Linus Magnusson

Characteristics of Occasional Poor Medium-Range Weather Forecasts for Europe

Medium-range weather prediction has become more skillful over recent decades, but forecast centers still suffer from occasional very poor forecasts, which are often referred to as “dropouts” or “busts.” This study focuses on European Centre for Medium-Range Weather Forecasts (ECMWF) day-6 forecasts for Europe. Although busts are defined by gross scores, bust composites reveal a coherent “Rex type” blocking situation, with a high over northern Europe and a low over the Mediterranean. Initial conditions for these busts also reveal a coherent flow, but this is located over North America and involves a trough over the Rockies, with high convective available potential energy (CAPE) to its east. This flow type occurs in spring and is often associated with a Rossby wave train that has crossed the Pacific. A composite on this initial flow type displays enhanced day-6 random forecast errors and some-what enhanced ensemble forecast spread, indicating reduced inherent predictability. Mesoscale convective systems, as...

Evaluation of forecast strategies for seasonal and decadal forecasts in presence of systematic model errors

This study discusses and compares three different strategies used to deal with model error in seasonal and decadal forecasts. The strategies discussed are the so-called full initialisation, anomaly initialisation and flux correction. In the full initialisation the coupled model is initialised to a state close to the real-world attractor and after initialisation the model drifts towards its own attractor, giving rise to model bias. The anomaly initialisation aims to initialise the model close to its own attractor, by initialising only the anomalies. The flux correction strategy aims to keep the model trajectory close to the real-world attractor by adding empirical corrections. These three strategies have been implemented in the ECMWF coupled model, and are evaluated at seasonal and decadal time-scales. The practical implications of the different strategies are also discussed. Results show that full initialisation results in a clear model drift towards a colder climate. The anomaly initialisation is able to reduce the drift, by initialising around the model mean state. However, the erroneous model mean state results in degraded seasonal forecast skill. The best results on the seasonal time-scale are obtained using momentum-flux correction, mainly because it avoids the positive feedback responsible for a strong cold bias in the tropical Pacific. It is likely that these results are model dependent: the coupled model used here shows a strong cold bias in the Central Pacific, resulting from a positive coupled feedback between winds and SST. At decadal time-scales it is difficult to determine whether any of the strategies is superior to the others.

Evaluation of Medium-Range Forecasts for Hurricane Sandy

AbstractOn 30 October 2012 Hurricane Sandy made landfall on the U.S. East Coast with a devastating impact. Here the performance of the ECMWF forecasts (both high resolution and ensemble) are evaluated together with ensemble forecasts from other numerical weather prediction centers, available from The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) archive. The sensitivity to sea surface temperature (SST) and model resolution for the ECMWF forecasts are explored. The results show that the ECMWF forecasts provided a clear indication of the landfall from 7 days in advance. Comparing ensemble forecasts from different centers, the authors find the ensemble forecasts from ECMWF to be the most consistent in the forecast of the landfall of Sandy on the New Jersey coastline. The impact of the warm SST anomaly off the U.S. East Coast is investigated by running sensitivity experiments with climatological SST instead of persisting the SST anomaly from the an...

Factors Influencing Skill Improvements in the ECMWF Forecasting System

AbstractDuring the past 30 years the skill in ECMWF numerical forecasts has steadily improved. There are three major contributing factors: 1) improvements in the forecast model, 2) improvements in the data assimilation, and 3) the increased number of available observations. In this study the authors are investigating the relative contribution from these three components by using the simple error growth model introduced in a previous study by Lorenz and extended in another study by Dalcher and Kalnay, together with the results from the ECMWF Re-Analysis Interim (ERA-Interim) forecasts where the improvement is only due to an increased number of observations. The authors are also applying the growth model on “lagged” forecast differences in order to investigate the usefulness of the forecast jumpiness as a diagnostic tool for improvements in the forecasts. The main finding is that the main contribution to the reduced forecast error comes from significant initial condition error reductions between 1996 and 20...

The Indian Ocean: The Region of Highest Skill Worldwide in Decadal Climate Prediction*

AbstractThe Indian Ocean stands out as the region where the state-of-the-art decadal climate predictions of sea surface temperature (SST) perform the best worldwide for forecast times ranging from the second to the ninth year, according to correlation and root-mean-square error (RMSE) scores. This paper investigates the reasons for this high skill by assessing the contributions from the initial conditions, greenhouse gases, solar activity, and volcanic aerosols. The comparison between the SST correlation skill in uninitialized historical simulations and hindcasts initialized from estimates of the observed climate state shows that the high Indian Ocean skill is largely explained by the varying radiative forcings, the latter finding being supported by a set of additional sensitivity experiments. The long-term warming trend is the primary contributor to the high skill, though not the only one. Volcanic aerosols bring additional skill in this region as shown by the comparison between initialized hindcasts tak...

