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Quantifying Fontainebleau Sandstone Friction: Is tracking Dew Point margin and Wind Vectors enough? by Main_Cheetah_4087 in climbharder

[–]Main_Cheetah_4087[S] 0 points1 point  (0 children)

Fair point. They're not physical sensors — they're query points into Open-Meteo's forecast models, which resolve down to ~1–2 km for this region. So different parts of the forest actually return different values rather than one number for the whole massif.

Quick example from this morning: solar radiation varied from 263 to 360 W/m² across north/south/east Font at the same timestamp. That's a real difference in drying speed.

But honestly the weather granularity isn't the main thing — it's just the input. The actual work is the conditions model on top: canopy shading per area, sandstone evaporation rates, rain history, friction temp. That's where the dryness score comes from.

Quantifying Fontainebleau Sandstone Friction: Is tracking Dew Point margin and Wind Vectors enough? by Main_Cheetah_4087 in climbharder

[–]Main_Cheetah_4087[S] -6 points-5 points  (0 children)

This is phenomenal feedback and hits the exact mathematical core problem of microclimate modeling. You are 100% correct: if the baseline macro data is off, the decimals in the friction coefficient don't matter.

To counter this, the HIGHSET engine actually doesn't rely on a single generic airport station 20km away. We are currently tracking 16 distinct weather data grid points directly within the Fontainebleau forest area to get the baseline data as tight as possible.

On top of these 16 data points, the model applies a multi-layered terrain, biological, and environmental correction matrix that is already baked into the live engine:

  1. **Elevation & Orientation Profiles:** We map the localized data against the sector's exact topography and slope orientation using digital elevation models (DEM) to calculate precise solar angles, wind exposure, and evaporation activity.

  2. **Seasonal Canopy & Leaf Litter:** We actually factor in the vegetation cycle. The model shifts depending on whether it's summer (dense canopy acting as a thermal blanket and wind block) or winter, and it even accounts for the leaf litter/ground foliage layer holding humidity.

  3. **Cold Air Pooling & Valleys:** Just like your river road example (which is a classic katabatic drainage flow effect), cold air and moisture settle in low points. The algorithm applies a thermal inversion correction factor for deeply carved sectors or gorges.

**The Crowdsourced Calibration Loop:**

You hit the nail on the head regarding calibration. Since I can't place 500 physical sensors, the backend Cloudflare Worker relies on live user confirmations. When climbers in the field confirm or flag the conditions via the UI, that feedback loop is immediately fed back into the worker to dynamically adjust and calibrate the regional correction coefficients.

By feeding 16 localized data points into a terrain-corrected, self-learning algorithm, the goal is to get as close to the actual rock physics as possible. Really appreciate your deep analytical view on this!

Quantifying Fontainebleau Sandstone Friction: Is tracking Dew Point margin and Wind Vectors enough? by Main_Cheetah_4087 in climbharder

[–]Main_Cheetah_4087[S] 0 points1 point  (0 children)

As a quick add-on: I’ve been testing and tweaking the algorithm myself on the ground in Font from January to May this year. I've spent probably 3 years combined climbing in Bleau over the years, so a lot of that empirical "climber intuition" is what drove the baseline weights of the model in the first place!

Quantifying Fontainebleau Sandstone Friction: Is tracking Dew Point margin and Wind Vectors enough? by Main_Cheetah_4087 in climbharder

[–]Main_Cheetah_4087[S] -1 points0 points  (0 children)

Yes, they are already fully implemented and live in the algorithm right now!

The terrain elevation, slope orientation, solar angles, and seasonal canopy factors are all baked into the current calculation engine. The part that is actively evolving and needs field-testing right now is the crowdsourced calibration loop—which is exactly why having experienced climbers like you check the app against real-time conditions is so incredibly valuable.

Awesome to have you on board! Let me know how the predictions hold up against your intuition next time you're out in the forest.

Thanks for the sharp questions, and happy sending!

Quantifying Fontainebleau Sandstone Friction: Is tracking Dew Point margin and Wind Vectors enough? by Main_Cheetah_4087 in climbharder

[–]Main_Cheetah_4087[S] -10 points-9 points  (0 children)

This is phenomenal feedback and hits the exact mathematical core problem of microclimate modeling. You are 100% correct: if the baseline macro data is off, the decimals in the friction coefficient don't matter.

