Description: Variational and ensemble methods have been developed separately by various research and development groups and each brings its own benefits to data assimilation. In the last decade or so various ways have been developed to combine these methods, especially with the aims of improving the background error covariance matrices and of improving efficiency. The field has become confusing, even to many specialists, and so there is now a need to summarise the methods in order to show how they work, how they are related, what benefits they bring, why they have been developed, how they perform, and what improvements are pending. This paper starts with a reminder of basic variational and ensemble techniques and shows how they can be combined to give the emerging ensemble-variational (EnVar) and hybrid methods. A key part of the paper includes details of how localisation is commonly represented. There has been a particular push to develop four-dimensional methods that are free of linearised forecast models. This paper attempts to provide derivations of the formulations of most popular schemes. These are otherwise scattered throughout the literature or absent. It builds on the nomenclature used to distinguish between methods, and discusses further possible developments to the methods, including the representation of model error.

Description: Two seemingly unrelated postprocessing concepts retaining inter-variable, spatial and temporal rank dependence structures from raw ensemble forecasts are compared and unified. The first concept combines ensemble model output statistics (EMOS), which fits and adjusts a parametric probability density function, with the ensemble copula coupling (ECC) reordering notion. The second concept is the member-by-member postprocessing (MBMP), based on a direct parametric adjustment of the raw ensemble members. It is shown that MBMP can be interpreted as a specific EMOS-ECC variant. In an application to ensemble forecasts for temperature, EMOS-ECC and MBMP succeed in outperforming the raw ensemble and conserving correlation structures.

Description: In this study, we propose a hypothesis, supported by numerical model simulations, concerning the role of cloud microphysical processes and aerosols in the invigoration of an extreme rainfall event over Uttarakhand in June 2013. The interactions among dynamics, thermodynamics and microphysical processes and their feedbacks play a vital role in the occurrence of extreme events. To test the proposed hypothesis, WRF simulations are carried out with three different microphysical schemes (i.e., WDM6, Morrison, and CLR). The role of aerosol indirect effects in the process of invigoration of precipitation is demonstrated with a high resolution regional model for the extreme event over the foothills of Himalayas. The extreme event is characterized by the strong north–south tropospheric temperature gradient and strong moisture convergence. Forced uplift beyond freezing level initiates precipitation process which involves cloud ice and mixed-phase cloud microphysics and latent heat release, further it invigorates convection and enhances precipitation. Results pinpoint that the role of microphysical processes are very crucial during such type of extreme events. Additionally, result accentuates the importance of aerosols on the deep convective cloud systems which has influence through invigoration and involvement of complex interactions between aerosol, large scale dynamics and cloud microphysics.

Description: Polar lows are intense, small-scale cyclones in the high latitudes. Typically, polar lows are initiated through baroclinic processes, but they sometimes evolve into a post-baroclinic mature stage where air-sea interaction becomes more important. In this stage some polar lows have developed hurricane-like cloud structures, and idealised axisymmetric hurricane models have indicated that air-sea interaction-fuelled pressure drops of up to 50 hPa are theoretically possible in polar environments. Here we study a polar low that formed in an extreme marine cold air outbreak over the Barents Sea and which had cloud structures with similarities to hurricanes. Using a high-resolution weather model, we artificially modified the sea surface temperatures (SSTs) to assess if the polar low was close to developing into a true hurricane-like system, with air-sea interaction processes leading to intensification in the post-baroclinic, mature phase of its life cycle. The polar low simulations with SSTs augmented by 2–6 K produced more intense mature phases than the control experiment (with unmodified SSTs). The intensity of the polar low in the latter of these, which it must be pointed out was unrealistic with an SST increase of 6 K, surpassed the intensity in the earlier baroclinic phase. The experiment where the SSTs were reduced by 2 K did not produce a much weaker polar low than the control run. Broadly speaking, our experiments suggest that in this case large and unrealistic SST increases would be needed for apparently hurricane-like polar lows to exhibit true hurricane-like behaviour, but nevertheless air-sea interaction did drive a more intense and prolonged mature phase.

Description: Based on skill estimates from hindcasts made over the last couple of decades, recent studies have suggested that considerable success has been achieved in forecasting winter climate anomalies over the Euro-Atlantic area using current-generation dynamical forecast models. However, previous-generation models had shown that forecasts of winter climate anomalies in the 1960s and 1970s were less successful than forecasts of the 1980s and 1990s. Given that the more recent decades have been dominated by the NAO in its positive phase, it is important to know whether the performance of current models would be similarly skilful when tested over periods of a predominantly negative NAO. To this end, a new ensemble of atmospheric seasonal hindcasts covering the period 1900 to 2009 has been created, providing a unique tool to explore many aspects of atmospheric seasonal climate prediction. In this study we focus on two of these: multi-decadal variability in predicting the winter NAO, and the potential value of the long seasonal hindcast data sets for the emerging science of probabilistic event attribution. The existence of relatively low skill levels during the period 1950s -1970s has been confirmed in the new dataset. The skill of the NAO forecasts is larger, however, in earlier and later periods. Whilst these inter-decadal differences in skill are, by themselves, only marginally statistically significant, the variations in skill strongly co-vary with statistics of the general circulation itself suggesting that such differences are indeed physically-based. The mid-century period of low forecast skill coincides with a negative NAO phase but the relationship between the NAO phase/amplitude and forecast skill is more complex than linear. Finally, we show how seasonal forecast reliability can be of importance for increasing confidence in statements of causes of extreme weather and climate events, including effects of anthropogenic climate change.

