Name
Abstract | ||
|---|---|---|
The Collaborative Adaptive Sensing of the Atmosphere (CASA) nowcasting system currently provides 0-30 min automated forecasts (nowcasts) of precipitation to National Weather Service forecasters, emergency managers, and researchers using composite X-band weather radar data. Nowcasting is accomplished in two steps. First, the Fourier-based Dynamic and Adaptive Radar Tracking of Storms (DARTS) technique computes a motion vector field representing precipitation pattern motion using a recently observed sequence of radar reflectivity fields. Then, future reflectivity fields are estimated by recursively advecting the latest observed or predicted field according to this motion vector field using a sine kernel-based method. This paper presents potential upgrades to the CASA nowcasting system. The performance of the current sine kernel-based advection method is compared to that of a backward mapping technique in terms of categorical (rain/no rain) assessments of accuracy. Because computational efficiency is an important concern given the high-resolution (0.5 km/1 min) nature of the CASA data, the respective computational efficiencies are also compared. A technique to perform temporal interpolation within the DARTS model with the potential application to data fusion is also presented and assessed. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/IGARSS.2012.6351001 | Geoscience and Remote Sensing Symposium |
Keywords | Field | DocType |
interpolation,pattern recognition,rain,sensor fusion,weather forecasting,CASA data,CASA nowcasting system,Collaborative Adaptive Sensing of the Atmosphere,DARTS technique,Fourier-based Dynamic and Adaptive Radar Tracking of Storms,National Weather Service,automated forecast,backward mapping technique,categorical assessment,composite X-band weather radar data,computational efficiency,data fusion,emergency management,kernel-based advection method,motion vector field,no rain assessment,precipitation nowcast,precipitation pattern motion representation,radar reflectivity field sequence,recursive advection,sine kernel-based method,temporal interpolation,Discrete Fourier transforms,interpolation,linear systems,meteorological radar,prediction methods | Kernel (linear algebra),Meteorology,Radar tracker,Weather radar,Computer science,Interpolation,Remote sensing,Sensor fusion,Advection,Weather forecasting,Nowcasting | Conference |
ISSN | ISBN | Citations |
2153-6996 E-ISBN : 978-1-4673-1158-8 | 978-1-4673-1158-8 | 1 |
PageRank | References | Authors |
0.39 | 2 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Evan Ruzanski | 1 | 21 | 3.89 |
| Chandrasekar, V. | 2 | 8 | 2.44 |