# Presentation: 200361

## The WRF EMS and Local Area Modeling 

**Robert Rozumalski****National SOO Science and Training Resource Coordinator****NOAA/NWS/OCWWS/FDTB****SAWS Workshop on NWP****October 2007**

## 
Benefits of running a local mesoscale model:
Model time steps
When to use a cumulus parameterization scheme
Impacts of model configuration on a forecast
Some general guidance for running a LAM

- Benefits of running a local mesoscale model:
- Model time steps
- When to use a cumulus parameterization scheme
- Impacts of model configuration on a forecast
- Some general guidance for running a LAM

- Tour of Talk

## 
To provide real-time data at temporal and spatial scales not available from operational sources
For studying local forecast problems and historically significant weather events
To provide an alternative to the configuration and physics of operational systems
To develop and test new diagnostic forecast techniques
To help in the training of forecasters on NWP-related issues

- Benefits of running a local mesoscale model:

- To provide real-time data at temporal and spatial scales not available from operational sources
- For studying local forecast problems and historically significant weather events
- To provide an alternative to the configuration and physics of operational systems
- To develop and test new diagnostic forecast techniques
- To help in the training of forecasters on NWP-related issues

*Supporting LAMs in the WFOs*

## 12km operational NAM

- 3km WRF-NMM

- Improved Spatial Resolution

*LAMs in the WFOs*

**Notes:**

A comparison of an 12km operational forecast on left to a 3km local model run on right over southern florida

Note the general difluence in the flow over western Florida in the operational run and compare that to
The distinct sea breeze in the high  resolution run

## Kain-Fritsch

- Betts-Miller-Janjić

- Varying Model Physics

- 24h Total Precipitation (in)

*LAMs in the WFOs*

**Notes:**

A comparison of 24-hour simulated precipitation when varying model physics

## 1900 UTC  20 September 2002

- Examining Locally Significant Weather Events

*LAMs in the WFOs*

**Notes:**

An example of running a LAM to study a historically significant weather event

Observed Tornado path through Central Indiana on left. F3  Tornado went through Marion County at 1900 UTC

1-hour total precip on right for 1900 UTC with maximum precipitation over marion County. Point being that
Model was able to capture the timing of event

## LAMs in the WFOs

- Cloud Base Vertical Mass Flux
- 1800 UTC 20 September 2002

*LAMs in the WFOs*

**Notes:**

An example of using new fields for forecasting. 

Simulated Cloud base vertical mass flux from the Kain-Fritsch  has shown to be a better at predicting the initiation  of observed convection.
Better than looking at the precipitation from convective schemes.

Note the maximum values are located just upstream of the tornadic activity 1 hour before the event. 

## Initialization with LAPS

- Using New Fields

- Simulated Reflectivity (dbz)
- 15 UTC 25 January 2007

**Notes:**
Another  example of using new fields for forecasting - Simulated reflectivity

## WRF EMS

- 	How does the selection of grid projection impact my model forecast?

- Frequently Asked Question:

## WRF EMS Grid Projection Primer

- Grid Spacing and Your Domain

**Notes:**

Note that there are problems with the SI GUI then specifying a domain with the NMM core (Rotated Lat-Lon) that crosses over the equator

## Grid Spacing and Your Domain

- Polar Stereographic

- Lambert Conformal

**32 km DX**

**32 km DX**

**Notes:**

Audience Participation:

Two Domains roughly over the same area; one polar stereographic and one Lambert Conformal

Question:  In the above polar stereographic projection, does the grid spacing increase, decrease, or remain the same as one
Moves towards the equator? 

## Grid Spacing and Your Domain

- Polar Stereographic

- Lambert Conformal

**32 km DX**

**22 km DX**

**32 km DX**

**Notes:**

Question:  In the above polar stereographic projection, does the grid spacing increase, decrease, or remain the same as one
Moves towards the Pole? 

## Grid Spacing and Your Domain

- Polar Stereographic

- Lambert Conformal

**32 km DX**

**39 km DX**

**22 km DX**

**32 km DX**

**Notes:**
Question:  In the above Lambert Conformal  projection, does the grid spacing increase, decrease, or remain the same as one
Moves towards the equator? 

## Grid Spacing and Your Domain

- Polar Stereographic

- Lambert Conformal

**32 km DX**

**39 km DX**

**22 km DX**

**32 km DX**

**25 km DX**

**Notes:**
Question:  In the above Lambert Conformal  projection, does the grid spacing increase, decrease, or remain the same as one
Moves towards the pole? 

