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Volume 1, Issue 2
October - December 2015

Microwave Integrated Retrieval System (MiRS) Version 11.1 Operational

Figure 1: S-NPP MiRS derived rain rates (left) and total precipitable water (right) for descending (top) and ascending (bottom) orbits on October 4, 2015

Figure 1: S-NPP MiRS derived rain rates (left) and total precipitable water (right) for descending (top) and ascending (bottom) orbits on October 4, 2015
(click to enlarge)

The Microwave Integrated Retrieval system (MiRS) has been operationally producing temperature and moisture profiles of the atmosphere, from a variety of microwave sensors on NOAA and non-NOAA satellites since 2009. Now, MiRS version 11.1 is operationally ingesting data from the Advanced Technology Microwave Sounder (ATMS) on board the Suomi-NPP satellite since October 15, 2015, providing updated versions of all operational products from ATMS data to users. MiRS 11.1 is implemented within the Suomi-NPP Data Exploitation (NDE) system. In addition, MiRS version 11.1 has also been integrated into the Community Satellite Processing Package (CSPP) developed at the University of Wisconsin/Space Science and Engineering Center for use with direct broadcast data from S-NPP/ATMS, as well as from AMSU-MHS data from MetopA, MetopB, NOAA-18, and NOAA-19. The significance of this update is that it replaces the previous operational version of MiRS (v9.2) and includes a number of important science and algorithm enhancements that yield improved temperature and water vapor retrievals, precipitation estimates, cryospheric products, as well as a transition to high resolution for all AMSU-MHS retrievals.

Figure 2: MiRS retrievals of hydrometeor and temperature structure around Typhoon Soudelor.

Figure 2: MiRS retrievals of hydrometeor and temperature structure around Typhoon Soudelor.
(click to enlarge)

Scientists at STAR, the Cooperative Institute for Research in the Atmosphere (CIRA) and the Cooperative Institute for Climate and Satellites-Maryland (CICS-MD) used Water vapor products from ATMS retrieved by the MiRS algorithms to track atmospheric profiles for Hurricane Joaquin in the Atlantic and Typhoon Soudelor in the Pacific. The retrievals show a good correlation between Hurricane Joaquin's moist environment and the strong mid-latitude disturbance that eventually dumped the excessive rain over South Carolina as shown in Figure 1. Note the two distinct areas of rainfall - one over South Carolina and one offshore (Joaquin) - yet, a close connection between the water vapor emanating from Joaquin and supporting the heavy precipitation over South Carolina. Additionally, the rain rate products performed well during the event.

Figure 2 depicts MiRS retrievals of hydrometeor and temperature structure around Typhoon Soudelor from Suomi-NPP/ATMS microwave observations at 0445 UTC on August 6, 2015. Panels show surface rain rate (top left), rain water 0.01 mm isosurface with temperature profile superimposed (top right), graupel water 0.05 mm isosurface with temperature profile superimposed (bottom left), and a vertical cross-section along 21 degrees north latitude of both rain and graupel water (bottom right)

Accurate near real-time estimates of tropical cyclone intensity and structure are a key component of generating reliable warnings to the public of related weather hazards. The retrieval of storm structure also provides an opportunity for researchers to test physical assumptions of weather forecast and radiative transfer models. Typhoon Soudelor struck both Taiwan and the mainland of China in August 2015, causing significant damage and loss of life due to high winds, heavy rainfall, and flooding. CICS-MD scientist Chris Grassotti and his colleagues at STAR applied the MiRS retrieval algorithm to Suomi-NPP/ATMS microwave data obtained on August 6, approximately 24 hours prior to landfall. The MiRS algorithm simultaneously retrieved not only the atmospheric profiles of temperature and water vapor, but also atmospheric rain water, graupel, and cloud, making it possible to reconstruct the 3-dimensional structure of the storm. The results (Figure 2) show that the 3-dimensional structure of atmospheric rain and ice, as well as the surface rain rate, are realistically retrieved, with maximum surface rain rates of 16 mm/h, and the storm core structure present in both rain and graupel fields.

