---

name: metocean-statistics description: Statistical analysis of metocean data including extreme value analysis, return periods, joint probability distributions, and directional statistics. Use for design criteria, fatigue analysis, and operational limits.

Metocean Statistics Skill

When to Use

Trigger this skill when the user asks about:

Extreme Value Analysis

• "Calculate extreme wave heights"
• "Return period analysis"
• "Fit GEV distribution"
• "100-year return period"
• "Peak-over-threshold analysis"
• "Annual maxima statistics"

Distribution Fitting

• "Joint probability distribution"
• "Hs-Tp joint distribution"
• "Environmental contours"
• "IFORM contours"
• "Fit distribution to wave data"

Temporal Statistics

• "Monthly statistics"
• "Seasonal trends"
• "Annual variability"
• "Exceedance duration"

Directional Analysis

• "Wave rose"
• "Wind rose"
• "Directional statistics"
• "Sector-based analysis"

Design Applications

• "Design criteria"
• "Fatigue analysis"
• "Operational limits"
• "Weather windows"

Analysis Types

Temporal Statistics

Monthly Aggregations

• Mean, max, min, standard deviation
• Percentiles (P50, P90, P99)
• Sample count and data availability

Seasonal Aggregations

• Winter (DJF), Spring (MAM), Summer (JJA), Autumn (SON)
• Seasonal extremes
• Inter-annual variability

Annual Trends

• Annual mean and max time series
• Trend analysis (linear regression)
• Climate variability indicators

Exceedance Duration

• Persistence above threshold
• Storm duration statistics
• Operational weather windows

Extreme Value Analysis

Block Maxima Method

• Annual maxima extraction
• Monthly maxima for shorter records
• Requires minimum 10 years for reliability
• Standard approach for design criteria

Peak-Over-Threshold (POT)

• Extract independent peaks above threshold
• Suitable for shorter records (3+ years)
• More efficient use of data
• Requires declustering of dependent events

GEV Distribution (Generalized Extreme Value)

• Three-parameter distribution (shape, location, scale)
• Encompasses Gumbel, Frechet, Weibull types
• Standard for block maxima analysis
• Shape parameter indicates tail behavior

GPD Distribution (Generalized Pareto)

• Two-parameter distribution for exceedances
• Paired with POT method
• Threshold selection critical
• More flexible for tail modeling

Return Periods

Standard Return Periods

Period	Application
1-year	Annual operating design
2-year	Seasonal planning
5-year	Short-term structure design
10-year	Installation operations
25-year	Platform design basis
50-year	Structure design life
100-year	Ultimate limit state
500-year	Abnormal/survival conditions

Confidence Intervals

• Profile likelihood method
• Bootstrap resampling
• Delta method approximation
• Typically report 95% CI

Joint Probability

Hs-Tp Joint Distribution

• Wave height and period correlation
• Scatter diagram representation
• Steepness limits (physical constraints)
• Critical for dynamic analysis

Wind-Wave Correlation

• Wind speed vs wave height
• Lag correlation analysis
• Sea state development
• Combined loading analysis

Environmental Contours

• IFORM method (Inverse First Order Reliability Method)
• Direct sampling approach
• Joint exceedance probability
• Design envelope definition

Directional Analysis

Wave Roses

• Direction vs magnitude distribution
• Sector occurrence frequency
• Mean and maximum by sector
• Seasonal directional variation

Wind Roses

• Similar to wave roses
• Different directional convention (from vs to)
• Fetch-limited conditions
• Dominant wind patterns

Sector-Based Statistics

• 8, 12, or 16 sector divisions
• Exceedance by direction
• Design wave by sector
• Directional spreading

