Mesoscale rainfall forecasts over New Zealand during SALPEX96: Characterization and sensitivity studies

Citation
N. Bormann et Cj. Marks, Mesoscale rainfall forecasts over New Zealand during SALPEX96: Characterization and sensitivity studies, M WEATH REV, 127(12), 1999, pp. 2880-2893
Citations number
44
Categorie Soggetti
Earth Sciences
Journal title
MONTHLY WEATHER REVIEW
ISSN journal
00270644 → ACNP
Volume
127
Issue
12
Year of publication
1999
Pages
2880 - 2893
Database
ISI
SICI code
0027-0644(199912)127:12<2880:MRFONZ>2.0.ZU;2-W
Abstract
Rainfall diagnostics from 48-h, 20-km mesoscale runs of the RAMS model conf igured for the New Zealand region have been characterized and compared to f orecasts from the U.K. Meteorological Office global model with a view to op erational use. The accuracy and precision of these diagnostics and their se nsitivity to various model parameters have been determined by conducting se veral parallel series of experiments for the month-long SALPEX96 observing period (October-November 1996) and by comparing model results with min gaug e data. A detailed validation reveals that the mesoscale configuration of RAMS adds significant value to rainfall forecasts from the global model in situation s of heavy orographic rain, particularly when the full RAMS microphysics sc heme is used. The higher spatial resolution of the mesoscale model allows a better representation of the steep New Zealand orography and the observed sharp rainfall gradients. The mesoscale model and the global model both ove rforecast light rain and perform more poorly for light rain than for modera te or heavy rain. In the sensitivity study it is found that snow, graupel, and aggregates pro vide important enhancement mechanisms for rainfall in the Southern Alps, an d modeling processes related to these hydrometeor species improves forecast s in the lee of the Southern Alps (the "spillover'' effect). It is also fou nd that the soil moisture initialization strongly affects forecasts of ligh t rain in our study, and that increasing the size of the mesoscale model do main does not always improve rainfall forecasts in the data sparse New Zeal and region. The implications of these findings for future data assimilation work are also discussed.