Overview¶
In this notebook, we utilize matplotlib contour functions to detect regions in gridded climate data with low precipitation values based on a defined threshold. We label these areas as individual features and follow them through various time steps. We visualize these regions across time on a map using frame-to-frame overlap, allowing us to analyze how the precipitation systems move and evolve over time.
Prerequisites¶
| Concepts | Importance | Notes |
|---|---|---|
| Xarray | Necessary | For reading in data |
| Matplotlib | Necessary | For contouring |
| Cartopy | Useful | For map projection |
| IPython | Useful | For inline animation |
Estimated time to learn: 30 minutes
Import Packages¶
# Import necessary libraries for identifying contours and plotting
import numpy as np
import xarray as xr
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation
from IPython.display import HTML
from skimage.measure import label, regionprops
import matplotlib.colors as mcolors
from matplotlib.cm import ScalarMappable
from matplotlib.path import Path
import cartopy.crs as ccrs
import cartopy.feature as cfeatureLoad in the data using xarray, and extract the variable of interest.
We want to look at the total precipitation of the extra-tropical cyclones, which is stored under the ETC_tp variable.
# Load in precipitation dataset
ds = xr.open_dataset('data/precip_ex.nc')
# Extratropical cyclone total precipitation
data = ds["ETC_tp"]Exploratory Data Analysis¶
Let’s take a look at this precipitation data for a single time stamp.
# Subsection the precipitation values at time = 0
hr_0_data = data.sel(time=data.time[0])
# Look at the range of total precipitation
plt.hist(hr_0_data.values.ravel(), bins=50)
plt.xlabel("Total Precipitation (mm LWE h$^{-1}$)")
plt.ylabel("Number of Grid Cells")
plt.title("Distribution of Precipitation at Hour 0")
plt.show()
The majority of precipitation values are between 0 and 2 mm. Let’s use that as a filter so we can visualize the data in a way that corresponds to the bulk of the data, rather than a few askew values.
# Plot precipitation at hour 0
fig, ax = plt.subplots(
figsize=(12, 4),
subplot_kw={"projection": ccrs.PlateCarree()}
)
im = ax.pcolormesh(
hr_0_data.longitude,
hr_0_data.latitude,
hr_0_data.values,
transform=ccrs.PlateCarree(),
cmap="turbo",
vmin=0,
vmax=2
)
# Add gridlines
ax.add_feature(cfeature.COASTLINE)
gl = ax.gridlines(draw_labels=True)
gl.top_labels = False
gl.right_labels = False
# Add colorbar and labels
fig.colorbar(im, ax=ax, orientation="horizontal", pad=0.1,
label='Total Precipitation (mm LWE h$^{-1}$)')
ax.set_title("Total precipitation at hour 0")
plt.show()/home/runner/micromamba/envs/feature-tracking-dev/lib/python3.14/site-packages/cartopy/io/__init__.py:242: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/110m_physical/ne_110m_coastline.zip
warnings.warn(f'Downloading: {url}', DownloadWarning)
Track Features Over Time¶
We will find the contours later using matplotlib.pyplot.contour.
First, we initialize the contour-detection parameters, and extract the latitude and longitude coordinates from the dataset. We then convert the coordinates into 2D grids so the found contours can be plotted in geographic space.
# Define parameters for contour finding
threshold = 100000
min_area = 20
min_overlap = 0.2
# Extract latitutde and longitude coordinates
lat = data[data.dims[1]].values # assumes dims like (time, lat, lon)
lon = data[data.dims[2]].values
# Make 2D grids for contour
lon2d, lat2d = np.meshgrid(lon, lat)The feature we want to detect is connected regions enclosed by a precipitation contour at or below our defined threshold.
We create a function to detect these features at each time step and assign each feature a track ID (track_id) so we can follow it from one time step to the next.
Detect features in the current field by drawing a contour at the precipitation threshold.
Each contour line is converted into a closed region (
mask).Only regions where precipitation is below the threshold are kept.
Very small regions (below
min_area) are removed.
For each detected feature, compare it with features from the previous time step.
Compute spatial overlap between the current feature mask and previous feature masks. If the new feature overlaps enough (exceeds
min_overlap) with an old feature, it keeps the sametrack_id. Otherwise, it is treated as a new feature and receives a new track_id.
Store the feature’s
track_id, centroid location, and mask for plotting or later analysis.
def detect_features(
field,
threshold=threshold,
min_area=min_area,
min_overlap=min_overlap
):
"""
Track regions with low total precipitation (total precipitation <= threshold) using Matplotlib contours.
