 234.20656, ..., 247.78218, 247.84874,
247.7748 ]]], dtype=float32)
Coordinates:
* lon (lon) float32 79.5 80.5 81.5 82.5 ... 157.5 158.5 159.5 160.5
* lat (lat) float32 -19.5 -18.5 -17.5 -16.5 ... 26.5 27.5 28.5 29.5
* dayofyear (dayofyear) int64 1 2 3 4 5 6 7 8 ... 360 361 362 363 364 365 366
Attributes:
standard_name: toa_outgoing_longwave_flux
long_name: NOAA Climate Data Record of Daily Mean Upward Longwave Fl...
units: W m-2
cell_methods: time: mean area: mean選取澳洲季風區，繪製氣候的時序圖。
nt, ny, nx = olrDayClim.shape
olrDayClim1 = xr.DataArray(data=olrDayClim.values,
dims=['dayofyear','lat','lon'],
coords=dict(dayofyear=range(1,nt+1),
lat=olrDayClim.lat,
lon=olrDayClim.lon))
olrDayClim_sm1 = xr.DataArray(data=olrDayClim_sm.values,
dims=['dayofyear','lat','lon'],
coords=dict(dayofyear=range(1,nt+1),
lat=olrDayClim.lat,
lon=olrDayClim.lon))
plt.figure()
(olrDayClim1.sel(lon=slice(115,150),lat=slice(-15,-2))
.mean(["lat","lon"])
.plot.line(x="dayofyear"))
(olrDayClim_sm1.sel(lon=slice(115,150),lat=slice(-15,-2))
.mean(["lat","lon"])
.plot.line(x="dayofyear"))
plt.show()
上圖藍線的部分為原始的氣候場，橘線的部分則是保留前三個諧函數的氣候場，縱軸為OLR，橫軸則是一年之中的天數。
用pandas.MultiIndex建立日期座標來計算氣候場#
以上的作法有一個小問題：由於datetime物件的日曆格式，一般年時dayofyear的範圍是1…365，但閏年時卻是1…366，因此同樣是3月1日，一般年的dayofyear是60，但閏年時卻是61，導致閏年的氣候場從3月1日開始相較於其它年會往後移動一天。但是這樣的差異，在進行多年的氣候平均，又做了前三個諧函數濾波之後，其實幾乎就沒有差異了。但如果還是想要避免這個誤差的話，可以參考Stack Overflow問答這篇的作法，使用Multi-index groupby，也就是將日期用兩種index (即月、日) 分類，按照相同的月／日分群進行平均就可以得到日氣候場。而要在xarray中建立Multi-index通常使用pandas。
grouper = xr.DataArray(
pd.MultiIndex.from_arrays(
[olr.time.dt.month.values, olr.time.dt.day.values],
names=['month', 'day']),
dims=['time'],
coords=[olr.time],
)
olrGB = olr.groupby(grouper)
olrDayClim = olrGB.mean()
olrDayClim
<xarray.DataArray 'olr' (group: 365, lat: 50, lon: 82)>
array([[[275.55728, 276.54013, 277.6245 , ..., 266.4256 , 261.806 ,
256.97592],
[271.42606, 273.49188, 275.5512 , ..., 265.4208 , 261.16055,
258.88416],
[269.327 , 272.3724 , 274.97632, ..., 266.53253, 263.97916,
260.42838],
...,
[256.7524 , 255.38773, 254.20909, ..., 258.3623 , 256.467 ,
255.85208],
[253.53944, 252.09029, 248.21416, ..., 254.66516, 253.68051,
252.92639],
[243.514 , 235.9991 , 223.12402, ..., 252.18465, 251.65887,
251.67668]],
[[266.20474, 268.61728, 269.68546, ..., 270.8527 , 267.73376,
265.09494],
[264.3772 , 268.11807, 272.9121 , ..., 267.90686, 267.9379 ,
265.27838],
[264.11343, 269.2573 , 272.4137 , ..., 265.5363 , 266.38144,
263.24698],
...
