5 th GOES Users’ Conference

1. 5th GOES Users’ Conference Bringing Environmental Benefits to a Society of Users Poster Preview Timothy J. Schmit NOAA/NESDIS/Satellite Applications and Research Advanced Satellite Products Branch (ASPB) Madison, WI January 23, 2008 Thanks to all the poster presenters!

2. JP1-03 Candidate approaches for the real-time generation of cloud properties from GOES-R ABI The GOES-R AWG Cloud Application Team + many others Andrew K. Heidinger, NOAA/NESDIS • 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.

3. JP1-05 5th GOES Users’ Conference: Development of the GOES-R AWG Product Processing System FrameworkWalter 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

4. JP1-06 GOES Re-Broadcast 5th GOES Users’ Conference: 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. Emergency Managers Weather Information Network Low-Rate Information Transmission System Data Collection System Search and Rescue

5. JP1-07 5th GOES Users’ Conference: 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?

6. JP1-08 Development of the GOES-R ABI OutgoingLongwave Radiation ProductHai-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 ‘ABI’ OLR 2005.10.01 Precise OLR diurnal variation information in every 15 min over full-disk at each 2km footprint. Acknowledgement Funded by GOES-R Risk Reduction and Algorithm Working Group. ABI Proxy data – Tong Zhu. 5th GOES Users’ Conference

7. JP1-11 5th GOES Users’ Conference: GOES-R Wind Retrieval Algorithm DevelopmentIliana Genkova, Steve Wanzong, Christopher Velden, David Santek, Erik Olson, Jason OtkinCIMSS/SSEC/University of Wisconsin - MadisonJaime Daniels - NOAA/NESDIS Wayne Bresky - IMSG Upper-Level Low-Mid Level 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. Acknowledgement: This research is funded by the NOAA/NESDIS GOES-R Risk Reduction and the GOES-R AWG.

8. JP1-12 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] }

9. JP1-13 5th GOES Users’ ConferenceValidation of the Community Radiative Transfer Model (CRTM) against AVHRR Clear-Sky Processor for Oceans (ACSPO) Radiances for improved cloud detection and physical SST retrievalsXingMing 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.

10. JP1-16 5th GOES Users’ Conference: The Global Space-based Inter-Calibration System (GSICS) Xiangqian Wu and Mitch Goldberg GSICS is WMO-sponsored international collaboration to enhance satellite instrument calibration and satellite data validation. ~3 K ~0 K 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.

11. JP1-17 5th GOES Users’ Conference: Inter-Calibration of Geostationary Imagers with MetOp/IASI Hyperspectral MeasurementsLikun 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.

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13. JP1-19 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

14. JP1-20 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 Large-scale WRF Model SimulationsUsedfor GOES-R Research ActivitiesJason Otkin, A. Huang, T. Greenwald, E. Olson, and J. SieglaffSSEC/CIMSS/University of Wisconsin-Madison

15. JP1-21 5th GOES-R Users’ Conference LUnar Spectral Irradiance and radiance (LUSI): Instrumentation to characterize the moon as an SI-traceable radiometric standard Allan W. Smith, Steven R. Lorentz, Thomas C. Stone, Howard Yoon, Raju V. Datla, Dave Pollock, and Joe Tansock • 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.

16. JP1-22 MTSAT-2 5th GOES Users’ Conference: 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 introducedinto estimating the visible channel sensitivity of GMS, GOES, and METEOSAT from 1978 to 2006.

17. JP1-23 5th GOES Users' Conference Synthesis of Angular Distribution Models (ADMs) for use in RadiativeFlux 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. 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 Anisotropic Factor at SZA 63.2°over desert for clear sky (a): simulation; (b): Bright Desert (CERES); (c) Dark Desert (CERES)

18. JP1-24 5th GOES Users' Conference Use of SEVIRI cloud properties to simulate SW fluxes from GOES-R ABIR. T. Pinker, R. Hollmann, H. Wangand 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 for optimal utilization of ABI information. • Will facilitate evaluation of cloud products from ABI.

