As an avid storm chaser and weather enthusiast, having access to accurate and timely weather model forecasts is crucial for both preparing for impactful weather events and analyzing meteorological mysteries. This is why I was immediately intrigued when I discovered Windy.com, an interactive weather platform that aggregates forecasts from a multiplicity of global and regional numerical weather prediction models.
Beyond collecting the model outputs, Windy‘s signature feature is enabling users to visualize predictions side-by-side, with the ability to contrast forecast renderings across models through both time and geographical space. This simultaneously presented multi-dimensional perspective exposes the uncertainties innate to weather forecasting in an understandable visual format.
In this extensive guide, I‘ll tap into my meteorology expertise to explore Windy‘s powerful comparison functionality for showcasing weather model strengths, weaknesses and disagreements. You‘ll learn:
- Technical specs and ideal applications for models like the ECMWF, NAM, ICON, HRRR and more
- Steps for setting up effective comparative views catered to your scenario
- Case studies demonstrating model uncertainties around extreme weather events
- Interviews with Windy developers on current limitations and future goals
Let’s dive deep into this innovative platform advancing public access to the complex art of meteorological modeling!
The CRUCIAL Role of Weather Models
First, what exactly are weather models? At the most fundamental level, they are computational simulations of the atmosphere underpinned by mathematical equations representing the physics governing motion, moisture, heat, gravity and more in air masses. Models ingest current observational data like temperatures, then project forward in time and space based on understood interplays between variables.
However, small errors in initial inputs or calculationscompound quicklyin chaotic weather systems. Models also rely on parameterizations, approximations for processes occurring below grid scale, bringing uncertainty. Different teams tune aspects like dynamics, data assimilation and sub-scale physics based on resources and regional focii.
Thus models diverge in their handling of elements like precipitation, storm systems and clouds. Checking multiple model outputs against eachother provides meteorologists context on possibilities and probabilities forcoming weather, centered around areas of agreement. Windy enables these comparisons visually.
Overview of Featured Weather Models
Windy sources predictive weather data from a multiplicity of global and regional numerical models, each with unique capabilities, strengths and weaknesses. Understanding the technical constraints and parametrization approaches explains when models excel or falter. Here’s an overview of some prominent featured models:
Global Models
ECMWF (European Centre for Medium-Range Weather Forecasts)
- 91 vertical atmospheric layers
- 30 km base horizontal resolution
- Specializes in precipitation and medium/long range outlooks
- Noteworthy handling of tropical cyclones and extratropical transitions
GFS (Global Forecast System)
- Runs at 13 km resolution out to 16 days
- Updates exceptionally frequently – hourly for 0-120 hours
- Leans markedly bullish on snowfall projections
ICON (German Weather Service model)
- 13 km global resolution
- Particularly strong conveying of low pressure dynamics
- 2.2 km ICON-D2 variant for detailed mountain terrain
See the full specifications table for 10+ global model metrics.
Regional Models
NAM (North America Mesoscale)
- 12 km resolution focused on North America
- Hourly updates useful for real-time precision like rainfall timing
HRRR (High Resolution Rapid Refresh)
- 3 km resolution, updated hourly out 39 hours
- Specializes in accurately locating storm initiation
Meteoblue
- 4 km global model explicitly handling mountain meteorology
- Uses hardware acceleration for rapid refresh rate
Why Resolution Matters
Higher resolution models can render smaller scale real-world features like terrain, sea breezes and thunderstorm updrafts directly rather than needing approximations. This leads to better resolving fine details of temperature gradients, convergence zones and precipitation areas which influence weather events.
However the computing power required for these hyper local models covering large domains lags behind demand. Ongoing exponential technology advances will continue expanding high resolution modeling capabilities.
Anatomy of a Weather Model Comparison
Windy’s signature feature is the ability to visualize weather model forecasts side-by-side. You can select and layer models over the same map area, advancing through timesteps to reveal the future projections. This exposes divergence versus alignment and highlights uncertainty.
To compare models on Windy:
- Define location – zoom into region of interest
- Select models & variables – choose factors like temperature or precipitation
- Activate Compare – split screen divides models
- Step through time – slide forecast into future
- Spot differences – toggle between model views
- Share compelling looks – export images or custom URLs
It’s ideal to narrow the map area and limit compared variables when learning to minimize complexity. I suggest global models like ECMWF and GFS as a base since their wider domain curbs edge effects. Layering a specialty model like NAM or ICON provides sharp second opinions.
Precipitation prediction disagreement between ECMWF (left) and NAM models (right)
Customization like opacity sliders helps parse signal from noise when model outputs obscure each other. The time dimension also reveals if a discrepancy reflects temporary run-to-run variation or lasting philosophical divergence.
Real-World Showcases Demonstrating Model Uncertainty
To demonstrate the revelatory power of visualizing weather models discrepancies, here are 3 high-impact scenarios where Windy’s comparison capabilities prove illuminating:
Hurricane Fiona’s Sinuous Track
In September 2022, models profoundly diverged on both the path and eventual landfall zone of Hurricane Fiona as it meandered erratically northward. While the GFS projected a race out to sea, other models like the ECMWF predicted a damaging hook back into Atlantic Canada.
