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Been on patio for 30!minutes and it’s down to 66 and actually chilly compared to living on Venus like we did

@Voyager Voyager’s Tamaqua Weather Station: A 91-Day QC Autopsy Voyager was kind enough to point me to his station in Tamaqua, so naturally I reacted in the most restrained and proportionate way possible. I downloaded 26,090 five-minute observations, catalogued 67 nearby station identities, untangled duplicate names and aliases, built several reference networks, pulled Pennsylvania Mesonet and ASOS data, aligned PRISM to its actual observation day, extracted 2,184 hourly Stage IV precipitation grids, separated rainfall into storm events, tested nearby personal stations one at a time, and dropped the whole thing onto a 30-meter terrain model. At some point this stopped being “Does Voyager’s thermometer read a little warm?” and became a minor federal research program, except it produced results and nobody had to attend a webinar. The analysis covers April 22 through July 21, 2026, a total of 91 days. The basic method, or how far down the rabbit hole went The core record was the station’s native five-minute archive. I preserved the raw observations and rebuilt hourly and daily values rather than relying entirely on precomputed summaries. For reference data, I used a professional-tier group consisting mainly of four Pennsylvania Mesonet stations, two airport ASOS sites, and one Pennsylvania DEP station. I also tested six nearby personal or CWOP stations against that professional group to see whether they were trustworthy enough to join the comparison. Then came PRISM. PRISM daily data does not use the normal local-calendar day. Its precipitation day ends at 12 UTC, which is 8 AM during daylight time. My first daily comparison used local midnight and briefly suggested that Voyager’s rainfall had almost no relationship to regional precipitation. This would have been a remarkable meteorological discovery. It was instead a timestamp problem. Once I rebuilt everything using the correct 12 UTC boundary, the relationship became extremely strong. The atmosphere was acquitted. The clocks were guilty. I then added NCEP Stage IV precipitation. That gave me 2,184 complete hourly multisensor precipitation fields covering the study period. Rather than comparing Voyager with one arbitrarily chosen grid cell, I extracted: the nearest Stage IV cell, the median of the surrounding 3×3 cells, and the median of the surrounding 5×5 cells. That matters because a rain gauge measures one point, while Stage IV estimates precipitation over an area. Thunderstorms remain stubbornly unwilling to distribute their rain evenly for the convenience of spreadsheets. Finally, I placed the station and reference network onto a real 30-meter digital elevation model of the Tamaqua area. This allowed me to compare the statistical behavior with the actual terrain rather than merely saying “mountains probably did something,” which is the traditional Pennsylvania forecasting method. Finding number one: the station record itself is structurally good The native archive is nearly continuous and passed the basic physical and internal-consistency checks. I did not find widespread impossible temperatures, broken timestamp ordering, random cumulative-rain resets, or the usual assortment of digital woodland creatures that live inside personal weather station archives. There is, however, one extremely clear processing artifact. The final five-minute observation of each local day contains corrupted daily high/low and precipitation-rate information. Across the 91 days: the mean high-minus-low spread in that final observation was 24.4°F, the median spread was 25.2°F, 80 of the 91 days showed a spread greater than 10°F, and all 36 isolated rain-rate blips in the entire record occurred in that same final local-day bin. Every one of them. Once I removed that final observation from precipitation-rate analysis, no isolated rain flickers remained. The genuine rainfall events had coherent multi-interval structure. This does not look like a thermometer or tipping-bucket failure. It looks like the daily summary fields are being folded into the final observation during the local-midnight rollover. The fix is wonderfully simple after all the detective work: Keep the raw archive, but exclude the final local-day five-minute observation from daily high/low and precipitation-rate analyses. Cumulative rainfall totals appear to remain usable. Temperature: good at night, warm under sunshine and weak wind Voyager’s overnight temperatures behaved quite well against the nearby reference network. The nighttime temperature bias was close to zero. The station and surrounding references generally agreed around the morning minimum. The daytime behavior was different. The station tended to run warmer during sunny conditions, and the warm bias increased when the wind was light. Under strong sun and calm conditions, the difference was roughly three degrees greater than during dark or overcast periods. Daily maximum temperatures also ran several degrees warmer than the PRISM estimate, while daily minima were close to unbiased. The timing helps tell the story. Voyager’s station typically reached its afternoon maximum about an hour earlier than the surrounding reference ensemble. That is what I would expect from a sensor environment responding too directly to solar heating or poor ventilation. A simple calibration error would shift the temperature almost equally at noon and at 5 AM. That is not what happened. Therefore, applying one flat temperature correction would be a bad idea. It would improve some sunny afternoons and make the nighttime values worse. My interpretation is: nighttime temperatures are generally reliable, daytime temperatures should be viewed with more caution during sunny and calm periods, and any future adjustment should depend on solar radiation and wind rather than being a fixed number. Humidity, dew point, and vapor-pressure deficit also showed systematic differences and should be used conditionally. They are mathematically tied to temperature, so a solar-heated temperature sensor can spread its mischief into several derived variables. One biased input variable, many exciting downstream consequences. Software engineers call this efficiency. Rainfall: the gauge did very well This was probably the strongest part of the entire station. Across 