# Presentation: 515483

## Application of TAMDAR data in the NCAR Auto-Nowcaster

*Eric Nelson – Research Applications Laboratory / National Center for Atmospheric Research*

*GFLE meeting 8-25-2005*

## Goal: Incorporate TAMDAR data into a short term forecast system

**IHOP High Resolution instability analyses**
**NCAR Auto-Nowcaster short-term (0-2hour) forecast system**
**VDRAS explicit forecast system**

## IHOP instability analyses

**Uses available soundings**

## IHOP instability analyses

**Uses available soundings**
**METAR and mesonet observations**

## High Resolution CAPE & CIN

**All available surface observations are lifted against the nearest available sounding to obtain CAPE and CIN.**
**CAPE and CIN values are then interpolated to a grid.**

## Analyses performed as frequently as data allows

**Notes:**
CIN movie here

## Slide 7

## Slide 8

## The Auto-nowcaster System

**...****.that trigger                        **
**thunderstorm initiation and impact  storm evolution**.

**The Auto-nowcaster System**
- is unique in its ability to provide ***nowcasts of storm initiation*** by.....

## Example of Auto-Nowcaster 

- Initiation Forecast

**1 hour forecast**

**Verification**

## Predictor Fields

- Large-Scale Environment

- B-L characteristics

- Satellite Cloud Typing

- Boundary
- characteristics

- Cumulus
- development

- Storm motion and trends

**Notes:**
Forecast pyramid.  RUC data serves as base of forecast.

## Flow Chart for the Auto-Nowcaster System

**Analysis Algorithms**

**Forecaster Input**

**Final Prediction**

**Notes:**
The next step is to pull these predictor fields into an algorithm that applies fuzzy logic to the fields:
i.e., uses forecaster defined membership functions to produce a likelihood field, weights each likelihood field for it relative importance in thunderstorm evolution, and then combines these likelihood fields together to produce the final prediction field

## Environmental conditions (RUC)
Frontal likelihood
Layered stability
CAPE (max between 900 and 700 mb)
Mean CIN (975 to 900 mb)
Mean 875 to 725 mb Relative Humidity
Boundary-layer
Convergence
LI (based on METARS)
Vertical velocity along boundary Boundary-relative steering flow
New storm development along boundary
Clouds
Clear or Cumulus
Vertical development as observed by drop in IR temps

**Environmental conditions (RUC)**
- Frontal likelihood
- Layered stability
- CAPE (max between 900 and 700 mb)
- Mean CIN (975 to 900 mb)
- Mean 875 to 725 mb Relative Humidity
**Boundary-layer**
- Convergence
- LI (based on METARS)
- Vertical velocity along boundary Boundary-relative steering flow
- New storm development along boundary
**Clouds**
- Clear or Cumulus
- Vertical development as observed by drop in IR temps

- Blue Regions  - **Little chance of storm development**
- Green Regions - **Moderate likelihood**
- Red Regions - **Areas of forecast initiation**

- Predictor Fields used for Combined Likelihood of Initiation

## Cloud model and its Adjoint with 4DVAR
Boundary layer winds and thermodynamics
Dual-processor DELL
Analysis every 6-12 min
Horizontal resolution 2-4 km (depending on domain size)
Vertical resolution 250 m up to 3 km

- Variation Doppler Radar Assimilation System
- Retrieval of boundary-layer winds from single Doppler radar data

**Cloud model and its Adjoint with 4DVAR**
**Boundary layer winds and thermodynamics**
**Dual-processor DELL**
**Analysis every 6-12 min**
**Horizontal resolution 2-4 km (depending on domain size)**
**Vertical resolution 250 m up to 3 km**

##   Full – Trop VDRAS 

**(explicit forecast of convection)**

- Deep domain and ideep = 1

- Reflectivity

**A short forecast cycle added between two assimilation cycles.**
**A recursive filter algorithm similar to that used in WRF/MM5 is implemented.**
**Equations for prediction of microphysical variables (rain water and total liquid water) are added to the system, so that VDRAS can be applied to the full troposphere.**
**    **

##   Real-Time Explicit Forecast of Convection

- July 22nd, 2004

***  ******Real-Time Explicit Forecast of Convection***
**VDRAS**
- Crook and Sun

## Ongoing work

**Analyze TAMDAR soundings **
**Modify stability fields within Auto-Nowcaster to inlcude TAMDAR data**
**Assimilate TAMDAR soundings into explicit VDRAS forecast**

## Many Thanks!

**Cindy Mueller, Rita Roberts, Tom Saxen, Huaqing Cai, Andrew Crook, Jenny Sun, Ying, Zhang, Nancy Rehak, Sue Dettling, **

## ANC Extrapolation & Trending1 hr Forecast

***Forecast***

***Verification***

## ANC1-hr Initiation Forecast

***Forecast***

***Verification***

## Why do we need a forecaster in the loop??

**Forecasters see the larger picture**
- Conceptual Models
- Ignore bad data points
- Understand limitations of NWP and observations

## Forecaster Entered Polygons

**Notes:**
Forecaster entered polygons (2 shown) allow the forecaster to manually adjust the convection initiation interest field.  Weight of the polygons denoted by the color.

## Ft. Worth WFO April 5, 2005

***Forecast***

***Verification***

## VDRAS Example

- Ht: 187 m