Central PA Winter 25/26 Discussion and Obs

[Blizzard of 93]

0z GFS keeps hope alive for tomorrow evening with up to 2 inches for the LSV this run.

[TheDreamTraveler]

Does anyone know why we've been having so many air quality alerts recently? I don't remember getting so many so often the past few months. Not a fan honestly.

[Jns2183]

So Sam Lilo did a ridiculous million winter simulation to try to quantify what Boston experienced that fabled 30 day stretch in 2015 when 90+ inches fell in 30 days. Reaching a conclusion it was a 1:26000 year event. I did similar for Harrisburg based upon 125 years data and 30,000 simulation years.

Based on the analysis of the simulation data (max30_histogram_bins_0p25.csv) and the historical seasonal maxima calculated from the Harrisburg records, the Gamma Distribution remains the best-fitting mathematical model.
1. The Model: Gamma Distribution
The Gamma distribution is ideal for modeling physical quantities that are always positive and exhibit a "heavy tail" (skewed towards high values), such as extreme snowfall events.
Variables Defined:
* x: The 30-day snowfall amount (in inches).
* \alpha (Shape): Determines the fundamental shape and peak of the curve.
* loc (Location): The shift/offset from zero.
* \theta (Scale): Controls the "spread" or horizontal stretch of the distribution.
* \Gamma(\alpha): The Gamma function (an extension of factorials for non-integers).
Equation with Derived Coefficients:
Using the simulation data for the fit:
* * * 2. Goodness of Fit
We used the Kolmogorov-Smirnov (KS) test to evaluate how well the model describes the data. A lower KS statistic (closer to 0) indicates a better fit.
| Dataset | KS Statistic | P-Value | Interpretation |
|---|---|---|---|
| Simulation Data | 0.0155 | 1.05 \times 10^{-6} | Excellent Fit. The model follows the simulation counts almost perfectly across the entire range of values. |
| Historical Observations | 0.1438 | 0.0097 | Moderate Fit. The model generalizes the history but over-predicts low-snow years and under-predicts the mid-range "typical" snowiest months. |
3. Analysis
* Simulation vs. Model: The Gamma curve is a near-perfect approximation of the simulation data. It successfully captures the rapid rise in probability between 0 and 10 inches and the long, thin tail extending past 60 inches.
* Historical Gap: As shown in the right-hand graph, the historical observations are more "clumped" than the model predicts. In reality, Harrisburg has a very strong peak of seasonal maxima between 15 and 25 inches. The model (derived from simulation) peaks slightly earlier (around 10 inches).
* Extreme Tails: The simulation model is much "bolder" than the historical record. It suggests a non-zero probability of 30-day periods exceeding 80 inches, whereas the historical record (spanning ~126 years) has never surpassed ~51 inches.
* Conclusion: The equation is a highly reliable model for the theoretical distribution of 30-day snowfall. However, if used to predict a typical winter in Harrisburg, it will slightly underestimate the "average" peak snow month while significantly overestimating the potential for unprecedented "megastorms."


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[Ruin]

14 minutes ago, TheDreamTraveler said:

Does anyone know why we've been having so many air quality alerts recently? I don't remember getting so many so often the past few months. Not a fan honestly.

yeah I even had them when its windy its total bs 

[Yardstickgozinya]

35 minutes ago, TheDreamTraveler said:

Does anyone know why we've been having so many air quality alerts recently? I don't remember getting so many so often the past few months. Not a fan honestly.

 Apparently it's from particulate and other pollutants  getting trapped under a persistent inversion facilitated by the extremely cold air we had as of late according to the NWS.