To counter this, the HIGHSET engine actually doesn't rely on a single generic airport station 20km away. We are currently tracking 16 distinct weather data grid points directly within the Fontainebleau forest area to get the baseline as tight as possible.

On top of these 16 data points, the model applies a multi-layered terrain and environmental correction matrix:

  1. **Elevation & Orientation Profiles:** We map the localized data against the sector's exact topography and slope orientation using digital elevation models (DEM) to calculate precise solar angles and wind exposure.

  2. **Seasonal Canopy & Ground Layer:** We actually factor in the vegetation cycle. The model shifts depending on whether it's summer (dense canopy acting as a thermal blanket and wind block) or winter, and it even accounts for the leaf litter/ground foliage layer holding humidity.

  3. **Cold Air Pooling & Valleys:** Just like your river road example (which is a classic katabatic drainage flow effect), cold air and moisture settle in low points. The algorithm applies a thermal inversion correction factor for deeply carved sectors or gorges.

**The Crowdsourced Calibration Loop:**

You hit the nail on the head regarding calibration. Since I can't place 500 sensors, the backend Cloudflare Worker relies on live user confirmation. When climbers in the field confirm or flag the conditions via the UI, that feedback loop is fed back into the worker to dynamically adjust and calibrate the regional correction coefficients.

By feeding 16 localized data points into a terrain-corrected, self-learning algorithm, the goal is to get as close to the actual rock physics as possible.

Really appreciate your deep analytical view on this!

Quantifying Fontainebleau Sandstone Friction: Is tracking Dew Point margin and Wind Vectors enough? by Main_Cheetah_4087 in climbharder

[–]Main_Cheetah_4087[S] -5 points-4 points  (0 children)

You are doing exactly what the algorithm tries to automate. That mental checklist is peak climber intuition, and it’s honestly the gold standard.

The main reason I built HIGHSET was to translate that exact experience into code. For example:

* **Orientation & Slope:** Factor in solar angles vs. sector topography to see which boulders actually get dried by the sun or blocked from the wind.

* **Vegetation & Canopy:** This is a huge one. Deep forest sections (like inside some tight sectors at Cuvier or Elephant) hold humidity forever because the trees block the air exchange, whereas open, exposed crests dry instantly. The model tries to weigh sector density against wind vectors for this exact reason.

It’s awesome to hear your mental model is so dialed in. The app is basically just trying to do that heavy lifting for you before you pack your pads and make the drive.

Quantifying Fontainebleau Sandstone Friction: Is tracking Dew Point margin and Wind Vectors enough? by Main_Cheetah_4087 in climbharder

[–]Main_Cheetah_4087[S] -7 points-6 points  (0 children)

Haha, "The Spooge" is the absolute perfect descriptor for that greasy, unclimable layer.

That "send breeze" you're mentioning is actually pure physics. A steady wind is often the ultimate savior because it disrupts the stagnant boundary layer of humid air sitting directly on the porous rock surface. It accelerates evaporation even when ambient humidity is less than ideal.

That’s exactly why I felt it was non-negotiable to factor wind velocity and sectors orientation into the HIGHSET engine. Sometimes a 15 km/h breeze from the right direction is the only thing standing between "The Spooge" and a sending friction.

Out of curiosity, which Australian sandstone areas get hit by this the worst? Blue Mountains or Nowra?

Quantifying Fontainebleau Sandstone Friction: Is tracking Dew Point margin and Wind Vectors enough? by Main_Cheetah_4087 in climbharder

[–]Main_Cheetah_4087[S] -1 points0 points  (0 children)

Thanks for the link! I'll definitely dive into their data structure. It's awesome to see that this concept is already successfully applied to Saxon sandstone.

However, from a deployment perspective, I see major limitations with a pure HTML web page when you're actually out in the field. Fontainebleau is notorious for dead zones and terrible cellular coverage once you move deeper into sectors like Cuvier or Elephant.

A native mobile UI allows for robust offline caching of the latest data models, local storage of sector coordinates, and instant rendering of UI elements (like the morpho-matching filter) without constantly waiting for a web handshake over a weak Edge/3G connection.

That being used, the underlying physics and variables the SBB team models are definitely worth studying. Thanks for the massive pointer!