scholarly journals Thunderstorms Do Not Get Butterflies

2016 ◽  
Vol 97 (2) ◽  
pp. 237-243 ◽  
Author(s):  
Dale R. Durran ◽  
Jonathan A. Weyn
Keyword(s):  
Large Scale ◽  
Squall Line ◽  
Small Scale ◽  
Lead Times ◽  
Initial State ◽  
Weather Forecasts ◽  
Kinetic Energy Spectrum ◽  
Initial Errors ◽  
Squall Lines ◽  
Homogeneous Environment

Abstract One important limitation on the accuracy of weather forecasts is imposed by unavoidable errors in the specification of the atmosphere’s initial state. Much theoretical concern has been focused on the limits to predictability imposed by small-scale errors, potentially even those on the scale of a butterfly. Very modest errors at much larger scales may nevertheless pose a more important practical limitation. We demonstrate the importance of large-scale uncertainty by analyzing ensembles of idealized squall-line simulations. Our results imply that minimizing initial errors on scales around 100 km is more likely to extend the accuracy of forecasts at lead times longer than 3–4 h than efforts to minimize initial errors on much smaller scales. These simulations also demonstrate that squall lines, triggered in a horizontally homogeneous environment with no initial background circulations, can generate a background mesoscale kinetic energy spectrum roughly similar to that observed in the atmosphere.

scholarly journals What Is the Predictability Limit of Midlatitude Weather?

2019 ◽  
Vol 76 (4) ◽  
pp. 1077-1091 ◽  
Author(s):  
Fuqing Zhang ◽  
Y. Qiang Sun ◽  
Linus Magnusson ◽  
Roberto Buizza ◽  
Shian-Jiann Lin ◽  
...  
Keyword(s):  
Initial Conditions ◽  
Weather Prediction ◽  
Forecast Model ◽  
Small Scale ◽  
Lead Times ◽  
Winter Storms ◽  
Weather Forecasts ◽  
Numerical Weather ◽  
Forecast Lead Time ◽  
Order Of Magnitude

Abstract Understanding the predictability limit of day-to-day weather phenomena such as midlatitude winter storms and summer monsoonal rainstorms is crucial to numerical weather prediction (NWP). This predictability limit is studied using unprecedented high-resolution global models with ensemble experiments of the European Centre for Medium-Range Weather Forecasts (ECMWF; 9-km operational model) and identical-twin experiments of the U.S. Next-Generation Global Prediction System (NGGPS; 3 km). Results suggest that the predictability limit for midlatitude weather may indeed exist and is intrinsic to the underlying dynamical system and instabilities even if the forecast model and the initial conditions are nearly perfect. Currently, a skillful forecast lead time of midlatitude instantaneous weather is around 10 days, which serves as the practical predictability limit. Reducing the current-day initial-condition uncertainty by an order of magnitude extends the deterministic forecast lead times of day-to-day weather by up to 5 days, with much less scope for improving prediction of small-scale phenomena like thunderstorms. Achieving this additional predictability limit can have enormous socioeconomic benefits but requires coordinated efforts by the entire community to design better numerical weather models, to improve observations, and to make better use of observations with advanced data assimilation and computing techniques.

The nonlinear properties of a large-scale dynamo driven by helical forcing

2002 ◽  
Vol 456 ◽  
pp. 219-237 ◽  
Author(s):  
FAUSTO CATTANEO ◽  
DAVID W. HUGHES ◽  
JEAN-CLAUDE THELEN
Keyword(s):  
Magnetic Field ◽  
Large Scale ◽  
Initial Conditions ◽  
Spatial Scales ◽  
Mean Field ◽  
Forced Flow ◽  
Small Scale ◽  
Dynamo Action ◽  
Initial State ◽  
The Magnetic Field

By considering an idealized model of helically forced flow in an extended domain that allows scale separation, we have investigated the interaction between dynamo action on different spatial scales. The evolution of the magnetic field is studied numerically, from an initial state of weak magnetization, through the kinematic and into the dynamic regime. We show how the choice of initial conditions is a crucial factor in determining the structure of the magnetic field at subsequent times. For a simulation with initial conditions chosen to favour the growth of the small-scale field, the evolution of the large-scale magnetic field can be described in terms of the α-effect of mean field magnetohydrodynamics. We have investigated this feature further by a series of related numerical simulations in smaller domains. Of particular significance is that the results are consistent with the existence of a nonlinearly driven α-effect that becomes saturated at very small amplitudes of the mean magnetic field.

scholarly journals Reply to “Comment on ‘Bias Correction, Quantile Mapping, and Downscaling: Revisiting the Inflation Issue’”

