scholarly journals A New Horizontal Length Scale for a Three-Dimensional Turbulence Parameterization in Mesoscale Atmospheric Modeling over Highly Complex Terrain

2019 ◽  
Vol 58 (9) ◽  
pp. 2087-2102 ◽  
Author(s):  
Brigitta Goger ◽  
Mathias W. Rotach ◽  
Alexander Gohm ◽  
Ivana Stiperski ◽  
Oliver Fuhrer ◽  
...  
Keyword(s):  
Complex Terrain ◽  
Prediction Models ◽  
Mesoscale Model ◽  
Weather Prediction ◽  
Atmospheric Modeling ◽  
Length Scale ◽  
Horizontal Shear ◽  
Valley Wind ◽  
Turbulence Parameterization ◽  
Horizontal Length

AbstractThe correct simulation of the atmospheric boundary layer (ABL) in highly complex terrain is a challenge for mesoscale numerical weather prediction models. An improvement in model performance is possible if horizontal contributions to turbulence kinetic energy (TKE) production, such as horizontal shear production, are implemented in the model’s turbulence parameterization. However, 3D turbulence parameterizations often only have a constant horizontal length scale that depends on the horizontal grid spacing. This is unphysical for mesoscale applications, because such parameterizations were initially developed for much smaller model grid spacings (e.g., for large-eddy simulations). In this study, we develop a new physically based horizontal length scale for the high-resolution mesoscale model COSMO. We analyze days dominated by thermally driven circulations (valley wind days) in the Inn Valley, Austria. Results show that the new horizontal length scale improves TKE simulations in the valley, when horizontal shear processes contribute to the overall TKE budget. Vertical profiles of TKE and transects across the valley indicate that the model simulates the ABL in a more realistic way than standard turbulence schemes, because the new scheme is able to account for terrain inhomogeneities. A model validation with 88 stations in Austria for four case study days indicates no change in the mean surface fields of temperature, relative humidity, and wind speed by the new turbulence parameterization.

The Bow and Arrow Mesoscale Convective Structure

2013 ◽  
Vol 141 (5) ◽  
pp. 1648-1672 ◽  
Author(s):  
Kelly M. Keene ◽  
Russ S. Schumacher
Keyword(s):  
Heavy Rainfall ◽  
Prediction Models ◽  
Weather Prediction ◽  
Warm Season ◽  
Severe Weather ◽  
Cold Pool ◽  
Horizontal Shear ◽  
Convective Systems ◽  
Wind Speeds ◽  
Bow And Arrow

Abstract The accurate prediction of warm-season convective systems and the heavy rainfall and severe weather associated with them remains a challenge for numerical weather prediction models. This study looks at a circumstance in which quasi-stationary convection forms perpendicular to, and above the cold-pool behind strong bow echoes. The authors refer to this phenomenon as a “bow and arrow” because on radar imagery the two convective lines resemble an archer’s bow and arrow. The “arrow” can produce heavy rainfall and severe weather, extending over hundreds of kilometers. These events are challenging to forecast because they require an accurate forecast of earlier convection and the effects of that convection on the environment. In this study, basic characteristics of 14 events are documented, and observations of 4 events are presented to identify common environmental conditions prior to the development of the back-building convection. Simulations of three cases using the Weather Research and Forecasting Model (WRF) are analyzed in an attempt to understand the mechanisms responsible for initiating and maintaining the convective line. In each case, strong southwesterly flow (inducing warm air advection and gradual isentropic lifting), in addition to directional and speed convergence into the convective arrow appear to contribute to initiation of convection. The linear orientation of the arrow may be associated with a combination of increased wind speeds and horizontal shear in the arrow region. When these ingredients are combined with thermodynamic instability, there appears to be a greater possibility of formation and maintenance of a convective arrow behind a bow echo.

scholarly journals C-Band Dual-Doppler Retrievals in Complex Terrain: Improving the Knowledge of Severe Storm Dynamics in Catalonia

