← Cuba hurricanes AA

7  Optimization - trigger selection and plotting

7.1 Rainfall aggregation

The trigger needs one rainfall number per storm, but IMERG gives a value for every pixel over Cuba. For each storm we take two-day rainfall totals in each pixel, over the days the storm was in the ZMA plus one day either side, and summarise them across the country as the mean or the 50th, 80th, 90th or 95th percentile. The storm’s value is the highest of these over its window. The optimizations in this part try each of these aggregations.

The figure shows this for Hurricane Oscar’s wettest two days (20-21 October 2024). Most of the rain fell in the far east, so the mean (25 mm) comes out above the 80th percentile (23 mm), while the 50th percentile is only 3 mm. The higher the percentile, the smaller the area of heavy rain it takes to push the value up.

Hurricane Oscar’s two-day rainfall over Cuba and the pixels at or above each percentile.

The figure is made by scripts/rainfall_aggregation_map.py. The script checks its 80th percentile against the value stored in the trigger data (q80_roll2, 22.6 mm for Oscar) and stops if they differ.

Code 
import ocha_stratus as stratus
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
from matplotlib.ticker import FuncFormatter

from src.constants import *
Code 
blob_name = (
    f"{PROJECT_PREFIX}/processed/storm_stats/stats_with_targets.parquet"
)
df_stats = stratus.load_parquet_from_blob(blob_name)
Code 
blob_name = f"{PROJECT_PREFIX}/processed/trigger_metrics_ibtracs_imerg.parquet"
df_results = stratus.load_parquet_from_blob(blob_name)
Code 
df_results
rain_col rain_thresh wind_speed_max wind_speed_max_landfall target_sum impact_sum target_with_cerf_sum cerf_sum n_years n_storms rain_agg rain_q rain_window
0 q50_total 18.190000 40.0 NaN 6 27069337 9 7 18 31 quantile 50 total
1 q50_total 18.190000 40.0 115.0 6 27069337 9 7 18 31 quantile 50 total
2 q50_total 18.190000 40.0 75.0 6 27069337 9 7 18 31 quantile 50 total
3 q50_total 18.190000 40.0 90.0 6 27069337 9 7 18 31 quantile 50 total
4 q50_total 18.190000 40.0 25.0 6 27069337 9 7 18 35 quantile 50 total
... ... ... ... ... ... ... ... ... ... ... ... ... ...
752233 q99_roll3_mean_abv 287.727478 65.0 100.0 5 23164562 8 6 12 15 mean_abv 99 roll3
752234 q99_roll3_mean_abv 287.727478 65.0 130.0 5 23164562 8 6 12 15 mean_abv 99 roll3
752235 q99_roll3_mean_abv 287.727478 65.0 30.0 5 23210058 8 6 13 18 mean_abv 99 roll3
752236 q99_roll3_mean_abv 287.727478 65.0 70.0 5 23164562 8 6 12 15 mean_abv 99 roll3
752237 q99_roll3_mean_abv 287.727478 65.0 110.0 5 23164562 8 6 12 15 mean_abv 99 roll3

