NOAA Center for Satellite Applications and Research banner
 
Intranet • Contact • Skip navigation
National Oceanographic & Atmospheric Administration website NOAA Center for Satellite Applications and Research website

Volume 2, Issue 1
January - March 2016


VIIRS Ice Surface Temperature Product Captures Extreme Arctic Warming Event

Arctic ice surface temperature (K) on 26 December 2015 (left) and 30 December (right).

Arctic ice surface temperature (K) on 26 December 2015 (left) and 30 December (right).
(click to enlarge)

On December 30, 2015, the central Arctic Ocean almost reached the melting point, an extreme warming event caused by the influx of warm air from the south due to "Storm Frank." The area near the North Pole was a similar temperature to Vienna and Chicago, and warmer than some parts of the U.S. Midwest. The temperature change from just a few days before was as high as 35° C in some areas. The enterprise VIIRS Ice Surface Temperature (IST) product captured this influx of warm air, as illustrated in the image and in a multi-day animation. The Washington Post published an article on the subject (among many others). (J. Key, E/RA2, 608-263-2605, jkey@ssec.wisc.edu; Y. Liu, CIMSS, 608-265-8620; R. Dworak, CIMSS, 608-265-8620)

Scientists Compare and Assess Uncertainty in Sea Surface Temperature Data Records

Averaged (1871-2005) total uncertainty (1σ) of (a) ERSST.v4, (b) HadSST3, and (c) COBE-SST2

Averaged (1871-2005) total uncertainty (1σ) of (a) ERSST.v4, (b) HadSST3, and (c) COBE-SST2
(click to enlarge)

A research paper by NOAA scientists and their university collaborators on the Extended Reconstructed Sea Surface Temperature (ERSST) Version 4 (v4) was published in the Journal of Climate. The team found that the global scale SST uncertainty is substantially smaller (0.03°-0.14°C) than that of the Hadley SST version 3 (HadSST3) and Centennial Observation-Based Estimates of SST version 2 (COBE-SST2), as shown in the image. This error arises primarily due to the parametric uncertainty (Up) results from using different parameter values in quality control, bias adjustments, and Empirical Orthogonal Teleconnection function definition, etc.

Huang, B., P. Thorne, T. Smith, W. Liu, J. Lawrimore, V. Banzon, H. Zhang, T. Peterson, and M. Menne, 2015, Further Exploring and Quantifying Uncertainties for Extended Reconstructed Sea Surface Temperature (ERSST) Version 4 (v4). J. Climate, DOI: 10.1175/JCLI-D-15-0430.1.

Snow Fall Rate Algorithm Captures East Coast Winter Storm 22-24 January 2016

Figure 1: ATMS and MHS SFR Performance Metrics When Compared With MRMS

Figure 1: ATMS and MHS SFR Performance Metrics When Compared With MRMS
(click to enlarge)

The Blizzard of 2016 swept through the Mid-Atlantic region on January 22-24, 2016 and produced record snowfall in many local areas. The NESDIS Snowfall Rate (SFR) product captured the evolution of the storm with five satellites including S-NPP (ATMS), NOAA-18, NOAA-19, Metop-A, and Metop-B (AMSU and MHS). Here is an SFR animation for the storm where snowfall rate between satellite overpasses is derived from weighted average of available swaths. The ATMS SFR product generally performed well over the legacy MHS SFR for the event with high correlation with Multi-Radar Multi-Sensor (MRMS) radar precipitation product (Figure 1).

 
Figure 2: Comparison of SFR (rows 1 and 3) and MRMS (rows 2 and 4) during the January snowstorm

Figure 2: Comparison of SFR (rows 1 and 3) and MRMS (rows 2 and 4) during the January snowstorm
(click to enlarge)

Comparison with radar also shows that SFR underestimated snowfall in the Washington, D.C. and surrounding areas early on January 23 due to strong convection in the region. This is a known issue with the current algorithm and is the focus of ongoing research supported by the JPSS PGRR Program. Figures 2 shows a comparison of SFR from individual passive microwave sensors on board NOAA 18, 19, Metop-B and S-NPP satellites with the Multi Radar/Multi Sensor (MRMS) observation system. These products were used by NWS Sterling Office (LWX) for the forecast of the snowstorm. Feedback from LWX indicated that the products were "Very Useful," and their impact was "Very Large" on the LWX forecast process. VIIRS imagery also captured the spatial extent of the snow cover (Figure 3).