External Tool Integration

metocean-stats Package

Installation:

pip install metocean-stats

Extreme Value Analysis:

from metocean_stats import EVA

# Block maxima approach
eva = EVA(data=wave_heights, method='block_maxima', block_size='year')
eva.fit('GEV')

# Get return levels
rp_10yr = eva.return_level(10)
rp_100yr = eva.return_level(100)

# Confidence intervals
rp_100yr_ci = eva.return_level_ci(100, alpha=0.05)

# Diagnostic plots
eva.plot_return_levels()
eva.plot_qq()
eva.plot_probability()

Peak-Over-Threshold:

from metocean_stats import EVA

# POT approach
eva_pot = EVA(
    data=wave_heights,
    method='peak_over_threshold',
    threshold=np.percentile(wave_heights, 95),
    decluster_time='48h'
)
eva_pot.fit('GPD')

# Return levels from POT
rp_values = eva_pot.return_level([1, 10, 50, 100])

Return Period Tables:

from metocean_stats import return_periods

# Multi-parameter return period table
rp_table = return_periods.calculate(
    data=df,
    parameters=['Hs', 'Tp', 'U10'],
    return_periods=[1, 2, 5, 10, 25, 50, 100],
    method='GEV',
    confidence_level=0.95
)

# Export to CSV
rp_table.to_csv('return_periods.csv')

Joint Probability Analysis:

from metocean_stats import joint_probability

# Kernel density estimation
jp = joint_probability.fit(
    df['Hs'],
    df['Tp'],
    method='kernel',
    bandwidth='scott'
)

# Contour plots
jp.plot_contours(levels=[0.1, 0.5, 0.9])

# Scatter diagram
jp.plot_scatter_diagram(hs_bins=20, tp_bins=20)

Environmental Contours:

from metocean_stats.contours import IFORM, DirectSampling

# IFORM contours
iform = IFORM(df['Hs'], df['Tp'])
contour_100yr = iform.contour(return_period=100, n_points=100)
iform.plot()

# Direct sampling contours
ds = DirectSampling(df['Hs'], df['Tp'], n_samples=10000)
contour_ds = ds.contour(return_period=100)
ds.plot()

Directional Statistics:

from metocean_stats import directional

# Sector statistics
dir_stats = directional.sector_stats(
    speeds=df['Hs'],
    directions=df['wave_dir'],
    sectors=16,
    statistics=['mean', 'max', 'p90', 'count']
)

# Wave rose plot
directional.plot_rose(
    speeds=df['Hs'],
    directions=df['wave_dir'],
    bins=[0, 1, 2, 3, 4, 5],
    title='Significant Wave Height Rose'
)

# Directional extremes
dir_extremes = directional.sector_extremes(
    speeds=df['Hs'],
    directions=df['wave_dir'],
    sectors=8,
    return_periods=[1, 10, 50, 100]
)

scipy.stats Integration

GEV Distribution Fitting:

from scipy import stats
import numpy as np

# Extract annual maxima
annual_max = df.groupby(df['time'].dt.year)['Hs'].max().values

# Fit GEV distribution
shape, loc, scale = stats.genextreme.fit(annual_max)

# Return level calculation
def return_level(T, shape, loc, scale):
    """Calculate return level for return period T years."""
    p = 1 - 1/T
    return stats.genextreme.ppf(p, shape, loc=loc, scale=scale)

# Calculate return levels
return_periods = [1, 2, 5, 10, 25, 50, 100, 500]
return_levels = {T: return_level(T, shape, loc, scale) for T in return_periods}

# Goodness of fit
ks_stat, p_value = stats.kstest(annual_max, 'genextreme', args=(shape, loc, scale))

GPD Distribution for POT:

from scipy import stats
import numpy as np

# Define threshold (e.g., 95th percentile)
threshold = np.percentile(wave_heights, 95)

# Extract exceedances
exceedances = wave_heights[wave_heights > threshold] - threshold

# Fit GPD
shape_gpd, loc_gpd, scale_gpd = stats.genpareto.fit(exceedances, floc=0)

# POT return level
def pot_return_level(T, threshold, shape, scale, rate):
    """Return level from POT analysis."""
    m = T * rate  # expected number of exceedances
    return threshold + scale / shape * ((m) ** shape - 1)

