Reading Weather Data on PlainClimate
How to interpret average temperatures, precipitation totals, snow days, sunshine hours, and degree days — and how to compare climates across US cities using NOAA 1991-2020 data.
Key Takeaway
Climate data becomes useful when you read it in context. A single number — annual average temperature, total rainfall — rarely tells the full story. The monthly profile, the range between high and low temperatures, the seasonality of precipitation, and how variables interact (cold + snow vs. cold + dry) are what make one city's climate feel fundamentally different from another's.
Temperature: High, Low, and Mean
Every city page on PlainClimate shows monthly temperature data in three forms: average high, average low, and mean. Together, these three numbers define the typical daily temperature experience for each month.
The average high tells you what the warmest part of the day typically looks like — late afternoon temperatures when you would be deciding whether to wear a jacket. The average low tells you the coldest part of the night, usually around dawn, which matters most for freezing pipes, cold-weather car starts, and whether you need heating on. The mean is the midpoint between the two, useful for energy demand calculations but less intuitive for daily planning.
The spread between high and low (the daily temperature range) also varies by location. Desert climates have wide daily ranges — Phoenix in June might have a 70°F low and a 106°F high, a 36°F swing within a single day. Coastal climates have narrow daily ranges because ocean temperatures moderate both the heat of afternoon and the cold of night.
Precipitation: Totals, Seasonality, and Type
Precipitation totals on PlainClimate are expressed in inches of liquid-equivalent precipitation per month, averaged across the 1991-2020 period. The most useful way to read this data is to look at the monthly profile — not just the annual total — to understand when and how precipitation falls.
| Precipitation Pattern | Example Cities | Character |
|---|---|---|
| Winter-concentrated | Portland OR, Seattle WA | Dry summers, rainy winters |
| Summer-concentrated | Phoenix AZ, Denver CO | Monsoon bursts, dry rest of year |
| Year-round even | Atlanta GA, New York NY | Consistent monthly totals |
| Spring-concentrated | Oklahoma City, Dallas TX | Peak spring, dry late summer |
When comparing two cities, a quick way to understand precipitation character is to find the wettest month and driest month and compare their values. A city where the wettest month has 8 inches and the driest has 0.3 inches has much more pronounced seasonality than one where the wettest month has 5 inches and the driest has 3 inches.
Compare city precipitation profiles using the precipitation rankings on PlainClimate.
Snowfall: Total Inches and What They Mean
Snowfall data is expressed in total inches of snow accumulation per year or per month. Unlike rainfall, snow accumulation is not density-adjusted — 12 inches of light powder and 12 inches of wet heavy snow contain very different amounts of water and require very different levels of effort to remove.
For practical interpretation, a rough framework for annual snowfall totals:
- 0-5 inches/year: Snow is a rare event. Infrastructure not designed for it.
- 5-20 inches/year: Light snow winters. Occasional disruption but generally manageable.
- 20-50 inches/year: Meaningful winter snow. Shoveling and plowing are regular activities.
- 50-100 inches/year: Heavy snow city. Winter driving, heating, and snow removal are major lifestyle factors.
- 100+ inches/year: Extreme snow regions. Primarily Great Lakes lake-effect zones and high mountain areas.
Also check which months carry the snowfall. A city with 40 inches concentrated in January-February (like Denver) is very different from one where snow falls from October through April (like Minneapolis). Browse state climate summaries to see regional snowfall patterns.
Sunshine Hours: Percent of Possible Sunshine
For cities where NOAA hourly observation data is available, PlainClimate shows "percent of possible sunshine" — the fraction of daylight hours when the sun was unobscured by clouds. This metric is far more comparable across cities than raw sunshine hours because it accounts for the fact that summer days are longer in northern latitudes.
Reading the monthly sunshine percentages reveals important seasonal patterns. Southern California cities typically maintain 70-80% sunshine year-round. Pacific Northwest cities might drop to 20-30% in winter months before climbing back to 60-70% in summer. The contrast between a city's sunniest and cloudiest months reveals whether seasonal mood variation is likely to be a factor.
A practical interpretation guide: 75%+ means most days are predominantly sunny; 50-75% means a mix of sunny and partly cloudy; 30-50% means frequent cloud cover; below 30% means predominantly grey skies. Winter values below 30% in a northern city (shorter days + cloud cover) can significantly affect mood and daily experience for people prone to seasonal depression.
Comparing Two Cities: A Step-by-Step Approach
To systematically compare two cities' climates, follow this sequence:
- Check the annual mean temperature for a rough sense of warmth level, then look at the monthly profile to see how stable that warmth is.
- Compare coldest month average low — this tells you winter severity. A -5°F mean low versus a 28°F mean low represents a vastly different winter experience even if annual averages are similar.
- Compare hottest month average high — this tells you summer peak heat. 95°F vs. 75°F is a major quality-of-life difference in summer.
- Compare annual precipitation and the monthly distribution to understand rain seasonality and intensity patterns.
- Compare annual snowfall for winter severity context.
- Check the comfort score for a composite summary that weights all these factors together.
Use PlainClimate's city pages to pull up full profiles for any two cities and compare them side by side. The rankings pages let you identify the best and worst cities along specific dimensions.
Frequently Asked Questions
What is the difference between average high, average low, and mean temperature?
