When researching weather prediction markets on platforms like Polymarket and Kalshi, the difference between a successful simulation and a flawed hypothesis often comes down to the fine print. Before you log a single simulated position, you must understand exactly how a market resolves. This process is known as a weather contract settlement source audit. It is the foundational step for any researcher looking to build a robust, repeatable workflow in simulation-only environments. By systematically verifying the contract rules, identifying the named observation source, checking units and rounding procedures, and separating forecast evidence from settlement authority, you protect your research from easily avoidable errors.
Many researchers jump straight into analyzing forecast models, looking for discrepancies between the predicted temperature and the current market probability. However, if you do not fully grasp the resolution criteria, your forecast analysis is effectively useless. A weather contract settlement source audit ensures that you are comparing apples to apples. It forces you to slow down, read the documentation, and confirm that the data you are tracking aligns perfectly with the data the platform will use to finalize the market. In this guide, we will walk through the essential steps of building a pre-simulation checklist tailored for weather market research.
Step 1: Reading and Understanding Contract Rules
The first phase of a weather contract settlement source audit involves a meticulous reading of the contract rules. Every market on Polymarket or Kalshi operates under a specific set of guidelines that dictate the resolution criteria. You must ask yourself: What exactly is this contract measuring? Is it the maximum daily temperature, the minimum overnight low, or the total accumulated precipitation over a specific window? Furthermore, you need to identify the exact timeframes involved. Weather data is often recorded in Coordinated Universal Time (UTC), but a contract might specify a local timezone. Failing to convert these timezones correctly is a common pitfall for researchers.
Additionally, you must look for edge cases defined in the rules. What happens if the primary data source goes offline during the measurement period? Does the contract specify a fallback source, or does it void the market? Understanding these contingencies is a critical part of your weather contract settlement source audit. By documenting these rules in your pre-simulation notes, you create a clear framework for your research. You will know exactly when the measurement period begins, when it ends, and what specific variable is being tracked, ensuring your simulated positions are based on accurate parameters.
Step 2: Identifying the Named Observation Source
Once you understand the rules, the next step in your weather contract settlement source audit is to identify the named observation source. Contracts do not resolve based on general city weather; they resolve based on specific, named weather stations. For example, a contract for New York City might specifically name the Central Park weather station (KNYC) or the LaGuardia Airport station (KLGA). These two locations can record significantly different temperatures and precipitation levels due to microclimate effects. Your research must focus exclusively on the station named in the contract.
To track historical data and understand station behavior, researchers often turn to authoritative archives. For instance, you might utilize the NCEI Integrated Surface Database. As part of your research, it is helpful to know that NCEI describes a global archive of hourly and synoptic surface observations. This type of historical context allows you to see how a specific named station typically behaves during certain weather patterns, which is invaluable when building your simulation hypothesis. Always verify that the station identifier in your data feed matches the station identifier in the contract rules.
Step 3: Checking Units, Rounding, and Thresholds
The third critical component of a weather contract settlement source audit is checking the units of measurement, the rounding rules, and the specific thresholds required for resolution. Weather data can be reported in Fahrenheit or Celsius, inches or millimeters. You must ensure that your forecast models and observation feeds are using the same units specified by the contract. If a contract resolves based on degrees Fahrenheit, but your primary model outputs Celsius, you must apply the correct conversion formula before logging your simulation data.
Rounding rules are equally important and often overlooked. Does the contract round to the nearest whole number, or does it use decimal precision? If a contract resolves positively if the temperature is 90 degrees or higher, and the official station records 89.6 degrees, does the platform round up to 90, or does it truncate the decimal? These minor details can be the difference between a contract resolving as Yes or No. Your weather contract settlement source audit must explicitly document these rounding procedures.
Always assume strict literal interpretation of the rounding rules as written in the contract documentation.
Step 4: Separating Forecast Evidence from Settlement Authority
A fundamental principle of weather market research is distinguishing between forecasts, observations, and platform-finalized settlement results. Forecasts are predictions of what might happen. Observations are real-time or historical records of what did happen. The platform-finalized settlement result is the ultimate authority that determines the outcome of the contract, regardless of what preliminary observations might suggest. Your weather contract settlement source audit must clearly separate these three concepts to maintain objective research.
During a live weather event, you might monitor real-time data feeds to gauge how the market is performing. For example, you might access the Aviation Weather Center Data API. When using this tool, you must remember that the API offers observation evidence but does not replace the contract platform's finalized result. Preliminary data is subject to quality control checks and revisions. A station might report a preliminary high of 92 degrees, but the official finalized report used by the platform might revise that to 91 degrees. Always wait for the platform's designated settlement authority to publish the final, official data before concluding your simulation.
Step 5: Structuring Your Pre-Simulation Checklist
To make the weather contract settlement source audit a seamless part of your routine, you should structure it into a repeatable pre-simulation checklist. Before you analyze any forecast models or look at market probabilities, force yourself to complete this checklist. Write down the contract name, the exact measurement period in both UTC and local time, the specific named station identifier, the required units, the rounding rules, and the designated settlement authority. This disciplined approach prevents confirmation bias and ensures you are researching the actual market, not an assumed version of it.
By standardizing this process, you can easily compare different markets and identify which ones offer the most transparent and predictable resolution mechanics. If a contract has ambiguous rules or relies on a settlement source that is frequently delayed or revised, you might choose to skip simulating that market entirely. For more insights on building effective research routines and managing your simulation data, be sure to explore other articles on our blog. Consistency in your audit process is the key to long-term educational value.
Executing Your Audit in Simulation Mode
Conducting a thorough weather contract settlement source audit is the hallmark of a disciplined researcher. It shifts your focus from merely guessing the weather to systematically understanding market mechanics. Remember that MeteoX is designed as a simulation-only environment. We do not provide financial advice, we do not guarantee profits, and we do not submit external orders to any prediction markets. Our platform is built strictly for educational research, allowing you to test your hypotheses and refine your analytical skills without financial risk.
By applying the principles outlined in this guide, you can elevate your understanding of Polymarket and Kalshi weather contracts. You will learn to navigate the complexities of timezone conversions, station specificities, and resolution authorities with confidence. If you are ready to apply these concepts and start building your own structured weather market simulations, we invite you to learn more about MeteoX Trade and discover how our simulation tools can support your educational journey in weather prediction markets.
Sources and further reading
- Aviation Weather Center Data API — The API offers observation evidence but does not replace the contract platform's finalized result.
- NCEI Integrated Surface Database — NCEI describes a global archive of hourly and synoptic surface observations.