GCOM AMSR-2 Products Become Operational

An example of the GCOM-W AMSR-2 total precipitable water product for November 9, 2015

An example of the GCOM-W AMSR-2 total precipitable water product for November 9, 2015
(click to enlarge)

On November 3, 2015, NESDIS declared operational status for a suite of products (imagery; rain rate; ocean surface wind speed, surface temperature, water vapor and cloud water) from the Advanced Microwave Scanning Radiometer 2 (AMSR2) onboard the Japanese GCOM-W satellite. The product system known as GAASP-GCOM-WAMSR2 Algorithm Software Processor, is a joint effort between several NESDIS organizations, including STAR (SOCD and CoRP), OSGS, and Cooperative Institutes (CICS and CIMSS).

Satellite Climate Studies Branch and CICS-MD contributed to the rain rate and ocean products (sea surface temperature, water vapor (see image), cloud water, and surface winds). Other products will be added in the near future, include snow cover, sea-ice, and soil moisture. The Cooperative Institute for Meteorological Satellite Studies (CIMSS) and the Advanced Satellite Products Branch are leading the cryosphere products (snow and sea-ice). More information can be found at

Megha-Tropiques Data and Products System Becomes Operational

Channel 5 brightness temperatures measured by the SAPHIR instrument produced by MTROPS

Channel 5 brightness temperatures measured by the SAPHIR instrument produced by MTROPS
(click to enlarge)

On December 3, 2015, the Megha-Tropiques Data and Products System (MTROPS) developed by STAR scientists became operational at OSPO. MTROPS produces microwave imagery, total precipitable water, and rain rate from the SAPHIR sensor. The near-real time data feed is obtained from EUMETSAT. The MTROPS was developed to tailor Megha-Tropiques data, and generate NOAA unique Level-2 science products to meet NOAA user requirements. An example of the Channel 5, a microwave water vapor channel, is shown in the image. Products are operationally available from OSPO.

New Geocolor Product from Himawari

Geocolor image from 12 Nov 2015 at 0950 UTC over Japan and the Korean peninsula

Geocolor image from 12 Nov 2015 at 0950 UTC over Japan and the Korean peninsula
(click to enlarge)

CIRA/RAMMB is producing a new experimental product known as Geocolor from Himawari-8 data. During the daytime, it's true color imagery, but at night, it transitions to a multispectral IR product that includes city lights and a differentiation between ice clouds and lower liquid water clouds. The GOES-R ABI will allow for a similar product, so the Himawari version will be perfected over the next year and eventually an ABI version will be developed. A nighttime example is shown in the image, and an animation showing the transition from day to night is also available.

The image depicts an example of nighttime Geocolor image from 12 November 2015 at 0950 UTC over Japan and the Korean peninsula. City lights are yellow/gold, ice clouds are white, and liquid water clouds are red/pink.

STAR Cooperative Institutes Contributions in Tracking Hurricanes Patricia and Joaquin

Figure 1: GOES-15 (GOES-West) Infrared (10.7 μm) image of Hurricane Patricia at 0215 UTC on 11/2/2015

Figure 1: GOES-15 (GOES-West) Infrared (10.7 μm) image of Hurricane Patricia at 0215 UTC on 11/2/2015
(click to enlarge)

On October 23, 2015 Patricia became the most intense hurricane on record in the National Hurricane Center area of responsibility (which includes the northern Atlantic Ocean and the eastern North Pacific Ocean east of 140° W). Geostationary Operational Environmental Satellite (GOES-15) infrared (Figure 1) and visible images of Category 5 Hurricane Patricia were posted on the Cooperative Institute for Meteorological Satellite Studies (CIMSS) Satellite Blog. Satellite imagery and derived products from the Cooperative Institute for Meteorological Satellites (CIMSS) and the Cooperative Institute for Research in the Atmosphere (CIRA) were widely used by both the National Weather Service and media outlets to track Hurricanes Patricia and Joaquin.

Figure 2: Plot of the Advanced Dvorak Technique run at CIMSS for Hurricane Patricia.

Figure 2: Plot of the Advanced Dvorak Technique run at CIMSS for Hurricane Patricia.
(click to enlarge)

The real-time Advanced Dvorak Technique (ADT) estimates (Figure 2) pegged the maximum winds at 175 knots. The ADT utilizes GOES imagery to objectively deduce storm structure and estimate intensity. The ADT intensity estimates will be used along with aircraft reconnaissance observations and other satellite-based intensity estimates in determining the final Best Track intensity for Patricia, and ultimately if the hurricane will keep its place at the top of the records book.