Windy’s side-by-side tracking of these outlooks clearly showcased the European model’s early recognition of the blocking ridge snaring Fiona from escaping out to sea. Post-storm analysis continues investigating the key factors differentiating the ECMWF‘s notable performance.
The spread of possible forecast tracks for Hurricane Fiona on September 21st, 2022.
Lake Effect Snowband Placement
As polar air traverses the Great Lakes, enhanced lift and moisture fuel notoriously bursty narrow bands of extreme snowfall exceeding 6 inches an hour. The mesoscale size means accurately projecting snowband setup relies on high resolution models skillfully handling fine details.
During prolific lake effect events, convection-allowing models like the HRRR and NAM nest revealed their advantage over global models for pinpointing zones receiving several feet of accumulation, while lower resolution models smeared snow more generally over the region.
3 km HRRR model (right) precisely locates heavy snowband versus smooth 12 km NAM model (left)
Madden Julian Oscillation Forecasting
The MJO describes a slow moving region of enhanced tropical rainfall propagating eastward over 30-90 days influencing mid-latitude weather. MJO phase forecasts determine long range outlooks but reliability remains stubbornly low beyond 15 days as models diverge.
Windy allows toggling between visualization of models’ handling of MJO propagation timing, a vital capability for anticipating week 3-4 winter storm, cold air outbreak and pattern flip potential. Ongoing MJO forecast skill research continually references Windy’s interfaces.
Week 3+ Forecast divergence on Madden Julian Oscillation tropical forcing
These cases emphasize the acceleration of meteorological understanding possible from Windy democratizing interactive comparison of both outputs and uncertainties across some of today’s most advanced weather models.
Interviews with Windy Founders on Model Choices
To learn about Windy’s process for curating their gallery of both widely used and specialty weather models, I sat down virtually with company CEO Ivo Lukačovič. Here are some excerpts:
What core considerations guide your weather model inclusion decisions?
“When evaluating adding new global models, we analyze historical accuracy across critical weather elements like surface air pressure and 500 mbar geopotential height over both Northern and Southern Hemisphere winters. Regional model assessment emphasizes performance on precipitation, 2m temperature and 10m windspeed.”
How do you handle balancing innovative niche models versus established sources?
“We definitely want to provide coverage of familiar powerhouse models like the ECMWF, while also keeping our ear to the ground on development of new limited area models extending capabilities for locations with complex terrain. A great example here is integration of the AROME Arctic model tailored to the unique meteorology of extremely northern latitudes.”
What excites you looking forward at future opportunities to improve on model comparisons?
“Continually increasing resolution unlocks realism – there are super exciting convection permitting models coming online leveraging GPU and AI-assisted computing like Flow-Based Lattices (FBL) and UCLA’s Deep Learning model. We’re also investing seriously in ensemble visualization to communicate forecast confidence ranges.”
Ivo’s commitment demonstrates Windy’s dedication towards advancing public access to the latest innovations in weather prediction science.
Additional Windy Features Enhancing Meteorological Analysis
Beyond the flagship comparative model visualizations, Windy packs an immense diversity of mapping layers and functionality for dodging raindrops on your next adventure and Marveling at meteorology alike.
Graphs & Statistics – Plots and tables for historical variables like temperature, wind and snow depth at surface observation stations
Webcams – Crowdsourced live photo feeds providing ground truth views augmentation
Cross-sections – Vertical model slice views revealing structure inside storms
Aviation Tools – Flight planning optimization overlays like turbulence, icing and ceiling
Ocean Layers – Sea surface temps, currents and wave height data
Air Quality – Airborne particulate matter and pollution monitoring
Model Uploads – Community custom model imports expanding niche options
The developer team also continues releasing updates to the platform weekly – on both existing feature upgrades and new datasets. The breadth reveals Windy’s ambition as one-stop weather visualization shop.
Conclusions on Windy’s Powerful Comparison Offerings
As discussed, Windy’s interactive weather model comparison features provide meteorological analysts:
- Frameworks to parse complex model technical specifications
- Interactive visual interfaces highlighting key areas of uncertainty
- Tools to export compelling forecast graphics with custom views
- Venues to discuss event case studies and predictability barriers
- Platforms to upload and share community-developed models
The exposed perspectives fundamentally enhance recognition of inevitable unpredictabilities around high impact weather. By accepting inherent limitations, collective forensic analysis afterward continues progressing atmospheric science and adaptation strategies.
Windy lowers barriers not just for finding reliable forecasts, but also for questioning modeling orthodoxies though public engagement. Its commitment to user experience refinement and shoveling the best available datasets into visually intuitive formats is admirable.
The platform has won my trust as preferred liaison helping weather wonders unfold with eyes wide open about probabilities, possibilities and uncertainties innate to my passion’s pursuit. I welcome you to join me in the journey!