2020 ◽  
Vol 12 (18) ◽  
pp. 2930 ◽  
Author(s):  
Anna del Moral ◽  
Tammy M. Weckwerth ◽  
Tomeu Rigo ◽  
Michael M. Bell ◽  
María Carmen Llasat
Keyword(s):  
Complex Terrain ◽  
Prediction Models ◽  
Weather Prediction ◽  
Warning System ◽  
Wind Fields ◽  
Severe Thunderstorm ◽  
Radar Network ◽  
Storm Dynamics ◽  
Storm Cells ◽  
First Time

Convective activity in Catalonia (northeastern Spain) mainly occurs during summer and autumn, with severe weather occurring 33 days per year on average. In some cases, the storms have unexpected propagation characteristics, likely due to a combination of the complex topography and the thunderstorms’ propagation mechanisms. Partly due to the local nature of the events, numerical weather prediction models are not able to accurately nowcast the complex mesoscale mechanisms (i.e., local influence of topography). This directly impacts the retrieved position and motion of the storms, and consequently, the likely associated storm severity. Although a successful warning system based on lightning and radar observations has been developed, there remains a lack of knowledge of storm dynamics that could lead to forecast improvements. The present study explores the capabilities of the radar network at the Meteorological Service of Catalonia to retrieve dual-Doppler wind fields to study the dynamics of Catalan thunderstorms. A severe thunderstorm that splits and a tornado-producing supercell that is channeled through a valley are used to demonstrate the capabilities of an advanced open source technique that retrieves dynamical variables from C-band operational radars in complex terrain. For the first time in the Iberian Peninsula, complete 3D storm-relative winds are obtained, providing information about the internal dynamics of the storms. This aids in the analyses of the interaction between different storm cells within a system and/or the interaction of the cells with the local topography.

Temporal Changes in Wind as Objects for Evaluating Mesoscale Numerical Weather Prediction

2009 ◽  
Vol 24 (5) ◽  
pp. 1374-1389 ◽  
Author(s):  
Daran L. Rife ◽  
Christopher A. Davis ◽  
Jason C. Knievel
Keyword(s):  
Numerical Weather Prediction ◽  
Prediction Models ◽  
Mesoscale Model ◽  
Weather Prediction ◽  
Grid Point ◽  
Surface Wind ◽  
Temporal Changes ◽  
High Temporal Resolution ◽  
Numerical Weather ◽  
Wind Events

Abstract The study describes a method of evaluating numerical weather prediction models by comparing the characteristics of temporal changes in simulated and observed 10-m (AGL) winds. The method is demonstrated on a 1-yr collection of 1-day simulations by the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) over southern New Mexico. Temporal objects, or wind events, are defined at the observation locations and at each grid point in the model domain as vector wind changes over 2 h. Changes above the uppermost quartile of the distributions in the observations and simulations are empirically classified as significant; their attributes are analyzed and interpreted. It is demonstrated that the model can discriminate between large and modest wind changes on a pointwise basis, suggesting that many forecast events have an observational counterpart. Spatial clusters of significant wind events are highly continuous in space and time. Such continuity suggests that displaying maps of surface wind changes with high temporal resolution can alert forecasters to the occurrence of important phenomena. Documented systematic errors in the amplitude, direction, and timing of wind events will allow forecasters to mentally adjust for biases in features forecast by the model.

scholarly journals Development of a hybrid variational-ensemble data assimilation technique for observed lightning tested in a mesoscale model

2014 ◽  
Vol 21 (5) ◽  
pp. 1027-1041 ◽  
Author(s):  
K. Apodaca ◽  
M. Zupanski ◽  
M. DeMaria ◽  
J. A. Knaff ◽  
L. D. Grasso
Keyword(s):  
Data Assimilation ◽  
Prediction Models ◽  
Mesoscale Model ◽  
Initial Conditions ◽  
Positive Impact ◽  
Weather Prediction ◽  
New Information ◽  
Forecasting System ◽  
Maximum Likelihood Ensemble Filter ◽  
The Impact

