Microphysical Observations and Mesoscale Model Simulation of a Warm Fog Case during FRAM Project

2008 ◽  
pp. 1161-1178 ◽  
Author(s):  
I. Gultepe ◽  
J.A. Milbrandt
Keyword(s):  
Mesoscale Model ◽  
Model Simulation

Mesoscale Model Simulation of Coastal Fog

1997 ◽  
Author(s):  
Christopher A. Davis
Keyword(s):  
Mesoscale Model ◽  
Model Simulation ◽  
Coastal Fog

scholarly journals Mesoscale Model Simulation of a Severe Summer Thunderstorm in The Netherlands: Performance and Uncertainty Assessment for Parameterised and Resolved Convection

Atmosphere ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 811
Author(s):  
Gert-Jan Steeneveld ◽  
Esther E.M. Peerlings
Keyword(s):  
Soil Moisture ◽  
The Netherlands ◽  
Mesoscale Model ◽  
Model Simulation ◽  
Mesoscale Convective System ◽  
Spatial Uncertainty ◽  
Moisture Distribution ◽  
Convective System ◽  
Grey Zone ◽  
Grid Spacing

On the evening of 23 June 2016 around 18:00 UTC, a mesoscale convective system (MCS) with hail and wind gusts passed the southern province Noord-Brabant in the Netherlands, and caused 675 millions of euros damage. This study evaluates the performance of the Weather Research and Forecasting model with three cumulus parameterisation schemes (Betts–Miller–Janjic, Grell–Freitas and Kain–Fritsch) on a grid spacing of 4 km in the ‘grey-zone’ and with explicitly resolved convection at 2 and 4 km grid spacing. The results of the five experiments are evaluated against observations of accumulated rainfall, maximum radar reflectivity, the CAPE evolution and wind speed. The results show that the Betts–Miller–Janjic scheme is activated too early and can therefore not predict any MCS over the region of interest. The Grell–Freitas and Kain–Fritsch schemes do predict an MCS, but its intensity is underestimated. With the explicit convection, the model is able to resolve the storm, though with a delay and an overestimated intensity. We also study whether spatial uncertainty in soil moisture is scaled up differently using parameterised or explicitly resolved convection. We find that the uncertainty in soil moisture distribution results in larger uncertainty in convective activity in the runs with explicit convection and the Grell–Freitas scheme, while the Kain–Fritsch and Betts–Miller–Janjic scheme clearly present a smaller variability.

Mesoscale model simulation of the meteorological conditions during the 2 June 2002 Double Trouble State Park wildfire

2010 ◽  
Vol 19 (4) ◽  
pp. 427 ◽  
Author(s):  
Joseph J. Charney ◽  
Daniel Keyser
Keyword(s):  
Wind Speed ◽  
Mesoscale Model ◽  
Model Simulation ◽  
Meteorological Conditions ◽  
High Momentum ◽  
East Central ◽  
Spring Frost ◽  
Early Afternoon ◽  
Late Spring Frost ◽  
Surface Drying

On the morning of 2 June 2002, an abandoned campfire grew into a wildfire in the Double Trouble State Park in east-central New Jersey, USA. The wildfire burned 526 ha (1300 acres) and forced the closure of the Garden State Parkway for several hours due to dense smoke. In addition to the presence of dead and dry fuels due to a late spring frost prior to the wildfire, the meteorological conditions at the time of the wildfire were conducive to erratic fire behaviour and rapid fire growth. Observations indicate the occurrence of a substantial drop in relative humidity at the surface accompanied by an increase in wind speed in the vicinity of the wildfire during the late morning and early afternoon of 2 June. The surface drying and increase in wind speed are hypothesised to result from the downward transport of dry, high-momentum air from the middle troposphere occurring in conjunction with a deepening mixed layer. This hypothesis is addressed using a high-resolution mesoscale model simulation to document the structure and evolution of the planetary boundary layer and lower-tropospheric features associated with the arrival of dry, high-momentum air at the surface coincident with the sudden and dramatic growth of the wildfire.

scholarly journals Urban Flow and Dispersion Simulation Using a CFD Model Coupled to a Mesoscale Model

