Development of quantitative precipitation nowcasts algorithm for high resolution weather radar

Abstract:

Statistical approaches for quantitative precipitation nowcasts (QPNs) have emerged with recent advances in sensors such as X-band radars that have high spatiotemporal resolution. Several investigations have reported that extrapolation-based prediction can be more effective than numerical weather model-based prediction for a short lead time of less than a few hours. This study introduces the algorithm based on extrapolation method to produce QPN products for high resolution radar located in Yonsei University. The algorithm first provides the 3-hour potential accumulated rainfall (PAR) for a very-short-range forecast using extrapolation and then calculates the probability of rainfall (POR) during the same time period through statistical methods. The algorithm for PAR first identifies rainfall features and computes the motion vectors of the identified rainfall features through extrapolation. The algorithm can then extrapolate future rainfall rates from current and previous rainfall rates outputs. In addition, the extrapolated precipitation fields at radar observation time intervals are used as inputs for the POR estimation process, which produces the rainfall probability during the same 3-h period. POR can provide a quantitative measure for the uncertainty in the PAR estimates.

Retrieved PAR and POR from the QPN algorithm are validated with 3-h accumulated rainfalls obtained from the radar observation. Forecasting accuracy of the PAR algorithm with time has also been investigated. The accuracy tends to decrease with increasing lead time, as expected. It may be due to the limitations of the extrapolation technique and degradation of the accuracy of the PAR estimates. The validation results will be discussed.

This work was supported by Korea Environment Industry & Technology Institute(KEITI) through Water Management Research Program, funded by Korea Ministry of Environment(MOE) (127557).