Abstract. Lightning measurements from the Geostationary Lightning Mapper (GLM) that will be aboard the Geostationary Operational Environmental Satellite – R Series will bring new information that can have the potential for improving the initialization of numerical weather prediction models by assisting in the detection of clouds and convection through data assimilation. In this study we focus on investigating the utility of lightning observations in mesoscale and regional applications suitable for current operational environments, in which convection cannot be explicitly resolved. Therefore, we examine the impact of lightning observations on storm environment. Preliminary steps in developing a lightning data assimilation capability suitable for mesoscale modeling are presented in this paper. World Wide Lightning Location Network (WWLLN) data was utilized as a proxy for GLM measurements and was assimilated with the Maximum Likelihood Ensemble Filter, interfaced with the Nonhydrostatic Mesoscale Model core of the Weather Research and Forecasting system (WRF-NMM). In order to test this methodology, regional data assimilation experiments were conducted. Results indicate that lightning data assimilation had a positive impact on the following: information content, influencing several dynamical variables in the model (e.g., moisture, temperature, and winds), and improving initial conditions during several data assimilation cycles. However, the 6 h forecast after the assimilation did not show a clear improvement in terms of root mean square (RMS) errors.

scholarly journals Hybrid variational-ensemble assimilation of lightning observations in a mesoscale model

2014 ◽  
Vol 1 (1) ◽  
pp. 917-952
Author(s):  
K. Apodaca ◽  
M. Zupanski ◽  
M. DeMaria ◽  
J. A. Knaff ◽  
L. D. Grasso
Keyword(s):  
Data Assimilation ◽  
Prediction Models ◽  
Mesoscale Model ◽  
Initial Conditions ◽  
Positive Impact ◽  
Weather Prediction ◽  
New Information ◽  
Forecasting System ◽  
Maximum Likelihood Ensemble Filter ◽  
The Impact

Abstract. Lightning measurements from the Geostationary Lightning Mapper (GLM) that will be aboard the Goestationary Operational Environmental Satellite – R Series will bring new information that can have the potential for improving the initialization of numerical weather prediction models by assisting in the detection of clouds and convection through data assimilation. In this study we focus on investigating the utility of lightning observations in mesoscale and regional applications suitable for current operational environments, in which convection cannot be explicitly resolved. Therefore, we examine the impact of lightning observations on storm environment. Preliminary steps in developing a lightning data assimilation capability suitable for mesoscale modeling are presented in this paper. World Wide Lightning Location Network (WWLLN) data was utilized as a proxy for GLM measurements and was assimilated with the Maximum Likelihood Ensemble Filter, interfaced with the Nonhydrostatic Mesoscale Model core of the Weather Research and Forecasting system (WRF-NMM). In order to test this methodology, regional data assimilation experiments were conducted. Results indicate that lightning data assimilation had a positive impact on the following: information content, influencing several dynamical variables in the model (e.g., moisture, temperature, and winds), improving initial conditions, and partially improving WRF-NMM forecasts during several data assimilation cycles.

scholarly journals Can machine learning improve the model representation of TKE dissipation rate in the boundary layer for complex terrain?

2020 ◽  
Author(s):  
Nicola Bodini ◽  
Julie K. Lundquist ◽  
Mike Optis
Keyword(s):  
Machine Learning ◽  
Numerical Weather Prediction ◽  
Complex Terrain ◽  
Dissipation Rate ◽  
Prediction Models ◽  
Weather Prediction ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Tke Dissipation Rate ◽  
Numerical Weather Prediction Models

Abstract. Current turbulence parameterizations in numerical weather prediction models at the mesoscale assume a local equilibrium between production and dissipation of turbulence. As this assumption does not hold at fine horizontal resolutions, improved ways to represent turbulent kinetic energy (TKE) dissipation rate (ε) are needed. Here, we use a 6-week data set of turbulence measurements from 184 sonic anemometers in complex terrain at the Perdigão field campaign to suggest improved representations of dissipation rate. First, we demonstrate that a widely used Mellor, Yamada, Nakanishi, and Niino (MYNN) parameterization of TKE dissipation rate leads to a large inaccuracy and bias in the representation of ε. Next, we assess the potential of machine-learning techniques to predict TKE dissipation rate from a set of atmospheric and terrain-related features. We train and test several machine-learning algorithms using the data at Perdigão, and we find that multivariate polynomial regressions and random forests can eliminate the bias MYNN currently shows in representing ε, while also reducing the average error by up to 30 %. Of all the variables included in the algorithms, TKE is the variable responsible for most of the variability of ε, and a strong positive correlation exists between the two. These results suggest further consideration of machine-learning techniques to enhance parameterizations of turbulence in numerical weather prediction models.

scholarly journals Can machine learning improve the model representation of turbulent kinetic energy dissipation rate in the boundary layer for complex terrain?