2009 ◽  
Vol 48 (8) ◽  
pp. 1667-1681 ◽  
Author(s):  
Jong-Jin Baik ◽  
Seung-Bu Park ◽  
Jae-Jin Kim
Keyword(s):  
Wind Speed ◽  
Boundary Conditions ◽  
Mesoscale Model ◽  
Model Simulation ◽  
Pollutant Dispersion ◽  
Time Dependent ◽  
Cfd Model ◽  
Ambient Wind ◽  
Mm5 Model ◽  
Urban Flow

Abstract Flow and pollutant dispersion in a densely built-up area of Seoul, Korea, are numerically examined using a computational fluid dynamics (CFD) model coupled to a mesoscale model [fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5)]. The CFD model used is a Reynolds-averaged Navier–Stokes equations model with the renormalization group k − ɛ turbulence model. A one-way nesting method is employed in this study. MM5-simulated data are linearly interpolated in time and space to provide time-dependent boundary conditions for the CFD model integration. In the MM5 simulation, four one-way nested computational domains are considered, and the innermost domain with a horizontal grid size of 1 km covers the Seoul metropolitan area and its adjacent areas, including a part of the Yellow Sea. The NCEP final analysis data are used as initial and boundary conditions for MM5. MM5 is integrated for 48 h starting from 0300 LST 1 June 2004 and the coupled CFD–MM5 model is integrated for 24 h starting from 0300 LST 2 June 2004. During the two-day period, a high-pressure system was dominant over the Korean peninsula, with clear conditions and weak synoptic winds. MM5 simulates local circulations characterized by sea breezes and mountain/valley winds. MM5-simulated synoptic weather and near-surface temperatures and winds are well matched with the observed ones. Results from the coupled CFD–MM5 model simulation show that the flow in the presence of real building clusters can change significantly as the ambient wind speed and direction change. Diurnally varying local circulations mainly cause changes in ambient wind speed and direction in the present simulation. Some characteristic flows—such as the double-eddy circulation, channeling flow, and vertical recirculation vortex—are simulated. Pollutant dispersion pattern and the degree of lateral pollutant dispersion are shown to be complicated in the presence of real building clusters and under varying ambient wind speed and direction. This study suggests that because of the sensitive dependency of urban flow and pollutant dispersion on variations in ambient wind, time-dependent boundary conditions should be used to better simulate or predict them when the ambient wind varies over the period of CFD model simulation.

A mesoscale model application to fire weather winds

1999 ◽  
Vol 9 (4) ◽  
pp. 255 ◽  
Author(s):  
José A. Gómez-Tejedor ◽  
María J. Estrela ◽  
Millán M. Millán
Keyword(s):  
Wind Field ◽  
Mediterranean Basin ◽  
Mesoscale Model ◽  
Model Simulation ◽  
Wind Flow ◽  
Fire Weather ◽  
Fire Event ◽  
Important Conclusion ◽  
Model Application ◽  
Simulation Results

In this work, a mesoscale model has been used to simulate the wind flow in a real fire situation in the Spanish Mediterranean basin in July 1991. The model simulation results for the wind field are shown, and compared to the fire evolution, and to some real observations taken in the area during the fire event. The most important conclusion is that, in spite of the presence of the fire, the mesoscale model was still able to predict the local winds accurately without taking the fire heating processes into account.

scholarly journals The IMPROVE-1 Storm of 1–2 February 2001. Part III: Sensitivity of a Mesoscale Model Simulation to the Representation of Snow Particle Types and Testing of a Bulk Microphysical Scheme with Snow Habit Prediction

2007 ◽  
Vol 64 (11) ◽  
pp. 3927-3948 ◽  
Author(s):  
Christopher P. Woods ◽  
Mark T. Stoelinga ◽  
John D. Locatelli
Keyword(s):  
Pacific Northwest ◽  
Mesoscale Model ◽  
Model Simulation ◽  
Velocity Versus ◽  
Constant Density ◽  
Snow Properties ◽  
The Pacific ◽  
Snow Particle ◽  
Microphysical Parameterization ◽  
Prediction Approach