Figure 2 depicts a plot of the Advanced Dvorak Technique run at CIMSS for Hurricane Patricia showing estimates of maximum winds. AdjT# and CI# are intensity parameters output by the ADT; the CI# is the final intensity estimate.

VIIRS and GOES imagery generated at RAMMB/CIRA was circulated widely in the media on October 23, 2015, as Hurricane Patricia intensified to a category 5 storm in the East Pacific as shown in Figure 3. The VIIRS image below was sent out via the NOAA Satellites twitter feed and subsequently received over 1300 retweets, a new record according to the CIRA social media coordinator.

Figure 3: VIIRS I-band-5 image of Hurricane Patricia on October 23, 2015 at 0925 UTC, near peak intensity

Figure 3: VIIRS I-band-5 image of Hurricane Patricia on October 23, 2015 at 0925 UTC, near peak intensity
(click to enlarge)

Tropical cyclone Intensity/Size estimation techniques developed by STAR and CIRA scientists, which use data from the Advanced Microwave Sounding Unit-A (AMSU-A), have been operationally used by the National Hurricane Center for monitoring and tracking hurricane intensities since 2006. These methods estimate intensity, in terms of maximum sustained wind and minimum central pressure, and surface wind structure, in terms of the maximum extent of 34-, 50-, and 64-knot winds in geographical quadrants of the storm (collectively known as wind radii). Wind radii estimates are based on statistical relationships between quantities (temperature, gradient winds, cloud liquid water, and pressure) estimated from the azimuthally averaged temperature and moisture retrievals. The intensity and structure information is disseminated to world-wide operational tropical cyclone centers in the form of a text file that conforms to the formats in the Automated Tropical Cyclone Forecast (ATCF) system that is used at the National Hurricane Center, the Central Pacific Hurricane Center, and the DOD's Joint Typhoon Warning Center. An example of the AMSU-A-based azimuthally-averaged temperature and gradient wind estimates for October 5, 2015 at 2104 UTC is the basis for the fix information mentioned in the NHC Tropical Cyclone discussions of Hurricane Joaquin wind radii issued 1100 PM AST October 5, 2015. At this time, NOAA-18 AMSU-A estimated Joaquin to have 84 kt winds a 968 hPa central pressure and 34-knot wind radii of 181, 207, 157, 140 nautical miles ( 1 nmi = 1.85 km).

This same methodology has been extended to the Advanced Technology Microwave Sounder (ATMS) that is aboard the Suomi-NPP satellite and was recently made an operational product as part of the NDE. These too are being disseminated in ATCF format and provide global tropical cyclone intensity and structure estimates. There are also plans to implement such estimates on the JPSS-1 and JPSS-2 satellites when the ATMS data from those satellites is available. This product and many other tropical cyclone information can be found for both active and past tropical cyclone events at the Regional and Mesoscale Meteorology Branch's TC real-time website.

STAR Scientists at CIMSS and CIRA Support Operational Volcanic Ash Cloud Tracking in Indonesia

Volcanic ash plume from Mount Rinjani extends westward over Bali, Indonesia on 11/3/2015.<br>Multi-spectral imagery (top left), ash cloud height (top right), ash effective radius (bottom left), and ash loading (bottom right), derived from Himawari-8.

Volcanic ash plume from Mount Rinjani extends westward over Bali, Indonesia on 11/3/2015.
Multi-spectral imagery (top left), ash cloud height (top right), ash effective radius (bottom left), and ash loading (bottom right), derived from Himawari-8.
(click to enlarge)

During the week of November 1, 2015, persistent volcanic ash emissions from Mount Rinjani severely disrupted air travel in regions of Indonesia popular with tourists, including Bali. The quantitative satellite-based volcanic cloud products developed by NOAA/NESDIS/STAR, in collaboration with the Cooperative Institute for Meteorological Satellite Studies (CIMSS), provided support to operational efforts to track and characterize the hazardous Rinjani ash clouds. The Australian Bureau of Meteorology and Indonesian authorities are using the near real-time volcanic cloud products, derived from Japanese Meteorological Agency (JMA) Himawari-8 satellite measurements, to assist in operational decision-making. An example of those products is illustrated in the image. Himawari-8 has very similar capabilities as the next generation of Geostationary Operational Environmental Satellites (GOES-R). As such, the generation of products from Himawari-8 measurements not only fosters collaboration with NOAA's international partners, but also demonstrates GOES-R capabilities.

