John R. Mecikalski & Wayne F. Feltz CIMSS Seminar on ASAP 3 December 2003 O U T L I N E Advanced...

John R. Mecikalski & Wayne F. FeltzCIMSS Seminar on ASAP 3 December 2003

O U T L I N EO U T L I N E

Advanced Satellite AviationAdvanced Satellite AviationWeather Products (ASAP):Weather Products (ASAP):A Collaborative Research Venture betweenA Collaborative Research Venture between

NASA & FAA, NCAR, CIMSS & UAHNASA & FAA, NCAR, CIMSS & UAH

1. History, Purpose & Goals

2. Update: People & Projects

3. Satellite-Derived Products and the FAA Product Development Teams at NCAR

4. Overview: Current Activities–PDT Table

5. Follow-on Work

6. David Johnson


History of ASAPHistory of ASAP• March 2001: First discussions with PDT members • August 27, 2002:

NASA Advanced Satellite Aviationweather Products (ASAP) Study ReportAs Prepared by the

Cooperative Institute for Meteorological Satellite Studies (CIMSS)The University of WisconsinMadison (UW)

Written in collaboration with

The National Center for Atmospheric Research (NCAR)For the National Aeronautic and Space Administration (NASA) and the

Federal Aviation Administration (FAA) Aviation Weather Research Program (AWRP)

Primary Authors:John R. Mecikalski, UWCIMSSDavid B. Johnson, NCAR RAP

Contributing Authors:John Murray, NASA Langley Research Center Rita Roberts, NCAR RAP

Bruce Carmichael, NCAR RAP Cathy Kessinger, NCAR RAPThomas Achtor, UWCIMSS Marcia Politovich, NCAR RAP

Wayne Feltz, UWCIMSS Cindy Mueller, NCAR RAPBob Sharman, NCAR RAP Barbara Brown, NCAR RAP

Tim Schmit, NOAA/NESDIS ASPT Paul Herzegh, NCAR RAPTimothy Olander, UWCIMSS Joleen Feltz, UWCIMSS

Suzanne Wetzel-Seemann, UWCIMSS Jun Li, UWCIMSSElaine Prins, NOAA/NESDIS ASPT


History of ASAPHistory of ASAP• May 2003: NASA Funding arrives for initial Product Research • Today:

- ~6 member Team at CIMSS - Monthly Teleconferencing- Delivery of about 4 main products- Web access/ data delivery - Ongoing discussions with NCAR on logistics- Preliminary work with larger components of PDT Teams- Seeking new in-house collaborators - Draw in strengths of outside collaborators (LaRC, MSFC, MIT-LL)

http://cimss.ssec.wisc.edu/asap/


Advanced Satellite Aviation-weather Products (ASAP) Initiative

NOAANESDIS

NOAANCEP

NOAAAWC

AirlineOps

FAAFSS

AWIN Providers

Government and University Labs

FAA AWRP

10 PDTsNASA UW-UAH

ASAP

ASAP: Developing High-Resolution Satellite Weather Products to Improve Aviation Safety

NASATestPilot


Purpose & GoalsPurpose & Goals

• The development and demonstration of methods for infusing current and advanced satellite-data analysis techniques into systems designed to increase aviation safety.

• The formation of a robust partnership between NCAR/FAA and UW-CIMSS/UAH that facilitates long-term aviation-oriented satellite-based product development and evolution.

• Coalesce scientists toward the common FAA and NASA goal of increasing aviation safety through improved use of remote-sensing systems.

The goals of this collaboration ...














Real-time Data5 Geostationary Satellites

4 NOAA Polar Orbiting SatellitesTERRA (Direct Broadcast)

FY1CNEXRAD WSR-88D

Model GridsPoint DataText Data

WEB ImagesCustomized Products

Archived Data5 Geostationary Satellites

Model GridsPoint Data

Customized Products


Cooperative Institute for Meteorological Satellite Studies (CIMSS)


University of Alabama in Huntsville (UAH)NASA Marshall Space Flight Center (MSFC)

UAH

ASAP

Lightning Research

Satellite Data Assimilation

Data Mining

New Instruments

Student/Basic Research


100

1000

Current GOES

Pre

ssu

re (

hP

a)

Water Vapor Weighting Functions100

1000

Pre

ssu

re (

hP

a)

Next Generation GOES

Non-Hyperspectral Data• “Broadband”• 10-50 Spectral Bands• Spectral Range ~600-2300 cm-1

