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Volume 2, Issue 3
July - September 2016


New Shortwave Infrared Polar Winds

Winds over the Arctic based on cloud tracking in a shortwave infrared (SWIR) band on MODIS

Winds over the Arctic based on cloud tracking in a shortwave infrared (SWIR) band on MODIS
(click to enlarge)

Polar winds from the Moderate Resolution Imaging Spectroradiometer (MODIS) 2.1 µm band are now routinely produced at the Cooperative Institute for Meteorological Satellite Studies (CIMSS) for the Arctic and Antarctic (website, labelled as "Terra-SW" and "Aqua-SW"). This experimental product (Figure 1) was created because clouds, which are tracked to derive winds, are notoriously difficult to detect over snow and ice due to the similarities between their temperature and reflectance properties and those of the underlying surface, resulting in less well-defined targets for tracking. In the shortwave infrared (SWIR), however, the scattering properties of liquid clouds and snow/ice are significantly different, and therefore the contrast between low clouds and the surface is large. In theory, SWIR data will provide more good features for cloud tracking and atmospheric motion vector derivation during the "daytime", especially for low clouds. This work will be extended to the VIIRS 1.6 µm band in the near future. Some numerical weather prediction centers are expected to begin testing the impact of the SWIR polar winds in the near future. (Team: Jeffrey Key, D. Santek, R. Dworak)

Does Satellite Radiometry Improve Sea Surface Salinity Estimates?

Monthly mean MODIS-Aqua SSS retrieved from the ANN algorithm during web (Oct. 2011) and dry (Oct. 2012 periods

Monthly mean MODIS-Aqua SSS retrieved from the ANN algorithm during web (Oct. 2011) and dry (Oct. 2012 periods
(click to enlarge)

Salinity is a critical factor in understanding and predicting physical, chemical, and biological processes in the coastal ocean, where these processes vary considerably in time and space. Unfortunately, estimating salinity of sufficient quality and resolution in coastal waters is difficult. Satellite ocean color radiometry may offer a method to provide estimates of sea surface salinity (SSS) at a medium spatial resolution (250 m to 1 km) in coastal waters for direct application and for assimilation into medium resolution hydrodynamic models. The Journal of Applied Remote Sensing published a new article from by Ronald Vogel and Christopher Brown on satellite detection of sea surface salinity. The article compares two satellite algorithms for retrieving Sea Surface Salinity (SSS) based on Ocean Color radiometry at a higher spatial and temporal resolution than is currently available. This work represents the first assessment of ocean color radiometric SSS retrievals, an advancing algorithm development field, for a coastal model application. The researchers used two different retrieval algorithms for the MODIS observations: The Generalized Additive Model (GAM) and an Artificial Neural Network (ANN), are shown in the Figure 2. The satellite retrievals are verified using in situ observation and the results of the NOAA's Chesapeake Bay Operational Forecast System (CBOFS) hydrodynamic model.

The SSS calculated by the CBOF model was more accurate than both satellite algorithms, so the study concluded that assimilating this data would not improve CBOFS forecasts of SSS in Chesapeake Bay.

Vogel, Ronald L., and Brown, Christopher W. (2016) Assessing satellite sea surface salinity from ocean color radiometric measurements for coastal hydrodynamic model data assimilation. J. Appl. Remote Sens., 10, 036003, DOI: 10.1117/1.JRS.10.036003.

 

Verification of Enterprise Cloud Mask Algorithms

Comparison of AVHRR PATMOS-x and MYD35 for winter 2003-2014 for day and night (all).