"""
ny, nx = data.isel(time=0).shape
yy, xx = np.mgrid[0:ny, 0:nx]
points = np.column_stack([xx.ravel(), yy.ravel()])
tracked = []
prev = {}
next_track_id = 1
for t in range(data.sizes["time"]):
field = data.isel(time=t).values
fig, ax = plt.subplots()
cs = ax.contour(field, levels=[threshold])
plt.close(fig)
current = {}
frame_info = []
for seg in cs.allsegs[0]:
path = Path(seg)
mask = path.contains_points(points).reshape(ny, nx)
# keep low precipitation regions only
mask = mask & (field <= threshold)
if mask.sum() < min_area:
continue
y, x = np.where(mask)
centroid = (y.mean(), x.mean())
best_id = None
best_overlap = 0
for track_id, old_mask in prev.items():
overlap = np.logical_and(mask, old_mask).sum() / mask.sum()
if overlap > best_overlap:
best_overlap = overlap
best_id = track_id
if best_overlap < min_overlap:
best_id = next_track_id
next_track_id += 1
current[best_id] = mask
frame_info.append({
"track_id": best_id,
"mask": mask,
"centroid_rc": centroid,
})
tracked.append(frame_info)
prev = current
return trackedNote that our function identifies these features for a single time step. So to track these regions over time, we must iterate through all time steps present in our data, and call detect_features on each interval.
Run the tracking function for each time step, and save the results in a variable tracked.
tracked = detect_features(
data,
threshold=threshold,
min_area=min_area,
min_overlap=min_overlap,
)In the visualization, we will have a colorbar that represent precipitation levels. We want the bar to accurately depict the bulk of the data, so we limit the lower end to the 1st percentile of the data, and the upper end to the 99th percentile.
# Find 1st percentile and 99th percentile of total precipitation values
vals = data.values
vmin = np.nanpercentile(vals, 1)
vmax = np.nanpercentile(vals, 99)
levels = np.linspace(vmin, vmax, 16) # evenly spaced intervals
norm = mcolors.Normalize(vmin=vmin, vmax=vmax) # normalize
cmap = 'turbo' # colormapWe can animate the storm tracked over time by plotting the low precipitation regions at each timestep.
def update(t):
"""
Animation function, goes through each timestep and its tracked precipitation.
"""
ax.clear()
ax.set_extent(extent, crs=ccrs.PlateCarree())
ax.set_aspect('auto')
title = fig.suptitle("")
field = data.isel(time=t).values # total precipitation data for current timestep
# Colored contour lines
ax.contour(
lon2d, lat2d, field,
levels=levels,
cmap=cmap,
norm=norm,
linewidths=0.9,
transform=ccrs.PlateCarree()
)
# Tracked feature outlines
for feat in tracked[t]:
mask = feat["mask"]
ax.contour(
lon2d, lat2d, mask.astype(int),
levels=[0.5],
colors="black",
linewidths=1.6,
transform=ccrs.PlateCarree()
)
y, x = feat["centroid_rc"]
iy = int(round(y))
ix = int(round(x))
ax.plot(
lon[ix], lat[iy],
"ko", ms=3,
transform=ccrs.PlateCarree()
)
ax.text(
lon[ix], lat[iy],
str(feat["track_id"]),
fontsize=8,
color="black",
ha="left",
va="bottom",
transform=ccrs.PlateCarree(),
bbox=dict(facecolor="white", edgecolor="none", alpha=0.7, pad=1)
)
# Create outlines for continents/coastlines and gridlines
ax.coastlines(color="0.55", linewidth=0.6)
gl = ax.gridlines(
draw_labels=True,
linewidth=0.4,
color="0.5",
alpha=0.25,
linestyle="--"
)
gl.top_labels = False
gl.right_labels = False
title.set_text(
f"Extratropical cyclone precipitation features at hour {t}"
)# Create map
fig, ax = plt.subplots(figsize=(12, 5), subplot_kw={"projection": ccrs.PlateCarree()})
extent = [lon.min(), lon.max(), lat.min(), lat.max()] # geographic boundaries of the map
ax.set_extent(extent, crs=ccrs.PlateCarree())
# Create colorbar
sm = ScalarMappable(norm=norm, cmap=cmap)
sm.set_array([])
cbar = fig.colorbar(
sm,
ax=ax,
orientation="horizontal",
pad=0.15
)
cbar.set_label("Total Precipitation (mm LWE h$^{-1}$)")
# Create animation
anim = FuncAnimation(
fig,
update,
frames=data.sizes["time"],
interval=300,
blit=False,
)
plt.close(fig)
HTML(anim.to_jshtml())This animation showcases the total precipitation values of low precipitation systems from hours 0 to 12. These feature systems appear between the latitudes 30°N and 70°N (hence the boundaries of the map) which is typical for an extratropical cyclone.
Through tracking, we see that most of the systems head towards the East as the hour increases. We also are able to track whether these precipitation areas break apart, which is typical behavior during an extratropical cyclone. For example, see the leftmost precipitation system on the map. It heads toward the East direction and splits at hour 7.