# -*- coding: utf-8 -*- """ EOF Analysis of Multiple Variables ========================================================================================================= Before proceeding with all the steps, first import some necessary libraries and packages """ import xarray as xr import matplotlib.pyplot as plt import cartopy.crs as ccrs import easyclimate as ecl # %% # Load and preprocess ERA5 reanalysis data (1982-2022 850hPa wind and total precipitation), # involving vertical level selection, unit conversion (``m/day`` to ``mm/day``), # and Dask lazy loading for memory optimization. # # .. code-block:: python # # u500_data = xr.open_dataset("u850_ERA5_1982-2022_N80.nc", chunks="auto").u.sel(level = 850).drop_vars("level") # v500_data = xr.open_dataset("v850_ERA5_1982-2022_N80.nc", chunks="auto").v.sel(level = 850).drop_vars("level") # tp_data = xr.open_dataset("tp_ERA5_1982-2022_N80.nc", chunks="auto").tp * 1000 # # .. tip:: # # You can download following datasets here: # # - :download:`Download u850_ERA5_1982-2022_N80.nc ` # - :download:`Download v850_ERA5_1982-2022_N80.nc ` # - :download:`Download tp_ERA5_1982-2022_N80.nc ` # - :download:`Download meof_analysis_result.zarr.zip ` # # # Spatially subset data to focus on East Asia (100°E–160°E, 10°N–60°N) and sort latitude # ascendingly to ensure consistent indexing and plotting. # # # .. seealso:: # # - Wang, B. (1992). The Vertical Structure and Development of the ENSO Anomaly Mode during 1979–1989. Journal of Atmospheric Sciences, 49(8), 698-712. https://journals.ametsoc.org/view/journals/atsc/49/8/1520-0469_1992_049_0698_tvsado_2_0_co_2.xml # - Wang, B., Wu, Z., Li, J., Liu, J., Chang, C., Ding, Y., & Wu, G. (2008). How to Measure the Strength of the East Asian Summer Monsoon. Journal of Climate, 21(17), 4449-4463. https://doi.org/10.1175/2008JCLI2183.1 # - Wu Bingyi, Zhang Renhe. 2011: Interannual variability of the East Asian summer monsoon and its association with the anomalous atmospheric circulation over the mid-high latitudes and external forcing. Acta Meteorologica Sinica (Chinese), (2): 219-233. http://qxxb.cmsjournal.net/article/doi/10.11676/qxxb2011.019 # - 武炳义, 张人禾. 2011: 东亚夏季风年际变率及其与中、高纬度大气环流以及外强迫异常的联系. 气象学报, (2): 219-233. DOI: https://dx.doi.org/10.11676/qxxb2011.019 # # .. code-block:: python # # u500_data_EA = u500_data.sortby("lat").sel(lon = slice(100, 160), lat = slice(10, 60)) # v500_data_EA = v500_data.sortby("lat").sel(lon = slice(100, 160), lat = slice(10, 60)) # tp_data_EA = tp_data.sortby("lat").sel(lon = slice(100, 160), lat = slice(10, 60)) # # Initialize a multivariate EOF (MEOF) model with cosine-of-latitude weighting # and seasonal cycle removal to jointly analyze wind-precipitation covariability. # # .. code-block:: python # # model = ecl.eof.get_EOF_model( # [u500_data_EA, v500_data_EA, tp_data_EA], # lat_dim = 'lat', lon_dim = 'lon', # remove_seasonal_cycle_mean = True, use_coslat = True # ) # # Execute EOF decomposition to compute spatial patterns, principal components, # and explained variance, saving results in Zarr format for efficient persistence and reuse. # # .. code-block:: python # # meof_analysis_result = ecl.eof.calc_EOF_analysis(model) # meof_analysis_result.to_zarr("meof_analysis_result.zarr") # # Here, we load the saved dataset. meof_analysis_result = ecl.open_datanode("meof_analysis_result.zarr") meof_analysis_result # %% # Extract and downsample (every 3rd grid point) EOF spatial patterns (precipitation and winds) for the first four modes to prepare visualized data. mode_num = 1 tp_draw_mode1 = meof_analysis_result["EOF/var2"].sel(mode = mode_num)["components"] *(-1) u_draw = meof_analysis_result["EOF/var0"].sel(mode = mode_num)["components"] v_draw = meof_analysis_result["EOF/var1"].sel(mode = mode_num)["components"] uv_draw_mode1 = xr.Dataset(data_vars={"u": u_draw, "v": v_draw}).thin(lon = 3, lat = 3) *(-1) # mode_num = 2 tp_draw_mode2 = meof_analysis_result["EOF/var2"].sel(mode = mode_num)["components"] u_draw = meof_analysis_result["EOF/var0"].sel(mode = mode_num)["components"] v_draw = meof_analysis_result["EOF/var1"].sel(mode = mode_num)["components"] uv_draw_mode2 = xr.Dataset(data_vars={"u": u_draw, "v": v_draw}).thin(lon = 3, lat = 3) # mode_num = 3 tp_draw_mode3 = meof_analysis_result["EOF/var2"].sel(mode = mode_num)["components"] u_draw = meof_analysis_result["EOF/var0"].sel(mode = mode_num)["components"] v_draw = meof_analysis_result["EOF/var1"].sel(mode = mode_num)["components"] uv_draw_mode3 = xr.Dataset(data_vars={"u": u_draw, "v": v_draw}).thin(lon = 3, lat = 3) # mode_num = 4 tp_draw_mode4 = meof_analysis_result["EOF/var2"].sel(mode = mode_num)["components"] u_draw = meof_analysis_result["EOF/var0"].sel(mode = mode_num)["components"] v_draw = meof_analysis_result["EOF/var1"].sel(mode = mode_num)["components"] uv_draw_mode4 = xr.Dataset(data_vars={"u": u_draw, "v": v_draw}).thin(lon = 3, lat = 3) # %% # Visualize the first four MEOF modes using contourf (precipitation) and quiver (downsampled winds) on a ``PlateCarree`` projection, # with geographic context (coastlines, gridlines) for wind-precipitation covariability analysis. proj = ccrs.PlateCarree(central_longitude = 200) proj_trans = ccrs.PlateCarree() fig, ax = plt.subplots(2, 2, figsize = (10, 7), subplot_kw={"projection": proj}) # axi = ax[0, 0] fg1 = tp_draw_mode1.plot.contourf( ax = axi, levels = 21, transform = proj_trans, add_colorbar = False ) cb1 = fig.colorbar(fg1, ax = axi, location = 'bottom', aspect = 50, pad = 0.1, extendrect = True) cb1.set_label('') cb1.formatter.set_powerlimits((0, 0)) cb1.formatter.set_useMathText(True) uv_draw_mode1.plot.quiver( ax = axi, x = "lon", y = "lat", u = "u", v = "v", transform = proj_trans, ) # axi = ax[0, 1] fg2 = tp_draw_mode2.plot.contourf( ax = axi, levels = 21, transform = proj_trans, add_colorbar = False ) cb2 = fig.colorbar(fg2, ax = axi, location = 'bottom', aspect = 50, pad = 0.1, extendrect = True) cb2.set_label('') cb2.formatter.set_powerlimits((0, 0)) cb2.formatter.set_useMathText(True) uv_draw_mode2.plot.quiver( ax = axi, x = "lon", y = "lat", u = "u", v = "v", transform = proj_trans, ) # axi = ax[1, 0] fg3 = tp_draw_mode3.plot.contourf( ax = axi, levels = 21, transform = proj_trans, add_colorbar = False ) cb3 = fig.colorbar(fg3, ax = axi, location = 'bottom', aspect = 50, pad = 0.1, extendrect = True) cb3.set_label('') cb3.formatter.set_powerlimits((0, 0)) cb3.formatter.set_useMathText(True) uv_draw_mode3.plot.quiver( ax = axi, x = "lon", y = "lat", u = "u", v = "v", transform = proj_trans, ) # axi = ax[1, 1] fg4 = tp_draw_mode4.plot.contourf( ax = axi, levels = 21, transform = proj_trans, add_colorbar = False ) cb4 = fig.colorbar(fg4, ax = axi, location = 'bottom', aspect = 50, pad = 0.1, extendrect = True) cb4.set_label('') cb4.formatter.set_powerlimits((0, 0)) cb4.formatter.set_useMathText(True) uv_draw_mode4.plot.quiver( ax = axi, x = "lon", y = "lat", u = "u", v = "v", transform = proj_trans, ) for axi in ax.flat: axi.coastlines() axi.gridlines(draw_labels=["left", "bottom"], color="grey", alpha=0.5, linestyle="--")