2014 ◽  
Vol 27 (4) ◽  
pp. 1821-1825 ◽  
Author(s):  
Douglas Maraun
Keyword(s):  
Large Scale ◽  
Weather Forecasting ◽  
Small Scale ◽  
Quantile Mapping ◽  
Spatiotemporal Structure ◽  
Weather Forecasts ◽  
Long Term Trends ◽  
Climate Signals ◽  
Local Variability

Abstract In his comment, G. Bürger criticizes the conclusion that inflation of trends by quantile mapping is an adverse effect. He assumes that the argument would be “based on the belief that long-term trends and along with them future climate signals are to be large scale.” His line of argument reverts to the so-called inflated regression. Here it is shown, by referring to previous critiques of inflation and standard literature in statistical modeling as well as weather forecasting, that inflation is built upon a wrong understanding of explained versus unexplained variability and prediction versus simulation. It is argued that a sound regression-based downscaling can in principle introduce systematic local variability in long-term trends, but inflation systematically deteriorates the representation of trends. Furthermore, it is demonstrated that inflation by construction deteriorates weather forecasts and is not able to correctly simulate small-scale spatiotemporal structure.

scholarly journals Indirect Taxis on a Fluctuating Environment

Mathematics ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 2052
Author(s):  
Andrey Morgulis ◽  
Konstantin Ilin
Keyword(s):  
Side Effects ◽  
Pattern Formation ◽  
Large Scale ◽  
Food Resource ◽  
Small Scale ◽  
Fluctuating Environment ◽  
Environmental Fluctuations ◽  
Sea Currents ◽  
Homogeneous Environment

In this article, we study a Patlak–Keller–Siegel (PKS) model of a community of two species placed in the inhomogeneous environment. We employ PKS law for modeling tactic movement due to interspecific taxis and in response to the environmental fluctuations. These fluctuations can arise for natural reasons, e.g., the terrain relief, the sea currents and the food resource distribution, and there are artificial ones. The main result in the article elucidates the effect of the small-scale environmental fluctuations on the large-scale pattern formation in PKS systems. This issue remains uncharted, although numerous studies have addressed the pattern formation while assuming an homogeneous environment. Meanwhile, exploring the role of the fluctuating environment is substantial in many respects, for instance, for predicting the side effects of human activity or for designing the control of biological systems. As well, it is necessary for understanding the roles played in the dynamics of trophic communities by the natural environmental inhomogeneities—those mentioned above, for example. We examined the small-scale environmental inhomogeneities in the spirit of Kapitza’s theory of the upside-down pendulum, but we used the homogenization instead of classical averaging. This approach is novel for the dynamics of PKS systems (though used commonly for other areas). Employing it has unveiled a novel mechanism of exerting the effect from the fluctuating environment on the pattern formation by the drift of species arising upon the homogenization of the fluctuations.

scholarly journals Concept of Large-scale Thermomechanical URL Experiments in the Nizhnekanskiy Rock Massif

2020 ◽  
Vol 12 (3) ◽  
pp. 101-111
Author(s):  
E. V. Moiseenko ◽  
◽  
N. I. Drobyishevsky ◽  
R. A. Butov ◽  
Yu. N. Tokarev ◽  
...  
Keyword(s):  
Host Rock ◽  
Large Scale ◽  
Mutual Influence ◽  
Thermomechanical Properties ◽  
Coupled Processes ◽  
Small Scale ◽  
Initial State ◽  
Excavation Damaged Zone ◽  
Damaged Zone ◽  
Engineered Barriers

Numerical simulation of thermomechanical processes in a deep underground radioactive waste repository requires information on the host rock and the engineered barriers properties at a scale of dozens of centimeters, meters and more. However, the extrapolation of the values obtained on small-scale samples in surface laboratories yields excessive uncertainties. The materials behavior is also influenced by conditions that cannot be reliably reproduced in a surface laboratory, such as water content or initial stress-strain state. Following experiments are planned to study the host rock and the engineered barriers behavior during heating under conditions similar to those expected in the repository, as well as to assess their large-scale thermomechanical properties. In the experiment focused on the excavation damaged zone thermal mechanics, the behavior of reinforced drift walls and vaults under heating will be studied. The experimental facility will involve two drifts with the same orientation as the planned repository ones. As a result, the spatial distribution of excavation damaged zone thermomechanical parameters and their evolution due to heating will be identified. The second experiment focuses on the host rock mass behavior under spatially nonuniform unsteady heating. The facility will feature two vertical boreholes with heaters. The experiment will be divided into several stages: study of the host rock initial state, estimation of the rock main thermomechanical properties, study of the temporal evolution of the stress field due to 3D temperature gradients and of the processes in the host rock occurring during its cooling and re-saturation with water. Following the completion of the separate-effect test program, an integrated experiment should be carried out to study the coupled processes with respect to their mutual influence. The obtained results will be used to refine the values of input parameters for numerical simulations and their uncertainty ranges, as well as to validate the computer codes.

