The GOES-R Air Quality Proving Ground: Building An Air Quality User Community for the Next Generation of Products Raymond M. Hoff, H. Zhang, A. Huff, S.

Slides:



Advertisements
Similar presentations
Air Quality and Health Scenario Stefan Falke, Rudy Husar, Frank Lindsay, David McCabe.
Advertisements

A Tutorial on MODIS and VIIRS Aerosol Products from Direct Broadcast Data on IDEA Hai Zhang 1, Shobha Kondragunta 2, Hongqing Liu 1 1.IMSG at NOAA 2.NOAA.
An Air Quality Proving Ground (AQPG) for GOES-R R. M. Hoff (UMBC GEST/JCET), S. A. Christopher (UAH), F. Moshary (CCNY), S. Kondragunta (STAR), R. B. Pierce.
NOAA GOES-R Air Quality Proving Ground July 30, 2011 Case Study Haze in Mid-Atlantic.
1 1. FY09 GOES-R3 Project Proposal Title Page Title: Trace Gas and Aerosol Emissions from GOES-R ABI Project Type: GOES-R algorithm development project.
Satellite based estimates of surface visibility for state haze rule implementation planning Air Quality Applied Sciences Team 6th Semi-Annual Meeting (Jan.
Transitioning unique NASA data and research technologies to operations GOES-R Proving Ground Activities at the NASA Short-term Prediction Research and.
11 th CMAS Meeting, RTP October 15 – 17, Fine Resolution Air Quality Forecasting Capability for limited-area domains – tested over Eastern Texas.
A Tutorial on MODIS and VIIRS Aerosol Products from Direct Broadcast Data on IDEA Hai Zhang 1, Shobha Kondragunta 2, Hongqing Liu 1 1.IMSG at NOAA 2.NOAA.
Update on Delivery System Options for ABI and VIIRS Air Quality Products Ray Hoff.
Transitioning research data to the operational weather community Use of VIIRS DNB Data to Monitor Power Outages and Restoration for Significant Weather.
Transitioning unique NASA data and research technologies to operations Relevant OCONUS Products Gary Jedlovec NASA / MSFC, Earth Science Office
Satellite and Above-Boundary Layer Observations for Air Quality Management Workshop Series – 2 nd Workshop Satellite and Above-Boundary Layer Observations.
Overview of Satellite and Above-Boundary Layer Observations for Air Quality Management Workshop Series and other activities relevant to GOES-R AQPG: Jim.
GOES-R AEROSOL PRODUCTS AND AND APPLICATIONS APPLICATIONS Ana I. Prados, S. Kondragunta, P. Ciren R. Hoff, K. McCann.
Upcoming AQPG Activities Amy Huff Battelle Memorial Institute NOAA GOES-R AQPG 2 nd Annual Advisory Group Workshop January 12, 2012.
Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Image: MODIS Land Group, NASA GSFC March 2000 Air Quality Products from.
Chapter 2: Satellite Tools for Air Quality Analysis 10:30 – 11:15.
Infusing satellite Data into Environmental Applications (IDEA): PM2.5 forecasting tool hosted at NOAA NESDIS using NASA MODIS (Moderate Resolution Imaging.
GOES-R Synthetic Imagery over Alaska Dan Lindsey NOAA/NESDIS, SaTellite Applications and Research (STAR) Regional And Mesoscale Meteorology Branch (RAMMB)
Synthetic Satellite Imagery: A New Tool for GOES-R User Readiness and Cloud Forecast Visualization Dan Lindsey NOAA/NESDIS, SaTellite Applications and.
