Maurice J. Schmeits

Bimodal Behavior of the Kuroshio and the Gulf Stream

For a long time, observations have indicated that the Kuroshio in the North Pacific Ocean displays bimodal meandering behavior off the southern coast of Japan. For the Gulf Stream in the North Atlantic Ocean, weakly and strongly deflected paths near the coast of South Carolina have been observed. This suggests that bimodal behavior may occur in the Gulf Stream as well, although less pronounced than in the Kuroshio. Evidence from a high-resolution ocean general circulation model (OGCM) and intermediate complexity models is given to support the hypothesis that multiple mean paths of both the Kuroshio and the Gulf Stream are dynamically possible. These paths are found as multiple steady states in an intermediate complexity shallow-water model. In the OGCM, transitions between similar mean paths are found, with the patterns having similarity to the ones in observations as well. To study whether atmospheric noise can induce transitions between the multiple steady states, a stochastic component is added to the annual mean wind stress forcing in the intermediate complexity model and differences between the transition behavior in the Gulf Stream and Kuroshio are considered.

A Comparison between Raw Ensemble Output, (Modified) Bayesian Model Averaging, and Extended Logistic Regression Using ECMWF Ensemble Precipitation Reforecasts

Abstract Using a 20-yr ECMWF ensemble reforecast dataset of total precipitation and a 20-yr dataset of a dense precipitation observation network in the Netherlands, a comparison is made between the raw ensemble output, Bayesian model averaging (BMA), and extended logistic regression (LR). A previous study indicated that BMA and conventional LR are successful in calibrating multimodel ensemble forecasts of precipitation for a single forecast projection. However, a more elaborate comparison between these methods has not yet been made. This study compares the raw ensemble output, BMA, and extended LR for single-model ensemble reforecasts of precipitation; namely, from the ECMWF ensemble prediction system (EPS). The raw EPS output turns out to be generally well calibrated up to 6 forecast days, if compared to the area-mean 24-h precipitation sum. Surprisingly, BMA is less skillful than the raw EPS output from forecast day 3 onward. This is due to the bias correction in BMA, which applies model output statisti...

Physics of the 9-Month Variability in the Gulf Stream Region: Combining Data and Dynamical Systems Analyses

Using nonseasonal altimeter data and SST observations of the North Atlantic, and more specifically the Gulf Stream region, dominant patterns of variability are determined using multivariate time series analyses. A statistically significant propagating mode of variability with a timescale close to 9 months is found, the latter timescale corresponding to dominant variability found in earlier studies. In addition, output from a high resolution simulation of the Parallel Ocean Climate Model (POCM) is analyzed, which also displays variability on a timescale of 9 months, although not statistically significant at the 95% confidence level. The vertical structure of this 9-month mode turns out to be approximately equivalent barotropic. Following the idea that this mode is due to internal ocean dynamics, steady flow patterns and their instabilities are determined within a barotropic ocean model of the North Atlantic using techniques of numerical bifurcation theory. Within this model, there appear to be two different mean flow paths of the Gulf Stream, both of which become unstable to oscillatory modes. For reasonable values of the parameters, an oscillatory instability having a timescale of 9 months is found. The connection between results from the bifurcation analysis, from the analysis of the observations, and from the analysis of the POCM output is explored in more detail and leads to the conjecture that the 9-month variability is related to a barotropic instability of the wind-driven gyres.

Probabilistic Forecasting of (Severe) Thunderstorms in the Netherlands Using Model Output Statistics

Abstract The derivation and verification of logistic regression equations for the (conditional) probability of (severe) thunderstorms in the warm half-year (from mid-April to mid-October) in the Netherlands is described. For 12 regions of about 90 km × 80 km each, and for projections out to 48 h in advance (with 6-h periods), these equations have been derived using model output statistics (MOS). As a source for the predictands, lightning data from the Surveillance et d’Alerte Foudre par Interferometrie Radioelectrique (SAFIR) network have been used. The potential predictor dataset mainly consisted of the combined (postprocessed) output from two numerical weather prediction (NWP) models. It contained 15 traditional thunderstorm indices, computed from the High-Resolution Limited-Area Model (HIRLAM), and (postprocessed) output from the European Centre for Medium-Range Weather Forecasts (ECMWF) model. The most important predictor in the thunderstorm forecast system is the square root of the ECMWF 6-h convecti...

