Accurate Initial Conditions Research Articles

Perturbing Surface Initial Conditions in a Regional Ensemble Prediction System

Two techniques for perturbing surface initial conditions in the regional ensemble system Aire Limitée Adaptation Dynamique Développement International-Limited Area Ensemble Forecasting (ALADIN-LAEF) are presented and investigated in this paper. The first technique is the noncycling surface breeding (NCSB), which combines short-range surface forecasts driven by perturbed atmospheric forcing and the breeding method for generating the perturbations on surface initial conditions. The second technique, which is currently used in the ALADIN-LAEF operational version, applies an ensemble of surface data assimilations (ESDA) in which the observations are randomly perturbed. Both techniques are evaluated over a two-month period from late spring to summer. The results show that the evaluation is more favorable to ESDA. In general, the ensemble forecasts of the observed near-surface meteorological variables (screen-level variables) of ESDA are more skillful than NCSB, in particular for 2-m temperature they are statistically more consistent and reliable. A slightly better statistical reliability for 2-m relative humidity and 10-m wind has been found as well. This could be attributed to the introduction of surface data assimilation in ESDA, which provides more accurate surface initial conditions. Moreover, the observation perturbation in ESDA helps to better estimate the initial condition uncertainties. For the forecast of precipitation and the upper-air variables in the lower troposphere, both ESDA and NCSB perform very similarly, having neutral impact.

Updated global soil map for the Weather Research and Forecasting model and soil moisture initialization for the Noah land surface model

AbstractA meteorological model requires accurate initial conditions and boundary conditions to obtain realistic numerical weather predictions. The land surface controls the surface heat and moisture exchanges, which can be determined by the physical properties of the soil and soil state variables, subsequently exerting an effect on the boundary layer meteorology. The initial and boundary conditions of soil moisture are currently obtained via National Centers for Environmental Prediction FNL (Final) Operational Global Analysis data, which are collected operationally in 1° by 1° resolutions every 6 h. Another input to the model is the soil map generated by the Food and Agriculture Organization of the United Nations ‐ United Nations Educational, Scientific and Cultural Organization (FAO‐UNESCO) soil database, which combines several soil surveys from around the world. Both soil moisture from the FNL analysis data and the default soil map lack accuracy and feature coarse resolutions, particularly for certain areas of China. In this study, we update the global soil map with data from Beijing Normal University in 1 km by 1 km grids and propose an alternative method of soil moisture initialization. Simulations of the Weather Research and Forecasting model show that spinning‐up the soil moisture improves near‐surface temperature and relative humidity prediction using different types of soil moisture initialization. Explanations of that improvement and improvement of the planetary boundary layer height in performing process analysis are provided.

The Richtmyer-Meshkov instability of a “V” shaped air/helium interface subjected to a weak shock

The Richtmyer-Meshkov instability of a “V” shaped air/helium gaseous interface subjected to a weak shock wave is experimentally studied. A soap film technique is adopted to create a “V” shaped interface with accurate initial conditions. Five kinds of air/helium “V” shaped interfaces with different vertex angles (60°, 90°, 120°, 140°, and 160°), i.e., different amplitude-wavelength ratios, are formed to highlight the effects of initial conditions, especially the initial amplitude, on the flow characteristics. The interface morphologies identified by the high-speed schlieren photography show that a spike is generated from the vertex after the shock impact, and grows constantly with time accompanied by the occurrence of the phase reversal. As the vertex angle increases, vortices generated on the interface become less noticeable, and the spike develops less pronouncedly. The linear growth rate of the interface mixing width of a heavy/light interface configuration after compression phase is estimated by a linear model and a revised linear model, and the latter is proven to be more effective for the interface with high initial amplitudes. It is found for the first time in a heavy/light interface configuration that the linear growth rate of interface width is a non-monotonous function of the initial perturbation amplitude-wavelength ratio. In the nonlinear stage, it is confirmed that the width growth rate of interface with high initial amplitudes can be well predicted by a model proposed by Dimonte and Ramaprabhu [“Simulations and model of the nonlinear Richtmyer-Meshkov instability,” Phys. Fluids 22, 014104 (2010)].

