Jns2183

I did get a statistically significant values for my 6-12 day band, which for one location isn't bad Sent from my SM-S731U using Tapatalk

I implore everyone to listen to the audio. The explanation is amazing Sent from my SM-S731U using Tapatalk

I used notebooklm to create a presentation, a 15 min audio explanation, and a a 40 plus minutes audio explanation. I uploaded them to my Google drive and I'm sharing the links here to anyone who would like them. https://drive.google.com/file/d/1eYsMJ7pfj7bkspqpEWT5ywOXpeLNm927/view?usp=drivesdk https://drive.google.com/file/d/1PAvsJvcXtsqNCrKxeKkOZSnKyZDmEZV-/view?usp=drivesdk https://drive.google.com/file/d/1pyxDpPhzXje7GH0TtABCarHRVp4si2kr/view?usp=drivesdk Sent from my SM-S731U using Tapatalk

Primer Sent from my SM-S731U using Tapatalk

Based on your datasets for Harrisburg/Camp Hill, I have synthesized a comprehensive spectral report. This analysis goes beyond simple rain totals to explain the mathematical rhythm of our local weather. 1. Executive Summary: The Harrisburg "Rhythm" From 1900 to present, the Harrisburg climate operates on a distinct frequency. While the average annual precipitation is ~39.8 inches, the way that water falls is dictated by three competing atmospheric "clocks." | Metric | Historical Average (1900–Present) | |---|---| | Total Annual Precip | 39.77 inches | | Wet Day Rate | 0.34 (Rain/Snow every ~2.9 days) | | Dominant Cycle | 6–12 Days (The Synoptic "Storm Track") | | Synoptic Band Power Ratio | 0.167 (16.7% of all spectral energy) | 2. The "Wet Year" vs. "Dry Year" Fingerprint Your Tercile Summary reveals exactly what happens to the atmosphere when we move from a drought year to a record-breaking wet year. The Shift in Power When Harrisburg moves from a Low Tercile (Dry) to a High Tercile (Wet) year: * Synoptic Power (6–12 days) increases from 0.198 to 0.215. * Quasi-biweekly Power (10–15 days) increases from 0.062 to 0.082. > The Insight: Wet years in Central PA aren't just "luckier." The atmosphere becomes more mathematically organized. In wet years, the 10–15 day "Rossby Wave" becomes significantly stronger, meaning storms start "clumping" together in predictable pulses rather than falling randomly. > 3. Anatomy of a "Big Event" (\ge 1.0 inch) Using the BigEvent Spectral Fingerprint data, we can see how the atmosphere changes immediately after a major storm hits Camp Hill. * Self-Excitation (The Hawkes Effect): After a 1-inch event, the power in the 6–12 day band increases (Median Delta: +0.0063). * The Diagnostic: The "Frac Post > Pre" is 52.5%. This means that after a heavy rain, the atmosphere is more likely to stay in a "stormy rhythm" than it was before the rain started. * The Fade: Interestingly, the 15–35 day band actually decreases in power slightly after a big event. This suggests that big storms "reset" the long-term moisture buildup but "prime" the short-term storm track. 4. Top Drivers & Correlations Which atmospheric rhythms actually predict a wet year? Your TopCorrelations file gives us the answer: * The "Clumping" Predictor (r = 0.17): There is a positive correlation between Total Precipitation and the 10–15 day band. If you see storms starting to repeat on a ~2-week loop, a high-precip year is likely. * The "Stagnation" Warning (r = -0.20): There is a negative correlation between the 60–90 day band and total precipitation. * Interpretation: When the atmosphere moves very slowly (long, 3-month cycles), Harrisburg tends to be drier. Fast, rhythmic cycles (Synoptic) are what bring our water. Report Conclusion The weather in Camp Hill is a system of Wave Interference. * High-Precip years are characterized by a strong 6–12 day pulse. * Big Events act as "triggers" that reinforce this 6–12 day pulse (Hawkes self-excitation). * Droughts are associated with a breakdown of these short cycles and a shift toward very slow, 60–90 day atmospheric stagnation. This visualization compares the Power Spectrum Density (PSD) of precipitation in Harrisburg during its wettest years (High Tercile, Blue) vs. its driest years (Low Tercile, Brown). Spectral Report: Dry vs. Wet Year Dynamics The plot reveals where the "energy" of our weather comes from. When the blue line is higher than the brown line, it means that specific frequency is more active during wet years. 