Why "Checking the Weather"
Still Fails Photographers
In a world of endless data, we're still missing the one notification that matters most.
We live in a golden age of weather data, yet landscape photographers still miss the best conditions. Why? Because the existing tools are built for a different job. They fall into three categories, each with a fatal flaw for chasing fog:
The NWS & Aviation | General Weather Apps | Niche Sunrise/Sunset Tools | |
|---|---|---|---|
| Focus | Transport Safety | Global Data Visualization | Golden Hour Color |
| The Flaw | Optimized for hazardous, zero-visibility conditions, not photogenic mist. A "Dense Fog Advisory" is often too late or too widespread for a specific valley shot. | "Pull" based. They have the data (like Windy's fog layer), but require you to manually open the app, navigate to each location, and interpret 5-7 variables every single day. | Groundbreaking for predicting color, but they don't forecast fog. More importantly, they lack proactive alerting, requiring daily manual checks. |
| The Result | ✗You miss the nuanced, photogenic conditions | ✗You spend time checking apps instead of shooting | ✗You miss misty mornings without colorful sunrises |
Fog-Index exists to fill this exact white space. We are the first "push" service dedicated to photogenic fog. We don't just give you the data; we interpret it and alert you, so you can stop checking apps and start planning your shoot.
Focused on the Light that Matters
Running advanced predictive models against global weather data is computationally expensive. To ensure Fog-Index remains accessible and sustainable, we concentrate our processing power where it counts most for photography.
The Sunrise Window
Our model actively analyzes conditions only during the prime photographic window: from 1 hour before sunrise to 2 hours after sunrise.
If fog happens outside this window, it's often too flat or too dense for dynamic photography. We prioritize the magical transition of light.
Tuned for the Pacific Northwest's "Quiet" Fog
Every region has a unique atmospheric fingerprint. Fog-Index was born and bred in the Pacific Northwest. Our current algorithm is painstakingly hand-tuned to thrive in the PNW's specific winter patterns: mid-latitude stratiform systems.
What this means:
We look for wide, continuous, stable low-cloud decks that settle into valleys overnight under calm, high-pressure skies. Our model rewards cold ground temperatures, high humidity trapped near the surface, and near-zero wind speeds.
Target Scenario: Stable Radiation/Valley Fog
Cross-section: Cold air settling in valley with inversion layer
Why One Model Can't (Yet) Predict the World
Atmospheric physics change drastically based on geography. A model perfectly tuned for Seattle's stable valleys will fail wildly in a tropical environment.
| System Type | Pacific Northwest | Costa Rica |
|---|---|---|
| System Type | Stable Stratiform (Current Focus) | Dynamic Convective (Future Focus) |
| Wind | Calm wind (near zero) | Strong wind (pushing upslope) |
| Temperature | Cold ground temperature | Warm ocean air |
| Cloud Type | Flat, uniform cloud layer | Tall, puffy cumulus clouds |
| Formation | Needs calm, stable air and clear skies above to let heat escape (radiative cooling). | Comes from unstable air, rapid uplift, or warm ocean air moving over land (advection). |
Conclusion: Our current model penalizes the very conditions (like high wind or active cloud formation) that cause fog in other regions.
The Essential Fog Types
The Photogenic Five: Understanding Your Target
A clean, modern guide to the five most photogenic fog formations. Each type has unique atmospheric conditions and visual characteristics.
Radiation Fog (Ground Fog)
Forms overnight on clear, calm nights as the ground rapidly cools, chilling the air immediately above it. It's typically shallow, patchy, and hugs flat terrain like fields or marshes. It is most common at dawn and creates iconic, low-lying layers that usually burn off quickly as the sun rises.
Valley Fog
A deep, dense type of radiation fog that fills basins like a bowl. Cold, heavy air drains down surrounding slopes at night and settles on the valley floor, building a thick layer of cloud that can look like a sea from above. In strong winter inversions, it can persist for days.
Advection Fog (Coastal Fog)
Created when warm, moist air moves horizontally over a much colder surface, like ocean water upwelling along a coast. It is thick, persistent, and can be driven deep inland by wind, blanketing coastlines at any time of day, distinct from sunrise fog.
Upslope Fog
Forms when moist wind is forced up the side of a mountain or hill. As the air rises along the terrain, it cools and condenses into a cloud that clings to the slope. It often appears as a persistent cloud bank capping a peak or shrouding forested hillsides in mist.
Steam Fog (Sea Smoke)
A visually striking phenomenon appearing as delicate wisps of vapor rising from a water surface. It occurs when very cold, dry air moves over much warmer water (like a lake in late fall), causing rapid evaporation that instantly condenses into wispy fog tendrils.
The Algorithm Report Card
We believe in radical transparency. This isn't a magic black box. It's an evolving tool specialized for specific jobs. Here is exactly how our current model performs against different fog types.
| Fog Type | Current Algorithm Fit | The Honest Truth (Notes) |
|---|---|---|
| Radiation Fog | 🟢🟢🟢🟢🟢Excellent | Our primary target. The model is perfectly tuned to detect the high-saturation, calm-wind, clear-sky cooling that creates these classic sunrise layers. |
| Valley Fog | 🟢🟢🟢🟢⚪Very Good | A core strength. Valley terrain bonuses, ground wetness tracking, and seasonal adjustments make the model highly effective at predicting these deep, persistent events. Still need coverage for inversions. |
| Advection Fog | 🟡🟡🟡⚪⚪Good | Captures the essential high humidity and saturation, but currently favors calmer winds than are typical for driving coastal fog inland. |
| Upslope Fog | 🟠🟠⚪⚪⚪Limited | Detects the moisture, but is blind to the key mechanism: wind direction forcing air up terrain. Currently penalizes the wind and elevation often required. |
| Steam Fog | 🔴⚪⚪⚪⚪Poor | Requires data we don't have yet: the specific temperature difference between cold air and warm water. The model will likely miss these localized events. |
The Horizon: Where We Go Next
This model is forever being honed. Today, it's hand-tuned by me based on data analysis. Tomorrow, it will be tuned by us.
Now: The Foundation
Tuning Radiation & Valley fog in the PNW. Establishing the baseline algorithm.
Next Up: The Marine Layer
Tackling Advection Fog. Developing a parallel algorithm branch to forecast the coastal marine layer pushing inland—a totally different physical process.
Future: Mobile Experience
Native iOS and Android apps for robust push notifications and location-based alerts on the go.
Long-Term: Community Tuning
The ultimate vision: allowing trusted users to "verify" fog events at specific locations, feeding real-world ground truth back into the model to micro-tune specific valleys and coastlines.
Ready to Stop Missing Perfect Mornings?
Join landscape photographers who trust Fog-Index to alert them when conditions align.