# Calculate exceedance rate (events per year)
n_years = (df['time'].max() - df['time'].min()).days / 365.25
rate = len(exceedances) / n_years

QQ Plot Diagnostics:

import matplotlib.pyplot as plt
from scipy import stats

# QQ plot for GEV fit
fig, ax = plt.subplots(figsize=(8, 6))
stats.probplot(annual_max, dist=stats.genextreme, sparams=(shape, loc, scale), plot=ax)
ax.set_title('GEV Q-Q Plot')
plt.savefig('qq_plot.png')

NREL Floating Wind Methodology

Site Conditions Assessment

The NREL floating wind methodology provides a standardized approach for characterizing offshore site conditions for floating wind turbine design.

Return Period Table Format:

Return Period	Hs (m)	Tp (s)	U10 (m/s)	Current (m/s)
1-year	5.2	10.5	18.3	0.45
10-year	7.8	12.1	23.4	0.62
50-year	9.4	13.2	27.1	0.78
100-year	10.2	13.8	28.9	0.85
500-year	12.1	15.1	32.4	1.02

Design Load Cases:

• DLC 1.6: Normal operation with extreme waves
• DLC 6.1: Parked, extreme conditions (50-year)
• DLC 6.2: Parked, extreme conditions (50-year) with fault

Joint Distribution Requirements:

• Hs-Tp scatter diagram (minimum 10 years)
• Environmental contours for DLC validation
• Conditional Tp given Hs for response analysis

Environmental Contour Methods

IFORM Method:

• Transform to standard normal space
• Apply reliability index for target probability
• Inverse transform to physical space
• Conservative for design applications

Direct Sampling:

• Monte Carlo simulation in physical space
• Empirical exceedance probability
• Better captures complex dependencies
• Computationally intensive

Python Workflow Examples

Complete Analysis Pipeline

import pandas as pd
import numpy as np
from scipy import stats
from datetime import date

async def run_extreme_analysis(station_id: str, start_year: int, end_year: int):
    """Complete extreme value analysis workflow."""

    # Import metocean module components
    from worldenergydata.modules.metocean.clients.ndbc_client import NDBCClient
    from worldenergydata.modules.metocean.processors.data_harmonizer import DataHarmonizer

    # Fetch historical data
    async with NDBCClient() as client:
        data = await client.fetch_historical(
            station_id=station_id,
            start_date=date(start_year, 1, 1),
            end_date=date(end_year, 12, 31)
        )

    # Harmonize data
    harmonizer = DataHarmonizer()
    records = []
    for obs in data.data:
        record = harmonizer.harmonize_ndbc(obs, data.latitude, data.longitude)
        records.append(record)

    df = pd.DataFrame(records)
    df['time'] = pd.to_datetime(df['observation_time'])

    # Extract annual maxima
    annual_max = df.groupby(df['time'].dt.year)['wave_height_m'].max()

    # Fit GEV distribution
    shape, loc, scale = stats.genextreme.fit(annual_max.values)

    # Calculate return levels with confidence intervals
    return_periods = [1, 2, 5, 10, 25, 50, 100, 500]
    results = []

    for T in return_periods:
        p = 1 - 1/T
        rl = stats.genextreme.ppf(p, shape, loc=loc, scale=scale)

        # Bootstrap confidence intervals
        n_bootstrap = 1000
        bootstrap_rl = []
        for _ in range(n_bootstrap):
            sample = np.random.choice(annual_max.values, size=len(annual_max), replace=True)
            s, l, sc = stats.genextreme.fit(sample)
            bootstrap_rl.append(stats.genextreme.ppf(p, s, loc=l, scale=sc))

        ci_lower = np.percentile(bootstrap_rl, 2.5)
        ci_upper = np.percentile(bootstrap_rl, 97.5)

        results.append({
            'return_period_years': T,
            'Hs_m': round(rl, 2),
            'Hs_lower_CI': round(ci_lower, 2),
            'Hs_upper_CI': round(ci_upper, 2)
        })

    return pd.DataFrame(results)

Monthly Statistics Calculation

def calculate_monthly_stats(df: pd.DataFrame) -> pd.DataFrame:
    """Calculate comprehensive monthly statistics for metocean parameters."""

