GOESR Algorithm Working Group AWG Meeting Objectives, Agenda, and AWG Overview

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GOES-R Algorithm Working Group (AWG)Meeting
Objectives, Agenda,and AWG Overview
Mitchell D. Goldberg
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Logistics

• Restrooms at both ends of the halls
• Lunch on your own, cafeteria end of long hall
  on right
• Breakouts end of long hall on left
• Wireless login/pw GOES/GOES
• Parking - 8.00 cash only
• ATM end of long hall, down stairs at bottom
• People online please mute your phones
• Poster boards will be up early Tuesday morning
• 2 sessions on Tuesday Baseline/Option 2
• 1 session on Thursday New Applications/Risk
  Reduction

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Objectives

• Review our end-to-end process for algorithm
  development and delivery
• Present last years progress and to discuss
  near-term major milestones
• Delivery of the ATBDs _at_ 80 for the baseline
  products
• Introduce the Algorithm Development Executive
  Board and their Independent Verification and
  Validation Team
• Feedback from our Technical Advisory Committee
• Hold the annual GOES-R Risk Reduction Meeting
• Discuss the end-to-end program

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GOES-R Algorithm/Product Readiness
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Algorithm Working Group
PURPOSE To develop, test, demonstrate, validate
and provide algorithms for end-to-end GOES-R
Ground Segment capabilities and to provide
sustained life cycle validation and product
enhancements

• Leverages nearly 100 scientists from NOAA, NASA,
  DOD, EPA, and NOAAs Cooperative Institutes
  (University partners)
• Apply first-hand knowledge of algorithms
  developed for POES, GOES, DMSP, AIRS, MODIS,
  MetOP and Space Weather.
• Leverage other programs experience (GOES,
  MODIS, AIRS, IASI, NPOESS and other prototype
  instruments and international systems)
• Facilitate algorithm consistency across platforms
  -- prerequisite for GEOSS (maximize benefits
  and minimizes integration)

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Capabilities and Experience

• Experience in Algorithm Delivery and
  Implementation
• Developed, tested, delivered and implemented
  operational product generation systems
• POES
• GOES
• DMSP (NOAA applications)
• AIRS, MODIS
• MeTOP (IASI, GOME, ASCAT)
• NPOESS (NDE Project)

• AWG End-to-End Capabilities
• Instrument Trade Studies
• Proxy Dataset Development
• Algorithm Development and Testing
• Product Demonstration Systems
• Development of Cal/Val Tools
• Integrated Cal/Val Enterprise System
• Sustained Radiance and Product Validation
• Algorithm and application improvements
• User Readiness and Education

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AWG Organizational Structure
GOES-R Program Office
GOES-R Program Management
Oversight and verification of Instrument
performance
GOES-R Cal/Val WG
Functional Responsibility
AWG Mgt Execution - Alg Selection Program
Guidance
Conducts program reviews, leads IVV, recommends
changes and provides direction
GOES-R AWG
Program Manager
Deputy Program Manager
Establishes requirements, standards,
infrastructure, architecture, integrates
software from the product development teams, and
prepares deliveries to system prime
Scientific Guidance
Integration Team
GOES-R Risk Reduction
Risk Reduction effort
Risk Reduction effort (includes exploratory
algorithms, processes and improved data
utilization)
Program Lead
Deputy Program Lead
Application Teams
Cooperative Institutes
Selects specialty area algs and provides special
guidance in area of expertise
JCSDA Others
Development Teams
Implements alg runoff, code dev, testing, etc.
AWG management structure and processes mitigate
risks associated with delivering algorithms on
schedule
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12/11/2009
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Defined Roles Responsibilities and Outcomes

• Application Teams plans and executes the
  activities to assess, select, develop, and
  deliver algorithms (including product validation)
  
• Development teams hosts and tests candidate
  algorithms in a scalable operational
  demonstration environment, and develop validation
  tools
• AWG Integration Team establishes requirements,
  standards, infrastructure, architecture,
  integrates software from the product development
  teams, and prepares deliveries to Ground Segment
  Project

