Wayne Feltz. , Kaba Bah. , Kristopher Lee Cronce. , Jordan Gerth

Slides:



Advertisements
Similar presentations
SPoRT Products in Support of the GOES-R Proving Ground and NWS Forecast Operations Andrew Molthan NASA Short-term Prediction Research and Transition (SPoRT)
Advertisements

Future Radar and Satellite Technology Daniel C. Miller National Weather Service Columbia, SC.
Title: Applications of the AWG Cloud Height Algorithm (ACHA) Authors and AffiliationsAndrew Heidinger, NOAA/NESDIS/STAR Steve Wanzong, UW/CIMSS Topics:
Transitioning unique NASA data and research technologies to operations GOES-R Proving Ground Activities at the NASA Short-term Prediction Research and.
Thunderstorm Nowcasting at NOAA-CREST Presented by Brian Vant-Hull, Robert Rabin CREST team: Arnold Gruber, Shayesteh Mahani, Reza Khanbilvardi CREST Students:
A. FY12-13 GIMPAP Project Proposal Title Page version 18 October 2011 Title: Daytime Enhancement of UWCI/CTC Algorithm For Daytime Operation In Areas of.
The GOES-R Proving Ground 2010 Spring Experiment at NOAA’s Hazardous Weather Testbed and Storm Prediction Center Christopher W. Siewert 1,2, Kristin M.
Convective Initiation Studies at UW-CIMSS K. Bedka (SSAI/NASA LaRC), W. Feltz (UW-CIMSS), J. Sieglaff (UW-CIMSS), L. Cronce (UW-CIMSS) Objectives Develop.
Using McIDAS-V for Satellite-Based Thunderstorm Research and Product Development Kristopher Bedka UW-Madison, SSEC/CIMSS In Collaboration With: Tom Rink,
McIDAS-V Support for the GOES-R Program William Straka 1, Tom Rink 1, Tom Achtor 1, Tim Schmit 2, Kaba Bah 1, Joleen Feltz 1 1 CIMSS/SSEC, University of.
UW-CIMSS/UAH MSG SEVIRI Convection Diagnostic and Nowcasting Products Wayne F. Feltz 1, Kristopher M. Bedka 1, and John R. Mecikalski 2 1 Cooperative Institute.
Improving Severe Weather Forecasting: Hyperspectral IR Data and Low-level Inversions Justin M. Sieglaff Cooperative Institute for Meteorological Satellite.
GOES-R Proving Ground NOAA’s Hazardous Weather Testbed Chris Siewert GOES-R Proving Ground Liaison OU-CIMMS / Storm Prediction Center.
Hazardous Weather Testbed / Storm Prediction Center 2011 Spring Experiment Chris Siewert Proving Ground Liaison OU-CIMMS / SPC.
Motivation Many GOES products are not directly used in NWP but may help in diagnosing problems in forecasted fields. One example is the GOES cloud classification.
Introduction and Methodology Daniel T. Lindsey*, NOAA/NESDIS/STAR/RAMMB Louie Grasso, Cooperative Institute for Research in the Atmosphere
Charleston, SC Weather Forecast Office Frank Alsheimer Science and Operations Officer NWS Charleston, SC.
Overshooting Convective Cloud Top Detection A GOES-R Future Capability Product GOES-East (-8/-12/-13) OT Detections at Full Spatial and Temporal.
University of Wisconsin Convective Initiation (UWCI) Developed by Justin Sieglaff, Lee Cronce, Wayne Feltz CIMSS UW-M ADISON, M ADISON, WI Kris Bedka SSAI,
NWS Field Perspective of the GOES-R Proving Ground Jeff Craven, Marcia Cronce, and Steve Davis NOAA/NWS Milwaukee-Sullivan WI 7 th GOES Users’ Conference.
GOES-R Proving Ground GOES-R Proving Ground Demonstration of Imagery Products at NOAA’s Storm Prediction Center and Hazardous Weather Testbed Chris Siewert.
GOES-R Proving Ground Storm Prediction Center and Hazardous Weather Testbed Chris Siewert 1,2, Kristin Calhoun 1,3 and Geoffrey Stano OU-CIMMS, 2.
1 CIMSS Participation in the Development of a GOES-R Proving Ground Timothy J. Schmit NOAA/NESDIS/Satellite Applications and Research Advanced Satellite.
