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GOES-R AWG Aviation Team: Flight Icing Threat (FIT)

June 14, 2011

William L. Smith Jr.NASA Langley Research Center

Collaborators:

Patrick Minnis, Louis Nguyen NASA Langley Research Center

Cecilia Fleeger, Doug Spangenberg, Rabindra PalikondaSSAI@ NASA Langley Research Center

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Outline

Executive Summary Algorithm Description ADEB and IPR Response Summary Requirements Validation Strategy Validation Results Summary

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Executive Summary This ABI Flight Icing Threat (FIT) detection algorithm generates an Option

2 product.

ATBD (100%) and Software Version 5 are on track for delivery for a July delivery.

Algorithm utilizes ABI cloud products to identify areas icing is likely to occur and estimates the probability for icing in two severity categories.

Validation Datasets: Icing PIREPS, TAMDAR, and ground-based icing remote sensing data.

Validation studies indicate that the product is meeting spec.

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Algorithm Description

In-flight Aircraft Icing depends on

GOES-R ABI can provide

● presence of super-cooled liquid water (SLW)

● liquid water content, LWC

● Droplet size distribution, N(r)

● Temperature, T(z)

● Cloud top temperature and phase: SLW

● liquid water path: LWP = f(LWC)

● effective radius, re = f(N(r))

Satellites detect icing conditions

Algorithm Description

Match ABI proxy cloud products with Pilot reports of icing and determine relationships

» Use NASA LaRC GOES products for prototype method/initial validation

» Tune technique for ABI (MODIS proxy with CAT products)

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Map satellite-derived cloud parameters to Flight Icing ThreatExploit high horizontal and temporal resolution

Effective Radius (μm)Liquid Water Path (g/m2)Cloud Top Phase

Icing PIREPS

0 100 200 300 600 800 5 7 11 13 1715 19 21 2590

Approach

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Algorithm Summary

The flight icing threat is determined using theoretically based cloud parameter retrievals.

The icing mask is first constructed (icing, none, unknown) using the cloud phase and optical depth (day and night).

The IR-only approach used by the CAT for Cloud Phase provides day/night consistency and takes advantage of the ABI’s improved spectral information (big advance over current GOES).

During daytime, the icing probability and severity are computed for each ‘icing’ pixel using the retrieved LWP and Re.

Additional output parameters include quality control flags and estimates of the icing altitude boundaries.

FIT is indeterminate when high clouds obscure view (see poster for new methods being developed to help alleviate this shortcoming).

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Flight Icing Threat Processing Schematic

Determine ‘icing’ pixels (optically thick SLW pixels)

INPUT: Cloud Phase, Tau, LWP, Re

Mask the remaining pixels as ‘none’ (cloud free, warm clouds) or ‘unknown’

(optically thick high clouds)

During daytime, estimate icing probability and severity from LWP, Re for icing pixels

Output Icing Mask and Index

AWG Cloud Phase

SEVIRI RGB 10/16/2009

Clear Liquid SLW Ice Mixed

AWG Liquid Water Path

10008006004002000

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Flight Icing Threat Product Output

Nighttime

CURRENT GOES

Daytime

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Algorithm Changes from 80% to 100%

Developed method to scale LWP to S-LWP for clouds that extend below freezing level.

» Requires knowledge LWC(z), the freezing level (NWP) and the cloud geometric thickness (NASA LaRC parameterization).

» Four LWC (Z) distributions: (1) Uniform LWC(z), (2) Adiabatic LWC(z) – inc w/altitude (3) dec w/altitude, (4) Climatology (from CloudSat) are being tested.

Added Icing Altitude Boundaries (Ztop from ACHA, Zbase from Ztop - ΔZ or freezing level, whichever is higher.

Adding snow cover dependence (requires dynamic snow map).

Metadata output added.

Quality flags standardized.

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Summary of ADEB and Independent Peer Review (IPR)

Feedback

Main issues from IPR

Concern regarding the usefulness of the accuracy requirement

- Agree but icing definition ill-defined (more later).

Concern regarding the dependence on unproven ABI cloud products and consistency with LaRC products used in prototype

- ABI products are looking reasonable and are being validated and compared to legacy products. We are working with CAT understand differences. The FIT can be tuned to the ABI cloud products. ABI-FIT validation will determine what level of algorithm tuning is needed, if any.

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Summary of ADEB and Independent Peer Review (IPR)

Feedback

All ATBD errors and clarification requests have been addressed.

No feedback required substantive modifications to the approach.

ADEB would like to see more validation with TAMDAR

- We agree and are working with AIRDAT to acquire additional TAMDAR datasets

Requirements

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Product Measurement Range

Accuracy Latency (mesoscale)

horizontal resolution

Flight Icing Threat

Day: None, Light, MOG (Moderate or greater)

Night: None, Possible Icing

50% correct classification

5 min 2 km

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Validation Approach

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Validation Approach: Datasets

• Icing PIREPS

Match satellite pixels within 20km and +/- 15 minutes of each PIREP

• TAMDAR icing sensor on ~400 commercial aircraft

4 closest pixels, +/- 15 minutes matched to each observation

• NIRSS - Ground-based icing remote sensing products at NASA GRC, Cleveland, OH

Match pixels within 20km of site and avg NIRSS data (time, altitude)

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Validation Approach: Qualifier

• Truth datasets each have their own associated uncertainties and limitations

PIREPS (geolocation errors, biased, severity subjective)

TAMDAR (‘no icing’ can be ambiguous due to sensor issues and knowledge of presence ‘in-cloud’

NIRSS a remote sensing concept, not direct

PODY (skill in positive detection) reliable but PODN, False Alarms difficult to ascertain and not meaningful. NIRSS could help.