752238 rows × 13 columns

Code 
def get_optimal_triggers(optimize_col: str, years_triggered: int):
    df_results_rp = df_results[df_results["n_years"] == years_triggered]
    df_results_top_single_col = df_results_rp[
        df_results_rp[optimize_col] == df_results_rp[optimize_col].max()
    ]
    df_results_top_duplicates = df_results_top_single_col[
        df_results_top_single_col["impact_sum"]
        == df_results_top_single_col["impact_sum"].max()
    ]
    df_results_top = (
        df_results_top_duplicates.sort_values("rain_thresh")
        .drop_duplicates(
            subset=["wind_speed_max", "wind_speed_max_landfall", "rain_col"]
        )
        .sort_values("wind_speed_max")
        .drop_duplicates(
            subset=["rain_thresh", "wind_speed_max_landfall", "rain_col"]
        )
        .sort_values("wind_speed_max_landfall")
        .drop_duplicates(subset=["rain_thresh", "wind_speed_max", "rain_col"])
    )
    return df_results_top
Code 
get_optimal_triggers("target_sum", 6)
rain_col rain_thresh wind_speed_max wind_speed_max_landfall target_sum impact_sum target_with_cerf_sum cerf_sum n_years n_storms rain_agg rain_q rain_window
689 q50_total 26.412498 115.0 105.0 5 22343481 6 4 6 8 quantile 50 total
Code 
get_optimal_triggers("target_with_cerf_sum", 6)
rain_col rain_thresh wind_speed_max wind_speed_max_landfall target_sum impact_sum target_with_cerf_sum cerf_sum n_years n_storms rain_agg rain_q rain_window
148901 q50_roll3_mean_abv 79.962158 115.0 105.0 5 22289476 7 5 6 8 mean_abv 50 roll3
44987 q50_total_mean_abv 82.303337 115.0 105.0 5 22289476 7 5 6 8 mean_abv 50 total
256955 q80_roll3 73.388992 115.0 105.0 5 22289476 7 5 6 8 quantile 80 roll3
81005 q50_roll2_mean_abv 70.131157 115.0 105.0 5 22289476 7 5 6 8 mean_abv 50 roll2
225077 q80_roll2 67.569000 115.0 105.0 5 22289476 7 5 6 8 quantile 80 roll2
167945 q80_total 77.296997 115.0 105.0 5 22289476 7 5 6 8 quantile 80 total
116609 q50_roll3 20.857498 115.0 105.0 5 22289476 7 5 6 8 quantile 50 roll3
16433 q50_total 22.917500 115.0 110.0 5 22289476 7 5 6 8 quantile 50 total
Code 
get_optimal_triggers("cerf_sum", 6)
rain_col rain_thresh wind_speed_max wind_speed_max_landfall target_sum impact_sum target_with_cerf_sum cerf_sum n_years n_storms rain_agg rain_q rain_window
148901 q50_roll3_mean_abv 79.962158 115.0 105.0 5 22289476 7 5 6 8 mean_abv 50 roll3
44987 q50_total_mean_abv 82.303337 115.0 105.0 5 22289476 7 5 6 8 mean_abv 50 total
256955 q80_roll3 73.388992 115.0 105.0 5 22289476 7 5 6 8 quantile 80 roll3
81005 q50_roll2_mean_abv 70.131157 115.0 105.0 5 22289476 7 5 6 8 mean_abv 50 roll2
225077 q80_roll2 67.569000 115.0 105.0 5 22289476 7 5 6 8 quantile 80 roll2
167945 q80_total 77.296997 115.0 105.0 5 22289476 7 5 6 8 quantile 80 total
116609 q50_roll3 20.857498 115.0 105.0 5 22289476 7 5 6 8 quantile 50 roll3
16433 q50_total 22.917500 115.0 110.0 5 22289476 7 5 6 8 quantile 50 total
Code 
get_optimal_triggers("target_sum", 7)
rain_col rain_thresh wind_speed_max wind_speed_max_landfall target_sum impact_sum target_with_cerf_sum cerf_sum n_years n_storms rain_agg rain_q rain_window
72319 q50_roll2 18.574999 115.0 100.0 6 26262081 8 6 7 9 quantile 50 roll2
119101 q50_roll3 21.314999 115.0 100.0 6 26262081 8 6 7 9 quantile 50 roll3
10633 q50_total 27.847499 115.0 100.0 6 26262081 8 6 7 9 quantile 50 total
Code 
get_optimal_triggers("target_with_cerf_sum", 7)
rain_col rain_thresh wind_speed_max wind_speed_max_landfall target_sum impact_sum target_with_cerf_sum cerf_sum n_years n_storms rain_agg rain_q rain_window
72319 q50_roll2 18.574999 115.0 100.0 6 26262081 8 6 7 9 quantile 50 roll2
119101 q50_roll3 21.314999 115.0 100.0 6 26262081 8 6 7 9 quantile 50 roll3
10633 q50_total 27.847499 115.0 100.0 6 26262081 8 6 7 9 quantile 50 total
Code 
get_optimal_triggers("cerf_sum", 7)
rain_col rain_thresh wind_speed_max wind_speed_max_landfall target_sum impact_sum target_with_cerf_sum cerf_sum n_years n_storms rain_agg rain_q rain_window
72319 q50_roll2 18.574999 115.0 100.0 6 26262081 8 6 7 9 quantile 50 roll2
119101 q50_roll3 21.314999 115.0 100.0 6 26262081 8 6 7 9 quantile 50 roll3
10633 q50_total 27.847499 115.0 100.0 6 26262081 8 6 7 9 quantile 50 total