GOES-R ABI PROXY DATA SET GENERATION AT CIMSS Mathew M. Gunshor, Justin Sieglaff, Erik Olson, Thomas Greenwald, Jason Otkin, and Allen Huang Cooperative.
A. FY12-13 GIMPAP Project Proposal Title Page version 27 October 2011 Title: Probabilistic Nearcasting of Severe Convection Status: New Duration: 2 years.
NASA SPoRT’s Pseudo Geostationary Lightning Mapper (PGLM) GOES-R Science Week Meeting September, 2011 Huntsville, Alabama Geoffrey Stano ENSCO, Inc./NASA.
AWIPS Tracking Point Meteogram Tool Ken Sperow 1,2, Mamoudou Ba 1, and Chris Darden 3 1 NOAA/NWS, Office of Science and Technology, Meteorological Development.
1 CIMSS Participation GOES-R Proving Ground Status January 2011 UW-Madison Contributors to this presentation: Tim Schmit, Wayne Feltz, Jordan Gerth, Scott.
GOES–R Applications for the Assessment of Aviation Hazards Wayne Feltz, John Mecikalski, Mike Pavolonis, Kenneth Pryor, and Bill Smith 7. FOG AND LOW CLOUDS.
Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Image: MODIS Land Group, NASA GSFC March 2000 Nearcasting Severe Convection.
1 CIMSS/ASPB Participation GOES-R Proving Ground Status September 2011 UW-Madison Contributors to this presentation: Tim Schmit, Wayne Feltz, Jordan Gerth,
 Rapidly developing convection is a known source of CIT  Satellite derived cloud top infrared (IR) cooling rate, overshooting tops (OT)/enhanced-V and.
Evaluation of the Pseudo-GLM GLM Science Meeting Huntsville, Alabama September 2013 Geoffrey Stano – NASA SPoRT / ENSCO Inc. Kristin Calhoun – NOAA.
GOES-R Recommendations from past GOES Users’ Conference: Jim Gurka Tim Schmit Tom Renkevens NOAA/ NESDIS Tony Mostek NOAA/ NWS Dick Reynolds Short and.
NOAA Hazardous Weather Test Bed (SPC, OUN, NSSL) Objectives – Advance the science of weather forecasting and prediction of severe convective weather –
High impact weather studies with advanced IR sounder data Jun Li Cooperative Institute for Meteorological Satellite Studies (CIMSS),
Transitioning research data to the operational weather community Overview of GOES-R Proving Ground Activities at the Short-term Prediction Research and.
Modeling GOES-R µm brightness temperature differences above cold thunderstorm tops Introduction As the time for the launch of GOES-R approaches,
Using Simulated Satellite Imagery in NWS Experiments and Testbeds Justin Sieglaff Wayne Feltz Tim Schmit Jordan Gerth Cooperative Institute for Meteorological.
Hands-on exercise showcasing ABI’s 16 channels with improved spatial resolution and temporal refresh rate (plus Weighting Functions and RGB ABI examples)
Developers: John Walker, Chris Jewett, John Mecikalski, Lori Schultz Convective Initiation (CI) GOES-R Proxy Algorithm University of Alabama in Huntsville.
1 New Developments in GOES-12 and GOES-R Advanced Baseline Imager Convective Initiation Detection Wayne F. Feltz*, Kristopher Bedka^, Lee Cronce*, and.
Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Image: MODIS Land Group, NASA GSFC March 2000 Nearcasting Severe Convection.
Methodology n Step 1: Identify MOG (EDR ≥ 0.25) observations at cruising altitude (≥ FL250). n Step 2: Account for ascending/descending flights by filtering.
HWT Experimental Warning Program: History & Successes Darrel Kingfield (CIMMS) February 25–27, 2015 National Weather Center Norman, Oklahoma.
2012 NHC Proving Ground Products Hurricane Intensity Estimate (HIE) Chris Velden and Tim Olander 1.
High impact weather nowcasting and short-range forecasting using advanced IR soundings Jun Li Cooperative Institute for Meteorological.
Assimilating Cloudy Infrared Brightness Temperatures in High-Resolution Numerical Models Using Ensemble Data Assimilation Jason A. Otkin and Rebecca Cintineo.
Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Combining GOES Observations with Other Data to Improve Severe Weather Forecasts.