 
Figure 3: Suomi NPP VIIRS true-color image showing the wide swath of fresh snow cover on 1/24/2016

Figure 3: Suomi NPP VIIRS true-color image showing the wide swath of fresh snow cover on 1/24/2016
(click to enlarge)

Kongoli, C., H. Meng, J. Dong, and R. Ferraro, 2015, A snowfall detection algorithm over land utilizing high-frequency passive microwave measurements - Application to ATMS. J. Geophys. Res. Atmos., 120, 1918–1932, DOI: 10.1002/2014JD022427.

Synthetic Alaska Imagery

Synthetic fog product. Frozen cloud fields are displayed as black while liquid cloud fields are displayed as blue.

Synthetic fog product. Frozen cloud fields are displayed as black while liquid cloud fields are displayed as blue.
(click to enlarge)

CIRA has been producing synthetic GOES-R ABI imagery for the Alaska NAM nest for over a year. Several of the GOES-R ABI bands - 3.9, 6.2, 6.95,734, 8.5, 10.35, and 12.3 μm - are displayed as individual channels. As a first step to aid forecasting in the Alaska region, a synthetic fog product - Tb(10.3)-Tb(3.9) - has been recently generated (see image). One main feature of this product is the distinction between liquid and frozen cloud fields. (D. Lindsey, E/RA1, L. Grasso, CIRA, 970-491-8446, Dan.Lindsey@noaa.gov, lewis.grasso@colostate.edu)

GOES-14 Imager and Sounder Data

The first week of the 2016 Geostationary Operational Environmental Satellite (GOES)-14 Super Rapid Scan Operations for GOES-R (SRSOR) imagery data collection campaign has been completed, which captured many interesting cases, including convective snow, severe thunderstorms, wildfires, blowing dust, and gravity waves. GOES-14 was re-activated in late January and began its SRSOR schedule on Monday, 1 February 2016. The primary purposes of the experiment are to support the VORTEX-Southeast field program and to collect 1-minute case studies in preparation for the ABI's regular mesoscale sector scans on the Advanced Baseline Imager (ABI). More information on the daily schedules and image center points is available here. CIRA collected the data at its ground station, converting to AWIPS-2 and NAWIPS formats, and is sending the data out via LDM to the National Weather Service. A large number of NWS offices and national centers are pulling in the data for use in operations and for evaluation. Real time loops can be found on RAMMB's webpage here. CIMSS is analyzing the mesoscale winds products that are being produced at 10-minute intervals with the atmospheric motion vector (AMV) algorithm. This project is in collaboration with the National Severe Storms Lab. Imagery is available here. Work is progressing on disseminating the wind product for evaluation and viewing in AWIPS2 workstations at NWS forecast offices and at the Hazardous Weather Testbed in Norman, OK.

Alaska Morphed Composite Cloud Products is Online

The online diagnostic viewer for the Alaska Cloud Product morphed composite is online. This is a major milestone of the PSDI project "Cloud Product Updates for NCEP and NWS-Alaska." The composite product uses real-time image morphing algorithms to create a seamless blend of polar-orbiter and geostationary retrievals by CLAVR-x (Clouds from AVHRR Extended) at 30-minute resolution up to the present time. The product can be viewed here.

Development of the Improved Algorithm for Displaying VIIRS TC Imagery

TC Ula examples of the high-resolution IR (left) and high-resolution visible (right) images

TC Ula examples of the high-resolution IR (left) and high-resolution visible (right) images
(click to enlarge)

As part of the JPSS-PGRR-C project, the real-time code for displaying global VIIRS TC-centered real-time imagery has been updated. The updates consist of bug fixes and additional capabilities. The software upgrade ensures that all available images are displayed online. The image shows an example of IR and DNB images for TC sh062016, Ula. The previous version of the code was unable to display that image.

