5 th GOES Users’ Conference

Poster Preview

Timothy J. Schmit

NOAA/NESDIS/Satellite Applications and Research

Advanced Satellite Products Branch (ASPB)

Madison, WI

January 23, 2008

Bringing Environmental Benefits to a Society of Users

Thanks to all the poster presenters!

5th GOES Users’ Conference

Candidate approaches for the real-time generation of cloud properties from GOES-R ABI

The GOES-R AWG Cloud Application Team + many others

• Prototype versions of the GOES-R AWG cloud algorithms exist.

• Candidate approaches are a blend of NOAA and NASA heritage plus some significant new science. We welcome all feedback.

• Our main tool for development and cal/val is the GEOstationary Cloud Algorithm Test-bed (GEOCAT).

• GEOCAT allows for multiple versions of the same algorithm to be run simultaneously to isolate algorithmic differences.

• GEOCAT is now running these prototype algorithms in near real-time on GOES and MSG as a demonstration.

•Algorithms implemented include those for cloud, fire, ozone and aviation products.

•GEOCAT is also running these algorithms on simulated ABI data for studying instrument performance impacts on algorithms.

JP1-03

Andrew K. Heidinger, NOAA/NESDIS

5th GOES Users’ Conference:

Development of the GOES-R AWG Product Processing System Framework

Walter Wolf, Lihang Zhou, P. Keehn, Q. Guo, S. Sampson, S. Qiu, and Mitchell D. Goldberg

● The GOES-R Algorithm Working Group (AWG) Product Processing System Framework is under development at NOAA/NESDIS/STAR

● The goal is to develop a processing system where GOES-R AWG algorithms can be developed and tested is a well defined and organized manner

● Details of the framework will be presented:● Framework description● Algorithm information and how it is applied to the framework● Input configuration files● Interface between the framework and the product algorithms● Hardware and software infrastructure

JP1-05


GOES-R Downlink Services for Users John Schimm, Larry Kincaid, Larry Urner, Eric Perez

The GOES-R series of satellites will continue to provide uplink and downlink services that users are familiar with, although with some changes.

User services addressed are GRB, EMWIN, LRIT, DCPR/DCPI, and SAR.

Similarities and changes to previous GOES Series satellites are highlighted.

GOES Re-Broadcast

Emergency ManagersWeather Information

Network

Search and Rescue

Data CollectionSystem

Low-Rate InformationTransmission System

JP1-06


GOES ABI Ground Processing Development System (GPDS)Jonathan P. Ormiston, Jon Blume, Joseph Ring, Jeff Yoder

ABI Level 0 to Level 1b in Real Time

Why is GPDS needed?• Implement ground processing algorithms• Real time performance

What does GPDS do?• INPUT: ABI instrument CCSDS data (Level 0)• Decompression• Calibration• Image Navigation and Registration• Resampling• OUTPUT: Images (Level 1b)

How does GPDS do it?

Hardware Software

11 computer nodes Implemented in C++

Dual Opteron Model 246, 2.0 GHz Utilizes open source libraries

4 GB Memory Platform independent

1 Gbps Ethernet (copper) Distributed processing application

Linux operating system (target platform)

JP1-07

Development of the GOES-R ABI OutgoingLongwave Radiation Product

Hai-Tien Lee(1), Istvan Laszlo(2) and Arnold Gruber(1)

(1)CICS/ESSIC-NOAA, University of Maryland College Park (2)NOAA/NESDIS/STAR & UMD/AOSC

A MESOSCALE OLR PRODUCT


Precise OLR diurnal variation information in every 15 min over full-disk at each 2km footprint.

AcknowledgementFunded by GOES-R Risk Reduction and Algorithm Working Group. ABI Proxy data – Tong Zhu.

‘AB

I’ O

LR 2

005.

10.0

1JP1-08


GOES-R Wind Retrieval Algorithm DevelopmentIliana Genkova, Steve Wanzong, Christopher Velden, David Santek, Erik Olson, Jason Otkin

CIMSS/SSEC/University of Wisconsin - MadisonJaime Daniels - NOAA/NESDIS Wayne Bresky - IMSG

Acknowledgement: This research is funded by the NOAA/NESDIS GOES-R Risk Reduction and the GOES-R AWG.

The NESDIS/CIMSS Atmospheric Motion Vector (AMV) processing code is being tested on the latest GOES-R AWG Proxy Team simulations. The CONUS domain simulation (shown above) contains 2 km spatial and 5, 15 and 30 minute temporal resolution imagery for all ABI bands. As part of the GOES-R Analysis Facility for Instrument Impacts on Requirements (GRAFIIR), the retrieval algorithm is also tested on altered data with variable-spec noise, navigation, calibration, and striping effects. Shown above is a representative AMV dataset retrieved from a set of unaltered IR and WV radiance imagery.

Upper-Level Low-Mid Level

JP1-11

5th GOES Users Conference: Validation of a GOES-R Broadband Shortwave Surface Transmission and TOA Albedo Look-Up-Table method.

Fred G. Rose, Istvan Laszlo, Thomas Charlock, Qiang Fu

• Output of broadband surface shortwave transmission and top-of-atmosphere albedo based on a multivariate look-up-table to the Langley Fu-Liou code output is tested.

• Cloud, aerosol and bulk atmosphere inputs consistent with the CERES full radiative transfer CRS (Cloud Radiation Swath) product are used in validation against five years of multiple validation site data.

= f { sza, pw, o3,Surface[albedo, elevation], Cloud[ fract, tau, Re, hgt], Aerosol[tau, ssa] }

JP1-12

5th GOES Users’ Conference Validation of the Community Radiative Transfer Model (CRTM) against

AVHRR Clear-Sky Processor for Oceans (ACSPO) Radiances for improved cloud detection and physical SST retrievals

XingMing Liang, Alexander Ignatov, Yury Kihai, Andrew Heidinger, Yong Han , Yong Chen

• Introduction ACSPO includes implementation of

CRTM, with NCEP fields as input, and an improved cloud detection algorithm for exploring real-time physical SST retrievals. This work demonstrates the consistency between CRTM simulated brightness temperatures with nighttime top-of-atmosphere AVHRR BTs in three thermal bands (ch3B, ch4 and ch5) onboard Metop-A, and NOAA-16 through 18, based on ACSPO ver1.0.

