Diagnosing Aircraft Icing Conditions Using Geostationary Satellite Data

P. Minnis, W. L. Smith, L. Nguyen

LaRC, Hampton, VA

R. Palikonda, D. Spangenberg, S. Houser, C. Yost, M. Khaiyer

SSAI, Hampton, VA

F.-L. Chang

NIA, Hampton, VA

P. W. Heck

CIMSS, Madison, WI

NASA Applied Sciences Weather Program Review

18-19 November 2008

Aircraft Icing

• Aircraft structures act as ice nuclei in supercooled clouds

- ice collects, weight increases, plane falls

• Pilots need to know where and when icing can occur

- PIREPS are first order

- sparse, aircraft dependent, location uncertain

- weather forecasts

- freezing levels, cloud expectations

- radar => precipitation

• All combined in NCAR/FAA/NOAA/NASA program to provide Current Icing Potential (CIP) & Future Icing Potential (FIP) products to pilots

- some inadequacies remain

- NWP uncertainties, intensity, altitude of icing, etc.

Objectives of LaRC Satellite-Based Cloud & Icing Products• Generate & provide, in near-real time, cloud and radiation products from satellites for a variety of applications, especially aircraft icing diagnosis & prediction, using NASA Earth Science data &/or algorithms

- apply cloud code developed for CERES-MODIS to GEO & LEO sats

- improve & update code

- develop I/O system to provide results to users in a timely manner

- validate to provide error bars for users

• Contribute to aircraft icing program

- nowcast: satellite only algorithm, cloud products => icing diagnosis

- quick, simple, useful for non-model domains, provides insight into diagnosis, not as good at night

- CIP: merge cloud products directly into CIP decision process

- still fast, merges all info together, all times of day (Haggerty)

- CIP/FIP: assimilate cloud products into RUC used by CIP (next talk + 1)

- provides forecast of weather, all times of day, icing diagnosed

- High IWC hazard (future)

Satellite Remote Sensing of Icing Conditions

ICING CONDITIONS ARE DETERMINED BY CLOUD

• liquid water content, LWC positive w/ intensity

• temperature, T(z) negative w/ intensity

• droplet size distribution, N(r) r positive w/ intensity

SATELLITE REMOTE SENSING CAN DETERMINE CLOUD• optical depth, • effective droplet size, re• liquid water path, LWP• cloud top temperature, Tc• Thickness, h

IN CERTAIN CIRCUMSTANCES

Current Products

0.65 µm Reflectance 3.7 µm Temperature 6.7 µm Temperature

10.8 µm Temperature 12 or 13.3-µm Temp 1.6 µm Reflectance

Skin Temperature Optical Depth Eff Radius/Diameter

Liq/Ice Water Path Cloud Eff Temp Cloud Top Pressure

Cloud Eff Pressure Cloud Top Height Cloud Eff Height

Cloud Phase Cloud Bot Height Cloud Mask

Cloud Bot Pressure Icing Potential Broadband SW Albedo

Broadband LW Flux Infrared Emittance

New products

Surface Flux (Gridded)

Multi Layer Cloud Mask & Layer Retrievals

LaRC Products from Geostationary & Polar-Orbiting Satellites

http://www-angler.larc.nasa.gov/satimage/products.html

2-D Cloud Retrieval Maps From GOES Have a 3rd Dimension: Cloud Thickness

Comparison of cloud base heights from GOES retrievals & ASOS ceilometer data

1900 UTC, 18 March 2004

overlap regime

ztop too low or cloud too thick

Primary Domain: CONUS

• Corresponds to RUC & CIP domains

• Apply CERES-MODIS analysis code to generate the cloud products

Analysis Applied to Two Satellites to Cover USA

GOES-11 RGB GOES-12 RGB

1815 UTC, 8 Jan 2008

Each image is analyzed and the results are combined

Combined GOES-11/12 Retrievals, 1815 UTC, 8 Jan 2008

RGB

Phase

re LWP

Light Blue - Supercooled

• LWP = LWC * h

• re = f[N(r)]

• Tc & h can yield depth of freezing layer

• zt is top of icing layer

• ceiling = zt - h

IN MANY CASES, SATELLITE REMOTE SENSING

SHOULD PROVIDE ICING INFORMATION

CLOUD PRODUCTS VS. ICING PARAMETERS

Dependence of Icing on LWP and re

Major dependence on LWP, minor on re

Formulation developed for icing potential

Limited Sampling

Icing Potential from GOES Data Alone 1815 UTC, 8 Jan 2008

Indeterminate areas (white)

• This method has been adopted for use in Europe with Meteosat

(Francis, 2007)

Upgrading Icing Nowcast Method

• Repeat analysis using greater dataset (2 seasons)

- only ~1 month of data used for initial parameterization

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PIREPS ICING INTENSITYNov-Mar, 2006/2007 and 2007/2008

Fre

quen

cy (

%)

2855

81212

10803

1689

4920

16 3 21

N=21,131

MOGlightSatellite Categories:

Most reports light or mod

Unfiltered dataset

Negative icing reports undersampled

Probability of icing always greater than 0.9

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Probability of icing PIREP as a function of Satellite LWP

No IcingTRC-LGT IcingMDT-HVY Icing

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LWP (g/m^2)

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Re = 5 umRe = 16 um

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LWP (g/m^2)

Filtered dataset but with negative icing reports tripled to account for sampling bias

488 g/m2 LWP threshold chosento discern light from MOG icing.

