Calibration of High-Impact Short-Range Quantitative Precipitation Forecast through Frequency-Matching Techniques

Short-lived and intense rainfall is common in Hong Kong during the wet season and often causes disruption to daily lives. As global numerical weather prediction (NWP) models are progressively improved, representation of sub-synoptic or mesoscale systems associated with intense rainfall is better parametrized and resolved than ever before, but their quantitative precipitation forecasts (QPFs) still tend to underestimate the magnitude of intense rainfall. This study calibrated model QPFs over the region of Hong Kong by two frequency-matching methods. In both methods, conversion schemes between the direct model output (DMO) and calibrated forecasts were first established by matching the cumulative distribution to that of the observed data. The “Adaptive Table” method updated the conversion scheme whenever the latest observation fell out of its expected range in the existing scheme, whereas the “Sliding Window” method reconstructed the conversion scheme using data from the most recent two years. The calibration methods had been verified against actual rainfall events with different thresholds, and it was found that both methods could improve model performance for moderate and heavy rainfall in short-range forecasts with similar effectiveness. They were also able to reduce the systematic bias of precipitation forecasts for significant rainfall throughout the verification period.