# Presentation: 025305

## BR-7100/051-1NASA Advanced Satellite Aviation Weather Products (ASAP) Benefits AnalysisMarch 31, 2003MCR Federal, Inc.1111 Jefferson Davis Highway, Suite 601Arlington, VA  22202(703) 416-9500

## Outline

- Purpose and Scope
- General Approach
- Data Collection
- ASAP Overview
- Summary Results of Proficiency Improvements
- ASAP Potential Benefits
- PDT Assessments
- Summary

## Purpose & Scope

- Determine both quantitative and qualitative improvements to be gained nationally by providing more accurate depictions of aviation weather phenomena through addition of satellite data to existing AWRP products
- Provide NASA and AWRP initial assessments of potential impacts of integrating ASAP into AWRP research and products

## General Approach

- Assess improvements to AWRP products through discussions with the meteorological community
- Identify proficiency improvements anticipated
- Quantify relative magnitude of proficiency improvements
- Determine risk or probability of achieving improvements
- Rank potential improvements to AWRP products based on combined anticipated proficiency improvements and risks
- Identify applicable set of operational benefits and determine overall magnitude

## Data Collection

**Data Collection**

- Literature Search
- NASA ASAP Plan, September 2002
- NASA Advanced Satellite Aviation Weather Products Study Report, July 2002
- NASA Aviation Weather Information Requirements Study, Georgia Tech Research Institute, June 2000
- FAA AWRP Product Development Team (PDT) 7-Year Plans (FY03-FY09)
- GOES Users’ Conference (October 2002)
- “Future Aviation Applications from GOES” by Fred Mosher(http://www.osd.noaa.gov/goes_R/goesrconf.html)
- Physics and Astronomy Online (_[www.Physlink.com](http://www.physlink.com/)__)_
- MeteoSat Transition Programme (_[www.eumetsat.de](http://www.eumetsat.de/)__)_

## Data Collection

**Data Collection**

- AWR PDT proficiency and risk assessment interviews
- In-flight Icing
- Oceanic Weather
- Turbulence
- Convective Weather
- National Ceiling and Visibility
- Terminal Ceiling and Visibility

## ASAP Overview

- ASAP would be a collaborative effort between NASA and the FAA Aviation Weather Research Program (AWRP) Product Development Teams (PDTs)
- ASAP Goals:
- Enhance the usage of satellite data sets within the existing AWRP PDT structure and transfer satellite expertise to PDTs
- Integrate new satellite information into the PDTs suite of weather products that undergo standard FAA procedures for testing and transition to operational use
- Facilitate early involvement of the AWRP PDTs in the development process for the next generation of satellite sensors and speed the use and incorporation of new technologies into the Nation’s aviation safety programs

## ASAP Overview

- Phase I
- Enhance AWRP use of current satellite data and processing techniques
- Increased use of operational satellites for current products
- Use research satellites to test future algorithms and validate current products
- Phase II  (overlaps with Phase I)
- Exploit the dramatic improvements in remote sensing technologies made possible through the next generation of satellites and hyperspectral instrumentation
- Identify opportunities for providing new or improved aviation weather products using these new technologies

## ASAP Overview

- _Phase I satellites_
- Geostationary orbit
- NOAA Geostationary Operational Environmental Satellites GOES-10 & GOES-12
- European Meteorological Satellites (METEOSAT & MSG)
- Japanese Meteorological Satellites (GMS-5 & MTSAT)
- Polar orbit
- NOAA  Polar Operational Environmental Satellites (POES)
- NASA Earth Observing System (EOS) Platforms
- Aqua & Terra

## ASAP Overview

- _Phase II satellites_
- Geostationary orbit
- GIFTS,  Geosynnchronous Imaging Fourier Transform Spectroradiometer
- GOES-R Advanced Baseline Imager (ABI)
- GOES-R Hyperspectral Environmental Sounder (HES)
- Polar orbit
- Future EOS Platforms
- Aqua & Terra
- Future National Polar  Operational Environmental Satellite System (NPOESS)
- Visible/Infrared Imager/Radiometer Suite (VIIRS)
- Crosstrack Infrared Sounder (CrIS)
- Advanced Technology Microwave Sounder (ATMS)

## Summary Results of Proficiency Improvements

- Two approaches used to assess proficiency improvements
- Detailed assessment of improvements to weather detection and forecasting proficiency provided by ASAP based on:
- Availability of new satellite information
- Follow-up question to determine broader assessment of overall improvements in weather detection and forecasting proficiency based on:
- Availability of new satellite information along with infusion of satellite expertise
- Extension of PDT vision and capabilities

