# Presentation: 066130

##  Mesoscale Models: How to Use Them

*By*
*Richard H. Grumm*
*National Weather Service *
*State College PA 16803*
*Version 1.2 *
*24 October 2001*

## Introduction

**Mesoscale model basics**
*What a single mesoscale model can do for you!*
*What is a mesoscale model*
*The ****6********** rule on predictability*
**Why we need ensembles**
*one model many potential initial states*!
**Effective use of mesoscale model ensembles**
- Lagged Average Forecasts and dProg/dt
***NCEP SREF**** data*
*display strategies*

## What one model can do

***One detailed mesoscale model will allow the user to make highly specific and detailed inaccurate forecast.***
*Uncertainties in data*
*Uncertainties in data verse resolution*
*Uncertainties in physics*
*live by a model, die by that model*
*to pick a model of the day, see line above!*

## What is a mesoscale Model

**Fujita 1981 JAS described scales**
- 4-400km mesoscale
**Orlanski- 3 mesoscales**
-  200-2000km (LFM,6-L PE)
-  20-200km (AVN,NGM,Eta)
-  2-20km (WS-Eta,Eta upgrade 2001)
**2001**
- all models in meso 
- some models in meso scale range!

## The 6 rule 

- Deals with the number of grid points needed to resolve a wave
- As general rule
- to fully resolve a wave you must have a grid spacing of 1/6 the length/width of the feature
- hence the **6****** rule
- Thunderstorm is ~2 km so need 2/6=~.333 km
- CSI band about ~30km wide 30/6=~5 km
- We have anticyclones and cyclones wickered!

## Scales thought experiment

- use sinusoidal wave
- wavelength 380 km
**first model grid** : 380 km (7L PE)
**second model grid** : 198 km (LFM)
**third model grids**: 80 km (Eta)
- mentally shift grid or wave
***resolution and detection***

*Scales thought experiment*

## 380 km grid and wave 

## 190 km grid 380km wave

## 80km grid

**X**

**X**

**X**

**X**

**X**

**X**

## Ensembles help us

*Deal with uncertainties in data*
*the ability to properly resolve the feature*
*Deal with uncertainties in data verse resolution of the model*
*Deal with uncertainties in physics*
*Displaying uncertainties in forecasts*
*spaghetti plots*
*probability charts (the most likely outcome)*
*consensus forecast charts *
*to visualize these is to see limits of any single solution.*

## Ensemble from LAF

***may convey uncertainty in initial conditions***

## AVN LAF12-hour model cycles only

## AVN dProg/dt at 18Z 30 Dec 2000

## Ensembles with different initial conditions

## Mesoscale Model Ensembles

- The more members the better
- ensemble YOUR model with NCEP’s
- Need ensemble member models with
*different initial conditions*
*different model physics*
*different model parameterizations*

## SREF Winter Storm Case

**Winter storm of 3-4 December 2000**
**Eta had more QPF and farther west then AVN**
**There was a trend in the models (dProg/dt)**
**SREF data**
*showed uncertainty*
*show Eta members cluster different then RSM members*
*allowed forecasters to view uncertainty*
*provided visual cues on high and low probability areas for WSW products.*

## 0000 UTC 2 December

- Cyclone Track difference
- 0.20 QPF forecasts
- advisory snow over most of region
- 0.50 QPF forecasts
- warning snow over most of region
- T850
- will it snow or rain?

## Slide 18

## Slide 19

## Slide 20

## Slide 21

## 1200 UTC 2 December

- Cyclone Track difference
- 0.20 QPF forecasts
- advisory snow over most of region
- 0.50 QPF forecasts
- warning snow over most of region
- T850
- will it snow or rain?

## Slide 23

## Slide 24

## Slide 25

## Slide 26

## SREF Benefits

- Visualize differences and uncertainty
- Visualize high probability outcome areas
- Visualize low probability outcome areas
- Can improve the WWA scenario and confidence
- In this case Eta/AVN clustered by model
- low confidence
- a screaming message
- pick one and if its wrong you cook your own goose
- Operational Eta was ***the wettest and farthest west solution***

## Conclusions

**One model is an all or nothing proposition**
**Mesoscale models**
- allow us to see features not in coarser models
- rely less on parameterizations and more on physics
- still limited by the **6****** rule
**Why we need ensembles**
*Deal with uncertainties in data*
*Deal with uncertainties in data verse resolution of the model*
*Deal with uncertainties in physics*

## Conclusions-II

**mesoscale model ensembles**
*allow us to visualize forecast differences*
*allow us to visualize uncertainties*
- consensus forecast should be a higher probability outcome
- 70-90% probabilities should normally be higher confidence areas for criteria
*related to QPF*
*Rain/snow lines*
*producing more effect WSW products*

## Conclusions-SREF

**NCEP SREF data**
- a valuable tool and resource
- can be used to exploit all visual concepts with ensembles
- should improve our winter weather WWA program
- remember: ***One detailed mesoscale model (******the model of the day******) will allow the user to make highly specific and detailed inaccurate forecast.***

## Presentations

- V_1.0 Millersville University 17 Oct
- V_1.1 WFO PIT 24 Oct