## Grid Spacing and Your Domain

- Polar Stereographic

- Lambert Conformal

**32 km DX**

**39 km DX**

**22 km DX**

**Notes:**

Understanding how the model grid spacing changes from the Center point has an impact on the configuration of a local model

## WRF EMS

- Understanding how the model grid spacing changes from the center point has an impact on the configuration of a local model

## WRF EMS can automatically select a time step
ARW Core: dt = 6 * dx (km), where dx is value at domain center (ignore units)
NMM Core: dt = 157.43*dx**2 + 376.08*dx, where dx is value at domain center

So, what’s the potential problem?

*ARW Core: dt = **6 * dx (km),** where dx is value at domain center (ignore units)*
*NMM Core: dt = **157.43*dx**2 + 376.08*dx,** where dx is value at domain center*
- So, what’s the potential problem?

- Model Time Step in the WRF EMS

- Grid Spacing and Your Domain

- A violation of the Courant-Friedrichs-Lewy (CFL) condition
*	*The CFL condition can be interpreted as a requirement that the distance C1/2Δt traveled by a wave in one timestep not exceed one spatial grid interval Δx, or dt < dx/c1/2
- Auto-calculated ARW timestep:  	6 x 32 (km) = 192s*
- Southern boundary ARW timestep:  6 x 22 (km) = 132s

**Notes:**
The CFL condition can be interpreted intuitively as requiring that the distance (or c1⁄2Δt) traveled by a wave in one time step not exceed one spatial step Δx, where c1⁄2 is the physical wave speed. The two solutions being considered for the operational model are to increase the divergence damping in the model, which effectively reduces the wind and wave speeds and/or a decrease of the model time step. 

## Temporal Resolution

- 	A violation of the CFL condition is most commonly manifested as numerical “noise” often followed by a model crash

**Notes:**

Remember this slide?

## As Stated: WRF EMS can automatically select a time step
NMM Core: dt = 157.43*dx**2 + 376.08*dx, where dx is value at domain center
NMM timestep is derived from best-fit curve through 4 dx-dt pairs provided by NCEP
No dt values for dx < 10km were provided.

So, what is the problem?

*NMM Core: dt = **157.43*dx**2 + 376.08*dx,** where dx is value at domain center*
- NMM timestep is derived from best-fit curve through 4 dx-dt pairs provided by NCEP
- No dt values for dx < 10km were provided.
- So, what is the problem?

- Additional NMM core timestep issue

- Grid Spacing and Your Domain

## Linear Instability in WRF

- 5s Time Step

- 3s Time Step

- 2km Grid Spacing

**Notes:**

On left: High resolution (2km) run with recommended 5s timestep

On right:  Same run with timestep reduced timestep

## Simulated Reflectivity Comparison

- 5s Time Step

- 3s Time Step

- Linear Instability in WRF

**Notes:**

Additional example with simulated reflectivity

## Potentially due to violation of the CFL condition
More common during cool season
Also observed on operational NAM at 12km DX
Potential operational solutions include increased divergence damping and/or decreased model time step

Typically seen originating along lateral boundaries, especially when co-located over complex terrain
Using the auto-calculate option for time step in NMM runs below 10km may result in linear instability
Temporary fix: manually define TIME_STEP in run_grid.conf file

- More common during cool season
- Also observed on operational NAM at 12km DX
- Potential operational solutions include increased divergence damping and/or decreased model time step
- Typically seen originating along lateral boundaries, especially when co-located over complex terrain
- Using the auto-calculate option for time step in NMM runs below 10km *may* result in linear instability
- Temporary fix: manually define TIME_STEP in run_grid.conf file

- Linear instability issues in WRF

- Linear Instability in WRF

## WRF EMS

- More Frequently Asked Questions:

- 	The grid spacing of 5 to 10km spans the no-man's land between explicit and parameterized convection. How should this problem be handled in a local model?

## Going Convective

- Due to the spatial scales on which individual convective cells occur, typically 2 to 10km, explicit representation of convection in operational models can not easily be achieved
- A convective parameterization scheme attempts to represent the collective effect of cumulus convection without predicting individual cloud elements

- But when is it acceptable to not use a cumulus scheme in a model simulation?

