CIRAs Plans for the GOESR Proving Ground

1
CIRAs Plans for the GOES-R Proving Ground

• Renate Brummer - CIRA
• Fairbanks
• 30 July 2008

CIRA
2
Proving Ground Related Work at CIRA

• CIRA has an in-house AWIPS already established
• NOAAPort data ingest
• AWIPS D-2D
• Weather Events Simulator (WES) installed

• Experimental GOES-R products are being developed
  in connection with the GOES-R Risk Reduction
  project
• Hazards (fog, smoke, fires volcanic ash)
• Severe weather
• Tropical cyclone track and intensity
• Winter weather
• Cloud climatology
• Synthetic ABI imagery

• Ongoing NWS Forecast Office and NCEP
  collaborations
• VISIT, SHyMet and COMET training programs
• GIMPAP program
• Joint Hurricane Testbed
• NCEP Storm Prediction Center Severe Weather
  Testbed
• Interaction with OAR/ESRL on AWIPS development

3
Project Staffing
Principal Developers

• Steven Miller (PI CIRA)
• CIRA project management, steering, NexSat
  application development
• Mark DeMaria (Co-PI NESDIS RAMMB)
• NOAA project management, steering
• Deb Molenar (Co-PI NESDIS RAMMB)
• Technical coordination
• Don Hillger (NESDIS RAMMB)
• RAMMB application development
• Renate Brummer (CIRA)
• Project coordination
• Hiro Gosden (CIRA)
• Technical support, AWIPS ingest, implementation
• Dave Watson (CIRA)
• General technical support

4
Staffing (Continued)
Weather Forecast Office Interface Training

• Bernie Connell (CIRA)
• Application development and training
• Ed Szoke (CIRA)
• Training/liaison with Boulder and Cheyenne
  Weather Forecast Office
• Cindy Combs, Dan Bikos, Jeff Braun (CIRA)
• Training/liaison with Cheyenne Weather Forecast
  Office
• Arunas Kuciauskas (NRL-Monterey Collaborator)
• Training/liaison with Monterey Weather Forecast
  Office

5
Plans for FY08

• Establishment of the Proving Ground at CIRA
• Staffing and equipment to ensure robust
  production
• Work began on moving RAMSDIS products into AWIPS
  and creating menu lists with assistance from
  CIMSS and NOAA/ESRL
• NexSat imagery ingest to require a novel approach

• Develop initial set of ABI prototype products
• Selected RAMSDIS On-Line products
• NexSat products where applicable
• CIMSS/SPoRT products where applicable
• Training materials for all demonstration products

• Foster interactions with neighboring offices
• Initial contact and site visits to Boulder,
  Cheyenne, and Miami offices have been established
• Establish protocols for product ingest usage
• Include additional NWS forecast offices
  (Monterey)

6
RAMSDIS

• RAMSDIS - RAMM Advanced Meteorological Satellite
  Demonstration and Interpretation System
• The RAMSDIS project was initiated in 1994. At
  that time, the project goal was to disseminate
  real-time, high quality, digital GOES data to
  select National Weather Service Forecast Offices
  (NWSFOs) via a powerful, low-cost, PC-based
  workstation for use in advanced satellite data
  display and analysis.
• The workstation is based on the University of
  Wisconsin Space Science and Engineering Center
  McIDAS software, with automatic product ingest,
  display and analysis applications developed by
  the RAMM Team. Research RAMSDIS workstations at
  CIRA are utilized to demonstrate new and
  experimental products.

7
Establishing the Contacts

• Boulder (BOU) Weather Forecast Office ?
• POCs Larry Mooney (MIC) and Eric Thaler (SOO)
• Proving Ground Liaison Ed Szoke

• Cheyenne Weather Forecast Office ?
• POCs Melissa Goering (SOO), John Eise (MIC), Ray
  Gomez (ITO)
• Proving Ground Liaisons Ed Szoke, Cindy Combs,
  Dan Bikos, Jeff Braun

• Miami Weather Forecast Office ?
• POC Pablo Santos (SOO)
• Proving Ground Liaison Mark DeMaria
• Note May include MSG applications over the
  Atlantic

• National Hurricane Center ?
• POC Bill Read (Director), Jack Beven (Senior
  Hurricane Specialist)
• Proving Ground Liaison Mark DeMaria
• Note Possible demonstrations in N-AWIPS

• Monterey Weather Forecast Office ?
• POC Dave Reynolds (MIC)
• Proving Ground Liaisons Arunas Kuciauskas, Steve
  Miller
• Note Expressed particular interest in low
  cloud/fog applications

8
Visit to Cheyenne

• GeoColor Imagery (GOES based) with a
  Low-Cloud/Fog Enhancement included
• MODIS Snow Cover Product
• MODIS Water Vapor (for detection of short waves,
  clear air waves)
• GOES Fire Detection Product (and MODIS for
  hi-resolution)
• GOES Soundings and Stability Indices
• GOES Convective Cloud Top Heights

Note Each office will select products from an
existing suite of CIMSS, CIRA, SPoRT
products.
9
Visit to Cheyenne
10
Initial Product Suite

• From RAMSDIS
• Shortwave albedo

11
RAMSDIS Shortwave Albedo
Day/night technique with low cloud/fog in light
gray to white
12
NexSat NRL/NPOESS Next-Generation Weather
Satellite Demonstration Project
http//www.nrlmry.navy.mil/NEXSAT.html
13
True Color Imagery

• Natural or True color satellite imagery is
  preferred by analysts over panchromatic visible
• Visually intuitive, less ambiguous, higher
  information content (feature recognition).
• Fabricated poorly by broadcast meteorologists
  from conventional VIS/IR data

Standard VIS

• Our best hope is to synthesize the missing green
  band that was de-manifested from the ABI through
  correlative relationships.

