H5, Evening School #2: Introduction to Satellite Applications to - - PowerPoint PPT Presentation

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H5, Evening School #2: Introduction to Satellite Applications to - - PowerPoint PPT Presentation

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H5, Evening School #2: Introduction to Satellite Applications to Nowcasting Steven J. Goodman GOES-R Program Chief Scientist NOAA/NESDIS WMO WSN16 Symposium On Nowcasting and Very-Short Range Forecasting 1 Hong Kong, 25-29 July, 2016 Outline

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H5, Evening School #2: Introduction to Satellite Applications to Nowcasting

Steven J. Goodman

GOES-R Program Chief Scientist NOAA/NESDIS

WMO WSN16 Symposium On Nowcasting and Very-Short Range Forecasting Hong Kong, 25-29 July, 2016

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Outline

  • Overview of New GEO Ring Capabilities
  • Conceptual Models and Archetypes
  • Pre-storm Boundaries and Convective Initiation
  • Satellite Cloud-Top Features
  • Extreme Thunderstorms
  • Training Resources

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AHI Band AHI Approximate Central Wavelength (μm) ABI Approximate Central Wavelength (μm) ABI Band Type Nickname 1 0.47 0.47 1 Visible Blue 2 0.51 Visible Green 3 0.64 0.64 2 Visible Red 4 0.86 0.86 3 Near-Infrared Veggie 1.4 4 Near-Infrared Cirrus 5 1.6 1.6 5 Near-Infrared Snow/Ice 6 2.3 2.2 6 Near-Infrared Cloud Particle Size 7 3.9 3.9 7 Infrared Shortwave Window 8 6.2 6.2 8 Infrared Upper-level Water Vapor 9 6.9 6.9 9 Infrared Mid-level Water Vapor 10 7.3 7.3 10 Infrared Lower-level Water Vapor 11 8.6 8.4 11 Infrared Cloud-Top Phase 12 9.6 9.6 12 Infrared Ozone 13 10.4 10.3 13 Infrared “Clean” Longwave Window 14 11.2 11.2 14 Infrared Longwave Window 15 12.4 12.3 15 Infrared “Dirty” Longwave Window 16 13.3 13.3 16 Infrared CO2 Longwave

Advanced Baseline Imager Spectral Bands

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The ABI visible and near-IR bands have many uses.

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Visible and near-IR channels on the ABI

Sample use only, many other uses

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GOES-R ABI Weighting Functions

Water Vapor Bands- 8, 9, 10

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ABI has many more bands than the current operational GOES imagers.

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The IR channels on the ABI

Sample use only, many other uses

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Challenges and Opportunities for Nowcasting with the New Generation Geo Satellities

Decision Aids

  • Cloud and Moisture Imagery (cloud top features,
  • vershooting tops, synthetic imagery, RGBs, environment)
  • Super Rapid Scan Imaging
  • Fused Products (satellite, radar, lightning, in-situ, NWP)
  • Probabilistic High Impact Hazards (tornado, hail, wind,

flood, lightning, fire, volash, fog, aircraft turbulence and icing)

Data Assimilation

  • Radiances, T,q profiles
  • High density AMVs
  • Total Lightning
  • Storm Scale NWP- WoF
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Grid-Based Probabilistic Threats

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Obs & Guidance

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The Forecaster

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Threat Grid Tools

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Useful Output

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Effective Response

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Verification

7 Integrated Social Sciences

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  • A science-driven paradigm delivering a continuous

stream of high-res, probabilistic hazard information extending from days to within minutes of event.

  • Optimized for user-specific decision-making through

comprehensive integration of social/behavioral sciences.

Integrated Social/Behavioral/Economic Sciences

Adapted from Lazrus (NCAR) 10

Forecasting a Continuum of Environmental Threats (FACETs):

The NOAA Next-Generation Hazardous Watch/Warning Paradigm

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Facet #1: Grid-Based Probabilistic Threats

  • Probabilistic Hazard

Information (PHI) – Forecasters convey threat probability on grids. – NOT (explicitly) watches or warnings, but…

30-Minute Threat: Tornado Probability

Valid 10:00 p.m. - 10:30 p.m. CDT Last updated: 2 minutes ago Grid-Based Probabilistic Threats

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Facet #1: Grid-Based Probabilistic Threats

  • Legacy products

(warnings/watches) result from pre-determined thresholds.

  • Opens the door for new

products and services.

  • For ALL weather hazards (not

just tornadoes)!!

