scholarly journals Impact of the Mesoscale Range on Error Growth and the Limits to Atmospheric Predictability

2020 ◽  
Vol 77 (11) ◽  
pp. 3769-3779
Author(s):  
Tsz Yan Leung ◽  
Martin Leutbecher ◽  
Sebastian Reich ◽  
Theodore G. Shepherd
Keyword(s):  
Weather Prediction ◽  
Growth Characteristic ◽  
Growth Behavior ◽  
Original Model ◽  
Two Dimensional ◽  
Error Growth ◽  
Synoptic Scale ◽  
Perfect Model ◽  
Per Se ◽  
Model Context

AbstractGlobal numerical weather prediction (NWP) models have begun to resolve the mesoscale k−5/3 range of the energy spectrum, which is known to impose an inherently finite range of deterministic predictability per se as errors develop more rapidly on these scales than on the larger scales. However, the dynamics of these errors under the influence of the synoptic-scale k−3 range is little studied. Within a perfect-model context, the present work examines the error growth behavior under such a hybrid spectrum in Lorenz’s original model of 1969, and in a series of identical-twin perturbation experiments using an idealized two-dimensional barotropic turbulence model at a range of resolutions. With the typical resolution of today’s global NWP ensembles, error growth remains largely uniform across scales. The theoretically expected fast error growth characteristic of a k−5/3 spectrum is seen to be largely suppressed in the first decade of the mesoscale range by the synoptic-scale k−3 range. However, it emerges once models become fully able to resolve features on something like a 20-km scale, which corresponds to a grid resolution on the order of a few kilometers.

scholarly journals A New Theoretical Framework for Understanding Multiscale Atmospheric Predictability

2020 ◽  
Vol 77 (7) ◽  
pp. 2297-2309
Author(s):  
Y. Qiang Sun ◽  
Fuqing Zhang
Keyword(s):  
Energy Spectrum ◽  
Numerical Models ◽  
Weather Prediction ◽  
Three Dimensional ◽  
Theoretical Framework ◽  
Growth Behavior ◽  
Small Scale ◽  
Growth Equation ◽  
Error Growth ◽  
Atmospheric Predictability

AbstractHere we present a new theoretical framework that connects the error growth behavior in numerical weather prediction (NWP) with the atmospheric kinetic energy spectrum. Building on previous studies, our newly proposed framework applies to the canonical observed atmospheric spectrum that has a −3 slope at synoptic scales and a −5/3 slope at smaller scales. Based on this realistic hybrid energy spectrum, our new experiment using hybrid numerical models provides reasonable estimations for the finite predictable ranges at different scales. We further derive an analytical equation that helps understand the error growth behavior. Despite its simplicity, this new analytical error growth equation is capable of capturing the results of previous comprehensive theoretical and observational studies of atmospheric predictability. The success of this new theoretical framework highlights the combined effects of quasi-two-dimensional dynamics at synoptic scales (−3 slope) and three-dimensional turbulence-like small-scale chaotic flows (−5/3 slope) in dictating the error growth. It is proposed that this new framework could serve as a guide for understanding and estimating the predictability limit in the real world.

scholarly journals Atmospheric brown clouds reach the Tibetan Plateau by crossing the Himalayas

2015 ◽  
Vol 15 (11) ◽  
pp. 6007-6021 ◽  
Author(s):  
Z. L. Lüthi ◽  
B. Škerlak ◽  
S.-W. Kim ◽  
A. Lauer ◽  
A. Mues ◽  
...  
Keyword(s):  
Tibetan Plateau ◽  
Weather Prediction ◽  
Mountain Range ◽  
The Tibetan Plateau ◽  
Pollution Transport ◽  
Synoptic Scale ◽  
Gangetic Plain ◽  
Dark Light ◽  
Air Masses ◽  
Pollution Episode

Abstract. The Himalayas and the Tibetan Plateau region (HTP), despite being a remote and sparsely populated area, is regularly exposed to polluted air masses with significant amounts of aerosols including black carbon. These dark, light-absorbing particles are known to exert a great melting potential on mountain cryospheric reservoirs through albedo reduction and radiative forcing. This study combines ground-based and satellite remote sensing data to identify a severe aerosol pollution episode observed simultaneously in central Tibet and on the southern side of the Himalayas during 13–19 March 2009 (pre-monsoon). Trajectory calculations based on the high-resolution numerical weather prediction model COSMO are used to locate the source regions and study the mechanisms of pollution transport in the complex topography of the HTP. We detail how polluted air masses from an atmospheric brown cloud (ABC) over South Asia reach the Tibetan Plateau within a few days. Lifting and advection of polluted air masses over the great mountain range is enabled by a combination of synoptic-scale and local meteorological processes. During the days prior to the event, winds over the Indo-Gangetic Plain (IGP) are generally weak at lower levels, allowing for accumulation of pollutants and thus the formation of ABCs. The subsequent passing of synoptic-scale troughs leads to southwesterly flow in the middle troposphere over northern and central India, carrying the polluted air masses across the Himalayas. As the IGP is known to be a hotspot of ABCs, the cross-Himalayan transport of polluted air masses may have serious implications for the cryosphere in the HTP and impact climate on regional to global scales. Since the current study focuses on one particularly strong pollution episode, quantifying the frequency and magnitude of similar events in a climatological study is required to assess the total impact.