## Summary Results of Proficiency ImprovementsDetailed Assessment

- Identified expected ASAP improvement areas for Phases I & II from each PDT
- Obtained PDT scoring of expected proficiencies for relevant performance metrics associated with baseline capability and each ASAP phase
- Scale from 0 – 10, from no proficiency to maximum proficiency
- Averaged across all relevant metrics, expected proficiency improvements over baseline based on equal weighting for each metric identified

## Summary Results of Proficiency ImprovementsDetailed Assessment

- Categorized average expected proficiency improvements by ASAP phase for each weather area
-      2.5 or more  =  substantial
-      1.5 to 2.49    =  moderate
-      0.5 to 1.49    =  minor
-      0 to 0.49       =  minimal
- Obtained PDT scoring and supporting rationale of expected risk associated with each improvement area identified by ASAP phase
-      1  =  low
-      2  =  medium-low
-      3  =  medium
-      4  =  medium-High
-      5  =  high

## Summary Results of Proficiency ImprovementsDetailed Assessment

- Averaged across all improvement areas, expected risk by ASAP phase based on equal weighting for each improvement area identified

## Summary Results of Proficiency ImprovementsDetailed Assessment

- Mapped average expected proficiency improvements and risk scores for each ASAP improvement for Phase I and Phase II based on availability of new satellite information
- Green = high potential
- Yellow = medium potential
- Red = low potential

## Summary Results of Proficiency ImprovementsBroad Assessment

- Obtained PDT rating of expected overall improvements in weather detection and forecasting proficiencies due to ASAP providing new satellite information along with infusion of satellite expertise and extension of PDT vision and capabilities
- Minimal
- Minor
- Moderate
- Substantial
- Obtained PDT scoring of expected risk associated with ASAP
- Low
- Medium-low
- Medium
- Medium-High
- High

## Summary Results of Proficiency ImprovementsBroad Assessment

- Mapped expected overall proficiency improvements and risk scores for each ASAP improvement based on availability of new satellite information along with infusion of satellite expertise and extension of PDT vision and capabilities
- Green = high potential
- Yellow = medium potential
- Red = low potential

## ASAP Potential Benefits Overview

- ASAP is expected to provide potential benefits in the following areas:
- In-flight Icing
- Turbulence
- Convective Weather
- Ceiling and Visibility (Terminal and National)
- Volcanic Ash
- Oceanic Weather

## ASAP Potential Benefits In-flight Icing

- Primary benefit is safety
- Accidents resulting in annual costs of fatalities, injuries, and aircraft damage of approximately $100M
- Existing PDT products estimated to reduce accident costs by 30%
- Also provide efficiency benefits (primarily for regional airlines operating turboprop aircraft)
- Existing PDT products estimated to provide benefits of $8.8M annually
- Re-routes due to severe icing or anti-ice/de-ice equipment failures
- Go/no-go decisions due to severe icing or inoperative anti-ice/de-ice equipment
- Implementation of weight restrictions due to one engine inoperative service ceiling requirements

## ASAP Potential Benefits Turbulence

- Primary benefit is safety
- Air carrier turbulence encounters resulting in annual costs of fatalities, injuries, and aircraft damage of approximately $100M based on NTSB records and industry inputs
- General aviation and air taxi turbulence encounters resulting in annual costs of fatalities, injuries, and aircraft damage of approximately $80M based on NTSB records
- Also provide efficiency benefits
- Turbulence avoidance
- Excessive routing
- Airspace congestion
- Airport capacity
- Turbulence encounters
- Penetration speed
- Escape routes
- Aircraft inspections

## ASAP Potential Benefits Convective Weather

- Significant efficiency improvements
- Ability to determine arrival fix, departure fix, and airport closures in the terminal environment could yield over $500M annually in benefits based on a 30 to 60 minute forecast
- Avoid implementing the closure prematurely
- Avoid not being prepared for the beginning or ending of the closure
- Better recognize when the closure will be prolonged
- Improved flight planning based on en route conditions could yield potential benefits of $8M to $55M
- Route and altitude decisions
- Alternative airport assignments
- Fuel loadings
- Also provide safety benefits with accidents resulting in annual costs of fatalities, injuries, and aircraft damage of approximately $45M