Comparison between Singular Vectors and Breeding Vectors as Initial Perturbations for the ECMWF Ensemble Prediction System

In this paper a study aimed at comparing the perturbation methodologies based on the singular vector ensemble prediction system (SV-EPS) and the breeding vector ensemble prediction system (BV-EPS) ...

Independent Estimations of the Asymptotic Variability in an Ensemble Forecast System

One desirable property within an ensemble forecast system is to have a one-to-one ratio between the root-mean-square error (rmse) of the ensemble mean and the standard deviation of the ensemble (spread). The ensemble spread and forecast error within the ECMWF ensemble prediction system has been extrapolated beyond 10 forecast days using a simple model for error growth. The behavior of the ensemble spread and the rmse at the time of the deterministic predictability are compared with derived relations of rmse at the infinite forecast length and the characteristic variability of the atmosphere in the limit of deterministic predictability. Utilizing this methodology suggests that the forecast model and the atmosphere do not have the same variability, which raises the question of how to obtain a perfect ensemble.

On the dependence of ENSO simulation on the coupled model mean state

Systematic model error remains a difficult problem for seasonal forecasting and climate predictions. An error in the mean state could affect the variability of the system. In this paper, we investigate the impact of the mean state on the properties of ENSO. A set of coupled decadal integrations have been conducted, where the mean state and its seasonal cycle have been modified by applying flux correction to the momentum-flux and a combination of heat and momentum fluxes. It is shown that correcting the mean state and the seasonal cycle improves the amplitude of SST inter-annual variability and also the penetration of the ENSO signal into the troposphere and the spatial distribution of the ENSO teleconnections are improved. An analysis of a multivariate PDF of ENSO shows clearly that the flux correction affects the mean, variance, skewness and tails of the distribution. The changes in the tails of the distribution are particularly noticeable in the case of precipitation, showing that without the flux correction the model is unable to reproduce the frequency of large events. For the inter-annual variability the momentum-flux correction alone has a large impact, while the additional heat-flux correction is important for the teleconnections. These results suggest that the current forecasts practices of removing the forecast bias a-posteriori or anomaly initialisation are by no means optimal, since they can not deal with the strong nonlinear interactions. A consequence of the results presented here is that the predictability on annual time-ranges could be higher than currently achieved. Whether or not the correction of the model mean state by some sort of flux correction leads to better forecasts needs to be addressed. In any case, flux correction may be a powerful tool for diagnosing coupled model errors and predictability studies.

Global Distribution of the Skill of Tropical Cyclone Activity Forecasts on Short- to Medium-Range Time Scales

AbstractOperational global medium-range ensemble forecasts of tropical cyclone (TC) activity (genesis plus the subsequent track) are systematically evaluated to understand the skill of the state-of-the-art ensembles in forecasting TC activity as well as the relative benefits of a multicenter grand ensemble with respect to a single-model ensemble. The global ECMWF, JMA, NCEP, and UKMO ensembles are evaluated from 2010 to 2013 in seven TC basins around the world. The verification metric is the Brier skill score (BSS), which is calculated within a 3-day time window over a forecast length of 2 weeks to examine the skill from short- to medium-range time scales (0–14 days). These operational global medium-range ensembles are capable of providing guidance on TC activity forecasts that extends into week 2. Multicenter grand ensembles (MCGEs) tend to have better forecast skill (larger BSSs) than does the best single-model ensemble, which is the ECMWF ensemble in most verification time windows and most TC basins. T...

Tropical cyclone sensitivity to ocean coupling in the ECMWF coupled model

We present an investigation of the performance of the ECMWF coupled atmosphere-waves-ocean model for different ocean and atmosphere resolutions on a series of tropical cyclones in the Western Pacific with the aim to better understand the coupled feedback mechanisms in these exterme conditions. For some of the test cases, we only find little impact of coupling the atmosphere to the ocean, while in others, we observe a very large impact. To further understand these differences, we have selected two tropical cyclones (TCs) as case studies: TC Haiyan (with small impact of coupling) and TC Neoguri (with large impact of coupling). The comparison between these two cases suggests that the upper ocean stratification is the key in determining the strength of the coupled feedback. A strong coupled feedback is found whenever the ocean heat content of the upper layer is low while a very weak coupled feedback is found whenever the ocean has a thick warm mixed layer. The oceanographic response to tropical cyclones for the two storms has been compared to sea surface temperature and derived surface currents from drifting buoys and to subsurface observations from Argo and ship launched XBTs. These comparisons show that we are able to realistically reproduce the atmospheric and oceanographic interaction during tropical cyclone conditions which gives us confidence that the coupled modelling system is physically sound.