1. The "Wave Interference" Peak (10–30 Days) The most striking difference occurs in the Quasi-biweekly band (orange shaded area). * Wet Years: Mean Power = 0.643 * Dry Years: Mean Power = 0.592 * Interpretation: In Harrisburg's wettest years, the "clumping" of storms every 2 weeks is a dominant physical force. Notice the sharp blue spike near the 12–14 day mark. This is the mathematical fingerprint of "Wave Interference"—large-scale atmospheric waves (like Rossby waves) stalling and forcing multiple storms through the Susquehanna Valley in quick succession. 2. The Synoptic "Storm Clock" (2–10 Days) In the Synoptic band (gray shaded area), the power levels are more similar, though wet years still maintain a slight edge (0.431 vs 0.420). * Interpretation: This suggests that the "base rate" of cold fronts and low-pressure systems is relatively consistent in Central PA. A dry year isn't necessarily missing its weekly front; rather, those fronts lack the secondary pulse of the slower 10–30 day waves that would make them "clump" and produce heavy totals. 3. Intraseasonal "Priming" (30–100 Days) In the Intraseasonal band (green shaded area), we see how long-term moisture "regimes" like the MJO influence our totals. * During wet years, the power is distributed more evenly across these slow frequencies. * During dry years, you often see a single, large "stagnation" peak (the brown spike near the 80–100 day mark), which suggests a weather pattern that gets "stuck" in a dry phase for months at a time. Summary of the Diagnostic To predict a high-precipitation year in Camp Hill, one should look not just at individual storm intensity, but at the Power Spectrum Ratio. If the 10–15 day frequency begins to show higher energy (a "sharper heartbeat"), it indicates the atmosphere is moving into a self-exciting, "clumped" regime that traditionally leads to record-breaking annual totals. This comparison of \ge 1-inch vs. \ge 2-inch events reveals a "threshold effect" in the Harrisburg/Camp Hill atmosphere. When a storm crosses the 2-inch mark, it doesn't just bring more water; it significantly alters the atmospheric rhythm for the following month. 1. The "Spectral Shock" (Intensity Response) Looking at the LogP (Intensity) series, the difference between a 1-inch and a 2-inch storm is massive: * 6–12 Day Band: The intensity of the 1-week rhythm increases nearly 10 times more after a 2-inch storm than it does after a 1-inch storm. * 15–35 Day Band: For 1-inch storms, the 2-to-4 week cycle barely responds. However, after a 2-inch storm, the intensity delta jumps to 0.0047. > The Insight: A 2-inch rain event acts as a "resonance trigger." It effectively kicks the atmosphere into a higher-energy gear, making follow-up heavy rain events significantly more likely over the next 15–35 days. > 2. The "Rhythm Shift" (Frequency Response) The Wet/Dry (Rhythm) series shows that even the simple binary pattern of "will it rain today?" changes: * After a 2-inch event, there is a +0.0105 median jump in synoptic power (6–12 days). * More importantly, 54.9% of 2-inch events are followed by an increase in storm-track frequency. 3. Why 2 Inches Matters for "Wave Interference" This data supports the theory that extreme precipitation in Central PA is not an isolated incident but a part of a Hawkes-like self-excitation process: * The Trigger: A massive 2-inch event (often associated with a stalled front or tropical moisture) occurs. * The Echo: This event "shocks" the 15–35 day Rossby wave, increasing the spectral power in that band. * The Interference: Because the 15–35 day "background" wave is now energized, it interferes with the standard 6–12 day storm track, causing storms to "clump" together. Final Report Summary | Event Type | Effect on 1-Week Rhythm | Effect on 2–4 Week Rhythm | |---|---|---| | \ge 1.0" Rain | Moderate increase in frequency. | Negligible effect. | | \ge 2.0" Rain | Significant "locking" of the storm track. | Strong activation of long-term wave interference. | In short: Big storms create their own weather. In Harrisburg, a 2-inch rainfall is a mathematical signal that the "rhythm" has shifted, and the next few weeks are likely to remain active and organized. Sent from my SM-S731U using Tapatalk