Code 
get_optimal_triggers("target_sum", 8)
rain_col rain_thresh wind_speed_max wind_speed_max_landfall target_sum impact_sum target_with_cerf_sum cerf_sum n_years n_storms rain_agg rain_q rain_window
68672 q50_roll2 17.484999 145.0 75.0 6 26586051 8 6 8 11 quantile 50 roll2
118892 q50_roll3 21.314999 90.0 75.0 6 26586051 8 6 8 11 quantile 50 roll3
72110 q50_roll2 18.574999 90.0 75.0 6 26586051 8 6 8 11 quantile 50 roll2
10424 q50_total 27.847499 90.0 75.0 6 26586051 8 6 8 11 quantile 50 total
73172 q50_roll2 25.957497 75.0 75.0 6 26586051 8 6 8 12 quantile 50 roll2
111260 q50_roll3 31.377502 75.0 75.0 6 26586051 8 6 8 12 quantile 50 roll3
Code 
get_optimal_triggers("target_with_cerf_sum", 8)
rain_col rain_thresh wind_speed_max wind_speed_max_landfall target_sum impact_sum target_with_cerf_sum cerf_sum n_years n_storms rain_agg rain_q rain_window
44571 q50_total_mean_abv 91.037247 115.0 90.0 6 26452081 9 7 8 10 mean_abv 50 total
270201 q80_roll3 76.439003 115.0 90.0 6 26452081 9 7 8 10 quantile 80 roll3
148899 q50_roll3_mean_abv 79.962158 115.0 90.0 6 26452081 9 7 8 10 mean_abv 50 roll3
161733 q80_total 88.023996 115.0 90.0 6 26452081 9 7 8 10 quantile 80 total
225075 q80_roll2 67.569000 115.0 90.0 6 26452081 9 7 8 10 quantile 80 roll2
81003 q50_roll2_mean_abv 70.131157 115.0 90.0 6 26452081 9 7 8 10 mean_abv 50 roll2
209433 q80_roll2 64.883003 145.0 90.0 6 26452081 9 7 8 10 quantile 80 roll2
129945 q50_roll3_mean_abv 77.877907 145.0 90.0 6 26452081 9 7 8 10 mean_abv 50 roll3
79437 q50_roll2_mean_abv 69.980583 145.0 90.0 6 26452081 9 7 8 10 mean_abv 50 roll2
167953 q80_total 77.296997 115.0 100.0 6 26452081 9 7 8 10 quantile 80 total
116617 q50_roll3 20.857498 115.0 100.0 6 26452081 9 7 8 10 quantile 50 roll3
256963 q80_roll3 73.388992 115.0 100.0 6 26452081 9 7 8 10 quantile 80 roll3
44995 q50_total_mean_abv 82.303337 115.0 100.0 6 26452081 9 7 8 10 mean_abv 50 total
Code 
get_optimal_triggers("cerf_sum", 8)
rain_col rain_thresh wind_speed_max wind_speed_max_landfall target_sum impact_sum target_with_cerf_sum cerf_sum n_years n_storms rain_agg rain_q rain_window
44571 q50_total_mean_abv 91.037247 115.0 90.0 6 26452081 9 7 8 10 mean_abv 50 total
270201 q80_roll3 76.439003 115.0 90.0 6 26452081 9 7 8 10 quantile 80 roll3
148899 q50_roll3_mean_abv 79.962158 115.0 90.0 6 26452081 9 7 8 10 mean_abv 50 roll3
161733 q80_total 88.023996 115.0 90.0 6 26452081 9 7 8 10 quantile 80 total
225075 q80_roll2 67.569000 115.0 90.0 6 26452081 9 7 8 10 quantile 80 roll2
81003 q50_roll2_mean_abv 70.131157 115.0 90.0 6 26452081 9 7 8 10 mean_abv 50 roll2
209433 q80_roll2 64.883003 145.0 90.0 6 26452081 9 7 8 10 quantile 80 roll2
129945 q50_roll3_mean_abv 77.877907 145.0 90.0 6 26452081 9 7 8 10 mean_abv 50 roll3
79437 q50_roll2_mean_abv 69.980583 145.0 90.0 6 26452081 9 7 8 10 mean_abv 50 roll2
167953 q80_total 77.296997 115.0 100.0 6 26452081 9 7 8 10 quantile 80 total
116617 q50_roll3 20.857498 115.0 100.0 6 26452081 9 7 8 10 quantile 50 roll3
256963 q80_roll3 73.388992 115.0 100.0 6 26452081 9 7 8 10 quantile 80 roll3
44995 q50_total_mean_abv 82.303337 115.0 100.0 6 26452081 9 7 8 10 mean_abv 50 total
Code 
def get_triggered_storms(selected_index):
    selected_trigger = df_results.loc[selected_index]
    print(selected_trigger)
    rain_col = selected_trigger["rain_col"]
    rain_thresh = selected_trigger["rain_thresh"]
    wind_speed_max = selected_trigger["wind_speed_max"]
    wind_speed_max_landfall = selected_trigger["wind_speed_max_landfall"]
    df_triggered = df_stats[
        (df_stats[rain_col] >= rain_thresh)
        & (
            (df_stats["wind_speed_max"] >= wind_speed_max)
            | (df_stats["wind_speed_max_landfall"] >= wind_speed_max_landfall)
        )
    ]
    return df_triggered
Code 
def plot_trigger_option(selected_index):
    selected_trigger = df_results.loc[selected_index]
    rain_col = selected_trigger["rain_col"]
    rain_thresh = selected_trigger["rain_thresh"]
    wind_speed_max = selected_trigger["wind_speed_max"]
    wind_speed_max_landfall = selected_trigger["wind_speed_max_landfall"]
    df_triggered = get_triggered_storms(selected_index)