19. JP1-25 REGISTRATION ACCURACY OF 56 µRad (2 km) 2 The tracking error lower limit (TELL) parameter which relates the INR errors to AMV errors TRACKING ERROR LOWER LIMIT 4 1 3 Lower bounds on AMV accuracy is proportional the feature movement uncertainty and inversely proportional to the image separation time. 15 MINUTE IMAGE SEPERATION IMAGE SEPERATION DATE / TIME IMAGE RESOLUTION 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. 5th GOES Users’ Conference: Effect of GOES-R Image Navigation Errors on Atmospheric Motion Vectors Gary Jedlovec • 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)  = (R + /2) / 

20. JP1-26 GOES-13 End-to-End INR Performance Verification and Post Launch TestingChristopher Carson, Chetan Sayal, James L. Carr GOES 13 INR Verification GOES 13 Navigation Performance GOES-13 INR improved over 100% versus GOES-12 GOES 12 Navigation Performance

21. JP1-30 5th GOES-R Users’ Conference:GOES-R Proxy Data Management SystemTong 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. GOES-R AWG Proxy Data Users:

22. JP1-31 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

23. JP1-32 5th GOES Users’ Conference: Multi-spectral Precipitation Estimation Using Artificial Neural Networks Ali Behrangi, Kuo-lin Hsu, Soroosh Sorooshian, and Bob Kuligowski 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.

24. JP1-33 Nowcast Using IR Window TB, Band Differencing, Cloud Top Cooling Rate, and NWP Stability Fields 5th GOES Users’ Conference: Improving Diagnosis and Nowcasting of Convective Storms Using MSG SEVIRI, MODIS, and GOES-12 for ABI Risk ReductionKristopher 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 Radar Reflectivity at Nowcast Time Nowcast Using Only IR Window TB and Band Differencing Criteria Radar Reflectivity 40 Mins Later FALSE ALARMS Objective cloud-top cooling rate + stability information is essential for nowcasting new convective storm initiation

25. JP1-35 5th GOES Users’ Conference: Proxy ABI datasets relevant for firedetectionthat are derived from MODIS dataScott 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) Remap and use ABI point spread function ABI Navigation MODIS imagery Simulated ABI imagery

26. JP1-36 Validating GOES Active Fire Detection Product Using ASTER and ETM+ Wilfrid Schroeder, Ivan Csiszar, Elaine Prins, Chris Schmidt & Mark Ruminski 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 ETM+ 30 m (RGB 7-5-2) @ 1344 UTC WFABBA GOES 08 @ 1345 UTC Date/loc: 26 Sep 2002 11.09S 52.62W ASTER 30 m (RGB 8-3-1) @ 1409 UTC WFABBA GOES 08 @ 1345 UTC Date/loc: 05 Oct 2002 13.33S 56.48W False Positive True Positive

27. JP1-38 5th GOES Users ConferencePoster– Quantifying Uncertainties in Fire Size and Temperature Measured by GOES-R ABIManajit Sengupta, Louie Grasso, Don Hillger, Renate Brummerand 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. 3.9 µm Point spread function Brightness temperature distribution AMS 2008

28. JP1-39 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

29. JP1-40 5th GOES Users’ Conference: Trade-off studies on future GOES hyperspectral infrared sounding instrumentJinlong 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

30. JP1-42 5th GOES Users’ Conference: Hyperspectral Infrared Alone Cloudy Sounding Algorithm DevelopmentElisabeth 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.

31. JP1-43 5th GOES Users’ Conference: Improved GOES Water Vapor Products over CONUS – Planning for GOES-RDan 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

32. JP1-47 5th GOES Users’ Conference: Overview of GOES-RAnalysis Facility for Instrument Impacts on Requirements (GRAFIIR) Planned Activities and Recent Progress Allen Huang, CIMSS/UW-Madison & Mitch Goldberg, STAR/NESDIS/NOAA 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.

33. JP1-48 5th GOES Users’ Conference: 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. ESA/NASA SOHO EIT U. Of Michigan (Gombosi et al.)

34. JP1-49 5th GOES Users’ Conference: Space Weather Products from the GOES-R MagnetometerPaul 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.)

35. JP1-50 Improving Space Weather ForecastsUsing Solar Coronagraph DataS.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’ Conference New Orleans, LA

36. 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 JP1-51 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.

37. JP1-53 Image #2 Image #1 <placeholder> <placeholder> Enhancing the Geostationary Lightning Mapper for Improved Performance 5th GOES Users’ Conference: 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.