2020 ◽  
Vol 13 (9) ◽  
pp. 4271-4285
Author(s):  
Nicola Bodini ◽  
Julie K. Lundquist ◽  
Mike Optis
Keyword(s):  
Machine Learning ◽  
Kinetic Energy ◽  
Complex Terrain ◽  
Dissipation Rate ◽  
Prediction Models ◽  
Weather Prediction ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Tke Dissipation Rate ◽  
Numerical Weather Prediction Models

Abstract. Current turbulence parameterizations in numerical weather prediction models at the mesoscale assume a local equilibrium between production and dissipation of turbulence. As this assumption does not hold at fine horizontal resolutions, improved ways to represent turbulent kinetic energy (TKE) dissipation rate (ϵ) are needed. Here, we use a 6-week data set of turbulence measurements from 184 sonic anemometers in complex terrain at the Perdigão field campaign to suggest improved representations of dissipation rate. First, we demonstrate that the widely used Mellor, Yamada, Nakanishi, and Niino (MYNN) parameterization of TKE dissipation rate leads to a large inaccuracy and bias in the representation of ϵ. Next, we assess the potential of machine-learning techniques to predict TKE dissipation rate from a set of atmospheric and terrain-related features. We train and test several machine-learning algorithms using the data at Perdigão, and we find that the models eliminate the bias MYNN currently shows in representing ϵ, while also reducing the average error by up to almost 40 %. Of all the variables included in the algorithms, TKE is the variable responsible for most of the variability of ϵ, and a strong positive correlation exists between the two. These results suggest further consideration of machine-learning techniques to enhance parameterizations of turbulence in numerical weather prediction models.

scholarly journals A New Visibility Parameterization for Warm-Fog Applications in Numerical Weather Prediction Models

2006 ◽  
Vol 45 (11) ◽  
pp. 1469-1480 ◽  
Author(s):  
I. Gultepe ◽  
M. D. Müller ◽  
Z. Boybeyi
Keyword(s):  
Numerical Weather Prediction ◽  
Prediction Models ◽  
Mesoscale Model ◽  
Weather Prediction ◽  
Liquid Water Content ◽  
Parameterization Scheme ◽  
Numerical Weather ◽  
Time Period ◽  
Simulation Domain ◽  
Forecast Models

Abstract The objective of this work is to suggest a new warm-fog visibility parameterization scheme for numerical weather prediction (NWP) models. In situ observations collected during the Radiation and Aerosol Cloud Experiment, representing boundary layer low-level clouds, were used to develop a parameterization scheme between visibility and a combined parameter as a function of both droplet number concentration Nd and liquid water content (LWC). The current NWP models usually use relationships between extinction coefficient and LWC. A newly developed parameterization scheme for visibility, Vis = f (LWC, Nd), is applied to the NOAA Nonhydrostatic Mesoscale Model. In this model, the microphysics of fog was adapted from the 1D Parameterized Fog (PAFOG) model and then was used in the lower 1.5 km of the atmosphere. Simulations for testing the new parameterization scheme are performed in a 50-km innermost-nested simulation domain using a horizontal grid spacing of 1 km centered on Zurich Unique Airport in Switzerland. The simulations over a 10-h time period showed that visibility differences between old and new parameterization schemes can be more than 50%. It is concluded that accurate visibility estimates require skillful LWC as well as Nd estimates from forecasts. Therefore, the current models can significantly over-/underestimate Vis (with more than 50% uncertainty) depending on environmental conditions. Inclusion of Nd as a prognostic (or parameterized) variable in parameterizations would significantly improve the operational forecast models.