• Summary Analysis of “CRTM-AVHRR” bias for

one week of data (Julian day 47 to 53, 2007) showed good cross-platform consistencies in three IR channels for all platforms, except the NOAA-16 AVHRR ch3B.

JP1-13


The Global Space-based Inter-Calibration System (GSICS)

Xiangqian Wu and Mitch Goldberg

MemberExecutive

PanelResearch

WGData WG

CMA N. Lu P. Zhang Z. Rong

CNES D. Renaut P. Henry

EUMETSAT J. Schmetz M. König V. Gätner*

JMA T. Kurino Y. Tahara T. Matsumoto

KMA M. Ou S. Chung

NOAA M. Goldberg* X. Wu* B. Barkstrom

WMOJ. Lafeuille (Secretary)

~3 K

~0 K

GSICS is WMO-sponsored international collaboration to enhance satellite instrument calibration and satellite data validation.

Built upon previous experiences, GSICS aims to first quantify the agreement between satellite measurements and, if warranted, to diagnose the possible cause of the difference.

JP1-16


Inter-Calibration of Geostationary Imagers with MetOp/IASI Hyperspectral Measurements

Likun Wang and Changyong Cao

Owing its high spectral-resolution nature and accurate spectral and radiometric radiance measurements, IASI potentially can serve as a baseline to independently verify the future Advanced Baseline Imager (ABI) flown on the GOES-R satellite.

A study of IASI and GOES imager inter-calibration is presented in this study to demonstrate the methodology of using high-resolution spectral measurements to inter-calibrate broadband instruments.

JP1-17

JP1-18

5th GOES Users’ Conference: Synthetic GOES-R Imagery Development and Uses

Louie Grasso, Daniel Lindsey, Manajit Sengupta, and Mark DeMaria

GOES-R AWG PROXY DATA FOR MESOSCALE WEATHER EVENTS.

GOES-R AWG PROXY DATA FOR FIRE HOT SPOTS

JP1-19

Large-scale WRF Model SimulationsUsedfor GOES-R Research Activities

Jason Otkin, A. Huang, T. Greenwald, E. Olson, and J. SieglaffSSEC/CIMSS/University of Wisconsin-Madison

Weather Research and Forecasting (WRF) model used to generate physically realistic atmospheric profile datasets

Range of capabilities from medium-sized domains with fine spatial resolution (< 2-km) to full disk sized domains with lower resolution (< 6-km)

TOA radiances are calculated using the Successive Order of Interaction (SOI) forward radiative transfer model

Example proxy ABI 11.2 m brightness temperature image shown to the left

JP1-20

LUnar Spectral Irradiance and radiance (LUSI):Instrumentation to characterize the moon as an SI-traceable radiometric standard

Goals of LUSI• Reduce the uncertainty of predicting the absolute

lunar irradiance to 1% (k=1) – Ensures low uncertainty relative spectral scale

that is needed for a cross-platform reference– An absolute scale allows validation of

instrument performance and models used to deduce climate variables

• Increase the spectral resolution—320 nm to 2500 nm continuous with a resolution of approximately 0.3 %.

– continuous coverage allows SI-traceable calibration of all satellite instrument bands

– Reduces sampling/interpolation errors when comparing sensors with different spectral bands

• Measure the lunar radiance to facilitate calibration and characterization of high spatial resolution sensors

• Use Earth-based instrumentation deployable to high-altitude balloon platforms and high-altitude mountaintop observatories to mitigate the effects of the atmosphere. Such an instrument can be based on the latest technology and calibrated in a laboratory frequently unlike satellite sensors that must use space-qualified components and are difficult to retrieve.

Allan W. Smith, Steven R. Lorentz, Thomas C. Stone, Howard Yoon, Raju V. Datla, Dave Pollock, and Joe Tansock

5th GOES-R Users’ ConferenceJP1-21


Preliminary Study of Lunar Calibrationfor Geostationary Imagers

Seiichiro Kigawa, Kengo Miyaoka / Japan Meteorological Agency

MTSAT-2 Imager can capture lunar and attenuated solar images. The lunar images were calibrated by the solar images, and were introduced into estimating the visible channel sensitivity of GMS, GOES, and METEOSAT from 1978 to 2006. MTSAT-2

JP1-22

5th GOES Users' Conference Synthesis of Angular Distribution Models (ADMs) for use in Radiative

Flux Estimates from the Advanced Baseline Imager (ABI) Xiaolei Niu and R. T. Pinker

• Prepared up-to-date (ADMs) for use with ABI’s TOA shortwave broadband

fluxes (clear and cloudy conditions).

• Synthesized theoretical simulations and (CERES) models.

Anisotropic Factor at SZA 63.2°over desert for clear sky (a): simulation; (b): Bright

Desert (CERES); (c) Dark Desert (CERES)

Difference in monthly mean all sky surface SW

downward flux (W/m2) January 2000 using

ERBE and CERES ADMs for all sky implemented

with GOES-8

a b c

JP1-23

http://www.ametsoc.org/meet/annual/index.html

5th GOES Users' Conference Use of SEVIRI cloud properties to simulate SW fluxes from GOES-R ABI

R. T. Pinker, R. Hollmann, H. Wang and H. Gadhavi

• Surface radiative fluxes are estimated with cloud properties from METEOSAT-8 provided by EUMETSAT CM-SAF SEVIRI observations.

• Similar information will become available from ABI on GOES-R. Of interest to evaluate resulting radiative fluxes against ground observations and products of CM-SAF.

• Should help to provide guidelines foroptimal utilization of ABI information.

• Will facilitate evaluation of cloud products from ABI.

JP1-24


Effect of GOES-R Image Navigation Errors on Atmospheric Motion Vectors

Gary Jedlovec

15 MINUTE IMAGE SEPERATION

DATE / TIME

Typical GOES-12 image-to-image registration errors range 60-90 µRad –

3 values (2.2-3.3 km) - vary as function of time, week, and season.