2,174 eligible station–Stage IV hourly comparisons: Voyager recorded 10.96 inches, the Stage IV 3×3 neighborhood median recorded 11.12 inches, the difference was only 0.16 inch, and the total ratio was 0.986. The hourly correlation with the 3×3 Stage IV median was 0.930. For wet hours only, the correlation was 0.899. When I grouped precipitation into 36 storm events, the event-total correlation rose to 0.957. The cumulative event ratio was 1.012, and the median absolute difference per event was only 0.05 inch. Timing was also good: the median storm-onset difference was zero hours, the median ending-time difference was zero hours, 81% of storm onsets were within one hour, and 85% of endings were within one hour. The station became more reliable as rainfall intensity increased: during Stage IV hours of 0.01–0.10 inch, Voyager also reported rain about 72% of the time, from 0.10–0.25 inch, detection rose to about 94%, and every Stage IV hour above 0.25 inch was detected. In other words, the disagreements were concentrated mainly in drizzle, traces, and highly localized light showers. Moderate and heavy rainfall was captured very well. That is exactly the pattern I would prefer to see. Trace precipitation is where point gauges, radar estimates, hour boundaries, virga, and the general perversity of Appalachian weather all begin arguing. The spatial comparison was especially revealing: nearest Stage IV cell: 12.75 inches, 3×3 median: 11.12 inches, Voyager: 10.96 inches, 5×5 median: 10.11 inches. The gauge falls between a wetter nearby grid cell and a drier broader neighborhood. That tells me the remaining differences are not simply “the gauge is low” or “the radar is high.” The local precipitation field genuinely varied across only a few grid cells. I found no evidence supporting a universal rain multiplier. My conclusion is that Voyager’s cumulative and storm-scale precipitation record is trustworthy, with normal uncertainty during light and highly localized precipitation. One caution: Stage IV is a multisensor areal estimate, not divine revelation descending from the radar dome. It should not be treated as exact point-gauge truth. Wind: local, messy, and behaving exactly like wind Wind was the least transferable variable, which is not the same as saying it was bad. Correlations with the Pennsylvania Mesonet sites were reasonable for stations separated by several miles, while correlations with the more openly exposed airport sites were weaker. Wind-direction differences were often large. That sounds alarming until one remembers where Tamaqua is located. Voyager’s site sits in a valley environment surrounded by complex ridges. Wind near the surface is blocked, redirected, accelerated through openings, and occasionally sent down the wrong hallway by terrain and nearby structures. An airport anemometer on an open field and a residential anemometer inside Appalachian terrain are not measuring quite the same atmosphere. Demanding identical values from them would be like comparing traffic speed on Interstate 81 with traffic speed on a Tamaqua side street and concluding that one speedometer is defective. Voyager’s wind data is useful for: local wind trends, frontal passages, gust timing, calm-versus-mixed regimes, and what the wind is actually doing at his property. It should not be expected to match an airport or ridge-top exposure exactly. Terrain provided the physical explanation The station’s terrain cell has: a mean elevation of about 274 meters, or 900 feet, roughly 86 meters of local relief, an average slope near 7.7 degrees, a low topographic-position value consistent with a valley setting, and predominantly western terrain exposure. That terrain context matches several findings: 1. Nighttime temperatures were close to the references. Valleys favor cold-air drainage and nighttime pooling. 2. The daytime warm bias increased when it was sunny and calm. A sheltered setting can retain locally heated air when ventilation is weak. 3. Wind direction and speed differed substantially from open reference sites. Valley channeling and terrain blocking are expected. This does not prove the exact sensor-level cause. A digital elevation model cannot see the station’s mounting pole, nearby roof, fence, tree, radiation shield, or neighbor who planted an ornamental shrub in precisely the wrong place. But the terrain provides a physically credible setting for the observed behavior. Pressure, solar, and the remaining odds and ends Relative pressure performed very well and was among the cleanest variables in the comparison. I found no reason to change the current pressure setting based on this study. Solar radiation was useful mainly for identifying the temperature bias regime. The station’s available solar field behaved well enough as a timing and peak-intensity indicator, but it was not a clean integrated-energy measurement. I would use it for relative solar conditions rather than treat it as laboratory-grade daily energy. I made no soil-moisture conclusions about Voyager’s station because it does not have a soil-moisture sensor. I studied nearby network soil sensors as context, but eventually resisted the temptation to grade equipment that does not exist. Scientific discipline occasionally survives. Final verdict Voyager does not have a failed station. He has a largely strong station record with: one sharply defined end-of-day processing artifact, a daytime temperature and humidity bias that depends on sunshine and ventilation, precipitation performance that is excellent at the event and seasonal-total scales, wind measurements that represent his immediate valley exposure rather than a regional open-air standard, and pressure data that performs very well. My practical recommendations are straightforward: preserve the native five-minute archive, filter the final local-day five-minute bin from high/low and precipitation-rate work, retain the cumulative rainfall totals, do not apply a flat temperature correction, interpret daytime temperature, humidity, and VPD in the context of sun and wind, and treat wind as a local microscale measurement. After 26,090 observations, 2,184 Stage IV grids, several reference networks, corrected day boundaries, storm-by-storm scoring, and one digital elevation model, my conclusion is reassuringly ordinary: Voyager’s station is good. It simply lives in Tamaqua...... tis was fun Sent from my SM-S731U using Tapatalk