Aircraft spiral on July 20, 2011 at 14 UTC Validation of GOES-R ABI Surface PM2.5 Concentrations using AIRNOW and Aircraft Data Shobha Kondragunta (NOAA),
Shobha Kondragunta and Ivan Csiszar NOAA/NESDIS Amy Huff Pennsylvania State University Hai Zhang and Pubu Ciren IMSG at NOAA Xiaoyang Zhang South Dakota.
Chapter 4: How Satellite Data Complement Ground-Based Monitor Data 3:15 – 3:45.
SATELLITE AIR QUALITY PROVING GROUND Amy Huff Shobha Kondragunta Ray Hoff.
NOAA GOES-R Air Quality Proving Ground (AQPG) Advisory Group Workshop September 14, 2010 University of Maryland, Baltimore County (UMBC) Raymond M. Hoff,
Estimates of Biomass Burning Particulate Matter (PM2.5) Emissions from the GOES Imager Xiaoyang Zhang 1,2, Shobha Kondragunta 1, Chris Schmidt 3 1 NOAA/NESDIS/Center.
Satellite Air Quality Proving Ground S. Kondragunta, NESDIS/STAR R. Hoff, UMBC A. Huff, PSU.
1 CIMSS Participation in the Development of a GOES-R Proving Ground Timothy J. Schmit NOAA/NESDIS/Satellite Applications and Research Advanced Satellite.
Air Quality Proving Ground R. M. Hoff 1, A. Huff 2, S. Kondragunta 4, P. Ciren 4, C. Xu 4, H. Zhang 1, S. Christopher 3, E.-Y. Su 3 1 UMBC 2 Battelle Memorial.
GOES-R ABI PROXY DATA SET GENERATION AT CIMSS Mathew M. Gunshor, Justin Sieglaff, Erik Olson, Thomas Greenwald, Jason Otkin, and Allen Huang Cooperative.
User Input from past GOES Users’ Conferences Jim Gurka Steve Goodman NOAA/NESDIS GOES-R Program Office Tim Schmit NOAA/NESDIS/ STAR 7 th GOES Users’ Conference.
1 1. FY08 GOES-R3 Project Proposal Title Page  Title: Hazards Studies with GOES-R Advanced Baseline Imager (ABI)  Project Type: (a) Product Development.
1 GOES-R AWG Product Validation Tool Development Aviation Application Team – Volcanic Ash Mike Pavolonis (STAR)
1 GOES-R AWG Product Validation Tool Development Aviation Application Team – Volcanic Ash Mike Pavolonis (STAR)
GOES and GOES-R ABI Aerosol Optical Depth (AOD) Validation Shobha Kondragunta and Istvan Laszlo (NOAA/NESDIS/STAR), Chuanyu Xu (IMSG), Pubu Ciren (Riverside.
GOES-R ABI Synthetic Imagery at 3.9 and 2.25 µm 24Feb2015 Poster 2 Louie Grasso, Yoo-Jeong Noh CIRA/Colorado State University, Fort Collins, CO
Update on Delivery System Options for ABI and VIIRS Air Quality Products Ray Hoff.
1 GOES-R Air Quality Proving Ground Leads: UAH UMBC NESDIS/STAR.
Significant contributions from: Todd Schaack and Allen Lenzen (UW-Madison, Space Science and Engineering Center) Mark C. Green (Desert Research Institute)
RGB Activities for the GOES-R Proving Ground Gary Jedlovec, NASA / MSFC / SPoRT Mark DeMaria NOAA / NESDIS / STAR Tim Schmit NOAA / NESDIS / CIMSS and.
Air Quality Proving Ground 3 rd Users Workshop Raymond Hoff, UMBC Shobha Kondragunta, NESDIS STAR Amy Huff, Penn State University Raymond Hoff, UMBC Shobha.
NESDIS Center for Satellite Applications and Research Air Quality Products from GOES-R Advanced Baseline Imager (ABI) S. Kondragunta, NOAA/NESDIS/STAR.
Overview of Case Studies of VIIRS Aerosol Products for Operational Applications Amy Huff Pennsylvania State University VIIRS Aerosol Science and Operational.
Satellite Air Quality Proving Ground Ray Hoff (UMBC) Amy Huff (Battelle Memorial Inst.) Shobha Kondragunta (NESDIS)