## ASAP Potential Benefits Ceiling and Visibility

- National Ceiling and Visibility
- Primary benefit is safety
- Accidents resulting in annual costs of fatalities, injuries, and aircraft damage of approximately $500M
- Accidents initiated by non-instrument rated pilots flying into areas of poor ceiling and visibility after receiving weather briefings represent approximately 20% of the total cost and are the most likely to be avoided
- Primary impact will be on General Aviation operations

## ASAP Potential Benefits Ceiling and Visibility

- Terminal Ceiling and Visibility
- Primary benefit is efficiency with an estimated potential of $5.5M annually for one major airport (e.g. SFO)
- Accurate forecast would provide improved knowledge of when stratus will clear at an airport
- Resulting air traffic management efficiencies will reduce unnecessary system delay
- Reduction in arrival delays as well as as downstream departure delays

## ASAP Potential Benefits Volcanic Ash

- Safety benefits
- Accidents associated with volcanic ash encounters have been estimated to cost the aviation industry in excess of $250M over the past 20 years or $12.5M annually
- Avoid loss of engine power
- Avoid extensive damage to aircraft
- Efficiency benefits
- Reduce excessive reroutes
- Increased knowledge concerning location and movement of volcanic ash
- Increased ability to reroute aircraft around volcanic ash more efficiently

## ASAP Potential Benefits Oceanic Weather

- Overall benefits of Oceanic Weather
- High potential efficiency benefits
- Low potential safety benefits relative to non-oceanic safety benefits
- Additional  analyses necessary to map specific ASAP improvements to potential benefits

## ASAP Potential Benefits Safety and Efficiency

- Mapped ASAP improvements into safety and efficiency benefits matrix to indicate potential operational benefits
- Highlights whether improvements lead to predominantly safety or efficiency benefits

## PDT Assessments

-       *Detailed AWR PDTs assessments of **              ASAP improvements*

## In-flight Icing Baseline Satellite Information

- GOES-8 and GOES-10
- Identification of existence of clouds and determination of cloud top temperature in Current Icing Potential (CIP) product
- Preliminary product for determination of existence of super-cooled water near cloud top in NCAR satellite icing algorithm – needs improvement for inclusion in CIP
- Preliminary approach for identification of large droplets near cloud top – no product yet, technique has plenty of room for improvement

## In-flight IcingPhase I Opportunities

- _Primary opportunities_
- More complete utilization of existing GOES to derive:
- Super-cooled water detection
- Cloud optical depth
- Super-cooled Water Droplet Diameter (SWDD)
- Phase  (ice crystals or water drops)
- Liquid water path, ice water path
- Addition of dedicated radiative transfer scientist to In-flight Icing PDT in FY04
- Verification of cloud top particle size and type using MODIS
- Some work previously accomplished with Bill Smith from NASA
- Additional comparisons of satellite data with observed data sets
- Leveraging of NASA funding for utilization of NASA Glenn verification data
- Verification of cloud layer product
- Collaborative effort between NASA GRC, NASA LRC, NCAR & CIMSS

## In-flight IcingPhase I Opportunities (cont’d)

- _Secondary opportunities_
- Satellite feature extraction and extrapolation to support Forecast Icing Potential (FIP) model initialization of clouds
- FIP purely model-based
- Considered this effort and dropped it because difficult problem, but could pick it back up
- Small possibility to assist in utilizing POES and AVHRR data
- Mostly FAA logistical problem
- Would be helpful if data could be made more available
- Data not real-time
- Large file sizes
- Use of University of Wisconsin receiver assets

## In-flight IcingPhase II Opportunities

- Ability to see through semi-transparent clouds or scattered to broken opaque clouds to get better temperature and water vapor profiles with hyperspectral instruments
- Ability to obtain higher-resolution (vertical and horizontal) profiles to combine with radar data and numerical weather model output
- Improved 3-D cloud depiction -- location, structure & microphysical phase

**In-flight Icing****Proficiency Assessments**

- Following proficiency metric improvements over baseline evaluated by phase for CONUS, AK and oceanic regions
- 	Icing Type/Intensity
- 	Forecast accuracy
- 	Update Rate
- 	Spatial Resolution
- Satellite data is particularly important in remote, data-sparse areas and for transferability to new geographical areas

**In-flight Icing****Proficiency Assessments**

- Average expected improvements across metrics determined for CONUS and AK by phase
- Minor expected improvements for Phase I
- Moderate expected improvements for Phase II
- Average Phase I = 1.0, Phase II = 2.0	           Average Phase I = 1.3, Phase II = 2.3