Comparison of AVHRR PATMOS-x and MYD35 for winter 2003-2014 for day and night (all).
(click to enlarge)

Clouds constitute one of the largest uncertainties in projecting future climate change. The Pathfinder Atmospheres Extended (PATMOS-x) is a NOAA project focused mainly on the generation of cloud and related satellite-derived climate data records (CDRs). The journal Remote Sensing published a paper on the performance of the NOAA Enterprise Cloud Mask. The NOAA Enterprise Cloud Mask (ECM) was implemented on the NASA Moderate Resolution Imaging Spectroradiometer (MODIS) and compared to the NASA Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) measurements (Figure 3). Since MODIS has analogous channels as the GOES-R Advanced Baseline Imager (ABI) and the JPSS Visible Imaging Radiometer Suite (VIIRS), this analysis was able to demonstrate the performance of the ECM on multiple sensors. This paper provides an important reference for users of the ECM. In addition, the entire AQUA record was processed through the ECM and the paper includes a study of the impact of spectral information on the decadal trends in cloud fraction from the ECM. The first author was Andrew Heidinger and the author list included 5 other scientists from the Cooperative Institute for Meteorological Satellite Studies (CIMSS). The reference for the paper is Remote Sens. 2016, 8(6), 511; DOI: 10.3390/rs8060511. (A. Heidinger)

In the image at right: Panel (a) shows the mean cloud fraction at 2.5° resolution; Panel (b) shows the PATMOS-x-MYD35 difference; (c) shows the PATMOS-x uncertainty from the naive Bayesian cloud detection scheme; Panel (d) shows the anomaly correlation of PATMOS-x and MYD35. Panel (e) shows the PATMOS-x linear trend, and Panel (f) shows a scatterplot of the PATMOS-x and MYD35 linear trends.

Evaporative Stress Index Goes Operational!

Evaporative stress index for the month of July, 2016

Evaporative stress index for the month of July, 2016
(click to enlarge)

CICS-MD Scientist Christopher Hain (STAR/SMCD/EMB) and his team have successfully transitioned the ALEXI model into operations at NOAA (Figure at right). This is the culmination of a six-year CICS project funded by the NOAA Climate Program Office (CPO), NASA Applied Sciences, and NOAA Product Systems Development and Integration (PSDI) program.

The ALEXI model produces daily maps of the Evaporative Stress Index, an early drought indicator derived from GOES thermal-infrared data and MODIS vegetation information. Unusually rapid changes in the ESI often precede periods of drought intensification and provide effective early warning of an increased risk for drought development. This work was featured on "Image of the Day" on September 13th on the NASA Earth Observatory website.

Real Time Eye Formation/Disintegration Forecasts on the Web

Examples of real-time eye probability forecasts for hurricane Matthew

Examples of real-time eye probability forecasts for hurricane Matthew
(click to enlarge)

An experimental product that predicts the probability of an infrared tropical cyclone eye structure forming in 6, 12, 18, 24, and 36 h is now available on the web as part of the TC-realtime web page. The application leveraged a JPSS project that provides an objective method for determining if an IR eye exists, trained on subjective estimates. That method was trained on 26 years of GOES data in the Atlantic and is now being applied globally. An example of the experimental product graphics for tropical cyclone Matthew is shown in Figure 5. This application was sponsored by GOES-R Risk Reduction. Team members: John Knaff, R. DeMaria, G. Chirokova, K. Micke, & K. Musgrave.

The image at right shows examples of real-time eye probability forecasts for hurricane Matthew from 1200 UTC on 9/28/16 to 1200 UTC on 10/02/16. The red line provides a history of the objective eye detection [probability) and the blue squares provide the forecasts at 6, 12, 18, 24, and 36 h lead times.

DEBRA Dust Product in NAWIPS for National Hurricane Center

Screen capture from NAWIPS of CIRA

Screen capture from NAWIPS of CIRA's DEBRA Dust product from the MSG satellite on 8-16-2016 at 1115 UTC. Yellow areas indicate regions of lofted dust
(click to enlarge)

The Dynamic Enhanced Background Reduction Algorithm (DEBRA) Dust product, produced by Steve Miller at CIRA, is now being generated from Meteosat Second Generation data in real time and converted to NAWIPS format for display in the National Hurricane Center's operational system. A screen capture is shown at right. The product provides a probability that lofted dust exists at each pixel, where the bright yellow areas are the highest probability. This product allows the NHC to easily track Saharan Air Layer dust as it advects westward across the Atlantic. (Team: S. Miller, A. Schumacher, D. Molenar, and D. Lindsey)

How do Satellite and Reanalyses Arctic Cloud Estimates Compare?

