# Presentation: 215481

## Day 1 Diagnosis & Forecast methodology

- J. LaDue
- Winter Wx workshop 2003
- Boulder, CO

## A miniscenario

- It is 05 UTC
- It’s Tuesday night and the first arctic outbreak of the season has arrived
- A large scale upper-level trough lies to the west.
- An passing shortwave failed to produce expected snow the previous afternoon
- The ETA is out and it’s time to start thinking of a forecast.

## You could just use model Omega and forecast precip

This could result in meteorological cancer

To avoid that, we will look at real data and conceptual models.

- This could result in meteorological cancer
- To avoid that, we will look at real data and conceptual models.

- A miniscenario

## 850 mb – 00 UTC

## 700 mb – 00 UTC

## 500 mb – 00 UTC

## 300 mb – 00 UTC

- DCVA

## A short-term forecast methodology

- Check accuracy of model analysis
- Diagnose model analysis for winter precipitation ingredients
- Planview maps and cross-sections
- Adjust model-based ingredients with real data
- Make the forecast
- Further adjustments with real data into forecast period

## Check the sanity of model analysis

- For example with ETA analysis, compare these parameters to raobs, profilers:
- SFC:   T, Td, wind, pressure
- 925 - 500 mb:   T, T-Td, wind, height
- 300 mb and up:  T, wind, height
- These offer direct comparisons to check validity of model analysis
- WV/IR imagery can be used to qualitatively evaluate strength of systems

## Diagnosing ingredients for winter precip

- Forcing
- Stability (response to forcing)
- Moisture
- Precip efficiency
- Precip type
- “Also helps to figure out where that dang precip came from”

## Diagnosing ingredients for winter precip

_**Forcing**_:  What type, where and how strong will it be?
_**Stability**_:  What will be the response to the forcing?
_**Moisture**_:  Will there be saturation (RH) and how much moisture (q) will there be?
_**Precip efficiency**_:  How will forcing, instability, and saturation coincide with the dendrite formation layer?
_**Precip type**_:  Top down approach

## Forcing

- Start with synoptics first – QG
- DCVA (differential cyclonic vort advection,
- 2WAA (local max in warm advection),
- QG frontogenesis
- Diagnostics
- Isentropic
- Q-vectors

## 850 mb – 00 UTC

- Frntgenesis

## 700 mb – 00 UTC

- Frntgenesis

- WAA

## 500 mb – 00 UTC

- DCVA

## 300 mb – 00 UTC

- DCVA

## Forcing

- Q-vectors
- Exist when geostrophic wind alters the thickness gradient vectors
- Anytime the thickness gradient changes the thermal wind goes out of balance
- Secondary ageostrophic circulations attempt to restore balance
- Ageostrophic winds keep us employed
- ·Q-vector

## Forcing – Flow inflections

- 

- +

- T1

- T

- T+T

- T2

- T3

- Q1

- Q2

- Q1

- Q2

- T1

- T2

- T2

- T3

- Qs = Q component parallel to isotherms

## Forcing – QG frontogenesis

- +2

- +

- 

- T

- T+T

- T1

- T2

- T+2T

- Q1

- T3

- T3

- Q2

- T1

- T2

- Q1

- Qn = Q component parallel to isotherms

## Forcing – QG  

- Forcing – QG

- +2

- +

- 

- T

- T+T

- T+2T

- Qn +Qs = total Q vector

- (Qn +  Qs)<< 0

## Q-vectors

## Forcing

- Best convergence means strongest
- QG forcing for vertical motion

## Forcing:  Q

- 650 mb
- 00 UTC
- ETA

## Forcing

- If QG cannot explain the vertical motion (fcst or real)...
- Then go downscale to mesoscale
- Next is 2-D full wind frontogenesis

## Frontogenesis (definition)

-  The 2-D frontogenesis function (*F*) – quantifies the change in (potential) horizontal temperature gradient following air parcel motion
***F*****>0 frontogenesis, *****F*****<0 frontolysis**
- Refer to Banacos’ presentation

## Forcing:  Frontogenesis

## Forcing:  Frontogenesis

## Forcing

- If 2-D frontogenesis fails to explain ascent, then
- Externally forced mesoscale lifting?
- Orographic ascent
- Frictional convergence –
- Shoreline
- Cyclonic sfc flow
- Local diabatic heating
- Water – land thermal differences
- Land cover thermal gradients
- Outflow boundaries

## Forcing

- Orographic ascent

- Weak cross-barrier winds or high stability
- KE < PE
- Fr  <   1           Fr = Froude #

- Strong cross-barrier winds or low stability
- KE > PE
- Fr  >   1

- http://www.meted.ucar.edu/mesoprim/flowtopo/

## Forcing

- Diabatic heating gradients

- In this case, lake effect enhancement due to land breeze circulations

- http://www.meted.ucar.edu/mesoprim/seabreez/

## Forcing

- Frictional convergence

******P**

**f**

******P**

**f**

**fr**

## Stability response

|  | gravitational | symmetric | inertial |
| --- | --- | --- | --- |
| Dry 
  | I 
Absolute instability | SI
Symmetric instability | Inertial instability |
| Moist saturated (potential) 
e
 | PI
Potential instability | PSI 
Potential symmetric instability
 |  |
| Moist/unsaturated
(conditional)
e* | CI
Conditional instability
 | CSI
Conditional symmetric instability
 |  |