• e.g., Current GOES Sounder

Hyperspectral Data• < 1.0 cm-1 Spectral Resolution• >1000 Spectral Bands• Spectral Range ~600-2300 cm-1

• e.g., Next Generation GOES Sounder

Hyperspectral DataHyperspectral Data

GOES-ABI, GOES-HES & MODIS


GOES Planning Launch Dates94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15

On-Orbit Storage Operations

GOES -8

GOES -9

GOES -10

GOES -11

GOES -M

GOES -N

GOES -O

GOES -P

GOES -R

4/94 8/01

5/95 7/98

4/97 4/02

5/00

8/00 5/055/05

7/014/02 4/07

1/03 5/05 5/10

4/05 4/07 5/12

4/074/07 5/10 5/15

4/10 5/12

5/17

as of August 2001

(ABI/HES)

(GIFTS)

2001

2003

2010

2012

2005

(GOES-12)


Presentation OutlinePresentation Outline





5. Follow-on Work

6. David Johnson


• John Mecikalski (PI–UAH): Convection, turbulence, product integration, satellite assimilation

• Wayne Feltz (PI–CIMSS): Cloud products, data management,product integration, validation efforts

• Kristopher Bedka: Convection, winds

• Sarah Thomas: Cloud properties, cloud products

• Ben Howell: Programming and scientific data support

• Scott Bachmeier: Volcanic ash

• Chris Schmidt: Ozone for turbulence

• Tony Wimmers: Water vapor imagery for turbulence

• Nate Uhlenbrock (Tim Schmit): Turbulence research

People & ProjectsPeople & Projects







5. Follow-on Work

6. David Johnson


What Meteorological Problems can Satellite What Meteorological Problems can Satellite Information Be Useful in Solving?Information Be Useful in Solving?

• Clouds• Visibility• Turbulence & Stability• Moist Convection• Icing & Winter

Weather• Over-Ocean Weather• Improved Prediction

through Satellite Data Assimilation

How can this information be most appropriately used at NCAR?

• Use within Expert Systems that forecast and nowcast particular weather

• Use in conjunction with numerical weather prediction systems

• Use within diagnostic programs

Satellites can provideinformation on:


Infusing Satellite Information into the Infusing Satellite Information into the problem of problem of Cloud Detection Cloud Detection && Quantification Quantification

• GOES, GIFTS, GOES-ABI, and GOES-HES can be used insignificant ways to address issues of cloud detection

• The quantification of cloud types, location and amounts canbe employed in a number of aviation-relevant problems:– ceiling determination– cloud top locations– cloud layers evaluations– rapid cloud growth accompanying convective initiation– identification of fog, haze & smoke

• Identification of the inversions that cloud occupy/form near areimportant for turbulence and stability detection


Infusing Satellite Information into the Infusing Satellite Information into the problem of problem of VisibilityVisibility Determination Determination

• GOES, GIFTS, GOES-ABI, and GOES-HES can be used toassess visibility on local and regional scales

• Fog detection/monitoring algorithms can be improved

• Identification of the low-level inversions are important forvisibility determination


Infusing Satellite Information into the Infusing Satellite Information into the problem of problem of TurbulenceTurbulence & & StabilityStability

• Identification of the low-level inversion strength is importantfor severe weather and convection-induced turbulence

• Cloud typing can help in the determination of regionalinstabilities (e.g., wave clouds)

• Hyperspectral geostationary satellite data (GIFTS, GOES-ABI,and GOES-HES ) will allow for the enhanced detection ofinversions


Infusing Satellite Information into the Infusing Satellite Information into the problems associated with problems associated with ConvectionConvection

• GOES, GIFTS, GOES-ABI, and GOES-ABS can be used toassess mesoscale regions of convective cloud development

• Quantification of convective clouds:– convective initiation; regions where this is or is not

possible– convective cloud growth & growth rates– areas of rapid thunderstorm growth– boundary-layer turbulence

• Simple models (Lagrangian) may be used to aid in nowcastingconvection and its initiation in the 0-2 h, and 2-6 h timeframes


Infusing Satellite Information into the Infusing Satellite Information into the problem of problem of In-Flight IcingIn-Flight Icing

• GIFTS, GOES-ABI, and GOES-HES can be used to evaluatecloud temperatures with significant accuracy to aid inaviation weather forecasting

• Algorithms to detect microphysical phase in clouds:– supercooled water clouds versus all-liquid clouds– layered clouds

• Identification of the widespread low-level cloud regionsassociated with potential icing