    ymax = df_stats[rain_col].max() * 1.1
    xmax = df_stats["wind_speed_max"].max() * 1.1

    fig, ax = plt.subplots(dpi=200, figsize=(7, 7))

    bubble_sizes = df_stats["Total Affected"].fillna(0)
    # Optional: scale for visual clarity
    bubble_sizes_scaled = (
        bubble_sizes / bubble_sizes.max() * 5000
    )  # Adjust 300 as needed

    # Plot bubbles
    ax.scatter(
        df_stats["wind_speed_max"],
        df_stats[rain_col],
        s=bubble_sizes_scaled,
        alpha=0.3,
        color="crimson",
        edgecolor="none",
        zorder=1,
    )

    for _, row in df_stats.iterrows():
        triggered = row["sid"] in df_triggered["sid"].to_list()
        ax.annotate(
            row["name"].capitalize() + "\n" + str(row["season"]),
            (row["wind_speed_max"], row[rain_col]),
            ha="center",
            va="center",
            fontsize=6,
            color="crimson" if row["cerf"] == True else "k",
            zorder=10 if row["cerf"] else 9,
            alpha=0.8,
            fontstyle="italic" if triggered else "normal",
            fontweight="bold" if triggered else "normal",
        )

    trig_color = "gold"
    ax.axvline(
        wind_speed_max,
        color=trig_color,
        linewidth=0.5,
        zorder=0,
    )
    ax.axvline(
        wind_speed_max_landfall,
        color=trig_color,
        linewidth=0.5,
        linestyle="--",
        zorder=0,
    )
    ax.axhline(
        rain_thresh,
        color=trig_color,
        linewidth=0.5,
        zorder=0,
    )
    ax.add_patch(
        mpatches.Rectangle(
            (wind_speed_max, rain_thresh),  # bottom left
            xmax - wind_speed_max,  # width
            ymax - rain_thresh,  # height
            facecolor=trig_color,
            alpha=0.1,
            zorder=0,
        )
    )

    for cat_value, cat_name in CAT_LIMITS:
        ax.annotate(
            cat_name + " -",
            (cat_value, 0),
            fontstyle="italic",
            color="grey",
            rotation=90,
            va="top",
            ha="center",
            fontsize=8,
        )

    ax.set_ylabel(rain_col)
    ax.set_xlabel("\nMax. wind speed while in ZMA (knots)")

    ax.set_xlim(left=0, right=xmax)
    ax.set_ylim(bottom=0, top=ymax)

    ax.spines.top.set_visible(False)
    ax.spines.right.set_visible(False)
Code 
# 6 triggered years, target_with_cerf_sum
plot_trigger_option(225077)
rain_col                   q80_roll2
rain_thresh                   67.569
wind_speed_max                 115.0
wind_speed_max_landfall        105.0
target_sum                         5
impact_sum                  22289476
target_with_cerf_sum               7
cerf_sum                           5
n_years                            6
n_storms                           8
rain_agg                    quantile
rain_q                            80
rain_window                    roll2
Name: 225077, dtype: object