Abstract A mesoscale model simulation of a wide cold-frontal rainband observed in the Pacific Northwest during the Improvement of Microphysical Parameterization through Observational Verification Experiment (IMPROVE-1) field study was used to test the sensitivity of the model-produced precipitation to varied representations of snow particles in a bulk microphysical scheme. Tests of sensitivity to snow habit type, by using empirical relationships for mass and velocity versus diameter, demonstrated the defectiveness of the conventional assumption of snow particles as constant density spheres. More realistic empirical mass–diameter relationships result in increased numbers of particles and shift the snow size distribution toward larger particles, leading to increased depositional growth of snow and decreased cloud water production. Use of realistic empirical mass–diameter relationships generally increased precipitation at the surface as the rainband interacted with the orography, with more limited increases occurring offshore. Changes in both the mass–diameter and velocity–diameter relationships significantly redistributed precipitation either windward or leeward when the rainband interacted with the mountain barrier. A method of predicting snow particle habit in a bulk microphysical scheme, and using predicted habit to dynamically determine snow properties in the scheme, was developed and tested. The scheme performed well at predicting the habits present (or not present) in aircraft observations of the rainband. Use of the scheme resulted in little change in the precipitation rate at the ground for the rainband offshore, but significantly increased precipitation when the rainband interacted with the windward slope of the Olympic Mountains. The study demonstrates the promise of the habit prediction approach to treating snow in bulk microphysical schemes.

scholarly journals Ozone, Temperature, and Wind Field Measurements in a Tropopause Fold: Comparison with a Mesoscale Model Simulation

1999 ◽  
Vol 127 (11) ◽  
pp. 2641-2653 ◽  
Author(s):  
F. Ravetta ◽  
G. Ancellet ◽  
J. Kowol-Santen ◽  
R. Wilson ◽  
D. Nedeljkovic
Keyword(s):  
Wind Field ◽  
Mesoscale Model ◽  
Model Simulation ◽  
Field Measurements

Predicting vineyard's evolution with the crop model IVINE driven by meteorological model forecasts: preliminary results.

2020 ◽  
Author(s):  
Valentina Andreoli ◽  
Claudio Cassardo ◽  
Massimiliano Manfrin
Keyword(s):  
Input Data ◽  
Mesoscale Model ◽  
Model Simulation ◽  
Circulation Model ◽  
Global Circulation Model ◽  
Meteorological Model ◽  
Environmental Forcing ◽  
Area Index ◽  
Climate Change Effects ◽  
Physiological Variables

<p><span><span>The crop growth model IVINE (Italian Vineyard Integrated Numerical model for Estimating physiological values) was developed at our Dept. of Physics in FORTRAN language as a research model in order to evaluate the environmental forcing effects on vine growth, being vines generally strongly sensitive to meteorological conditions, and with the idea of using it for assessing climate change effects on grape growth. IVINE requires a set of hourly meteorological and soil data as boundary conditions. Input data that are more relevant for the model to correctly simulate the plant growth are air temperature and soil moisture. Among the principal IVINE outputs, we mention: the main phenological stages (dormancy exit, bud-break, fruit set, veraison, and harvest), the Leaf Area Index, the yield, the berry sugar concentration and the predawn leaf water potential. IVINE model requires to set some experimental parameters depending on the cultivar; at present, IVINE is optimized for Nebbiolo and other northern Italy autocthonous and common varieties. In order to use the model for forecasting purposes, the set of input data required by IVINE must be retrieved by the simulation's outputs of a mesoscale model, in turn driven by a Global Circulation Model simulation. In our Department, a voluntary meteorological forecasting service has been working for several years; for this task four daily 5-days simulations are performed over Piedmont Italian region with WRF (Weather Research and Forecast) mesoscale model driven by the GFS (Global Forecast</span> <span>System). Taking advantage of these runs, we have organized a system able to extract, for each simulation, the hourly values of the parameters needed by IVINE. The input dataset is updated every six hours using the values coming by the new simulation, while considering past values acquired. Since IVINE simulation must start from the previous season, in order to correctly simulate the dormancy exit, we have carried out several simulations with IVINE by starting in the same date (</span><span>January 1</span><sup><span>st</span></sup><span> 2018</span><span>) and ending at the fifth day of the last available WRF simulation. In this way, we were able to made a sort of temporal ensemble meteogram for the last five days; where the results of the most recent simulation were displayed with those of previuos runs and the number of simulations was gradually decreasing from 20 to 1 with the progress of the time.</span></span></p><p><span><span>The simulations were performed for the whole 2019 year over 156 WRF grid points distributed in the Langhe, Roero and Monferrato wine areas of Piedmont. Here some </span><span>pheno-physiological variables in vineyards </span><span>are analyzed, relative to some significant points and events, and the main results are discussed.</span></span></p>

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