Climate Variable Data & Visualization

Data source: Monthly aggregated Water Vapor MODIS MCD19A2 (1 km): Long-term data (2000-2022)

pip install GDAL

import numpy as np
from osgeo import gdal
import matplotlib.pyplot as plt
import seaborn as sns

fig = plt.figure(figsize = (14, 3))
i = 0
for month in [5, 6]:
    dataset = gdal.Open(r'wv_mcd19a2v061.seasconv.m.m0{}_p50_1km_s_20000101_20221231_go_epsg.4326_v20230619.tif'.format(month))
    mat = np.array(dataset.GetRasterBand(1).ReadAsArray()[4500 : 8000, 6600 : 13100]).astype(float)
    mat[mat == -1] = np.nan
    ax = fig.add_subplot(1, 2, i + 1)
    ax = sns.heatmap(mat,  cmap = "Spectral_r", vmin = 0, vmax = 4500,
                     cbar_kws = {"shrink": 0.5, 'label': r'Water vapor ($10^{-3}$cm)'})
    plt.axis('off')
    if month == 5:
        plt.title('May (2000-2022)')
    elif month == 6:
        plt.title('June (2000-2022)')
    i += 1
plt.show()

import numpy as np
from osgeo import gdal
import matplotlib.pyplot as plt
import seaborn as sns

dataset = gdal.Open(r'wv_mcd19a2v061.seasconv.m.m06_p50_1km_s_20000101_20221231_go_epsg.4326_v20230619.tif')
mat = np.array(dataset.GetRasterBand(1).ReadAsArray()[4500 : 8000, 6600 : 13100]).astype(float)
mat[mat == -1] = np.nan
fig = plt.figure(figsize = (6.5, 3))
ax = sns.heatmap(mat,  cmap = "Spectral_r", vmin = 0, vmax = 4500,
                  cbar_kws = {"shrink": 0.5, 'label': r'Water vapor ($10^{-3}$cm)'})
plt.axis('off')
plt.title('June (2000-2022)')
plt.show()

import numpy as np
from osgeo import gdal

for i in ['01', '02', '03', '04', '05', '06',
          '07', '08', '09', '10', '11', '12']:
    dataset = gdal.Open(r'wv_mcd19a2v061.seasconv.m.m{}_p50_1km_s_20000101_20221231_go_epsg.4326_v20230619.tif'.format(i))
    mat = np.array(dataset.GetRasterBand(1).ReadAsArray()[4500 : 8000, 6600 : 13100]).astype(float)
    mat[mat == -1] = np.nan
    np.savez_compressed('water_vapor_month_{}.npz'.format(i), mat)

import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
fig = plt.figure(figsize = (14, 3))
i = 0
for month in [5, 6]:
    mat = np.load('water_vapor_month_0{}.npz'.format(month))['arr_0']
    ax = fig.add_subplot(1, 2, i + 1)
    ax = sns.heatmap(mat,  cmap = "Spectral_r", vmin = 0, vmax = 4500,
                     cbar_kws = {"shrink": 0.5, 'label': r'Water vapor ($10^{-3}$cm)'})
    plt.axis('off')
    if month == 5:
        plt.title('May (2000-2022)')
    elif month == 6:
        plt.title('June (2000-2022)')
    i += 1
plt.show()

def resolute(mat):
    a, b = mat.shape
    a1 = int(a / 2)
    b1 = int(b / 2)
    mat1 = (mat[:: 2, :: 2] + mat[:: 2, 1 :: 2] 
            + mat[1 :: 2, :: 2] + mat[1 :: 2, 1 :: 2])
    return mat1

M, N = resolute(np.load('water_vapor_month_01.npz')['arr_0']).shape
tensor = np.zeros((M * N, 12))
for month in range(12):
    if month + 1 < 10:
        tensor[:, month] = resolute(np.load('water_vapor_month_0{}.npz'.format(month + 1))['arr_0']).reshape([M * N], order = 'F')
    else:
        tensor[:, month] = resolute(np.load('water_vapor_month_{}.npz'.format(month + 1))['arr_0']).reshape([M * N], order = 'F')
ind = np.where(~np.isnan(tensor[:, 0]))
a = ind[0].shape[0]
mat = np.zeros((a, 12))
for month in range(12):
    mat[:, month] = tensor[ind[0], month]
del tensor

tensor_hat = np.zeros((M, N, 4))
for month in range(12):
    temp = np.empty(M * N)
    temp[:] = np.nan
    temp[ind[:]] = mat[:, month]
    tensor_hat[:, :, month] = temp.reshape([M, N], order = 'F')

xinychen/climate-tensor

Climate Variable Data & Visualization