*Going Convective*

## Illustration of Cumulus Processes

## Going Convective

- Grid Scale Vs. Cumulus Scheme Sensitivity Experiments

- All experiments were run with the WRF ARW and the following configuration:
- 02, 04, 06, 08, 10, 12, 14, and 16km horizontal grid spacing
- Number of horizontal grid points ranged 301x301 (2km) to 39x39 (16km)
- KF cumulus scheme for CU scheme runs
- 24 hour simulation initialized 12 UTC March 1 2007

*Going Convective*

## Going Convective

*Going Convective*

**Area of Interest**

**Simulation Domain**

**Notes:**

Observed 24 hour precipitation for the same period as that used  in the model simulations

Only the “Area of Interest” was used for the comparisons

## Going Convective

*Going Convective*

**Area of Interest**

**Notes:**

Note: The streakiness of the observed precipitation pattern

## Going Convective

*Going Convective*

- Total KF and Grid Scale Precipitation Vs. Grid Spacing

**Notes:**

Graph depicts the Total area averaged (Over region of interest) precipitation for model runs using the Kain-Fritsch scheme (in green) and 
without (no convective scheme; orange) as a function of grid spacing.

Note that:

The runs with the Kain-Fritsch scheme generated more area averaged precipitation than those with no cumulus scheme
 The disparity between the KF and GS runs diminished as grid spacing decreased

## Going Convective

*Going Convective*

- KF and Grid Scale Precipitation Vs. Grid Spacing

- Grell-Devenyi

**Notes:**

Graph depicts the contribution to the total area averaged precipitation by the convective (KF) scheme (in green) and grid scale scheme (orange) 
for the cumulus scheme runs only as a function of grid spacing.

Note that:

 At DX >= 6km, The amount of precipitation from the KF scheme is much larger than that from the grid scale scheme.
 The ratio of work being done by the cumulus to gridscale schemes decreases rapidly with grid spacing less than 6km
At 4km, the contribution to the total precipitation by the Grell Scheme dominates that of the gridscale scheme

## Going Convective

*Going Convective*

- Maximum KF and Grid Scale Precipitation Vs. Grid Spacing

**Notes:**

Graph depicts the magnitude of the precipitation maximum for model runs using the Kain-Fritsch scheme (in green) and 
without (no convective scheme; orange) as a function of grid spacing.

Note that:

1. While the area averaged precipitation was greater with the cumulus scheme runs, the precipitation maxima are greater in the grid scale only runs

## Going Convective

- 24 hour total precipitation – 12 km simulations

*Going Convective*

- No CU Scheme

- Kain-Fritsch

**Notes:**

12km simulations of 24 hour total precip

Note the difference in pattern between the GS only and KF runs

GS – More streaky
KF – more granular in appearance

## Going Convective

*Going Convective*

- No CU Scheme

- Kain-Fritsch

**Notes:**

6km simulations of 24 hour total precip

Note the difference in pattern between the GS only and KF runs

GS – More streaky
KF – more granular in appearance

## Going Convective

- 24 hour total precipitation - 02 km simulations

*Going Convective*

- No CU Scheme

- Kain-Fritsch

**Notes:**

02km simulations of 24 hour total precip

The convergence in the patterns is due to the GS scheme doing a larger percentage of the work relative to the cumulus scheme

## Going Convective

- 24 hour total precipitation - 04 km simulations

*Going Convective*

- Grell-Devenyi

- Kain-Fritsch

**Notes:**

A comparison of the precipitation patterns for the4km Grell and KF scheme runs. 

The Grell run is much more granular and does not show any suggestion of the precipitation streaks because the contribution
To the total precipitation is dominated by the convective component.

## Going Convective

- 24 hour total precipitation - 04 km simulations

*Going Convective*

- No CU w/5-class Lin et al.

- No CU w/6-class Thompson

**Notes:**

A comparison of 4km grid scale only (No CU scheme) simulations with two different microphysics. – little difference

## Going Convective

- 24 hour total precipitation - 04 km simulations

*Going Convective*

- KF with 5-class Lin et al.

- KF with 6-class Thompson

**Notes:**

A comparison of 4km  cumulus scheme simulations with two different microphysics. – little difference

## Going Convective

- What have we learned from this somewhat limited and quasi-scientific sensitivity study?