Cloud amount anomalies (%) in (top) January 2013 and (bottom) June 2013

Cloud amount anomalies (%) in (top) January 2013 and (bottom) June 2013
(click to enlarge)

Cloud cover is one of the largest uncertainties in model predictions of the future Arctic climate. Previous studies have shown that cloud amounts in global climate models and atmospheric reanalyses vary widely and may have large biases. However, many climate studies are based on anomalies rather than absolute values, for which biases are less important. A paper "Assessment of Arctic Cloud Cover Anomalies in Atmospheric Reanalysis Products Using Satellite Data" by Yinghui Liu (CIMSS) and Jeff Key (NOAA/NESDIS) has been published in the Journal of Climate. This paper examines the performance of five atmospheric reanalysis products in depicting monthly mean Arctic cloud amount anomalies against MODIS and CALIPSO.

The figure at right shows the monthly mean cloud amount anomalies in January 2013 and June 2013 from CALIPSO, MODIS, ERA-Interim, MERRA, MERRA-2, NCEP R1, and NCEP R2. There are significant differences in the monthly mean Arctic cloud amount in reanalysis products, and none of the reanalysis products examined here resembles the annual cycle of cloud amount from MODIS or CALIPSO satellite products. Despite differences in the mean cloud amount, the reanalysis datasets do exhibit some capability for depicting the monthly mean cloud amount anomalies, as demonstrated for the period 2000-14. They all perform best in July, August, and September and worst in November, December, and January. All reanalysis datasets have better performance over land than over ocean. This study identifies the magnitudes of errors in Arctic mean cloud amounts and anomalies and provides a useful tool for evaluating future improvements in the cloud schemes of reanalysis products. The paper is available at DOI: 10.1175/JCLI-D-15-0861.1. (Yinghui Liu)

Tracking NOx Emissions from Space and Linking them with Economics

NAQFC NOx emissions before (a) and after (b) the 2012 major updates and their difference<br>(2012 minus 2005) in emission rate (c) and percentage (d) during summertime (July)

NAQFC NOx emissions before (a) and after (b) the 2012 major updates and their difference
(2012 minus 2005) in emission rate (c) and percentage (d) during summertime (July)
(click to enlarge)

As of December 2013, the United States Environmental Protection Agency (US EPA) estimates that more than one-third of the US population lives in areas that exceed the national ambient air quality standards (NAAQS) for either O3 or PM2.5. NOx are emitted from both anthropogenic sources (transportation, power plants, and fertilizers) and natural sources (biomass burning, lightning, and soils). Can satellite-based emission data provide reliable information in order to rapidly update NOx emission inventories and thereby support NAQFC-type air quality applications? CICS-MD researchers Tong et al. have shown that both the Ozone Monitoring Instrument (OMI) and the Air Quality System (AQS) detect substantial downward trends from 2005 to 2012, with a seven-year total of -35% according to OMI and -38% according to AQS. Both OMI and AQS datasets display distinct emission reduction rates before, during, and after the 2008 global recession in some cities, but the detailed changing rates are not consistent across the OMI and AQS data (see figure at right).

Their paper was highlighted in the August 2016 issue of the NOAA Scientific Publications Report as "the first paper to present a credible assessment of the 2008 global recession on air quality in the United States."

Daniel Q. Tong, Lok Lamsal, Li Pan, Charles Ding, Hyuncheol Kim, Pius Lee, Tianfeng Chai, Kenneth E. Pickering, and Ivanka Stajner. (2015) Long-term NOx trends over large cities in the United States during the Great Recession: Comparison of satellite retrievals, ground observations, and emission inventories. Atmos. Environ., 107, 70-84, DOI: 10.1016/j.atmosenv.2015.01.035.

What are the Differences in Arctic Sea Ice Estimates from Different Satellites?