SNPP VIIRS Cloud Climatology

Monthly mean ice cloud fraction from SNPP VIIRS. Jan 2016 occurred within the current El Niño event

Monthly mean ice cloud fraction from SNPP VIIRS. Jan 2016 occurred within the current El Niño event
(click to enlarge)

The majority of the NOAA Enterprise cloud algorithms have been run routinely at the Cooperative Institute for Meteorological Satellite Studies (CIMSS) since January 2015 to prepare for their operational implementation in the summer of 2016. The cloud team has been doing some preliminary climate-scale analysis of these products and is exploring the extension of NOAA Pathfinder Atmospheres Extended (PATMOS-x) cloud records with data from the Suomi National Polar-Orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS). The image shows the NOAA Enterprise ice cloud fraction for January 2015 and January 2016. January 2016 occurred during the current El Niño, which began in the middle of 2015. These images show the dramatic change in cloudiness during El Niño events. As these images show, the mass of Tropical ice cloud shifts from the Western Pacific to the Central Pacific. We hope to include SNPP cloud analysis in the upcoming Bulletin of the American Meteorological Society (BAMS) State of the Climate. (A. Heidinger, E/RA2, 608-263-6757, andrew.heidinger@noaa.gov, D. Botambekov, CIMSS, denis.botambekov@ssec.wisc.edu)

GOES-R ABI Visibility Product

Frequency reduced aerosol visibility values (red line) and frequency of WFABBA detected fires (blue line)

Frequency reduced aerosol visibility values (red line) and frequency of WFABBA detected fires (blue line)
(click to enlarge)

A manuscript entitled "Development and validation of satellite-based estimates of surface visibility" discusses a satellite-based surface visibility retrieval developed using Moderate Resolution Imaging Spectroradiometer (MODIS) measurements as proxies for Advanced Baseline Imager (ABI) data from the next generation Geostationary Operational Environmental Satellites (GOES-R). The manuscript demonstrates that the aerosol component of the GOES-R ABI visibility retrieval can be used to augment measurements from the United States Environmental Protection Agency (EPA) and National Park Service (NPS) Interagency Monitoring of Protected Visual Environments (IMPROVE) network and provide useful information to the regional planning offices responsible for developing mitigation strategies required under the EPA's Regional Haze Rule, particularly during regional haze events associated with smoke from wildfires. The image shows the frequency reduced aerosol visibility values (>20 deciview, dV) (red line plot) and frequency of Wildfire Automated Biomass Burning Algorithm (WFABBA) detected fires (blue line plot) by month in the United States for January 2010 through December 2013. A deciview is metric of haze proportional to the logarithm of the atmospheric extinction with values >20dV associated with visible haze. Reduced visibility over the United States occurs in May-September and is most frequent during years with significant wild fires (2011 and 2012). The quality of the ABI visibility retrieval is indicated by the shading with green being good (Heidke skill score ~0.3), medium (Heidke skill score ~0.2), and use with caution. (R.B. Pierce, E/RA2, 608-890-1892, brad.pierce@noaa.gov, J. Brunner, CIMSS, A. Lenzen CIMSS)

Brunner, J., R. B. Pierce, and A. Lenzen, 2016, Development and validation of satellite-based estimates of surface visibility. Atmos. Meas. Tech., 9, 409-422, DOI: 10.5194/amt-9-409-2016.

Routine True Color Imagery from Japan's AHI

True Color Imagery from the Himawari-8 Satellite, produced by the NOAA Viz Lab

True Color Imagery from the Himawari-8 Satellite, produced by the NOAA Viz Lab
(click to enlarge)

An improved version of the Simple Hybrid Contrast Stretch (SHCS) algorithm for generating true color imagery from Advanced Himawari Imager (AHI) data has been developed and delivered to the NOAA Environmental Visualization Laboratory (see image). They are now routinely posting these images to the web. 10-mintue full disk imagery. (T. Schmit, E/RA2, 608-263-0291, tim.j.schmit@noaa.gov; Y. Sumida, JMA)

Real-Time Satellite-Based Wind Radii Graphic

Example 34-knot wind radii graphic for TC Winston

Example 34-knot wind radii graphic for TC Winston
(click to enlarge)