Special Issue on Uncertainties in Tsunami Effects

Special Issue on Uncertainties in Tsunami Effects

LES of spray in crossflow – Effect of droplet distortion

The present investigation is focused on modeling of spray in crossflow using Large Eddy Simulations (LES). The modeling efforts are supported by experiments which are used both to provide accurate boundary and initial conditions and to evaluate droplet shapes in the near nozzle region. The droplets are modeled as Lagrangian parcels in an Eulerian continuum. Droplets in such configuration have been found to be distorted and not in perfect spherical shape from experimental results of our previous study. Droplet distortion is computed by Taylor-Analogy Breakup (TAB) distortion model. Each droplet is modelled as damped spring-mass system, where surface tension acts as a spring on the mass of the droplet and viscous dissipation provides the damping effect. The effort is to examine the effect of drag law used and the effect of this distortion on the droplet sizes produced in the flow field. Spray wind-ward trajectory and droplet sizes obtained from simulations are compared with the experimental results available. Although computational spray trajectory shows a reasonable match with experimental values, droplet sizes using the standard TAB model are found to be larger than that from experimental observation. To account for this distortion and its role in early breakup of droplets, constants of the TAB model are modified and the droplet sizes are found to be in good agreement with the experimental data.

Improvement of the forecast of convective activity from the AROME‐France system

AROME‐France is a convective‐scale numerical weather prediction system which has been running operationally at Météo‐France since the end of 2008. In order to determine its initial conditions, it uses a 3D‐Var assimilation scheme at the same resolution as the model in a continuous data assimilation cycle. In addition to conventional and satellite observations used in global data assimilation systems, dedicated observations for the mesoscale such as surface observations and radar measurements (radial winds and reflectivities) are assimilated. A major update of this system occurred in April 2015 with, among several improvements, (i) an increase of both horizontal and vertical resolutions (1.3 km and 90 vertical levels versus 2.5 km and 60 levels), and (ii) the reduction of the period of the data assimilation cycle from 3 to 1 h (as a result of the model spin‐up reduction and the tuning of the background‐error covariances). This study presents the preparatory work to these modifications and explores the main impact expected on convective activity forecasts. (i) appears to result in more realistic convective cells and better rainfall and wind gust scores and (ii) allows assimilation of more observations with information at the mesoscale which provides more accurate initial conditions and hence better subsequent rainfall forecasts. The benefits of using both (i) and (ii) in a pre‐operational configuration are shown using objective precipitation scores and illustrated by a case‐study.

An Offline-Online Riemann Solver for One-Dimensional Systems of Conservation Laws

In this paper, we present an exact Riemann solver for one-dimensional systems of conservation laws. The method is based on an offline-online computational decomposition. During the offline stage, we generate an accurate surrogate model for the solution to the Riemann problem for arbitrary left and right states. Then, during the online stage, we employ the surrogate model to generate accurate initial conditions for an iterative Newton solver. We present a mathematical analysis of the Riemann problem to justify the proposed approach. Finally, we illustrate its effectiveness by means of two numerical examples.

Second-order variational equations forN-body simulations

First-order variational equations are widely used in N-body simulations to study how nearby trajectories diverge from one another. These allow for efficient and reliable determinations of chaos indicators such as the Maximal Lyapunov characteristic Exponent (MLE) and the Mean Exponential Growth factor of Nearby Orbits (MEGNO). In this paper we lay out the theoretical framework to extend the idea of variational equations to higher order. We explicitly derive the differential equations that govern the evolution of second-order variations in the N-body problem. Going to second order opens the door to new applications, including optimization algorithms that require the first and second derivatives of the solution, like the classical Newton's method. Typically, these methods have faster convergence rates than derivative-free methods. Derivatives are also required for Riemann manifold Langevin and Hamiltonian Monte Carlo methods which provide significantly shorter correlation times than standard methods. Such improved optimization methods can be applied to anything from radial-velocity/transit-timing-variation fitting to spacecraft trajectory optimization to asteroid deflection. We provide an implementation of first- and second-order variational equations for the publicly available rebound integrator package. Our implementation allows the simultaneous integration of any number of first- and second-order variational equations with the high-accuracy IAS15 integrator. We also provide routines to generate consistent and accurate initial conditions without the need for finite differencing.