Sea ice thickness from APP-x (a); CryoSat-2 (b); SMOS (c); and PIOMAS (d). These are the monthly mean results for March 2012

Sea ice thickness from APP-x (a); CryoSat-2 (b); SMOS (c); and PIOMAS (d). These are the monthly mean results for March 2012
(click to enlarge)

Sea ice affects the exchange of heat, energy, mass, and momentum between the atmosphere and ocean, and has a significant impact on society in terms of marine transportation, security, fisheries, hazards, recreation, and hunting. Quantifying its variability in space and time is critical for improving our understanding of climate sensitivity at high latitudes. A paper titled "Comparison of sea ice thickness from satellites, aircraft, and PIOMAS data" was recently published in the journal Remote Sensing (2016, 8, 713, DOI: 10.3390/rs8090713). The paper, by Xuanji Wang (CIMSS), Jeff Key (STAR), Ron Kwok (NASA JPL), and Jinlun Zhang (University of Washington), compares Arctic sea ice thickness from the Advanced Very High Resolution Radiometer (AVHRR) Polar Pathfinder-extended (APP-x) using the algorithm developed for VIIRS, the ICESat laser altimeter, the CryoSat- 2 radar altimeter, the IceBridge aircraft campaign laser altimeter and snow radar, the Soil Moisture and Ocean Salinity (SMOS) sensor, and the Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS) ice model (see figure at right).

All satellite-retrieved ice thickness products and PIOMAS overestimate the thickness of thin ice (1 m or less) compared to IceBridge. The spatial correlation between the datasets indicates that APP-x and PIOMAS are the most similar, followed by APP-x and CryoSat-2. (Team: Jeff Key and X. Wang)

TV Interference with AMSR2

Schematic illustration of a potential occurrence of TFI over land, on January 5, 2014

Schematic illustration of a potential occurrence of TFI over land, on January 5, 2014
(click to enlarge)

The Advanced Microwave Scanning Radiometer 2 (AMSR2) is the only remote sensing instrument onboard the Global Change Observation Mission-Water 1 (GCOM-W1) satellite, which was successfully launched onto a sun-synchronous orbit at an altitude of 700 km on May 18, 2012. AMSR2 measurements at K-band channels are used for snow retrieval. However, television (TV) signals transmitted from DirecTV satellites at the K-band, if reflected by snow surfaces, could enter the antenna of AMSR2 to introduce errors in AMSR2 snow products. CICS-MD Scientist Xiaolei Zou and her graduate student Xiaoxu Tian have a new article in IEEE Geoscience & Remote Sensing Letters. It identifies a source of potential interference in satellite-based snow measurements caused by satellite television. The AMSR2 can detect and measure snow using its K-Band channels. The article shows that DirecTV satellites transmit signals at the K-Band that could reflect off snow surfaces and degrade AMSR2 accuracy.

The figure at right is a schematic illustration of a potential occurrence of television frequency interference (TFI) over land, showing the AMSR2-retrieved snow depth (in centimeters, shaded in color) on January 5, 2014, and the coverage of DirecTV-12 with its signal intensity indicated in contours of purple (55 dbW), light purple (52 dbW), and black contours (< 52 dbW). The authors developed a detection algorithm based on principal component analysis to detect TFI. Tian, Xiaoxu, and Xiaolei Zou, Television frequency interference in AMSR2 K-Band measurements over reflective surfaces, IEEE Geosci. Remote Sens. Lett., DOI: 10.1109/LGRS.2016.2598058 (in press 2016).

What is the Diurnal Variability of Tropospheric Relative Humidity in Tropical Regions?

Diurnal peak time in local time for RHI based on Fourier series fit. Depicts from top to bottom are for SAPHIR channels 1-6

Diurnal peak time in local time for RHI based on Fourier series fit. Depicts from top to bottom are for SAPHIR channels 1-6
(click to enlarge)

Despite the importance of water vapor especially in the tropical region, the diurnal variations of water vapor have not been completely investigated in the past due to the lack of adequate observations. CICS-MD scientist Isaac Moradi and STAR researchers calculated the mean amplitude and diurnal peak time of relative humidity for each gridpoint in the tropical region using measurements from the Sondeur Atmosphérique du Profil d'Humidité Intertropicale par Radiométrie (SAPHIR) onboard the low inclination Megha-Tropiques satellite (figure at right).

The results showed great variability in these critical factors, with peak times occurring at different times of day. While the diurnal amplitudes is less than 10% in the middle to upper troposphere, it is up to 30% in the lower troposphere over land.