[251.53069, 252.98149, 255.76465, ..., 254.974 , 255.07802,
256.7515 ],
[250.71376, 250.6916 , 250.74278, ..., 247.69414, 248.49252,
249.42802],
[247.05325, 242.05045, 228.88042, ..., 242.61389, 242.83167,
242.44658]],
[[278.89948, 278.50262, 279.78247, ..., 266.84204, 260.7723 ,
256.52045],
[273.61664, 273.47092, 275.00528, ..., 264.80386, 259.15552,
256.94455],
[269.77542, 270.22888, 273.07074, ..., 263.5273 , 259.77316,
259.5152 ],
...,
[255.64615, 253.61986, 252.87692, ..., 254.2591 , 254.60645,
255.94365],
[250.28539, 250.46992, 247.14093, ..., 247.19354, 248.55852,
247.40335],
[243.30751, 237.3538 , 224.35126, ..., 243.6021 , 245.26958,
244.68266]]], dtype=float32)
Coordinates:
* lon (lon) float32 79.5 80.5 81.5 82.5 83.5 ... 157.5 158.5 159.5 160.5
* lat (lat) float32 -19.5 -18.5 -17.5 -16.5 -15.5 ... 26.5 27.5 28.5 29.5
* group (group) object MultiIndex
* month (group) int64 1 1 1 1 1 1 1 1 1 1 ... 12 12 12 12 12 12 12 12 12 12
* day (group) int64 1 2 3 4 5 6 7 8 9 10 ... 23 24 25 26 27 28 29 30 31
Attributes:
standard_name: toa_outgoing_longwave_flux
long_name: NOAA Climate Data Record of Daily Mean Upward Longwave Fl...
units: W m-2
cell_methods: time: mean area: mean同理，將即時的olr資料進行月/日分類，然後再減去氣候場，就可以得到距平值。
# 另一組grouper。
grouper1 = xr.DataArray(
pd.MultiIndex.from_arrays(
[olrrt.time.dt.month.values, olrrt.time.dt.day.values],
names=['month', 'day']),
dims=['time'],
coords=[olrrt.time],
)
olrrtGB = olrrt.groupby(grouper1)
olra = olrrtGB - olrDayClim
olra
<xarray.DataArray 'olr' (time: 31, lat: 50, lon: 82)>
array([[[ 13.704773 , 15.109009 , 12.878937 , ..., -54.183212 ,
-73.50813 , -68.62732 ],
[ 19.4534 , 19.543701 , 18.546448 , ..., -52.459564 ,
-60.05954 , -50.794464 ],
[ 24.460175 , 23.976593 , 23.317444 , ..., -47.908966 ,
-50.708405 , -54.80075 ],
...,
[ 15.7473755 , 17.249878 , 15.353302 , ..., 23.260834 ,
23.680878 , 20.842041 ],
[ 10.4704895 , 9.605225 , 9.93811 , ..., 20.312744 ,
21.57254 , 19.565369 ],
[ 9.06192 , 10.407959 , 8.299484 , ..., 30.240723 ,
26.335663 , 28.194885 ]],
[[ 12.072968 , 12.46286 , 11.166382 , ..., 3.3347168 ,
-2.691681 , -17.87941 ],
[ 19.253296 , 16.912842 , 14.450348 , ..., 11.276276 ,
-16.642563 , -24.297821 ],
[ 27.846039 , 25.520111 , 20.153198 , ..., 19.735596 ,
-9.782639 , -18.835938 ],
...