    # Ensure datetime index
    df = df.copy()
    df['month'] = pd.to_datetime(df['observation_time']).dt.month

    # Define aggregation functions
    agg_funcs = {
        'wave_height_m': ['mean', 'max', 'min', 'std', 'count',
                          lambda x: x.quantile(0.5),
                          lambda x: x.quantile(0.9),
                          lambda x: x.quantile(0.99)],
        'wave_period_s': ['mean', 'max', 'std'],
        'wind_speed_ms': ['mean', 'max', 'std']
    }

    # Calculate monthly statistics
    monthly = df.groupby('month').agg(agg_funcs)

    # Flatten column names
    monthly.columns = ['_'.join(col).strip() for col in monthly.columns.values]

    # Add month names
    month_names = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun',
                   'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
    monthly.index = [month_names[i-1] for i in monthly.index]

    return monthly

Seasonal Statistics

def calculate_seasonal_stats(df: pd.DataFrame) -> pd.DataFrame:
    """Calculate seasonal statistics for metocean parameters."""

    df = df.copy()
    df['time'] = pd.to_datetime(df['observation_time'])
    df['month'] = df['time'].dt.month

    # Define seasons
    season_map = {12: 'Winter', 1: 'Winter', 2: 'Winter',
                  3: 'Spring', 4: 'Spring', 5: 'Spring',
                  6: 'Summer', 7: 'Summer', 8: 'Summer',
                  9: 'Autumn', 10: 'Autumn', 11: 'Autumn'}

    df['season'] = df['month'].map(season_map)

    # Calculate seasonal statistics
    seasonal = df.groupby('season').agg({
        'wave_height_m': ['mean', 'max', 'std', 'count'],
        'wind_speed_ms': ['mean', 'max', 'std']
    })

    # Order seasons
    season_order = ['Winter', 'Spring', 'Summer', 'Autumn']
    seasonal = seasonal.reindex(season_order)

    return seasonal

Directional Analysis

def calculate_directional_stats(
    df: pd.DataFrame,
    speed_col: str = 'wave_height_m',
    dir_col: str = 'wave_direction_deg',
    sectors: int = 16
) -> dict:
    """Calculate statistics by directional sector."""

    df = df.copy()
    sector_width = 360 / sectors

    # Assign sectors (centered on N, E, S, W for 8 sectors)
    df['sector'] = ((df[dir_col] + sector_width/2) / sector_width).astype(int) % sectors
    df['sector_center'] = df['sector'] * sector_width

    stats_by_sector = {}
    total_count = len(df.dropna(subset=[speed_col, dir_col]))

    for sector in range(sectors):
        sector_center = sector * sector_width
        sector_data = df[df['sector'] == sector][speed_col].dropna()

        if len(sector_data) > 0:
            stats_by_sector[sector_center] = {
                'direction_deg': sector_center,
                'direction_label': _get_direction_label(sector_center),
                'mean': round(sector_data.mean(), 2),
                'max': round(sector_data.max(), 2),
                'std': round(sector_data.std(), 2),
                'p90': round(sector_data.quantile(0.9), 2),
                'count': len(sector_data),
                'occurrence_pct': round(len(sector_data) / total_count * 100, 1)
            }
        else:
            stats_by_sector[sector_center] = {
                'direction_deg': sector_center,
                'direction_label': _get_direction_label(sector_center),
                'mean': None, 'max': None, 'std': None, 'p90': None,
                'count': 0, 'occurrence_pct': 0
            }

    return stats_by_sector


def _get_direction_label(degrees: float) -> str:
    """Convert degrees to compass direction label."""
    directions = ['N', 'NNE', 'NE', 'ENE', 'E', 'ESE', 'SE', 'SSE',
                  'S', 'SSW', 'SW', 'WSW', 'W', 'WNW', 'NW', 'NNW']
    index = int((degrees + 11.25) / 22.5) % 16
    return directions[index]

Joint Probability Distribution

def calculate_joint_distribution(
    df: pd.DataFrame,
    var1: str = 'wave_height_m',
    var2: str = 'wave_period_s',
    bins1: int = 20,
    bins2: int = 20
) -> pd.DataFrame:
    """Calculate Hs-Tp joint distribution (scatter diagram)."""