Probabilistic Forecasts of (Severe) Thunderstorms for the Purpose of Issuing a Weather Alarm in the Netherlands

The development and verification of a new model output statistics (MOS) system is described; this system is intended to help forecasters decide whether a weather alarm for severe thunderstorms, based on high total lightning intensity, should be issued in the Netherlands. The system consists of logistic regression equations for both the probability of thunderstorms and the conditional probability of severe thunderstorms in the warm half-year (from mid-April to mid-October). These equations have been derived for 12 regions of about 90 km 80 km each and for projections out to 12 h in advance (with 6-h periods). As a source for the predictands, reprocessed total lightning data from the Surveillance et d’Alerte Foudre par Interferometrie Radioelectrique (SAFIR) network have been used. The potential predictor dataset not only consisted of the combined postprocessed output from two numerical weather prediction (NWP) models, as in previous work by the first three authors, but it also contained an ensemble of advected radar and lightning data for the 0–6-h projections. The NWP model output dataset contained 17 traditional thunderstorm indices, computed from a reforecasting experiment with the High-Resolution Limited-Area Model (HIRLAM) and postprocessed output from the European Centre for Medium-Range Weather Forecasts (ECMWF) model. Brier skill scores and attributes diagrams show that the skill of the MOS thunderstorm forecast system is good and that the severe thunderstorm forecast system generally is also skillful, compared to the 2000–04 climatology, and therefore, the preoperational system was made operational at the Royal Netherlands Meteorological Institute (KNMI) in 2008.

Internal Variability Of The North Atlantic Wind-Driven Ocean Circulation

A new approach to understand the physical processes that govern internal variability of the large scale North Atlantic ocean circulation is outlined and current methods and results are reviewed. In this approach, based on the theory of dynamical systems, internal variability is viewed as arising through successive transitions when parameters are changed. The potential of the approach is demonstrated through analysesof solutions of intermediate complexity models of the wind-driven ocean circulation in the North Atlantic. In a quasi-geostrophic modelfor the flow in a rectangular basin with idealized wind forcing, the basic transitions are already found and physical mechanisms at work can be described in detail. Qualitatively, this transition behavior remains robust in more realistic models, having shallow water dynamics, realistic wind forcingand continental geometry, although patterns and time scales changethrough the model hierarchy. The relevance of the results is discussed inrelation to those of observations and of ocean general circulation models.

Skill improvement of dynamical seasonal Arctic sea ice forecasts

We explore the error and improve the skill of the outcome from dynamical seasonal Arctic sea ice reforecasts using different bias correction and ensemble calibration methods. These reforecasts consist of a five-member ensemble from 1979 to 2012 using the general circulation model EC-Earth. The raw model reforecasts show large biases in Arctic sea ice area, mainly due to a differently simulated seasonal cycle and long term trend compared to observations. This translates very quickly (1-3months) into large biases. We find that (heteroscedastic) extended logistic regressions are viable ensemble calibration methods, as the forecast skill is improved compared to standard bias correction methods. Analysis of regional skill of Arctic sea ice shows that the Northeast Passage and the Kara and Barents Sea are most predictable. These results show the importance of reducing model error and the potential for ensemble calibration in improving skill of seasonal forecasts of Arctic sea ice.

A Stratified Sampling Approach for Improved Sampling from a Calibrated Ensemble Forecast Distribution

AbstractBefore using the Schaake shuffle or empirical copula coupling (ECC) to reconstruct the dependence structure for postprocessed ensemble meteorological forecasts, a necessary step is to sample discrete samples from each postprocessed continuous probability density function (pdf), which is the focus of this paper. In addition to the equidistance quantiles (EQ) and independent random (IR) sampling methods commonly used at present, the stratified sampling (SS) method is proposed. The performance of the three sampling methods is compared using calibrated GFS ensemble precipitation reforecasts over the Xixian basin in China. The ensemble reforecasts are first calibrated using heteroscedastic extended logistic regression (HELR), and then the three sampling methods are used to sample calibrated pdfs with a varying number of discrete samples. Finally, the effect of the sampling method on the reconstruction of ensemble members with preserved space dependence structure is analyzed by using EQ, IR, and SS in E...

A Comparative Verification of High-Resolution Precipitation Forecasts Using Model Output Statistics

AbstractVerification of localized events such as precipitation has become even more challenging with the advent of high-resolution mesoscale numerical weather prediction (NWP). The realism of a forecast suggests that it should compare well against precipitation radar imagery with similar resolution, both spatially and temporally. Spatial verification methods solve some of the representativity issues that point verification gives rise to. In this paper, a verification strategy based on model output statistics (MOS) is applied that aims to address both double-penalty and resolution effects that are inherent to comparisons of NWP models with different resolutions. Using predictors based on spatial precipitation patterns around a set of stations, an extended logistic regression (ELR) equation is deduced, leading to a probability forecast distribution of precipitation for each NWP model, analysis, and lead time. The ELR equations are derived for predictands based on areal-calibrated radar precipitation and SYN...