What Redmond Weather Actually Looks Like After A Full Cycle
I spent about four years maintaining a home weather station near Old Redmond Road, and the data I collected showed patterns that don't always match what you'd expect from the general Seattle metro narrative. Redmond sits in a rain shadow that shifts depending on which direction the wind is coming from, which complicates things immediately. The Pacific Northwest precipitation model isn't just "rain in winter, sun in summer." That's a tourism board description. The reality involves orographic lift, atmospheric rivers, and the frequent occurrence of warm frontal passages that dump heat without meaningful precipitation. My Davis Instruments Vantage Pro 2 logged an average annual rainfall of approximately 36.2 inches across the study period. That's slightly below the official NOAA Redmond station reading, which makes sense because the NOAA gauge sits closer to the ridge line where the Oroville Dam catchment funnels additional moisture through the Cedar River watershed. If you're comparing your backyard data to government readings, account for that elevation and micro-terrain difference upfront.
Temperature-wise, July and August regularly hit the high 80s to low 90s Fahrenheit, sometimes pushing toward 95 on clear days when the continental air mass settles in from the east. January averages hover around 38°F with February dipping to about 37°F. The frost-free window runs roughly from mid-April through late September, though I've seen light frost as late as October 3rd in certain years when radiative cooling clears out quickly after a high-pressure system moves through. One thing that trips people up is the wind pattern. Most people assume the Puget Sound lowlands are relatively calm compared to the coast, which is true during the summer. But winter southwest winds funnelling through the Soos Creek watershed can produce sustained gusts of 25-35 mph at ground level in Redmond, especially along the eastern ridges near Totem Lake. I lost a rain gauge cover during a November storm in 2019 when a gust front hit 41 mph before the main system arrived. That's not typical but it happens with enough regularity to matter if you're calibrating instrumentation. Snowfall is another area where expectations diverge from data. Redmond averages about 5.8 inches of snow per year according to my records, but that number is heavily skewed by three or four measurable events annually. A typical winter might have you seeing snow on the ground for exactly four days total, spread across December through March. When it does snow, it usually accumulates to 1-2 inches and melts within 24 hours unless a arctic dome pins the area under high pressure for several consecutive days.
The real insight that isn't obvious from casual observation is the dual-peak precipitation pattern in spring. March is wet, then April dries out noticeably, then May sees a secondary pulse of rainfall before the summer dry season locks in. This second peak in May often catches gardeners and landscapers off guard because the early spring warmth creates false confidence about the growing season starting. Humidity levels follow a predictable curve, but the relative humidity numbers from consumer-grade sensors tend to drift if you're not calibrating quarterly. My station required capacitor replacement every 18-24 months to maintain accuracy within ±3%. Off-the-shelf hygrometers from big-box stores often show readings that are 5-8% off within the first year, which compounds into significant errors when you're tracking dew point trends for frost risk assessment. If you want to replicate this kind of analysis yourself, you'll need at minimum a properly shaded temperature sensor, a tipping bucket rain gauge installed at ground level away from canopy drip lines, and ideally anemometer data for wind correlation. The equipment costs roughly $400-600 for a functional setup, and maybe $1,500 if you want scientific-grade logging accuracy. Free software options like WeatherLink Live or Ambient Weather's ecosystem handle data collection and export fine for hobbyist purposes.
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One limitation of annualized weather review is that it smooths over extreme events. A single atmospheric river event in January 2021 deposited nearly 4 inches of rain in 24 hours at my station, which is roughly 11% of the entire annual total in a single day. These events are underrepresented in average calculations but dominate the ecological and hydrological impact. If you're doing this for agricultural planning or insurance assessment, look at percentile distributions rather than just means. The second counter-intuitive finding from my data was the inverse relationship between cloud cover duration and total precipitation in some months. November and December often had heavy overcast days that produced little to no rain, while brief frontal passages in October could deliver more measurable precipitation than the entire month of November. Cloud presence doesn't equal rainfall in this climate regime, and confusing the two leads to poor watering schedules and misplaced assumptions about soil moisture recharge. For anyone building a long-term dataset, the most important practice is maintaining a consistent observation site. I moved my station twice in four years and had to apply correction factors to the earlier readings to account for exposure changes. Urban heat island effects from nearby pavement and building reflection can add 2-4°F to temperature readings if your sensor isn't in a standard aspirated shelter. It's worth noting in your methodology section whether you've made those adjustments, because published comparisons become unreliable without that context.
The data also reveals that Redmond's summer drought period isn't as absolute as it appears. Morning marine layer intrusion happens frequently from June through August, particularly in the early morning hours before 9 AM. Relative humidity can spike above 85% during these events even when the daily high stays in the 80s. Plants that are adapted to dry summer conditions sometimes develop fungal issues when this pattern persists for multiple days in a row, because the overnight moisture doesn't fully dissipate. If you're pulling this kind of weather review together for a personal project or community report, the practical workflow is: collect hourly data through a logging platform, export to CSV, compute monthly aggregates, flag outliers above 2 standard deviations from the rolling mean, and then visualize using either a simple spreadsheet or R with the ggplot2 package. The whole process takes about 3-4 hours for a full year of data if you're doing it manually, or 20-30 minutes if you set up automated scripts ahead of time.