Numerical Simulation of Squall line in idealized SINGV and WRF Models

2020 ◽  
Author(s):  
Ragi Rajagopalan ◽  
Anurag Dipankar ◽  
Xiang-Yu Huang
Keyword(s):  
Weather Prediction ◽  
Numerical Weather Prediction Model ◽  
Squall Line ◽  
Cross Sectional ◽  
Spatial And Temporal Scales ◽  
Weather Forecasts ◽  
Convective Systems ◽  
Squall Lines ◽  
Rain Events ◽  
Onset Location

<p>Squall lines are the prominent feature over Singapore region creating strongly localized rain events due to vigorous localized convective activity. These convective systems have relatively small spatial and temporal scales compared to other atmospheric features like monsoons, thus the prediction of these features lack accuracy. The SINGV numerical weather prediction model is able to provide improved weather forecasts over Singapore region, however, challenges still exist in predicting the thunderstorm/squall line events in onset, location, intensity and lead time. A few real-time case studies of squall lines indicate that SINGV could not capture these features appropriately, while WRF did a better forecasting. To understand the issues with SINGV model, idealized simulations replicating the Weismann & Klemp ‘82 case are conducted keeping similar physics in both the models. Preliminary results indicate that both models behave differently: WRF displays organized convection whereas in SINGV the storm splits at the early stages. Cross-sectional details along the propagating squall line suggest that the updrafts and downdrafts, at the storm development stages, are moderately higher in SINGV compared to WRF. It is speculated that these stronger updrafts in SINGV carry anomalously large amount of liquid water to the upper troposphere where these are converted into rain, which in turn result in stronger downdrafts facilitating the splitting of initial storm. Further analysis is required to conclude our speculation.</p>

New Efficient Sparse Space–Time Algorithms for Superparameterization on Mesoscales

2009 ◽  
Vol 137 (12) ◽  
pp. 4307-4324 ◽  
Author(s):  
Yulong Xing ◽  
Andrew J. Majda ◽  
Wojciech W. Grabowski
Keyword(s):  
Large Scale ◽  
Space Time ◽  
Horizontal Velocity ◽  
Specific Humidity ◽  
Scale Model ◽  
Small Scale ◽  
Squall Lines ◽  
Scale Models ◽  
New Algorithms ◽  
Small Scale Model

Abstract Superparameterization (SP) is a large-scale modeling system with explicit representation of small-scale and mesoscale processes provided by a cloud-resolving model (CRM) embedded in each column of a large-scale model. New efficient sparse space–time algorithms based on the original idea of SP are presented. The large-scale dynamics are unchanged, but the small-scale model is solved in a reduced spatially periodic domain to save the computation cost following a similar idea applied by one of the authors for aquaplanet simulations. In addition, the time interval of integration of the small-scale model is reduced systematically for the same purpose, which results in a different coupling mechanism between the small- and large-scale models. The new algorithms have been applied to a stringent two-dimensional test suite involving moist convection interacting with shear with regimes ranging from strong free and forced squall lines to dying scattered convection as the shear strength varies. The numerical results are compared with the CRM and original SP. It is shown here that for all of the regimes of propagation and dying scattered convection, the large-scale variables such as horizontal velocity and specific humidity are captured in a statistically accurate way (pattern correlations above 0.75) based on space–time reduction of the small-scale models by a factor of ⅓; thus, the new efficient algorithms for SP result in a gain of roughly a factor of 10 in efficiency while retaining a statistical accuracy on the large-scale variables. Even the models with ⅙ reduction in space–time with a gain of 36 in efficiency are able to distinguish between propagating squall lines and dying scattered convection with a pattern correlation above 0.6 for horizontal velocity and specific humidity. These encouraging results suggest the possibility of using these efficient new algorithms for limited-area mesoscale ensemble forecasting.