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Was a good performer here as we finished up with 2.23" for the day. 6.67" MTD Nice to see the D5 being trimmed back a lot.

Forrest Gump voice...and just like that, another rapid onset drought was forecast.

So tiny! Just like the Ashby MN 2020 vortex. Too bad she freaked and put the cam down temp, it was historic. I'll need the official link to the FB vid as a screen cap just won't do. Also, random lady gets some of the the best tor footage of 2026 sans the cam sag

High of 82 before the rains came. Ended up with .89" for the day.

BOS is +3.1F. Which is impressive

Today was gorgeous especially with dewpoints in the low 50’s!!

Nonsoon again, completely skunked here. Try again. Not just here, but the coverage that was forecast never really happened.

Rules x jelly

Today should be the 8th day in a row of BN temps for ORH. Strong start out of the gates for the month, but fading down the back stretch +1.5F MTD

Today was a great day to work outside. Still a touch warm for my liking on where I was on the eastern shore. Prefer 72 over 82 with full sunshine this time of year but I'll take what I can get with these dew points today.

High of 82 and windows open all day with inside humidity dropping to 47%

I’m not sure why I’m seeing people touting the -PDO + super nino combo as a cold/snowy signal for the east when that isn’t supported by historical precedence. The super nino obviously isn’t going anywhere, so I would think those rooting for a snowier outcome in the east would want the PDO to rise, not remain negative.

Today broke a record cool high in Raleigh: 74°. The old record was 76° in 1921. Greensboro tied a record cool high of 73 degrees.

Ended up with 2.85" today. Finally passed 20" for the year. Would love to get some more tomorrow. We're making significant inroads against the drought, hopefully we can keep it up.

Gotta' love how Florida is still in the snow despite being south of the mean slp track depicted on that map. The "A" part I believe...the "I" part, nasso' much.

Canadian looks like it did an OK job on precipitation for July in the US. It had a bad read on the SE/SW US placement, but pretty closer for northern precip. The cooler than average middle of the US didn't verify at all though. I think we'll see similar model issues all Fall-Spring. Moisture is much more directly tied to the subtropical jet/SOI/how strong the El Nino is. The temperature pattern is PDO centric. Most interesting outcome for winter would be if subtropical jet remains very strong and PDO weakens enough to not be a major influence, with the Arctic/Pacific patterns fighting for dominance week to week or month to month.

2.35 storm total

Today's Highs PHL: 84 New Brnswck: 83 EWR: 82 TEB: 81 LGA: 81 ACY: 80 TTN: 80 BLM: 79 JFK: 79 ISP: 79 NYC: 79

They’re all at my house. That’s what I use for my Cocorahs station, I like it a lot more than the standard gauge I used before. It’s worth the price upgrade. The little metal rods to deter birds are great- my standard gauge was a porta-potty for every bird passing through.

Yes, I agree. 30-yr does have practical uses, but for long-term climate, and I am talking on geological scales, it is useless. Things exist in a rather large range from a local to global scale when you consider how long the Earth has been around, and that's ok. Geez, the range of things from a climatic POV over millions of years? Also, I have seen things done w/ 30-year normals, both on a deliberate and accidental level. Using some random 30-year period to scale wx and records in the present. Which 30-year normal baseline is best to quantify changes or trends to the present? You see one can pick and chose to get desired results here either way. And why 30 years? Is this something significant climate-wise about 30 years? Given that is a tiny speck geologically, I would say not. It sounds more like a human convenient period (generational?) than anything else! We have a tendency to scale and build things around what humans consider a "long-time," but that does not work on geological scales. And then you run into period of record varying greatly for many locations across the globe, so there is big challenge here. We have to have something for a baseline, but having something does not mean it works well for all things.