scholarly journals Prediction of synoptic-scale sea level pressure over the Indian monsoon region using deep learning

2021 ◽  
Author(s):  
Aryaman Sinha ◽  
Mayuna Gupta ◽  
K S S Sai Srujan ◽  
Hariprasad Kodamana ◽  
Sandeep Sukumaran
Keyword(s):  
Deep Learning ◽  
Sea Level ◽  
Short Term Memory ◽  
Weather Forecasting ◽  
Weather Prediction ◽  
Learning Model ◽  
Synoptic Scale ◽  
Sea Level Pressure ◽  
Level Pressure ◽  
Deep Learning Model

<div><div><div><p>The synoptic-scale (3 - 7 days) variability is a dominant contributor to the Indian summer monsoon (ISM) seasonal precipitation. An accurate prediction of ISM precipitation by dynamical or statistical models remains a challenge. Here we show that the sea level pressure (SLP) can be used as a proxy to predict the active-break cycle as well as the genesis of low- pressure-systems (LPS), using a deep learning model, namely, convolutional long short-term memory (ConvLSTM) networks. The deep learning model is able to reliably predict the daily SLP anomalies over Central India and the Bay of Bengal at a lead time of 7 days. As the fluctuations in SLP drive the changes in the strength of the atmospheric circulation, the prediction of SLP anomalies is useful in predicting the intensity of ISM. It is demonstrated that the ConvLSTM possesses better prediction skill compared to a conventional numerical weather prediction model, indicating the usefulness of a physics guided deep learning model in medium range weather forecasting.</p></div></div></div>

scholarly journals Application of Boyd’s Periodization and Relaxation Method in a Spectral Atmospheric Limited-Area Model. Part II: Accuracy Analysis and Detailed Study of the Operational Impact

2012 ◽  
Vol 140 (10) ◽  
pp. 3149-3162 ◽  
Author(s):  
Daan Degrauwe ◽  
Steven Caluwaerts ◽  
Fabrice Voitus ◽  
Rafiq Hamdi ◽  
Piet Termonia
Keyword(s):  
Weather Prediction ◽  
Relaxation Method ◽  
Companion Paper ◽  
Water Model ◽  
Limited Area ◽  
Perfect Model ◽  
Depth Study ◽  
Nwp Model ◽  
The Impact ◽  
Operational Impact

Abstract Spectral limited-area models face a particular challenge at their lateral boundaries: the fields need to be made periodic. Boyd proposed a windowing-based method to improve the periodization and relaxation. In a companion paper, the implementation of this windowing method in the operational semi-implicit semi-Lagrangian spectral HARMONIE system was described and some first reproducibility tests, comparing this method to the old existing one, were presented. The present paper provides an in-depth study of the impact of this method for different configurations of the implementation. This is carried out in three steps in well-controlled experimental setups of increasing complexity. First, different aspects of Boyd’s method are analyzed in an idealized perfect-model test using a representative 1D shallow-water model. Second, the implementation is tested in an adiabatic 3D numerical weather prediction (NWP) model with perfect-model experiments. Finally, the impact of using Boyd’s method in a more operational-like NWP context is investigated as well. The presented tests show that, while the implementation of Boyd’s method is neutral in terms of scores, it is superior to the existing spline method in the case of strong dynamical forcings at the lateral boundaries.