    # Remove NaN values
    data = df[[var1, var2]].dropna()

    # Define bin edges
    var1_edges = np.linspace(data[var1].min(), data[var1].max(), bins1 + 1)
    var2_edges = np.linspace(data[var2].min(), data[var2].max(), bins2 + 1)

    # Calculate 2D histogram
    hist, _, _ = np.histogram2d(
        data[var1], data[var2],
        bins=[var1_edges, var2_edges]
    )

    # Convert to probability
    prob = hist / hist.sum()

    # Create DataFrame
    var1_centers = (var1_edges[:-1] + var1_edges[1:]) / 2
    var2_centers = (var2_edges[:-1] + var2_edges[1:]) / 2

    scatter = pd.DataFrame(
        prob,
        index=np.round(var1_centers, 2),
        columns=np.round(var2_centers, 2)
    )

    return scatter

Exceedance Duration Analysis

def calculate_exceedance_duration(
    df: pd.DataFrame,
    parameter: str = 'wave_height_m',
    threshold: float = 2.5,
    time_col: str = 'observation_time'
) -> dict:
    """Calculate exceedance duration statistics."""

    df = df.copy()
    df['time'] = pd.to_datetime(df[time_col])
    df = df.sort_values('time')

    # Identify exceedance events
    df['exceeds'] = df[parameter] > threshold

    # Find contiguous exceedance periods
    df['event_id'] = (df['exceeds'] != df['exceeds'].shift()).cumsum()

    events = df[df['exceeds']].groupby('event_id').agg({
        'time': ['min', 'max', 'count']
    })
    events.columns = ['start', 'end', 'observations']
    events['duration_hours'] = (events['end'] - events['start']).dt.total_seconds() / 3600

    # Calculate statistics
    total_hours = (df['time'].max() - df['time'].min()).total_seconds() / 3600
    exceedance_hours = events['duration_hours'].sum()

    return {
        'threshold': threshold,
        'n_events': len(events),
        'total_exceedance_hours': round(exceedance_hours, 1),
        'exceedance_percentage': round(exceedance_hours / total_hours * 100, 2),
        'mean_duration_hours': round(events['duration_hours'].mean(), 1),
        'max_duration_hours': round(events['duration_hours'].max(), 1),
        'min_duration_hours': round(events['duration_hours'].min(), 1)
    }

YAML Configuration Templates

Extreme Value Analysis Configuration

analysis:
  name: extreme_value_analysis
  type: extreme_value

  # Method selection
  method: block_maxima  # Options: block_maxima, peak_over_threshold
  block_size: year      # For block_maxima: year, month, season

  # Distribution fitting
  distribution: GEV     # Options: GEV, Gumbel, GPD
  fitting_method: MLE   # Options: MLE, PWM, LMOM

  # POT-specific settings (if method: peak_over_threshold)
  pot_settings:
    threshold_percentile: 95
    decluster_time_hours: 48
    min_separation: 3

  # Return periods
  return_periods: [1, 2, 5, 10, 25, 50, 100, 500]

  # Confidence intervals
  confidence:
    level: 0.95
    method: bootstrap   # Options: bootstrap, profile_likelihood, delta
    n_bootstrap: 1000

  # Output options
  output:
    format: csv
    include_diagnostics: true
    plot_return_levels: true
    plot_qq: true

Joint Probability Configuration

joint_analysis:
  name: hs_tp_joint_distribution

  # Variables
  variables:
    x: wave_height_m
    y: wave_period_s

  # Distribution fitting
  method: kernel        # Options: kernel, parametric, copula
  bandwidth: scott      # Options: scott, silverman, custom