More information

New Lightning Website and Training Guide Released

The ENTLN quick guide by Dr. Scott Rudlosky

The ENTLN quick guide by Dr. Scott Rudlosky
(click to enlarge)

SCSB and CICS-MD scientists launched a new website that aims to better orient operational users of lightning data. This website includes a wealth of supplementary information that will help forecasters better use current lightning data, which will ultimately prepare them for the GOES-R GLM era. Additionally, Dr. Scott Rudlosky developed a Quick Guide (see image) to the Earth Networks Total Lightning Network (ENTLN) data to introduce forecasters to this new dataset. Forecasters have had access to data from the Earth Networks Total Lightning Network (ENTLN) for nearly a year, but very little training has accompanied this new dataset. The ENTLN quick guide follows a similar format to a previous quick guide for the Global Lightning Dataset 360 (GLD360). The quick guide will be distributed to the NWS via regional SSD chiefs and SOOs at the local WFOs. The lack of training on this dataset re-emphasizes the need to prepare GLM training well in advance of launch.


Two Papers by NOAA Scientist at CIMSS in Top 10 Most Read Articles

CIMSS scientist James P Kossin has two papers listed as the Top Ten Most read in their respective journals.

James P. Kossin, 2015: Validating Atmospheric Reanalysis Data using Tropical Cyclones as Thermometers. Bull. Amer. Meteor. Soc., 96, DOI: 10.1175/BAMS-D-14-00180.1.

James P. Kossin, 2015: Hurricane Wind-Pressure Relationship and Eyewall Replacement Cycles. Wea. Forecasting, 30, DOI: 10.1175/WAF-D-14-00121.1.

SSEC/CIMSS Rooftop Antenna Upgraded for Future GOES-R Reception

Feed horn being mounted on the SSEC 7.3 meter antenna for GOES Rebroadcast reception

Feed horn being mounted on the SSEC 7.3 meter antenna for GOES Rebroadcast reception
(click to enlarge)

The University of Wisconsin SSEC's 7.3 meter antenna (see image), located on the southwest corner of the penthouse roof on top of the 15 story Atmospheric and Oceanic Science (AOS) building, has been upgraded in preparation for the reception of GOES Rebroadcast (GRB) after the launch of GOES-R in October 2016. The upgrade, which includes a new feed, a new mounting plate, new cabling, and a new demodulator, was completed in late September 2015. Since the new feed is compatible with the existing GVAR broadcast, the GVAR reception was tested while pointed at GOES-15 (135 W) during October and November of 2015. The upgraded antenna can be pointed at any geostationary satellite between 75 W and 137 W. (W. Feltz, CIMSS, 608-265-6283, J. Robaidek)

Article by CREST Student on Sea Surface Temperature Warming in the Spotlight

Annual SST trends per season per year from 1982 to 2012 for the Intra-Americas Region

Annual SST trends per season per year from 1982 to 2012 for the Intra-Americas Region
(click to enlarge)

CREST graduate student and former CICS-MD intern Equisha Glenn has published an article on her PhD research in an upcoming issue of Geophysical Research Letters. The article documents sea surface temperature warming trends in the Caribbean region over the last 30 years (see image). She found that the warming was the greatest in the Gulf of Mexico. It reflected an increase in the magnitude and intensity of the Atlantic Warming Pool. Extreme increases and decreases correlated with ENSO. Glenn coauthored the article with CICS-MD Scientists Daniel Comarazamy and Tom Smith.

The charts at right show annual SST trends per season per year from 1982 to 2012 for the Intra-Americas Region during the (a) early rainfall season, (b) late rainfall season, and (c) dry season. The color bar represents °C yr-1, and hatch lines indicate values that were not determined to be significant.

AGU has chosen this article to be featured in "Research Spotlight" on its website:

Glenn, E., D. Comarazamy, J. E. González, and T. Smith, 2015, Detection of recent regional sea surface temperature warming in the Caribbean and surrounding region. Geophys. Res. Lett., 42, DOI: 10.1002/2015GL065002.