The Story of the Dent-Gouge Fracture Model

2020 ◽  
Author(s):  
Andrew Cosham ◽  
Phil Hopkins
Keyword(s):  
Empirical Model ◽  
Factor Of Safety ◽  
Original Model ◽  
Full Scale ◽  
Fracture Model ◽  
Burst Pressure ◽  
Two Dimensional ◽  
Dimensional Representation ◽  
Full Scale Tests ◽  
Semi Empirical

Abstract Once upon a time the dent-gouge fracture model was developed by the then British Gas Corporation to estimate the burst pressure of a dent and gouge subject to internal pressure. The dent-gouge fracture model is based on a two-dimensional representation of a dent and gouge; it assumes an infinitely long, longitudinally-orientated gouge (a crack) at the base of infinitely long, longitudinally-orientated dent. The model was calibrated using the results of 109 ring tests and 23 vessel tests conducted by the British Gas Corporation; a dent was introduced and then a slot was machined in the base of the dent (all at zero pressure). It is a semi-empirical model. Part 12 of API 579-1/ASME FFS-1 2016 quotes the original dent-gouge fracture model. A number of variations on the theme of the original dent-gouge fracture model have been developed. The variants have not significantly improved the accuracy of the original model, as is demonstrated by comparing the variants against the results of burst tests on rings and vessels containing a dent and gouge (or notch) reported in the published literature. The dent-gouge fracture model is deconstructed in order to illustrate its component parts. The deconstruction clearly identifies the parts of the model that could be improved. It also highlights where semi-empiricism is embedded in the model. The effect of changes to the original model is illustrated using the results of the full-scale tests. The difficulties introduced by the scatter in the full-scale tests are discussed, noting that a number of different methods have been used to introduce the dent and gouge (or notch) into the ring or vessel. A factor of safety is proposed. Pointers are given to how the dent-gouge fracture model might be improved or replaced. The need for a dent-gouge model is also considered, in the context of the guidance given in API Recommended Practice 1160 and ASME B31.8S.

scholarly journals Deep Learning for Spatially Explicit Prediction of Synoptic-Scale Fronts

2019 ◽  
Vol 34 (4) ◽  
pp. 1137-1160 ◽  
Author(s):  
Ryan Lagerquist ◽  
Amy McGovern ◽  
David John Gagne II
Keyword(s):  
Deep Learning ◽  
Weather Prediction ◽  
Potential Temperature ◽  
Probability Of Detection ◽  
Neural Nets ◽  
Specific Humidity ◽  
Synoptic Scale ◽  
Cold Fronts ◽  
Critical Success ◽  
The North

AbstractThis paper describes the use of convolutional neural nets (CNN), a type of deep learning, to identify fronts in gridded data, followed by a novel postprocessing method that converts probability grids to objects. Synoptic-scale fronts are often associated with extreme weather in the midlatitudes. Predictors are 1000-mb (1 mb = 1 hPa) grids of wind velocity, temperature, specific humidity, wet-bulb potential temperature, and/or geopotential height from the North American Regional Reanalysis. Labels are human-drawn fronts from Weather Prediction Center bulletins. We present two experiments to optimize parameters of the CNN and object conversion. To evaluate our system, we compare the objects (predicted warm and cold fronts) with human-analyzed warm and cold fronts, matching fronts of the same type within a 100- or 250-km neighborhood distance. At 250 km our system obtains a probability of detection of 0.73, success ratio of 0.65 (or false-alarm rate of 0.35), and critical success index of 0.52. These values drastically outperform the baseline, which is a traditional method from numerical frontal analysis. Our system is not intended to replace human meteorologists, but to provide an objective method that can be applied consistently and easily to a large number of cases. Our system could be used, for example, to create climatologies and quantify the spread in forecast frontal properties across members of a numerical weather prediction ensemble.

scholarly journals Mapping of Snow Depth by Blending Satellite and In-Situ Data Using Two-Dimensional Optimal Interpolation—Application to AMSR2

2019 ◽  
Vol 11 (24) ◽  
pp. 3049 ◽  
Author(s):  
Cezar Kongoli ◽  
Jeffrey Key ◽  
Thomas M. Smith
Keyword(s):  
North America ◽  
Snow Depth ◽  
Weather Prediction ◽  
Weighted Average ◽  
Optimal Interpolation ◽  
Horizontal Distance ◽  
Two Dimensional ◽  
In Situ Data ◽  
The Difference

The development of a snow depth product over North America is investigated by applying two-dimensional optimal interpolation to passive microwave satellite-derived and in-situ measured snow depth. At each snow-covered satellite footprint, the technique computes a snow depth increment as the weighted average of data increments, and updates the satellite-derived snow depth accordingly. Data increments are computed as the difference between the in-situ-measured and satellite snow depth at station locations surrounding the satellite footprint. Calculation of optimal weights is based on spatial lag autocorrelation of snow depth increments, modelled as functions of horizontal distance and elevation difference between pairs of observations. The technique is applied to Advanced Microwave Scanning Radiometer 2 (AMSR2) snow depth and in-situ snow depth obtained from the Global Historical Climatology Network. The results over North America during January–February 2017 indicate that the technique greatly enhances the performance of the satellite estimates, especially over mountain terrain, albeit with an accuracy inferior to that over low elevation areas. Moreover, the technique generates more accurate output compared to that from NOAA’s Global Forecast System, with implications for improving the utilization of satellite data in snow assessments and numerical weather prediction.