CIRA and RAMMB have developed several satellite-based techniques to estimate the wind structure associated with tropical cyclones. To highlight these efforts, which are based on a combination GOES-R, GIMPAP, POES, and JPSS funded research, PSDI funded R20 transitions, and experimental products run at CIRA, real-time graphical products have been developed that show wind radii in geographic quadrants. The image shows an example of these products for the 34-knot (gale-force) wind radii for Tropical Cyclone Winston (SH112016). Shown are several operational microwave sounder based methods from NCEP (NOAA15, NOAA18, NOAA19, METOPA) from NSOF (MIRS-NOAA18, MIRS-NOAA19, MIRS-META, MIRS-METB), multi-platform methods from NSOF (MTCSWA-OPS) and CIRA/RAMMB (MTCSWA-EXP), and experimental IR-based methods that make use of the operational Dvorak intensity and center locations (DVRK-JTWC/PGTW, DVRK-TAFB, DVRK-SAB/KNES) based on Knaff et al. (2016). Similar plots are available for 50- and 64-knot wind radii and are available and archived in real time on the TC_Realtime web page. (J. Knaff, E/RA1, K. Micke, CIRA, 970-491-8446, John.Knaff@noaa.gov, Kevin.Micke@colostate.edu)

Synthetic Imagery Over Hawaii

Synthetic 10.4 μm image based on an 18-hour forecast of the NAM Hawaii Nest model (left), and the corresponding observed GOES-15 IR image

Synthetic 10.4 μm image based on an 18-hour forecast of the NAM Hawaii Nest model (left), and the corresponding observed GOES-15 IR image
(click to enlarge)

As part of a GOES-R Risk Reduction project, synthetic imagery is now being generated from the NAM Hawaii Nest Model (3 km grid spacing). An example is shown in the image. CIRA will distribute the imagery to the Honolulu NWS office for display in AWIPS-2. (D. Lindsey, E/RA1, L. Grasso, CIRA, 970-491-8446, Dan.Lindsey@noaa.gov, Lewis.Grasso@colostate.edu)

VIIRS Principal Component Images

RGB combination of PCIs created from VIIRS M-band imagery on 3 March 2016 at 11:00 UTC

RGB combination of PCIs created from VIIRS M-band imagery on 3 March 2016 at 11:00 UTC
(click to enlarge)

Software to implement a Principal Component Analysis (PCA) of the VIIRS I-band or M-band spectral information has been coded to run randomly, yet regularly, in order to look for spectral-band difference information that might reveal meteorologically-significant cloud or surface features not normally seen in single-band VIIRS imagery. The PCA output in image form has been coined "Principal Component Imagery (PCI)" in the published literature by this author. The first PCI software run, on a granule over Eastern Europe, yielded a large number of aircraft contrails in one of the higher-order PCIs. The image shown is a false-color RGB combination of 3 PCIs that were chosen to highlight the aircraft contrails in contrast to other cloud and surface features that normally appear in single-band visible and IR imagery. Aircraft contrails are featured in red, at high contrast to the underlying clouds in yellow and clear areas in green that would normally be the most-significant features in single-band imagery. There were only slight indications of the aircraft contrails in single-band VIIRS imagery. The VIIRS bands that contributed most significantly to this PCI are M14 (8.5 μm) and M16 (12.0 μm), as well as a few other bands with much less/lower contribution/weight to the image. PCIs can be used as a guide to creating new image products by using the bands that contributed most-significantly to features in the resulting PCI. This work is part of the exploratory mission of the VIIRS Imagery Team. (D. Hillger, E/RA1, 970-491-8446, Don.Hillger@noaa.gov)

Validation of Seasonal and Annual Precipitation Estimates from Satellites

Average conditional precipitation composites during 2010–14 for (a) SCaMPR, (b) 3B42RT, (c) GPI, and (d) Hydro-Estimator

Average conditional precipitation composites during 2010–14 for (a) SCaMPR, (b) 3B42RT, (c) GPI, and (d) Hydro-Estimator
(click to enlarge)

A manuscript was published in the Journal of Operational Meteorology titled "Seasonal and Annual Validation of Operational Satellite Precipitation Estimates." This manuscript originated as part of the Precipitation Cal/Val activities at SCSB/CICS-MD, and involved contributions from two UMD students. The study analyzed the performance of five satellite-derived precipitation products relative to ground-based gauge observations. The results (see image) characterized biases in satellite precipitation estimates to better inform the user community and help researchers improve future versions of their operational products. Average conditional composite maps result from summing the precipitation in each grid cell on days with >0.01 mm/day and dividing by the number of days when the satellite, gauge, and/or radar observed >0.01 mm/day.