Foliage Forecast in the News

Fall foliage condition as observed by NPP VIIRS sensor in October 2015

Fall foliage condition as observed by NPP VIIRS sensor in October 2015
(click to enlarge)

The nonprofit website "Nature's Notebook," which strives to connect people with nature to benefit our changing planet, is incorporating a JPSS leaf color prediction product in one of its campaigns. The project, called "Green Wave Northeast," ask citizen-scientists to observe one or more maple, oak, or poplar trees in their region to document the spread of seasonal color across the country in autumn and the flush of green of "leaf-out" in the Spring. This information will be used to fill in data gaps for scientists and policymakers. They recommend volunteers check the Fall Foliage Coloration 10-day Prediction along with the Near-Real-Time Foliage Phase maps available from NOAA JPSS Environmental Data Records at CICS scientists Xiaoyang Zhang and Lingling Liu collaborated with Yunyue Yu (STAR/SMCD/EMB) to create these maps from the NDVI vegetation index and land surface temperature from S-NPP VIIRS.

Examples of these maps in late October 2015 are shown in the image. More on the Nature's Notebook citizen-scientist project is available at Products like the Fall Foliage Prediction map provide useful information to the public and citizen-scientist projects help build climate literacy

Fourth NASA-NOAA GPM MOU Working Group Meeting

Multi-Radar Multi-Sensor (MRMS) radar snowfall rate and NESDIS satellite snowfall rate (SFR) merged product, mSFR

Multi-Radar Multi-Sensor (MRMS) radar snowfall rate and NESDIS satellite snowfall rate (SFR) merged product, mSFR
(click to enlarge)

On October 7, SCSB hosted the 4th meeting of the NASA-NOAA Global Precipitation Measurement (GPM) Mission Memorandum of Understanding (MOU) working group. Approximately 20 participants attended in person or remotely to review previous action items (from the March 2015 meeting) and report progress related to GPM and other relevant precipitation activities. NASA reported that the GPM mission is functioning and well and fuel estimates would keep it in orbit for at least another 10 years. A comprehensive summary of the ATMS and MHS snowfall rate (SFR) products including algorithms, validation, application, and assessment in operation was given by SCSB scientists. The SFR product is of interest to NASA because GPM scientists are developing snowfall rate algorithms following different approaches. In addition, the SCSB/CICS group will also develop a GPM/GMI snowfall rate product with the support of JPSS PGRR Program. The image is an example of a radar-satellite merged snowfall rate product that has been developed at SCSB/CICS. The goal of the product is to use satellite SFR to fill in radar precipitation gaps.

The image shows the Multi-Radar Multi-Sensor (MRMS) radar snowfall rate and NESDIS satellite snowfall rate (SFR) merged product, mSFR. Panels: (a) mSFR, (b) a zoomed in area in (a), (c) satellite SFR, and (d) MRMS. The light grey color indicates that radar quality index is 0, i.e., no valid radar snowfall rate retrieval.

image: tag cloud of research-related words

Blonski, S., & Cao, C. (2015). Suomi NPP VIIRS Reflective Solar Bands Operational Calibration Reprocessing. Remote Sensing, 7(12), 15823. [10.3390/rs71215823]

Canty, T. P., Hembeck, L., Vinciguerra, T. P., Anderson, D. C., Goldberg, D. L., Carpenter, S. F., Allen, D. J., Loughner, C. P., Salawitch, R. J., & Dickerson, R. R. (2015). Ozone and Nox Chemistry in the Eastern US: Evaluation of CMAQ/Cb05 with Satellite (OMI) Data. Atmospheric Chemistry and Physics, 15(19), 10965-10982. [10.5194/acp-15-10965-2015]

Chen, X., Liang, S., Cao, Y., He, T., & Wang, D. (2015). Observed Contrast Changes in Snow Cover Phenology in Northern Middle and High Latitudes from 2001-2014. Scientific Reports, 5. [10.1038/srep16820]

Claverie, M., Vermote, E. F., Franch, B., & Masek, J. G. (2015). Evaluation of the Landsat-5 Tm and Landsat-7 ETM + Surface Reflectance Products. Remote Sensing of Environment, 169, 390-403. [10.1016/j.rse.2015.08.030]

Crow, W. T., Lei, F., Hain, C., Anderson, M. C., Scott, R. L., Billesbach, D., & Arkebauer, T. (2015). Robust Estimates of Soil Moisture and Latent Heat Flux Coupling Strength Obtained from Triple Collocation. Geophysical Research Letters, 42(20), 8415-8423. [10.1002/2015gl065929]