= (R + /2) /

The tracking error lower limit (TELL) parameter which relates the INR errors to AMV errors

125250

5001000

20004000

60

15

4

0

1

10

100

IMAGE RESOLUTION

TRACKINGERRORLOWERLIMIT

IMAGESEPERATION

REGISTRATION ACCURACY

OF 56 µRad (2 km)

1

2

3

4

Lower bounds on AMV accuracy is proportional

the feature movement uncertainty and inversely proportional to the image

separation time.

GOES-12 image-to-image registration accuracy (~70µRad) contributes to about 3.3 ms-1 (1.6 ms-1 for visible data) error in AMVs.

Analysis of GOES-13 INR data shows substantial improvements over GOES-12 Anticipated nominal GOES-R registration accuracy of < 28 µRad should reduce error contribution to <1.0 ms-1 (<0.5 ms-1 for visible data)

JP1-25

GOES-13 End-to-End INR Performance Verification and Post Launch Testing

Christopher Carson, Chetan Sayal, James L. Carr

GOES-13 INR improved over 100% versus GOES-12

GOES 13 Navigation Performance

GOES 12 Navigation Performance

GOES 13 INR Verification

Spacecraft Attitude

SIADBus StearingJitter Control

Ground LoopOATSRPM

GTACS

InstrumentsImager

Sounder

Image Motion Compensation

Orbital ErrorThermal error

Performance Evaluation System (PES)Model based Matlab Simulation

System Functional TestSystem Design Verification and Validation Real-time Operations (BSE/OATS/GTACS)

Post Launch Test (PLT)System Design Verification and Validation

S/C Launch

Bus Activation & Calibration (ACT)SIAD

Star Tracker Fine AlignmentDMC

Payload Activation & Calibration (ACT)

IMC CharacterizationInst. Boresight Alignment

OAD

Systems Performance and Operations Testing (SPOT)INR normal mode specification testing, imaging during eclipse , yaw flip operations and system preparation and performance before and after all

thruster maneuver operations

ICSTIMC/DMC

S/C Optical TestingWFC2

CalibrationAlignment

Fixed Pattern Error

TestingAlgorithmsInterface

MST and ACS PAR

FSW Validation

JP1-26

5th GOES-R Users’ Conference: GOES-R Proxy Data Management System

Tong Zhu, Min-Jeong Kim, Fuzhong Weng, Mitch Goldberg,Allen Huang, Manajit Sengupta, Daniel Zhou, Zhanqing Li, and Ben Ruston

This poster will give an update of all proxy datasets produced by GOSE-R AWG Proxy Data Group with focusing on new data and activities.

The observation datasets include measurements from SEVIRI, GOES-08/10, MODIS, SURFRAD, AERONET, NAST-I, and HIRS-X.

The numerical models simulated datasets are from MM5, WRF, RAMS mesoscale models.

New proxy datasets include: NAST-I simulated ABI with co-incident radiosondes and dropsondes; SEVIRI data for AEROSE field campaigns with hourly rate in 94 days during 2004, 2006 and 2007; ABI Proxy Dataset of 11 hurricanes; RAMS Simulation of Severe Weather, Lake Effect Snow, Fire in Kansas; and four monthly emissivities for ABI in 2007 with 1-km resolution.

Aviation

Lightning

Clouds

Land

System Prime

Cal/ValAir QualityOceans

Cryosphere

Winds

Hydrology

RadiationSounding

Proxy Data

GOES-R AWG Proxy Data Users:

JP1-30

5th GOES-R Users’ Conference: Simulation of GOES Radiances for OSSE

Tong Zhu, Fuzhong Weng, Jack Woollen, Michiko Masutani, Steve Lord, Yucheng Song, Quanhua Liu, Sid Boukabara

In this poster, we will present some results of the simulation of GOES radiances based on OSSE nature run output and the evaluation against observations.

A case study will be performed to analysis ECMWF T511 natural run results. ABI instrument properties and geometry factors are simulated based on current GOES and MSG SEVIRI sensors.

The JCSDA Community Radiative Transfer Model (CRTM) is used to simulate ABI radiances with the natural run atmospheric profiles.

The simulated radiances are evaluated by comparing with MSG SEVIRI and current GOES observations.

CRTM simulated GOES-12 Imager with ECMWF T511 NR data

JP1-31

Multi-spectral information is processed for rainfall estimation using an Artificial Neural Network. The improved rainfall estimation is obtained from using multi-spectral bands rather than a single spectral band. The classification map of ANN provides additional insight into the rainfall and spectral feature relationship.


Multi-spectral Precipitation Estimation Using Artificial Neural Networks

Ali Behrangi, Kuo-lin Hsu, Soroosh Sorooshian, and Bob Kuligowski

JP1-32

5th GOES Users’ Conference: Improving Diagnosis and Nowcasting of Convective Storms Using

MSG SEVIRI, MODIS, and GOES-12 for ABI Risk Reduction Kristopher M. Bedka, Wayne F. Feltz, Justin Sieglaff, and John R. Mecikalski

MSG SEVIRI, MODIS, and rapid-scan GOES-12 imagery serve as useful proxy datasets to evaluate the future benefits provided by ABI for convection diagnosis and nowcasting

5-minute temporal resolution will provide improvement in cumulus cloud growth rate products to better recognize new convective initiation and exisitng thunderstorm intensification

Increased spectral coverage and spatial resolution will provide improved convective cloud identification and cloud-top microphysical retrievals

Nowcast Using IR Window TB, Band Differencing, Cloud Top Cooling Rate, and NWP Stability Fields

Nowcast Using Only IR Window TB and Band Differencing Criteria

Radar Reflectivity at Nowcast Time

Radar Reflectivity 40 Mins Later

FALSE ALARMSFALSE ALARMS

Objective cloud-top Objective cloud-top cooling rate + stability cooling rate + stability

information is essential information is essential for nowcasting new for nowcasting new

convective storm initiationconvective storm initiation

JP1-33

5th GOES Users’ Conference: Proxy ABI datasets relevant for fire

detectionthat are derived from MODIS data Scott S. Lindstrom, Christopher C. Schmidt, Elaine M. Prins, Jay Hoffman, Jason C. Brunner, and Timothy J. Schmit

Use simulated ABI navigation, raw MODIS data and the ABI point spread function to create simulated ABI imagery.