30-Minute Threat: Tornado Probability

Valid 10:00 p.m. - 10:30 p.m. CDT Last updated: 2 minutes ago

“Byproduct” Tornado Warning

Proximity (Yellow) Alert??

Grid-Based Probabilistic Threats

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Warning-on-Detection v.s. Warn-on-Forecast Nowcasting v.s. NWP

The quoted NWP models are not initialized with radar and/or other high- res data observing explicit weather and/or their resolution is too low Properly initialized NWP models should

  • ut-perform extrapolation/statistical

nowcasting models from time zero Fast nowcasting systems are useful for very short ranges because they are fast, not because they are intrinsically better

(Jim Wilson 2006)

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Jim Wilson, NCAR

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QLCS - Quasi-Linear Convective Systems

Convective Modes for Severe Storms Supercell, Squall Lines

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Conceptual Model

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Many long-lived multicell convective systems have a propensity to take lives and damage property with high winds, hail, and

  • tornadoes. Damage often occurs over a broad swath

encompassing multiple county warning areas. Danger to public in these situation is obvious and often more extreme, impact-wise, than a single tornado storm. Challenges for a warning forecaster

  • n the threat assessment level include:
  • Recognition of the intensity of these type of events
  • Determination of duration and movement, and
  • Determination of all the threats associated with these type
  • f events.

Conceptual Models

Note that there are can be significant differences in the evolution

  • f multicell systems based on the strength of the cold pool,

ambient shear, and attendant RIJ.

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GOES-14 Super Rapid Scan 1-min Imagery of a QLCS

GOES-14 IR brightness temperature, GOES-R overshooting cloud top (OT) detection algorithm output, cloud-top height derived from the length of shadow produced by OT penetration above the surrounding anvil, WSR-88D derived vertically-integrated liquid (VIL) and precipitation echo top height, and total lightning from the Northern Alabama Lightning Mapping Array (NALMA) and Earth Networks Total Lightning Network (ENTLN).

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GOES-14, 1 min rapid-scan of Florida Sea Breeze

“The 1-min data gives a more continuous depiction of how meteorological features are evolving, versus the ‘snapshot’ approach of coarser temporal resolution images.”

GOES-14 SRSOR Vis and IR incorporated into SPC

  • perational NAWIPS
  • T. Schmit, D. Lindsey
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Mesoscale and Satellite Cloud Top Features colliding boundaries initiate thunderstorms

GOES-14 1-min Imagery

Overshooting Top Outflow boundary Vortex cloud streets (horizontal rolls) Forecasters can monitor the interactions between air masses, outflow boundaries and storms leading to increased situational awareness and confidence

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  • Vertical shear

– Cloud motion and height – Moisture motion

  • Evolving instability field

– Surface heating – Detailed moisture field – Evolving instability

  • Thunderstorm cold pool production

– Sounder in synergy with imager

  • Updraft strength

– IR top temperature and characteristics – Overshooting top height – Updraft efficiency (height and instability)

  • Anvil characteristics & storm environment interaction

– Growth and detailed upper level atmospheric motion and water vapor behavior – Imager and sounder with spectral fidelity

  • Rotating overshooting top

– Rapid scan imager

  • Storm damage

– Combined polar and geostationary imager products

Nowcasting requires detailed information on mesoscale thermodynamic structure of atmosphere, cloud type and vertical wind shear Important for Nowcasting Convection and Severe Weather

  • Vertical wind shear
  • Evolving instability field
  • Strength of storm

produced cold pool

  • Updraft strength
  • Anvil characteristics

Development Temperature structure

  • Storm-environment

interaction

  • Cloud top rotation
  • Storm damage
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Overshooting Tops What do they mean?

Overshooting Tops

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Overshooting Top (OT)

  • Indication of a very

strong updraft

  • 71% had a CG strike

within 10 km (Bedka et. Al 2010)

  • 55% of severe reports

were near overshoots and 75% of those

  • vershoots occurred

before the report (Dworak et. Al 2012)

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OT Detection

  • Small clusters (< 15 km in

diameter) where IR BT’s are significantly colder than the surrounding anvil cloud.