• Outcome -- Demonstrated algorithms,
  documentation and test data sets delivered to
  the Ground Segment Project
• Algorithm Theoretical Basis Documents (ATBD)
• Proxy datasets
• Pre-operational code with all supporting
  materials test plans, software, data sets (with
  results for comparison) and implementation
  documentation
• Routine cal/val tools

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Follow High Maturity Process

• Standards
• Algorithm Theoretical Basis Document
• Metadata (FGDC guidelines)
• Interface Control
• System Description
• Users Manual
• Fortran Programming
• C and C Programming
• Test Plan
• Algorithm Implementation Instructions
• Latency Reports

• Initial Requirements Analysis
• Preliminary Design Review
• Critical Design Review
• Test Readiness Review (before 80 delivery)
• System (Algorithm) Readiness Review (before 100
  delivery)

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GOES-R OPERATIONAL PRODUCTS
OPTION 2
BASELINE

• Cloud Layer/Heights
• Cloud Ice Water Path
• Cloud Liquid Water
• Cloud Type
• Convective Initiation
• Turbulence
• Low Cloud and Fog
• Visibility
• Surface Albedo
• Upward and Downward Longwave Radiation
• Upward and Absorbed Shortwave Radiation
• Total Ozone
• SO2 Detections (Volcanoes)
• Surface Emissivity
• Aerosol Particle Size
• Vegetation Index
• Vegetation Fraction
• Snow Depth
• Flood Standing Water

• Clouds and Moisture Imagery
• Clear Sky Masks
• Temperature and Moisture Profiles
• Total Precipitable Water
• Stability Parameters (Lifted Index)
• Cloud Top Pressure and Height
• Cloud Top Phase
• Cloud Particle Size Distribution
• Cloud Optical Path
• Rainfall Rate
• Aerosols Optical Depth
• Atmospheric Motion Vectors (AMVs)
• Hurricane Intensity
• Volcanic Ash
• Fire/Hot Spot Characterization
• Land and Sea Surface Temperature
• Snow Cover
• Downward Surface Insolation
• Lightning Detection

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Application Teams
GOES-R Products Mapped to Algorithm Application
Teams

• Soundings (Chris Barnet, Tim Schmit)
• Winds (Jaime Daniels)
• Clouds (Andy Heidinger)
• Aviation (Ken Pryor, Wayne Feltz)
• Aerosols / Air Quality / Atmospheric Chemistry
  (Shobha Kondragunta)
• Hydrology (Robert Kuligowski)
• Land Surface (Bob Yu)
• SST and Ocean Dynamics (Alexander Ignatov)
• Cryosphere (Jeff Key)
• Radiation Budget (Istvan Lazslo)
• Lightning (Steve Goodman)
• Space Environment (Steven Hill)
• Proxy Data (Fuzhong Weng)
• Cal/Val (Changyong Cao)
• Algorithm Integration (Walter Wolf)
• Product System Integration
• KPP/Imagery/Visualization
• Product Tailoring

Example AAA Application Team Make-up
Kondragunta, Shobha (STAR), Chair Ackerman,
Steven (CIMSS) Hoff, Raymond (UMBC) Pierce, Brad
(NASA -gt STAR) Szykman, James (EPA) Laszlo,
Istvan (STAR) Lyapustin, Alexie (NASA) Li,
Zhanqing (CICS)) Schmidt, Chris (CIMSS)
GOES-R Program requested the AWG to establish
broad and cross-cutting support for the
algorithms and products
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Delivery Schedule

• September 2008
• Draft ATBD
• September 2009
• 80 ATBD and Algorithm Package for Baseline
• September 2010
• 100 ATBD and Algorithm Package for Baseline
• 80 ATBD and Algorithm Package for Option 2
• September 2011
• 100 ATBD and Algorithm Package for Option 2

Algorithms deliveries are followed by development
of monitoring tools for sustained product
monitoring. AWG will also develop tools for
sustained validation of products and will be
responsible for the sustained scientific
performance of the algorithms
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Delivered Algorithm Package (DAP) Content

