Quantitative Precipitation Nowcasting: A Lagrangian Pixel-Based Approach

Quantitative Precipitation Nowcasting: A Lagrangian Pixel-Based Approach

Short-term high-resolution precipitation forecasting has important implications for navigation, flood forecasting, and other hydrological and meteorological concerns. This article introduces a pixel-based algorithm for Short-term Quantitative Precipitation Forecasting (SQPF) using radar-based rainfall data. The proposed algorithm called PixelBased Nowcasting (PBN) tracks severe storms with a hi...

Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting

The goal of precipitation nowcasting is to predict the future rainfall intensity in a local region over a relatively short period of time. Very few previous studies have examined this crucial and challenging weather forecasting problem from the machine learning perspective. In this paper, we formulate precipitation nowcasting as a spatiotemporal sequence forecasting problem in which both the in...

Nowcasting of Precipitation

Short-term quantitative precipitation forecasting using an object-based approach

Center for Hydrometeorology and Remote Sensing (CHRS), The Henry Samueli School of Engineering, Department of Civil and Environmental Engineering, University of California, Irvine, California, E/4130 Engineering Gateway, Irvine, CA 92697, United States NOAA/National Severe Storms Laboratory, Norman, Oklahoma, 120 David L. Boren Blvd., Rm. 4745, Norman, OK 73072, United States Department of Civi...

A Lagrangian Study of Precipitation-Driven Downdrafts*

Precipitation-driven downdrafts are an important component of deep convective systems. They stabilize the atmosphere by injecting relatively cold and dry air into the boundary layer. They have also been invoked as responsible for balancing surface latent and sensible heat fluxes in the heat and moisture budget of tropical boundary layers. This study is focused on precipitation-driven downdrafts...