1. 1 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 Palikonda SSAI@ NASA Langley Research Center

2. 2 Outline  Executive Summary  Algorithm Description  ADEB and IPR Response Summary  Requirements  Validation Strategy  Validation Results  Summary

3. 3 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.

4. 4 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, r e = f(N(r)) Satellites detect icing conditions

5. 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) 5 Map satellite-derived cloud parameters to Flight Icing Threat Exploit high horizontal and temporal resolution Effective Radius (μm) Liquid Water Path (g/m 2 )Cloud Top Phase Icing PIREPS 0100200 300 600800 571113171519212590 Approach

6. 6 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).

7. 7 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 ClearLiquid SLW IceMixed AWG Liquid Water Path 1000800 600400 2000

8. 8 Flight Icing Threat Product Output Nighttime CURRENT GOES Daytime

9. 99 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 (Z top from ACHA, Z base from Z top - ΔZ or freezing level, whichever is higher.  Adding snow cover dependence (requires dynamic snow map).  Metadata output added.  Quality flags standardized.

10. 10 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.

11. 11 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

12. Requirements 12 ProductMeasurement Range AccuracyLatency (mesoscale) horizontal resolution Flight Icing Threat Day: None, Light, MOG (Moderate or greater) Night: None, Possible Icing 50% correct classification 5 min2 km

13. 13 Validation Approach

14. 14 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)

15. 15 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.

16. 16 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

17. 17 Validation Results Flight Icing Threat Numerous Icing PIREPs confirm ABI flight icing threat Current GOES (LaRC Cloud Algorithms) Validation with PIREPS: Use data from several winters

18. Validation with PIREPS Winter 2008-2010 18 Satellite PIREPS L M LM 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 53 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

19. 19 Validation Approach: NIRSS 19 LWP and Cloud Boundaries Icing Severity profiles Convert LWC to Severity Ground-based Sensors at NASA GRC (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)

20. Validation with NIRSS Winter 2008-2010 20 Satellite PIREPS L M LM 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 00 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)

21. 21 Flight Icing Threat Product Output Oct 16, 2009 (1400 UTC) SEVIRI Phase LWP FIT AIT Framework tests work as expected

22. 22 Flight Icing Threat Product Output Oct 16, 2009 (1400 UTC) SEVIRI Flight Icing Threat AWG Cloud Phase ClearLiquid SLW IceMixed RGB x x X – MDT Icing PIREPs confirm ABI icing threat

23. 23 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 096 FEB 1019 DEC 10

24. 24 MODIS (ABI Cloud Algorithms) Validation Results 16 MODIS granules have been run by the CAT to test FIT and validate with PIREPS 23 NOV 0925 NOV 096 FEB 1019 DEC 10 24

25. 25 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) %

26. 26 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