Code 
# 6 triggered years, cerf_sum
plot_trigger_option(226352)
rain_col                   q80_roll2
rain_thresh                98.341003
wind_speed_max                  75.0
wind_speed_max_landfall         75.0
target_sum                         4
impact_sum                  22612062
target_with_cerf_sum               6
cerf_sum                           4
n_years                            6
n_storms                           7
rain_agg                    quantile
rain_q                            80
rain_window                    roll2
Name: 226352, dtype: object

Code 
# 7 triggered years
plot_trigger_option(72319)
rain_col                   q50_roll2
rain_thresh                18.574999
wind_speed_max                 115.0
wind_speed_max_landfall        100.0
target_sum                         6
impact_sum                  26262081
target_with_cerf_sum               8
cerf_sum                           6
n_years                            7
n_storms                           9
rain_agg                    quantile
rain_q                            50
rain_window                    roll2
Name: 72319, dtype: object

Code 
# 8 triggered years, target_with_cerf_sum
plot_trigger_option(225075)
rain_col                   q80_roll2
rain_thresh                   67.569
wind_speed_max                 115.0
wind_speed_max_landfall         90.0
target_sum                         6
impact_sum                  26452081
target_with_cerf_sum               9
cerf_sum                           7
n_years                            8
n_storms                          10
rain_agg                    quantile
rain_q                            80
rain_window                    roll2
Name: 225075, dtype: object

Code 
# 8 triggered years, target_sum
plot_trigger_option(73172)
rain_col                   q50_roll2
rain_thresh                25.957497
wind_speed_max                  75.0
wind_speed_max_landfall         75.0
target_sum                         6
impact_sum                  26586051
target_with_cerf_sum               8
cerf_sum                           6
n_years                            8
n_storms                          12
rain_agg                    quantile
rain_q                            50
rain_window                    roll2
Name: 73172, dtype: object

Code 
df_stats
sid valid_time_min valid_time_max wind_speed_max wind_speed_max_landfall q50_total q50_total_mean_abv q50_roll2 q50_roll2_mean_abv q50_roll3 ... q99_roll3_mean_abv cerf Total Affected Amount in US$ season name name_season cerf_str target target_with_cerf
0 2000233N12316 2000-08-24 00:00:00.000040 2000-08-24 12:00:00.000040 40.0 NaN 18.190000 35.262634 18.152500 34.911701 18.189999 ... 70.882492 None 0 NaN 2000 DEBBY Debby 2000 nan False False
1 2000260N15308 2000-09-19 12:00:00.000040 2000-09-20 18:00:00.000040 30.0 NaN 26.412498 68.799995 21.250000 62.718685 23.750000 ... 184.656494 None 0 NaN 2000 HELENE Helene 2000 nan False False
2 2001303N13276 2001-11-04 06:00:00.000040 2001-11-05 06:00:00.000040 120.0 115.0 94.369999 214.020828 76.807495 195.712280 94.297501 ... 404.398499 None 5900012 NaN 2001 MICHELLE Michelle 2001 nan True True
3 2002258N10300 2002-09-19 18:00:00.000040 2002-09-21 18:00:00.000040 110.0 75.0 78.592495 207.540146 40.644997 143.821213 56.139999 ... 794.184509 None 42500 NaN 2002 ISIDORE Isidore 2002 nan False False
4 2002265N10315 2002-09-29 00:00:00.000040 2002-10-02 00:00:00.000040 90.0 90.0 54.195000 89.869759 37.119995 67.565186 43.507500 ... 183.679001 None 281470 NaN 2002 LILI Lili 2002 nan False False
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
57 2023321N15278 2023-11-18 00:00:00.000039 2023-11-18 00:00:00.000039 30.0 NaN 3.947500 38.300976 3.947500 38.281227 3.947500 ... 161.707001 False 0 NaN 2023 UNNAMED Unnamed 2023 False False False
58 2024216N20284 2024-08-02 12:00:00.000039 2024-08-04 00:00:00.000039 35.0 30.0 54.164997 103.377747 40.412502 75.217384 49.295002 ... 240.353485 False 0 NaN 2024 DEBBY Debby 2024 False False False
59 2024268N17278 2024-09-25 12:00:00.000039 2024-09-25 12:00:00.000039 65.0 NaN 25.417499 116.488297 21.440001 99.156189 25.417500 ... 307.214508 False 0 NaN 2024 HELENE Helene 2024 False False False
60 2024293N21294 2024-10-20 06:00:00.000039 2024-10-22 06:00:00.000039 75.0 75.0 12.205000 61.957958 9.135000 50.185165 12.029999 ... 533.183960 True 320000 3499569.0 2024 OSCAR Oscar 2024 True True True
61 2024309N13283 2024-11-06 06:00:00.000039 2024-11-07 06:00:00.000039 100.0 100.0 39.124998 102.100563 34.349998 83.028404 37.614998 ... 287.727478 True 4000000 5999888.0 2024 RAFAEL Rafael 2024 True True True

