TY - JOUR
T1 - Evaluating the effects of image filtering in short-term radar rainfall forecasting for hydrological applications
AU - Van Horne, Matthew P.
AU - Vivoni, Enrique R.
AU - Entekhabi, Dara
AU - Hoffman, Ross N.
AU - Grassotti, Christopher
PY - 2006/9
Y1 - 2006/9
N2 - Radar rainfall nowcasting at short lead times has important hydrometeorological applications in the fields of weather prediction and flood forecasting. The predictability of rainfall events can vary significantly with scale as smaller storm features are less predictable than the storm envelope motion. As a result, various techniques have been developed for filtering a radar image and deriving short-term forecasts from the more predictable, larger storm scales. In this study, the effects of image filtering on radar nowcasting performance using the Storm Tracker Nowcasting Model (STNM) are evaluated. Radar rainfall nowcasts are evaluated for three storms exhibiting varying degrees of organisation over the Arkansas-Red River basin. In each case, it is found that the nowcast skill decreases with the forecast lead time, increases with the verification area used around a forecast location, and decreases with higher rainfall thresholds. Furthermore, it is demonstrated that a set of properly tuned filtering nowcasts are superior to simple 'persistence' and slightly better than 'uniform advection'. At the scale of a large hydrologic basin (∼ 6000 km2), filter-based nowcasting is shown to capture the temporal variation in rainfall amount and its spatial distribution based on a set of catchment-based metrics. Finally, a method for relating changes in nowcasting skill to errors associated with storm dynamics not captured by image filtering techniques is evaluated.
AB - Radar rainfall nowcasting at short lead times has important hydrometeorological applications in the fields of weather prediction and flood forecasting. The predictability of rainfall events can vary significantly with scale as smaller storm features are less predictable than the storm envelope motion. As a result, various techniques have been developed for filtering a radar image and deriving short-term forecasts from the more predictable, larger storm scales. In this study, the effects of image filtering on radar nowcasting performance using the Storm Tracker Nowcasting Model (STNM) are evaluated. Radar rainfall nowcasts are evaluated for three storms exhibiting varying degrees of organisation over the Arkansas-Red River basin. In each case, it is found that the nowcast skill decreases with the forecast lead time, increases with the verification area used around a forecast location, and decreases with higher rainfall thresholds. Furthermore, it is demonstrated that a set of properly tuned filtering nowcasts are superior to simple 'persistence' and slightly better than 'uniform advection'. At the scale of a large hydrologic basin (∼ 6000 km2), filter-based nowcasting is shown to capture the temporal variation in rainfall amount and its spatial distribution based on a set of catchment-based metrics. Finally, a method for relating changes in nowcasting skill to errors associated with storm dynamics not captured by image filtering techniques is evaluated.
KW - Extrapolation forecast
KW - Nowcasting errors
KW - Precipitation
KW - Storm dynamics
KW - Watershed
KW - Weather radar
UR - http://www.scopus.com/inward/record.url?scp=33747771383&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33747771383&partnerID=8YFLogxK
U2 - 10.1017/S1350482706002295
DO - 10.1017/S1350482706002295
M3 - Article
AN - SCOPUS:33747771383
SN - 1350-4827
VL - 13
SP - 289
EP - 303
JO - Meteorological Applications
JF - Meteorological Applications
IS - 3
ER -