  # Scatter diagram
  scatter_diagram:
    x_bins: 20
    y_bins: 20
    normalize: probability

  # Environmental contours
  contours:
    method: IFORM       # Options: IFORM, direct_sampling, highest_density
    return_periods: [10, 50, 100]
    n_points: 100

  # Physical constraints
  constraints:
    max_steepness: 0.1  # Hs/Lp limit
    min_tp_factor: 3.0  # Tp >= factor * sqrt(Hs)

Directional Analysis Configuration

directional_analysis:
  name: wave_directional_stats

  # Sector definition
  sectors: 16           # Options: 8, 12, 16
  convention: from      # Options: from, to (direction waves come from)

  # Parameters to analyze
  parameters:
    - name: wave_height_m
      statistics: [mean, max, p90, occurrence]
    - name: wave_period_s
      statistics: [mean, max]

  # Rose plot settings
  rose_plot:
    bins: [0, 1, 2, 3, 4, 5, 6]
    colors: viridis
    legend_title: "Hs (m)"

  # Directional extremes
  extremes:
    method: sector_maxima
    return_periods: [1, 10, 50, 100]

Monthly Statistics Configuration

temporal_analysis:
  name: monthly_statistics

  # Aggregation period
  period: monthly       # Options: monthly, seasonal, annual

  # Statistics to calculate
  statistics:
    - mean
    - max
    - min
    - std
    - p50
    - p90
    - p99
    - count

  # Parameters
  parameters:
    - wave_height_m
    - wave_period_s
    - wind_speed_ms
    - wind_direction_deg

  # Output format
  output:
    format: csv
    include_plots: true
    plot_type: heatmap

Output Formats

Return Period Table (CSV)

return_period_years,Hs_m,Hs_lower_CI,Hs_upper_CI,Tp_s,Tp_lower_CI,Tp_upper_CI,U10_ms,U10_lower_CI,U10_upper_CI
1,5.2,4.8,5.6,10.5,9.8,11.2,18.3,16.9,19.7
2,6.1,5.6,6.6,11.2,10.4,12.0,20.5,18.9,22.1
5,7.1,6.5,7.7,11.8,10.9,12.7,22.1,20.3,23.9
10,7.8,7.1,8.5,12.1,11.2,13.0,23.4,21.4,25.4
25,8.7,7.9,9.5,12.6,11.6,13.6,25.2,23.0,27.4
50,9.4,8.4,10.4,13.2,12.1,14.3,27.1,24.7,29.5
100,10.2,9.0,11.4,13.8,12.6,15.0,28.9,26.2,31.6
500,12.1,10.5,13.7,15.1,13.7,16.5,32.4,29.2,35.6

Summary Statistics (JSON)

{
  "parameter": "wave_height_m",
  "unit": "m",
  "period": {
    "start": "2014-01-01",
    "end": "2024-12-31",
    "years": 11
  },
  "statistics": {
    "mean": 1.45,
    "std": 0.82,
    "min": 0.1,
    "max": 8.7,
    "p50": 1.3,
    "p90": 2.6,
    "p99": 4.1,
    "count": 96432,
    "missing_pct": 2.3
  },
  "monthly": {
    "Jan": {"mean": 2.1, "max": 7.2, "std": 1.1},
    "Feb": {"mean": 2.0, "max": 6.8, "std": 1.0},
    "...": "..."
  },
  "annual_maxima": {
    "2014": 6.2,
    "2015": 7.1,
    "...": "..."
  },
  "gev_parameters": {
    "shape": -0.12,
    "location": 5.8,
    "scale": 1.2
  }
}

Directional Statistics (JSON)

