Interdisciplinary Modeling for Acquatic Ecosystems 11 18 July 2005 Atmospheric Modeling Vanda...

Interdisciplinary Modeling for AcInterdisciplinary Modeling for Acquatic Ecosystemsquatic EcosystemsInterdisciplinary Modeling for AcInterdisciplinary Modeling for Acquatic Ecosystemsquatic Ecosystems

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Atmospheric ModelingAtmospheric Modeling

Vanda GrubišićDesert Research Institute

Division of Atmospheric Sciences

Vanda GrubišićDesert Research Institute

Division of Atmospheric Sciences

18 July 200518 July 2005 Interdisciplinary Modeling for Acquatic EcosystemsInterdisciplinary Modeling for Acquatic Ecosystems

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Atmospheric Model Atmospheric Model

• A component of complex ecosystem models

• Provides external “forcing” (e.g., precipitation, temperature, winds, relative humidity, radiation, etc.) for a variety of other constituent models

• In jargon of many environmental modeling disciplines often referred to as “meteorology”

• A component of complex ecosystem models

• Provides external “forcing” (e.g., precipitation, temperature, winds, relative humidity, radiation, etc.) for a variety of other constituent models

• In jargon of many environmental modeling disciplines often referred to as “meteorology”


33

Model vs. Computer Model Model vs. Computer Model

• Model: A mathematical representation of a process (analytical model, parameterized model - insight is a key, empirical models - regression fit)

• Computer (Numerical) Model: Discretized model equations numerically solved with use of computers

• Model: A mathematical representation of a process (analytical model, parameterized model - insight is a key, empirical models - regression fit)

• Computer (Numerical) Model: Discretized model equations numerically solved with use of computers


44

How sophisticated atmospheric model one needs?

How sophisticated atmospheric model one needs?

• Dictated by the importance of atmospheric forcing to the problem at hand (e.g. Lake Tahoe clarity vs. algae growth)

• Always be aware of uncertainties and errors (especially if atmospheric forcing is a key input into your model!)

• Dictated by the importance of atmospheric forcing to the problem at hand (e.g. Lake Tahoe clarity vs. algae growth)

• Always be aware of uncertainties and errors (especially if atmospheric forcing is a key input into your model!)


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Important ScalesImportant Scales• Atmospheric processes encompass a wide range of

scales• Spatial and Temporal Scales Example Process

– Molecular (<< 2 mm, >min) Diffusion – Microscale (2 mm - 2 km, hours) In cloud processes– Mesoscale (2 - 2000 km, Tornadoes to

hours to days) Thunderstorms – Synoptic (500 - 10,000 km Weather Systems:

days to weeks) Anticyclones, Cyclones, Fronts

– Planetary (> 10,000 km, > weeks)Global Circulation

• Atmospheric processes encompass a wide range of scales

• Spatial and Temporal Scales Example Process– Molecular (<< 2 mm, >min) Diffusion – Microscale (2 mm - 2 km, hours) In cloud processes– Mesoscale (2 - 2000 km, Tornadoes to

hours to days) Thunderstorms – Synoptic (500 - 10,000 km Weather Systems:

days to weeks) Anticyclones, Cyclones, Fronts

– Planetary (> 10,000 km, > weeks)Global Circulation


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What Type of Atmospheric Numerical Model to Choose?

What Type of Atmospheric Numerical Model to Choose?

• Scales Model– Molecular (<< 2 mm, >min) Diffusion Equation– Microscale Microphysical and Cloud – Mesoscale Mesoscale (limited area)– Synoptic Weather Prediction/

Regional Climate (regional to hemispheric)

– Planetary Global Circulation Model

• Scales Model– Molecular (<< 2 mm, >min) Diffusion Equation– Microscale Microphysical and Cloud – Mesoscale Mesoscale (limited area)– Synoptic Weather Prediction/

Regional Climate (regional to hemispheric)

– Planetary Global Circulation Model


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What about Vertical Scale?What about Vertical Scale?