Effects of Horizontal and Vertical Grid Spacing on Mixing in Simulated Squall Lines and Implications for Convective Strength and Structure

2015 ◽  
Vol 143 (11) ◽  
pp. 4355-4375 ◽  
Author(s):  
Z. J. Lebo ◽  
H. Morrison
Keyword(s):  
Convective Transport ◽  
Squall Line ◽  
Mature Stage ◽  
Small Scale ◽  
Grid Spacing ◽  
Squall Lines ◽  
Vertical Grid ◽  
Passive Tracers ◽  
Storm Characteristics ◽  
Horizontal Grid

Abstract The sensitivity of an idealized squall line to horizontal and vertical grid spacing is investigated using a new approach. Simulations are first performed at a horizontal grid spacing of 1 km until the storm reaches its mature stage. The model output is then interpolated to smaller (and larger) grid spacings, and the model is restarted using the interpolated state plus small thermodynamic perturbations to spin up small-scale motions. This framework allows an investigation of the sensitivity of the storm to changes in without complications from differences in storm initiation and early evolution. The restarted simulations reach a quasi steady state within approximately 1 h. Results demonstrate that there are two -dependent regimes with the transition between regimes occurring for between 250 and 500 m. Some storm characteristics, such as the mean convective core area, change substantially for 250 m but show limited sensitivity as is decreased below 250 m, despite better resolving smaller-scale turbulent motions. This transition is found to be independent of the chosen . Mixing in the context of varying and is also investigated via passive tracers that are initialized 1 h after restarting the simulations (i.e., after the spin up of small-scale motions). The tracer field at the end of the simulations reveals that entrainment and detrainment are suppressed in the simulations with 500 m. For decreasing , entrainment and detrainment are substantially more important, limiting the flux of low-level tracer to the upper troposphere, which has important implications for modeling studies of convective transport from the boundary layer through the troposphere.

scholarly journals Hurricane Intensity Predictability

2016 ◽  
Vol 97 (10) ◽  
pp. 1847-1857 ◽  
Author(s):  
Chanh Q. Kieu ◽  
Zachary Moon
Keyword(s):  
Large Scale ◽  
Initial Conditions ◽  
Numerical Models ◽  
Absolute Intensity ◽  
Limited Range ◽  
Mature Stage ◽  
Lead Times ◽  
Initial State ◽  
Hurricane Intensity ◽  
Chaotic Nature

Abstract Weather has long been projected to possess limited predictability due to the inherent chaotic nature of the atmosphere; small changes in initial conditions could lead to an entirely different state of the atmosphere after some period of time. Given such a limited range of predictability of atmospheric flows, a natural question is, how far in advance can we predict a hurricane’s intensity? In this study, it is shown first that the predictability of a hurricane’s intensity at the 4–5-day lead times is generally determined more by the large-scale environment than by a hurricane’s initial conditions. This result suggests that future improvement in hurricane longer-range intensity forecasts by numerical models will be most realized as a result of improvement in the large-scale environment rather than in the storm’s initial state. At the mature stage of a hurricane, direct estimation of the leading Lyapunov exponent using an axisymmetric model reveals, nevertheless, the existence of a chaotic attractor in the phase space of the hurricane scales. This finding of a chaotic maximum potential intensity (MPI) attractor provides direct information about the saturation of a hurricane’s intensity errors around 8 m s−1, which prevents the absolute intensity errors at the mature stage from being reduced below this threshold. The implication of such intensity error saturation to the limited range of hurricane intensity forecasts will be also discussed.

scholarly journals An Attempt to Improve the Forecasting of Persistent Severe Rainfall Using the Spectral Nudging and Update Cycle Methods

2017 ◽  
Vol 32 (2) ◽  
pp. 713-723 ◽  
Author(s):  
Yanfeng Zhao ◽  
Donghai Wang ◽  
Jianjun Xu
Keyword(s):  
Large Scale ◽  
Wrf Model ◽  
Weather Research And Forecasting ◽  
Brier Score ◽  
Small Scale ◽  
Lead Times ◽  
Spectral Nudging ◽  
Numerical Predictions ◽  
Forecast Lead Time ◽  
Accumulated Rainfall

Abstract Using the interior spectral nudging and update cycle (SN+UIC) methods in the regional Weather Research and Forecasting (WRF) Model, the numerical predictions of four persistent severe rainfall (PSR) events during the preflood season in south China were investigated, based on the fact that the global model has an advantage in predicting the large-scale atmospheric variation and the regional model is better in terms of simulating small-scale changes. The simulation results clearly indicated that the SN+UIC improved the prediction of the PSR events’ daily precipitation for moderate, heavy, and torrential rains (10–100 mm day−1). It also improved the simulative forecasts of the two categories of rain with accumulated precipitation above 50 and 100 mm at lead times of 5–11 days. Moreover, the longer the forecast lead time is, the larger the decrease in the Brier score. Additionally, the SN+UIC method decreased the root-mean-square error for accumulated rainfall (6.2%) and relative humidity (5.67%).

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