Diurnal variation of tropospheric relative humidity in tropical regions. (2016). Moradi, Isaac, Philip Arkin, Ralph Ferraro, Patrick Eriksson, and Eric Fetzer, Atmos. Chem. Phys., 16, 6913–6929, DOI: 10.5194/acp-16-6913-2016. This research is highlighted on the Global Energy and Water cycle Experiment (GEWEX) website.

 

Robert Pierce Wins the Prestigious 2016 Administrator’s and Technology Transfer Award

Brad Pierce (left) and his colleague Tim Schmit (seated)

Brad Pierce (left) and his colleague Tim Schmit (seated)
(click to enlarge)

Real time ABI imagery generated by the GOES-R ground system using proxy data on 2nd August 2016.

Real time ABI imagery generated by the GOES-R ground system using proxy data on 2nd August 2016.
(click to enlarge)

Dr. Robert Pierce from the Advanced Satellite Product Branch is a recipient of the prestigious 2016 Administrator’s and Technology Transfer Award for providing robust, real-time, simulated data of the next generation geostationary satellite imagers, reducing risk in post- launch operations. Dr. Pierce developed a system to simulate the data that will be obtained by the Advanced Baseline Imager (ABI) which will fly on GOES-R, the next generation of geostationary satellite. The simulated, or "proxy", data allows product developers to test and validate their algorithms, and it provides users such as the National Weather Service (NWS) with the information they need to evaluate future products even before the satellite is launched. He was able to build the capability by partnering with the relevant experts and stakeholders, both locally (University of Wisconsin-Madison) and across the country.

The proxy data generation system Pierce developed couples a complex numerical weather model, the Weather Research and Forecasting (WRF) model, with a chemistry model (WRF-Chem), and an even more complex model of atmospheric chemistry known as the Real-Time Air Quality Modeling System (RAQMS). Near real time proxy data that was generated by this system was successfully used to test the GOES-R product generation system before launch, resulting in significant time and cost savings and possibly millions of dollars in potential post-launch software redevelopment. Without this capability that Dr. Pierce developed, working with others both at the CIMSS and the NWS, the verification of the product generation capability within the GOES-R ground segment would be at high risk of not generating the Key Performance Parameters at launch. In the composite image above, the images were transmitted to a select group of NWS test sites and are displayed on an AWIPS-II terminal.

2016 CoRP Symposium in Colorado

CoRP Symposium Group Photo, July 2016

CoRP Symposium Group Photo, July 2016
(click to enlarge)

The 2016 CoRP Symposium was held 18-19 July at the CIRA/Atmospheric Science complex on the foothills campus of Colorado State University in Fort Collins, Colorado. The Symposium consisted of two days of presentations and posters, including a few invited speakers, but featuring mostly oral and poster presentations by younger scientists from several Cooperative Institutes. The poster presenters also gave 1- to 2-minute oral summaries of their posters in a poster introductory session. At the end of the Symposium, award certificates were presented to the three best posters as determined by poster judges. See the Symposium website for details. Symposium master of ceremonies was C. Kummerow. The Symposium was organized by personnel from the Regional And Mesoscale Meteorology Branch and the Cooperative Institute for Research in the Atmosphere. (Team: D. Hillger, D. Lindsey, R. Brummer, D. Watson, S. Miller, C. Kummerow)

GOES-14 1-minute imagery used by NWS

GOES-14 visible image from 8-9-2016 showing a severe storm over southeast Montana,<br>with some inflow feeder clouds, a severe storm signature, denoted

GOES-14 visible image from 8-9-2016 showing a severe storm over southeast Montana,
with some inflow feeder clouds, a severe storm signature, denoted
(click to enlarge)

GOES-14 was recently reactivated and it began collecting 1-minute imagery in SRSOR mode on Aug. 9. The primary goal of the experiment is to prepare for regular 1-minute scans with GOES-R. RAMMB/CIRA and the CIMSS Data Center, is collecting the data, making the appropriate format conversions, and sending the data to the National Weather Service with approximately 3-minute total latency between satellite scan and display in AWIPS. The sector on Aug. 9 included the northern high plains, and a large storm formed in southeast Montana, a region that has poor radar coverage. The forecasters in Billings, MT, were pulling in the SRSOR GOES-14 data in real time from CIRA’s feed, and their Science and Operations Officer (Marc Singer) said in an email that "The really awesome part about this case is that Super Rapid Scan satellite imagery was available over our CWA, and it played a major factor in our warning operations." The storm produced 4" diameter hail. A VISIT blog entry was made for this case, including satellite links, and can be found here.