38. JP1-54 All required technology elements developed & tested North American Monsoon Compact receivers Northeast Winter Storms & Extratropical Cyclones North Atlantic Hurricanes Great Plains MCS Tornados Florida Diurnal Storms Low-power MMICs Innovative array layout East Pacific Hurricanes • Correlator: • Efficient • Redundant • OK for ASICs First images at 50 GHz by aperture synthesis Feedhorns: Low mutual coupling LO phase switching system: Ultrastable operation 5th GOES Users’ Conference: 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 Observational focus on hurricanes & severe storms GeoSTAR technology development well under way

39. 5th GOES Users’ ConferenceComparison of GOES Cloud Classification AlgorithmsEmploying Explicit and Implicit PhysicsRichard L. Bankert, Cristian Mitrescu, Steven D. Miller, Robert H. Wade JP1-58 Pixel-to-pixel comparison of two GOES-11 cloud classifiers Implicit Physics • Supervised learning • Expert labeled samples • Feature representation • 1-nearest neighbor • Classical cloud classes Validate each other - Establish confidence - Confirm limitations - Expose problem areas Analyze disagreements - Establish reasons - Produce combined classifier output Enhance development of future classifiers Explicit Physics • Cloud mask • Series of tests • Channel thresholding • Phase types

40. JP1-59 5th GOES Users’ Conference:Estimation of Sea and Lake Ice Characteristics with GOES-R ABIXuanji Wang, Jeff Key, Yinghui Liu, William Straka III 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). MODIS Aqua true color image (left) on March 31, 2006 over Kara Sea, and derived surface skin temperature (middle), and ice concentration (right). 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. MODIS true color image over Caspian Sea on January 27st, 2006 An example of ice motion over the Arctic from MODIS on May 7, 2007. This poster does not reflect the views or policy of the GOES-R Program Office.

41. JP1-61 5th GOES Users’ Conference:Operational GOES-SST and MSG-SEVIRI Productsfor GOES-R Risk ReductionEileen Maturi, Andy Harris, Jonathan Mittaz, John Sapper METHODOLOGY The careful analysis of both proxy data sets from the operational GOES-Imager and the MSG-SEVIRI combined 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 instrument parameters. GOES-E MSG MT-SAT GOES-W NOAA/NESDIS GOES-SST Operational Product Coverage

42. JP1-62 5th GOES Users’ Conference: Overview of the NESDIS heritage AVHRR Sea Surface Temperature Calibration/Validation System Dilkushi de Alwis, Alexander Ignatov, John Sapper, Prasanjit Dash, William Pichel, Yury Kihai, Xiaofeng Li Introduction NOAA 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 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. 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 to0.47 during the night time.

43. 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 JP1-65 5th GOES-R Users Conference:Poster – Status Update from the GOES-R Hydrology Algorithm TeamR. J. Kuligowski AMS 2008

44. JP1-68 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 algorithms IR Observed Simulated Visible Observed Simulated

45. JP1-69 5th GOES Users’ Conference: 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.

46. JP1-71 5th GOES Users’ Conference: NearCasting Convective Destabilization using Objective Tools that Optimize the Impact of Sequences of GOES Moisture Products Ralph A. Petersen & Robert Aune, CIMSS University 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). 3 Hr Nowcast Convectively Stable 5 Hr Nowcast Unstable

47. JP1-72 Comparison of Satellite-Based (pre) Nowcasting Algorithms over the New York City Area Grad Students: Mr. Bernard.Mhando & Ms. Nasim Nourozi,Department of Civil Engineering, City College of New York at CUNY Supervisors: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: brianvh@ce.ccny.cuny.edu RDTHydro-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.

48. JP1-73 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 Valliappa.Lakshmanan@noaa.gov

49. JP1-74 Key: = Hours 0 100 200 300 400 500 600 700 Yearly Imaging AvailabilityGOES Series Satellites Larry Urner (larry.urner@ngc.com), Jose Castellon, Mark Hanson, Scott Sawyer Yearly per Satellite “Planned” Imaging Outage (Hours) GOES Series Satellites • 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 2 Hours 1 GOES-R 224 Hours 2 GOES N-P 588 Hours 3 GOES I-M GOES_R spacecraft functional and performance specification FPS 417-R-SPEC-0014 Performance specification for the Geostationary Operational Environmental Satellite GOES - N, O, P, QAugust 26, 1997 Attachment B, S-415-22 Compiled from Operations Data GOES - 8, 10, 12 ImprovedGOES-R availability enhances NWS ability to warn of severe weather events This poster does not reflect the views or policy of the GOES-R Program Office.

50. JP1-75 5th GOES Users’ Conference: Determination of aircraft icing threat from satelliteWilliam L. Smith Jr., Patrick Minnis, Stephanie Houser, Doug Spangenburg, J. Kirk Ayers, Michele Nordeen Aircraft icing threat derived from GOES theoretically-based Tc, LWP and Re retrievals Nov. 16, 2006 1745 UTC Verified by Icing PIREPS Icing Threat from GOES-11,12 PIREPS Icing Intensity Multichannel-RGB