Teleconnections between Ethiopian summer rainfall and sea surface temperature: part II. Seasonal forecasting

A seasonal forecasting system that is capable of skilfully predicting rainfall totals on a regional scale would be of great value to Ethiopia. Here, we describe how a statistical model can exploit the teleconnections described in part 1 of this pair of papers to develop such a system. We show that, in most cases, the predictors selected objectively by the statistical model can be interpreted in the light of physical teleconnections with Ethiopian rainfall, and discuss why, in some cases, unexpected regions are chosen as predictors. We show that the forecast has skill in all parts of Ethiopia, and argue that this method could provide the basis of an operational seasonal forecasting system for Ethiopia.

A singular vector perspective of 4DVAR: The spatial structure and evolution of baroclinic weather systems

The extent to which the four-dimensional variational data assimilation (4DVAR) is able to use information about the time evolution of the atmosphere to infer the vertical spatial structure of baroclinic weather systems is investigated. The singular value decomposition (SVD) of the 4DVAR observability matrix is introduced as a novel technique to examine the spatial structure of analysis increments. Specific results are illustrated using 4DVAR analyses and SVD within an idealized 2D Eady model setting. Three different aspects are investigated. The first aspect considers correcting errors that result in normal-mode growth or decay. The results show that 4DVAR performs well at correcting growing errors but not decaying errors. Although it is possible for 4DVAR to correct decaying errors, the assimilation of observations can be detrimental to a forecast because 4DVAR is likely to add growing errors instead of correcting decaying errors. The second aspect shows that the singular values of the observability matrix are a useful tool to identify the optimal spatial and temporal locations for the observations. The results show that the ability to extract the time-evolution information can be maximized by placing the observations far apart in time. The third aspect considers correcting errors that result in nonmodal rapid growth. 4DVAR is able to use the model dynamics to infer some of the vertical structure. However, the specification of the case-dependent background error variances plays a crucial role.

Toward an integrated seasonal forecasting system for South America

This study proposes an objective integrated seasonal forecasting system for producing well-calibrated probabilistic rainfall forecasts for South America. The proposed system has two components: ( i) an empirical model that uses Pacific and Atlantic sea surface temperature anomalies as predictors for rainfall and ( ii) a multimodel system composed of three European coupled ocean - atmosphere models. Three-month lead austral summer rainfall predictions produced by the components of the system are integrated ( i. e., combined and calibrated) using a Bayesian forecast assimilation procedure. The skill of empirical, coupled multimodel, and integrated forecasts obtained with forecast assimilation is assessed and compared. The simple coupled multimodel ensemble has a comparable level of skill to that obtained using a simplified empirical approach. As for most regions of the globe, seasonal forecast skill for South America is low. However, when empirical and coupled multimodel predictions are combined and calibrated using forecast assimilation, more skillful integrated forecasts are obtained than with either empirical or coupled multimodel predictions alone. Both the reliability and resolution of the forecasts have been improved by forecast assimilation in several regions of South America. The Tropics and the area of southern Brazil, Uruguay, Paraguay, and northern Argentina have been found to be the two most predictable regions of South America during the austral summer. Skillful rainfall forecasts are generally only possible during El Nino or La Nina years rather than in neutral years.

How well does the ECMWF Ensemble Prediction System predict blocking?

Forecasting atmospheric blocking is one of the main problems facing medium-range weather forecasters in the extratropics. The European Centre for Medium-Range Weather Forecasts (ECMWF) Ensemble Prediction System (EPS) provides an excellent basis for medium-range forecasting as it provides a number of different possible realizations of the meteorological future. This ensemble of forecasts attempts to account for uncertainties in both the initial conditions and the model formulation. Since 18 July 2000, routine output from the EPS has included the field of potential temperature on the potential vorticity (PV) D 2 PV units (PVU) surface, the dynamical tropopause. This has enabled the objective identification of blocking using an index based on the reversal of the meridional potential-temperature gradient. A year of EPS probability forecasts of Euro-Atlantic and Pacific blocking have been produced and are assessed in this paper, concentrating on the Euro-Atlantic sector. Standard verification techniques such as Brier scores, Relative Operating Characteristic (ROC) curves and reliability diagrams are used. It is shown that Euro-Atlantic sector-blocking forecasts are skilful relative to climatology out to 10 days, and are more skilful than the deterministic control forecast at all lead times. The EPS is also more skilful than a probabilistic version of this deterministic forecast, though the difference is smaller. In addition, it is shown that the onset of a sector-blocking episode is less well predicted than its decay. As the lead time increases, the probability forecasts tend towards a model climatology with slightly less blocking than is seen in the real atmosphere. This small under-forecasting bias in the blocking forecasts is possibly related to a westerly bias in the ECMWF model. Copyright © 2003 Royal Meteorological Society