Simulated data are then used to test fire-detection software with ABI data in different fire regions (yellow squares, above)

ABI Navigation MODIS imagery

Remap and use ABI point spread function

Simulated ABI imagery

JP1-35

ETM+ 30 m (RGB 7-5-2) @ 1344 UTCWFABBA GOES 08 @ 1345 UTCDate/loc: 26 Sep 2002 11.09S 52.62W

ASTER 30 m (RGB 8-3-1) @ 1409 UTCWFABBA GOES 08 @ 1345 UTCDate/loc: 05 Oct 2002 13.33S 56.48W

Validating GOES Active Fire Detection Product Validating GOES Active Fire Detection Product Using ASTER and ETM+Using ASTER and ETM+

Wilfrid Schroeder, Ivan Csiszar, Elaine Prins, Chris Schmidt & Mark Ruminski

False Positive

True Positive

Apply 30 m resolution ASTER and ETM+ active fire masks to validate

WFABBA

Commission and omission errors are derived and compared to MODIS

Thermal Anomalies (MOD14) product

JP1-36

5th GOES Users ConferencePoster– Quantifying Uncertainties in Fire Size and Temperature Measured by

GOES-R ABIManajit Sengupta, Louie Grasso, Don Hillger, Renate Brummer and Mark DeMaria

• Create Point Spread function for GOES-R ABI at high resolution.

Extract information from low resolution tables provided by MIT Lincoln Labs/U Wisconsin.

Use the information as constraint to create high resolution normalized point spread function assuming bivariate normal distribution.

• Create brightness temperatures distributions to characterize fire uncertainty

Compute brightness temperatures for 3 GOES-R ABI channels using assumed atmospheric profile and varying fire temperature.

Create distributions of GOES-R ABI pixel brightness temperatures for varying sub-pixel fire location within a GOES-R ABI pixel keeping the fire size and temperature constant.

Analyze uncertainty based on the brightness temperature distributions for different fire sizes and temperatures.

AMS 2008

3.9 µm Point spread function

Brightness temperature distribution

JP1-38

5th GOES Users’ Conference: Quality Assurance of the GOES-R AWG Product Processing System

Lihang Zhou, Walter Wolf, S. Qiu, P. Keehn, Q. Guo, S. Sampson, and Mitchell D. Goldberg

● Quality assurance procedures have been developed for the algorithm development lifecycle, to assure the quality of the products and performance of the GOES-R AWG product processing system.

● Following aspects of quality assurance will be presented:● Algorithm verification and testing procedure● Coding Standards/Documentation Guidelines● System Monitoring● Products Monitoring and visualization● V&V datasets and technique

JP1-39


Trade-off studies on future GOES hyperspectral infrared sounding instrument

Jinlong Li, Jun Li, Timothy J. Schmit, and James J. Gurka

To optimize the geostationary hyperspectral IR sounding instrument that meets the users’ requirement, trade-off study has been carried out to address follow issues by using the fast forward radiative transfer model:

• Spectral coverage and spectral resolution• Spatial and temporal resolutions• Signal to noise ratio• Detector Optical Ensqaured Energy• Comparison with current GOES Sounder

JP1-40


Hyperspectral Infrared Alone Cloudy Sounding Algorithm Development

Elisabeth Weisz, Jun Li, Chian-Yi Liu, Daniel K. Zhou, Hung-Lung Huang, Mitchell D. Goldberg

• To prepare for the synergistic use of data from the high-temporal resolution ABI (Advanced Baseline Imager) on GOES-R and hyperspectral sounders on polar-orbiting satellites a retrieval algorithm has been developed to obtain sounding profiles under all weather conditions.

• The algorithm is applied to AIRS (Atmospheric Infrared Sounder) and IASI (Infrared Atmospheric Sounding Interferometer) data, and the results as well as validation with analysis fields, operational products and radiosonde observations are presented.

JP1-42


Improved GOES Water Vapor Products over CONUS – Planning for GOES-R

Dan Birkenheuer, Seth Gutman, Susan Sahm, and Kirk Holub

•GPS zenith derived integrated precipitable water is compared to traditional GOES TPW product data

•Bias error is characterized

•Bias corrections are demonstrated to be viable in real time

•GPS data is compared to GOES-R proxy ABI TPW product data generated from MODIS

•Results are favorable

JP1-43

5th GOES Users’ Conference: Overview of GOES-R Analysis Facility for Instrument Impacts on Requirements (GRAFIIR) Planned Activities and Recent Progress

GRAFIIR is a facility established to leverage existing capabilities and those under development for both current GOES and its successor in data processing and product evaluation to support GOES-R analysis of instruments impacts on meeting user and product requirements. GRAFIIR is to effectively adopt component algorithms toward analyzing the sensor measurements with different elements of sensor characteristic (i.e. noise, navigation, band to band co-registration, diffraction, etc.) and its impact on products.

Allen Huang, CIMSS/UW-Madison & Mitch Goldberg, STAR/NESDIS/NOAA

JP1-47


GOES-R Algorithm Working Group Space Weather Team Update

S. Hill, H. Singer, T. Onsager, R. Viereck, D. Biesecker, C. Balch, D. Wilkinson, M. Shouldis, P. Loto’aniu, J. Gannon, L. Mayer

In the past year, the Space Weather Application Team has inventoried existing algorithms, held an algorithm design review, created algorithm flowcharts, developed proxy data, and created a product validation approach.

This poster presents highlights of the past year’s progress, planned activities for the next year, and provides example maps for products from data source to product to customer.

U. Of Michigan (Gombosi et al.)ESA/NASA SOHO EIT

JP1-48


Space Weather Products from the GOES-R Magnetometer Paul T.M. Loto’aniu and Howard J. Singer

• Since their inception in the 1970's, the GOES satellites have monitored Earth’s highly variable magnetic field using magnetometers. The GOES-R magnetometer requirements are similar to the tri-axial fluxgates that have previously flown.

• Products for the GOES-R magnetometer will be an integral part of the NOAA space weather operations providing, for example, information on the general level of geomagnetic activity and permitting detection of magnetopause crossings and storm sudden commencements.

• Models of the Earth’s magnetosphere are dependent on GOES magnetometer data for validation and assimilation. The GOES-R space weather products will include the ability to compare measurements to quiet magnetic field models in near real-time.