Creation of a high resolution precipitation data set by merging gridded gauge data and radar observations for Sweden

Summary Hydrological forecasting systems require accurate initial conditions, particularly for real time precipitation data, which are problematic to retrieve. This is especially difficult for high temporal and spatial resolutions, e.g. sub-daily and less than 10–20 km. Forecasting fast processes such as flash flood are, however, dependent on such high resolution data. Gridded gauge data produces too smooth fields and underestimates small scale phenomena, such as convection, whereas radar composites contain the small scale information, but suffer from inconsistencies between individual radars and have poor long term statistics. Here, we present a method to merge a radar composite with daily resolution gridded gauge data for Sweden for the time period 2009–2014 to produce a one hourly 4 × 4 km2 data set. The method consists of a main step where monthly accumulations of the radar data are scaled by those retrieved from the gridded data for each month. An optional quantile mapping based bias correction step makes sure that the daily intensity distribution agrees with the gridded observations. Finally, the data are dis-aggregated to an hourly time resolution. This results in a data set which has the same long-term spatial properties as the gridded observations, but with the spatial and temporal details of the radar data. Validation of the method is performed with high resolution gauge data, and shows a high quality of the derived product.

Impact of land-surface initialization on sub-seasonal to seasonal forecasts over Europe

Land surfaces and soil conditions are key sources of climate predictability at the seasonal time scale. In order to estimate how the initialization of the land surface affects the predictability at seasonal time scale, we run two sets of seasonal hindcasts with the general circulation model EC-Earth2.3. The initialization of those hindcasts is done either with climatological or realistic land initialization in May using the ERA-Land re-analysis. Results show significant improvements in the initialized run occurring up to the last forecast month. The prediction of near-surface summer temperatures and precipitation at the global scale and over Europe are improved, as well as the warm extremes prediction. As an illustration, we show that the 2010 Russian heat wave is only predicted when soil moisture is initialized. No significant improvement is found for the retrospective prediction of the 2003 European heat wave, suggesting this event to be mainly large-scale driven. Thus, we confirm that late-spring soil moisture conditions can be decisive in triggering high-impact events in the following summer in Europe. Accordingly, accurate land-surface initial conditions are essential for seasonal predictions.

A Hybrid Global Ocean Data Assimilation System at NCEP

Abstract Seasonal forecasting with a coupled model requires accurate initial conditions for the ocean. A hybrid data assimilation has been implemented within the National Centers for Environmental Prediction (NCEP) Global Ocean Data Assimilation System (GODAS) as a future replacement of the operational three-dimensional variational data assimilation (3DVar) method. This Hybrid-GODAS provides improved representation of model uncertainties by using a combination of dynamic and static background error covariances, and by using an ensemble forced by different realizations of atmospheric surface conditions. An observing system simulation experiment (OSSE) is presented spanning January 1991 to January 1999, with a bias imposed on the surface forcing conditions to emulate an imperfect model. The OSSE compares the 3DVar used by the NCEP Climate Forecast System (CFSv2) with the new hybrid, using simulated in situ ocean observations corresponding to those used for the NCEP Climate Forecast System Reanalysis (CFSR). The Hybrid-GODAS reduces errors for all prognostic model variables over the majority of the experiment duration, both globally and regionally. Compared to an ensemble Kalman filter (EnKF) used alone, the hybrid further reduces errors in the tropical Pacific. The hybrid eliminates growth in biases of temperature and salinity present in the EnKF and 3DVar, respectively. A preliminary reanalysis using real data shows that reductions in errors and biases are qualitatively similar to the results from the OSSE. The Hybrid-GODAS is currently being implemented as the ocean component in a prototype next-generation CFSv3, and will be used in studies by the Climate Prediction Center to evaluate impacts on ENSO prediction.

