Why "Checking the Weather"
Still Fails Photographers
Despite living in a golden age of data, photographers still miss the best atmosphere. Why? Because standard tools aren't built for us.
The NWS & Aviation | General Weather Apps | Niche Sunrise/Sunset Tools | |
|---|---|---|---|
| Examples | NOAA, National Weather Service | Accuweather, Windy | Skyfire, Escaype |
| 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 fills the gap. It's the first proactive "push" service dedicated to photogenic fog. It interprets the raw data 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 keep Fog-Index accessible and sustainable, the processing power is concentrated where it counts most for photography.
The Sunrise Window
The model actively analyzes conditions only during the prime photographic window: from 1 hour before sunrise to 2 hours after sunrise.
Ignoring the flat, midday haze allows for the most accurate predictions when the light is best.
Born in the Pacific Northwest
Every region has a unique atmospheric fingerprint. Fog-Index was born and bred in the Pacific Northwest. The current algorithm is painstakingly hand-tuned to thrive in the PNW's specific winter patterns: mid-latitude stratiform systems.
The Algorithm Report Card
I believe you should know exactly where the model excels and where it's still learning.
| Fog Type | Current Algorithm Fit | The Honest Truth (Notes) |
|---|---|---|
| Radiation Fog | 🟢🟢🟢🟢🟢Excellent | The 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. |
| Precipitation Fog | 🟠🟠⚪⚪⚪Limited | The model detects the high humidity and wet ground (saturation), but misses the key thermodynamic driver: warm rain falling into colder air. |
| Steam Fog | 🔴⚪⚪⚪⚪Poor | Requires data that isn't available yet: the specific temperature difference between cold air and warm water. The model will likely miss these localized events. |
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: The 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 Six: Understanding Your Target
Radiation Fog (Ground Fog)
The classic "morning mist." Forms overnight on clear, calm ground. Hugs valleys and burns off quickly after sunrise.
Valley Fog
A sea of clouds. Cold air drains into basins, creating thick, persistent layers that can last for days in winter inversions.
Advection Fog (Coastal Fog)
The coastal blanket. Warm, moist air rolls over cold water. Thick, persistent, and driven inland by wind.
Upslope Fog
The mountain capper. Moist wind forced up terrain cools and clings to peaks and forested slopes.
Steam Fog (Sea Smoke)
Sea smoke. Wispy vapor rising from warm water into biting cold air. Surreal, fleeting, and distinct.
Precipitation Fog
Frontal fog. Forms when warm rain falls through cold air, saturating it. Widespread, soft, and moody.
The Horizon: What's 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.
The Foundation
Tuning Radiation & Valley fog in the PNW. Establishing the baseline algorithm.
The Marine Layer
Tackling Advection Fog. Developing a parallel algorithm branch to forecast the coastal marine layer pushing inland.
Mobile Experience
Native iOS and Android apps for robust push notifications and location-based alerts on the go.
Community Tuning
The ultimate vision: allowing trusted users to "verify" fog events, feeding real-world ground truth back into the model.
Ready to Stop Missing Perfect Mornings?
Join landscape photographers who trust Fog-Index to alert them when conditions align.