diff --git a/scripts/plotting/ensemble_track_error_ellipses.py b/scripts/plotting/ensemble_track_error_ellipses.py
new file mode 100755
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+++ b/scripts/plotting/ensemble_track_error_ellipses.py
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+#!/usr/bin/env python3
+
+"""Generates error ellipses from atcfunix files of an ensemble forecast.
+
+This is a temporary location for this script; the plot_ellipses()
+function should be lifted out and put somewhere more sensible.
+
+Command-line arguments:
+1. Basin (AL, EP, IO, etc.)
+2. Numeric storm id (3, 10, 91, etc.)
+3. Analysis time in YYYYMMDDHH format (20251028)
+4. Model identifier (GFSO, HFSA, etc.)
+5. List of atcfunix filenames
+
+Model identifiers with AVN in atcfunix files are renamed to GFS (ie. AVNO becomes GFSO).
+
+`./ellipses.py AL 13 20251028 HFSA H*/*2025102800*atcfunix`
+"""
+
+import sys
+import collections
+import numpy
+import datetime
+import math
+
+import cartopy
+
+import matplotlib
+from matplotlib import pyplot as plt
+from cartopy import crs as ccrs
+
+class AtcfUnixLine:
+    """
+    Utility class with track information.
+    """
+    def __init__(self, line):
+        # Line looks like this:
+        # AL, 10, 2025100712, 03, HFSA, 000, 110N,  430W
+        # 0000000000111111111122222222223333333333444444
+        # 0123456789012345678901234567890123456789012345
+
+        self.basin2 = line[0:2]
+        self.stormid = int(line[4:6].strip(), 10)
+        self.YYYYMMDDHH = line[8:18]
+        self.atime = datetime.datetime.strptime(self.YYYYMMDDHH, '%Y%m%d%H')
+        self.model = line[24:28].strip().replace('AVN', 'GFS')
+        self.lead_time_hour = int(line[30:33].strip(), 10)
+
+        lat_int = int(line[35:38].strip(), 10)
+        latNS = line[38]
+        self.lat = lat_int / 10.0
+        if latNS == 'S':
+            self.lat = -self.lat
+
+        lon_int = int(line[41:45].strip(), 10)
+        lonEW = line[45]
+        self.lon = lon_int / 10.0
+        if lonEW == 'W':
+            self.lon = -self.lon
+
+    def set_ensemble_id(self, ensid):
+        self.ensemble_id = ensid
+
+def read_atcfunix(filename, filter_basin2, filter_stormid, filter_atime, filter_model):
+    """
+    Reads one atcfunix file, scanning for lines that match the filters.
+
+    @returns a list of AtcfUnixLine objects sorted by lead time.
+    """
+    #print(f'Scan {filename} for basin {filter_basin2} stormid {filter_stormid} atime {filter_atime} model {filter_model}')
+    by_time = []
+    with open(filename, 'rt') as fd:
+        for line in fd:
+            parsed = AtcfUnixLine(line)
+
+            if filter_basin2 is not None and filter_basin2 != parsed.basin2:
+                #print(f'bad basin {parsed.basin2}: {line}')
+                continue
+            if filter_stormid is not None and filter_stormid != parsed.stormid:
+                #print(f'bad stormid {parsed.stormid}: {line}')
+                continue
+            if filter_atime is not None and filter_atime != parsed.atime:
+                #print(f'bad atime: {line}')
+                continue
+            if filter_model is not None and filter_model != parsed.model:
+                #print(f'bad model: {model}')
+                continue
+
+            #print(f'accept line {line}')
+            by_time.append(parsed)
+
+    by_time.sort(key=lambda x: x.lead_time_hour)
+    return by_time
+
+def recentered_longitudes(inlon):
+    """Ensures no break in longitudes when the ensemble crosses a dateline.
+    Longitudes are shifted to min(inlon) ... 360+min(inlon)"""
+    minlon = min(inlon)
+    outlon = [ minlon + (l - minlon) % 360 for l in inlon ]
+    return outlon
+
+def read_ensemble(filenames, filter_basin2, filter_stormid, filter_atime, filter_model):
+    """
+    Reads a list of atcfunix files, one per ensemble member.
+
+    @returns A list, sorted by ensemble id, of lists of AtcfUnixLine objects. The inner lists are the result of a call to read_atcfunix() on one file.
+    """
+    by_id = [ read_atcfunix(filename, filter_basin2, filter_stormid, filter_atime, filter_model)
+              for filename in filenames]
+    ensemble_id = -1
+    for lines in by_id:
+        ensemble_id += 1
+        for line in lines:
+            line.set_ensemble_id(ensemble_id)
+    return by_id
+
+def fixes_by_lead_time(by_id):
+    """
+    Obtains fixes for each lead time and ensemble member.
+
+    @param by_id the return value of read_ensemble()
+
+    @returns A three-dimensional array fixes[lead_time][ensemble_id][2]. Outer dimension is the lead time, sorted by increasing lead time. Middle dimension is the ensemble id, sorted numerically increasing. Inner dimension is the fix location as [ lon, lat ]
+    """
+
+    fixes = collections.defaultdict(dict)
+    for by_lead_time in by_id:
+        for line in by_lead_time:
+            fixes[line.lead_time_hour][line.ensemble_id] = [ line.lon, line.lat ]
+    return fixes
+
+def covariance_and_mean_by_lead_time(fixes):
+    """
+    Calculates covariance matrixes and mean locations for each lead time.
