Grid Point Models

Surface Data

Models: Types

• Spectral Models (AVN)– Data is not represented on grid– Data represented by wave functions– Resolution is a function of # waves used in model– Computational errors generally less– Not well-suited for mesoscale modeling

Models: Types

• Hydrostatic Models (ETA, AVN, NGM)– Cannot produce vertical accelerations– Vertical motions determined by the continuity

equation

• Non-Hydrostatic Models (Some MM5)– Can produce vertical accelerations– Calculate Vertical Motions explicitly– Used in mesoscale applications (conv)

Models: The Basics

• Domain: Area covered by the model– IDD grids

– Regional vs. Global– Nested models

Models: The Basics

• Resolution: Distance between grid points– High and low resolution models

• http://www.unidata.ucar.edu/packages/gempak/examples/models/grids/grid211.gif• http://www.unidata.ucar.edu/packages/gempak/examples/models/grids/grid215.gif

Models: Resolution

Model Resolution

Should have 5 to 7 grid points to resolve feature

Model Resolution

Should have 5 to 7 grid points to resolve feature

Models: The Basics

• What can’t models simulate?– Processes neglected in simplified equations– Processes unknown– Processes that are sub-grid scale

• How’s a model to cope?

Models: The Basics

• Parameterizations– Model’s attempt to ““simulate”” (incorporate)

important sub-grid scale processes – Examples:

• Convection• Microphysical processes of precipitation• Surface/Boundary layer fluxes

Model Parameterizations:CONVECTION

Model Parameterizations:CONVECTION

Model Parameterizations:CONVECTION

Model Parameterizations:CONVECTION

Why are model forecasts imperfect?

• Imperfect Initial Conditions– Too few observations– “Continuous atmosphere = Non-continuous sampling”

• some areas worse than others– Bad observations

• instrument error– Errors in the initialization procedure

• First guess & objective analysis

“GI = GO”

Imperfect Models: Accurate Ob = Good ob?

Good ObservationOr Bad Observation?

Why are model forecasts imperfect?

• Imperfect Models– Simplified equations

• many “unimportant” terms = 0– Neglected Processes

• that’s why we still have field projects!– Resolution

• can’t simulate small scale stuff• ‘good’ ob can be a bad ob

Trend of Numerical Models

• Resolution increasing!

• Run more frequently!

• More models!– Computer power increasing– Cost decreasing

Trend of Numerical Models

• Implications: Higher Resolution– Improved initialization– More small-scale effects will be predicted!– Will these small-scale phenomena be correct?

• If terrain-forced weather phenomena = YES!• Density obs VS. density grid points• Heightened sensitivity to initial conditions

Higher Resolution: Improves Initialization

Good ObservationOr Bad Observation?

Higher Resolutionwill help but not solve the problem!

Model Resolution

Should have 5 to 7 grid points to resolve feature

Higher Resolution: Improves Terrain-forced weather!

Model Terrain vs. Actual Terrain

Model Terrain

• ETA 80km– http://lnx21.wwb.noaa.gov/orog/80km_east_crop.gif

• ETA 32km– http://lnx21.wwb.noaa.gov/orog/32km_east.gif

• ETA 10km– http://lnx21.wwb.noaa.gov/orog/10km_east_crop.gif

• Actual terrain– http://fermi.jhuapl.edu/states/us/big_us_color.gif

Orographic: Differential Heating

Orographic: Differential Heating

Density of OBS vs. Grid points

What if grid density (aka. model resolution) exceeds observation density?

Sensitivity to Initial Conditions