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Validation Approach: Qualifier

• There is currently no accepted definition of severity by cloud microphysics parameters (e.g LWC, Re) or accretion rate on an airplane (considering the variety of airplanes and their aerodynamics, this would also carry some uncertainty), thus PIREPS, etc will have to do

FIT Severity validation and calibration are difficult

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Validation Results

Flight Icing Threat Numerous Icing PIREPsconfirm ABI flight icing threat

Current GOES (LaRC Cloud Algorithms)

Validation with PIREPS:

Use data from several winters

Validation with PIREPSWinter 2008-2010

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Satellite

PIREPS

L

M

L M

Probability of Detecting Light/Moderate Icing

3346 992

2633 1498

POD(light) = 55% POD(mdt) = 60%

PODY = 99.9% CSI= 93% FAR=7% SS= 99.8%

Probability of Detecting Icing

Satellite

PIREPS

Y

N

Y

6229 442

5 3

N

• Algorithm developed with with independent data (3700 PIREPS: winter 2006-2008

• Excellent detection of icing conditions (overcast SLW scenes)

• False reports common, but small % of total - ‘No Icing’ not reported often • Classification of severity has skill but not as much as icing detection

• Icing severity often subjective & depends on A/C

SS = (YY – NY) / (YY +NY) skill in positive detection

191919

Validation Approach: NIRSS

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LWP and Cloud Boundaries

Icing Severity profilesConvert LWC to Severity

Ground-based Sensors atNASA GRC

(Reehorst et al. 2009)(Reehorst et al. 2009)MWR

Cloud Radars

NIRSS approach takes data from microwave radiometer, cloud radars, experience/data from aircraft icing program to derive super-cooled LWC profiles which can be mapped to icing severity profiles

NASA Icing Remote Sensing System

Tuned to airfoil model (Politovitch 2003)

Validation with NIRSSWinter 2008-2010

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Satellite

PIREPS

L

M

L M

Probability of Detecting Light/Moderate Icing

145 (144) 30 (19)

41 (42) 16 (27)

PODL = 78 (77) % PODM = 35 (59)%

PODY = 100% CSI= 93% FAR=7% SS= 100%

Probability of Detecting Icing

Satellite

PIREPS

Y

N

Y

232 17

0 0

N

• Similar detection skill as found using PIREPS

• Need more data (coming soon)

• Classification of severity good for V3. Not as good for V5

- FIT algorithm is tuned to PIREPS

- NIRSS tuned to airfoil model

SS = (YY – NY) / (YY +NY) skill in positive detection

FIT V5 (V3)

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Flight Icing Threat Product Output

Oct 16, 2009 (1400 UTC) SEVIRIPhase

LWP

FIT

AIT Framework tests work as expected

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Flight Icing Threat Product Output

Oct 16, 2009 (1400 UTC) SEVIRI

Flight Icing Threat

AWG Cloud Phase

Clear Liquid SLW Ice Mixed

RGB

x

x

X – MDT Icing PIREPsconfirm ABI icing threat

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MODIS (ABI Cloud Algorithms)

Validation Results

16 MODIS granules have been run by the CAT to test FIT and validate with PIREPS

23 NOV 09 25 NOV 09 6 FEB 10 19 DEC 10

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MODIS (ABI Cloud Algorithms)

Validation Results

16 MODIS granules have been run by the CAT to test FIT and validate with PIREPS

23 NOV 09 25 NOV 09 6 FEB 10 19 DEC 10

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Validation Summary (Ver 5)Product Measurement Range

Product Measurement Accuracy

Icing Validation using GOES-11/12/13 and MODIS data over CONUS

Icing Detection

Binary Yes/No

(OVC SLW Scenes)

Skill Score

Icing Validation using GOES/MODIS data over CONUS

Two-Category Severity POD

(Light, MOG)

Daytime Only

Day: Unknown, None, Light, Moderate or Greater (MOG);

Night: Unknown, None, Possible Icing

50% correct classification

PIREPS

GOES: 100% (N=6699)

ABI MODIS: 100% (N=39)

TAMDAR

GOES: 75% (N=12082)

NIRSS:

GOES: 100% (N=336 )

FAR ~ 7%

PIREPS

GOES: Light: 55%

MOG: 60%

ABI MODIS: (V3)

Light: 63 (93) %

MOG: 56 (22) %

NIRSS

GOES: Light: 78 (77) %

MOG: 59 (35) %

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Summary

The ABI Flight Icing Threat algorithm provides a new capability for objective detection of the in-flight icing threat to aircraft

Version 5 software and 100% ATBD on track for delivery in July Improved ABI spectral coverage, spatial and temporal resolution should

offer better detection capability over current GOES particularly at night Algorithm meets all performance and latency requirements. Some

tuning probably required for ABI for severity component Have not yet quantified missed detection due to cloud phase errors We are working closely with Cloud Team to acquire ABI cloud products,

on cloud product validation, FIT algorithm tuning and validation 30-40% of atmospheric icing still undetected due to high cloud

obscuration (not accounted for in validation). See poster for potential solutions