• Algorithm Theoretical Basis Document
• Datasets
• Input Proxy and/or simulated datasets
• Output product datasets , Ancillary datasets
• Source Code
• For each product algorithm
• Scripts and/or makefiles used in the build
  process
• Tools required for compilation
• NetCDF and HDF libraries and compilers
• Test Information
• Descriptions, plans, and/or procedures on how to
  test algorithms
• Performance testing results
• Documentation
• Software and Algorithm Implementation Documents

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12/11/2009
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High Confidence in ABI Algorithms Meeting
Requirements

• Algorithms from MODIS and current GOES program
  are being leveraged
• EUMETSAT SEVIRI Instrument serves as excellent
  proxy
• High fidelity simulated datasets for ABI
• Government and University expertise from relevant
  current programs

Similar spectral channel experience provides
confidence the algorithms will be delivered with
minimal program risk while meeting the required
accuracies
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High Confidence in GLM Algorithms Meeting
Requirements

• Lightning algorithm maturity from over 12 years
  of on-orbit experience with NASAs
• Optical Transient Detector (OTD) (1995-2000)
• Tropical Rainfall Measuring Missions (TRMM)
  Lightning Imager Sensor (LIS) (1997-Present)
• ATBD for Global Lightning Mapper (GLM) lightning
  detection based on LIS
• Proxy data sets derived from LIS and from ground
  based total lightning mapping arrays
• Government and University expertise from current
  programs

Lightning Clustering Algorithm, Mach et al., JGR,
2007)
Similar experience provides confidence the
algorithms will be delivered with minimal program
risk while meeting the required accuracies
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High Confidence in Space Weather Algorithms
Meeting Requirements

• Algorithms for space weather cover both solar and
  in situ observations
• Solar Extreme Ultraviolet and X-ray Irradiance
  Suite (EXIS) and Solar Ultraviolet Imager (SUVI)
• In Situ Space Environment In Situ Suite (SEISS)
  and Magnetometer (MAG)
• Algorithms from current GOES program are being
  leveraged
• Current GOES instrument data serve as excellent
  proxies
• High fidelity simulated datasets for SUVI derived
  from GOES SXI and ESA/NASA SOHO EIT
• Government and University expertise from relevant
  current programs

NASA/ESA SOHO EIT 28.4nm
External research results help validate GOES
magnetometer products.
SXI and EIT provide basis for temporal and
spectral characteristics of SUVI observations
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Summary

• Experienced Developed the algorithms for NOAAs
  satellite programs since their inception over 40
  years ago
• Knowledgeable Understand how to calibrate,
  validate and verify algorithms using techniques
  appropriate for instrument, product, and
  spectral characteristics
• Efficient Capable of generating proxy data sets
  for all GOES-R instruments (ABI, GLM, Space Wx)
  for use in program activities
• Coordinated Will develop, host, demonstrate,
  document, and deliver algorithms to meet program
  specifications
• Consistent Established AWG management processes
  with a defined schedule that is aligned with
  GOES-R Program to provide status and track
  progress
• On Track Demonstrated clear progress toward our
  algorithm development plan
• - 23/25 of test readiness reviews have been
  completed for the baseline products (Snow cover
  and lighting detection August)
• - Numerous proxy and simulated datasets have been
  created
• - Nearly all algorithms are meeting
  specifications.

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AIRS measurements overlay on GOES IR image
(Hurricane Dean)  
LEO data have limitation on monitoring hurricane
due to orbital gap and low temporal resolution.
High temporal resolution is unique aspect of
GEO IR measurements
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TAC

• Advisors
• Tim Schmit
• Chris Veldon
• Bill Smith Sr.
• Reza Khanbilvardi
• Fuzhong Weng
• Walter Wolf
• Allen Huang
• Hank Revercomb
• Laurie Rokke
• Jim Gurka
• Steve Goodman

• Core
• Steve Koch (OAR)
• Mike Johnson (NWS)
• Steve Hill (NWS)
• Jim Yoe (NESDIS/OSD)
• Awdesh Sharma (NESDIS/OSDPD)
• Dennis Chesters (NASA/GSFC)