The forecast skill horizon

Numerical weather prediction has seen, in the past 25 years, a shift from a ‘deterministic’ approach, based on single numerical integrations, to a probabilistic one, with ensembles of numerical integrations used to estimate the probability distribution function of forecast states. This shift to a probabilistic approach enabled a better extraction of predictive signals at longer lead times and provided a meaningful framework for extending the forecast length beyond 10 days. In this work, the limit of predictive skill is assessed for ECMWF monthly ensemble forecasts at different spatial and temporal scales. The forecast skill horizon is defined as the lead time when the ensemble ceases to be more skilful than a climatological distribution, using a continuous ranked probability score as metric. Results based on 32‐day ensemble forecasts indicate that the forecast skill horizon is sensitive to the spatial and temporal scale of the predicted phenomena, to the variable considered and the area analysed. On average over 1 year of forecasts, the forecast skill horizon for instantaneous, grid‐point fields is between 16 and 23 days, while it is considerably longer for time‐ and spatial‐average fields. Forecast skill horizons longer than the 2 weeks that were thought to be the limit are now achievable thanks to major advances in numerical weather prediction. More specifically, they are possible because forecasts are now framed in probabilistic terms, with a probability distribution estimated using ensembles generated using forecast models that include more components (e.g. a dynamical ocean and ocean waves) and more faithfully represent processes. Moreover, the forecasts start from more accurate initial conditions constructed using better data‐assimilation methods and more observational data.

An efficient semi‐implicit subgrid method for free‐surface flows on hierarchical grids

SUMMARYWe present a new modelling strategy for improving the efficiency of computationally intensive flow problems in environmental free‐surface flows. The approach combines a recently developed semi‐implicit subgrid method with a hierarchical grid solution strategy. The method allows the incorporation of high‐resolution data on subgrid scale to obtain a more accurate and efficient hydrodynamic model. The subgrid method improves the efficiency of the hierarchical grid method by providing better solutions on coarse grids. The method is applicable to both steady and unsteady flows, but we particularly focus on river flows with steady boundary conditions. There, the combined hierarchical grid–subgrid method reduces the computational effort to obtain a steady state with factors up to 43. For unsteady models, the method can be used for efficiently generating accurate initial conditions on high‐resolution grids. Additionally, the method provides automatic insight in grid convergence. We demonstrate the efficiency and applicability of the method using a schematic test for the vortex shedding around a circular cylinder and a real‐world river case study. Copyright © 2015 John Wiley & Sons, Ltd.

Need for Accurate Initial Conditions to Simulate Flexible Structures in Motion

Flexible structures are often important components of mechanical assemblies in motion. A flexible structure sometimes must go through assembly steps that cause it to be in a pre-stressed condition when in the starting position for operation. A virtual prototype of the assembly must also bring the model of the flexible structure into the same pre-stressed condition in order to obtain accurate simulation results. This case study is presented regarding the simulation of a constant velocity joint, with a focus on the flexible boot. The case study demonstrates that careful definition of the initial conditions of the boot and flexible body contacts yields high-fidelity simulation results.

Target observations for improving initialization of high-impact ocean-atmospheric environmental events forecasting

Abstract In this paper, we emphasize the importance of accurate initial conditions in predicting high-impact ocean-atmospheric environmental events, such as El Niño-Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), tropical cyclone (TC), and Kuroshio large meander (KLM), by reviewing recent progresses toward target observations for improving the initialization of these events forecasting. Since field observations are costly and will never be dense enough to fully cover the vast space of these events, it is necessary to develop methodologies that guide the design of efficient and effective observation strategy. Of particular interest is a method called conditional non-linear optimal perturbation (CNOP), which has been shown to be very useful in determining the sensitive areas for target observations applicable to the predictions of ENSO, IOD, TC, and KLM. Further studies are needed to understand the predictability of these events under the influence of climate change, and to explore the possibility of implementing field programs of target observations. These studies are challenging but are crucially important for improving our forecast skill of the high-impact ocean-atmospheric environmental events, and thus for disaster prevention, climate change mitigation, and sustainable socio-economic development.