- In experiments with grid spacing >= 6km:
- A comparison between the experiments with and without parameterized convection showed that the precipitation patterns in the Kain-Fritsch simulations have a more “granular” appearance and distributed over the entire domain while the non-cumulus scheme experiments have a more realistic “streaky” appearance.
- Grid-area averaged total precipitation was greater for the simulations with the Kain-Fritsch scheme than those with no cumulus scheme
- Precipitation maxima were greater in the  simulations without a convective scheme across all grid spacings
- In the simulations with the Kain-Fritsch scheme, the contribution to the total precipitation from parameterized convection was much larger than that from the grid scale scheme

*Going Convective*

**Notes:**

Summary for those runs with grid spacing  greater than or equal to 6km

## Going Convective

- What have we learned from this somewhat limited and quasi-scientific sensitivity study?

- In experiments with grid spacing <= 4km:
- Precipitation patterns in the Kain-Fritsch simulations looked more similar in appearance to those of the non-cumulus scheme experiments.
- Contribution to the total precipitation by the convective parameterization decreased relative to the grid scale contribution
- Precipitation maxima were greater in the  simulations without a convective scheme
- In the simulations with the Kain-Fritsch scheme, the contribution to the total precipitation from parameterized convection decreased as grid spacing decreased

*Going Convective*

**Notes:**
Summary for those runs with grid spacing  less than or equal to 4km

## Going Convective

- What have we learned from this somewhat limited and quasi-scientific sensitivity study?

- 			“If in doubt, run without”

*Going Convective*

**Notes:**

General Guidance

## WRF EMS

- How does domain size affect a local model forecast?

- Even More Frequently Asked Questions:

##   The influence of lateral boundary conditions
  Model execution time
 	Development of computational grid-scale forcing
  Spin-up of precipitation

-   Model execution time
-  	Development of computational grid-scale forcing
-   Spin-up of precipitation

-  The proximity of the lateral boundaries to the area of interest in a model forecast may impact:

***You do not want to sacrifice domain size for resolution***

*LAMs in the WFOs*

- Lateral Boundary Condition Primer

**Notes:**
Development of computational grid-scale forcing  from the large scale initialization grid to the fine mesh simulation domain

##  	Errors in interpolation from external grid
 	Temporal aliasing of information
 	Inability to feed back information to larger scale (1-way nests)
  An incompatibility between physical-process parameterizations 

-  	Usually provided by coarser model run
- Operational GFS
- Reanalysis data
- Previous WRF  Model run
-  	Act to constrain numerical solution – *unlike global models*
-  	Influence on forecast is somewhat controllable

-  LBCs

-  	Errors in interpolation from external grid
-  	Temporal aliasing of information
-  	Inability to feed back information to larger scale (1-way nests)
-   An incompatibility between physical-process parameterizations

-  LBCs can degrade a forecast through

-  Errors propagate via advection and gravity wave modes

*LAMs in the WFOs*

## LAMs in the WFOs

- Lateral boundary condition experiments

- All control experiments were run with the WRF ARW core and the following configuration:
- Three different domains – Weak, Moderate, and Strong local forcing
- 32km horizontal grid spacing
- KF cumulus scheme
- Initialized from a 00 hour operational GFS on 0.5 degree grid
- 3-hourly 0.5 degree GFS used as lateral BCs through 36 hours
- Forecasts were compared to the control experiment over a region of interest

*LAMs in the WFOs*

## 307x307 Control Domain

**307x307 Control Domain**

**Region of Interest**

**Middle of Nowhere (Weak local forcing) **

**Notes:**
Weak local Forcing

Dark orange = 303x307 32km Control simulation Domain

Yellow – Subset of 23 (total) sensitivity study domains

Red – Region of interest (ROI) for each of the experiments

For each of the sensitivity study domains, various fields over the region of interest (ROI) were compared to those from the 307x307 control domain

## 307x307 Control Domain

**307x307 Control Domain**

**Region of Interest**

**US Gulf Coast (Moderate local forcing) **

**Notes:**

Moderate local Forcing

Dark orange = 303x307 32km Control simulation Domain

Yellow – Subset of 23 (total) sensitivity study domains

Red – Region of interest (ROI) for each of the experiments

For each of the sensitivity study domains, various fields over the region of interest (ROI) were compared to those from the 307x307 control domain

