GOES Sounder Hyper-spectral Environmental Suite (HES) Data from the HES will revolutionize short-term weather forecasting Impact on short-term weather.

Slides:

Advertisements

Similar presentations

World Wide Weather Briefing – Event Week 2013 Satellite Imagery Interpretation of Severe Thunderstorms in the USA. Dan Bikos Cooperative Institute for.

Advertisements

Stratus. Outline  Formation –Moisture trapped under inversion –Contact layer heating of fog –Fog induced stratus –Lake effect stratus/strato cu  Dissipation.

Forecasting convective outbreaks using thermodynamic diagrams. Anthony R. Lupo Atms 4310 / 7310 Lab 10.

The Utility of GOES-R and LEO Soundings for Hurricane Data Assimilation and Forecasting Jun Timothy J. Schmit #, Hui Liu &, Jinlong and Jing.

Convective Storm Forecasting 1-6 Hours Prior to Initiation Dan Lindsey (STAR/RAMMB) John Mecikalski (UAH) Chris Velden (CIMSS) Bob Rabin (NSSL) Brian Vant-Hull.

Danielle M. Kozlowski NASA USRP Intern. Background Motivation Forecasting convective weather is a challenge for operational forecasters Current numerical.

1 GOES Users’ Conference October 1, 2002 GOES Users’ Conference October 1, 2002 John (Jack) J. Kelly, Jr. National Weather Service Infusion of Satellite.

Microwave Imagery and Tropical Cyclones Satellite remote sensing important resource for monitoring TCs, especially in data sparse regions Passive microwave.

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.

Geostationary Lightning Mapper (GLM) 1 Near uniform spatial resolution of approximately 10 km. Coverage up to 52 deg latitude % flash detection day.

1 High impact weather nowcasting and short- range forecasting with advanced IR soundings Jun Tim Schmit &, Hui Liu #, Jinlong Jing

Improving Severe Weather Forecasting: Hyperspectral IR Data and Low-level Inversions Justin M. Sieglaff Cooperative Institute for Meteorological Satellite.

CORP Symposium Fort Collins, CO August 16, 2006 Session 3: NPOESS AND GOES-R Applications Tropical Cyclone Applications Ray Zehr, NESDIS / RAMM.

GOES-R Risk Reduction Status Dan Lindsey and Andy Heidinger Cooperative Research Program (CoRP) NOAA/NESDIS/STAR NOAA SATELLITE SCIENCE WEEK, 2015 February.

Introduction and Methodology Daniel T. Lindsey*, NOAA/NESDIS/STAR/RAMMB Louie Grasso, Cooperative Institute for Research in the Atmosphere

Aviation Seminars1 #3410. At approximately what altitude above the surface would the pilot expect the base of the cumuliform clouds if the surface air.

Assessment of the vertical exchange of heat, moisture, and momentum above a wildland fire using observations and mesoscale simulations Joseph J. Charney.

1 Tropical cyclone (TC) trajectory and storm precipitation forecast improvement using SFOV AIRS soundings Jun Tim Schmit &, Hui Liu #, Jinlong Li.

UNCLASSIFIED Navy Applications of GOES-R Richard Crout, PhD Naval Meteorology and Oceanography Command Satellite Programs Presented to 3rd GOES-R Conference.

Tropical Cyclone Applications of GOES-R Mark DeMaria and Ray Zehr NESDIS/ORA, Fort Collins, CO John Knaff CIRA/CSU, Fort Collins, CO Applications of Advanced.

1. HAZARDS  Wind shear  Turbulence  Icing  Lightning  Hail 3.

Lecture 2a Severe Thunderstorm Primer Synoptic Laboratory II – Mesoscale Professor Tripoli.

Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 STAR Science Support Science Support for VISIT and SHyMet Training Mark.

Polar Communications and Weather Mission Canadian Context and Benefits.

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 Comparison of Two Microwave Retrieval Schemes in the Vicinity of Tropical Storms Jack Dostalek Cooperative Institute for Research in the Atmosphere,

Jack Dostalek Louie Grasso Manajit Sengupta Mark DeMaria Synthetic GOES-R and NPOESS Imagery of Mesoscale Weather Events Overview This poster contains.