• Help estimate timing of freezing drizzle events


Infusing Satellite Information into the Infusing Satellite Information into the problem of Observing Weather over problem of Observing Weather over OceansOceans

• GOES, GIFTS, GOES-ABI, and GOES-HES can be used toassess weather on local and regional scales

• The quantification of clouds and accompanying weather:– convective regions– cloud bases and cloud tops– middle and upper level turbulence– satellite data to improve numerical forecast models– simulated satellite imagery with numerical models– contrails as tracers for regional turbulence

• Volcanic ash, smoke, dust and haze regions


Infusing Satellite Information into the Infusing Satellite Information into the problem of NWP Enhancementproblem of NWP Enhancement

• Satellite data assimilation

• Simulation of satellite imagery by numerical models

• Improvement of input data for models (e.g., RUC, WRF,MM5)


What NCAR Product Development Teams What NCAR Product Development Teams might Benefit from Satellite Data?might Benefit from Satellite Data?

The Product Development Teams (PDT)s are:

• In-Flight Icing• Aviation Forecasts• Quality Assessment• Turbulence • Winter Weather Research • Convective Weather • National Ceiling and Visibility • NEXRAD Enhancements • Juneau Terrain-Induced Turbulence • Model Development and Enhancement• Oceanic Weather


What NCAR Product Development Teams What NCAR Product Development Teams might Benefit from Satellite Data?might Benefit from Satellite Data?

The Product Development Teams (PDT)s are:

• In-Flight Icing• Aviation Forecasts• Quality Assessment• Turbulence • Winter Weather Research • Convective Weather • National Ceiling and Visibility • NEXRAD Enhancements • Juneau Terrain-Induced Turbulence • Model Development and Enhancement• Oceanic Weather







5. Follow-on Work

6. David Johnson


Current ActivitiesCurrent Activities• Certain SSEC/CIMSS satellite products (low-hanging fruit) deemed useful to the FAA PDT’s are already in progress

• All products will be converted to Unidata NetCDF format and compressed

• A dedicated ASAP server and web page are online to provide the FAA PDT’s a means for data sharing:

ftp://ftp.ssec.wisc.edu/asap/

• ASAP has provided an opportunity to organize the CIMSS/SSEC satellite products into one location in a unified format


Product Platform Units Resolution Status SourceGlobalCloudMask

MODIS,AVHRR,variousGeostationary

Cloud mask(yes/no) forclouds over10K feet ,cloud toppressureestimates

0.1x0.1degrees

Betaversioncomplete

11 micronIR mosaicthrough DaveSantek

GlobalWinds

VariousGeostationary

Vectors 0.5x0.5degrees

Betaversioncomplete

GOES asciiproductsproduced byVelden et al.reinterpolatedto flight levels

CONUSCloudProducts

GOES-10/12Sounder,GOES-12imager, andMODIS 1km

Cloud toppressure,cloud mask,cloud phase,overlap

10 kmsounder, 4km onceimager, 1kmMODIS

InProgress

CIMSS cloudgroup viaSchriener etal., MODIS1km fromBaum et al

ConvectiveCloudMask

GOES-12,GOES-10

Cloud mask 1km InProgress

GOES-Usingbrightnessthresholdingand gradients

GlobalShear

GOES, MODIS Shear 0.5x0.5degrees

InProgress

Derived fromASAP Windsabove

Convectivesignature/automatedalgorithms

GOES Convectiveassessment

1 km Inprogress

GOES imagervisible and IR

Current Satellite ProductsCurrent Satellite Products


Global Cloud MaskGlobal Cloud Mask

11 m channel satellite mosaic cloud mask for all clouds greater than 10,000 feet in altitude (Dave Santek et al.)

Data has low time latency and is a composite of Geostationary and Polar orbit satellites

Main user for the data would be the Oceanic Weather PDT


Global Cloud Binned HeightGlobal Cloud Binned Height

11 m channel satellite mosaic cloud binned heights determined from temperature profile from GFS model

Future research include adding 6.7 m water vapor channel (common on most of the satellites) to improve cloud height determination accuracy

Main user for the data would be the Oceanic Weather PDT


Global WindsGlobal Winds

Satellite derived winds (Velden et al.) are being interpolated to standard aviation flight levels.


CONUS Cloud Top PressureCONUS Cloud Top Pressure

GOES Sounder

Hourly

10km

CONUS

GOES-12 Imager

4km

30 minute

Eastern U.S

GOES cloud heights/mask needed by Ceiling-Visibility PDT

Fog?