## 307x307 Control Domain

**307x307 Control Domain**

**Region of Interest**

**Pacific Northwest (Strong local forcing) **

**Notes:**
Strong local Forcing

Dark orange = 303x307 32km Control simulation Domain

Yellow – Subset of 23 (total) sensitivity study domains

Red – Region of interest (ROI) for each of the experiments

For each of the sensitivity study domains, various fields over the region of interest (ROI) were compared to those from the 307x307 control domain

## LAMs in the WFOs

***Forecast Difference Vs Distance – ******Atlantic Domain***

*LAMs in the WFOs*

***  **Smaller                                      Larger ***

**Notes:**

Weak local forcing experiments

Many fields compared (surface pressure, precipitation, 2 m RH, and 10m winds). Areal averaged vector difference in the 10m winds betwwen the
Control and sensitivity simulation are shown because they best represent the results.

Explained: magnitude of the vector difference at each grid point between the control and sensitivity experiment averaged over the entire region of 
interest and then averaged over the 10 day period.

Distance from Lateral Boundary – Distance from ROI boundary to lateral boundary of sensitivity experiment. Smaller distance means smaller domain.

## LAMs in the WFOs

***Forecast Difference Vs Distance – ******Gulf Domain***

*LAMs in the WFOs*

***  **Smaller                                      Larger ***

**Notes:**
Moderate local forcing experiments

## LAMs in the WFOs

***Forecast Difference Vs Distance – ******Mountain Domain***

*LAMs in the WFOs*

***  **Smaller                                      Larger ***

**Notes:**
strong local forcing experiments

Note that region with strong local forcing has less sensitivity to lateral  boundary location relative to the moderate and weak local forcing domains.

## Model domains increasing in size from 33x71 to 141x251

**33x71**

**39x81**

**45x91**

**51x101**

**57x111**

**69x131**

**75x141**

**99x181**

**141x251**

**Notes:**

Similar Study over region of moderate local forcing – Gulf coast of US showing sensitivity of 24 hour total precipitation to the proximity of the lateral
Boundaries.

## 4 min

**57x111**

**99x181**

**33x71**

**39x81**

**45x91**

**69x131**

**87x161**

**111x201**

**123x221**

**Notes:**

In contrast to experiments over region of moderate local forcing

Similar Study over region of strong  local forcing – West coast of US showing sensitivity of 24 hour total precipitation to the proximity of the lateral
Boundaries.

Note relative lack of sensitivity

## 24 hour Total precipitation (inches)

- 123x221 22km domain

- 123x221 5km domain

**Notes:**

Two simulations with identical number of grid points but different grid spacing over a region of strong local forcing

Potentially more value in the 5km simulation

## WRF EMS

- 	How important is it to consider the following when setting up a local simulation?

- Still More Frequently Asked Questions:

- The spatial resolution of the grids used to initialize and provide boundary conditions for a local model
- The temporal resolution of the boundary conditions
- The spatial resolution of the initial conditions
- The vertical resolution of the computational domain
- The model time step

**Notes:**

Lot of questions require lots of experiments

## LAMs in the WFOs

- Lateral boundary condition sensitivity experiments

- Degrading the spatial resolution of the initial conditions
- Degrading the spatial resolution of the lateral boundary conditions
- Degrading the temporal resolution of the lateral boundary conditions
- Decreasing model timestep by 1/2
- Increasing the number of levels by 2x

*LAMs in the WFOs*

- Results for each domain were directly compared to those of the control experiment for the same computational domain.

**Notes:**

Sensitivity experiments were conducted over the moderate local forcing domain

## LAMs in the WFOs

**Gulf Domain: ****Difference between same domain control **
**simulation and ****1⁄2 time step****  sensitivity experiment**

*LAMs in the WFOs*

***  **Smaller                                      Larger ***

**Notes:**

Decreasing the model time step by 1⁄2 had little impact on the simulation with a mean domain average vector difference of ~ 0.10 m/s

Little sensitivity to location of lateral boundary conditions or hour of forecast

## LAMs in the WFOs

**Gulf Domain: ****Difference between same domain control **
**simulation and ****1 degree BC**** sensitivity experiment**

*LAMs in the WFOs*

***  **Smaller                                      Larger ***

**Notes:**

Degrading the spatial resolution of the lateral boundary conditions by 1⁄2 also had little impact on the simulation with a mean domain average vector difference of ~ 0.10 m/s

Little sensitivity to location of lateral boundary conditions or hour of forecast