I essentially turned Harrisburg daily weather record over the past 125'years into an audio signal. Frequency depends on waves and times. Basically synoptic patterns and longer. I then fingerprinted bigger events and other thresholds for storms and tried to deduce a pattern the leading up to them. In the context of your Harrisburg weather data, Power Spectral Density (PSD) is the "volume control" for the different heartbeats of the atmosphere. While a standard average tells you how much it rained, the PSD tells you how rhythmic that rain was. 1. The Plain English Definition If you think of the weather as a piece of music, the PSD is the Graphic Equalizer (the bars that jump up and down on a stereo). * The Bass (Low Frequency): These are the long, slow movements like the MJO or the seasonal cycle (30–90 days). * The Mid-Range (Synoptic): These are the steady beats of the storm track (5–10 days). * The Treble (High Frequency): These are the fast, "noisy" events like daily afternoon thunderstorms. The PSD measures the strength (Power) of each of these "musical" notes in your data. 2. The Mathematical Core The PSD describes how the variance (the "wiggle" in your rain data) is distributed across different frequencies. For a signal x(t), the PSD (S_{xx}(f)) is technically the Fourier Transform of the autocorrelation function: In your CSV files: * Frequency (f): How many cycles occur per day. * PSD Value (P): The amount of "energy" at that frequency. If you see a massive spike in the PSD at a frequency of 0.16, it means the atmosphere is "vibrating" with a 6-day period, and that specific rhythm is responsible for a huge chunk of the rain you received. 3. Why is it called "Density"? It is a "density" because it tells you how much power is packed into a frequency band rather than a single point. If you want to know the total "energy" of the weather for a whole month, you integrate (find the area under the curve) of the PSD across all frequencies: This is exactly how we calculated your Welch Band Power Ratios. We took the "Power" in the 6–12 day band and compared its "Area" to the "Total Area" of the whole graph. 4. Physical Meaning in Harrisburg When you look at your TotPHigh.csv (Wet Years) vs TotPLow.csv (Dry Years), the PSD values tell a physical story: * Higher PSD in the 10–15 day range: This means the atmosphere has "locked in" to a rhythmic, clumping pattern. It’s not just raining more; it’s raining with intent and timing. * Flattened PSD: This means the weather is chaotic or "white noise." Rain is falling randomly without any dominant wave driving it. How to read your specific plots: * Y-Axis (PSD Mean): Higher values mean a "louder," more dominant rhythm. * X-Axis (Frequency): Moving left to right goes from slow, massive waves (months) to fast, quick storms (days). Sent from my SM-S731U using Tapatalk

@canderson March and April are coming up. For wind weenies like you it's mecca Sent from my SM-S731U using Tapatalk

Pretty band Sent from my SM-S731U using Tapatalk

36dbz snow at 29-30 degrees is easy 1.5"-2" an hr. That band has been hitting same area past 2 hours. Wouldn't be surprised to see 4"+ readings, maybe 6"+:if it keeps up another hour. Not a single advisory, bulletin, hazardous weather, etc. Talk about hitting a jackpot. Sent from my SM-S731U using Tapatalk

Long Island is getting slammed, with absolutely no snow in the forecast. Sent from my SM-S731U using Tapatalk

