Total Annual Precipitation at Cornell University Using NRCC CLIMOD 2 Data

combined_precipitation.csv

ithaca_precipitation_chart.py

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

# Read the data
df = pd.read_csv('combined_precipitation.csv')

# Filter for Ithaca Cornell University
ithaca_row = df[df['Station'] == 'ITHACA CORNELL UNIV'].iloc[0]

# Extract years and precipitation values
years = list(range(1980, 2025))
precip_values = []

for year in years:
    value = ithaca_row[str(year)]
    if value == 'M' or pd.isna(value):
        precip_values.append(np.nan)
    else:
        precip_values.append(float(value))

# Create DataFrame for easier handling
data = pd.DataFrame({
    'Year': years,
    'Precipitation': precip_values
})

# Identify valid (non-missing) data points
valid_mask = ~np.isnan(data['Precipitation'])
valid_years = data['Year'][valid_mask].values
valid_precip = data['Precipitation'][valid_mask].values

# Create moving average trend line
# Use 5-year moving average to show long-term trends
window_size = 5
if len(valid_years) >= window_size:
    # Calculate moving average for valid data
    moving_avg = pd.Series(data['Precipitation']).rolling(window=window_size, center=True).mean()
    
    # Get non-null moving average values and corresponding years
    valid_ma_mask = ~moving_avg.isna()
    ma_years = data['Year'][valid_ma_mask].values
    ma_values = moving_avg[valid_ma_mask].values

# Create the plot
fig, ax = plt.subplots(figsize=(14, 8))

# Plot actual data points
ax.scatter(valid_years, valid_precip, color='#2E86AB', s=50, alpha=0.8, 
           label='Observed Annual Precipitation', zorder=3)

# Plot moving average trend line
if len(valid_years) >= window_size:
    ax.plot(ma_years, ma_values, color='#A23B72', linewidth=2.5, 
            alpha=0.7, label=f'{window_size}-Year Moving Average', zorder=2)

# Connect consecutive valid points with light lines
ax.plot(valid_years, valid_precip, color='#2E86AB', linewidth=1, 
        alpha=0.4, zorder=1)

# Mark missing data years
missing_years = data['Year'][~valid_mask].values
if len(missing_years) > 0:
    ax.scatter(missing_years, [ax.get_ylim()[0] + 1] * len(missing_years), 
               marker='x', color='red', s=60, alpha=0.6, 
               label='Missing Data', zorder=3)

# Calculate and add statistics
mean_precip = np.nanmean(data['Precipitation'])
ax.axhline(y=mean_precip, color='#F18F01', linestyle='--', alpha=0.7, 
           linewidth=2, label=f'Long-term Average ({mean_precip:.1f} inches)')

# Styling
ax.set_xlabel('Year', fontsize=12, fontweight='bold')
ax.set_ylabel('Annual Precipitation (inches)', fontsize=12, fontweight='bold')
ax.set_title('Annual Precipitation Trends at Ithaca Cornell University\n1980-2024', 
             fontsize=14, fontweight='bold', pad=20)

ax.grid(True, alpha=0.3, linestyle='-', linewidth=0.5)
ax.set_xlim(1979, 2025)

# Add some styling
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_linewidth(0.5)
ax.spines['bottom'].set_linewidth(0.5)

# Legend
ax.legend(loc='upper left', frameon=True, fancybox=True, shadow=True)

# Add summary statistics as text box
stats_text = f"""Data Summary:
• Valid observations: {len(valid_years)}/{len(years)} years
• Missing data: {len(missing_years)} years
• Average precipitation: {mean_precip:.1f} inches
• Min: {np.nanmin(data['Precipitation']):.1f} inches ({data.loc[data['Precipitation'].idxmin(), 'Year']})
• Max: {np.nanmax(data['Precipitation']):.1f} inches ({data.loc[data['Precipitation'].idxmax(), 'Year']})"""

ax.text(0.02, 0.98, stats_text, transform=ax.transAxes, fontsize=9,
        verticalalignment='top', bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.8))

plt.tight_layout()
plt.savefig('ithaca_precipitation_1980_2024.png', dpi=300, bbox_inches='tight')
plt.show()

# Print missing years for reference
print("Missing data years:", missing_years.tolist() if len(missing_years) > 0 else "None")
print(f"Data coverage: {len(valid_years)}/{len(years)} years ({len(valid_years)/len(years)*100:.1f}%)")