.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/ftle/plot_merra_ftle_ridges.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_ftle_plot_merra_ftle_ridges.py: MERRA-2 FTLE ridges =================== Compute the FTLE field and ridges for atmospheric flow at time of Godzilla dust storm using MERRA-2 data which is vertically averaged over pressure surfaces ranging from 500hPa to 800hPa. .. GENERATED FROM PYTHON SOURCE LINES 10-22 .. code-block:: Python # Author: ajarvis # Data: MERRA-2 - Global Modeling and Assimilation Office - NASA import numpy as np from math import copysign import matplotlib.pyplot as plt from numbacs.flows import get_interp_arrays_2D, get_flow_2D from numbacs.integration import flowmap_grid_2D from numbacs.diagnostics import C_eig_2D, ftle_from_eig from numbacs.extraction import ftle_ordered_ridges from scipy.ndimage import gaussian_filter .. GENERATED FROM PYTHON SOURCE LINES 23-31 Get flow data -------------- Load in atmospheric velocity data, dates, and coordinates. Set domain for FTLE computation and integration span. Create interpolant and retrieve flow. .. note:: Pandas is a simpler option for storing and manipulating dates but we use numpy here as Pandas is not a dependency. .. GENERATED FROM PYTHON SOURCE LINES 31-68 .. code-block:: Python # load in atmospheric data dates = np.load("../data/merra_june2020/dates.npy") dt = (dates[1] - dates[0]).astype("timedelta64[h]").astype(int) t = np.arange(0, len(dates) * dt, dt, np.float64) lon = np.load("../data/merra_june2020/lon.npy") lat = np.load("../data/merra_june2020/lat.npy") # NumbaCS uses 'ij' indexing, most geophysical data uses 'xy' # indexing for the spatial coordintes. We need to switch axes and # scale by 3.6 since velocity data is in m/s and we want km/hr. u = np.moveaxis(np.load("../data/merra_june2020/u_500_800hPa.npy"), 1, 2) * 3.6 v = np.moveaxis(np.load("../data/merra_june2020/v_500_800hPa.npy"), 1, 2) * 3.6 nt, nx, ny = u.shape # set domain on which ftle will be computed dx = 0.15 dy = 0.15 lonf = np.arange(-100, 35 + dx, dx) latf = np.arange(-5, 45 + dy, dy) # set integration span and integration direction day = 16 t0_date = np.datetime64(f"2020-06-{day:02d}") t0 = t[np.nonzero(dates == t0_date)[0][0]] T = -72.0 params = np.array([copysign(1, T)]) # get interpolant arrays of velocity field grid_vel, C_eval_u, C_eval_v = get_interp_arrays_2D(t, lon, lat, u, v) # set integration direction and retrieve flow # set spherical = 1 since flow is on spherical domain and lon is from [-180,180) params = np.array([copysign(1, T)]) funcptr = get_flow_2D(grid_vel, C_eval_u, C_eval_v, spherical=1) .. GENERATED FROM PYTHON SOURCE LINES 69-72 Integrate --------- Integrate grid of particles and return final positions. .. GENERATED FROM PYTHON SOURCE LINES 72-74 .. code-block:: Python flowmap = flowmap_grid_2D(funcptr, t0, T, lonf, latf, params) .. GENERATED FROM PYTHON SOURCE LINES 75-78 CG eigenvalues, eigenvectors, and FTLE ---------------------------------------------- Compute eigenvalues/vectors of CG tensor from final particle positions and compute FTLE. .. GENERATED FROM PYTHON SOURCE LINES 78-91 .. code-block:: Python # compute eigenvalues/vectors of Cauchy Green tensor eigvals, eigvecs = C_eig_2D(flowmap, dx, dy) eigval_max = eigvals[:, :, 1] eigvec_max = eigvecs[:, :, :, 1] # compute FTLE from max eigenvalue ftle = ftle_from_eig(eigval_max, T) # smooth ftle field, usually a good idea for numerical velocity field sigma = 1.2 ftle_c = gaussian_filter(ftle, sigma, mode="nearest") .. GENERATED FROM PYTHON SOURCE LINES 92-95 Ridge extraction ---------------- Compute ordered FTLE ridges. .. GENERATED FROM PYTHON SOURCE LINES 95-115 .. code-block:: Python # set parameters for ridge function # function is fast after first call so experiment with these parameters percentile = 30 sdd_thresh = 0.0 # identify ridge points, link points in each ridge in an ordered manner, # connect close enough ridges dist_tol = 5e-1 ridge_curves = ftle_ordered_ridges( ftle_c, eigvec_max, lonf, latf, dist_tol, percentile=percentile, sdd_thresh=sdd_thresh, min_ridge_pts=25, ) .. GENERATED FROM PYTHON SOURCE LINES 116-120 Plot ---- Plot the results. Using the cartopy package for plotting geophysical data is advised but it is not a dependency so we simply use matplotlib. .. GENERATED FROM PYTHON SOURCE LINES 120-130 .. code-block:: Python coastlines = np.load("../data/merra_june2020/coastlines.npy") fig, ax = plt.subplots(dpi=200) ax.scatter(coastlines[:, 0], coastlines[:, 1], 1, "k", marker=".", edgecolors=None, linewidths=0) ax.contourf(lonf, latf, ftle.T, levels=80, zorder=0) for rc in ridge_curves: ax.plot(rc[:, 0], rc[:, 1], "r", lw=0.5) ax.set_xlim([lonf[0], lonf[-1]]) ax.set_ylim([latf[0], latf[-1]]) ax.set_aspect("equal") plt.show() .. image-sg:: /auto_examples/ftle/images/sphx_glr_plot_merra_ftle_ridges_001.png :alt: plot merra ftle ridges :srcset: /auto_examples/ftle/images/sphx_glr_plot_merra_ftle_ridges_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 12.227 seconds) .. _sphx_glr_download_auto_examples_ftle_plot_merra_ftle_ridges.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_merra_ftle_ridges.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_merra_ftle_ridges.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_merra_ftle_ridges.zip ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_