[ 28.224838 , 25.6931 , 17.891449 , ..., 2.3854675 ,
-1.5048828 , -9.9785 ],
[ 31.848648 , 29.524582 , 23.120224 , ..., -1.6408081 ,
-3.447403 , -6.5221863 ],
[ 30.846802 , 25.651062 , 22.978333 , ..., 3.466629 ,
-3.4124298 , -2.3715515 ]],
[[ 15.812714 , 24.250153 , 24.052948 , ..., -6.747101 ,
10.18869 , 8.011688 ],
[ 20.912537 , 26.442352 , 25.50885 , ..., -4.769104 ,
12.966644 , 14.544495 ],
[ 20.990906 , 24.864594 , 22.321228 , ..., 7.7335815 ,
21.59378 , 27.109741 ],
...,
[ 15.216736 , 11.739334 , 9.068115 , ..., 14.774506 ,
14.711273 , 20.791306 ],
[ 17.75206 , 15.910721 , 7.664322 , ..., 14.993011 ,
20.03865 , 22.741272 ],
[ 14.902145 , 10.488312 , 9.08168 , ..., 13.184586 ,
12.378677 , 11.702133 ]]], dtype=float32)
Coordinates:
* time (time) datetime64[ns] 2017-12-01 2017-12-02 ... 2017-12-31
* lon (lon) float32 79.5 80.5 81.5 82.5 83.5 ... 157.5 158.5 159.5 160.5
* lat (lat) float32 -19.5 -18.5 -17.5 -16.5 -15.5 ... 26.5 27.5 28.5 29.5
group (time) object MultiIndex
month (time) int64 12 12 12 12 12 12 12 12 12 ... 12 12 12 12 12 12 12 12
day (time) int64 1 2 3 4 5 6 7 8 9 10 ... 22 23 24 25 26 27 28 29 30 31得到的距平場olra的時空範圍和olr完全一樣。
自定義函式庫#
以上的手續其實略嫌繁瑣，因此如果可以直接建立一個自建函式庫檔案 (例如命名為clim_n_anml.py)，放在執行程式的資料夾，就可以直接import進程式使用。以下是寫好的函式範例：
import xarray as xr
import numpy as np
import pandas as pd
from scipy.fft import rfft, irfft
def clmDayTLL(data):
DayClm = data.groupby("time.dayofyear").mean("time")
return(DayCLm)
def smthClmDay(clmDay, nHarm):
nt, ny, nx = clmDay.shape
cf = rfft(clmDay.values, axis=0) # xarray.DataArray.values 可將DataArray 轉換成numpy.ndarray。
cf[nHarm,:,:] = 0.5*cf[nHarm,:,:] # mini-taper.
cf[nHarm+1:,:,:] = 0.0 # set all higher coef to 0.0
icf = irfft(cf, n=nt, axis=0) # reconstructed series
clmDaySmth = clmDay.copy(data=icf, deep=False)
return(clmDaySmth)
def calcDayAnom(data, daily_clim):
anml = data.copy(
data=(data.groupby('time.dayofyear') - daily_clim),
deep=False)
return(anml)
測試一下：
import sys
sys.path.append('udef_func/') # 指定函式庫存放的資料夾位置
import clim_n_anml as clm
olrDayClim = clm.clmDayTLL(olr)
olrDayClim_sm = clm.smthClmDay(olrDayClim,3)
olra = clm.calcDayAnom(olrrt,olrDayClim_sm )
olra
<xarray.DataArray 'olr' (time: 31, lat: 50, lon: 82)>
array([[[ 13.158997 , 13.107758 , 11.475281 , ..., -62.923843 ,
-82.93059 , -78.43939 ],
[ 17.55783 , 16.720917 , 15.455994 , ..., -63.033905 ,
-71.00287 , -61.41597 ],
[ 18.432892 , 18.253174 , 17.552612 , ..., -58.26001 ,
-60.75856 , -65.8367 ],
...,
[ 9.908081 , 11.472931 , 10.322662 , ..., 29.79602 ,
30.32196 , 27.26532 ],
[ 6.590454 , 6.7529297 , 6.64328 , ..., 27.686188 ,
28.347534 , 26.190308 ],
[ 5.759613 , 8.028198 , 7.4326935 , ..., 33.577255 ,
30.970673 , 33.40158 ]],
[[ 10.150513 , 10.651581 , 8.884491 , ..., -1.2956848 ,
-6.2937927 , -23.936508 ],
[ 14.880341 , 13.246948 , 11.273285 , ..., 6.2802734 ,
-22.045303 , -30.698395 ],
[ 21.674652 , 22.969727 , 19.048096 , ..., 11.945557 ,
-20.773972 , -29.57405 ],
...