Folmer, M. J., DeMaria, M., Ferraro, R., Beven, J., Brennan, M., Daniels, J., Kuligowski, R., Meng, H., Rudlosky, S., Zhao, L., Knaff, J., Kusselson, S., Miller, S. D., Schmit, T. J., Velden, C., & Zavodsky, B. (2015). Satellite Tools to Monitor and Predict Hurricane Sandy (2012): Current and Emerging Products. Atmospheric Research, 166, 165-181. [10.1016/j.atmosres.2015.06.005]

Gochis, D., Schumacher, R., Friedrich, K., Doesken, N., Kelsch, M., Sun, J., Ikeda, K., Lindsey, D., Wood, A., Dolan, B., Matrosov, S., Newman, A., Mahoney, K., Rutledge, S., Johnson, R., Kucera, P., Kennedy, P., Sempere-Torres, D., Steiner, M., Roberts, R., Wilson, J., Yu, W., Chandrasekar, V., Rasmussen, R., Anderson, A., & Brown, B. (2015). The Great Colorado Flood of September 2013. Bulletin of the American Meteorological Society, 96(9), 1461-1487. [10.1175/bams-d-13-00241.1]

Gultepe, I., Zhou, B., Milbrandt, J., Bott, A., Li, Y., Heymsfield, A. J., Ferrier, B., Ware, R., Pavolonis, M., Kuhn, T., Gurka, J., Liu, P., & Cermak, J. (2015). A Review on Ice Fog Measurements and Modeling. Atmospheric Research, 151, 2-19. [10.1016/j.atmosres.2014.04.014]

Han, Y., Suwinski, L., Tobin, D., & Chen, Y. (2015). Effect of Self-Apodization Correction on Cross-Track Infrared Sounder Radiance Noise. Applied Optics, 54(34), 10114-10122. [10.1364/ao.54.010114]

Hillger, D., Kopp, T., Seaman, C., Miller, S., Lindsey, D., Stevens, E., Solbrig, J., Straka III, W., Kreller, M., Kuciauskas, A., & Terborg, A. (2015). User Validation of VIIRS Satellite Imagery. Remote Sensing, 8(1), 11. [10.3390/rs8010011]

Huang, M., Tong, D., Lee, P., Pan, L., Tang, Y., Stajner, I., Pierce, R. B., McQueen, J., & Wang, J. (2015). Toward Enhanced Capability for Detecting and Predicting Dust Events in the Western United States: The Arizona Case Study. Atmospheric Chemistry and Physics, 15(21), 12595-12610. [10.5194/acp-15-12595-2015]

Janetos, A. C., & Kenney, M. A. (2015). Developing Better Indicators to Track Climate Impacts. Frontiers in Ecology and the Environment, 13(8), 403-403. [10.1890/1540-9295-13.8.403]

Jiang, L.-Q., Feely, R. A., Carter, B. R., Greeley, D. J., Gledhill, D. K., & Arzayus, K. M. (2015). Climatological Distribution of Aragonite Saturation State in the Global Oceans. Global Biogeochemical Cycles, 29(10), 1656-1673. [10.1002/2015gb005198]

Kaplan, J., Rozoff, C. M., DeMaria, M., Sampson, C. R., Kossin, J. P., Velden, C. S., Cione, J. J., Dunion, J. P., Knaff, J. A., Zhang, J. A., Dostalek, J. F., Hawkins, J. D., Lee, T. F., & Solbrig, J. E. (2015). Evaluating Environmental Impacts on Tropical Cyclone Rapid Intensification Predictability Utilizing Statistical Models. Weather and Forecasting, 30(5), 1374-1396. [10.1175/waf-d-15-0032.1]

Key, J., Goodison, B., Schoner, W., Godoy, O., Ondras, M., & Snorrason, A. (2015). A Global Cryosphere Watch. Arctic, 68(S1), 48-58. [10.14430/arctic4476]

Knaff, J. A., Longmore, S. P., & Molenar, D. A. (2015). An Objective Satellite-Based Tropical Cyclone Size Climatology (Vol 27, Pg 455, 2014). Journal of Climate, 28(21), 8648-8651. [10.1175/jcli-d-15-0610.1]