Hyperspectral Data Applications: Convection & Turbulence Overview: Application Research for MURI Atmospheric Boundary Layer Turbulence Convective Initiation.

The National Hurricane Center and Geostationary Sounders: Needs and Issues NATIONAL HURRICANE CENTER Jack Beven WHERE AMERICA’S CLIMATE AND WEATHER SERVICES.

Computing Deep-Tropospheric Vertical Wind Shear Analyses for TC Applications: Does the Methodology Matter? Christopher Velden and John Sears Univ. Wisconsin.

Hurricane Intensity Estimation from GOES-R Hyperspectral Environmental Suite Eye Sounding Fourth GOES-R Users’ Conference Mark DeMaria NESDIS/ORA-STAR,

Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Image: MODIS Land Group, NASA GSFC March 2000 Nearcasting Severe Convection.

Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Image: MODIS Land Group, NASA GSFC March 2000 Improving Hurricane Intensity.

1 Using water vapor measurements from hyperspectral advanced IR sounder (AIRS) for tropical cyclone forecast Jun Hui Liu #, Jinlong and Tim.

Hyperspectral Infrared Alone Cloudy Sounding Algorithm Development Objective and Summary To prepare for the synergistic use of data from the high-temporal.

Aviation Weather Forecaster Training Plans LeRoy Spayd NWS Office of Meteorology Chief, Science & Training Core Aviation Weather Forum July 26, 2000.

GOES-R: Bringing Critical New Capability to Automated Aviation Weather Products Paul H. Herzegh National Center for Atmospheric Research With acknowledgements.

Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 STAR Science Support Science Support for VISIT and SHyMet Training Mark.

High impact weather studies with advanced IR sounder data Jun Li Cooperative Institute for Meteorological Satellite Studies (CIMSS),

Preparing for GOES-R: old tools with new perspectives Bernadette Connell, CIRA CSU, Fort Collins, Colorado, USA ABSTRACT Creating.

Studies of Advanced Baseline Sounder (ABS) for Future GOES Jun Li + Timothy J. Allen Huang+ W. +CIMSS, UW-Madison.

Future GOES Satellite Product Upgrades Donald G. Gray Office of Systems Development NOAA/NESDIS, Washington, DC Satellite Direct Readout Users Conference.

Satellite based instability indices for very short range forecasting of convection Estelle de Coning South African Weather Service Contributions from Marianne.

The Hyperspectral Environmental Suite (HES) and Advanced Baseline Imager (ABI) will be flown on the next generation of NOAA Geostationary Operational Environmental.

Development of a Rapid Intensification Index for the Eastern Pacific Basin John Kaplan NOAA/AOML Hurricane Research Division Miami, FL and Mark DeMaria.

DIRECT READOUT APPLICATIONS USING ATOVS ANTHONY L. REALE NOAA/NESDIS OFFICE OF RESEARCH AND APPLICATIONS.

PRODUCTS Sea Ice Characteristics SSTs River Ice (Monitor/Detect) Snow Cover/Depth Surface Temperature and Moisture (Permafrost) Vertical Temp/Humidity.

Earth Observing Satellites Update John Murray, NASA Langley Research Center NASA Aviation Weather Satellites Last Year NASA’s AURA satellite, the chemistry.

Doppler Lidar Winds & Tropical Cyclones Frank D. Marks AOML/Hurricane Research Division 7 February 2007.

Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Image: MODIS Land Group, NASA GSFC March 2000 Weather and Water Theme Introduction.

Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Image: MODIS Land Group, NASA GSFC March 2000 Nearcasting Severe Convection.

Matthew Lagor Remote Sensing Stability Indices and Derived Product Imagery from the GOES Sounder

High impact weather nowcasting and short-range forecasting using advanced IR soundings Jun Li Cooperative Institute for Meteorological.

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,

4 th Workshop on Hyperspectral Science of UW-Madison MURI, GIFTS, and GOES-R Hyperspectral Applications for Aviation Advanced Satellite Aviation-weather.

Intelligent Use of LAPS By Ed Szoke 16 May 2001.

Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Combining GOES Observations with Other Data to Improve Severe Weather Forecasts.

Dynamic NearCasting of Severe Convection using Sequences of GOES Moisture Products - Applicability to NASA Aviation Program? - Ralph Petersen 1 and Robert.

S-244 Field Observer & Fire Effects Monitor ENVIRONMENTAL OBSERVATIONS.

Training Activities at CIRA National –VISIT and SHyMet Programs International –Partnership with RTCs in Costa Rica, Barbados, Argentina, and Brazil –WMO.

Training Session: Satellite Applications on Tropical Cyclones

NWS Forecast Office Assessment of GOES Sounder Atmospheric Instability

Geostationary Sounders

In the past thirty five years NOAA, with help from NASA, has established a remote sensing capability on polar and geostationary platforms that has proven.

Advanced Satellite Products Branch at the Cooperative Institute for Meteorological Satellite Studies (CIMSS) University of Wisconsin-Madison The Advanced.

A-J Punkka Weather Warning Service, FMI

Status of recent/near future VISIT/SHyMet as it relates to the Satellite Foundational Course for GOES-R (SatFC-G) NOAA Satellite Proving Ground / User.

UNSTABLE Science Question 1: ABL Processes

Rita Roberts and Jim Wilson National Center for Atmospheric Research

Advanced Satellite Products Branch at the Cooperative Institute for Meteorological Satellite Studies (CIMSS) University of Wisconsin-Madison The Advanced.

Presentation transcript:

GOES Sounder Hyper-spectral Environmental Suite (HES) Data from the HES will revolutionize short-term weather forecasting Impact on short-term weather forecasting will be comparable to inception of WSR-88D network The goal of this presentation is to justify why these two statements are true.

Comparison: HES versus current GOES sounder

VERTICAL SAMPLING FOR GOESR_HES FWD SKEWT 6 KM5 KM4 KM 3 KM2 KM1 KMGOES

Simulated CAPE HES loop - Severe Weather event - 8 May 2003

Simulated CIN HES loop - Severe Weather event - 8 May 2003

Using the CAPE and CIN fields from the HES, a forecaster can: Assess different air masses (unstable / stable) much more easily with the improved temporal / spatial resolution. Notice indications of convective initiation BEFORE the towering cumulus stage detectable on visible imagery. Provide a much better assessment of cap strength in answering the question “where will the cap break”? Observe regions that have been stabilized by storms, and help identify outflow boundaries more readily.

What about cloud obscuration preventing the retrievals? 50 km 10 km 30 km 4 km

The HES will result in a much improved: 3 dimensional “view” of temperature and moisture profile of the atmosphere. Operational derived products (i.e. winds). Climatology to better understand thresholds for various fields (i.e., anomaly fields). Initial analysis for NWP models. Fire weather forecast. Dispersion nowcasting / modeling (volcanic ash, pollutants, hazardous materials etc.)

Weather Forecasting Improvements Winter weather: Pre-storm environment Cyclogenesis Magnitude of moisture / temperature profiles in the vicinity of extra-tropical cyclones Rapid intensification of extra-tropical cyclones Lake-effect snow Temperature / wind chill (assuming no clouds) Clear air turbulence / Icing

Weather Forecasting Improvements Severe weather: Assessing the magntiude, depth and advection of moisture. Temperature information yields cap strength (inversion characteristics), lapse rates, magnitude of instability. 5 minute temporal resolution soundings would be well suited to convective forecasting, where signficant changes occur on short time scales.

Weather Forecasting Improvements Tropical weather: Track forecast improvements through better feature track winds Tropical cyclone (TC) intensity monitoring via eye soundings in relatively cloud-free eyes (MSLP) Improved temporal and spatial monitoring of environmental stability, and moisture Better understanding/monitoring of rapid intensification events Improved data assimilation potential for advanced modeling efforts leading to improved analyses and forecasts Improved genesis/formation monitoring and prediction Improved intensity diagnosis and prediction Improved structure diagnosis and prediction

Credits CIRA: Dan Bikos Louie Grasso Mark DeMaria John Knaff Jeff Braun Dan Lindsey CIMSS: Justin Sieglaff