A CIMSS Satellite Blog entry for this case has been posted at: http://cimss.ssec.wisc.edu/goes/blog/archives/21693. The figure at right illustrates a single GOES-14 image from the blog entry with one of the severe storm signatures noted. (Team: D. Lindsey, D. Bikos, T. Schmit)

AMSR2 Day-2 Products Declared Operational

AMSR2 snow depth over the Northern Hemisphere on January 15, 2015

AMSR2 snow depth over the Northern Hemisphere on January 15, 2015
(click to enlarge)

On September 21, 2016, the NESDIS Satellite Products and Services Review Board (SPSRB) declared the GCOM-W1 Advanced Microwave Scanning Radiometer-2 (AMSR2) "Day-2" products as operational. The Day-2 products include Snow Cover/Depth (figure at right), Snow Water Equivalent and Sea Ice Characteristics, and Soil Moisture. The snow and ice products were developed by the AMSR2 Cryosphere Team at the Cooperative Institute for Meteorological Satellite Studies (CIMSS) and by an affiliate of the National Snow and Ice Data Center (NSIDC). The products, except for Sea Ice Characteristics, are available from OSPO. The AMSR-2 Sea Ice Characteristics product will be made operational available to users early next year when NDE 2.0 goes operational. (Team: Jeff Key, Y-K. Lee)

 

VIIRS Day Night Band Detects Power Outage is Puerto Rico

A new VISIT Blog, titled 'Puerto Rico Power Outage' can be found at http://rammb.cira.colostate.edu/training/visit/blog/. Excerpted images below. (J. Torres, CIRA)

Before the power outage in Puerto Rico, via VIIRS DNB

Before the power outage in Puerto Rico, via VIIRS DNB
(click to enlarge)

The NCC product highlighting the emitted lights from cities and towns on the island of Puerto Rico, is shown at right. The satellite image was taken on 21 September 2016 @0627 UTC before the power outage occurred. The Aguierre Power Plant where the fire first started and took out the power-grid in Puerto Rico is also seen. In the top-right corner of the figure one can see the approximate moon phase of the lunar cycle, where there is a correlation between the distinct satellite imagery and moon phase.

 
During the power outage in Puerto Rico, via VIIRS DNB

During the power outage in Puerto Rico, via VIIRS DNB
(click to enlarge)

The NCC product at right shows the decrease in emitted light from cities and towns on the island of Puerto Rico on 22 September 2016 @0608Z after the power outage occurred. In the top-right corner of the figure one can see the approximate moon phase of the lunar cycle.

 
image: tag cloud of research-related words

Arvani, B., Pierce, R. B., Lyapustin, A. I., Wang, Y. J., Ghermandi, G., & Teggi, S. (2016). Seasonal Monitoring and Estimation of Regional Aerosol Distribution over Po Valley, Northern Italy, Using a High-Resolution MAIAC Product. Atmospheric Environment, 141, 106-121. [10.1016/j.atmosenv.2016.06.037]

Baylon, P. M., Jaffe, D. A., Pierce, R. B., & Gustin, M. S. (2016). Interannual Variability in Baseline Ozone and Its Relationship to Surface Ozone in the Western US. Environmental Science & Technology, 50(6), 2994-3001. [10.1021/acs.est.6b00219]

Brunner, J., Pierce, R. B., & Lenzen, A. (2016). Development and Validation of Satellite-Based Estimates of Surface Visibility. Atmospheric Measurement Techniques, 9(2), 409-422. [10.5194/amt-9-409-2016]

