# Presentation: 441359

## ASAP In-Flight Icing Research at NCAR

- J. Haggerty, F. McDonough, J. Black,
- S. Landolt, C. Wolff,  and S. Mueller
*In collaboration with:*
- P. Minnis and W. Smith, Jr.
*NASA Langley Research Center, Hampton, Virginia*

**NASA Applied Sciences Weather Program Review, 18-19 Nov 2008, Boulder, CO **

## Objective

-    Use advanced satellite cloud products to detect areas of supercooled liquid and improve FAA operational icing products

- Approach

-    Combine satellite products with other data sources using the Current Icing Product (CIP) fuzzy logic scheme

## Products

- FAA/NCAR Current Icing Product (CIP)
- Uses fuzzy logic methods and decision tree technology to combine data sets
- Produces estimates of icing probability, supercooled large droplet (SLD) potential, and icing severity over CONUS
- NASA LaRC Satellite Cloud Products (“ASAP Products”)
- Based on daytime GOES imager data
- Available every half hour
- Hydrometeor phase, cloud top height, cloud effective temperature, liquid water path, effective radius

## Current Icing Product (CIP)

**Model**

**Satellite**

**Surface**
**Obs**

**Radar**

**Pilot**
**Reports**

**NO**

**YES**

**Determine  Vertical Cloud Structure and Weather Scenario**

**Match data to each 3-D model grid box**

**Cloudy?**

**ICING PROBABILITY and SLD POTENTIAL FIELDS**

**ICING=0.0**
**SLD=0.0**

**Lightning**

**Apply interest maps. Calculate icing probability and potential for supercooled large drop (SLD). **

**Current Icing Product (CIP)**

**Notes:**
ASAP tasks focused on pink fields.

## FY08 Accomplishments

- Icing probability estimates now use ASAP products
- experimental CIP available at _[http://www.rap.ucar.edu/icing/cip/gdcp/](http://www.rap.ucar.edu/icing/cip/gdcp/)_
- Improved cloud top height estimates using ASAP products
- Icing probability comparison: experimental vs. operational CIP
- Use of ASAP products for icing severity estimates in progress

## Integration of ASAP Products into CIPIcing Probability

| Cloud screening | Hydrometeor phase |
| --- | --- |
| Cloud top temperature interest map | Cloud effective temperature
Hydrometeor phase |
| Cloud top height estimate | Cloud top height
Cloud effective temperature |

-         CIP Function               Satellite Product Used

## Cloud Top Height Estimation – Current CIP Scheme

*GOES IR brightness temperature*

*Model sounding*

*Observed cloud top*

- Height (m)

- Temperature (K)

**Cloud Top Height Estimation – Current CIP Scheme**

*CIP cloud top*

## Matching CIP to CloudSat

**Notes:**
CloudSat reflectivity (X-band), minimum detectable -30 dBz
Jan – Mar 2007, 650 CloudSat passes matched with CIP

## 	         CTZ ≤ 3 km			 3 km < CTZ ≤ 6 km

- 	         CTZ ≤ 3 km			 3 km < CTZ ≤ 6 km

- 	   6 km < CTZ ≤ 9 km		         CTZ > 9 km

**Notes:**
CIP minus CloudSAT

## Hybrid Method

- Blend satellite-derived cloud top height and effective temperature products with model profiles
- Apply fuzzy logic to incorporate qualitative info about location of cloud tops in a sounding, e.g.,
- RH < 100%
- Inversion in θe
- Presence of wind shear
- Develop interest maps; combine weighted values to determine cloud top height

## Errors in Cloud Top Height Estimates

-       CIP Method             Satellite Method         Hybrid Method

**Errors in Cloud Top Height Estimates**
**                         ****(Compared to TOP-REPS)**

- x

## Cloud Top Height -- 19 January 2005

- Operational CIP Method                        Experimental Hybrid Method

**1000 m**

**3000 m**

**5000 m**

## Icing Probability at 3500 m -- 19 January 2005

- Operational CIP Method                Experimental Hybrid Method

## Statistical ValidationOperational CIP vs. Experimental CIP

- Analyze 6 weeks of 2005 winter icing data
- Compare icing volume and probability of detection (POD) of icing PIREPS

## Volume Comparison

- Experimental CIP
- 8.4% of grid contains icing
- 83% of the cases had less volume

- Operational CIP
- 9.0% of grid contains icing

- Comparison limited to gridpoints where both versions agreed on cloudy/clear status

## Probability of Detecting Icing PIREPS

- Experimental CIP
- PODy = 74%
- PODn = 68%

- Operational CIP
- PODy = 83%
- PODn = 58%

- Need to examine loss of PODy in the experimental version

## Icing SeverityASAP Products vs. PIREPs

- LWP/IWP, phase, tau, effective radius
- Compared to PIREPS for each CIP scenario

## ASAP Products – CIP Icing Severity

- Combine the NASA Langley retrievals with other CIP input data sets
*Appropriately* apply fields such as LWP/IWP
- Scenarios
- Liquid
- Small drop (effective radius)
- Large drop (effective radius)
- Multi Layer
- Ice
- Glaciated
- Mixed phase

## Create membership function

## Example: liquid - small drop 

- Supercool liquid

- LWP = 300 g/m2

## Example: liquid - small drop 

- Small drop

- Example: liquid - small drop

## Example - cont

- Liq. Cloud top
- LWP 300 g/m2

- LWP_map
- applied to all grid
- points within cloud layer

- Model sounding

## Two layer example

- LWP_map applied

- LWP_map
- not applied

- Model sounding

- Liq. Cloud top
- LWP 300 g/m2

## Future Plans

- Experimental CIP now runs routinely at NCAR; evaluate against operational CIP in winter 2008-2009 season
- Integrate ASAP products into icing severity algorithm
- Investigate use of multi-layer cloud product for improved layer placement in CIP
- Transition to operations; dependent upon operational availability of ASAP products