3-D simulation of geomagnetic field (Gombosi et al.)

JP1-49

Improving Space Weather ForecastsUsing Solar Coronagraph Data

S.P. Plunkett, A. Vourlidas, D.R. McMullin, K. Battams, R.C. Colaninno

• Quantified CME morphology in coronagraph images and its impact on forecasts of geomagnetic activity.– Identified a metric to predict

shock arrival at Earth with 93% accuracy.

• Determined a set of rules for forecasters to use in selecting input data source for CME speed when using models to predict CME arrival times at Earth.

5th GOES Users’ ConferenceNew Orleans, LA

JP1-50

5th GOES Users’ Conference: GOES-N EUVS Observations During Post-

Launch TestingD. J. Strickland, J. S. Evans, W. K. Woo, D. R. McMullin, S. P.

Plunkett, and R. A. Viereck

Example at right of EUVS data from its five channels and comparisons with TIMED/SEE and SOHO/SEM data. Data will be shown for the months of Sep through Nov as well. EUVS Flare data will be presented from Dec 5 2006 observations.

There is agreement among sensors within their experimental uncertainties.

JP1-51

Enhancing the Geostationary Lightning Mapper for Improved Performance


David B. Johnson

The GLM specification calls for 10 km resolution at nadir, but earth curvature effects will significantly degrade the resolution of the instrument as you move away from nadir. This will significantly reduce the utility of the observations for many applications. Modifying the instrument optics using anamorphic imaging techniques can provide uniform resolution data over the full earth disk.

Image #1<placeholder>

Image #2<placeholder>

JP1-53


A Microwave Sounder for Geostationary Orbit Bjorn Lambrigtsen, JPL

“GeoSTAR” concept: AMSU-equivalent performance from GEO– Technology development under way at JPL/NASA; Further risk reduction planned for next 3 years– Proof-of-concept prototype built; adequate performance demonstrated

“PATH” GEO/MW mission is identified in NRC Decadal Survey– Perfect candidate for joint NASA-NOAA Research-to-Ops mission in 2014-18 time frame– NASA: Develop instrument — Both: Implement demo mission — NOAA: Operate system

GeoSTAR technology development well under wayObservational focus on hurricanes & severe storms

East Pacific Hurricanes

North Atlantic Hurricanes

Great Plains MCS

FloridaDiurnalStorms

Tornados

NorthAmericanMonsoon

NortheastWinter Storms

&Extratropical

Cyclones

Compact receivers

Low-power MMICs

LO phase switching system: Ultrastable operation

Correlator:• Efficient• Redundant• OK for ASICs

Feedhorns:Low mutual

coupling

Innovative array layout

All required technology elements developed & tested

First imagesat 50 GHz

by aperturesynthesis

JP1-54

5th GOES Users’ Conference Comparison of GOES Cloud Classification Algorithms

Employing Explicit and Implicit Physics Richard L. Bankert, Cristian Mitrescu, Steven D. Miller, Robert H. Wade

Pixel-to-pixel comparison of two GOES-11 cloud classifiers

Implicit Physics

Explicit Physics

Supervised learning Expert labeled samples Feature representation 1-nearest neighbor Classical cloud classes

Cloud mask Series of tests Channel thresholding Phase types

Validate each other - Establish confidence - Confirm limitations - Expose problem areas

Analyze disagreements - Establish reasons - Produce combined classifier output

Enhance development of future classifiers

JP1-58


Estimation of Sea and Lake Ice Characteristics with GOES-R ABI

Xuanji Wang, Jeff Key, Yinghui Liu, William Straka III

MODIS Aqua true color image (left) on March 31, 2006 over Kara Sea, and derived surface skin temperature (middle), and ice concentration (right).

Satellite retrieved sea ice thickness with AVHRR data (left) on March 12, 2004 at 04:00 LST for the entire Arctic region (left) and Hudson Bay area (right).

An example of ice motion over the Arctic from MODIS on May 7, 2007.

MODIS true color image over Caspian Sea on January 27st, 2006

The Cryosphere exists at all latitudes and in about one hundred countries. It has profound socio-economic value due to its role in water resources and its impact on transportation, fisheries, hunting, herding, and agriculture.

The Cryosphere not only plays a significant role in climate; its characterization and distribution are critical for accurate weather forecasts. A number of ice characterization algorithms have been improved and/or developed for GOES-R ABI, including ice identification, ice surface temperature, ice concentration, ice extent, ice thickness and age, and ice motion. Preliminary tests are promising, and we expect that accuracy specifications will be met for most of the Cryosphere products in the 2009-2010 timeframe.

ICE

Surface ice concentration (SIC) (%) retrieved from SEVIRI Data on the same date as the left image.

This poster does not reflect the views or policy of the GOES-R Program Office.

JP1-59


Operational GOES-SST and MSG-SEVIRI Productsfor GOES-R Risk Reduction

Eileen Maturi, Andy Harris, Jonathan Mittaz, John Sapper

METHODOLOGY

The careful analysis of both proxy data sets from the operational GOES-Imager and the MSG-SEVIRIcombined with simulated ABI data will be used. The findings will be extrapolated on the basis of Instrument and retrieval physics in order to build the best estimate model for the ABI performance. Tools will also be developed to quickly diagnose on-orbit problems in terms of physical instrumentparameters.

GOES-W GOES-E MSG MT-SAT

NOAA/NESDIS GOES-SST Operational Product Coverage

JP1-61

Dilkushi de Alwis, Alexander Ignatov, John Sapper, Prasanjit Dash, William Pichel,Yury Kihai , Xiaofeng Li

IntroductionNOAA satellites provide repetitive daily global coverage of the Earth. For over two decades, the National Environmental Satellite, Data, and Information Service (NESDIS) has been generating Sea Surface Temperature (SST) products operationally from the Advanced Very High Resolution Radiometer (AVHRR). Global AVHRR SSTs are merged with in-situ SSTs and organized into monthly match-up files, which are used to calibrate SST algorithms (early in satellite mission); and then routinely validate SST products (for the lifetime of a platform). Climatological Bauer-Robinson (1985) SSTs, and many other ancillary data from both satellite and in-situ files, are also available on the match-up datasets.