Foster, M. J., Ackerman, S. A., Bedka, K., Frey, R. A., DiGirolamo, L., Heidinger, A. K., Sun-Mack, S., Maddux, B. C., Menzel, W. P., Minnis, P., Stengel, M., & Zhao, G. (2016). [Hydrological Cycle] Cloudiness [in "State of the Climate in 2015"]. Bull. Amer. Meteor. Soc., 97(8), S28-S29. [10.1175/2016BAMSStateoftheClimate.1]

Goni, G. J., Knaff, J. A., & Lin, I. I. (2016). [the Tropics] Tropical Cyclone Heat Potential [in "State of the Climate in 2015"]. Bull. Amer. Meteor. Soc., 97(8), S120-S123. [10.1175/2016BAMSStateoftheClimate.1]

Grasso, L., Lindsey, D. T., Seaman, C. J., Stocks, B., & Rabin, R. M. (2016). Satellite Observations of Plume-Like Streaks in a Cloud Field in Canada. Pure and Applied Geophysics, 173(9), 3103-3110. [10.1007/s00024-015-1076-z]

Holmes, T. R. H., Hain, C. R., Anderson, M. C., & Crow, W. T. (2016). Cloud Tolerance of Remote-Sensing Technologies to Measure Land Surface Temperature. Hydrol. Earth Syst. Sci., 20(8), 3263-3275. [10.5194/hess-20-3263-2016]

Koner, P. K., Harris, A. R., & Dash, P. (2016). A Deterministic Method for Profile Retrievals from Hyperspectral Satellite Measurements. IEEE Transactions on Geoscience and Remote Sensing, 54(10), 5657-5670. [10.1109/TGRS.2016.2565722]

Kumar, S. V., Zaitchik, B. F., Peters-Lidard, C. D., Rodell, M., Reichle, R., Li, B. L., Jasinski, M., Mocko, D., Getirana, A., De Lannoy, G., Cosh, M. H., Hain, C. R., Anderson, M., Arsenault, K. R., Xia, Y. L., & Ek, M. (2016). Assimilation of Gridded GRACE Terrestrial Water Storage Estimates in the North American Land Data Assimilation System. Journal of Hydrometeorology, 17(7), 1951-1972. [10.1175/Jhm-D-15-0157.1]

Liu, Y. H., & Key, J. R. (2016). Assessment of Arctic Cloud Cover Anomalies in Atmospheric Reanalysis Products Using Satellite Data. Journal of Climate, 29(17), 6065-6083. [10.1175/jcli-d-15-0861.1]

Saide, P. E., Thompson, G., Eidhammer, T., da Silva, A. M., Pierce, R. B., & Carmichael, G. R. (2016). Assessment of Biomass Burning Smoke Influence on Environmental Conditions for Multiyear Tornado Outbreaks by Combining Aerosol-Aware Microphysics and Fire Emission Constraints. Journal of Geophysical Research: Atmospheres, 121(17), 10,294-210,311. [10.1002/2016jd025056]

Smith, T. M., Shen, S. S. P., & Ferraro, R. R. (2016). Superensemble Statistical Forecasting of Monthly Precipitation over the Contiguous United States, with Improvements from Ocean-Area Precipitation Predictors. Journal of Hydrometeorology, 17(10), 2699-2711. [10.1175/jhm-d-16-0018.1]

Staten, P. W., Kahn, B. H., Schreier, M. M., & Heidinger, A. K. (2016). Subpixel Characterization of HIRS Spectral Radiances Using Cloud Properties from AVHRR. Journal of Atmospheric and Oceanic Technology, 33(7), 1519-1538. [10.1175/Jtech-D-15-0187.1]

Vogel, R. L., & Brown, C. W. (2016). Assessing Satellite Sea Surface Salinity from Ocean Color Radiometric Measurements for Coastal Hydrodynamic Model Data Assimilation. Journal of Applied Remote Sensing, 10(3), 036003-036003. [10.1117/1.JRS.10.036003]

Wang, X., Key, J., Kwok, R., & Zhang, J. (2016). Comparison of Arctic Sea Ice Thickness from Satellites, Aircraft, and Piomas Data. Remote Sensing, 8(9), 713. [10.3390/rs8090713]

 

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