Stratus?


MODIS 1km Cloud PropertiesMODIS 1km Cloud Properties

SSEC Direct Broadcast 1km MODIS Cloud phase (B. Baum and G. McGarragh) may be important to Icing and C/V PDT, disadvantage ~6 hourly resolution

Ice

Overlap

Mixed-Uncertain

Water


Convective Cloud Mask Animation, 1900-1945 UTC, May 4, 2003 Red: Immature Cumulus, Green: Mature Cumulonimbus/Cirrus Technique uses 2-D brightness thresholds/gradients, 10.7 μm TB and 6.5-10.7 μm thresholds


Preliminary CI Nowcasts, April 6th and May 4th, 2003 Red: Pixels with High CI potential, Grey: Cirrus Clouds

1445 UTC 1600 UTC 1945 UTC 2030 UTC







5. Follow-on Work

6. David Johnson


Follow-on Work: 2004-2005Follow-on Work: 2004-2005

For ASAP there are clear avenues for new research. Much ofthis research is already occurring, and thus would only haveto be (re-)directed toward ASAP.

The following pages outline “new” and “potential” areas of(basic) research that will be fostered though the ASAP initiative,both at UW-CIMSS and UAH/MSFC.

Graduate student involvement in such efforts can connect themdirectly to NCAR scientists and national research endeavors.

The following pages outline active research area.


Potential Satellite Products:

• Synthetic/Simulated Satellite Imagery• Enhanced 4-D Visualization• Use of AVHRR for Cloud Typing products• Microwave from POES satellites

• Improved “supercooled” cloud and fog detection

In-Flight Icing PDT


Water/Ice Clouds and Snow/Lake Ice ABI Simulations (from MODIS data)

3-color composite (Visible/1.6 μm/8.5-11 μm)12 February 2001; 1627 UTC

UW/CIMSS

Vis/1.6m/8.5-11m

Water cloud

Ice cloud

Lake Ice

Snow

Super-Cooled


GOES CLOUD PRODUCT & NWP MODELS (CRAS)

24 hr Forecast w/o Sat CTP & PW

24 hr Forecast w Sat CTP & PW

GOES-8 11m Image

•The NWP model is initialized with Sat. CTP & PW

•Prior to start of forecast, Sat. CTP is inserted at 3 hourly intervals

•With Sat. data positive impact is seen over the eastern Pacific and central part of US


New Satellite Products:

• Convective Cloud Information/Pattern Recognition and Trends• Convective Outflow Boundaries• Initiation Regions of Convection• MODIS 1 km water vapor bands for turbulence identification

• Wave Cloud detection (e.g., mountain waves)• Integration of wind, waves and ozone

• Exploit GOES Sounder Total Ozone (better with GIFTS)• GOES “true” water vapor

Turbulence


Wave Patterns in GOES Imager Fields Correlated with Severe Turbulence


GOES Winds as Applied to Jet Streak Detection, Region of High Horizontal Shear (Low Inertial

Stability) and High Vertical Wind Shear

40 to 10 kPa GOES Winds

95 to 70 kPa GOES Winds

High Shear Zone

Low Inertial Stability



• MODIS/GOES Cloud Properties….Ice Crystal Growth, Ice Clouds

• Synthetic/Simulated GOES Satellite Imagery• Duration of Frozen Precipitation

Winter Weather Research


Diagnostics for Winter Convection

white contours diagnose forcing for upward vertical motion

colored contours indicate a layer of conditional instability

GOES-8 Visible image shows convection in southeast Wisconsin; thundersnow was reported

Where forcing and instability are co-located, convection may occur: green contours


5 March 2001 - Nocturnal Fog/Stratus Over the Northern Plains

GOES-10 4 minus 11 μm Difference ABI 4 minus 11 μm Difference

FogBoth images are shown in the GOES projection.