Winter 2006/2007 and 2007/2008 dataset

(c) Mean Cloud Parameters (Filtered data)

(d) Standard Deviations (Filtered data)

Filtered dataset

This method is the starting operational algorithm for the NOAA GOES-R project.

GOES Example Jan 11, 2008 (1745 UTC)

Terra

This method is the starting operational algorithm for the NOAA GOES-R project.

Main Problem Areas

• Overlapped clouds- false icing- indeterminate pixels

• Snow- overestimate of optical depth => LWP

false icing, too severe

• Nighttime, terminator- discrimination of optical depth for thick clouds difficult- phase more uncertain

Finding More Icing in Indeterminate AreasMultilayer Cloud Detection & Retrieval

• Some indeterminate pixels are overlapped ice-over-water clouds

- multilayered cloud detection used to find those areas where icing is a problem (Detect with CO2 method, Chang & Li, 2005)

• Use a multilayered VISST to derive low cloud properties Minnis et al. JGR 2007

2 methods developed using Aqua & Terra data

Upper cloud

Lower cloud

Single cloud

SL VISST ML VISST

Multilayered Cloud Detection Using 11 & 13.3 µm Channels

• Use sounding to predict 11 & 13.3 BTs

• Use radiative transfer to obtain solution satisfying both channels by adjusting background temperature and upper layer cloud optical depth

- OD(UL)

- T(UL)

- T(LL)

• Use UL cloud OD with 2-layer VISST to determine OD of LL

• If OD(LL) > 6 and T(LL) < 273 K => ML icing

• Only applies to GOES-12 over CONUS (Meteosat, MODIS)

Multi-layered Cloud Detection, 13.3/10.8 µm,1815 UTC, 8 Jan 2008

Magenta areas indicate multilayer ice-over-water

Icing Potential

1815 UTC, 7 Jan 2008

RGB Standard retrievals + ML results

Multilayer retrievals pick up additional areas with icing that were formerly indeterminate

… some areas remain undetected

Icing Potential

1915 UTC 10 Jan 2008

RGB Standard retrievals + ML results

Multilayer retrievals pick up additional areas with icing that were formerly indeterminate

… some areas remain undetected

ICING-Non ML ICING-ML enhanced PIREP PIREP ------------------------ --------------------------- S YES NO S YES NO

A YES 933 66 A YES 1070 81 T NO 114 17 T NO 124 17 ---------------------- --------------------------- pody=yy/(yy+ny)=PODy=89.1% PODy = 89.6% podn=nn/(yn+nn)=PODn=20.5% PODn = 17.3%Ntot=1130 Ntot=1292Indeterminate GOES icing excluded

Summary of PIREPS Comparisons

GOES-12, Jan, Oct, Nov 2008

• Identifies icing ~ 90% of the time

• Multilayer method as accurate as single-layer technique

• No matching of altitudes performed

Single-Layer GOES (G) PIREPS (P)----------------------------------------- Icing (G), Icing (P): 56.4% No Icing (G), No Icing: (P) 0.9% Indeterminate (G), No Icing (P): 1.8% Indeterminate (G), Icing (P): 30.9% No Icing (G), Icing (P): 7.3% Icing (G), No Icing (P): 2.6%----------------------------------------

Multi-layer enhanced ----------------------------------------Icing (G), Icing (P): 66.8% No Icing (G), No Icing: (P) 0.9%Indeterminate (G), No Icing (P): 1.2%Indeterminate (G), Icing (P): 19.6%No Icing (G), Icing (P): 8.1%Icing (G), No Icing (P): 3.4%

Total number of compared events: 1614 ----------------------------------------

PIREPS vs GOES icing detection returns: frequency of occurrence

• Increases area of knowledge

- 25% more cloudy area included in base

• Cuts indeterminate area by ~40%

Limitations on Multilayer Detection

• Multilayer retrieval depends on having a 13.3-µm channel

- GOES-12 only (on Meteosat)

- expand G-12 CONUS domain (high angles in west)?

