Improving Radar Rainfall Estimate with Kalman Filter Based Bias Adjustment Methods

Abstract

This study evaluates three real-time Kalman filter (KF) approaches—Mean Field (MKF), Site-Specific (SKF), and Lagrangian (LKF)—for radar rainfall bias correction in northern China. While SKF addresses spatial heterogeneity using independent gauge-location filters, LKF advects a gridded log-bias correction field with storm motion estimated from optical flow, reducing the erroneous transfer of large correction factors into trailing light-rain areas. Using 176,008 15-min validation radar-gauge samples from 449 held-out gauges, LKF reduced radar bias from − 35.0% to -3.5% and achieved the lowest overall error. However, gains weakened beyond about 10 km, showing that gauge adjustment remains constrained by network density and radar data quality. The optimized parameters further indicate that when measurement variance (S) becomes comparable to or larger than process variance (Q), the filter updates more conservatively; therefore, 2Q is used as a diagnostic guideline rather than a strict threshold.