+
+    @param fixes from fixes_by_lead_time()
+
+    @returns A list (one per lead time) of lists. Inner lists have five elements: covariance matrix, mean longitude, mean latitude, list of longitude fixes, and list of latitude fixes.
+    """
+    cov_and_mean = {}
+    for lead_time_hour, fixes_by_id in fixes.items():
+        lon = []
+        lat = []
+        for ensemble_id, fix in fixes_by_id.items():
+            lon.append(fix[0])
+            lat.append(fix[1])
+        if not lon and not lat:
+            continue # no data at this time
+
+        lon = recentered_longitudes(lon)
+
+        meanlon = sum(lon) / len(lon)
+        meanlat = sum(lat) / len(lat)
+        if len(lon) < 2:
+            # Need at least 2 points for an error estimate
+            cov_and_mean.append([ None, meanlon, meanlat ])
+            continue
+        normlon = [ x - meanlon for x in lon ]
+        normlat = [ y - meanlat for y in lat ]
+        cov = numpy.cov(numpy.asarray(normlon), numpy.asarray(normlat))
+        cov_and_mean[lead_time_hour] = [ cov, meanlon, meanlat, lon, lat ]
+    return cov_and_mean
+
+def plot_ellipses(cov_and_mean, plot_hours, colors):
+    """
+    Plots a track with error ellipses.
+
+    @param cov_and_mean from covariance_and_mean_by_lead_time
+    @param plot_hours list of forecast lead times to plot
+    @param colors list of colors cycled through for each lead time
+    """
+    i = -1
+    center_x = []
+    center_y = []
+    fig = plt.figure(figsize=(9,6), dpi=168)
+    ax = fig.add_subplot(1,1,1, projection=ccrs.PlateCarree())
+    legend_contents = []
+    for lead_time_hour, cov_meanlon_meanlat_lon_lat in cov_and_mean.items():
+        if lead_time_hour not in plot_hours:
+            #print(f'skip {lead_time_hour}hr lead time which was not requested')
+            continue
+
+        i += 1
+
+        cov, meanlon, meanlat, lon, lat = cov_meanlon_meanlat_lon_lat
+        eig = numpy.linalg.eig(cov)
+        del cov
+
+        center_x.append(meanlon)
+        center_y.append(meanlat)
+
+        val0 = eig.eigenvalues[0]
+        val1 = eig.eigenvalues[1]
+
+        vec0 = eig.eigenvectors[0]
+        vec1 = eig.eigenvectors[1]
+
+        del eig
+
+        if val0 > val1:
+            anglefrom='val0'
+            angle = math.atan2(vec0[1], vec0[0]) * 180 / math.pi
+        else:
+            anglefrom='val1'
+            angle = math.atan2(vec1[1], vec1[0]) * 180 / math.pi
+
+        stdev0 = math.sqrt(val0)
+        stdev1 = math.sqrt(val1)
+
+        del val0, val1, vec0, vec1
+
+        color = colors[i % len(colors)]
+
+        stdfac = 2 # one standard deviation
+
+        plt.scatter(lon, lat, 2, color=color, transform=ccrs.PlateCarree())
+        ellipse = matplotlib.patches.Ellipse([meanlon, meanlat], width=stdev0*stdfac, height=stdev1*stdfac, angle=angle, edgecolor=color, fill=False, transform=ccrs.PlateCarree(), linewidth=1.5)
+        ax.add_patch(ellipse)
+
+        legend_contents.append(matplotlib.patches.Patch(facecolor=color, alpha=0.7, edgecolor=color, label=f'{lead_time_hour} hrs'))
+
+
+    print(repr(legend_contents))
+    gl = ax.gridlines(draw_labels=True, dms=True, x_inline=False, y_inline=False, alpha=0.5)
+    gl.top_labels=False
+    gl.right_labels = False
+    ax.coastlines()
+    plt.plot(center_x, center_y, 'kx-', transform=ccrs.PlateCarree())
+    plt.legend(handles=legend_contents)
+    plt.show()
+
+
+def main():
+    """Main program. Arguments from sys.argv; see module description for details."""
+    filter_basin2 = sys.argv[1]
+    filter_stormid = int(sys.argv[2], 10)
+    filter_atime = datetime.datetime.strptime(sys.argv[3], '%Y%m%d%H')
+    filter_model = sys.argv[4]
+    filenames = sys.argv[5:]
+    by_id = read_ensemble(filenames, filter_basin2, filter_stormid, filter_atime, filter_model)
+    fixes = fixes_by_lead_time(by_id)
+    cov_and_mean = covariance_and_mean_by_lead_time(fixes)
+
+    colors = [ 'tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:cyan', 'tab:pink', 'tab:gray', 'tab:olive', 'tab:brown' ]
+    hours = [ 0, 24, 48, 72, 96, 120 ]
+    plot_ellipses(cov_and_mean, hours, colors)
+
+if __name__ == '__main__':
+    main()