1. GINA OCONUS Support in Alaska Carl Dierking, Tom Heinrichs, Eric Stevens, Jessica Cherry, Jiang Zhu, Dayne Broderson Geographic Information Network of Alaska (GINA) / University of Alaska Fairbanks (UAF)

2. Sandy Supplemental Project  Completed Sep 2015 – Distribution of Near Real Time (NRT) Polar Satellite Products for AWIPS  Satellite Antennas  3.6 m X-band at UAF/GINA  3.0 m X,S,L band at NESDIS FCDAS facility – Gilmore Creek  Processing Capabilities:  SNPP VIIRS  MODIS (Aqua & Terra)  AVHRR (POES: NOAA15, 18, 19, METOP B)  DMSP  CSPP Products distributed to Alaska NWS Offices via LDM  http://nrt-status.gina.Alaska.edu/products http://nrt-status.gina.Alaska.edu/products  Support: http://support.gina.alaska.eduhttp://support.gina.alaska.edu

3. GINA Single Channel AWIPS Products (Sandy) 3 NicknameVIIRSMODISCorReflAVHRR Day Night Band0.70 (DNB) Blue Band0.49 (M3)0.47 (3)Y Green Band0.56 (M4)0.56 (4)Y Red Band0.64 (I1)0.64 (1)Y Veggie Band0.86 (I2)0.86 (2)Y Cirrus Band1.4 (M9)1.4 (26) Snow-Ice Band1.6 (I3)1.6 (6)Y1.6 (3a) Cloud Particle Size Band 2.3 (M11)2.1 (7) Shortwave IR Window 3.7 (I4)3.7 (20)3.7 (3b) Fire Band4.0 (M13)4.0 (23) Upper Lvl Trop WV Band Mid Lvl Trop WV Band 6.7 (27) Lower Lvl WV Band 7.3 (28) Cloud Top Phase Band 8.6 (M14)8.5 (29) Ozone Band9.7 (30) Clean IR Longwave Band 10.8 (M15)11.0 (31)10.8 (4) IR Longwave Window 11.4 (I5) Dirty Longwave Window 12.0 (M16)12.0 (32)12.0 (5)  VIIRS: bands similar to GOES-R  Plus DNB: Adaptive and Dynamic ERF  Plus 0.56 um Green Visible  No Water Vapor, Ozone, or CO2 Bands  MODIS Terra & Aqua: similar bands as VIIRS  Plus 2 WV bands  No DNB or CO2 Bands  Corrected Reflectances: VIIRS & MODIS  AVHRR: POES 15,18,19 & MetOp  Bands 1 (.64um), 2 (.86um), 3a (1.6um), 3b (3.7um), 4 (10.8 um), 5 (12.0um)  Requested NOAA POES 3a & 3b switching be turned back on * Colors highlight sensor differences from GOES-R

4. GINA Partner Support  CIMSS  CSPP  OCONUS technical expertise  Virtual Machine (DR data access)  GEOCAT distribution  SPoRT  Virtual Machine (DR data access)  RGB product production  CIRA  Virtual Machine (DR data access)  DNB Dynamic ELF distribution  GMU/CCNY/CIMSS  VM River Flood and River Ice products (Dr. Sanmei Li)  AFS  Fire detection  Smoke identification  AWIPS Polar Imagery for FxCAVE

5. AAWU Volcanic Ash Monitoring  Ash RGB - “On the Fly”  BTD 10.8 – 12.0 um (Ash Plume)  BTD 10.8 – 8.6 um (SO 2)  AVHRR BTD 10.8 – 12.0 um

6. Natural Color RGB (R=1.6um G=.86um B=.64um)  Sea Ice Extent  Snow / cloud differentiation  Smoke & Burn Scars  Large fire Identification  Using 2.3 um instead of 1.6 um more sensitive to hot spots  Similar ice/snow properties  Sampling for pixel color pct. (“on the fly”) RED: 30.0 counts (11%) GREEN: 157.0 counts (68%) BLUE: 181.0 counts (71%)

7. Multispectral Imagery “On the Fly”  Benefits:  Minimizes impact on bandwidth and storage  Products are only generated when needed  Sampling helps clarify contributions of bands and/or differences  Local offices and regions can participate (“Best Practices”)  Channel Differences  More direct relationship between bands  Simpler to assess channel characteristics  Requires good color curves that may have some variability  RGB  More efficient composite of multiple bands  Channel differences can be represented by a color for an additional level of complexity  Can be confusing because of color mixing  Sampling is important to understand individual

8. AWIPS Time Matching Problem for Polar Data  Difficult in AWIPS for forecasters to view all relevant polar data because of irregular times.  Time Match Basis of one sensor can skip passes of other sensor.  Can switch TimeMatchBasis in popup dialog, but…  Rare to see all sensors at once

9. Client-Side Mosaic Script  Python script runs on AWIPS data server  Composites similar bands across multiple sensors.  Provides summary of all recent polar data  Works for any channel being ingested into AWIPS  Retains 1 km resolution  minimal parallax displacement or limb effectsl