## Stability response

|  | gravitational | symmetric | inertial |
| --- | --- | --- | --- |
| Dry 
  | I 
/Z < 0 | SI
Geostrophic potential vort
PVg < 0 | Inertial instability
g < 0 |
| Moist unsaturated (potential) 
e
 | PI
e/Z < 0 | PSI 
Moist geostrophic potential vort
MPVg < 0
 |  |
| Moist saturated
(conditional)
e* | CI
e*/Z < 0 | CSI
Saturated geostrophic potential vort
 MPVg* < 0
 |  |

## Stability response

|  | gravitational | symmetric | inertial |
| --- | --- | --- | --- |
| Dry 
  | I 
/Z < 0
Subtract  from two levels in the VB | SI
 PVg < 0
 | Inertial instability
g < 0 |
| Moist unsaturated (potential) 
e
 | PI
e/Z < 0
Subtract e from two levels in the VB | PSI 
MPVg < 0
Not avail in VB |  |
| Moist saturated
(conditional)
e* | CI
e*/Z < 0
Add mean RH.
Assume e e* If mean RH > 90% | CSI
MPVg* < 0
Labeled as saturated geos EPV* in VB |  |

## Stability response

|  | gravitational | symmetric | inertial |
| --- | --- | --- | --- |
| Dry 
  | I 
/Z < 0
Subtract  from two levels in the VB | SI
 PVg < 0
 | Inertial instability
g < 0 |
| Moist unsaturated (potential) 
e
 | PI
e/Z < 0
Subtract e from two levels in the VB | PSI 
MPVg < 0
Not avail in VB |  |
| Moist saturated
(conditional)
e* | CI
e*/Z < 0
Add mean RH.
Assume e e* If mean RH > 90% | CSI
MPVg* < 0
Labeled as saturated geos EPV* in VB |  |

## Stability response

|  | gravitational | symmetric | inertial |
| --- | --- | --- | --- |
| Dry 
  | I 
/Z < 0
Subtract  from two levels in the VB | SI
 PVg < 0
 | Inertial instability
g < 0 |
| Moist unsaturated (potential) 
e
 | PI
e/Z < 0
Subtract e from two levels in the VB | PSI 
MPVg < 0
Not avail in VB |  |
| Moist saturated
(conditional)
e* | CI
e*/Z < 0
Add mean RH.
Assume e e* If mean RH > 90% | CSI
MPVg* < 0
Labeled as saturated geos EPV* in VB |  |

- You can get away with
- Looking at **only** EPV* to
- Evaluate CSI and CI

## Stability response

- Courtesy Pete Banacos

## Stability response

- http://www.nssl.noaa.gov/~schultz/csi.shtml

## Stability response

- http://www.nssl.noaa.gov/~schultz/csi.shtml

- This is where  MPVg* < 0

## Combined forcing and stability

- PVQ = (Q) (MPVg*) for negative MPVg* and negative Q
-          = 0 		    for positive MPVg*  and positive  Q

- Negative PVQ means both Q-vector convergence and CSI, CI or both are occurring in the same layer
- This will not occur often as the best instability lies on a different layer than the best forcing

## PVQ

## Moisture

- Relative Humidity
- Mixing Ratio

## Precipitation efficiency

- See Dan Baumgardt’s talk
- http://www.crh.noaa.gov/arx/micrope.html

## Precipitation efficiency

## Precipitation efficiency

- See Dan
- Baumgardt’s talk
- http://www.crh.noaa.gov/arx/micrope.html

## Precipitation type

- Refer to the top-down approach in Dan Baumgardt’s presentation
- http://www.cira.colostate.edu/ramm/visit/ptype/title.asp

## Model Forecast 

- Add to typical displays
- 4-panel ingredients for 600-650mb, 650-700mb, 700-750mb with 80 km model
- Ingredients cross-sections across thickness gradients in areas of forcing
- Full frontogenesis
- Ensembles

## Adjust model forecast 

- How will analysis errors affect the ingredients?
- Does the current data agree with model forecasts?
- If not, then how will you adjust model forecasts?

## A short-term forecast methodology

- Check accuracy of model analysis
- Diagnose model analysis for winter precipitation ingredients
- Planview maps and cross-sections
- Adjust model-based ingredients with real data
- Make the forecast
- Further adjustments with real data into forecast period

## Related links

- Ingredients-based methodology
- http://cimss.ssec.wisc.edu/goes/visit/ingredients.html
- CSI, mesoscale circulations online training
- http://www.meted.ucar.edu/topics_meso.php
- Precipitation type and efficiency
- http://www.cira.colostate.edu/ramm/visit/ptype.html
- This workshop linked to
- http://www.wdtb.noaa.gov

## Precipitation efficiency