Average high (or "normal high") is the average of daily maximum temperatures across all days in that month over 30 years. Average low is the average of daily minimum temperatures. Mean temperature is simply the average of those two numbers: (average high + average low) / 2. Mean temperature is the most commonly cited single number but hides the full story — a city with a 50°F mean could have an 85°F average high (hot days) and a 15°F average low (very cold nights), or a much narrower 60°F/40°F spread. Always check both high and low to understand the full temperature experience.
What do monthly precipitation totals actually tell me?
Monthly precipitation totals (in inches) represent the combined liquid equivalent of all rain, melted snow, sleet, and freezing rain that fell during that month, averaged across 30 years. A 4-inch monthly total does not tell you whether it rained every day for a little bit or poured heavily a few times. It also doesn't tell you what percentage of days were rainy. That said, the monthly profile is useful for understanding seasonality — whether a city gets its rain concentrated in winter (Pacific Northwest), summer (Southwest monsoon), spring (tornado corridor), or distributed year-round (Southeast). Comparing months reveals the pattern far better than the annual total alone.
How do I read snowfall data — total inches vs. snow days?
Snowfall data on PlainClimate is expressed as total inches of snowfall per year or per month. One inch of snow contains approximately 0.1 inches of liquid water equivalent (though this ratio varies widely with snow type — dry powder can be 30:1, wet heavy snow can be 5:1). Total annual snowfall tells you the volume; number of snow days (where available) tells you how frequently it occurs. A city with 30 inches spread across 20 events has different day-to-day impact than one with 30 inches falling in two major storms. Both numbers together give you the clearest picture for planning.
What are heating degree days and cooling degree days?
Heating degree days (HDD) and cooling degree days (CDD) are measures of energy demand for climate control. HDD are calculated by taking the difference between 65°F and that day's mean temperature on any day when the mean is below 65°F. A day with a mean of 45°F contributes 20 HDD. CDD work in reverse — days above 65°F contribute to CDD. Annual HDD totals indicate how much heating fuel a building needs; annual CDD totals indicate air conditioning demand. Cities with high HDD (Minneapolis, MN: ~8,200 HDD) have substantially higher winter heating costs than low-HDD cities (Miami, FL: ~200 HDD). These metrics are used extensively by energy analysts, HVAC engineers, and utility planners.
How should I compare sunshine data between cities?
Sunshine on PlainClimate is expressed as "percent of possible sunshine" — the fraction of daylight hours during which the sun was actually visible (not obscured by clouds). A city with 70% possible sunshine in July will have bright, sunny days for the majority of summer daylight hours. Compare cities using this percentage rather than absolute hours, because absolute sunshine hours vary with latitude and season (longer summer days in northern cities mean more potential daylight hours even if the sky is often cloudy). For practical planning, any city above 60% annual sunshine feels consistently sunny; below 45% means frequent overcast conditions.
What is a frost date and why does it matter?
Frost dates are the average dates of the last spring frost (after which freezing temperatures are unlikely) and first fall frost (after which freezing temperatures become likely again). The period between these dates is the "frost-free season" or growing season. For gardeners, the last spring frost date determines when to plant tender annuals and transplant seedlings outdoors. For property owners, frost dates signal when to winterize irrigation systems and when spring lawn care can begin. NOAA calculates frost dates based on 30-year historical probabilities — the dates shown represent the 50% probability level (equal chance of frost before or after that date). Some gardeners use the 10% or 90% probability dates for more conservative or more aggressive planting schedules.
Explore Climate Data
Related Guides
Sources
- NOAA National Centers for Environmental Information — U.S. Climate Normals 1991-2020 (v1.0.1)
- NOAA — Technical Documentation: U.S. Climate Normals Quick Guide
- NOAA NCEI — Degree Days: Base Temperature and Calculation Methods
This guide is for informational and educational purposes only. Climate data represents 30-year historical averages and does not predict specific future conditions. For current weather forecasts and real-time conditions, consult the National Weather Service (weather.gov).
Understanding the Data
The information presented throughout this guide is informed by publicly available public records published by federal and state government agencies. Our database aggregates and standardizes these records to make them more accessible and easier to interpret for general audiences. When we reference specific statistics or trends, they are drawn directly from these authoritative sources unless explicitly noted otherwise.
It is important to understand the limitations of any large-scale data dataset. Records may contain errors from the original data collection process, some fields may be incomplete for older entries, and classification systems may have changed over time. Our analysis accounts for these factors by clearly labeling data vintage, flagging records with missing critical fields, and noting when temporal comparisons span methodology changes in the source data.
For readers who want to conduct their own research, we recommend going directly to the source whenever possible. federal and state government agencies provides detailed documentation on collection methodology, sampling frames, and known data quality issues. Our goal is not to replace primary sources but to make them more approachable and to highlight patterns that may not be immediately obvious when browsing raw records.
How We Analyze Data Records
Our analytical approach involves several steps designed to surface meaningful insights from large datasets. First, we clean and standardize the raw data, handling variations in naming conventions, date formats, and categorical labels. Then we compute summary statistics, distributions, and comparative benchmarks across relevant dimensions such as geography, time period, and category type.
Key metrics we examine include statistical records, geographic distributions, temporal trends. These indicators provide a multi-dimensional view of each entity in our database, allowing users to understand not just individual records but how they compare to peers, regional averages, and national benchmarks. We believe this contextual approach is far more valuable than presenting raw numbers in isolation.