[ 18.367126 , 16.839813 , 11.049927 , ..., -0.47988892,
-4.4154816 , -11.702835 ],
[ 24.341888 , 21.68283 , 16.428009 , ..., -6.494049 ,
-7.722763 , -9.957443 ],
[ 27.46399 , 21.651993 , 16.81337 , ..., -1.6997223 ,
-8.444962 , -7.742798 ]],
[[ 20.622894 , 28.359894 , 29.258667 , ..., -2.3112793 ,
9.432922 , 4.0237427 ],
[ 21.72287 , 27.290466 , 27.49649 , ..., -0.30877686,
12.429962 , 12.740112 ],
[ 20.229828 , 24.448975 , 24.218536 , ..., 13.534119 ,
24.76941 , 30.98462 ],
...,
[ 9.899933 , 3.9648438 , -0.20733643, ..., 11.259216 ,
11.405579 , 18.342407 ],
[ 10.268677 , 8.310822 , -2.1844635 , ..., 9.669418 ,
15.873459 , 17.33287 ],
[ 8.221268 , 2.23172 , -1.1888123 , ..., 9.007721 ,
9.79422 , 8.591064 ]]], dtype=float32)
Coordinates:
* time (time) datetime64[ns] 2017-12-01 2017-12-02 ... 2017-12-31
* lon (lon) float32 79.5 80.5 81.5 82.5 83.5 ... 157.5 158.5 159.5 160.5
* lat (lat) float32 -19.5 -18.5 -17.5 -16.5 -15.5 ... 26.5 27.5 28.5 29.5
Attributes:
standard_name: toa_outgoing_longwave_flux
long_name: NOAA Climate Data Record of Daily Mean Upward Longwave Fl...
units: W m-2
cell_methods: time: mean area: meanplt.figure()
fig,ax = plt.subplots(1,1,subplot_kw={'projection':proj})
clevs = [-50,-40,-30,-20,-10,-5,5,10,20,30,40,50,60]
olrPlot = (olra.mean(axis=0)
.plot.contourf("lon", "lat",
transform=proj,
ax=ax,
levels=clevs,
cmap=olr_anml_cmap,
add_colorbar=True,
extend='both',
cbar_kwargs={'orientation': 'horizontal', 'aspect': 30, 'label': ' ', 'ticks':clevs})
)
ax.coastlines()
plt.show()
<Figure size 640x480 with 0 Axes>
/Users/waynetsai/micromamba/envs/p3/lib/python3.10/site-packages/shapely/predicates.py:798: RuntimeWarning: invalid value encountered in intersects
return lib.intersects(a, b, **kwargs)
Homework 2#
Homework #2
請以xarray開啟CMORPH降雨資料，繪製CMORPH降雨的月氣候場和月距平。
以
groupby計算降雨的daily climatology (1998-2020)，然後將5月做月平均並繪製五月降雨氣候平均地圖。計算CMORPH降雨的daily anomaly，將2022年5月1-5, 6-10, 11-15日分別平均起來繪製成侯 (pentad) 平均降雨地圖，以及侯距平 (pentad anomaly) 平均地圖 (有3侯，請畫6張圖)。
請根據所畫出來的地圖描述五月降雨氣候特徵，以及所選的3侯降雨有什麼樣的特色。
繪圖的範圍請限制在40˚~180˚E, 20˚S-40˚N。繳交的時候請上傳程式碼 (.py或.ipynb皆可)、圖 (總共7張圖) 和降雨特色的描述 (可以是Word, PPT, PDF格式)。