A. FY12-13 GIMPAP Project Proposal Title Page version 26 October 2011 Title: WRF Cloud and Moisture Verification with GOES Status: New GOES Utilization.
CIMSS Board of Directors Meeting 12 December 2003 Personnel: John Mecikalski (Principal Investigator) and Kristopher Bedka Objective: Develop methods to.
Australian VLab Centre of Excellence National Himawari-8 Training Campaign Forecaster use of Rapid Scan Data: Part A.
Hands-on exercise showcasing ABI’s 16 channels with improved spatial resolution and temporal refresh rate (plus RGB ABI examples) Mat Gunshor1, Tim.
GOES-R ABI and Himawari-8 AHI Training using SIFT
GOES-R ABI and Himawari-8 AHI Training using SIFT
User Preparation for new Satellite generations
GOES-R Proving Ground Status
USING GOES-R TO HELP MONITOR UPPER LEVEL SO2
GOES-R Risk Reduction Research on Satellite-Derived Overshooting Tops
Preparation for use of the GOES-R Advanced Baseline Imager (ABI)
NWS Forecast Office Assessment of GOES Sounder Atmospheric Instability
ABI Visible/Near-IR Bands
ASAP Convective Weather Analysis & Nowcasting
GOES-R Hyperspectral Environmental Suite (HES) Requirements
Geostationary Sounders
GOES-R Proving Ground Activities at SPoRT
Objective Overshooting Top and Cold V Detection
Promoting New Satellite Applications Within the AWIPS Environment
Visualization of Model Forecasts as Satellite Visible Imagery
Generation of Simulated GIFTS Datasets
Presentation transcript:

University of Wisconsin GOES-R Proving Ground Participation in NOAA HWT Wayne Feltz *, Kaba Bah*, Kristopher Bedka@, Lee Cronce*, Jordan Gerth*, Jack Kain#, Scott Lindstrom*, Jason Otkin*, Tim Schmit#, Justin Sieglaff*, Chris Siewert$, and Robert Rabin# *CIMSS University of Wisconsin-Madison, @SSAI NASA LaRC, #NOAA/NESDIS/STAR #National Severe Storms Laboratory, %NASA Langley Research Center, $University of Oklahoma - CIMMS P813 1. INTRODUCTION 3. WRF Derived Synthetic ABI Radiances 2. University of Wisconsin - CONVECTIVE INITIATION (See Sieglaff et al P9.4) UW-CIMSS provided real-time access to University of Wisconsin-Madison Convective Initiation (UWCI), GOES-R Overshooting-top enhanced-V proxy, and WRF simulated ABI radiance decision support products via N-AWIPS (Advanced Weather Information Processing System) to the Storm Prediction Center (SPC) as part of the Hazardous Weather Testbed (HWT) Spring 2010 Experiment. This poster overviews the products, training, and forecaster interaction during the NOAA HWT May 17 – June 18th 2010. A summary of GOES-R proxy decision support products provided by UW-CIMSS to NOAA HWT is contained within table below. Simulated GOES-R ABI imagery generated from the NSSL-WRF 00Z 4km model run was provided within the HWT N-AWIPS. UW-CIMSS provided simulated satellite data for all GOES-R ABI IR bands from the 12 Z through 03 Z forecast times. The UWCI and associated cloud-top cooling rate product has been delivered to the SPC since the 2009 Spring Experiment. The product is currently provided within SPC operations and was provided within the HWT via the EFP in N-AWIPS gridded format, and the EWP in AWIPS gridded format for the 2010 Spring Experiment. The product utilizes GOES-13 infrared (IR) window brightness temperature changes based on an operational day/night cloud mask to infer cloud-top cooling as a proxy for vertical development in growing cumulus clouds as described by Sieglaff et al. (2010). UWCI is generated at the University of Wisconsin for each GOES-13 scan, including rapid-scans, and distributed via LDM in GRIB2 format to AWIPS and N-AWIPS systems 1 1 1 “ABI” 21 UTC “ABI” 00 UTC Demonstration Product (contacts) Category PG Testbed Activity Cloud and Moisture Imagery (WRF ARW simulated) (Feltz/Schmit) Baseline SPC HWT and WES Overshooting-Top/Enhanced-V (Feltz) Option 2 SPC HWT, AWC GOES Imager Convective Initiation (Feltz) GIMPAP SPC HWT, NWS, DOD, SMG 2000 UTC 30 March 2005 GOES-13 21:15 UTC GOES-13 23:45 UTC SPC Storm Reports GOES-R Overshooting-top/ Enhanced-V Proxy (See Dworak et al. P9.6) The OTTC product is a new addition within the 2010 Spring Experiment. The product utilizes GOES-13 IR window brightness temperature spatial testing to identify overshooting-top