Calibration/Validation System Results :Validation

Overview of the NESDIS heritage AVHRR Sea Surface Temperature Calibration/Validation System

Day Night

µ

Shown above are the validation Bias (µ) and RMSE () for day time and night time data, after removing outliers based on median statistics. Resulting RMSE’s are from 0.38 to 0.52 during the day time and from 0.31 to 0.47 during the night time.


Independent match-up data are used to assess the accuracy of operational SST, by analyzing the global Bias and the Root Mean Squared Error (RMSE) of satellite SST minus In-situ SST, from 2001 till the present.

JP1-62

5th GOES-R Users Conference:Poster – Status Update from the GOES-R Hydrology Algorithm Team

R. J. Kuligowski

• GOES-R Algorithm Working Group (AWG) Hydrology Algorithm Team (AT)– Provide recommended, demonstrated, and validated algorithms for processing

GOES-R observations into• Probability of rainfall (0-3 h)• Rainfall potential (0-3 h)• Rainfall Rate / QPE

– Members from NOAA, NASA, ESSIC, UC-Irvine• Current Status

– Four rain rate estimation and three nowcasting algorithms were modified by the developers using SEVIRI data as an ABI proxy

– Developers provided evaluation fields to the Hydrology AT for 16 days in January, April, July, October 2005 over selected regions

– Intercomparison is underway; selection will be completed by end of February 2008

• Next Steps:– Derive the probability of rainfall algorithm from the selected rain rate and

nowcasting algorithms by calibrating against ground validation data– Integrate the source code from the selected algorithms into the AWG processing

framework, meeting requirements for code format and for internal and external documentation AMS 2008

JP1-65

Verifying Large-Scale High-Resolution Simulations of Clouds for GOES-R Activities

Tom Greenwald, Erik Olson, Justin Sieglaff, Hung-Lung Huang,Jason Otkin, Mat Gunshor

A system to generate ABI proxy data sets from WRF model

simulations is validated in cloudy areas using GOES-12 imager

data. These proxy data sets are important for testing cloud and

wind algorithmsObserved Simulated

IR

Observed Simulated

Visible

JP1-68


Retrieving Cloud Properties for MultilayeredClouds Using Simulated GOES-R Data

Fu-Lung Chang, Patrick Minnis, Bing Lin, Rabindra Palikonda, Mandana Khaiyer, Sunny Sun-Mack, Ping Yang

• This study presents a multi-spectral satellite retrieval algorithm for retrieving the multi-layered cloud properties.

• The retrievals are presented by applying to the satellite data available from GOES-12, -13, Meteosat-8, -9, and MODIS.

JP1-69

Ralph A. Petersen & Robert Aune, CIMSSUniversity of Wisconsin – Madison

• Increase the utility of current and future multi-layer GOES DPI moisture products

by:– Adding short-range predictive component

to satellite observations ( 1-6 hr projections )

• Use Lagrangian (trajectory) methods to retain all data and to get predictions to forecasters in real time

– Showing that frequently updated NearCasts of differential low- and mid-level moisture transport can detect areas becoming convective unstable 4-6 hrs in advance

– Validating GOES DPI NearCasts using observed convection and independent observations (e.g., GPS).

Convectively Stable

Unstable

3 Hr Nowcast

5 Hr Nowcast


NearCasting Convective Destabilization using Objective Tools that Optimize the Impact

of Sequences of GOES Moisture Products

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Comparison of Satellite-Based (pre) NowcastingComparison of Satellite-Based (pre) NowcastingAlgorithms over the New York City AreaAlgorithms over the New York City Area

Grad Students: Mr. Bernard.Mhando & Ms. Nasim Nourozi, Department of Civil Engineering, City College of New York at CUNYSupervisors:Dr. Brian Vant-Hull, Dr. Shayesteh Mahani, Dr. Arnold Gruber, and Dr. Reza Khanbilvardi, NOAA-CREST at the City College of New York of CUNY

E-Mail: [email protected]

RDT Hydro-Estimator Radar Rainfall

NOAA/CREST is assisting the GOES AWG in the selection of a operational thunderstorm nowcasting algorithm. Currently EeMETSET’s Rapidly Developing Thunderstorm (RDT) and NESDIS’ HydroNowcaster (HN) algorithms are under investigation. But the first step in nowcasting is to select the features to be extrapolated into the near future. HN uses rainfall output created by the Hydro-Estimator (HE) as the features of interest; RDT uses convective cells and then calculates trends. Since RDT does not actually perform a nowcast, it is best to compare the ‘pre-nowcast’ features used by each algorithm.

RDT: • Detects convective cells by temperature growth rates and spatial gradients at the cell peripheries. • Uses single channel thermal IR (BT) to track and characterize cells.HE: • Estimates rainfall based on BT thresholds modified by local BT statistics and NWF stability and water vapor. • Temporal information is not used.

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Nowcasting of Thunderstorms from GOESInfrared and Visible Imagery

Courtesy: Yang et. al (2006)

• Sidesteps problems inherent in other tracking methods

• As accurate at large scales as optical flow methods

• As accurate at small scales than object-tracking methods

• Does not have to deal with splits/merges

[email protected]

JP1-73

Yearly Imaging AvailabilityGOES Series Satellites

This poster does not reflect the views or policy of the GOES-R Program Office.