## LAMs in the WFOs

**Gulf Domain: ****Difference between same domain control **
**simulation and ****6 hourly BC**** sensitivity experiment**

*LAMs in the WFOs*

***  **Smaller                                      Larger ***

**Notes:**

Degrading the temporal resolution of the lateral boundary conditions by 1⁄2 (3 to 6-hourly) also had little impact on the simulation with a mean domain average vector difference of ~ 0.15 m/s

Some sensitivity to location of lateral boundary conditions for smaller domains

## LAMs in the WFOs

**Gulf Domain: ****Difference between same domain control **
**simulation and ****1 degree IC**** sensitivity experiment**

*LAMs in the WFOs*

***  **Smaller                                      Larger ***

**Notes:**

Degrading the spatial resolution of the initial conditions by 1⁄2 (0.5 to 1.0 degree) had a moderate impact on the simulation with a mean domain average vector difference of ~ 0.30 m/s

Little sensitivity to location of lateral boundary conditions or hour of forecast

## LAMs in the WFOs

**Gulf Domain: ****Difference between same domain control **
**simulation and ****2x levels**** sensitivity experiment**

*LAMs in the WFOs*

***  **Smaller                                      Larger ***

**Notes:**

Decreasing the number of vertical levels 2x had the greatest impact on the simulation with a mean domain average vector difference of ~ 0.70 m/s

Little sensitivity to location of lateral boundary conditions or hour of forecast

Limited runs due to increase in computational time ~ 2x

## LAMs in the WFOs

**Gulf Domain: ****Mean difference in 10m wind field between control **
**simulation and sensitivity experiments**

*LAMs in the WFOs*

|  | 2x levels | 1 deg IC | 6hr BC | 1/2DT | 1 deg BC |
| --- | --- | --- | --- | --- | --- |
| 12hr Fcst | 0.60 | 0.25 | 0.15 | 0.10 | 0.10 |
| 24hr Fcst | 0.65 | 0.34 | 0.20 | 0.10 | 0.11 |
| 36hr Fcst | 0.70
  1 | 0.36
  2
   | 0.22
  3 | 0.10
  4 | 0.10
  4 |

**Notes:**

To summarize  in order of greatest to least impact for moderate local forcing domain experiments

## LAMs in the WFOs

**Atlantic Domain: ****Mean difference in 10m wind field between control **
**      ****simulation and sensitivity experiments**

*LAMs in the WFOs*

|  | 1 deg IC | 6hr BC | 1 deg BC |
| --- | --- | --- | --- |
| 12hr Fcst | 0.55 | 0.27 | 0.10 |
| 24hr Fcst | 0.90 | 0.45 | 0.31 |
| 36hr Fcst | 1.00
  1
   | 0.52
  2 | 0.38
  3 |

**Notes:**

Similar results with the weak local forcing domain experiments

## LAMs in the WFOs

**Mountain Domain: ****Mean difference in 10m wind field between **
**                       ****control simulation and sensitivity experiments**

*LAMs in the WFOs*

|  | 1 deg IC | 6hr BC | 1 deg BC |
| --- | --- | --- | --- |
| 12hr Fcst | 0.30 | 0.12 | 0.10 |
| 24hr Fcst | 0.30 | 0.22 | 0.12 |
| 36hr Fcst | 0.45
  1
   | 0.36
  2 | 0.18
  3 |

**Notes:**
Similar results with the strong local forcing domain experiments

## Going Convective

- What have we learned from another somewhat limited and quasi-scientific sensitivity study?

- Decreasing the time step by 1⁄2 and increasing the number of vertical levels by 2x increase the computational time of a model simulation by the same amount; however, increasing the number of levels had >6 times the impact on the forecast.
- Spatially degrading the model initial conditions had approximately 3 times the impact on a model forecast as those with spatially degraded lateral boundary condition.
- Temporally degrading the model lateral boundary conditions had approximately 2 times the impact on a model forecast as those with spatially degraded lateral boundary condition.