Rudlosky, S. D., M. A. Nichols, P. C. Meyers, and D. F. Wheeler, 2016, Seasonal and annual validation of operational satellite precipitation estimates. J. Operational Meteor., 4 (5), 58–74, DOI: 10.15191/nwajom.2016.0405.

1-Minute Geostationary Satellite Data Use by Storm Prediction Center

1855 UTC 5/11/2014 GOES-14 visible-channel image from GOES-14. The arrow points an orphan anvil.

1855 UTC 5/11/2014 GOES-14 visible-channel image from GOES-14. The arrow points an orphan anvil.
(click to enlarge)

A paper has been published in the American Meteorological Society (AMS) Weather and Forecasting journal, entitled "Use of Geostationary Super Rapid Scan Satellite Imagery by the Storm Prediction Center [SPC]". NOAA/NWS/SPC forecasters utilized the Geostationary Operational Environmental Satellite (GOES)-14 1-minute imagery extensively in operations when available over convectively active regions. They found it (see image) provided them with unique insight into relevant features and processes before, during, and after convective initiation. This paper introduces how the 1-minute datasets from GOES-14 were used by SPC forecasters and how these data are likely to be applied when available operationally from GOES-R. More information about the data. (T. Schmit, E/RA2, 608-263-0291, tim.j.schmit@noaa.gov; D. Lindsey, RAMMB)

Line, W., T. Schmit, D. Lindsey and S. Goodman, 2016, Use of Geostationary Super Rapid Scan Satellite Imagery by the Storm Prediction Center [SPC]. Weather and Forecasting, 31:2, 483-494, DOI: 10.1175/WAF-D-15-0135.1.

Observing the Solar Eclipse Shadow Over the Pacific

Himawari-8 AHI true color image (Simple Hybrid Contrast Stretch) at 0150 UTC on March 9, 2016

Himawari-8 AHI true color image (Simple Hybrid Contrast Stretch) at 0150 UTC on March 9, 2016
(click to enlarge)

Several animations were built and posted from five geostationary imagers showcasing the solar eclipse shadow as it moved across the Pacific Ocean on March 9, 2016 (see image). The animation from Japan's Advanced Himawari Imager (AHI), due to the color and temporal resolution is especially spectacular. Feedback on this animation from Geostationary Operational Environmental Satellite (GOES)-R Education Proving Ground teachers included: "AWESOME! Thanks so much for sharing! My classes and I enjoyed it!" and, "I've never thought about the eclipse from that angle". More information can be found regarding this case on the Cooperative Institute for Meteorological Satellite Studies (CIMSS) Satellite Blog. Given the 10-min full disk temporal sampling by Himawari, the moon's shadow can easily be tracked across the Pacific in this animation. (T. Schmit, E/RA2, 608-263-0291, tim.j.schmit@noaa.gov; Y. Sumida, JMA; S. Bachmeier, CIMSS, 608-263-3958; D. Lindsey, E/RA1, 970-491-8446, Dan.Lindsey@noaa.gov; S. Lindstrom, CIMSS/SSEC, (608) 263-4425)

 

CICS-MD SBN Antenna Installed

NOAAPORT Satellite Broadcast Network (SBN) antenna

NOAAPORT Satellite Broadcast Network (SBN) antenna
(click to enlarge)

Following nearly three years of effort, a NOAAPORT Satellite Broadcast Network (SBN) antenna, receiver, and server have been installed at CICS-MD. The NOAAPORT will provide nearly identical feeds to those received at National Weather Service (NWS) offices, allowing CICS-MD to simulate operational environments for the first time.

This equipment is integral to the planned CICS-MD Proving Ground and Training Center, which will promote interactions between scientists, students, and forecasters. CICS-MD will help to develop and visualize existing and new products within the AWIPS software. Training also will be developed to accompany any products that we implement.