  • Typically > 6.5 K colder

than anvil BT’s (Bedka et. al 2010)

  • Make sure cloud top is

above equilibrium level

Colder than ET Coldest pixel

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CTT Cooling Rate Pre-Anvil

  • Relatively strong CTC precede significant radar

signatures (Hartung et al. 2013)

  • Can provide lead time in favorable

environments

  • Future increased resolution will improve

detection and accuracy

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Observed IR Cloud Top Features

  • Enhanced-V
  • Cold Area (CA)
  • Close-in Warm Area (CWA)
  • Warm-cold couplet
  • Distant Warm Area (DWA)

McCann (1983): Storms with enhanced-V have about 70% probability of producing severe weather. Median lead time from the

  • nset of the V to the first severe weather is

about 30 minutes. Adler et al. (1985): 75% of storms with the-V feature have severe weather, but 45% of severe storms don’t have this feature

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Model simulation of storm top temperature fields

Courtesy Pao Wang

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Tornadic Storm: Denver International Airport

DIA

Dan Lindsey - CIRA

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  • C. Velden, CIMSS

1-min meso-scale AMVs

21-May-2014

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Lightning Jumps and Severe Storms

Improved forecaster situational awareness and confidence results in more accurate severe storm warnings (i.e., improved lead times and reduced false alarms)

Schultz et al. 2011

  • Using lightning data alone, predicted severe weather

with 20.65 min lead time

  • 79% probability of detection (POD)
  • 36% false alarm rate (FAR)

Rudlosky et al. 2013

  • Severe = 1.44 jumps h-1; Non-severe = 0.92 jumps h-1
  • Adding a 10 mm Maximum Expected Size of Hail (MESH)

threshold:

  • Severe = 1.25 jumps h-1; Non-severe = 0.61 jumps h-1

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NWS Vision to Integrate Satellite Products with Other Data and Models

A Potential Operational Example: Convective Initiation/Severe Wx How can we integrate the information in future tools?

Why NWS needs this? Situational Awareness Warning confidence Decision Support (venues)

CI

Over- shooting tops

Lightning Jumps

Next Generation Warning System

Situational Awareness:

User comment: ‘Cloud Top Cooling product is an excellent source of enhancing the situational awareness for future convective initiation, particularly in rapid scan mode’. AWC Testbed forecaster (June 2012)

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Derecho/Lightning/Tornado (June 13, 2013)

Courtesy of Scott Rudlosky, CICS-MD

GOES-R Fusion of 1-min Imagery With Total Lightning

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Short-range NWP Forecasts of Lightning with NSSL WRF

25 April 2010

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Lightning Forecast Algorithm (LFA) Methodology

  • Compare WRF forecasts of graupel flux

(GFX) at -15C (main neg charge region) to LMA observations of peak FRD within storm outbreaks

  • Find best linear fit of peak WRF proxy

to LMA peak FRD

  • Generate additional WRF LTG proxy

using vertically integrated ice (VII), and rescale its peak value to match that from GFX

  • Threshold GFX to zero where GFX < 1.5
  • Create a blend of GFX and VII threats

to achieve correct threat areal coverage (0.95) GFX + (0.05) VII

Carey et al., 2014, Vaisala International Lightning Meteorology Conference, Tucson, AZ

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HRRR Forecast Fields

Lightning Threat 3 used for Prob LTG forecast out to 9 hours

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HRRR LFA Forecasts on 28 April 2014 from 14z/15z/16z:

9 AM CDT 10 AM CDT 11 AM CDT 8 hr fcst 7 hr fcst 6 hr fcst

All Lightning Forecasts Valid 5 PM CDT (22z) 28 April

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NCAR (Glen Romine) 10-member Ensemble (ensemble.ucar.edu)

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Vaisala Lightning Valid 0140 UTC 28 July 2016

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WLLN Data 27 July 2016

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SPC Storm Reports and Lightning Validation, 27 July 2016

