Abstract
Summary Short-term Quantitative Precipitation Forecasting (SQPF) is critical for flash-flood warning, navigation safety, and many other applications. The current study proposes a new object-based method, named PERCAST (PERsiann-ForeCAST), to identify, track, and nowcast storms. PERCAST predicts the location and rate of rainfall up to 4 h using the most recent storm images to extract storm features, such as advection field and changes in storm intensity and size. PERCAST is coupled with a previously developed precipitation retrieval algorithm called PERSIANN-CCS (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System) to forecast rainfall rates. Four case studies have been presented to evaluate the performance of the models. While the first two case studies justify the model capabilities in nowcasting single storms, the third and fourth case studies evaluate the proposed model over the contiguous US during the summer of 2010. The results show that, by considering storm Growth and Decay (GD) trends for the prediction, the PERCAST-GD further improves the predictability of convection in terms of verification parameters such as Probability of Detection (POD) and False Alarm Ratio (FAR) up to 15–20%, compared to the comparison algorithms such as PERCAST.
Highlights
Short-term Quantitative Precipitation Forecasting (SQPF), or ‘‘nowcasting’’, is important for a number of hydrometeorological applications (Ganguly and Bras, 2003; Afshar et al, 2010)
While Brightness Temperature (BT) less than 245 K can satisfactorily identify MCSs, usually the temperature between 228 K and 235 K has been proposed for the summer season, which is based on the assumption that deep convection penetrates in the upper troposphere (Machado et al, 1998; Vila et al, 2008)
For the purposes of this study, the Q2 data have been resampled to the IR satellite resolution. Both PERCASST and PERCAST-Growth and Decay (GD) nowcasting scenarios have been verified vs. both satellite-based observed rainfall (PERSIANN-CCS) and ground-based radar rainfall observation
Summary
Short-term Quantitative Precipitation Forecasting (SQPF), or ‘‘nowcasting’’, is important for a number of hydrometeorological applications (Ganguly and Bras, 2003; Afshar et al, 2010) Both Numerical Weather Prediction (NWP) models and extrapolationbased techniques are widely used in SQPF. While these two methods are different in terms of their approaches, they play an effective and complementary role for SQPF (Golding, 1998; Ganguly and Bras, 2003; Wilson et al, 2004; Sokol, 2006; Liang et al, 2010).
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