Langford, A. O., Senff, C. J., Alvarez, R. J., II, Brioude, J., Cooper, O. R., Holloway, J. S., Lin, M. Y., Marchbanks, R. D., Pierce, R. B., Sandberg, S. P., Weickmann, A. M., & Williams, E. J. (2015). An Overview of the 2013 Las Vegas Ozone Study (LVOS): Impact of Stratospheric Intrusions and Long-Range Transport on Surface Air Quality. Atmospheric Environment, 109, 305-322. [10.1016/j.atmosenv.2014.08.040]

Laviola, S., Dong, J., Kongoli, C., Meng, H., Ferraro, R., & Levizzani, V. (2015). An Intercomparison of Two Passive Microwave Algorithms for Snowfall Detection over Europe. Geoscience and Remote Sensing Symposium (IGARSS), 2015 IEEE International, 886-889. [10.1109/IGARSS.2015.7325907]

Lee, Y.-K., Kongoli, C., & Key, J. (2015). An in-Depth Evaluation of Heritage Algorithms for Snow Cover and Snow Depth Using AMSR-E and AMSR2 Measurements. Journal of Atmospheric and Oceanic Technology, 32(12), 2319-2336. [10.1175/jtech-d-15-0100.1]

Li, J., Rao, Y., Sun, Q., Wu, X., Jin, J., Bi, Y., Chen, J., Lei, F., Liu, Q., Duan, Z., Ma, J., Gao, G. F., Liu, D., & Liu, W. (2015). Identification of Climate Factors Related to Human Infection with Avian Influenza a H7n9 and H5n1 Viruses in China. Scientific Reports, 5. [10.1038/srep18094]

Li, Z., Grotenhuis, M., Wu, X., Schmit, T. J., Schmidt, C., Schreiner, A. J., Nelson, J. P., III, Yu, F., & Bysal, H. (2014). Geostationary Operational Environmental Satellite Imager Infrared Channel-to-Channel Co-Registration Characterization Algorithm and Its Implementation in the Ground System. Journal of Applied Remote Sensing, 8. [10.1117/1.jrs.8.083530]

Liu, Y., Key, J., Tschudi, M., Dworak, R., Mahoney, R., & Baldwin, D. (2015). Validation of the Suomi NPP VIIRS Ice Surface Temperature Environmental Data Record. Remote Sensing, 7(12), 15880. [10.3390/rs71215880]

Ma, Y., & Zou, X. (2015). Striping Noise Mitigation in ATMS Brightness Temperatures and Its Impact on Cloud Lwp Retrievals. Journal of Geophysical Research-Atmospheres, 120(13), 6634-6653. [10.1002/2015jd023162]

Meier, W. N., Hovelsrud, G. K., van Oort, B. E. H., Key, J. R., Kovacs, K. M., Michel, C., Haas, C., Granskog, M. A., Gerland, S., Perovich, D. K., Makshtas, A., & Reist, J. D. (2014). Arctic Sea Ice in Transformation: A Review of Recent Observed Changes and Impacts on Biology and Human Activity. Reviews of Geophysics, 52(3), 185-217. [10.1002/2013rg000431]

Moradi, I., Ferraro, R. R., Eriksson, P., & Weng, F. (2015). Intercalibration and Validation of Observations from ATMS and SAPHIR Microwave Sounders. IEEE Transactions on Geoscience and Remote Sensing, 53(11), 5915-5925. [10.1109/tgrs.2015.2427165]

Moradi, I., Ferraro, R. R., Soden, B. J., Eriksson, P., & Arkin, P. (2015). Retrieving Layer-Averaged Tropospheric Humidity from Advanced Technology Microwave Sounder Water Vapor Channels. IEEE Transactions on Geoscience and Remote Sensing, 53(12), 6675-6688. [10.1109/tgrs.2015.2445832]

Pusede, S. E., VandenBoer, T. C., Murphy, J. G., Markovic, M. Z., Young, C. J., Veres, P. R., Roberts, J. M., Washenfelder, R. A., Brown, S. S., Ren, X., Tsai, C., Stutz, J., Brune, W. H., Browne, E. C., Wooldridge, P. J., Graham, A. R., Weber, R., Goldstein, A. H., Dusanter, S., Griffith, S. M., Stevens, P. S., Lefer, B. L., & Cohen, R. C. (2015). An Atmospheric Constraint on the No2 Dependence of Daytime near-Surface Nitrous Acid (Hono). Environmental Science & Technology, 49(21), 12774-12781. [10.1021/acs.est.5b02511]

