How to Build a Profitable Trading Bot for Polymarket Weather Markets: Bias Correction Breakdown

If you're seriously building a bot for daily high/low temperature markets on Polymarket, then you need to understand bias correction

I studied the paper "A Machine Learning–Based Bias Correction Method for GFS 2-m Temperature" (Cui et al., Weather and Forecasting, May 2026) and here’s how to do bias correction properly

> What the Paper is About

GFS is one of the most popular models that almost every bot uses. However, it has systematic errors (bias) in 2-meter temperature forecasts.

Especially a cold bias (the model underestimates temperature), which varies depending on time of day, region, and forecast horizon.

> What the Authors Did 

• Developed a model called BC-Unet - a U-Net convolutional neural network with CBAM attention block
• Trained it on GFS outputs + ERA5 reanalysis (as ground truth)
• Covered the entire CONUS (USA)
• Trained on one horizon (72 hours) and then applied it to all horizons from 6 hours to 10 days
• Compared results against both ERA5 and real station observations

> Key Results

• Reduced average RMSE down to 0.35°C. 
• Reduced cold bias down to 0.8°C (in 00 UTC cycle). 
• Excellent performance on diurnal cycle (day/night) - critical for high/low bins. 
• Outperforms traditional methods (like MOS).

> How to Calculate Bias

Bias = Forecast − Truth (where Truth = ERA5 or real station observation)

If bias is negative → model systematically underestimates temperature (cold bias)

Bias is calculated separately by: 
• Region 
• Time of day (day/night - diurnal cycle) 
• Forecast horizon 
• Season

> How to Reduce Bias

1. Simple Classical Methods (baseline): 

• Moving average, Kalman filter, MOS (Model Output Statistics) - linear regression on past errors.

2. ML Approach (BC-Unet): 

• The neural network learns nonlinear relationships between raw GFS forecast, orography, time of day, season, and the actual error
• CBAM attention helps the network focus on important areas (mountains, cities, water bodies). 
• Train once - apply to all horizons. 
• Result: bias is almost eliminated, and the model follows the real diurnal temperature cycle much better

This kind of post-processing (bias correction) turns raw GFS from an average source into a much more accurate one

On Polymarket markets with 1°C steps, this gives a noticeable edge in probability estimation