 |
| Titel |
A study on WRF radar data assimilation for hydrological rainfall prediction |
| VerfasserIn |
J. Liu, M. Bray, D. Han |
| Medientyp |
Artikel
|
| Sprache |
Englisch
|
| ISSN |
1027-5606
|
| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 17, no. 8 ; Nr. 17, no. 8 (2013-08-02), S.3095-3110 |
| Datensatznummer |
250018955
|
| Publikation (Nr.) |
 copernicus.org/hess-17-3095-2013.pdf |
|
|
|
|
|
| Zusammenfassung |
| Mesoscale numerical weather prediction (NWP) models are gaining more attention in providing high-resolution
rainfall forecasts at the catchment scale for real-time flood forecasting.
The model accuracy is however negatively affected by the "spin-up" effect
and errors in the initial and lateral boundary conditions. Synoptic studies
in the meteorological area have shown that the assimilation of operational
observations, especially the weather radar data, can improve the reliability of
the rainfall forecasts from the NWP models. This study aims at investigating
the potential of radar data assimilation in improving the NWP rainfall
forecasts that have direct benefits for hydrological applications. The
Weather Research and Forecasting (WRF) model is adopted to generate 10 km
rainfall forecasts for a 24 h storm event in the Brue catchment
(135.2 km2) located in southwest England. Radar reflectivity from the
lowest scan elevation of a C-band weather radar is assimilated by using the
three-dimensional variational (3D-Var) data-assimilation technique.
Considering the unsatisfactory quality of radar data compared to the rain
gauge observations, the radar data are assimilated in both the original form
and an improved form based on a real-time correction ratio developed
according to the rain gauge observations. Traditional meteorological
observations including the surface and upper-air measurements of pressure,
temperature, humidity and wind speed are also assimilated as a bench mark to
better evaluate and test the potential of radar data assimilation. Four modes
of data assimilation are thus carried out on different types/combinations of
observations: (1) traditional meteorological data; (2) radar reflectivity;
(3) corrected radar reflectivity; (4) a combination of the original
reflectivity and meteorological data; and (5) a combination of the corrected
reflectivity and meteorological data. The WRF rainfall forecasts before and
after different modes of data assimilation are evaluated by examining the
rainfall temporal variations and total amounts which have direct impacts on
rainfall–runoff transformation in hydrological applications. It is found that
by solely assimilating radar data, the improvement of rainfall forecasts are
not as obvious as assimilating meteorological data; whereas the positive
effect of radar data can be seen when combined with the traditional
meteorological data, which leads to the best rainfall forecasts among the
five modes. To further improve the effect of radar data assimilation,
limitations of the radar correction ratio developed in this study are
discussed and suggestions are made on more efficient utilisation of radar
data in NWP data assimilation. |
| |
|
| Teil von |
|
|
|
|
|
|
|
|