- use full disk imagery (limited temporal coverage)

NASA LaRC GOES Icing Product Nov 8, 2008 (1745 UTC)

Satellite analysis indicates large area of icing conditions extend from the Dakotas eastward to PA, NY and WV

Impact of Snow on Icing Diagnoses

NCAR Icing Analysis with PIREPS OverlayNov 8, 2008 (1900 UTC)

HVY

Note: HVY icing report in Ohio. Numerous MDT reports. Good correspondence with satellite analysis

PIREPS confirm significant icing

Aviation Weather Center PIREPS Display Tool

Nov 8, 2008

NASA LaRC Cloud Analysis Input Nov 8, 2008 (1745 UTC)

GOES RGB NOAA Daily Snow-Ice Map

• Magenta areas are snow covered• Clouds identified properly• Icing areas correctly identified• Icing severity overestimated, LWP overestimated

Addressing Snow Problem

• More spectral channels

- MODIS, future GOES (1.6, 2.2 µm)

• Improve retrieval with existing channels

- use snow map to define snow areas

- estimate surface albedo

- retrieve using both CO2 method and VISST

Cloud Phase, 1745 UTC, 9 November 2008

Retrievals over Snow Background, GOES-12, 1745 UTC, Nov 9, 2008Predicted clr-sky reflectance, with model snow Predicted clr-sky reflectance snow correction

- using snow-corrected background information reduces cloud optical depth & water path

LWP using no-snow background LWP difference (snow – no snow)

Snow Corrections

• Procedure in place for using snow map & surface-type snow albedos

- regional albedos may differ => no retrieval

- exception handling (IR for thin, adjust albedo for thick clouds)

• Test against MODIS retrievals using 1.6/3.7 µm method

• Snow map from yesterday

- examine use of RUC snow amounts to guide snow cover & albedo

• Note: This problem primarily affects LWP and, hence only icing severity. Icing is still well-detected over

snow!

Other Refinements

• Nighttime retrieval improvements- Detection of clouds in overlap conditions- More optical depth information (IR limitations)

- affects severity- Refine phase algorithm (tune w/MODIS 8.5-µm channel)- Multilayer detection enhancement- Improved terminator cloud mask

- use of hour-to-hour memory

• Cloud height improvements- low: new variable lapse rate approach- high: new rough ice crystal models (reduce OD)

• Daytime: better snow discrimination (use snow maps) higher resolution background (RUC scale, ~13 km)

Domain Expansion

• Algorithms operating on all 5 GEOsats, AVHRR, MODIS- leverage from ARM, CERES, & SMD R&A- in-house calibration program

• Icing in remote areas still difficult to predict- nowcast product would be valuable

• Cloud input to larger domain applications- CONUS results do not cover RR domain- expanded CIP needs more data- NASA GEOS-5 needs global data

• Full-disk imagery being processed at low resolution- 9 km, 3 hr, long lag- no refinement of input, spectral responses- shoestring effort

Expanded Domains

• Full-disk Products online

• Analyses of POES datasets also available

Full-Disk Icing Potential, GOES-1214 Mar 2008, 1745 UTC

Most icing occurs poleward of 30° latitude

Icing Potential, 1800 UTC, 8 Nov 2008

GOES-11 GOES-12

Meteosat-9

MTSAT

Global GEOSat Composite, 15 Z 9-9-08 to 21Z, 11-9-08

RGB

Significant problems with FY2-C

Global GEOSat Composite, 15 Z 9-9-08 to 21Z, 11-9-08

Cloud Top Height

Progress Toward “Operational” Status

• Operational = running 24/7 support with short lag time

- NASA Columbia supercomputer

• Porting had been completed at beginning of year

- new compiler on Columbia would not run our software environment (McIDAS)

• New computer analyst to make McIDAS run with new compiler

- 2-3 months required for security approval- began work in earnest 2 weeks ago

- testing of McIDAS modules underway- developing backup system at LaRC to ensure 24/7

- Shuttle operations take over Columbia

• Expect CONUS retrievals “operational” in mid-to-late January 09

Summary & Future Research

• Multilayer method implemented for GOES-12, Meteosat- increases area of potential icing detection

- maintains current accuracy of detection- will enhance CIP & RUC assimilation- will be applied to more of CONUS (full disk)

• Analysis problems identified & potential solutions in the works- snow, night, cloud heights

• New domains available

• “Operations”- CONUS retrievals should be 24/7 on Columbia at end of January- backup systems to be developed at LaRC- alter algorithm as improvements are completed- full disk dependent on other proposals (MAP 08)

Improvement of Operational Aircraft Icing Forecasts and Diagnoses by Assimilation of Satellite Cloud/Surface Properties in the RUC/WRF

P. Minnis, W. L. Smith, L. Nguyen

LaRC, Hampton, VA

S. Benjamin, S. Weygandt

NOAA ERSL GSD

NASA Applied Sciences Weather Program Review

18-19 November 2008

Objectives

• Use NASA satellite products in a Decision Support System for Air Safety

Approach

• Evaluation - Compare RUC and GOES WP & other parameters

- iterate with new code & input

• Verification- Establish LARC data accuracy criteria

- Compare LaRC data w/other input & independent measurements

- iterate w/retrieval improvements

• Validation- Develop/test LWP/IWP assimilation code

- iterate with revised code

• Documentation- Present results at conferences- Summarize results in peer-reviewed papers- Document code as needed

Tasks

• Retrieval code improvements (Minnis)- discussed earlier in icing presentation

• Benchmarking, validation, evaluation- Bill Smith

• Assimilation tests- Smith & Benjamin

• Future- Benjamin