Investigating the dynamics of thermally driven up-slope flows: analysis of data from measurements in the Inn Valley (Austria) and comparison with a simple model

2021 ◽  
Author(s):  
Mattia Marchio ◽  
Sofia Farina ◽  
Dino Zardi
Keyword(s):  
Surface Temperature ◽  
Prediction Models ◽  
Weather Prediction ◽  
Slope Angle ◽  
Turbulent Fluxes ◽  
Surface Energy Budget ◽  
Valley Wind ◽  
Slope Winds ◽  
Slope Wind ◽  
Slope Flow

<p><span>Diurnal wind systems typically develop in mountainous areas following the daytime heating and nighttime cooling of sloping surfaces. While down-slope winds have been extensively treated in the literature, up-slope winds have received much less attention. In particular, the physical mechanisms associated with the development of these winds, as well as the search for appropriate parameterization of turbulent fluxes of mass, momentum, and heat over slopes in numerical weather prediction models are still open research topics.</span></p><p><span>Here we present some preliminary results from the analysis of turbulence data (sonic wind speed, temperature, humidity, and turbulent fluxes) collected at two slope stations which are part of the i-Box initiative. The i-Box project (Rotach et al. 2017) aims at studying turbulent exchange processes in complex terrain areas. The experimental setup is composed of six stations disseminated in the surroundings of the alpine city of Innsbruck, in the Inn Valley. The two stations adopted for the present study are located at different points on the valley sidewalls, one with a slope angle of 27° (labelled NF27) and one with a slope angle of 10° (NF10). Both stations are located over slopes covered by alpine meadow and at an altitude of about 1000 m MSL (400 m above the valley floor). The station NF27 has two measurement points, 1.5 and 6.8 m AGL, while the station NF10 has one measurement point, at 6.2 m AGL.</span></p><p><span>The analysis shows that criteria proposed in the literature for the selection of valley-wind days may not apply for the identification of slope-wind days. Furthermore, from the analysis of second order moments, scaling relationships are derived for up-slope flow conditions. In addition, measurements representing the evolution of the up-slope flow structure from the early morning to the mid-afternoon are compared with an existing, simplified, analytical model, which provides the evolution of the vertical profiles of temperature and along-slope wind velocity as generated by a sinusoidal forcing representing the daily cycle of surface temperature. An improvement of the existing model, where the surface energy budget is considered as the boundary condition for the surface temperature, is also tested.</span></p>

scholarly journals Downscaling surface wind predictions from numerical weather prediction models in complex terrain with WindNinja

2016 ◽  
Author(s):  
N. S. Wagenbrenner ◽  
J. M. Forthofer ◽  
B. K. Lamb ◽  
K. S. Shannon ◽  
B. W. Butler
Keyword(s):  
Numerical Weather Prediction ◽  
Complex Terrain ◽  
Prediction Models ◽  
Wildland Fire ◽  
Weather Prediction ◽  
Surface Wind ◽  
Atmospheric Stability ◽  
Wind Model ◽  
Near Surface ◽  
Numerical Weather

Abstract. Wind predictions in complex terrain are important for a number of applications. Dynamic downscaling of numerical weather prediction (NWP) model winds with a high resolution wind model is one way to obtain a wind forecast that accounts for local terrain effects, such as wind speed-up over ridges, flow channeling in valleys, flow separation around terrain obstacles, and flows induced by local surface heating and cooling. In this paper we investigate the ability of a mass-consistent wind model for downscaling near-surface wind predictions from four NWP models in complex terrain. Model predictions are compared with surface observations from a tall, isolated mountain. Downscaling improved near-surface wind forecasts under high-wind (near-neutral atmospheric stability) conditions. Results were mixed during upslope and downslope (non-neutral atmospheric stability) flow periods, although wind direction predictions generally improved with downscaling. This work constitutes evaluation of a diagnostic wind model at unprecedented high spatial resolution in terrain with topographical ruggedness approaching that of typical landscapes in the western US susceptible to wildland fire.

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