• Air is a continuously stratified fluid (density function of height)

• All interesting meteorological phenomena occur in the troposphere

• Air is a continuously stratified fluid (density function of height)

• All interesting meteorological phenomena occur in the troposphere

Interdisciplinary Modeling for AcInterdisciplinary Modeling for Acquatic Ecosystemsquatic EcosystemsInterdisciplinary Modeling for AcInterdisciplinary Modeling for Acquatic Ecosystemsquatic Ecosystems

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MesoscaleMesoscale

The most interesting phenomenology

The most challenging forecasting

The most demanding computationally

The most interesting phenomenology

The most challenging forecasting

The most demanding computationally


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Synoptic MesoscaleSynoptic Mesoscale

Weather

SevereWeather


1010

MesoscaleNon-Hydrostatic Effects Important

MesoscaleNon-Hydrostatic Effects Important

Buoyancy and Topographic Effects Dominate

Hydrostatic Equilibrium vs. Lack of It


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Equations and ApproximationsEquations and Approximations• Set of coupled partial differential equations describing

the motion (conservation of momentum), thermodynamic state of the atmosphere (1st law of thermodynamics), and continuity equations for air (+particles+chemical spiecies) (conservation of mass)

• Set of coupled partial differential equations describing the motion (conservation of momentum), thermodynamic state of the atmosphere (1st law of thermodynamics), and continuity equations for air (+particles+chemical spiecies) (conservation of mass)

€

∂ r

v

∂t+ (

r v • ∇

r v ) = − fˆ z ×

r v −∇φ −

1

ρ a

∇pa + ν a∇2r v +

1

ρ a

(∇ • ρ aKm∇)r v

€

∂θv

∂t+ (

r v • ∇θv ) =

1

ρ a

(∇ • ρ aK h∇)θv +θv

c pT

dQn

dtn=1

Neh

∑

€

∂q

∂t+ (

r v • ∇q) =

1

ρ a

(∇ • ρ aK h∇)q + Rn

n=1

Nem

∑


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Momentum EquationMomentum Equation

€

∂ r

v

∂t+ (

r v • ∇

r v ) = − fˆ z ×

r v −∇φ −

1

ρ a

∇pa + ν a∇2r v +

1

ρ a

(∇ • ρ aKm∇)r v

€

Dr v

Dt

Lagrangian Derivative

}

Coriolis Force

Gravity

Pressure Gradient Force

Diffusion

Eddy Diffusion “Turbulence”

€

rv = (u,v,w) Air motion vector (wind vector)

Function of space and time

€

rv =

r v (x, y,z, t)

€

ν ∫


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First Attempts at Atmospheric Numerical Modeling

First Attempts at Atmospheric Numerical Modeling

• Lewis Fry Richardson, 1913-1919 experiment (Richardson 1922) Numerical solutions to a simplified set of equations obtained by human “computers”

• John von Neumman 1946 Numerical solutions to a (different) simplified set obtained by an electronic computer (ENIAC)

• Lewis Fry Richardson, 1913-1919 experiment (Richardson 1922) Numerical solutions to a simplified set of equations obtained by human “computers”

• John von Neumman 1946 Numerical solutions to a (different) simplified set obtained by an electronic computer (ENIAC)


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Common Theme That Continues to Today…Common Theme That Continues to Today…

• It is impossible to explicitly numerically resolve all scales and processes simplifications, approximations, and parameterizations necessary even as model resolution increases (grid spacing decreases)

• Lack of data for verification: Density of observational networks continues to lag increases in model resolutions (due to computing technology advances)

• It is impossible to explicitly numerically resolve all scales and processes simplifications, approximations, and parameterizations necessary even as model resolution increases (grid spacing decreases)

• Lack of data for verification: Density of observational networks continues to lag increases in model resolutions (due to computing technology advances)


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How Mesoscale Models Work?How Mesoscale Models Work?


1616

Limited Area Models Limited Area Models

Need initial and boundary conditions from a larger-scale model!


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Grid-Point ModelsResolution

Horizontal and Vertical

Grid-Point ModelsResolution

Horizontal and Vertical


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Vertical Coordinateand Resolution

Vertical Coordinateand Resolution


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Mesoscale Models Effects of Increased Resolution

Mesoscale Models Effects of Increased Resolution

Price to be PaidSeveral-fold increase in computational time and cost!


2020

How to Increase Resolution without Making Computation

Prohibitively Expansive?

How to Increase Resolution without Making Computation

Prohibitively Expansive?

• Answer: Domain Nesting• Answer: Domain NestingHorizontal resolution increased by the factor of 3 for each successive nested domain (two-way nesting)

Nested domains can be spawned at any time

Vertical resolution (commonly) the same in all domains


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Importance of BC Updates and Assimilation of Observations

Importance of BC Updates and Assimilation of Observations

• Keep Models from Veering Off into Virtual Reality

• Keep Models from Veering Off into Virtual Reality


2222

Parameterizations of Subgrid-Scale Processes

Parameterizations of Subgrid-Scale Processes

• Parameterizations: Modeling the effect of a process (emulation) rather than modeling the process itself (simulation)

• Why do we need parameterizations?– Processes either too small or too complex to be

resolved and directly simulated– Processes not understood enough – Yet, important for obtaining accurate simulation

and/or forecast

• Parameterizations: Modeling the effect of a process (emulation) rather than modeling the process itself (simulation)

• Why do we need parameterizations?– Processes either too small or too complex to be

resolved and directly simulated– Processes not understood enough – Yet, important for obtaining accurate simulation

and/or forecast


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ParameterizationsParameterizations

Near Surface Processes Convective Mixing


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How are Mesoscale Models Used?