Roebeling, R., Baum, B., Bennartz, R., Hamann, U., Heidinger, A., Meirink, J. F., Stengel, M., Thoss, A., Walther, A., & Watts, P. (2015). Summary of the Fourth Cloud Retrieval Evaluation Workshop. Bulletin of the American Meteorological Society, 96(4), ES71-ES74. [10.1175/bams-d-14-00184.1]

Saide, P. E., Spak, S. N., Pierce, R. B., Otkin, J. A., Schaack, T. K., Heidinger, A. K., da Silva, A. M., Kacenelenbogen, M., Redemann, J., & Carmichael, G. R. (2015). Central American Biomass Burning Smoke Can Increase Tornado Severity in the US. Geophysical Research Letters, 42(3), 956-965. [10.1002/2014gl062826]

Sampson, C. R., & Knaff, J. A. (2015). A Consensus Forecast for Tropical Cyclone Gale Wind Radii. Weather and Forecasting, 30(5), 1397-1403. [10.1175/waf-d-15-0009.1]

Schmit, T. J., Goodman, S. J., Gunshor, M. M., Sieglaff, J., Heidinger, A. K., Bachmeier, A. S., Lindstrom, S. S., Terborg, A., Feltz, J., Bah, K., Rudlosky, S., Lindsey, D. T., Rabin, R. M., & Schmidt, C. C. (2015). Rapid Refresh Information of Significant Events: Preparing Users for the Next Generation of Geostationary Operational Satellites. Bulletin of the American Meteorological Society, 96(4), 561-576. [10.1175/bams-d-13-00210.1]

Shao, M., Xu, J., Powell, A. M., Jr., Kogan, F., & Guo, W. (2015). Global Land Vegetation and Marine Fishery Responses to Atmospheric and Oceanic Decadal Variability. International Journal of Remote Sensing, 36(21), 5523-5536. [10.1080/01431161.2015.1103919]

Stein, A. F., Draxler, R. R., Rolph, G. D., Stunder, B. J. B., Cohen, M. D., & Ngan, F. (2015). NOAA's Hysplit Atmospheric Transport and Dispersion Modeling System. Bulletin of the American Meteorological Society, 96(12), 2059-2077. [10.1175/BAMS-D-14-00110.1]

Sutton-Grier, A. E., Wowk, K., & Bamford, H. (2015). Future of Our Coasts: The Potential for Natural and Hybrid Infrastructure to Enhance the Resilience of Our Coastal Communities, Economies and Ecosystems. Environmental Science & Policy, 51, 137-148. [10.1016/j.envsci.2015.04.006]

Turner, M. D., Henze, D. K., Capps, S. L., Hakami, A., Zhao, S., Resler, J., Carmichael, G. R., Stanier, C. O., Baek, J., Sandu, A., Russell, A. G., Nenes, A., Pinder, R. W., Napelenok, S. L., Bash, J. O., Percell, P. B., & Chai, T. (2015). Premature Deaths Attributed to Source-Specific Bc Emissions in Six Urban US Regions. Environmental Research Letters, 10(11). [10.1088/1748-9326/10/11/114014]

Vadrevu, K. P., Lasko, K., Giglio, L., & Justice, C. (2015). Vegetation Fires, Absorbing Aerosols and Smoke Plume Characteristics in Diverse Biomass Burning Regions of Asia. Environmental Research Letters, 10(10). [10.1088/1748-9326/10/10/105003]

Wu, L., Tian, W., Liu, Q., Cao, J., & Knaff, J. A. (2015). Implications of the Observed Relationship between Tropical Cyclone Size and Intensity over the Western North Pacific. Journal of Climate, 28(24), 9501-9506. [10.1175/jcli-d-15-0628.1]

Wu, M., Zhang, X., Huang, W., Niu, Z., Wang, C., Li, W., & Hao, P. (2015). Reconstruction of Daily 30 M Data from HJ CCD, GF-1 WFV, Landsat, and MODIS Data for Crop Monitoring. Remote Sensing, 7(12), 15826. [10.3390/rs71215826]

Yin, J., Zheng, Y., Zhan, X., Hain, C. R., Zhai, Q., Duan, C., Wu, R., Liu, J., & Fang, L. (2015). An Assessment of Impacts of Land-Cover Changes on Root-Zone Soil Moisture. International Journal of Remote Sensing, 36(24), 6116-6134. [10.1080/01431161.2015.1111539]


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