The Core Difference in Weather Data Sources
When researching prediction markets on platforms like Polymarket and Kalshi, understanding the fundamental difference between a forecast grid versus weather station data is essential. Many new researchers assume that a weather model predicts the exact temperature or precipitation for the specific physical sensor at an airport. However, this is a misconception that can lead to flawed simulation research and a misunderstanding of market dynamics.
To understand why, we must look at how weather data is generated and recorded. Forecast data is generated by supercomputers running complex mathematical models. These models divide the Earth's atmosphere into a three-dimensional grid. When you query a forecast, you are looking at the predicted conditions for a specific grid box. As detailed in the Open-Meteo Forecast API, the forecast API documents requested coordinates, model selection, and returned forecast-grid coordinates. The returned data represents the model's calculation for that entire grid area, not a single pinpoint location.
In contrast, the actual ground truth used to resolve weather contracts comes from physical hardware. The NWS Automated Surface Observing Systems (ASOS) documentation explains the operational observing network behind many airport weather reports. These are physical sensors sitting at specific coordinates on an airfield, measuring the exact conditions at that exact spot. These physical sensors measure temperature, dew point, wind speed, wind direction, and precipitation amounts at regular intervals, providing the critical ground truth needed for aviation safety and climatological records. The discrepancy between the broad grid box and the pinpoint sensor is a primary source of variance in weather simulation research.
How Grid Offset Impacts Simulation Research
Grid offset occurs because the center of a weather model's grid box rarely aligns perfectly with the physical location of an airport's ASOS sensor. When you request a forecast for a specific airport's coordinates, the model returns the data for the grid box that contains those coordinates. The center of that grid box might be several miles away from the actual runway where the sensor is located.
This offset introduces a spatial discrepancy. For example, if the grid box center is located three miles north of the airport, the forecast is technically predicting the weather for that northern point, averaged over the grid area. If a localized rain shower passes directly over the airport but misses the center of the grid box, the physical sensor will record precipitation while the forecast grid might show dry conditions.
When conducting simulation research for weather contracts, accounting for grid offset is a critical step. Researchers must evaluate how far the returned forecast-grid coordinates are from the actual ASOS station. A larger offset generally implies a higher potential for variance between the modeled forecast and the eventual physical observation. Recognizing this spatial gap helps researchers better understand the uncertainty inherent in any weather prediction.
The Role of Model Resolution in Weather Contracts
Model resolution refers to the size of the grid boxes used by a specific weather model. High-resolution models use smaller grid boxes, while low-resolution models use larger ones. The resolution of the model you choose for your simulation research directly impacts the severity of the grid offset and the precision of the forecast.
Global models, such as the Global Forecast System (GFS), typically have a lower resolution. A single grid box in a global model might cover an area of ten to fifteen square miles. When comparing a low-resolution forecast grid versus weather station data, the forecast represents an average of the atmospheric conditions across that massive area. This broad averaging can smooth out extreme temperature spikes or localized precipitation events that a physical airport sensor would easily detect. When researchers understand the limitations of a fifteen-square-mile grid box, they can better anticipate why a forecast might miss a highly localized weather event that ultimately triggers a contract settlement.
Conversely, high-resolution models like the High-Resolution Rapid Refresh (HRRR) use much smaller grid boxes, often just a couple of miles wide. These smaller boxes reduce the grid offset and provide a forecast that is spatially closer to the physical ASOS station. However, high-resolution models are computationally expensive and typically only forecast a short time into the future. Researchers must balance the need for spatial precision with the lead time required for their specific simulation goals.
Local Terrain and Microclimates
Even if a model has high resolution and minimal grid offset, local terrain can still cause significant deviations between the forecast grid and the physical weather station. Weather models calculate conditions based on the average elevation and terrain characteristics of the entire grid box. Physical sensors, however, are subject to the immediate microclimate of their exact location.
Airports are generally built on large, flat expanses of land, often surrounded by concrete and asphalt. This environment can create an artificial microclimate. For instance, the vast amount of pavement can absorb heat during the day and radiate it at night, causing the physical sensor to record higher temperatures than the surrounding natural terrain. If the forecast grid box includes a mix of the airport, nearby forests, and perhaps a body of water, the model will average these terrain types together.
This terrain averaging means the forecast might predict a cooler temperature than what the airport sensor actually records. Similarly, if an airport is located in a valley, cold air might pool at the sensor's location overnight, leading to lower minimum temperatures than the grid box average would suggest. Understanding the specific topography surrounding the ASOS station is vital for interpreting why a forecast grid might consistently diverge from the physical observations.
Forecasts, Observations, and Final Settlement
To conduct rigorous simulation research, it is absolutely crucial to clearly distinguish between forecasts, observations, and platform-finalized settlement results. Conflating these three distinct phases is a common error that can invalidate an entire research workflow.
Forecasts are purely predictive. They are the output of numerical models, representing what the grid box calculations expect to happen in the future. Forecasts are inherently uncertain and will change as new data is ingested into the models. Observations, on the other hand, are the historical facts recorded by the physical ASOS sensors. They represent what actually happened at the specific coordinates of the weather station.
Finally, platform-finalized settlement results are the ultimate truth for prediction markets. While Polymarket and Kalshi rely on observations from physical sensors, they also apply specific contract rules to determine the final outcome. For example, a contract might specify that only data published in a specific daily climate report is valid, or it might have rules for handling missing sensor data. An observation is not a settlement result until the platform applies its rules and finalizes the market. Your simulation research must account for these specific contract rules, not just the raw sensor data. Understanding this distinction ensures that your simulation logs accurately reflect the reality of how prediction markets operate, rather than relying solely on raw meteorological data.
Conducting Simulation-Only Research with MeteoX
Navigating the complexities of grid offsets, model resolutions, and terrain biases requires a structured approach to research. By systematically comparing historical forecast grids with the corresponding physical observations, you can identify patterns of variance and build a deeper understanding of atmospheric predictability.
We encourage researchers to explore the tools available on the MeteoX Trade homepage to enhance their analytical workflows. Our platform is designed specifically for simulation-only research, allowing you to track forecast evolution, compare model outputs, and log your observations without any financial risk. Please remember that MeteoX operates strictly in simulation-only mode; we do not facilitate real-money trading, nor do we submit external orders to any prediction markets.
By maintaining a disciplined simulation log and continuously refining your understanding of how forecast grids interact with physical weather stations, you can develop a more objective view of weather contract dynamics. For more insights on building robust research routines and analyzing market structures, be sure to read the other educational articles available on our blog.
Sources and further reading
- Open-Meteo Forecast API — The forecast API documents requested coordinates, model selection and returned forecast-grid coordinates.
- NWS Automated Surface Observing Systems — ASOS documentation explains the operational observing network behind many airport weather reports.