## In-flight Icing Proficiency Assessments

- _Forecast accuracy_
- Baseline product is fairly accurate
- More mature product than other PDT products
- Probability of Detection (POD) of positive icing reports approximately 80%, POD of negative icing reports is about 70%
- POES  data and additional fields helpful for Alaska
- _Icing Type/Severity_
- Icing type product currently on hold, needs improvement
- Cloud-top super-cooled water droplet size and liquid water path from current GOES expected to be helpful for determining icing type/severity in Phase I  (new NASA LRC products look promising**)**
- Real profiles of liquid in clouds expected to be helpful in determining icing type and severity in Phase II

## In-flight Icing Proficiency Assessments

- _Icing Type/Severity (cont’d)_
- CIP does not provide icing severity, but  off-line product is being tested which could be improved
- _Update Rate_
- Current one hour update could be improved to 15 minutes for Phase I
- May not be much operational value to update more frequently than 15 minutes
- _Spatial Resolution_
- National-scale products currently at 40 km resolution with planned improvement to 20 km resolution
- Terminal-scale high resolution product possible with new satellite technology

## In-flight IcingRisk

- _ASAP Phase I risk_
- Overall medium-low risk
- Medium-low risk for primary Phase I opportunities
- Excellent opportunity for more complete utilization of existing GOES data
- Medium risk for secondary Phase I opportunities  to medium-high for verification of MODIS data
- More difficult work
- _ASAP Phase II risk_
- Medium-low for all Phase II opportunity areas

## TurbulenceBaseline Satellite Information

- Satellites not currently utilized
- Considered valuable, but no funding available, historically
- Lots of potential, magnitude of benefit proportional to funding
- Satellite data more important for Global aviation applications than for CONUS

## Turbulence Phase I Opportunities

- Identification of potential research areas
- Pattern identification in high-level clouds and correlation with turbulence
- Some work done on this in past
- Software development to use pattern identification in product
- Performance comparisons to pilot reports

## Turbulence Phase II Opportunities

- New sensing capabilities such as ozone sensing to detect stratospheric folding
- Determination of turbulence features through high resolution data
- Possible opportunities from improved flight level winds
- Improved vertical resolution is critical to identify shear

**Turbulence****Proficiency Assessments**

- Following proficiency metric delta improvements over baseline evaluated by phase for various turbulence types
- Forecast accuracy
- Coverage area
- Spatial resolution
- Severity
- Lower altitude forecasting

**Turbulence****Proficiency Assessments**

- 			Average Phase I = 1.4, Phase II = 2.8

- Average expected proficiency improvements  by phase
- Minor expected improvements for Phase I for CAT and CIT, moderate for mountain wave turbulence
- Substantial expected improvements for Phase II
- Substantial improvements for CAT and CIT
- Moderate improvements for mountain wave turbulence

**Turbulence****Proficiency Assessments****Clear Air Turbulence**

- 			Average Phase I = 1.3, Phase II = 3.0

- Average expected proficiency improvements over baseline across metrics determined
- Minor expected improvements for Phase I
- Substantial expected improvements for Phase II

**Turbulence****Proficiency Assessments****Clear Air Turbulence**

- _Forecast Accuracy_
- Currently using ITFA baseline system which predicts CAT at upper levels, but does not use satellite information
- PODyes and PODno approximately 0.7
- Cloud pattern imagery might improve forecasting in Phase I
- Higher resolution with Phase II expected to improve forecasting
- POD of approximately 0.8 expected for Phase II
- _Severity Identification_
- Related to PODyes, with correlated improvements

**Turbulence****Proficiency Assessments****Clear Air Turbulence**

- _Lower Altitude Forecasting_
- Current capability substantially less than capability for upper level turbulence
- Opportunities for improvement at lower altitudes may be greater than for higher altitudes
- Satellite imagery may improve feature identification, especially near surface

**Turbulence****Proficiency Assessments****Convection-Induced Turbulence**

- 			Average Phase I = 1.3, Phase II = 3.0

- Average expected proficiency improvements over baseline across metrics determined
- Minor expected improvements for Phase I
- Substantial expected improvements for Phase II

**Turbulence****Proficiency Assessments****Convection-Induced Turbulence**

- _Forecast Accuracy_
- Baseline product not developed, and no POD estimates available
- Improved feature identification may be possible
- _Coverage Area_
- High resolution data expected to increase global coverage
- Global identification of clouds necessary to identify CIT

