Abstract
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.
Highlights
Hong Kong receives abundant amounts of rainfall in the wet season due to the influence of the southwest monsoon, monsoon troughs and tropical cyclones [1]
This study focuses on the calibration of quantitative precipitation forecasts (QPFs) through frequency-matching, and the Adaptive Table method applied in this study is inspired and modified from a statistical post-processing method developed by the Japan Meteorological Agency (JMA) [26]
Owing to the fact that intense rainfall in Hong Kong is mostly brought about by severe convections and tropical cyclones, and global Numerical weather prediction (NWP) models are relatively weak in convective parameterization, the purpose of this study is to provide a more accurate shortrange QPF guidance based on global models by correcting the systematic underestimation of significant rainfall over the region of Hong Kong
Summary
Hong Kong receives abundant amounts of rainfall in the wet season due to the influence of the southwest monsoon, monsoon troughs and tropical cyclones [1]. As Hong Kong is densely populated and many buildings are situated on or near slopes, short-lived and intense rainfall can cause disruptions to daily lives and even pose threats to lives and properties. Numerical weather prediction (NWP) models provide essential input to weather forecasting generally up to a medium range, but it remains a challenge to predict precipitation quantitatively at a specific time and location due to its intermittent nature, high variability, and dependence on spatiotemporal scales [4]. The forecast uncertainty of intense rainfall in numerical models is high, because convective cells are short-lived and rely heavily on the performance of cumulus parametrization in the models [5]. Intense precipitation events are usually studied within a small area in the context of short-range forecasts
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