GOES-ABI (3.9 m)Based on GOES Imager Ch 2

For Fog, Snow, Cloud, and Fire detection



• Cloud Typing• Automated Detection of Convective Clouds, Cloud Lines• Automated Detection of Convective Initiation• GOES Sounder (CAPE, LI, cloud top glaciation, Tskin, etc)

• Possibilities for detection of Elevated Convection • Lagrangian Model for CI Nowcasting• Incorporation of Lightning Data

Improved methods to Detection of Convective Initiation

Convective Weather


Convective Initiation (CI) as Determined fromGeostationary Satellite Data: Precursors to CI

General CI Interest Field

Growing/Expanding Cumulus

Deep Precipitating Cb’s

Outflow Boundaries

Cirrus Shield (no interest)

ABL Rolls/Cumulus Lines

Sea-breeze Boundary

GOES-8 1 km visible imagery: 17:45 UTC 10 September 1999GOES-8 1 km visible imagery: 17:45 UTC 10 September 1999

ToweringCumulus

No CI Interest

OceanicCloud Lines

High-resolution cloud featuresHigh-resolution cloud features

Sea Breeze

ABL Rolls

CI Interest


Severe Convection IR windows

25 February 2001The simulated ABI clearly captures the over-shooting (cold) cloud tops, while the current GOES Imager does not.

(Images shown in GOES projection.)

MODIS (1 km) GOES-ABI (2 km)

GOES-8 (4 km)


GOES-ABI (1.61 m)Example of MAS 0.66 and 1.61 m

convective cumulonimbus surrounded by lower-level water clouds(King et al., JAOT, August 1996)

Ice Clouds

Water Clouds



• 0.47 m Dust detection via MODIS• 11-12 m Dust from GOES• Cloud Base & Top Heights (Ceilings)• AERI for Fog Development

• Aerosol and dust detection for visibility• Enhanced cloud products (e.g., cloud base)

• Fog Product for Timing of Onset/Burn-off• Low Cloud Typing• Cloud Microphysics (ice versus water) for De-Icing• Potential for direct Visibility Assessments

National/Terminal Ceiling and Visibility


D C

B

A

Arc of Deforestation

1995

1997

1996

1998

1999

Longitude °W70 60 50 40 30 20 10

Longitude °W

70 60 50 40 30 20 10Longitude °W

70 60 50 40 30 20 10

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-10

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Overview of Fires, Opaque Clouds, and Smoke/Aerosol Coverage in South AmericaDerived from the GOES-8 ABBA and MACADA: 1995 - 1999

FIRES SMOKE/AEROSOLOPAQUE CLOUDS



• 4-D Visualization• GOES clouds and Simulated Satellite Data

• RUC collaboration• WRF collaboration• Satellite Data Assimilation through the JCSDA/SPoRT

Model Development and Enhancement



• Port many existing Cloud, Wind and Turbulence Products• Locating Jets (Jet Streaks) with Imager Winds; Shear Zones• Sounder Water Vapor Winds• “CO2 Slicing” for Height of Volcanic Plume• Sounder Ozone• 0.47 m and Visibility; Aerosol and Smoke Detection• Turbulence and Convection• 8.5 m on MODIS for Volcanoes• Improved SST

Oceanic Weather


GOES-ABI spatial coverage rate versus the currentGOES Imager: Example for 5 minutes

ABI coverage in 5 minutes GOES coverage in 5 minutesThe ABI may take full disk images every 15 min. + CONUS images every 5 min.


GOES-9 Detected Asian Dust Event Over Pacific, GOES Sounder Derived Winds Indicate Strength

and Intensity of Jet Advecting the Dust

Asian Dust Plume


Aleutian Islands

Mt. Cleveland

MODIS 11m - 12m Brightness Temperature Difference MODIS 3.9m - 12m Brightness Temperature

MODIS views the ash plume from the eruption of Mt. Cleveland: 2310 UTC February 19, 2001







5. Follow-on Work

6. David Johnson


Overview: Satellite Data per Scientific Overview: Satellite Data per Scientific Problem (i.e. PDT)Problem (i.e. PDT)

Table: PDT versus Satellite Products...

P

T

D

Clouds WinterWx OceanWx NWPIcingConvectionTurb/StabVisibility

IFI

AF/QA

TURB

WINT

CONV

NCV

OCEAN

MDE


Summary & ConclusionSummary & Conclusion

• NCAR/FAA PDTs will benefit by CIMSS/UAH providing a large number of already-available, and PDT-specific satellite-based products.

• NCAR/FAA PDTs will benefit by CIMSS/UAH providing expert knowledge on future satellite systems. This will minimize the time between launch of new sensors and their use.

• CIMSS/UAH, in conjunction with NOAA/NASA, have experience in developing satellite-based meteorological products.

• NCAR/FAA PDTs can direct satellite-based development; CIMSS/UAH can forge new basic research paths rooted in remote sensing technologies and data processing.

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John R. Mecikalski & Wayne F. Feltz CIMSS Seminar on ASAP 3 December 2003 O U T L I N E Advanced...

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