Parameter variations in prediction skill optimization at ECMWF

Peer reviewe

Resonant gravity-wave drag enhancement in linear stratified flow over mountains

High-drag states produced in stratified flow over a 2D ridge and an axisymmetric mountain are investigated using a linear, hydrostatic, analytical model. A wind profile is assumed where the background velocity is constant up to a height z1 and then decreases linearly, and the internal gravity-wave solutions are calculated exactly. In flow over a 2D ridge, the normalized surface drag is given by a closed-form analytical expression, while in flow over an axisymmetric mountain it is given by an expression involving a simple 1D integral. The drag is found to depend on two dimensionless parameters: a dimensionless height formed with z_1, and the Richardson number, Ri, in the shear layer. The drag oscillates as z_1 increases, with a period of half the hydrostatic vertical wavelength of the gravity waves. The amplitude of this modulation increases as Ri decreases. This behaviour is due to wave reflection at z_1. Drag maxima correspond to constructive interference of the upward- and downward-propagating waves in the region z < z_1, while drag minima correspond to destructive interference. The reflection coefficient at the interface z = z_1 increases as Ri decreases. The critical level, z_c, plays no role in the drag amplification. A preliminary numerical treatment of nonlinear effects is presented, where z_c appears to become more relevant, and flow over a 2D ridge qualitatively changes its character. But these effects, and their connection with linear theory, still need to be better understood

Evaluating Data Assimilation Algorithms

Data assimilation leads naturally to a Bayesian formulation in which the posterior probability distribution of the system state, given the observations, plays a central conceptual role. The aim of this paper is to use this Bayesian posterior probability distribution as a gold standard against which to evaluate various commonly used data assimilation algorithms. A key aspect of geophysical data assimilation is the high dimensionality and low predictability of the computational model. With this in mind, yet with the goal of allowing an explicit and accurate computation of the posterior distribution, we study the 2D Navier-Stokes equations in a periodic geometry. We compute the posterior probability distribution by state-of-the-art statistical sampling techniques. The commonly used algorithms that we evaluate against this accurate gold standard, as quantified by comparing the relative error in reproducing its moments, are 4DVAR and a variety of sequential filtering approximations based on 3DVAR and on extended and ensemble Kalman filters. The primary conclusions are that: (i) with appropriate parameter choices, approximate filters can perform well in reproducing the mean of the desired probability distribution; (ii) however they typically perform poorly when attempting to reproduce the covariance; (iii) this poor performance is compounded by the need to modify the covariance, in order to induce stability. Thus, whilst filters can be a useful tool in predicting mean behavior, they should be viewed with caution as predictors of uncertainty. These conclusions are intrinsic to the algorithms and will not change if the model complexity is increased, for example by employing a smaller viscosity, or by using a detailed NWP model

Working group 3: What are and how do we measure the pros and cons of existing approaches?

WG3 discussed both the pros and cons of existing schemes as well as metrics to measure relative advantages and disadvantages. We first provide a list of the current operational techniques and their respective advantages and disadvantages that were discussed in the WG. We do not claim that the list is complete, and we note that the pros and cons are neither exhaustive nor quantitative. Nevertheless, it may be useful to note the WG’s consensus on the general advantages and disadvantages of the most commonly-used schemes. We then list our recommendations for evaluating model uncertainty schemes. At the end is a short list pertaining to recommendations for further development of methods to represent model uncertainty

Current and emerging developments in subseasonal to decadal prediction

Weather and climate variations of subseasonal to decadal timescales can have enormous social, economic and environmental impacts, making skillful predictions on these timescales a valuable tool for decision makers. As such, there is a growing interest in the scientific, operational and applications communities in developing forecasts to improve our foreknowledge of extreme events. On subseasonal to seasonal (S2S) timescales, these include high-impact meteorological events such as tropical cyclones, extratropical storms, floods, droughts, and heat and cold waves. On seasonal to decadal (S2D) timescales, while the focus remains broadly similar (e.g., on precipitation, surface and upper ocean temperatures and their effects on the probabilities of high-impact meteorological events), understanding the roles of internal and externally-forced variability such as anthropogenic warming in forecasts also becomes important. The S2S and S2D communities share common scientific and technical challenges. These include forecast initialization and ensemble generation; initialization shock and drift; understanding the onset of model systematic errors; bias correct, calibration and forecast quality assessment; model resolution; atmosphere-ocean coupling; sources and expectations for predictability; and linking research, operational forecasting, and end user needs. In September 2018 a coordinated pair of international conferences, framed by the above challenges, was organized jointly by the World Climate Research Programme (WCRP) and the World Weather Research Prograame (WWRP). These conferences surveyed the state of S2S and S2D prediction, ongoing research, and future needs, providing an ideal basis for synthesizing current and emerging developments in these areas that promise to enhance future operational services. This article provides such a synthesis

Sensitivity experiments for ensemble forecasts of the extratropical transition of typhoon Tokage (2004)

The article of record as published may be located at http://dx.doi.org/10.1002/qj.527The extratropical transition (ET) of tropical cyclones often has a detrimental impact on predictability in the vicinity of the event and downstream. Ensemble forecasts provide an appropriatemeans by which to investigate both the uncertainty and the dynamical development leading to the different ET scenarios. Sensitivity experiments are presented using the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble prediction system (EPS) to investigate different methods of perturbing the ensemble forecast of the ET of Typhoon Tokage (2004). During ET these perturbations have a notable impact on the ensemble spread representing the uncertainty. Three experiments were performed: one of them without singular vectors (SVs) targeted on the tropical cyclone, the second without stochastic physics and the third excluding both perturbation methods. The targeted perturbations are most important for sufficient spread in track and intensity. Without the targeted perturbations, the analysis is not contained within the ensemble spread. Stochastic physics leads to stronger reintensification of the ensemble members after ET. The higher track spread leads to higher variability in processes such as lower tropospheric latent heat release. This can be related to a higher spread in the upper-level midlatitude flow for both perturbation methods. A connection is drawn between the strength of ET and the modification of the downstream midlatitude flow pattern. The uncertainty due to the targeted perturbations propagates downstream with a Rossby wave train excited during Tokage’s ET. For the case of stochastic physics, the uncertainty spreads to the ridge directly downstream of the ET system but is not evident further downstream.This study was sponsored by the Office of Naval Research, Marine Meteorology Program