*Going Convective*

## LAMs in the WFOs

**Gulf Domain:**** Weighted impact on model forecast**

*LAMs in the WFOs*

|  | Impact | Comments |
| --- | --- | --- |
| 1⁄2 DT | 1 | 2x increase in computational time |
| 2x BC dx | 1 | Minor increase in time to download and process |
| 2x BC dt | 2 | 2x increase in time to download and process |
| 2x IC dx | 3 | Slight increase in time to download and process |
| 2x vertical levels | 6 | 2x increase in computational time |

*** *Smaller impact Larger**

**Notes:**

Relative impact – doubling the number of vertical levels had 6x the impact on a forecast as decreasing the time step by 1⁄2 (And takes about same time to run)

## Model execution time (real-time forecasts)
Domain size
Horizontal resolution
Vertical resolution
Grid navigation
Resolution of initialization dataset
Phenomena of interest

- Domain size
- Horizontal resolution
- Vertical resolution
- Grid navigation
- Resolution of initialization dataset
- Phenomena of interest

- What to consider when setting up a LAM

*LAMs in the WFOs*

**Notes:**

Some General guidance on setting up a local area model run.

## LAMs in the WFOs

- The most important factor to consider when setting up a real-time forecast system. Less so for local modeling studies
- A forecast that is not timely is useless to forecasters
- A timely, skillful forecast will require both          computational power and a wisely configured model

*LAMs in the WFOs*

## First determine the areal coverage of your computational domain
Larger domain needed during the cool season
Increase size for longer period forecasts
Move the domain upstream of your region of interest
DO NOT locate lateral boundaries along regions of strong surface forcing such as complex terrain

Determine how much time you have for each run

Run some experiments to establish the allowable resolution
It may be > 10km – that is OK!
More resolution if you have strong local forcing

- Larger domain needed during the cool season
- Increase size for longer period forecasts
- Move the domain upstream of your region of interest
- DO NOT locate lateral boundaries along regions of strong surface forcing such as complex terrain
- Determine how much time you have for each run
- Run some experiments to establish the allowable resolution
- It may be > 10km – that is OK!
- More resolution if you have strong local forcing

- When setting up your LAM for real-time forecasting

*LAMs in the WFOs*

## 
If the phenomenon of interest is locally forced, you may sacrifice areal coverage for model resolution
Increasing the resolution at the expense of domain size may improve the forecast value

Otherwise, it is more important to provide a larger computational domain

- If the phenomenon of interest is locally forced, you may sacrifice areal coverage for model resolution
- Increasing the resolution at the expense of domain size may improve the forecast value
- Otherwise, it is more important to provide a larger computational domain

- When setting up your LAM for real-time forecasting

*LAMs in the WFOs*

## LAMs in the WFOs

- Region
- of interest

- Upstream flow

- Shift model domain to reduce BC influences

*LAMs in the WFOs*

## LAMs in the WFOs

- The scale of a given atmospheric phenomenon is proportional to its forcing
-  The ability to simulate a given response will depend upon either:
- The particular forcing mechanism being fully resolved in the forecast initial state
- The ability of the model to develop the forcing during integration
***The scale contraction processes take time!***

*LAMs in the WFOs*

##   Topography is a very strong forcing mechanism
Well-known and well-resolved
Knowledge of large-scale flow may only be necessary to generate high-value forecasts
  Other non-transient (0-24 hrs) forcing mechanisms include
Land/water interfaces
Strong soil moisture gradients
Surface roughness gradients
Sharp transitions in surface albedo (sea/ice, snow, etc.)
Gradients in water surface temperature

** **** **Topography is a very strong forcing mechanism
- Well-known and well-resolved
- Knowledge of large-scale flow may only be necessary to generate high-value forecasts
** **** **Other non-transient (0-24 hrs) forcing mechanisms include
- Land/water interfaces
- Strong soil moisture gradients
- Surface roughness gradients
- Sharp transitions in surface albedo (sea/ice, snow, etc.)
- Gradients in water surface temperature

*LAMs in the WFOs*

## LAMs in the WFOs

*LAMs in the WFOs*

- In local area modeling, do not assume that a model initialized closer to a verification time will always provide a better forecast
- Critical, small-scale phenomena may require time (> 6hours) to develop during model integration
- Especially true when initializing from a relatively coarse grid
- The “spin-up” of precipitation is an example of this process

*Non-local model forcing** *

## 
Benefits of running a local mesoscale model:
Determining model time steps
When to use a cumulus parameterization scheme
Impacts of model configuration on a forecast
Some general guidance for running a LAM

- Benefits of running a local mesoscale model:
- Determining model time steps
- When to use a cumulus parameterization scheme
- Impacts of model configuration on a forecast
- Some general guidance for running a LAM

- Review of Talk