Home  About us  Contact
 

Rainfall Accumulations (rainfall + accumulation)

Distribution by Scientific Domains
Earth and Environmental Science100%

Selected Abstracts

Use of a stochastic precipitation nowcast scheme for fluvial flood forecasting and warning

ATMOSPHERIC SCIENCE LETTERS, Issue 1 2005
Clive Pierce
Abstract In collaboration with the Bureau of Meteorology (Melbourne, Australia), the Met Office (Joint Centre for Hydro,Meteorological Research, UK) has developed a stochastic precipitation nowcast scheme, designed to model and predict the PDF of surface rain rate and rain accumulation in space and time. Here we demonstrate the range of probabilistic products generated by the scheme, and their potential applications for fluvial flood forecasting and warning. With the aid of a hydrological model (the PDM), we consider the use of ensembles of predicted catchment rain accumulation in evaluating the range of possible river flow responses from a given catchment. When employed in conjunction with a catchment specific, cost-based decision-making model, we highlight the value of PDFs of forecast catchment rainfall accumulation and river flow as an aid to objective decision making within the flood warning process. Crown Copyright 2005. Reproduced with the permission of Her Majesty's Stationery Office. Published by John Wiley & Sons, Ltd. [source]

The effects of aerosols on intense convective precipitation in the northeastern United States,

THE QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, Issue 643 2009
Alexandros A. Ntelekos
Abstract A fully coupled meteorology-chemistry-aerosol mesoscale model (WRF-Chem) is used to assess the effects of aerosols on intense convective precipitation over the northeastern United States. Numerical experiments are performed for three intense convective storm days and for two scenarios representing ,typical' and ,low' aerosol conditions. The results of the simulations suggest that increasing concentrations of aerosols can lead to either enhancement or suppression of precipitation. Quantification of the aerosol effect is sensitive to the metric used due to a shift of rainfall accumulation distribution when realistic aerosol concentrations are included in the simulations. Maximum rainfall accumulation amounts and areas with rainfall accumulations exceeding specified thresholds provide robust metrics of the aerosol effect on convective precipitation. Storms developing over areas with medium to low aerosol concentrations showed a suppression effect on rainfall independent of the meteorological environment. Storms developing in areas of relatively high particulate concentrations showed enhancement of rainfall when there were simultaneous high values of convective available potential energy, relative humidity and wind shear. In these cases, elevated aerosol concentrations resulted in stronger updraughts and downdraughts and more coherent organization of convection. For the extreme case, maximum rainfall accumulation differences exceeded 40 mm. The modelling results suggest that areas of the northeastern US urban corridor that are close to or downwind of intense sources of aerosols, could be more favourable for rainfall enhancement due to aerosols for the aerosol concentrations typical of this area. Copyright © 2009 Royal Meteorological Society [source]

Analysis of scale dependence of quantitative precipitation forecast verification: A case-study over the Mackenzie river basin

THE QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, Issue 620 2006
Olivier Bousquet
Abstract Six-hour rainfall accumulations derived from radar observations collected during a 3-day summertime precipitation event over central Alberta (Canada) are used to assess the performance of a regional Canadian numerical weather prediction system for quantitative precipitation forecast verification. We show that radar data provide a simple and efficient way to significantly reduce model phase errors associated with misplacement of predicted precipitation patterns. Using wavelet analysis, we determine that the limiting spatial scale of predictability of the model is about six times its grid resolution for 6 h accumulated fields. The use of longer accumulation periods is shown to smooth out forecast errors that may have resulted from slight phase or time shift errors but does not change the limiting scale of predictability. The scale decomposition of the mean-square forecast error also reveals that scales which cannot be accurately reproduced by the model account for about 20% of the total error. Using classical continuous and categorical scores, we show that significantly better model performance can be achieved by smoothing out wavelengths that cannot be predicted. Copyright © 2006 Royal Meteorological Society [source]

Numerical simulations of the 12,13 August 2002 flooding event in eastern Germany

THE QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, Issue 600 2004
G. Zängl
Abstract In this paper, high-resolution numerical simulations of the 12,13 August 2002 flooding event in eastern Germany are presented. The simulations are performed with the Penn State/National Center for Atmospheric Research mesoscale model MM5 in a four-domain configuration with a finest horizontal resolution of 1 km. Sensitivity experiments are performed with coarser resolutions (3 and 9 km), with different cloud microphysical parametrizations and with a different date of initialization. Moreover, tests with 1 km resolution but the smoothed topography of the 9 km runs are conducted in order to isolate the contribution of the model topography to the differences between the 1 km runs and the 9 km runs. The results show that the high-resolution runs reproduce the observed structure of the precipitation field very well. In particular, the location of the rainfall maximum is correct to within 15 km. The quantitative agreement between model results and observations is fairly good in regions with light to moderate rain, but large amounts of precipitation tend to be underpredicted. For observed 36-hour rainfall accumulations exceeding 200 mm, the negative bias typically ranges between 15 and 30 Copyright © 2004 Royal Meteorological Society. [source]