Quantitative Precipitation Nowcasting: A Lagrangian Pixel-Based Approach
Highlights
► We describe Pixel-Based Nowcasting (PBN) algorithm. ► The algorithm has hydrological and meteorological applications. ► It has shown promising performance in severe storms forecasting.
Section snippets
Introduction and literature review
Nowcasting is referred as forecasting the future state of the atmosphere within a very short time (e.g., 0 ~ 3 hr) at a given location. For such short forecast lead times, an effective estimation and extrapolation of existing storms from the current observations (radar and satellite images) is critical (Golding, 1998).
Two primary approaches are used frequently for storm nowcasting depending on the length of prediction and the forecast skill. These approaches are: (1) the application of storm-scale
Pixel-Based Nowcasting (PBN) algorithm
Thunderstorms usually have relatively small-scale high-rainfall cores that should be predicted accurately. Regardless of their sizes and relatively short lifetimes, the advection-based nowcasting algorithm should enhance the prediction of the storms’ future positions. Therefore, the PBN algorithm forecasts storms associated with intensive rainfall more accurately using a pixel-based storm-tracking process to catch each storm dynamic advection process using radar imagery, and then an
Data and Case Studies
The next step involves the application and testing (verification) of the proposed PBN algorithm presented above. For this purpose, radar observations are used. Radar images have been used frequently in detecting severe storms. For this study, the Q2 radar-based quantitative precipitation estimation data set with 0.01o spatial and 2.5-5-min temporal resolution over the entire conterminous U.S. (CONUS) produced by the NOAA-NSSL is used (Vasiloff et al., 2007). This study focuses on the
Verification and Results
The proposed PBN approach is compared with two nowcasting algorithms that have been presented in the literature (Montanari et al., 2006). Both of these algorithms are based on Eq. (1) and are described below.
Summary and Conclusions
In this manuscript, we introduce a new nowcasting algorithm named Pixel-Based Nowcasting (PBN) to improve the predictability of severe thunderstorms. The proposed PBN algorithm is particularly suitable for very short-duration forecasts useful for hydrological modeling applications, such as flash-flood forecasting. In testing the PBN prediction capabilities, ten severe storms were selected for their features, including relatively short lifetime, smaller-scale, damaging winds, and rainfall. The
Acknowledgments
This research was supported by the Center for Hydrometeorology and Remote Sensing (CHRS) at the University of California, Irvine. Partial financial support was provided by NOAA/NESDIS/NCDC (prime award NA09NES4400006, NCSU CICS subaward 2009-1380-01), ARO (grant W911NF-11-1-0422) and NASA NEWS (grant NNX06AF93G). The graduate fellowship support provided by the Hydrologic Research Lab of the U.S. National Weather Service (HRL-NWS) is also greatly appreciated. Part of the research was carried out
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2021, Journal of HydrologyCitation Excerpt :Nevertheless, gauge recordings are point measurements that don’t provide sufficient information about the spatial variability (Quirmbach & Schultz, 2002). Radar data on the other hand can capture the spatial–temporal variability of rainfall at 1 km2 and 5 min time steps, and form the basis for tracking and extrapolating rainfall storms in the near future (Berenguer et al., 2012; Jensen et al., 2015; Lin et al., 2005; Zahraei et al., 2012). The radar based nowcast is typically done in three steps: 1) rainfall identification – initializing the nowcast, 2) rainfall tracking – calculating the displacement vectors of the storm and 3) rainfall prediction – extrapolating the rainfall field into the future at different lead times based on the displacement vectors.
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