scholarly journals Practical Predictability of Supercells: Exploring Ensemble Forecast Sensitivity to Initial Condition Spread

2018 ◽  
Vol 146 (8) ◽  
pp. 2361-2379 ◽  
Author(s):  
Montgomery L. Flora ◽  
Corey K. Potvin ◽  
Louis J. Wicker
Keyword(s):  
Lead Time ◽  
Weather Prediction ◽  
Object Oriented ◽  
Comprehensive Analysis ◽  
Initial Condition ◽  
Forecast System ◽  
Model Assumption ◽  
Ensemble Forecasts ◽  
Severe Thunderstorms ◽  
Perfect Model

Abstract As convection-allowing ensembles are routinely used to forecast the evolution of severe thunderstorms, developing an understanding of storm-scale predictability is critical. Using a full-physics numerical weather prediction (NWP) framework, the sensitivity of ensemble forecasts of supercells to initial condition (IC) uncertainty is investigated using a perfect model assumption. Three cases are used from the real-time NSSL Experimental Warn-on-Forecast System for Ensembles (NEWS-e) from the 2016 NOAA Hazardous Weather Testbed Spring Forecasting Experiment. The forecast sensitivity to IC uncertainty is assessed by repeating the simulations with the initial ensemble perturbations reduced to 50% and 25% of their original magnitudes. The object-oriented analysis focuses on significant supercell features, including the mid- and low-level mesocyclone, and rainfall. For a comprehensive analysis, supercell location and amplitude predictability of the aforementioned features are evaluated separately. For all examined features and cases, forecast spread is greatly reduced by halving the IC spread. By reducing the IC spread from 50% to 25% of the original magnitude, forecast spread is still substantially reduced in two of the three cases. The practical predictability limit (PPL), or the lead time beyond which the forecast spread exceeds some prechosen threshold, is case and feature dependent. Comparing to past studies reveals that practical predictability of supercells is substantially improved by initializing once storms are well established in the ensemble analysis.

scholarly journals Large-Eddy Simulation of Post-Cold-Frontal Continental Stratocumulus

2010 ◽  
Vol 67 (12) ◽  
pp. 3835-3853 ◽  
Author(s):  
David B. Mechem ◽  
Yefim L. Kogan ◽  
David M. Schultz
Keyword(s):  
Boundary Layer ◽  
Climate Models ◽  
Weather Prediction ◽  
Vertical Motion ◽  
Surface Fluxes ◽  
Synoptic Scale ◽  
Eddy Simulation ◽  
Turbulent Statistics ◽  
Large Eddy ◽  
Stratocumulus Clouds

Abstract Previous large-eddy simulations (LES) of stratocumulus-topped boundary layers have been exclusively set in marine environments. Boundary layer stratocumulus clouds are also prevalent over the continent but have not been simulated previously. A suite of LES runs was performed for a case of continental post-cold-frontal stratocumulus observed by the Atmospheric Radiation Measurement Program (ARM) Climate Research Facility (ACRF), located in northern Oklahoma. Comparison with fixed, ground-based sensors necessitated an Eulerian approach in which it was necessary to supply to the model estimates of synoptic-scale advection and vertical motion, particularly given the quickly evolving, baroclinic nature of the synoptic environment. Initial analyses from the Rapid Update Cycle model supplied estimates for these forcing terms. Turbulent statistics calculated from the LES results are consistent with large-eddy observations obtained from millimeter-wave cloud radar. The magnitude of turbulence is weaker than in typical marine stratocumulus, a result attributed to highly decoupled cloud and subcloud circulations associated with a deep layer of negative buoyancy flux arising from the entrainment of warm, free-tropospheric air. Model results are highly sensitive to variations in advection of temperature and moisture and much less sensitive to changes in synoptic-scale vertical velocity and surface fluxes. For this case, moisture and temperature advection, rather than entrainment, tend to be the governing factors in the analyzed cloud system maintenance and decay. Typical boundary layer entrainment scalings applied to this case do not perform very well, a result attributed to the highly decoupled nature of the circulation. Shear production is an important part of the turbulent kinetic energy budget. The dominance of advection provides an optimistic outlook for mesoscale, numerical weather prediction, and climate models because these classes of models represent these grid-scale processes better than they do subgrid-scale processes such as entrainment.

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