Larry Urner ([email protected]), Jose Castellon, Mark Hanson, Scott Sawyer

1. GOES_R spacecraft functional and performance specification FPS 417-R-SPEC-00142. Performance specification for the Geostationary Operational Environmental Satellite GOES - N, O, P, Q

August 26, 1997 Attachment B, S-415-223. Compiled from Operations Data GOES - 8, 10, 12

• The GOES-R series of Satellites annual “planned” imaging outages are orders of magnitude less than previous GOES series satellites

• This improvement in operational availability will enhance NWS ability to provide timely and accurate warning of potentially life-and-property threatening weather events such as thunder storms and tornados

Yearly per Satellite “Planned” Imaging Outage (Hours) GOES Series Satellites

Improved GOES-R availability enhances NWS ability to warn of severe weather eventsImproved GOES-R availability enhances NWS ability to warn of severe weather events

GOES-R

GOES N-P

GOES I-M

2 Hours 1

224 Hours 2

588 Hours 3

Key: = Hours

0 100 200 300 400 500 600 700

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mailto:[email protected]

5th GOES Users’ Conference: Determination of aircraft icing threat from satellite

William L. Smith Jr., Patrick Minnis, Stephanie Houser, Doug Spangenburg, J. Kirk Ayers, Michele Nordeen

Multichannel-RGB

Icing Threat from GOES-11,12 Verified by

Icing PIREPS

Nov. 16, 20061745 UTC

PIREPS Icing Intensity

Aircraft icing threat derived from GOES theoretically-based Tc, LWP and Re retrievals

JP1-75


Cloud statistics over agricultural and mixed forest areas Valentine Anantharaj, Udaysankar Nair, Denise Berendes, Salvi Asefi, and

Jonathan Fairman

The heterogeneous landscape pattern in the lower Mississippi river valley can be readily seen in satellite imagery due to the different vegetation and soil types. This results in differential heating and cooling.

Visible imagery from GOES-12 have been analyzed for 4 years. The convective cloud patterns are different across the agricultural and forested areas.

JP1-76

5th GOES Users’ Conference: Development of Severe Weather Products for the

GOES-R Advanced Baseline Imager Daniel T. Lindsey, Don Hillger, Louie Grasso

Simulated GOES-R ABI data is being used to develop severe weather products

One example is a boundary layer moisture depth product

Principal Component (PC) Analysis is applied to the simulated data to determine which spectral bands provide the most information about boundary layer water vapor

Figure: PC1 in a simulated domain in which moisture depth increases from west to east and temperature increases from north to south

JP1-78

5th GOES Users’ Conference: Algorithm and Software Development of

Atmospheric Motion Vector Products for the GOES-R ABI

Jaime M. Daniels1,Wayne Bresky2,Chris Velden3,Iliana Genkova3,Steve Wanzong3,and David Santek3

NOAA/NESDIS, Center for Satellite Applications and Research (STAR)1

IMSG, Inc.2

Cooperative Institute for Meteorological Satellite Studies (CIMSS)3

The GOES-R Algorithm Working Group (AWG) Winds team is working on development of algorithms and software for the generation of Atmospheric Motion Vectors (AMVs) from the GOES-R Advanced Baseline Imager (ABI) to be flown on the next generation of GOES satellites.

GOES-R AMV software development and testing is being done within a framework that supports a tiered algorithm processing approach. MSG/SEVERI imagery is serving as a primary GOES-R ABI proxy data source for the development, testing, and validation of the AMV algorithms. This poster will highlight the AMV algorithms and results from recent testing.

AMVs derived from a Meteosat-8 SEVERI image triplet centered at 1215Z on 04 August 2006

JP1-79

5th GOES Users’ Conference: An enhanced IDEA product with GOES AOD

Hai Zhang ([email protected]), R. M. Hoff, S. Kondragunta, I. Laszlo, and A. Wimmers

In this new product, the Aerosol Optical Depth imagery has beenenhanced with GOES Aerosol and Smoke Product (GASP), which has highertemporal resolution. The enhanced IDEA is designed to allow the GOES-R AOD to be directly substituted for GASP once GOES-R data is available. The GASP IDEA beta test site is at http://idea.umbc.edu/index_new.php and comments are welcome.

IDEA (Infusing satellite Data into Environmental Applications) was created through a NASA-EPA-NOAA cooperative effort and involves the near-real time dissemination of MODIS aerosol optical depth data and forecast of air quality.

JP1-80


An Initial Assessment of the GOES Microburst Windspeed Potential Index

Kenneth L. Pryor The GOES-sounder derived

microburst products diagnose risk based on conceptual models of prototype environmental profiles. The Microburst Windspeed Potential Index (MWPI) algorithm incorporates CAPE, the temperature lapse rate between 670 and 850 mb, and the dew point depression difference between the typical level of a convective cloud base at 670 mb and the sub-cloud layer at 850 mb. Initial validation during the 2007 convective season indicated a strong correlation between MWPI values and observed surface downburst wind gusts.

JP1-81

5th GOES Users’ Conference: GOES–R Applications for the Assessment of Aviation Hazards

Wayne Feltz, John Mecikalski, Mike Pavolonis, Kenneth Pryor, and Bill Smith

A suite of products has been developed and evaluated to assess meteorological hazards to aircraft in flight derived from the current generation of Geostationary Operational Environmental Satellite (GOES). The existing suite of products includes derived images to address seven major aviation hazards: fog, aircraft icing, microbursts, turbulence, volcanic ash, convective initiation, and enhanced-v and overshooting top detection. It is proposed to adapt the current suite of aviation product algorithms, with modifications and enhancements, for the GOES-R Advanced Baseline Imager (ABI).

JP1-82


Weather Information and Decision SystemsWeather Information and Decision Systems(WxIDS): Looking Into the Future of (WxIDS): Looking Into the Future of

Data Processing and Decision Support SystemsData Processing and Decision Support Systems Dylan Powell, John Dutton, Jeremy Ross, Jeff Sroga, Chung-Fu Chang, Rod Pickens,

Kyle Leesman, Shanna Pitter, George Young, Paul Knight, Nelson Seaman, Jon Nese4, Glenn Haselfeld, Robert Wessels, Mike Dhondt

Future Observing Systems will Provide:

• Improved spatial, spectral, and temporal resolution and coverage

• Increased data volume and data rates

WxIDS Forecast and Decision Process

•Automate the identification, analyses, and decision processes necessary for early warning of atmospheric events

•Assemble, prioritize and integrate the relevant data necessary to support the users decision processes

Substantial increases in

the amount of data delivered

to the end users

Example: Severe Convective Weather Prediction

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Fifth GOES Users’ Conference:

DEVELOPMENT OF SIMULATED GOES PRODUCTS FOR GFS AND NAM

Hui-Ya Chuang and Brad Ferrier

NCEP EMC started generating simulated GOES products operationally for GFS in Sep. 07 and for NAM in Nov. 07. This paper will describe the methodology used to derive these products and their qualitative validation against observations.