3 Tornadoes in Iowa, Wind Damage Tennessee North Alabama LMA Total Lightning

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Lofted Dust

Somalia Ethiopia Kenya Uganda Tanzania Sudan South Sudan Congo

Thunderstorm Shadows Lightning Flash Over Lake Victoria

Suomi-NPP DNB Nighttime View of Africa

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17 Oct 2014: http://weather.msfc.nasa.gov/sport/servirModelingAfrica/kenya/14290/servir_modeling_20141017_0000_kenVIIRS.html 21 Oct 2014: http://weather.msfc.nasa.gov/sport/servirModelingAfrica/kenya/14294/servir_modeling_20141021_0000_kenVIIRS.html 03 Mar 2015: http://weather.msfc.nasa.gov/sport/servirModelingAfrica/kenya/15062/servir_modeling_20150303_0000_kenVIIRS.html 03 Apr 2015: http://weather.msfc.nasa.gov/sport/servirModelingAfrica/kenya/15093/servir_modeling_20150403_0000_kenVIIRS.html 17 Apr 2015: http://weather.msfc.nasa.gov/sport/servirModelingAfrica/kenya/15107/servir_modeling_20150417_0000_kenVIIRS.html 20 Apr 2015: http://weather.msfc.nasa.gov/sport/servirModelingAfrica/kenya/15110/servir_modeling_20150420_0000_kenVIIRS.html 29 Apr 2015: http://weather.msfc.nasa.gov/sport/servirModelingAfrica/kenya/15119/servir_modeling_20150429_0000_kenVIIRS.html 9 May 2015: http://weather.msfc.nasa.gov/sport/servirModelingAfrica/kenya/15129/servir_modeling_20150509_0000_kenVIIRS.html

Lake Victoria SWFDP

Satellite-Lightning-NWP Case Studies

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Lake Victoria Project Satellite-Lightning Nowcasting

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Satellite Training Timeline

FY15 Data L+88 & Beyond Nov 2016 Launch

Application Prerequisites Making it Stick Foundation Exercise Continuous Learning

Training Stages

‒ Prerequisites – overall basics ‒ Foundation – satellite specifics ‒ Application – operational setting ‒ Exercise – simulations, practice ‒ Making it Stick – multi-situational, sharing ‒ Continuous Learning – evolve and update

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Satellite Foundational Course

  • GOESR Introduction and SatMet Background Track (240 minutes)
  • Basic principles of radiation (15 minutes)
  • Basic operation of the GOES-R satellites (15 minutes)
  • Spectral bands (90 minutes)
  • Multichannel interpretation approaches (30 minutes)
  • Baseline products (90 minutes)
  • Geostationary Lightning Mapper Track (40 minutes)
  • Mesoscale/Convection Track (120 minutes)
  • Synoptic Features Track (80 minutes)
  • NWP/Data Assimilation Track (30 minutes)
  • Interactive Lessons and Simulations - Provide an opportunity to view &

interact with the GOES- R imagery & products to firmly establish understanding and interpretation.

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Training Modules and Sample Data

  • Cooperative Program for Operational Meteorology,

Education, and Training (COMET): https://www.meted.ucar.edu/

  • Satellite Hydrology and Meteorology for Forecasters

(SHyMet): http://rammb.cira.colostate.edu/training/shymet/

  • Short-term Prediction Research and Transition

Center (SPoRT) product training modules: http://weather.msfc.nasa.gov/sport/training/

  • Virtual Institute for Satellite Integration Training

(VISIT) Training Resources: http://rammb.cira.colostate.edu/training/visit/

  • GOES-R Education Proving Ground:

http://cimss.ssec.wisc.edu/education/goesr/

  • Sample data: http://www.goes-

r.gov/products/samples.html

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http://www.goes-r.gov/users/training.html

GOES-R Short Course links

http://cimss.ssec.wisc.edu/goes/shortcourse/links.html

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Education and Public Outreach Update

70 5,600+ FB “Likes!”

www.facebook.com/ GOESRsatellite https://www.youtube.com/ user/goesrsatellites

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Printed Materials

  • ABI Bands Quick

Information Guides

  • Fact Sheets
  • User Readiness Plan
  • GRB Downlink

Specifications

  • Product Users’ Guides
  • Proving Ground

Demonstration Final Reports and Annual Reports

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ABI Bands Quick Information Guides

  • http://www.goes-r.gov/education/ABI-bands-quick-info.html
  • Reference Guide for Forecasters

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Proving Ground Blogs

http://cimss.ssec.wisc.edu/goes/b log/ http://rammb.cira.colostate.edu/r esearch/goes- r/proving_ground/blog/ https://satelliteliaisonblog.wordpr ess.com/ http://fusedfog.ssec.wisc.edu/ http://goesrawt.blogspot.com/ http://goesrhwt.blogspot.com/ https://nasasport.wordpress.com/

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Summary

  • GOES-R Launch Nov 2016

– Most significant improvement to United States geostationary weather satellites in over 20 years. – ABI will significantly improve upon current Imager

  • Spatial, spectral, temporal, calibration

– GLM will provide total lightning detection – Training development well underway

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Thank you!

For more information visit www.goes-r.gov www.facebook.com/GOESRsatellite www.youtube.com/user/ NOAASatellites twitter.com/NOAASatellites www.flickr.com/photos/ noaasatellites

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