and thermal couplet (also known as enhanced-V) features within mature convective storm cloud-tops as described by Bedka et al. (2010). The OTTC product provides detections and relative magnitudes of overshooting-top and thermal couplet features in real-time. Similar to the UWCI product, the OTTC product is generated at the University of Wisconsin for each GOES-13 scan, including rapid-scans, and distributed via LDM in GRIB2 format to AWIPS and N-AWIPS systems. NLDN CG Lightning UW-CIMSS NSSL-WRF simulated GOES-R ABI band 9 imagery (left) and GOES-13 water vapor imagery for 2300 UTC on 19 May 2010 show within N-AWIPS display. Forecaster Feedback: There is much excitement regarding the possibilities of making simulated satellite imagery readily available alongside all the traditional and other experimental model fields. There is also a strong recommendation for simulating GOES-R products and channel differences using the simulated satellite imagery as a decision ai In the future we expect to leverage other high resolution model runs, such as the High Resolution Rapid Refresh (HRRR) model, to better simulate the GOES-R ABI temporal and spatial resolutions. . References: Bedka, Kristopher; Brunner, Jason; Dworak, Richard; Feltz, Wayne; Otkin, Jason and Greenwald, Thomas. Objective satellite-based detection of overshooting tops using infrared window channel brightness temperature gradients. Journal of Applied Meteorology and Climatology, Volume 49, Issue 2, 2010, pp.181-202. Call Number: Reprint # 6245 Sieglaff, J., L. Cronce, K. Bedka, W. F. Feltz, K. M. Bedka, M. J. Pavolonis, and A. K. Heidinger, 2010. Nowcasting Convective Storm Initiation Using Satellite Based Box-averaged Cloud Top Cooling and Cloud Typing Trends. Jour. Appl. Meteor. and Clim., Accepted for publication, on-line format. 5. Acknowledgments Acknowledgments: The support of the research sponsor, the GOES-R Program Office is greatly appreciated. The authors would also like to thank Steve Goodman, Mitch Goldberg, and Greg Mandt for their support in this endeavor. 4-panel display within AWIPS of GOES-R products provided within EWP including 8-km Psuedo-GLM (top left), UWCI convective initiation (top right), UWCI cloud top cooling rate (bottom left), and overshooting-top magnitude (bottom right) for 24 May 2008 archive case event. Overshooting-top magnitudes overlaid on visible satellite imagery within AWIPS at 2131 UTC on 8 June 2010 Forecaster Feedback: In general, forecasters found that the UWCI products are a useful tool to help them increase situational awareness prior to warning operations during severe weather days. Forecasters also noticed lead-times on their subjective interpretation of convective initiation based on signals from radar generally of about 5 to 30 minutes. When comparing UWCI to the first occurrence of CG lightning detected by the NLDN, forecasters found that UWCI lead times extended, often to 60 minutes. More valuable at night Forecasters did mention some frustration with the temporal resolution of the product as provided from GOES-13, RSO mode optimal Product unavailable due to thin cirrus. Cloud top cooling preferred over convective initiation flag Forecaster Feedback: In general, while forecasters found the idea of the OTTC product exciting, the limitations of the current observational system severely limited the OTTC product as demonstrated in severe weather warning operations. The coarse horizontal IR resolution (4-km) of GOES-13 was often unable to detect overshoots, easily seen in visible imagery since they are generally smaller than the GOES-13 IR footprint. The temporal resolution of the current observational systems also limited the evaluation of the OTTC product in severe weather warning operations. . “ The OTTC product was most useful in indicating locations where storm strength was at a relative maximum… Quickly highlighting the strongest thunderstorms on the visible satellite imagery where it can be hard to distinguish storms due to similar brightness.” Corresponding author address: Wayne F. Feltz University of Wisconsin-Madison,1225 W. Dayton, Madison, WI 53706 E-mail: wayne.feltz@ssec.wisc.edu http://www.ssec.wisc.edu/~waynef