Predictability and prediction skill of the boreal summer intraseasonal oscillation in the Intraseasonal Variability Hindcast Experiment

Boreal summer intraseasonal oscillation (BSISO) is one of the dominant modes of intraseasonal variability of the tropical climate system, which has fundamental impacts on regional summer monsoons, tropical storms, and extra-tropical climate variations. Due to its distinctive characteristics, a specific metric for characterizing observed BSISO evolution and assessing numerical models’ simulations has previously been proposed (Lee et al. in Clim Dyn 40:493–509, 2013). However, the current dynamical model’s prediction skill and predictability have not been investigated in a multi-model framework. Using six coupled models in the Intraseasonal Variability Hindcast Experiment project, the predictability estimates and prediction skill of BSISO are examined. The BSISO predictability is estimated by the forecast lead day when mean forecast error becomes as large as the mean signal under the perfect model assumption. Applying the signal-to-error ratio method and using ensemble-mean approach, we found that the multi-model mean BSISO predictability estimate and prediction skill with strong initial amplitude (about 10 % higher than the mean initial amplitude) are about 45 and 22 days, respectively, which are comparable with the corresponding counterparts for Madden–Julian Oscillation during boreal winter (Neena et al. in J Clim 27:4531–4543, 2014a). The significantly lower BSISO prediction skill compared with its predictability indicates considerable room for improvement of the dynamical BSISO prediction. The estimated predictability limit is independent on its initial amplitude, but the models’ prediction skills for strong initial amplitude is 6 days higher than the corresponding skill with the weak initial condition (about 15 % less than mean initial amplitude), suggesting the importance of using accurate initial conditions. The BSISO predictability and prediction skill are phase and season-dependent, but the degree of dependency varies with the models. It is important to note that the estimation of prediction skill depends on the methods that generate initial ensembles. Our analysis indicates that a better dispersion of ensemble members can considerably improve the ensemble mean prediction skills.

Effect of Observation Network Design on Meteorological Forecasts of Asian Dust Events

Abstract To improve the prediction of Asian dust events on the Korean Peninsula, meteorological fields must be accurately predicted because dust transport models require them as input. Accurate meteorological forecasts could be obtained by integrating accurate initial conditions obtained from data assimilation processes in numerical weather prediction. In data assimilation, selecting the appropriate observation location is important to ensure that the initial conditions represent the surrounding meteorological flow. To investigate the effect of observation network configuration on meteorological forecasts during Asian dust events on the Korean Peninsula, observing system simulation experiments using several simulated and real observation networks were tested with the Weather Research and Forecasting modeling system for 11 Asian dust events affecting the Korean Peninsula during a recent 6-yr period. First, the characteristics of randomly fixed and adaptively selected observation networks were investigated with various observation densities. The adaptive observation strategy could reduce forecast errors more efficiently than the fixed observation strategy. For both the fixed and adaptive observation strategies, the mean forecast error reduction rates increased as the number of assimilated observations and the distance between observation sites increased up to 300 km. Second, the effects of redistributing the real observation sites and adding observation sites to the real observation network based on the adaptive observation strategy were investigated. Adding adaptive observation sites to the real observation network in statistically sensitive regions improved the forecast performance more than redistributing real observation sites did. The strategy of adding adaptive observation sites is used to suggest the optimal meteorological observation network for meteorological forecasts of Asian dust transport events on the Korean Peninsula.

Effects of Fuel Flow inside the Nozzle on Spray Characteristics of Butanol/Diesel Blends

In order to build an accurate spray model for studying the characteristics of n-butanol and diesel blends. First, the calculation of gas-liquid two phase flow inside the nozzle was carried on, the birth and development of cavitation and the non-uniformity of the velocity field inside the nozzle were analysed. Then the spray model coupled flow inside the nozzle was established and verified. The spray simulation calculation of n-butanol and diesel blends was carried on by using the model, and compared the results with the results of flow inside the nozzle not coupled. The results show that the droplet speed, spray penetration and SMD of D100 and N40 is all bigger under the condition of without coupling flow inside the nozzle. The flow inside nozzle will affect the whole spray process and spray characteristics, which will ultimately affect the combustion and emission characteristics. So the spray model coupled flow inside the nozzle can provide accurate initial condition for spray calculation, which can reflects the fuel spray characteristics truly.