14
Comparing Against Truth
Truth
Gunnison Bay
Gilbert Bay
Great Salt Lake
15
The GeoColor Concept

• Originally designed to illustrate the concept of
  natural color imagery from geostationary orbit.
• Technique blends VIS/IR satellite imagery with
  MODIS blue marble and OLS backgrounds.
• It soon became apparent that this dynamic
  blending approach held far more potential for
  multi-parameter visualization.

? Attempts to consolidate multiple enhancement
techniques into a single value-added image
16
(No Transcript)
17
Hurricane Katrina in GeoColor
GOES-12 27 August 2005
18
Taking it to the Next Level
Above dynamic blending of three layers
(Infrared/Low-cloud/Background).

• It doesnt have to stop herewe can extend to
  N-layers (dust/aerosol, snow cover, SST/LST
  fields, etc.) to provide a one-stop utility.

19
Contrasting Information Content
Standard Infrared
GeoColor (Pinklow cloud)
? Blending of N-dimensional datasets in the
vertical and horizontal previews the kinds of
products anticipated from NPOESSGOES-R data
synergy
20
Blue Light Absorption Technique for Mineral Dust
Enhancement
Application Use the NDDI in place of the red
channel of a natural color composite to enhance
the dusty portion of the scene in pink/red
tonality for rapid identification by analysts.
21
U.S. Dust Storm Examples
True Color
Lake Tahoe
Texas
Nevada
California

• The ABI will include all bands required to
  reproduce the MODIS dust enhancement.

22
Volcanic Ash Enhancement

• Blue Light Absorption
• Technique
• Use the difference in
• NDDI between white
• clouds and ash (instead of dust)

• Principal Component
• Image Analysis
• Applied to volcanic ash

Mount Etna (Italy) 28 October 2002 MODIS Aqua
image
23
Etna (Mediterranean)
October 30, 2002 1110 Z November
7, 2002 1143 Z
24
Manam (Papua New Guinea)
October 24, 2004 0355 Z November
29, 2004 0040 Z
25
Okmok Visible Loop
26
Okmok (Alaska)
July 12, 2008 2145 Z
27
Principal Component Image (PCI) Analysis
Volcanic Ash Enhancement
Analysis of Initial Okmok Eruption Imagery for
the Okmok (Alaska Aleutian) volcano eruption from
12/13 July 2008 has been analyzed thru Principal
Component Image (PCI) analysis. PCIs extract
dominant image combinations from the available
GOES bands.
28
PCI Analysis -- Image Loop
29
Principal Component Image (PCI) Analysis
PCIs are combined in this image using RGB
(3-color) analysis. The colors chosen to enhance
the ash cloud, with PCI-2, 3, and 5 as Red,
Green, and Blue, respectively. Clear areas in
the image are deep purple, high clouds are mainly
green, lower clouds are yellow, and
heavily-ash-dominated cloud is orange. Note the
higher concentration of ash in the plume south of
the volcano vs. the plume east of the volcano.
30
PCI Analysis -- Image Loop
PCI Analysis of Initial Okmok Eruption 12/13 July
2008 PCIs are enhanced with RGB (Red, Green,
Blue 3-color) analysis to better show the
associated clouds and ash in the images.
31
PCI Analysis -- Image Loop
PCI Analysis of Initial Okmok Eruption 12/13July
2008
32
Snow/Cloud Discrimination

• High spatial resolution snow cover improved via
  cirrus filtering using the 1.38 ?m band

33
Protocol for RAMSDIS Product Integration within
AWIPS

• Remap the products from NESDIS server to match
  AWIPS sectors.
• Convert products from McIDAS to netCDF format.
• Copy the converted netCDF products to local AWIPS
  machine inject into LDM product queue.
• Create a separate Proving Ground menu for
  display within AWIPS (menu bar at top of the GUI)
• Thanks to Jordan Gerth (SSEC/CIMSS) for his
  assistance in getting us up to speed on this
  process.

34
Protocol for NexSat Product Integration within
AWIPS

• Many NexSat products are 24-bit depth images,
  but AWIPS can only handle 8-bit depth(!).
• Requires a reduction of NexSat imagery depth,
  optimal specification of a 256 color palette.
• Using Photoshop to define optimized palette
• Translate each pixel R/G/B to closest palette
  index
• Use AreaToNetCDF with corresponding McIDAS image,
  manually enter into AWIPS.
• Currently working on an self-contained version
  which writes directly to AWIPS netCDF.

35
24-bit to 8-bit Reduction
Original
? Gives hope that most NexSat applications can be
adapted to AWIPS
36
24-bit to 8-bit Reduction into AWIPS
Original
We are confident that most NexSat applications
can be adapted to AWIPS
37
24-bit to 8-bit Reduction into AWIPS
Original
38
Plans for FY09 and Beyond

• Refine applications based on user feedback.

• Introduce additional applications from other
  proxy data (e.g., IASI), case studies, training.

• After establishing a working concept of
  operations with local offices, expand to remote
  WFOs.

• Develop distributed interactions
• E.g., NCEP, TPC, HPC, SPC, OPC
• River Forecast Centers

• Conduct workshops for participants.

• Prepare for migration to AWIPS-II.

39
Summary

• GOES-R Proving Ground status can be viewed at
• http//cimss.ssec.wisc.edu/goes_r/proving-ground.h
  tml