**Turbulence****Proficiency Assessments****Mountain Wave Turbulence**

- 			Average Phase I = 1.5, Phase II = 2.3

- Average expected proficiency improvements over baseline across metrics determined
- Moderate expected improvements for Phase I
- Moderate expected improvements for Phase II

**Turbulence****Proficiency Assessments****Mountain Wave Turbulence**

- _Forecast Accuracy_
- Prototype product still in early stages, but PODs  approximately 0.7
- POD expected to remain about 0.7 for Phase I
- POD expected to improve to approximately 0.85 by Phase II
- Easier problem than for CAT due to known cause of turbulence
- _Coverage Area_
- GOES currently covers entire CONUS
- Additional data needed for Global coverage
- _Lower Altitude Forecasting_
- Forecasting based on patterns in low-level clouds
- Current low-level POD is approximately 0.6
- Hope to achieve 0.8 POD for Phase II

## TurbulenceRisk

- _ASAP Phase I risk_
- Overall medium-low risk for pattern identification in high-level clouds and correlation with turbulence
- _ASAP Phase II risk_
- Overall low risk

## Convective WeatherBaseline Satellite Information

- Satellite cloud field can be used as a proxy for stability
- GOES visible and IR data
- Satellite data helpful for seeing clouds before detectable by radar
- Better depiction of initiation,  15-30 min additional lead time
- Helps determine regions where convection is unlikely
- For example, areas of stratiform clouds

## Convective Weather Phase I Opportunities

- Data Quality and Availability
- Ground Registration  (for merging with radar data)
- Bad pixels or missing scan lines
- Cloud line detection and growth
- Difficult problem, presents an opportunity
- Interesting work being done by University of Wisconsin
- Cloud typing and characterization
- Utilization of high resolution data
- Monitoring changes over time
- Rapid scanning
- More rapid scanning needed in convective regions

## Convective Weather Phase II Opportunities

- Hyperspectral instrument soundings
- Initialization of high resolution, non-hydrostatic numerical models
- Monitor clear air stability
- Higher vertical and spatial resolution
- Hyperspectral imaging
- Improved IR sampling in Advanced Baseline Imager (ABI)
- Faster update rate and improved spatial resolution for a better  match with radar data
- Determine band signatures that characterize storm development
- Distinguish liquid particles from ice through band differences

**Convective Weather****Proficiency Assessments**

- Proficiency metric delta improvements over baseline were not provided by Convective Weather PDT, but qualitative proficiency improvement estimates were provided
- Radar coverage excellent over CONUS;  mature storms well monitored by radar
- The forecast challenge is in convective initiation, growth, and decay
- Already use satellite observations for convective initialization
- Expect some improvements in convective initiation and boundary detection expected from Phase I research
- Some improvements with rapid scanning are possible
- Most improvements associated with Phase II which would be substantial if accomplished
- Possibility of early initiation forecast and boundary detection

**Convective Weather****Proficiency Assessments**

- _Convective Initiation & Growth_
- Some proficiency with current algorithms
- Satellite data gives better depiction of convective initiation
- Could be improved with more research
- Phase I considered valuable  ( in particular, algorithms for satellite data processing and cloud classification)
- _Convective Dissipation_
- Not as much benefit here as compared to initiation problem
- Phase 2 satellites may be able to identify stable areas
- Might be able to monitor if storm has generated a gust front that is becoming separated from the storm core

**Convective Weather****Proficiency Assessments**

- _Pop-up Storms_
- Higher update rates ideal for identifying pop-up storm growth
- No high-level obscurations associated with pop-up storms
- Phase I considered valuable
- _Forecast Window_
- Cloud observations with hyperspectral instruments not expected to extend window, only improve forecast within 0-2 hour window
- May be able to improve lead times 15 – 30 minutes through effective use of satellite data in combination with supporting data on stability structure and surface forcing.
- Improvements for forecasts beyond 2-hours are possible through improved model initialization from hyperspectral soundings

## Convective Weather Risk

- _ASAP Phase I risk_
- Overall medium risk
- Low for ground registration
- Not scientific risk, only operational/logistical issue
- Medium-high for data quality
- Unknown if missing pixels can be replaced to improve data
- Medium-high for cloud line detection and growth
- Medium for cloud typing and characterization
- Low for rapid scanning
- Known ability to execute scan strategy
- Operational trade-off between regions only issue
- _ASAP Phase II risk_
- Overall medium risk
- Medium-high for hyperspectral soundings
- Medium for hyperspectral imaging