How are Mesoscale Models Used?

• Real-Time Weather Forecasting (NWS-USA, Universities-regional forecasting efforts)

• Research Tool – Real-data simulations (“Case and Sensitivity Studies”)– Idealized simulations (uniform wind and/or stability

profiles, simplified topography, simple initial and BC, 2D,…)

• Real-Time Weather Forecasting (NWS-USA, Universities-regional forecasting efforts)

• Research Tool – Real-data simulations (“Case and Sensitivity Studies”)– Idealized simulations (uniform wind and/or stability

profiles, simplified topography, simple initial and BC, 2D,…)


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Open QuestionsOpen Questions

• Continuous need for high-resolution observations for model verification [mesoscale field campaigns, e.g. Terrain-induced Rotor Experiment (T-REX) 2006 in Sierra Nevada, CA]

• Increase in horizontal resolution does not always lead to better results [e.g., Quantitative Precipitation Forecasting, model skill worse at 4.5 and 1.5 km than at 13.5 km, Grubišić et al. (2005), Colle et al. (2002)

• Range of validity of parameterizations

• Continuous need for high-resolution observations for model verification [mesoscale field campaigns, e.g. Terrain-induced Rotor Experiment (T-REX) 2006 in Sierra Nevada, CA]

• Increase in horizontal resolution does not always lead to better results [e.g., Quantitative Precipitation Forecasting, model skill worse at 4.5 and 1.5 km than at 13.5 km, Grubišić et al. (2005), Colle et al. (2002)

• Range of validity of parameterizations


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ResourcesResources

• Beyond Meteorology 101 University Corporation for Atmospheric Research (UCAR) MetEd (Meteorology Education & Training)

COMET Program pages http://meted.ucar.edu

Some of My Favorites: • Rain Gauges: Are They Really Ground Truth?

• How Models Produce Precipitation & Clouds

• Intelligent Use of Model-Derived Products

• Beyond Meteorology 101 University Corporation for Atmospheric Research (UCAR) MetEd (Meteorology Education & Training)

COMET Program pages http://meted.ucar.edu

Some of My Favorites: • Rain Gauges: Are They Really Ground Truth?

• How Models Produce Precipitation & Clouds

• Intelligent Use of Model-Derived Products

http://meted.ucar.edu/

http://meted.ucar.edu/


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Resources Resources Mesoscale Models - Large Community Models, Open Source

1) MM5 - Pennsylvania State University/National Center for Atmospheric Research (PSU/NCAR) Mesoscale Model v5 http://www.mmm.ucar.edu/mm5

2) COAMPS - Naval Research Laboratory's Coupled Ocean/Atmosphere Prediction System http://www.nrlmry.navy.mil/coamps-web/web/home

3) WRF - Weather Research & Forecasting Model National Center for Atmospheric Research (NCAR), National Oceanic and Atmospheric Administration (NOAA) Forecast System Laboratory (FSL) and the National Centers for Environmental Prediction (NCEP), Air Force Weather Agency (AFWA), Naval Research Laboratory (NRL), University of Oklahoma, Federal Aviation Administration (FAA) http://www.wrf-model.org

Mesoscale Models - Large Community Models, Open Source

1) MM5 - Pennsylvania State University/National Center for Atmospheric Research (PSU/NCAR) Mesoscale Model v5 http://www.mmm.ucar.edu/mm5

2) COAMPS - Naval Research Laboratory's Coupled Ocean/Atmosphere Prediction System http://www.nrlmry.navy.mil/coamps-web/web/home

3) WRF - Weather Research & Forecasting Model National Center for Atmospheric Research (NCAR), National Oceanic and Atmospheric Administration (NOAA) Forecast System Laboratory (FSL) and the National Centers for Environmental Prediction (NCEP), Air Force Weather Agency (AFWA), Naval Research Laboratory (NRL), University of Oklahoma, Federal Aviation Administration (FAA) http://www.wrf-model.org

http://www.mmm.ucar.edu/mm5

http://www.nrlmry.navy.mil/coamps-web/web/home

http://www.wrf-model.org/

http://www.mmm.ucar.edu/mm5

http://www.nrlmry.navy.mil/coamps-web/web/home

http://www.wrf-model.org/

Interdisciplinary Modeling for Acquatic Ecosystems 11 18 July 2005 Atmospheric Modeling Vanda...

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