62 rows × 44 columns

Code 
def color_df(val):
    if val == "Yes":
        return "background-color: crimson"
    elif val == "No":
        return "background-color: dodgerblue"
    elif val == "Trig.":
        return "background-color: darkorange"
    else:
        return ""
Code 
plot_triggers = [
    (226352, "4.3-yr RP"),
    # (225077, "4.3-yr RP<br>[B]"),
    (72319, "3.7-yr RP<br>"),
    (225075, "3.3-yr RP"),
    # (73172, "3.3-yr RP<br>[B]"),
]
Code 
df_disp = df_stats.copy()
df_disp = df_disp.rename(columns={"cerf_str": "CERF", "name_season": "Storm"})
df_disp = df_disp.set_index("Storm")
df_disp["CERF"] = df_disp["CERF"].replace(
    {"True": "Yes", "False": "No", "nan": "pre-CERF"}
)


for trig_index, trig_name in plot_triggers:
    df_triggered = get_triggered_storms(trig_index)
    df_disp[trig_name] = (
        df_disp["sid"]
        .isin(df_triggered["sid"].to_list())
        .apply(lambda x: "Trig." if x else "No trig.")
    )

trig_cols = [x[1] for x in plot_triggers]

df_disp = df_disp.sort_values(["Total Affected"] + trig_cols, ascending=False)

cols = trig_cols + ["CERF", "Total Affected"]