GOES-R Satellite Liaison Training with SIFT

The participants and instructors of the GOES-R workshop held in Kansas City

The participants and instructors of the GOES-R workshop held in Kansas City
(click to enlarge)

A GOES-R training workshop was held March 1-3, 2016 in Kansas City at NOAA's Aviation Weather Center (AWC). The workshop was held for the Geostationary Operational Environmental Satellite (GOES)-R liaisons and others and used the Satellite Information Familiarization Tool (SIFT) which was developed at the University of Wisconsin-Madison Space Science and Engineering Center (SSEC). The workshop consisted of both lecture and hands-on exercises. The comments from the participants were overall very positive (really liked, beneficial, great hands-on, excellent course, educational, really valuable, etc.). (T. Schmit, E/RA2, 608-263-0291, tim.j.schmit@noaa.gov; J. Gerth, CIMSS; S. Lindstrom, CIMSS)

Pavolonis Wins 2015 American Astronautical Society Earth Science and Applications Award

Michael Pavolonis with his award

Michael Pavolonis with his award
(click to enlarge)

Michael Pavolonis (NOAA/NESDIS/STAR) received the 2015 American Astronautical Society (AAS) Earth Science and Applications Award. The award, which was presented on March 9, 2016 at the 54th Robert H. Goddard Memorial Symposium in Greenbelt, MD, is for "developing cutting-edge methods to convert satellite data into actionable information for mitigating hazards caused by volcanic eruptions and severe convection." As conveyed in Dr. Pavolonis' acceptance remarks, extracting information from environmental satellites that helps identify and forecast natural hazards strongly supports NOAA's mission. As NOAA transitions to the next generation of environmental satellites in the near future, applied research will help ensure that those satellites play a significant role in protecting life and property. (J. Key, E/RA2, 608-263-2605, Jeff.Key@noaa.gov)

UW-Madison Space Science and Engineering Center Completes 50 Years!

A milestone year for the UW-Madison Space Science and Engineering Center (SSEC), 2015 marked our first half-century of research, discovery, and innovation. Since 1965, SSEC scientists have striven to provide the people of Wisconsin - and around the globe - with the best information about the Earth and its atmosphere. Selected stories provide a window into the continuing commitment by SSEC and the Cooperative Institute for Meteorological Satellite Studies (CIMSS) to the advancement of science through collaborative research, education, and public engagement are available here.

Highly Cited Paper Designation

As of July/August 2015, "An Objective Satellite-Based Tropical Cyclone Size Climatology" by J. Knaff, S. Longmore, and D. Molenar, which was published in J. Climate in January of 2014 is now considered a "highly cited paper" by Thomson Reuters' Web of Science. This means that it has received enough citations to place it in the top 1% of the academic field of Geosciences based on a highly cited threshold for the field and publication year. Since publication this paper has been cited 20 times. (J. Knaff, D. Molenar, E/RA1, S. Longmore, CIRA, 970-491-8446, John.Knaff@noaa.gov, Debra.Molenar@noaa.gov, Scott.Longmore@colostate.edu)

Second Asia Global Cryosphere Watch Workshop

Asian countries with representatives at the 2nd Asia CryoNet Workshop. Salekhard is indicated by the circled X

Asian countries with representatives at the 2nd Asia CryoNet Workshop. Salekhard is indicated by the circled X
(click to enlarge)

The World Meteorological Organization (WMO) Global Cryosphere Watch (GCW) program held its Second Asia CryoNet meeting in Salekhard, Russia, western Siberia, 2-5 February 2016. CryoNet is the core of the GCW surface observing network. The workshop was organized by the Arctic and Antarctic Research Institute (AARI) of Roshydromet and the Administration of the Yamal-Nenets Autonomous Okrug (YaNAO). There were three days of plenary meetings in the YaNAO Administration building and one day of field training on Ob River north of Salekhard. Jeff Key, the GCW Senior Science Advisor, attended and presented on recent accomplishments and the status of GCW. The main objective of the workshop was to develop practical aspects of the implementation of CryoNet in Asia based on the decisions of the Seventeenth World Meteorological Congress (25 May - 12 June 2015). This includes, among others, identifying new stations/sites that could become CryoNet or contributing sites in both terrestrial and marine environments in Asia, reviewing existing observing practices for cryospheric observations and examining advances in measurement techniques, and discussing data policies. There was considerable media coverage of the event, including Key and three of the other workshop organizers discussing the cryosphere on a local TV show. Media coverage is listed on the GCW website. The workshop was very productive, with more than 20 sites in the "Third Pole" (Himalaya) region and Siberia being proposed as new CryoNet sites. (J. Key, E/RA2, 608-263-2605, jkey@ssec.wisc.edu)

