A new methodology for pixel-quantitative precipitation nowcasting using a pyramid Lucas Kanade optical flow approach

Abstract

Keywords:Quantitative precipitation nowcasting Pyramid Lucas-Kanade optical flow method Pixel level s u m m a r y Short-term high-resolution Quantitative Precipitation Nowcasting (QPN) has important implications for navigation, flood forecasting, and other hydrological and meteorological concerns. This study proposes a new algorithm called Pixel-based QPN using the Pyramid Lucas-Kanade Optical Flow method (PPLK), which comprises three steps: employing a Pyramid Lucas-Kanade Optical Flow method (PLKOF) to estimate precipitation advection, projecting rainy clouds by considering the advection and evolution pixel by pixel, and interpolating QPN imagery based on the space-time continuum of cloud patches. The PPLK methodology was evaluated with 2338 images from the geostationary meteorological satellite Fengyun-2F (FY-2F) of China and compared with two other advection-based methods, i.e., the maximum correlation method and the Horn-Schunck Optical Flow scheme. The data sample covered all intensive observations since the launch of FY-2F, despite covering a total of only approximately 10 days. The results show that the PPLK performed better than the algorithms used for comparison, demonstrating less time expenditure, more effective cloud tracking, and improved QPN accuracy.

Subpixel-Based Precipitation Nowcasting with the Pyramid Lucas–Kanade Optical Flow Technique

“…The first model is used widely in QPF and nowcasting systems [27,28]. The second model was proposed by Liu in 2015 [7]. The last one is a new algorithm that is introduced in this paper.…”

“…The extrapolation-based QPF algorithms, often based on the extrapolation of the radar reflectivity data or remote sensing data, can produce a reliable forecast with high spatial and temporal resolution in a very short time (0-2 h) [2,[5][6][7][8]. Extrapolation-based QPF has developed quickly with the advance of the observed skill and computing techniques.…”

“…The pixel-based method considers the motion between two consecutive radar images and extrapolates the observations at the pixel level [13]. Pixel-based nowcasting algorithms are widely used in the nowcasting systems [2,7,14,17]. However, the pixel-based algorithms often mislocate rainy events because of the coarse spatial resolution of the radar images, and may introduce large uncertainty into the nowcasting system or overestimate the storm location.…”

“…The OF algorithm can handle successive images in short time intervals, and has been applied successfully in computer vision tasks. Due to its efficiency in tracking moving objects, the OF algorithm has been used in rainfall forecast, and has been proven to be an effective algorithm in precipitation nowcasting [7,23]. However, the OF method still suffers from two limitations: firstly, the extracted wind fields are easily chaotic, due to their dependence on a maximum and statistically noisy operation.…”

Typhoon Cloud System Identification and Forecasting Using the Feng-Yun 4A/Advanced Geosynchronous Radiation Imager Based on an Improved Fuzzy Clustering and Optical Flow Method

“…At present, the main nowcasting methods include the crosscorrelation technique [17], centroid tracking [18], and the optical flow method and its variants [19]. Carvalho and Jones [20] studied the automatic identification and tracking of mesoscale convective systems using the maximum spatial correlation tracking method.…”