display(
    df_disp[cols]
    .style.bar(
        subset="Total Affected",
        color="mediumpurple",
        # vmax=500000,
        props="width: 400px;",
    )
    .map(color_df)
    .set_table_styles(
        {
            "Total Affected": [
                {"selector": "th", "props": [("text-align", "left")]},
                {"selector": "td", "props": [("text-align", "left")]},
            ]
        }
    )
    .format({"Total Affected": "{:,}"})
)
rain_col                   q80_roll2
rain_thresh                98.341003
wind_speed_max                  75.0
wind_speed_max_landfall         75.0
target_sum                         4
impact_sum                  22612062
target_with_cerf_sum               6
cerf_sum                           4
n_years                            6
n_storms                           7
rain_agg                    quantile
rain_q                            80
rain_window                    roll2
Name: 226352, dtype: object
rain_col                   q50_roll2
rain_thresh                18.574999
wind_speed_max                 115.0
wind_speed_max_landfall        100.0
target_sum                         6
impact_sum                  26262081
target_with_cerf_sum               8
cerf_sum                           6
n_years                            7
n_storms                           9
rain_agg                    quantile
rain_q                            50
rain_window                    roll2
Name: 72319, dtype: object
rain_col                   q80_roll2
rain_thresh                   67.569
wind_speed_max                 115.0
wind_speed_max_landfall         90.0
target_sum                         6
impact_sum                  26452081
target_with_cerf_sum               9
cerf_sum                           7
n_years                            8
n_storms                          10
rain_agg                    quantile
rain_q                            80
rain_window                    roll2
Name: 225075, dtype: object
  4.3-yr RP 3.7-yr RP
 3.3-yr RP CERF Total Affected
Storm          
Irma 2017 Trig. Trig. Trig. Yes 10,000,000
Michelle 2001 Trig. Trig. Trig. pre-CERF 5,900,012
Rafael 2024 Trig. Trig. Trig. Yes 4,000,000
Ian 2022 No trig. Trig. Trig. Yes 3,200,000
Dennis 2005 Trig. Trig. Trig. pre-CERF 2,500,000
Gustav 2008 No trig. Trig. Trig. No 450,019
Oscar 2024 No trig. No trig. No trig. Yes 320,000
Lili 2002 No trig. No trig. No trig. pre-CERF 281,470
Charley 2004 No trig. No trig. No trig. pre-CERF 244,005
Noel 2007 No trig. No trig. No trig. No 192,488
Matthew 2016 No trig. No trig. Trig. Yes 190,000
Sandy 2012 Trig. Trig. Trig. Yes 162,605
Wilma 2005 No trig. No trig. No trig. pre-CERF 100,000
Paloma 2008 Trig. Trig. Trig. No 49,445
Isaac 2012 No trig. No trig. No trig. No 45,496
Isidore 2002 No trig. No trig. No trig. pre-CERF 42,500
Alberto 2018 No trig. No trig. No trig. No 40,000
Alex 2022 No trig. No trig. No trig. No 6,780
Ivan 2004 No trig. No trig. No trig. pre-CERF 3,245
Michael 2018 No trig. No trig. No trig. No 540
Isaias 2020 No trig. No trig. No trig. No 500
Alberto 2006 No trig. No trig. No trig. No 268
Ike 2008 Trig. Trig. Trig. No 0
Debby 2000 No trig. No trig. No trig. pre-CERF 0
Helene 2000 No trig. No trig. No trig. pre-CERF 0
Unnamed 2002 No trig. No trig. No trig. pre-CERF 0
Bonnie 2004 No trig. No trig. No trig. pre-CERF 0
Jeanne 2004 No trig. No trig. No trig. pre-CERF 0
Arlene 2005 No trig. No trig. No trig. pre-CERF 0
Rita 2005 No trig. No trig. No trig. pre-CERF 0
Alpha 2005 No trig. No trig. No trig. pre-CERF 0
Chris 2006 No trig. No trig. No trig. No 0
Ernesto 2006 No trig. No trig. No trig. No 0
Barry 2007 No trig. No trig. No trig. No 0
Olga 2007 No trig. No trig. No trig. No 0
Fay 2008 No trig. No trig. No trig. No 0
Hanna 2008 No trig. No trig. No trig. No 0
Ida 2009 No trig. No trig. No trig. No 0
Bonnie 2010 No trig. No trig. No trig. No 0
Nicole 2010 No trig. No trig. No trig. No 0
Paula 2010 No trig. No trig. No trig. No 0
Tomas 2010 No trig. No trig. No trig. No 0
Don 2011 No trig. No trig. No trig. No 0
Emily 2011 No trig. No trig. No trig. No 0
Rina 2011 No trig. No trig. No trig. No 0
Dorian 2013 No trig. No trig. No trig. No 0
Hermine 2016 No trig. No trig. No trig. No 0
Nate 2017 No trig. No trig. No trig. No 0
Philippe 2017 No trig. No trig. No trig. No 0
Beryl 2018 No trig. No trig. No trig. No 0
Gordon 2018 No trig. No trig. No trig. No 0
Laura 2020 No trig. No trig. No trig. No 0
Marco 2020 No trig. No trig. No trig. No 0
Eta 2020 No trig. No trig. No trig. No 0
Elsa 2021 No trig. No trig. No trig. No 0
Fred 2021 No trig. No trig. No trig. No 0
Ida 2021 No trig. No trig. No trig. No 0
Arlene 2023 No trig. No trig. No trig. No 0
Idalia 2023 No trig. No trig. No trig. No 0
Unnamed 2023 No trig. No trig. No trig. No 0
Debby 2024 No trig. No trig. No trig. No 0
Helene 2024 No trig. No trig. No trig. No 0

7.2 Data for the email scatter plot

The data behind the scatter plot used in the monitoring emails.

Code 
blob_name = (
      f"{PROJECT_PREFIX}/processed/storm_stats/stats_with_targets.parquet"
)

df_stats_raw = stratus.load_parquet_from_blob(blob_name)