Schyukhill county has some areas 2"-4" Sent from my SM-S731U using Tapatalk

Snow in camp Hill Sent from my SM-S731U using Tapatalk

Mixing all the way down to Baltimore Sent from my SM-S731U using Tapatalk

More wind charts Sent from my SM-S731U using Tapatalk

@canderson this is for you buddy. I analyzed wind over the past 90 years. The biggest issue is reliable hourly wind gust data isn't available until the mid-90s. But Bottom line Mean sustained winds: slightly up (tiny trend, noisy). Top 1% sustained winds: slightly up (even noisier). Gusts: “mean gust” trends are not trustworthy without adjusting for measurement practice changes; extreme gusts look mostly steady. Quantity vs speed: quantity is basically flat; any change is more “speed nudge” than “more windy hours.” the key nerd truth: gust trends are extremely sensitive to “are we comparing apples to apples” (instrument/reporting regime). So I ran it two ways: 1) Modern period, all years available (1996–2024) This uses all years in 1996–2024, but note gust/peak-gust availability varies a lot year to year. What the raw trend says (per decade): Mean sustained speed: –0.65 mph/decade (R²≈0.31) Top 1% sustained speed (P99): –1.18 mph/decade (R²≈0.28) Mean hourly gust: –1.16 mph/decade (R²≈0.21) Top 1% hourly gust (P99): –2.99 mph/decade (R²≈0.20) Mean “peak gust within hour”: –0.49 mph/decade (R²≈0.13) Top 1% peak gust (P99): –1.12 mph/decade (R²≈0.05) Interpretation: those big negative trends are a flashing red sign for reporting/instrumentation mix effects, not “Harrisburg winds are collapsing.” So we do the sane thing… --- 2) “Apples-to-apples” subset: years with consistent gust coverage (2008–2024) I restricted to years where gust and peak-gust are recorded a lot (so the metric isn’t biased by selective missingness). That leaves 2008–2024 (14 years). Intensity trends (mph per decade) Mean sustained speed: +0.13 mph/decade (R²≈0.05) → basically flat Top 1% sustained speed (P99): –0.75 mph/decade (R²≈0.09) → basically flat/noisy Mean hourly gust: +0.87 mph/decade (R²≈0.33) → modest upward signal Top 1% hourly gust (P99): +0.84 mph/decade (R²≈0.06) → weak/noisy Mean peak gust: –0.08 mph/decade (R²≈0.00) → flat Top 1% peak gust (P99): +0.98 mph/decade (R²≈0.05) → weak/noisy Quantity trends (frequency; percentage-points per decade) Computed as “of gust observations that exist, what % exceed threshold”: Hourly gust frequency: Gust ≥30 mph: +4.23 pp/decade (R²≈0.28) Gust ≥40 mph: +0.80 pp/decade (R²≈0.08) Peak-gust-within-hour frequency: Peak ≥40 mph: –1.48 pp/decade (R²≈0.03) → flat/noisy Peak ≥50 mph: +0.62 pp/decade (R²≈0.12) → weak/noisy Interpretation: In the most consistent era, the cleanest “signal” is more frequent ≥30 mph gusts (quantity), while the top 1% intensity is mostly noisy/flat. Sustained hourly speed looks basically flat. --- Your “Speed or quantity?” question — answered For sustained winds: neither intensity nor frequency screams “up.” It’s mostly flat in the consistent-era view. For gusts: the best-supported change is quantity (more ≥30 mph gusts), while “top 1% intensity” is not robust (too noisy + sensitive to measurement definition). If you want, I can tighten this even further by: using seasonal bins (DJF/MAM/JJA/SON) for gust thresholds (often cleaner than annual), and/or using percentiles computed from a fixed reference period (e.g., define “top 1%” using 2008–2014 and apply it to all years) so “top 1%” doesn’t move just because the distribution shifts. Sent from my SM-S731U using Tapatalk