Description: Increasing the resolution of numerical models has played a large part in improving the accuracy of weather and climate forecasts in recent years. Until now, this has required the use of ever more powerful computers, the energy costs of which are becoming increasingly problematic. It has therefore been proposed that forecasters switch to using more efficient ‘reduced precision’ hardware capable of sacrificing unnecessary numerical precision to save costs. Here, an extended form of the Lorenz ’96 idealised model atmosphere is used to test whether more accurate forecasts could be produced by lowering numerical precision more at smaller spatial scales in order to increase the model resolution. Both a scale-dependent mixture of single- and half-precision – where numbers are represented with fewer bits of information on smaller spatial scales – and ‘stochastic processors’ – where random ‘bit-flips’ are allowed for small-scale variables – are emulated on conventional hardware. It is found that high-resolution parameterised models with scale-selective reduced precision yield better short-term and climatological forecasts than lower resolution parameterised models with conventional precision for a relatively small increase in computational cost. This suggests that a similar approach in real-world models could lead to more accurate and efficient weather and climate forecasts.

Description: To post-process ensemble predictions to a particular location, statistical methods are often used, especially in complex terrain such as the Alps. When expanded to several stations, the post-processing has to be repeated at every station individually thus losing information about spatial coherence and increasing computational cost. Therefore, the ensemble post-processing is modified and applied simultaneously at multiple locations. We transform observations and predictions to standardized anomalies. Seasonal and site-specific characteristics are eliminated by subtracting a climatological mean and dividing by the climatological standard deviation from both observations and numerical forecasts. This method allows to forecast even at locations where no observations are available. The skill of these forecasts is comparable to forecasts post-processed individually at every station, and even better on average.

Description: Previous work has identified a number of vertical discretizations of the nonhydrostatic compressible Euler equations that optimally capture the propagation of acoustic, inertio-gravity, and Rossby waves. Here, that previous work is extend to apply to a general equation of state, making it applicable to a wider range of geophysical fluid systems. It is also shown that several choices of prognostic thermodynamic variables and vertical staggering that were previously thought to be suboptimal can, in fact, give optimal wave propagation when discretized in an appropriate way. The key idea behind constructing these new optimal discretizations is to ensure that their corresponding linear system is equivalent to that of a certain, most fundamental, optimal configuration.

Description: Uncertainties in parameterized processes in general circulation models can be represented as stochastic perturbations to the model formulation. The European Centre for Medium-Range Weather Forecasts (ECMWF) has pioneered approaches to represent these model errors in forecasting systems. In particular, the stochastically perturbed physical tendency (SPPT) scheme for the atmosphere is used in their operational ensemble system for medium- and long-range predictions. Recent studies have shown that these stochastic approaches can both increase the reliability of the probabilistic forecasts and reduce long-term mean biases of the model climate. Towards developing a seamless prediction system in the future, these benefits of stochastic parameterization for both short-term and long-term forecasts make it an essential component of the next generation Earth System Models. We present results of the impact of different configurations of the SPPT scheme in ECMWF’s seasonal forecasting System 4 on the mean and variability in tropical precipitation. Small scale perturbations in the SPPT scheme play a significant role in reducing the mean biases in tropical precipitation. The stochastic physics also non-linearly rectifies the convection and precipitation during different phases of El Niño Southern Oscillation events and improves the reliability of the ensemble forecasts for the Madden-Julian Oscillation (MJO). They impact the MJO dynamics by modulating the convective and suppressed phases of the MJO. Finally, we discuss some of the caveats to this analysis and some future prospects.

Description: Gaining a deeper physical understanding of the high impact weather events which repeatedly affected the Western Mediterranean Basin in the last years on the coastal areas of Eastern Spain, Southern France and Northern Italy is strongly motivated by the social request to reduce the casualties and the economical impacts due to these highly-localized and hardly-predictable phenomena. In October 2014, an extreme event hit the Genoa city centre, less than three years after a very similar event, which occurred in November 2011. Taking advantage of the availability of both observational data and modelling results at the micro-α meteorological scale, this paper provides insights about the triggering mechanism and the subsequent spatio-temporal evolution of the Genoa 2014 back-building Mesoscale Convective System. The major finding is the effect of a virtual mountain created on the Ligurian sea by the convergence of a cold and dry jet outflowing from the Po valley and a warm and moist low level south-easterly jet within the PBL.