ASOS Sensor Precision: The Technical Reality of Kalshi Weather Settlement
TL;DR / Key Takeaways
- Kalshi weather contracts settle against official NOAA station observations, not app forecasts.
- ASOS sensor precision, truncation, and station micro-climates can move a binary contract across its threshold.
- A bot needs settlement-station mappings and bias corrections to avoid modeling the wrong target.
- Historical ASOS data is useful for understanding systematic station-level differences.
Most weather traders think about "the temperature in Chicago." I think about the specific hardware sensor at O'Hare International Airport (KORD), its calibration tolerances, and whether it reports in whole degrees or with decimals.
That difference matters when a Kalshi contract settles on a specific threshold and the actual temperature lands within a degree of it.
The settlement sensor is the truth
Every Kalshi weather contract specifies a settlement station by ICAO code. When the contract expires, Kalshi pulls the official recorded high or low temperature from that specific station — not from a weather service average, not from a regional model output, not from the nearest NWS forecast point. The sensor at that airport is the final authority.
If you are trading a Washington DC temperature contract and your GFS ensemble interpolation is targeting the regional DC forecast grid point rather than the Reagan National Airport (KDCA) station specifically, your probability estimates are systematically off. Not by much in normal conditions. But at the margins — which is exactly where binary contracts get decided — it matters.
Sensor hardware and truncation
| Data source | Measurement target | Precision issue | Trading implication | | --- | --- | --- | --- | | Weather app forecast | Broad city forecast | Often rounded and generalized | Useful context, not settlement truth | | NOAA ASOS station | Specific official sensor | Sensor precision, truncation, local exposure | Must match Kalshi contract settlement | | Model grid point | Forecasted atmosphere over an area | May not align with station micro-climate | Needs station bias adjustment |
The ASOS temperature sensors, typically the HO-1088 hygrothermometer, are calibrated to high standards and generally accurate. But they have operational tolerances, and more importantly, they report in a specific format.
Standard ASOS hourly observations report temperature in whole degrees Fahrenheit. The underlying continuous measurement is rounded. The rounding convention matters:
Underlying reading: 74.4°F → Official observation: 74°F
Underlying reading: 74.5°F → Official observation: 75°F
Underlying reading: 74.6°F → Official observation: 75°F
If a Kalshi contract asks whether the daily high will reach or exceed 75°F, and the underlying sensor registers 74.6°F, the contract settles YES. If it reads 74.4°F, it settles NO. That 0.2°F difference in continuous temperature produces completely opposite contract outcomes.
This truncation behavior means that for contracts right at the threshold, there is an inherent uncertainty that the raw ensemble probability does not fully capture. The ensemble might say 72% probability the temperature exceeds 75°F. But if most of the ensemble mass is clustered between 74.3°F and 75.2°F, the actual settlement probability depends heavily on where within that range the single sensor reading falls. The effective probability is less certain than the ensemble headline number suggests.
Understanding this is not academic. It directly affects how I set minimum confidence thresholds for contracts where the ensemble mean is within two degrees of the Kalshi threshold.
Station micro-climate and systematic bias
Sensors at major airports sit in the middle of large areas of asphalt and concrete. They measure what the atmosphere is doing at that specific microenvironment, which is not identical to "the weather" as experienced anywhere else in the metro area. Airport sensors consistently run warmer than suburban or rural stations nearby, particularly on calm, clear days when radiative heating from pavement is strongest.
This is not a bug in the system. It is a known, measurable, and correctable bias.
For the Predict & Profit system, I have built station-specific bias corrections by comparing historical ASOS readings from each Kalshi settlement station against the GFS model output at the same grid coordinate. The result is a per-station offset that gets applied to the ensemble probability before scoring.
The KDCA (Reagan National Airport) example is one I know well: during southwest wind events, the station consistently reads approximately 1.2°F warmer than the regional GFS average. During northeast flow, the bias is much smaller. I factor in both the magnitude and the wind-direction conditioning when calculating the adjusted probability.
The stations I have documented systematic biases for include:
- KDCA (Washington DC) — warm bias during SW flow
- KORD (Chicago O'Hare) — cold bias during lake breeze events
- KLAX (Los Angeles) — marine layer influence varies significantly by season
- KBOS (Boston Logan) — sea breeze timing affects afternoon high readings
These corrections are small — usually under 1°F — but on binary contracts settled by a single degree threshold, a 0.8°F correction on a calibrated model estimate can shift the probability meaningfully.
What happens when these factors combine
The worst-case scenario for an automated weather trading bot is a contract where:
- The ensemble mean is within 1-2 degrees of the threshold
- The settlement station has a documented micro-climate bias in the current wind pattern
- The sensor reading will be rounded through the threshold boundary
In this scenario, the raw ensemble probability is nearly meaningless as a basis for the contract probability. The Predict & Profit system flags these trades for additional scrutiny and typically skips them. The minimum ensemble confidence filter (0.30) catches many of these cases automatically — if the ensemble members are spread across the threshold, the confidence score is low regardless of the mean.
But the station bias correction catches cases the raw confidence score misses: a high-confidence ensemble that is confidently predicting the wrong reference point because it is not accounting for the KDCA heat island effect.
Why most bots do not have this
Most Kalshi weather bots I have seen — including several on GitHub — pull GFS ensemble data and interpolate to city coordinates from the model grid. That is a reasonable starting point. But none of the ones I have reviewed implement station-specific bias correction.
The data to build these corrections is freely available. NOAA maintains decades of ASOS historical observations through the ISD (Integrated Surface Database). The GFS model output archive goes back years. A weekend of Python data work gives you the bias estimates. The question is whether you care enough to build it.
I do, because the settlement threshold edge cases are exactly where the difference between winning and losing contracts lives.
The station-specific bias correction system, settlement station mappings, and the full ensemble probability pipeline are all included in the Predict & Profit Python source code.
Frequently Asked Questions
Q: Why does ASOS station precision matter for Kalshi settlement?
A: Kalshi settles on official station observations. A one-degree threshold can be decided by sensor behavior, rounding, truncation, or station micro-climate rather than the broader city forecast.
Q: What is station-level bias?
A: Station-level bias is the repeatable difference between a forecast grid point or city forecast and the actual settlement station. It can come from airport location, elevation, surface type, and local heat effects.
Q: Can ensemble forecasts automatically correct for ASOS bias?
A: Not completely. Ensembles model atmospheric conditions, but the bot still needs settlement-station mapping and historical bias analysis to align forecasts with the exact observation used for settlement.