STAT meeting in Boulder 1-4 February 2016

The Satellite Training Advisory Team (STAT) met at COMET in Boulder on 1-4 February 2016. The goal of the meeting was to discuss the development of the Satellite Foundational Course for GOES-R (SatFC-G). This course will be delivered in October 2016 and will be required training for NWS forecasters. The STAT meeting included training developers from COMET, CIRA, CIMSS, CIMMS and OCLO. CIRA will be responsible for 17 training modules that are part of the SatFC-G course. The course will rely heavily on Himawari examples displayed in AWIPS-2 so as to emulate as close as possible what forecasters will see with GOES-R data. Discussion also included a future course on JPSS modeled after this course. The meeting was attended in person and remotely (due to a snowstorm) across 4 days by D. Bikos, B. Connell, E. Szoke and J. Torres. (D. Bikos, B. Connell, E. Szoke, J. Torres, CIRA, 970-491-8446, Dan.Bikos@colostate.edu, Bernie.Connell@colostate.edu, Edward.J.Szoke@noaa.gov, Jorel.Torres@colostate.edu)

 
image: tag cloud of research-related words

Anderson, D. C., Nicely, J. M., Salawitch, R. J., Canty, T. P., Dickerson, R. R., Hanisco, T. F., Wolfe, G. M., Apel, E. C., Atlas, E., Bannan, T., Bauguitte, S., Blake, N. J., Bresch, J. F., Campos, T. L., Carpenter, L. J., Cohen, M. D., Evans, M., Fernandez, R. P., Kahn, B. H., Kinnison, D. E., Hall, S. R., Harris, N. R. P., Hornbrook, R. S., Lamarque, J.-F., Le Breton, M., Lee, J. D., Percival, C., Pfister, L., Pierce, R. B., Riemer, D. D., Saiz-Lopez, A., Stunder, B. J. B., Thompson, A. M., Ullmann, K., Vaughan, A., & Weinheimer, A. J. (2016). A Pervasive Role for Biomass Burning in Tropical High Ozone/Low Water Structures. Nature Communications, 7. [10.1038/ncomms10267]

Banzon, V., Smith, T. M., Liu, C., & Hankins, W. (2016). A Long-Term Record of Blended Satellite and in Situ Sea Surface Temperature for Climate Monitoring, Modeling and Environmental Studies. Earth Syst. Sci. Data Discuss., 2016, 1-13. [10.5194/essd-2015-44]

Gladkova, I., Ignatov, A., Shahriar, F., Kihai, Y., Hillger, D., & Petrenko, B. (2016). Improved VIIRS and MODIS SST Imagery. Remote Sensing, 8(1), 79. [10.3390/rs8010079]

Glenn, E., Comarazamy, D., Gonzalez, J. E., & Smith, T. (2015). Detection of Recent Regional Sea Surface Temperature Warming in the Caribbean and Surrounding Region. Geophysical Research Letters, 42(16), 6785-6792. [10.1002/2015GL065002]

Greenwald, T. J., Pierce, R. B., Schaack, T., Otkin, J., Rogal, M., Bah, K., Lenzen, A., Nelson, J., Li, J., & Huang, H.-L. (2016). Real-Time Simulation of the GOES-R ABI for User Readiness and Product Evaluation. Bulletin of the American Meteorological Society, 97(2). [10.1175/bams-d-14-00007.1]

Hillger, D., Kopp, T., Seaman, C., Miller, S., Lindsey, D., Stevens, E., Solbrig, J., Straka, W., III, Kreller, M., Kuciauskas, A., & Terborg, A. (2016). User Validation of VIIRS Satellite Imagery. Remote Sensing, 8(1). [10.3390/rs8010011]

Holz, R. E., Platnick, S., Meyer, K., Vaughan, M., Heidinger, A., Yang, P., Wind, G., Dutcher, S., Ackerman, S., Amarasinghe, N., Nagle, F., & Wang, C. (2015). Resolving Ice Cloud Optical Thickness Biases between Caliop and MODIS Using Infrared Retrievals. [Discussion Paper]. Atmospheric Chemistry and Physics Discussions, 15(20), 29455-29495. [10.5194/acpd-15-29455-2015]

Karnauskas, K. B., Jenouvrier, S., Brown, C. W., & Murtugudde, R. (2015). Strong Sea Surface Cooling in the Eastern Equatorial Pacific and Implications for Galapagos Penguin Conservation. Geophysical Research Letters, 42(15), 6432-6437. [10.1002/2015gl064456]