10. Mosaic Pass Age Includes composite of the pass “time delta” or age from mosaic time.

11. Multispectral Mosaics  Can be used for “on the fly” channel differences and RGBs.  Works for any channel… but best if common to all sensors 11-12um BTD Mosaic

12. Multispectral Sounding WRF assimilation  GINA-WRF short-term forecast  GINA conducts study on data assimilation into the regional WRF model. The goal is to evaluate improvements made from bringing in the near realtime polar orbiting datasets into the weather model.  A regional WRF model is running in GINA. It runs 4 times each day for analysis times of 00, 06,12, and 18Z. The CrIS/ATMS sounding profile data (EDR) are assimilated to improve initial condition of every WRF run. Level1 CrIS/ATMS data (SDR) are received by GINA/NOAA satellite facility at Gilmore Creek, they are then processed into CrIS/ATMS sounding profile (EDR) data with the NUCAPS algorithm.  24-hour forecasts are produced for each analysis time. The WRF outputs are shown at http://gina-wrf-2-web.s3-website- us-west-2.amazonaws.com/ http://gina-wrf-2-web.s3-website- us-west-2.amazonaws.com

13. Assessing Forecaster Use of Satellite Data “Great stuff… but forecast shifts are busy, and there’s not enough time to look at all those new channels” “Most of my time is spent working on GFE… I need information that helps with my grids” Roadblocks to full utilization Education & Training

14. Promote “Efficient” Use of New Satellite Data  Promote Multispectral products  RGBs, Channel Differences  Extract pertinent information from multiple channels (2 – 6)  Expand satellite influenced GFE grids  Some satellite data could be imported into GFE, especially “value added” products  Fog mask, probabilities  Cloud types, phases, icing  QPE (rain, snow)  Dust, Ash  Winds ?  Knowledge of individual channel strengths and weaknesses is essential:  to understand channel inputs intelligently for multispectral and value added products (know what’s valid)  To target information appropriate to the problem (what’s applicable to the situation)  To get beyond “black box” approach and build confidence

15. Multi-level Training Approach TypesSubject MatterDevelopersTrainers Quick GuidesBandsCI (SPoRT, GINA) GOES-R Program Office Self Study AWIPS Integrated Training Tool (ITT) Bands Products Forecast Best Practices CI (CIMSS, SPoRT, GINA) GOES-R Program Office Self Study Web Modules - STAT (CBT) Foundational: Bands Products Case Studies COMET CI (CIMSS, SPoRT, CIRA, GINA) Self Study Office SeminarsBands Products Case Studies CI (CIMSS, SPoRT, CIRA, GINA)Liaisons SOO Sat FP Distance Seminars Products Case Studies COMET CI (CIMSS, SPoRT, CIRA, GINA) SOO Sat FP Liaisons One-on-oneForecast Best Practices Case Studies SOO, Sat FP, LiaisonsSOO Sat FP Others?

16. Quick Guides and Training Library  More planned in 2016  Make available within AWIPS (Integrated Training Tool)  Training Library for Training Documents, Blogs, & other references http://gina.alaska.edu/projects/gina-training-library

17. Channel Differences & RGBs “On the Fly” Understanding Ingredients  Nighttime Microphysics RGB  Satellite Differences can highlight ingredient contribution  12.0 um – 10.8 um (red)  10.8 um – 3.7 um (green)  10.8 um (blue)  Sampling RED: 157.0 counts (68%) GREEN: 181.0 counts (71%) BLUE: 30.0 counts (11%)

18. Alaska Forecaster Training Workshop?  Multi-day to reach entire staff  GOES-R and SNPP/JPSS individual channel characteristics  Multispectral Products  Hands-On using SIFT tool  Practical Applications  Case Studies  Assessments

19. OCONUS Action Items  Evaluate justification for getting global SNPP/JPSS data through Gilmore Creek  Sep 2015 – completion Sandy Project: Distribution of Near Real Time (NRT) Polar Satellite Products for AWIPS  Composite circum-arctic mosaic loops for overlaying satellite products.  MOSAIC script  Extend simulated imagery to Alaska (run post-processed at GINA)  Sent directly to Forecast Offices from CIRA  Use hyperspectral data (AIRS, CrIS, IASI) in the WRF/HRRR runs. Assess retrieval accuracy  Ongoing research by Jiang Zhu (Online output: http://gina.alaska.edu/projects/gina-wrf-short-term-forecast)http://gina.alaska.edu/projects/gina-wrf-short-term-forecast  Work on resolving color issues (RGB) for color blind  No Change  Validate the science with the users for all GOES-R Baseline Products  Quick Guides started in 2015. Additional training planned through remainder 2016  Develop quantitative air mass product  No Change. Air Mass training at all Forecast Offices conducted Oct 2015 and planned for Oct 2016.  Provide sample ice products to the Alaska Ice Desk  No Change

20. Thank you Questions??