{
  "parameter": "wave_height_m",
  "sectors": 8,
  "convention": "direction_from",
  "data": [
    {"direction": 0, "label": "N", "mean": 1.2, "max": 5.4, "p90": 2.1, "occurrence_pct": 8.2},
    {"direction": 45, "label": "NE", "mean": 1.4, "max": 6.1, "p90": 2.4, "occurrence_pct": 12.5},
    {"direction": 90, "label": "E", "mean": 1.1, "max": 4.8, "p90": 1.9, "occurrence_pct": 9.8},
    {"direction": 135, "label": "SE", "mean": 0.9, "max": 4.2, "p90": 1.6, "occurrence_pct": 7.1},
    {"direction": 180, "label": "S", "mean": 1.3, "max": 5.8, "p90": 2.2, "occurrence_pct": 15.3},
    {"direction": 225, "label": "SW", "mean": 1.8, "max": 8.7, "p90": 3.1, "occurrence_pct": 22.4},
    {"direction": 270, "label": "W", "mean": 1.6, "max": 7.2, "p90": 2.8, "occurrence_pct": 16.2},
    {"direction": 315, "label": "NW", "mean": 1.3, "max": 5.9, "p90": 2.3, "occurrence_pct": 8.5}
  ]
}

Best Practices

Data Requirements

Minimum Record Lengths:

• Block maxima (annual): Minimum 10 years recommended
• Peak-over-threshold: Minimum 3 years with high-frequency data
• Monthly statistics: Minimum 3 years for seasonal patterns
• Directional analysis: Minimum 1 year continuous data

Data Quality:

• Check for gaps and document missing periods
• Validate against neighboring stations
• Remove spurious values (instrument errors)
• Ensure consistent time zone and conventions

Distribution Fitting

GEV Fitting:

• Use maximum likelihood estimation (MLE)
• Check shape parameter range (-0.5 to 0.5 typical for ocean waves)
• Validate with QQ plots and probability plots
• Consider regional frequency analysis for short records

Threshold Selection for POT:

• Use mean residual life plot
• Test sensitivity to threshold choice
• Ensure independence of peaks (declustering)
• Typical threshold: 90th-95th percentile

Uncertainty Quantification

Always Report:

• Confidence intervals for return levels
• Sample size and data availability
• Distribution fit quality metrics
• Assumptions and limitations

Validation Methods:

• Split-sample testing
• Cross-validation with nearby stations
• Comparison with historical extremes
• Sensitivity analysis

Design Applications

Conservative Approach:

• Use upper confidence interval for design
• Consider climate change trends
• Account for measurement uncertainty
• Apply appropriate safety factors

Common Workflows

1. Design Criteria Development

Step 1: Data Collection
  - Fetch 10+ years of hourly data
  - Check data quality and gaps
  - Document data source and period

Step 2: Annual Maxima Extraction
  - Extract annual maximum for each year
  - Check for outliers
  - Ensure independence

Step 3: Distribution Fitting
  - Fit GEV distribution
  - Validate fit with diagnostics
  - Check shape parameter

Step 4: Return Period Calculation
  - Calculate return levels (1-500 year)
  - Compute confidence intervals
  - Create return period table

Step 5: Export Results
  - CSV table for engineering use
  - JSON for database storage
  - Plots for reports

2. Fatigue Analysis Support

Step 1: Load Time Series
  - Hourly Hs-Tp pairs
  - Minimum 3 years data

Step 2: Joint Distribution
  - Calculate scatter diagram
  - Fit joint distribution
  - Define Hs-Tp bins

Step 3: Occurrence Matrix
  - Calculate occurrence hours per bin
  - Normalize to annual basis
  - Export for fatigue tools

Step 4: Validate
  - Check physical constraints
  - Compare with design basis
  - Document assumptions

3. Operational Weather Windows

Step 1: Define Operational Limits
  - Maximum Hs for operation
  - Maximum wind speed
  - Minimum visibility

Step 2: Monthly Exceedance
  - Calculate monthly exceedance %
  - Identify seasonal patterns
  - Calculate weather window statistics

Step 3: Persistence Analysis
  - Duration above threshold
  - Duration below threshold
  - Waiting time statistics

Step 4: Planning Output
  - Monthly operability table
  - Seasonal summary
  - Risk assessment input