Chapter 8: Daily Analysis of Air Quality in Central America: the “Blog de Calidad del Aire” 3:15 – 4:00.
Near-Real-Time Simulated ABI Imagery for User Readiness, Retrieval Algorithm Evaluation and Model Verification Tom Greenwald, Brad Pierce*, Jason Otkin,
Satellite Imagery ARSET - AQ Applied Remote SEnsing Training – Air Quality A project of NASA Applied Sciences NASA ARSET- AQ – EPA Training September 29,
GOES-R Recommendations from past GOES Users’ Conference: Jim Gurka Tim Schmit Tom Renkevens NOAA/ NESDIS Tony Mostek NOAA/ NWS Dick Reynolds Short and.
GOES-R Air Quality Proving Ground H. Zhang 1, R. M. Hoff 1, S. Kondragunta 2, A. Huff 3, M. Green 4, S. A. Christopher 5, B. Pierce.
As components of the GOES-R ABI Air Quality products, a multi-channel algorithm similar to MODIS/VIIRS for NOAA’s next generation geostationary satellite.
An Air Quality Proving Ground (AQPG) for GOES-R 6. ABI outputs will be integrated into an IDEA-like platform which will be the AQPG testbed for dissemination.
Transitioning research data to the operational weather community Overview of GOES-R Proving Ground Activities at the Short-term Prediction Research and.
What’s Next for the AQPG Ray Hoff and Amy Huff. AQPG Testbed – Summer 2011 Next summer, we plan to have an AQPG testbed running for a 1-2 week intensive.
Satellite Basics MAC Smog Blog Training CATHALAC, Panama, Sept 11-12, 2008 Jill Engel-Cox & Erica Zell Battelle Memorial Institute
CIRA / SPoRT Update 10/31/12 1 Update: “A Multisensor 4-D Blended Water Vapor Product for Weather Forecasting” Stan Kidder John Forsythe Andy Jones.
CLOUD PHYSICS LIDAR for GOES-R Matthew McGill / Goddard Space Flight Center April 8, 2015.
User Readiness Issues for GOES-R Jim Gurka Tim Schmit (NOAA/ NESDIS) Tony Mostek (NOAA/NWS) Dick Reynolds (Short and Associates) 4 th GOES Users’ Conference.
Summary of User Feedback on GOES-R Air Quality Proving Ground Summer 2011 Experiment Shobha Kondragunta, Amy Huff, and Raymond Hoff Proving Ground All-Hands.
Welcome to the PRECIS training workshop
Real-time Display of Simulated GOES-R (ABI) Experimental Products Donald W. Hillger NOAA/NESDIS, SaTellite Applications and Research (STAR) Regional And.
Update on Satellite Proving Ground Activities at the Operations Proving Ground Chad Gravelle GOES-R/JPSS All-Hands Call – 20 Jan 2016.
Update on Satellite Proving Ground Activities at the Operations Proving Ground Chad Gravelle GOES-R/JPSS All-Hands Call – 13 July 2015.
NOAA GOES-R Air Quality Proving Ground March 25, 2011 Case Study Smoke in Southeastern U.S.
GOES-R ABI AS A WARNING AID Louie Grasso, Renate Brummer, and Robert DeMaria CIRA, Fort Collins, CO Dan Lindsey, Don Hillger, NOAA/NESDIS/RAMMB, Fort Collins,
A. FY12-13 GIMPAP Project Proposal Title Page version 26 October 2011 Title: WRF Cloud and Moisture Verification with GOES Status: New GOES Utilization.
User Preparation for new Satellite generations
SNPP VIIRS Aerosol Product Latencies
NOAA GOES-R Air Quality Proving Ground Case Study 2 May 24, 2007
NOAA GOES-R Air Quality Proving Ground July 4, 2012 Case Study
Presentation transcript:

The GOES-R Air Quality Proving Ground: Building An Air Quality User Community for the Next Generation of Products Raymond M. Hoff, H. Zhang, A. Huff, S. Kondragunta, C. Xu, P. Ciren, S. A. Christopher, and E. S. Yang Paper AMS Annual Meeting

Current NOAA Satellite Products for AQ Community HMS Fire and Smoke Product 8/24/06 GOES VisibleGOES IRGOES Water Vapor GASP AOD 2

Uses of Satellite Products by AQ Community Routine AQ forecasting and event analyses: – Identify meteorological features that affect air pollutant build-up and transport (e.g., cloud cover, convection, frontal boundaries) – Identify and evaluate significant air pollution events (e.g., wildfire smoke, windblown dust, haze) – Advanced warning of upwind significant events (especially wildfires) Retrospective and Exceptional Event analyses: – Document the overall meteorological setting of events – Document the location, severity, timing, transport, and extent of events – Utilize this evidence in regulatory exemptions under the Exceptional Event Rule 3

NOAA’s IDEA Site (dynamic flat webpages)

Air Quality Proving Ground (AQPG) NOAA has created the AQPG – a subset of the GOES-R Proving Ground – focusing on the aerosol products that will be available from the ABI. Goal: build a user community that is ready to use GOES-R air quality products as soon as they become available. This distinction is important because the air quality community has very different needs than the majority of NOAA users (NWS meteorologists). AQPG is using simulated GOES-R ABI data for training and interaction with the user community. 5

AQPG Activities Created an Advisory Group of forecasters and analysts who are providing feedback on products. – User community feedback is critical for improving product quality, usage, and distribution, and for the development of new applications (including specific data formats) Working with NOAA to prototype the delivery system for GOES-R air quality products. Creating simulated GOES-R ABI products for at least 10 case studies of past air quality events. Providing training on GOES-R and ABI products to Advisory Group and general AQ community at workshops and conferences. 6

User Group (eye chart …)

AQPG Trainings Held three full workshops with the User Group (September 2010), the National Air Quality Conference attendees (60 people, March 2011) and January 12, 2012 at UMBC Workshops are tutorial in nature, presenting GOES-R 101 basics and then working with Case Studies. Users provide immediate and follow up feedback on the products and questions about utility of the AQ products in their daily forecasting. Since most of the AQ forecasters in the country work for state and local agencies, data and imagery access continues to be their primary concern. 8

3. CRTM Run on NOAA STAR computer orbit006l with 12GB memory and 16 CPUs. Code based on FORTRAN, IDL, and shell scripts 5. ABI AOD algorithm run on orbit006l for daytime scenes 4. GOES-R ABI Synthetic Radiances (6 bands: 0.468, 0.633, 0.86, 1.38, 1.61, 2.25  m) 1. Hourly outputs of aerosol and met fields from 48-hour WRF-SMOKE-CMAQ forecast run at 12-km resolution (ftp.nsstc.org/outgoing/yes) 36 min 6 hrs 7. Post-processing of proxy ABI aerosol products using IDL and ImageMagic to generate display imagery files 2. Re-formatting of CMAQ outputs to prepare for CRTM run 2 min50 min 1 min 6. Proxy ABI aerosol products 8. Proxy ABI aerosol imagery 9. Web display: 30 min Process for Creating Proxy ABI Aerosol Products 9

Case Studies of Simulated ABI Aerosol Data Case studies are designed to help users envision what actual GOES-R ABI data will look like, particularly GOES-R’s high temporal resolution, and anticipate how to use the data. Simulated ABI data are prepared for past air quality events and are used for training and to obtain feedback from the air quality community. Simulated ABI data are based on model data processed through the ABI algorithm, so they are not completely faithful representations of past conditions. 10

Proxy ABI Aerosol Optical Depth (AOD) AOD indicates areas of high particulate concentrations in atmosphere AOD is unitless; high AOD values (yellow, orange, red) indicate high particulate concentrations Clouds block AOD retrievals 11

12 Proxy ABI Aerosol Type New product - not available with current GOES imager Qualitative and untested Useful for distinguishing between smoke and dust but can be noisy, especially at low AOD values

13 Proxy ABI Synthetic Natural Color (RGB) No green band on ABI 2 methods to generate RGBs: –CIRA/SPoRT –UMBC/STAR UMBC/STAR method used for Summer 2011 experiment Algorithm development underway to improve RGB product

Interactive Web Display for Proxy ABI Products Proxy ABI aerosol products were streamed to users through the NESDIS IDEA website ( Users selected the day/time of interest using a pull-down menu and flipped between the three ABI proxy products using radial buttons 14

Planning for the Next NRT Experiment AQPG team has incorporated user feedback from the Summer 2011 NRT experiment and is planning another NRT experiment for September, Volunteers are welcome To generate additional proxy ABI products, the AQPG needs real-time model aerosol output with a CONUS domain – Hope to use NCEP/NWS forecast products as basis for the next AQPG NRT experiment in

Acknowledgements Steve Goodman (NOAA NESDIS) Bonnie Reed (NOAA NWS) Mike Johnson (NOAA NWS) Jaime Daniels (NOAA NESDIS) 16 This work was funded by NOAA NESDIS STAR grants NA09NES , NA10NES ,and NA11NES The opinions here do not necessarily represent those of NOAA or the Department of Commerce.