## National Ceiling and VisibilityBaseline Satellite Information

- GOES-8
- Currently using GOES-East imagery and will be using GOES-West imagery in a couple months
- Synthesizing of GOES-East with GOES-West which has better viewing angle on Western U.S.
- Doing cloud masking to determine cloud location
- Plan to incorporate gap filling between METARS stations in data sparse regions within year
- Plan to use polar orbiting satellites for cloud masking and gap filling with AK product which is in early test phases

## National Ceiling and Visibility  Phase I & II Opportunities

- _Access_
- Access to NASA data, including low-cost access to NOAA polar orbiter
- _Functionality_
- Low cloud identification
- Discrimination between low clouds and snow covered surfaces which have similar temperatures
- Identification of low clouds and fog
- Cloud base
- Cloud top thickness and height
- Used together to help determine cloud base
- Cloud masking to determine cloud location
- Determining visibility outside cloudy areas
- Aerosols can affect visibility

**National Ceiling and Visibility****Proficiency Assessments**

- Following proficiency metric improvements over baseline evaluated by phase
- CONUS forecast accuracy
- CONUS nowcast accuracy
- AK forecast/nowcast accuracy
- Forecast window
- Cloud cover location
- Mid-atmosphere slant range visibility
- Surface visibility related to aerosol forecasting

**National Ceiling and Visibility ****Proficiency Assessments**

- 			Average Phase I = 3.1, Phase II = 3.7

- Average expected improvements across metrics determined by phase
- Substantial expected improvements for Phases I & II

**National Ceiling and Visibility ****Proficiency Assessments**

- _Forecast Accuracy_
- Capabilities and methodologies of models to assimilate data primary concerns for Phase I, rather than data availability
- _AK Nowcast / Forecast Accuracy_
- Coverage area improved only by supporting AK development
- Forecast uses Eta model only
- _Forecast Window_
- Currently extends to six hours
- Could easily be extended to 12 hours with current RUC data

**National Ceiling and Visibility ****Proficiency Assessments**

- _Mid-atmosphere Slant Range Visibility_
- Current forecasts are surface level visibility forecasts
- Could expand forecasts beyond surface to forecasts in mid-atmosphere at flight altitudes in future
- _Surface Visibility Related to Aerosol Forecasting_
- Aerosols more important than dust to National C&V product
- Aerosols can be persistent in some areas, like L.A. marine area

## National Ceiling and Visibility Risk

- _ASAP Phase I risk_
- Overall medium risk
- Medium for low cloud identification
- Medium for cloud top thickness
- Medium-low for cloud top height
- High for cloud base
- Most important improvement
- Might be able to bound it
- Medium-low for cloud masking
- Refined techniques already exist
- Medium-high for visibility outside cloudy areas
- _ASAP Phase II risk_
- Overall medium risk
- Marginal reduction in risk from Phase I for all efforts

## Terminal Ceiling and VisibilityBaseline Satellite Information

- Two testbed locations (San Francisco & Northeast Corridor)
- Using GOES visible channel information for estimation of cloud brightness and aerial coverage for prediction of stratus burn-off for SFO
- Plan to use similar approach for Northeast Corridor
- Likely to also include IR
- May also include satellite tracking of cloud advection

## Terminal Ceiling and Visibility Phase I & II Opportunities

- Data Quality and Redundant Data Channels
- Improved registration techniques for insuring accurate geographical cloud mapping
- Additional processing sometimes required to account for registration jitter
- Differentiation among cloud layers
- Differentiation of cloud types
- Discrimination of cloud vs snow cover

**Terminal Ceiling and Visibility****Proficiency Assessments**

- Expected proficiency metric improvements over baseline
- Low cloud clearing time error
- Forecast model confidence level
- Spatial resolution
- Opportunities at similar airports
- Data quality & registration

**Terminal Ceiling and Visibility****Proficiency Assessments**

-                                             Average Phase I = 1.2, Phase II = 2.4

- Average expected improvements across metrics determined by phase
- Minor expected improvements for Phase I, Moderate improvements for Phase II

**Terminal Ceiling and Visibility****Proficiency Assessments**

- _Stratus Clearing Time Error_
- Primary metric is forecast median error
- Varies by forecast time
- Might expect 10% error reduction with ASAP
- Expected to be worthwhile operational improvement
- _Opportunities at Similar Airports_
- Stronger reliance on satellite technology improves tech transfer to other airports
- Better opportunity to exploit improvements from SFO at other airports
- Low level pollutants detection might also be helpful at LA