Key, J., Wang, X., Liu, Y., Dworak, R., & Letterly, A. (2016). The AVHRR Polar Pathfinder Climate Data Records. Remote Sensing, 8(3), 167. [10.3390/rs8030167]

Knaff, J. A., Slocum, C. J., Musgrave, K. D., Sampson, C. R., & Strahl, B. R. (2016). Using Routinely Available Information to Estimate Tropical Cyclone Wind Structure. Monthly Weather Review, 144(4), 1233-1247. [10.1175/MWR-D-15-0267.1]

Letterly, A., Key, J., & Liu, Y. (2016). The Influence of Winter Cloud on Summer Sea Ice in the Arctic, 1983-2013. Journal of Geophysical Research: Atmospheres, 121(5), 2178-2187. [10.1002/2015jd024316]

Line, W. E., Schmit, T. J., Lindsey, D. T., & Goodman, S. J. (2016). Use of Geostationary Super Rapid Scan Satellite Imagery by the Storm Prediction Center. Weather and Forecasting, 31(2), 483-494. [10.1175/WAF-D-15-0135.1]

Meyers, P. C., & Ferraro, R. R. (2016). Precipitation from the Advanced Microwave Scanning Radiometer 2. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, PP(99), 1-8. [10.1109/JSTARS.2015.2513666]

Miller, S. D., Schmit, T. L., Seaman, C., Lindsey, D. T., Gunshor, M. M., Kohrs, R. A., Sumida, Y., & Hillger, D. (2016). A Sight for Sore Eyes—the Return of True Color to Geostationary Satellites. Bulletin of the American Meteorological Society, 0(0), null. [10.1175/BAMS-D-15-00154.1]

Rabin, R. M., Gultepe, I., Kuligowski, R. J., & Heidinger, A. K. (2015). Monitoring Snow Using Geostationary Satellite Retrievals During the Saawso Project. Pure and Applied Geophysics, 1-18. [10.1007/s00024-015-1195-6]

Rudlosky, S. D., Nichols, M. A., Meyers, P. C., & Wheeler, D. F. (2016). Seasonal and Annual Validation of Operational Satellite Precipitation Estimates. Journal of Operational Meteorology, 4(5), 58-74. [10.15191/nwajom.2016.0405]

Seidel, D. J., Li, J., Mears, C., Moradi, I., Nash, J., Randel, W. J., Saunders, R., Thompson, D. W. J., & Zou, C.-Z. (2016). Stratospheric Temperature Changes During the Satellite Era. Journal of Geophysical Research-Atmospheres, 121(2), 664-681. [10.1002/2015jd024039]

Simons, G., Bastiaanssen, W., Ngô, L., Hain, C., Anderson, M., & Senay, G. (2016). Integrating Global Satellite-Derived Data Products as a Pre-Analysis for Hydrological Modelling Studies: A Case Study for the Red River Basin. Remote Sensing, 8(4), 279. [10.3390/rs8040279]

Yin, J., Zhan, X., Zheng, Y., Hain, C. R., Ek, M., Wen, J., Fang, L., & Liu, J. (2016). Improving Noah Land Surface Model Performance Using near Real Time Surface Albedo and Green Vegetation Fraction. Agricultural and Forest Meteorology, 218, 171-183. [10.1016/j.agrformet.2015.12.001]

You, Y., Wang, N.-Y., & Ferraro, R. (2015). A Prototype Precipitation Retrieval Algorithm over Land Using Passive Microwave Observations Stratified by Surface Condition and Precipitation Vertical Structure. Journal of Geophysical Research-Atmospheres, 120(11), 5295-5315. [10.1002/2014jd022534]

 

Data, algorithms, and images presented on STAR websites are intended for experimental use only and are not supported on an operational basis.  More information

Level A conformance icon, W3C-WAI Web Content Accessibility Guidelines 1.0 and Valid HTML 4.01 IconDept. of Commerce  •  NOAA  •  NESDIS  •  Website Owner: STAR  •  Contact webmaster  •  Last revised: April 15, 2016
Heartbleed Notice  •  Privacy Policy  •  Disclaimers  •  Information Quality  •  Accessibility  •  Search  •  Customer Survey
icon: valid HTML 4.01 transitional. Level A conformance icon, W3C-WAI Web Content Accessibility Guidelines 1.0