GOES 12 Ch 3 NAM analysis Ch 3 GFS analysis Ch 4

GOES 12 Ch 4 NAM analysis Ch 4 GFS analysis Ch 4

JP1-85


Current GOES Sounder applications and future needsJun Li, Timothy J. Schmit, James J. Gurka, Jaime Daniels, Mitch Goldberg, and W. Paul Menzel

The temperature and moisture profiles, along with derived lifted index (LI) product from the current GOES Sounder provide useful information for severe weather nowcasts. Ideally, future geostationary sounder improvements include: spatial coverage, vertical temperature and moisture information. AIRS and IASI are used for demonstrating the advantage of hyperspectral IR data in depicting the hurricane thermodynamic structure.

Forecast LI GOES Sounder LI

22 UTC on 24 April 2007

Tornado that killed 10 and injured 120 persons in the Eagle Pass, Texas area

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5th GOES User’s Conference:

GOES-R ABI Proxy Dataset Generation at CIMSSMathew M. Gunshor, E. Olson, J. Sieglaff, T. Greenwald, A. Huang, and J. Otkin

CIMSS/SSEC - UW-Madison

Simulated GOES-R Advanced Baseline Imager from June 04, 2005 at 22:00 UTC

JP1-87

GOES-R Mesoscale Product Development Renate Brummer, Bernie Connell, John F. Dostalek, Dusanka Zupanski

CIRA – Colorado State University, Fort Collins, COMark DeMaria and John A. Knaff

NOAA - NESDIS - Office of Research and Applications, Fort Collins, CO


Current Activities at CIRA

2) Mesoscale Weather Database

3) Synthetic GOES-R data generation and analysis

1) Prototype Hazard and Fire Products

6) Information content analysis using MLEF data assimilation

7) Training Activities

5) Severe Weather and Winter Weather Product Development

4) Tropical Cyclone Product Development

Goes-R at 10.35 μm

Hurricane Lili Oct02 Lake Effect Snow Feb03 P1.88

JP1-88


GOES-R/ABI legacy profile algorithm evaluation with MSG/SEVIRI Xin Jin, Jun Li, Timothy J. Schmit , Jinlong Li, Elisabeth Weisz, and Zhenglong Li

With the brightness temperatures measured by MSG/SEVIRI, along with the regressed profiles as first guess, the moisture profiles are significantly improved by physical retrieval but the temperature profiles are not. Relative to weather forecast, ABI/SEVIRI can provide full disk TPW and integrated water vapor products at three significant layers (WV1, WV2 and WV3) with higher temporal/spatial resolution and higher accuracy.

JP1-89

GOES

POES

5th GOES Users’ Conference: High Spatial and Temporal Resolution Retrievals Obtained from the Combination of GOES-R Multispectral ABI and Joint Polar Satellite Hyperspectral Radiances

W.L. Smith, S. Kireev, D.K. Zhou, M.D. Goldberg, and E. Maturi

JP1-90


Real-time Display of Simulated GOES-R (ABI) Experimental Products

Donald W. Hillger ([email protected])

Simulated GOES-R Advanced Baseline Imager (ABI) products developed at RAMMB/CIRA are being ported to the Web, as in the accompanying screen shot. http://rammb.cira.colostate.edu/ramsdis/online/goes-r.asp

Current products include a daytime fog/stratus product and a blowing dust product, both generated from ABI-equivalent MSG bands.

Future additions include other product variants, as in the image to the right, as well as experimental products for smoke from fires and volcanic ash.

JP1-91

http://rammb.cira.colostate.edu/ramsdis/online/goes-r.asp










Looking Ahead to GOES-R Space Weather Data Archive, Access, and User Services

Daniel C. Wilkinson and William F. Denig

The first of the GOES-R series of satellites is scheduled for launch in 2014 and will introduce four space weather monitoring instrument suites.

This poster will give an overview of the steps being taken in the area of archive, access and data services for the data collected from these instruments. Our goal is to engage the user community in this process so that the end result best reflects their needs.

JP1-93

5th GOES Users’ Conference:Remapping GOES Imager Instrument Data for South American

Operations, Implementing the XGOHI System Shahram Tehranian, James L. Carr, Shu Yang, Houria Madani, Subir Vasanth,

Robert DiRosario, Keith McKenzie, Tim Schmit, Anand Swaroop

GOES-10 remapped Imager data (band 1) recorded on October 2nd 2007GOES-10 Inclination

0

1

2

3

4

5

6

12/04 12/05 12/06 12/07 12/08 12/09 12/10 12/11

Date

Inclination (deg)

GOES-10 Saturated its IMC Dynamic Range in September 2007The orbital inclination of GOES-10 positioned at 60° W longitude has currently passed 2 degrees. There is no more fuel available to control the orbital inclination, so it will continue to increase at the rate of approximately 0.95 degrees per year

Why Resample for IMC?Allows extending GOES-10, GOES-11, and GOES-12 Imager INR Ops when orbital inclination exceeds 2 degrees No change in Sounder operations The first GOES I-M S/C to Operate in XGOHI mode is GOES-10Resampling implies that onboard IMC function will be performed on the groundResampling On is equivalent to onboard IMC On and allows to generate GVAR with “fixed grid”Fixed grid GVAR is “user-friendly” – no need to implement time-variant ELUG routines on user’s ingest systemsImages in the loop are stable – land & grids are stationary – only clouds are movingGVAR format adaptations for XGOHI are transparent to the users

As of October 2007, XGOHI is operational at Wallops Command and Data Acquisition Station (WCDAS) and the Wallops Backup Unit (WBU) at Goddard in Maryland

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GOES-10 @ 60 West – A Wisconsin PerspectiveTimothy J. Schmit, Jun Li, James P. Nelson III, Anthony J. Schreiner,

Gary S. Wade, and Zhenglong Li

GOES-10 routinely scans the southern hemisphere with both the Sounder and Imager instruments.

Many uses of the GOES-10 data stream through out the hemisphere.

CIMSS at University of Wisconsin-Madison is producing experimental Sounder products.

http://cimss.ssec.wisc.edu/goes/rt/goes10.php

GOES-10 Sounder Lifted Index (LI) Derived Product Image (DPI)

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5 th GOES Users’ Conference