## Terminal Ceiling and Visibility Risk

- _ASAP Phase I risk_
- Overall medium-high risk
- Low for registration
- Medium-high for differentiation among cloud layers
- Medium-high for discrimination of fog/cloud/snow cover
- _ASAP Phase II risk_
- Overall medium risk
- Low for registration
- Medium for soundings
- Medium for differentiation among cloud layers
- Medium for discrimination of fog/cloud/snow cover

## Oceanic WeatherBaseline Satellite Information

- Satellite data is crucial over data sparse, oceanic regions
- Techniques developed by other PDT’s may have to be modified to make more use of satellite observations over oceanic regions
- Currently use GOES-8 and GOES-10,  both visible and IR
- Primarily uses  GOES West and GMS for Pacific coverage
- Use IR for relative cloud top height, and visible for cloud location and classification for convection
- Use IR for wind height, and for trending wind speed and direction for flight level winds
- Use visible and IR to identify location of Clear Air Turbulence (CAT)
- Use Global Numerical Model  (not satellite data) for Convective Induced Turbulence (CIT) detection
- Limited use of polar orbiters to mosaic satellite information together to fill in gaps

## Oceanic WeatherPhase I Opportunities

- Utilization of split-channel techniques
- Working with University of Wisconsin to understand how to use these techniques with current technologies
- Identification of ice crystal versus liquid water to indicate cloud make-up and cumulus growth
- Split-channel brightness/temperature differential from different channels to distinguish water vapor from volcanic cloud ash
- Merging of satellite data with other model data
- For example, determination of relative cloud top height to identify areas of icing potential and flight level cloud height by merging satellite IR data with model data

## Oceanic WeatherPhase II Opportunities

- Utilization of hyperspectral instruments to provide more information
- 	_Turbulence_
- Better CAT forecasting due to better characterization of wind fields at all levels
- Better diagnosis of turbulence location through improved wind characterization, and identification of aerosols and motion
- _Convection_
- Characterization of cloud make-up through determination of particle size and density
- Diagnosis of heavy convection, icing, hail, and cloud lightning
- _Volcanic Ash_
- Separation of volcanic ash plume from clouds
- Plume tracking
- Determination of particle density, size, and makeup

**Oceanic Weather****Proficiency Assessments**

- Assessment conducted for weather phenomena related to oceanic weather
- In-flight Icing evaluated by In-flight Icing PDT
- Turbulence evaluate by Turbulence PDT
- Convection evaluated by Oceanic Weather PDT
- Flight-level winds evaluated by Oceanic Weather PDT
- Volcanic ash evaluated by Oceanic Weather PDT

**Oceanic Weather****Overall Proficiency Assessments**

- Average expected proficiency improvements over baseline across metrics determined for weather phenomena by phase
- Minor expected improvements for Phase I
- Substantial expected improvements for Phase II
- 	                               Average Phase I = 1.4, Phase II = 3.0

** ****Oceanic Weather****Specific Proficiency Assessments****In-flight Icing **

- Average expected proficiency improvements over baseline across metrics determined
- Moderate expected improvements for Phase I
- Substantial expected improvements for Phase II
- 		                    Average Phase I = 2.0, Phase II = 3.0

**Oceanic Weather****Specific Proficiency Assessments****Turbulence**

- 		          Average Phase I = 2.5, Phase II = 4.8

- Average expected proficiency improvements over baseline across metrics determined
- Substantial CAT improvements and moderate CIT improvements expected for Phase I
- Substantial improvements expected for CAT and CIT  for Phase II

**Oceanic Weather****Specific Proficiency Assessments****Turbulence**

-      Average Phase I = 3.0, Phase II = 6.0                                      Average Phase I = 2.0, Phase II = 3.7

**Oceanic Weather****Specific Proficiency Assessments****Convective Weather**

- Average expected proficiency improvements over baseline across metrics determined
- Minor expected improvements for Phase I
- Substantial expected improvements for Phase II
- 			              Average Phase I = 0.8, Phase II = 2.8

**Oceanic Weather****Specific Proficiency Assessments****Convective Weather**

- _Forecast accuracy_
- Currently running early prototype with no POD estimates
- Uses satellite data and infrequently updated model data
- Lower accuracy over oceanic areas due to sparse data
- 20 – 30 aircraft reports per day for oceanic versus approximately 100,000 per day for CONUS
- _Forecast Window_
- Initially nowcast
- Plan 0 – 2 hour forecast, then 2 – 6 hour forecast, and then ultimately 12 hour forecast
- Any forecast over 2 hours will be difficult
- May get more stability over ocean due to better persistence
- Phase II may not provide much improvement because going beyond 2 hour forecast could be difficult

