Interpreting Historical Weather Summaries

First published: October 2026
Last updated: October 23, 2026

[!NOTE] Before calibrating stochastic parameters or running simulations, a thorough inspection of the historical climate record is vital. This ensures you understand the local climate regime and catch potential data anomalies early.

The Historical Data Analysis card provides an intuitive visual dashboard that breaks down decades of raw weather observations into clear hydrologic metrics.

The Key Metric & Quality Cards

At the top of the Historical panel, the summary banner and metric cards provide an immediate snapshot of the record's breadth and quality:

• Record Length & Completeness: Shows total calendar years (e.g., May 1, 1985 to June 8, 2020), overall data completeness percentage ($98.4\%$), and total missing observation days.
• Max 1-Day Peak: The single largest 24-hour storm depth recorded in the gauge history (e.g., $1.86\text{ in}$).
• Max Dry & Wet Spells: The longest continuous stretch of consecutive dry days ($55\text{ days}$) and consecutive wet storm days ($10\text{ days}$).

Figure 1: The Historical Data Analysis dashboard showing record completeness, peak storm metrics, probability density fitting (Log-Pearson III vs. Gamma), and long-term precipitation trend analysis.

Understanding the Charts

1. Probability Density Fit & Long-Term Trend

• Annual Precipitation Probability Density: Aggregates daily observations into annual rainfall depths and fits theoretical probability distributions:
• Gamma Distribution (2-Parameter): Benchmark hydrologic curve bounded at zero.
• Log-Pearson Type III (LP3): Recommended by USGS Bulletin 17B/17C for modeling hydrologic skewness and rare flood/drought tails.
• K-S Goodness-of-Fit Test: Kolmogorov-Smirnov test p-values ($p > 0.05$ indicates strong agreement between the historical gauge record and the theoretical distribution).
• Long-Term Trend Analysis: Uses the Mann-Kendall test and Sen's Slope estimator to determine whether precipitation shows a statistically significant upward or downward trend over decades.

2. Monthly Precipitation Extremes & Wet/Dry Days

Figure 2: Monthly precipitation extremes (mean, maximum, and minimum monthly totals) alongside average monthly counts of wet days versus dry days.

• Monthly Extremes: Tracks the historical mean monthly rainfall alongside historical wettest and driest months on record.
• Wet vs. Dry Day Breakdown: Displays the average number of rainy days ($P \ge 0.01\text{ in}$) versus sunny dry days per month, revealing seasonal storm frequencies.
• Seasonal Trends: Evaluates trends across meteorological seasons: Winter (DJF), Spring (MAM), Summer (JJA), and Fall (SON).

3. Seasonal Temperature Envelopes (Thermograph)

Switching to the Temperature tab reveals the thermal regime of the site:

Figure 3: Temperature analysis dashboard featuring all-time temperature records, mean frost days, long-term warming/cooling trends, and 12-month diurnal envelopes.

• All-Time Records & Frost Days: Instant display of recorded extreme highs ($102.0^\circ\text{F}$), extreme lows ($-6.0^\circ\text{F}$), and average annual frost days ($84\text{ days/yr}$).
• 12-Month Climatology & Diurnal Envelope:
• Red Curve ($T_{\max}$): Mean monthly daytime maximum temperature.
• Blue Curve ($T_{\min}$): Mean monthly nighttime minimum temperature.
• Shaded Band: Diurnal temperature range ($T_{\max} - T_{\min}$).
• Warmest and Coldest Months: Identifies peak summer heating (e.g. July at $89.8^\circ\text{F}$) and winter freezing (January at $26.9^\circ\text{F}$).

What to Look For: Sanity Check Checklist

Before proceeding to CLIGEN Parameter Calibration: - [ ] Are the seasonal wet and dry months consistent with your site's geography? - [ ] Does the freeze/thaw transition align with local snowpack observations? - [ ] Are there any unphysically high single-day rainfall spikes caused by gauge errors? - [ ] Is data completeness high (preferably $> 95\%$)? If not, proceed to the next chapter to apply date filtering.