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Rainfall Forecast (rainfall + forecast)

Distribution by Scientific Domains

Earth and Environmental Science	85%

Selected Abstracts

Retro-active skill of multi-tiered forecasts of summer rainfall over southern Africa

INTERNATIONAL JOURNAL OF CLIMATOLOGY, Issue 1 2001
Willem A. Landman
Abstract Sea-surface temperature (SST) variations of the oceans surrounding southern Africa are associated with seasonal rainfall variability, especially during austral summer when the tropical atmospheric circulation is dominant over the region. Because of instabilities in the linear association between summer rainfall over southern Africa and SSTs of the tropical Indian Ocean, the skilful prediction of seasonal rainfall may best be achieved using physically based models. A two-tiered retro-active forecast procedure for the December,February (DJF) season is employed over a 10-year period starting from 1987/1988. Rainfall forecasts are produced for a number of homogeneous regions over part of southern Africa. Categorized (below-normal, near-normal and above-normal) statistical DJF rainfall predictions are made for the region to form the baseline skill level that has to be outscored by more elaborate methods involving general circulation models (GCMs). The GCM used here is the Centre for Ocean,Land,Atmosphere Studies (COLA) T30, with predicted global SST fields as boundary forcing and initial conditions derived from the National Centres for Environmental Prediction (NCEP) reanalysis data. Bias-corrected GCM simulations of circulation and moisture at certain standard pressure levels are downscaled to produce rainfall forecasts at the regional level using the perfect prognosis approach. In the two-tiered forecasting system, SST predictions for the global oceans are made first. SST anomalies of the equatorial Pacific (NIÑO3.4) and Indian oceans are predicted skilfully at 1- and 3-month lead-times using a statistical model. These retro-active SST forecasts are accurate for pre-1990 conditions, but predictability seems to have weakened during the 1990s. Skilful multi-tiered rainfall forecasts are obtained when the amplitudes of large events in the global oceans (such as El Niño and La Niña episodes) are described adequately by the predicted SST fields. GCM simulations using persisted August SST anomalies instead of forecast SSTs produce skill levels similar to those of the baseline for longer lead-times. Given high-skill SST forecasts, the scheme has the potential to provide climate forecasts that outscore the baseline skill level substantially. Copyright © 2001 Royal Meteorological Society [source]

Can ensemble forecasts improve the reliability of flood alerts?

JOURNAL OF FLOOD RISK MANAGEMENT, Issue 4 2009
J. Dietrich
Abstract A probabilistic evaluation of ensemble forecasts can be used to communicate uncertainty to decision makers. We present a flood forecast scheme, which combines forecasts from the European COSMO-LEPS, SRNWP-PEPS and COSMO-DE (lagged average) ensemble prediction systems with a rainfall,runoff model. The methodology was demonstrated with a case study for the Central European Mulde River basin. In this paper, we summarize results from hindcast simulations for seven events from 2002 to 2008. The ensemble spread resulting from uncertainty in rainfall forecast was very high at 2,5 days lead time. The median of the medium- and short-range forecasts and a lagged average ensemble of the very short-range forecasts proved to be reliable regarding the probability of exceeding flood alert levels. However, the limited number of observed events does not allow for the postulation of prescriptive binary decision rules. Flood managers have to adapt their decisions when new information becomes available. [source]

Erratum: Bias-free rainfall forecast and temperature trend-based temperature forecast using T-170 model output during the monsoon season

METEOROLOGICAL APPLICATIONS, Issue 3 2010
Rashmi Bhardwaj
No abstract is available for this article. [source]

Three-dimensional variational assimilation of Special Sensor Microwave/Imager data into a mesoscale weather-prediction model: A case study

THE QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, Issue 626 2007
C. Faccani
Abstract Assimilation of data from the Special Sensor Microwave/Imager (SSM/I) is performed in order to improve the forecast of a heavy-precipitation case (IOP2b, 20,21 September 1999) of the Mesoscale Alpine Programme 1999. The three-dimensional variational data assimilation technique of the MM5 model is used. Either brightness temperatures or precipitable water and surface wind speed are assimilated. The sensitivity of the model to SSM/I data is also tested by selectively excluding SSM/I frequencies and changing the size of the thinning box. All the experiments are performed using the European Center for Medium range Weather Forecasting (ECMWF) analysis on pressure level. The new initial conditions show considerable underestimation of the surface wind component V, and, even more, of the surface water vapour mixing ratio. This last error is partially corrected by assimilation of precipitable water alone, although these data produce a large increase in the mean error of the other surface variables (U, V and T). However, the forecast with this new set of initial conditions shows a good agreement (high correlation coefficient) with the rain gauge observations for the 1 h accumulated precipitation 3 h after the initial time. With a doubled box size, there is low sensitivity to the density of the observations used. In this case, the effect of the SSM/I data is slight, and the rainfall pattern produced is comparable to that obtained without any data assimilation. The model performance is also degraded if the 22 GHz brightness temperatures are removed from the assimilated measurements: the correlation coefficient for the precipitation is lower than in the case where all the frequencies are assimilated, and it decreases over time. In general, the use of precipitable water and surface wind speed affects the early stages (3 h) of the rainfall forecast, reducing the model spin-up. Brightness temperatures affect the forecast at a longer range (10 h). Copyright © 2007 Royal Meteorological Society [source]

Statistical prediction of global sea-surface temperature anomalies

INTERNATIONAL JOURNAL OF CLIMATOLOGY, Issue 14 2003
A. W. Colman
Abstract Sea-surface temperature (SST) is one of the principal factors that influence seasonal climate variability, and most seasonal prediction schemes make use of information regarding SST anomalies. In particular, dynamical atmospheric prediction models require global gridded SST data prescribed through the target season. The simplest way of providing those data is to persist the SST anomalies observed at the start of the forecast at each grid point, with some damping, and this strategy has proved to be quite effective in practice. In this paper we present a statistical scheme that aims to improve that basic strategy by combining three individual methods together: simple persistence, canonical correlation analysis (CCA), and nearest-neighbour regression. Several weighting schemes were tested: the best of these is one that uses equal weight in all areas except the east tropical Pacific, where CCA is preferred. The overall performance of the combined scheme is better than the individual schemes. The results show improvements in tropical ocean regions for lead times beyond 1 or 2 months, but the skill of simple persistence is difficult to beat in the extratropics at all lead times. Aspects such as averaging periods and grid size were also investigated: results showed little sensitivity to these factors. The combined statistical SST prediction scheme can also be used to improve statistical regional rainfall forecasts that use SST anomaly patterns as predictors. Copyright © Crown Copyright 2003. Published by John Wiley & Sons, Ltd. [source]