Odds on weather: probabilities and the public

Will weather forecasting ever be perfect? Looking at the history of numerical weather prediction (NWP), a continuous upward trend in predictive skill is evident. This may give the impression that achieving the perfect forecast is only a matter of time. A problem, however, lies in the fact that the atmosphere is chaotic, which results in an inherent unpredictability in our forecasts. While more accurate initial conditions and a better physical model can help combat this, chaos can never be completely overcome. Currently the best way of showing this uncertainty quantitatively is through ensembles. Although meteorologists have been using ensemble forecasts for years, there seems to be hesitation in giving the user this information. A common assumption made by meteorologists is that the layperson would not be interested in a forecast that had multiple options: it would complicate the forecast, or it may be seen as unnecessary. Two papers have been published recently that seem to contradict this train of thought. In Morss (2008) and Stephens et al. (2011), surveys were issued to the general public regarding their understanding of probabilistic forecasts (see also Peachey et al., 2013). The findings of these surveys suggest that people are more interested in probabilistic forecasts than may have been previously thought. In Morss (2008), the survey was aimed at gauging the American public’s perception and understanding of probabilistic forecasting. The results from this research showed that there was an enthusiasm for uncertainty information to be given out with weather forecasts. Morss found that people already infer uncertainty in deterministic models, and inclusion of probabilities would prevent users inferring the wrong uncertainty in the forecast. In Stephens et al. (2011), the UK Met Office (UKMO) developed an ‘online game’ designed to investigate two assumptions: (1) the public do not understand uncertainty and (2) uncertainty is too complex to communicate. It was found that, contrary to popular belief, probabilities did not cause confusion amongst users. In fact, when compared with the deterministic forecast, the probabilistic forecasts helped give the user better decision-making ability. In our current study, a survey was issued to the Irish public in order to gauge their level of understanding of probabilistic forecasting and to see if they would be willing to use these types of forecasts. Contact was also made with a number of meteorologists who gave their opinions on how best to issue these forecasts to the public.

Galaxy evolution in groups and clusters: satellite star formation histories and quenching time-scales in a hierarchical Universe

Satellite galaxies in groups and clusters are more likely to have low star formation rates (SFR) and lie on the red-sequence than central (field) galaxies. Using galaxy group/cluster catalogs from SDSS DR7, together with a cosmological N-body simulation to track satellite orbits, we examine the star formation histories and quenching timescales of satellites of M_star > 5 x 10^9 M_sun at z=0. We first explore satellite infall histories: group preprocessing and ejected orbits are critical aspects of satellite evolution, and properly accounting for these, satellite infall typically occurred at z~0.5, or ~5 Gyr ago. To obtain accurate initial conditions for the SFRs of satellites at their time of first infall, we construct an empirical parametrization for the evolution of central galaxy SFRs and quiescent fractions. With this, we constrain the importance and efficiency of satellite quenching as a function of satellite and host halo mass, finding that satellite quenching is the dominant process for building up all quiescent galaxies at M_star < 10^10 M_sun. We then constrain satellite star formation histories, finding a 'delayed-then-rapid' quenching scenario: satellite SFRs evolve unaffected for 2-4 Gyr after infall, after which star formation quenches rapidly, with an e-folding time of < 0.8 Gyr. These quenching timescales are shorter for more massive satellites but do not depend on host halo mass: the observed increase in satellite quiescent fraction with halo mass arises simply because of satellites quenching in a lower mass group prior to infall (group preprocessing), which is responsible for up to half of quenched satellites in massive clusters. Because of the long time delay before quenching starts, satellites experience significant stellar mass growth after infall, nearly identical to central galaxies. This fact provides key physical insight into the subhalo abundance matching method.