**Oceanic Weather**** Specific Proficiency Assessments****Convective Weather**

- _Update Rate_
- Some current satellites do not provide 30 minute update, but improvement anticipated during Phase II with GIFTS
- _Storm Intensity_
- May do fairly well in this area
- _Pop-up Storms_
- Unsure if can do well in this area at all

**Oceanic Weather****Specific Proficiency Assessments****Convective Weather**

- _Spatial Resolution_
- Currently doing fairly well
- Phase I may not help much
- Phase II expected to be of help
- _Lightning Diagnosis_
- Currently have little capability
- Improvements anticipated during Phase II using hyperspectral instruments and lightning sensors on-board satellites

**Oceanic Weather****Specific Proficiency Assessments****Flight-level Winds**

- Possible Phase I, stand-along gridded wind product
- Anticipate a much improved Phase II product

**Oceanic Weather****Specific Proficiency Assessments****Flight-level Winds**

- Average expected proficiency improvements over baseline across metrics determined
- Minor expected improvements for Phase I
- Moderate expected improvements for Phase II
- 		                      Average Phase I = 0.5, Phase II = 1.8

**Oceanic Weather****Specific Proficiency Assessments****Flight-level Winds**

- _Forecast Accuracy (Wind Error)_
- Currently have good nowcast and forecast product due to good wind fields
- Low resolution product which assimilates satellite winds with model winds to get higher resolution wind fields
- _Forecast Window_
- 0 – 12 hour forecasting currently being done
- _Spatial Resolution_
- Hyperspectral instruments will provide more point winds predicting wind speed and direction at each point on 3-D grid due to greater resolution

**Oceanic Weather****Specific Proficiency Assessments****Volcanic Ash**

- Average expected proficiency improvements over baseline across metrics determined
- Minor expected improvements for Phase I
- Substantial expected improvements for Phase II
- 		                         Average Phase I = 1.1, Phase II = 2.9

**Oceanic Weather****Specific Proficiency Assessments****Volcanic Ash**

- _Forecast Accuracy_
- No POD measurements because no truth data sets available
- Differencing techniques should provide improvements in Phase I
- Hyperspectral instruments should provide additional improvements in Phase II
- _Forecast Window_
- Goal of 6 hours which would satisfy FAA user need requirements
- _Eruption Detection_
- Need better global coverage
- Modest improvements anticipated for Phase I & Phase II
- _Plume Dispersion_
- Difficult problem, opportunities for improvement

**Oceanic Weather****Specific Proficiency Assessments****Volcanic Ash**

- _Height Estimation_
- Channel differencing techniques anticipated to help distinguish water vapor from harmful ash in Phase I
- Hyperspectral instruments anticipated to provide further improvements in Phase II
- _Material Diagnosis_
- Relates to particle size and make-up
- Pilots will not fly where plume is located if they do not know whether plume is hazardous
- More research needed in this area

## Oceanic WeatherRisk

- _ASAP Phase I risk_
- Overall medium-low risk for both split-channel techniques and merging satellite data with other model data
- Have understanding of how to enhance products with satellite data, but have not done it yet
- Some risk associated with split-channel differencing technique not indicating the correct information
- Risk distinguished by weather phenomena
- Medium-low for turbulence, convection, and flight-level winds
- Medium for in-flight icing, and volcanic ash

## Oceanic WeatherRisk

- _ASAP Phase II risk_
- Overall medium risk
- Results are more unknown than for Phase I for which some work has already begun
- Unknown value from additional sensing equipment
- Plan to use GIFTS which is not yet fully tested
- Risk distinguished by weather phenomena
- Medium-low for turbulence and flight-level winds
- Medium for convection, and in-flight icing
- Medium-high for volcanic ash

## Summary

- Conducted proficiency improvement assessments using two approaches
- Detailed assessment based on availability of new satellite information
- Broader assessment based on availability of new satellite information along with infusion of satellite expertise and extension of PDT vision and capabilities
- Identified and characterized potential operational benefits
- Efficiency
- Safety
- Potential future research
- Update benefits assessment annually based on research developments
- Further study of potential oceanic benefits for AWRP