This data set has been collected from a custom built battery prognostics testbed at the NASA Ames Prognostics Center of Excellence (PCoE). Li-ion batteries were run through 3 different operational profiles (charge, discharge and Electrochemical Impedance Spectroscopy) at different temperatures. Discharges were carried out at different current load levels until the battery voltage fell to preset voltage thresholds. Some of these thresholds were lower than that recommended by the OEM (2.7 V) in order to induce deep discharge aging effects. Repeated charge and discharge cycles result in accelerated aging of the batteries. The experiments were stopped when the batteries reached the end-of-life (EOL) criteria of 30% fade in rated capacity (from 2 Ah to 1.4 Ah). Data Acquisition: The testbed comprises: * Commercially available Li-ion 18650 sized rechargeable batteries, * Programmable 4-channel DC electronic load, * Programmable 4-channel DC power supply, * Voltmeter, ammeter and thermocouple sensor suite, * Custom EIS equipment, * Environmental chamber to impose various operational conditions, * PXI chassis based DAQ and experiment control, and MATLAB based experiment control, data acquisition and prognostics algorithm evaluation setup (appx. data acquisition rate is 10Hz). Parameter Description: Data Structure: cycle: top level structure array containing the charge, discharge and impedance operations type: operation type, can be charge, discharge or impedance ambient_temperature: ambient temperature (degree C) time: the date and time of the start of the cycle, in MATLAB date vector format data: data structure containing the measurements for charge the fields are: Voltage_measured: Battery terminal voltage (Volts) Current_measured: Battery output current (Amps) Temperature_measured: Battery temperature (degree C) Current_charge: Current measured at charger (Amps) Voltage_charge: Voltage measured at charger (Volts) Time: Time vector for the cycle (secs) for discharge the fields are: Voltage_measured: Battery terminal voltage (Volts) Current_measured: Battery output current (Amps) Temperature_measured: Battery temperature (degree C) Current_charge: Current measured at load (Amps) Voltage_charge: Voltage measured at load (Volts) Time: Time vector for the cycle (secs) Capacity: Battery capacity (Ahr) for discharge till 2.7V for impedance the fields are: Sense_current: Current in sense branch (Amps) Battery_current: Current in battery branch (Amps) Current_ratio: Ratio of the above currents Battery_impedance: Battery impedance (Ohms) computed from raw data Rectified_impedance: Calibrated and smoothed battery impedance (Ohms) Re: Estimated electrolyte resistance (Ohms) Rct: Estimated charge transfer resistance (Ohms) Intended Use: The data sets can serve for a variety of purposes. Because these are essentially a large number of Run-to-Failure time series, the data can be set for development of prognostic algorithms. In particular, due to the differences in depth-of-discharge (DOD), the duration of rest periods and intrinsic variability, no two cells have the same state-of-life (SOL) at the same cycle index. The aim is to be able to manage this uncertainty, which is representative of actual usage, and make reliable predictions of Remaining Useful Life (RUL) in both the End-of-Discharge (EOD) and End-of-Life (EOL) contexts.

MY NASA DATA (MND) is a tool that allows anyone to make use of satellite data that was previously unavailable.Through the use of MND’s Live Access Server (LAS) a multitude of charts, plots and graphs can be generated using a wide variety of constraints. This site provides a large number of lesson plans with a wide variety of topics, all with the students in mind. Not only can you use our lesson plans, you can use the LAS to improve the ones that you are currently implementing in your classroom.

Experiments on a milling machine for different speeds, feeds, and depth of cut. Records the wear of the milling insert, VB. The data set was provided by the UC Berkeley Emergent Space Tensegrities (BEST) Lab.

WELDAKMO.015 was decommissioned on December 2, 2019. Users are encouraged to use the improved monthly Global Web-Enabled Landsat Data (GWELD) Version 3, 3.1, and 3.2 datasets.NASA’s Web-Enabled Landsat Data (WELD) are generated from composited 30 meter (m) Landsat Enhanced Thematic Mapper Plus (ETM+) mosaics of the United States and Alaska from 2002 to 2012. These mosaics provide consistent data for deriving land cover, geophysical, and biophysical products for regional assessments of surface dynamics for effective study of Earth system function. The Alaska Monthly products are defined with respect to each calendar year by the days in each month. This product includes Top of Atmosphere (TOA) Reflectance and Brightness Temperature, along with the Normalized Difference Vegetation Index (NDVI) generated from Band 3 and Band 4 TOA Reflectance. These products are distributed in Hierarchical Data Format 4 (HDF4).The WELD project is funded by the National Aeronautics and Space Administration (NASA) and is a collaboration between the United States Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center and the South Dakota State University (SDSU) Geospatial Sciences Center of Excellence (GSCE). Known Issues WELD Version 1.5 known issues can be found in the WELD Version 1.5 User Guide.Improvements/Changes from Previous Version Version 1.5 is the original version.

WELDAKWK.015 was decommissioned on December 2, 2019. Users are encouraged to use the improved monthly Global Web-Enabled Landsat Data (GWELD) Version 3, 3.1, and 3.2 datasets.NASA’s Web-Enabled Landsat Data (WELD) are generated from composited 30 meter (m) Landsat Enhanced Thematic Mapper Plus (ETM+) mosaics of the United States and Alaska from 2003 to 2012. These mosaics provide consistent data to derive land cover, geophysical, and biophysical products for regional assessments of surface dynamics for effective study of Earth system function. This product includes Top of Atmosphere (TOA) Reflectance and Brightness Temperature, along with the Normalized Difference Vegetation Index (NDVI) generated from Band 3 and Band 4 TOA Reflectance. WELDAKWK is distributed in Hierarchical Data Format 4 (HDF4).The WELD weekly products are defined with respect to each calendar year and as consecutive 7 day products with the following examples defined for the weeks: Week01: January 1 to January 7 Week02: January 8 to January 14 Week52: December 24 to December 30 (non-leap years) Week52: December 23 to December 29 (leap years) Week53: December 31 (non-leap years) Week53: December 30 to December 31 (leap years) The WELD project is funded by the National Aeronautics and Space Administration (NASA) and is a collaboration between the United States Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center and the South Dakota State University (SDSU) Geospatial Sciences Center of Excellence (GSCE). Known Issues WELD Version 1.5 known issues can be found in the WELD Version 1.5 User Guide.Improvements/Changes from Previous Version Version 1.5 is the original version.

This dataset provides flight track and aircraft navigation data from the NASA Atmospheric Tomography Mission (ATom). Flight track information is available for the four ATom campaigns: ATom-1, ATom-2, ATom-3, and ATom-4. Each ATom campaign consists of multiple individual flights and flight navigational information is recorded in 10-second intervals. Data available for each flight includes research flight number, date, and start and stop time of each 10-second interval. In addition, latitude, longitude, altitude, pressure and temperature is included at each 10-second interval. NASA's ATom campaign deploys an extensive gas and aerosol payload on the NASA DC-8 aircraft for systematic, global-scale sampling of the atmosphere, profiling continuously from 0.2 to 12 km altitude. Flights occurred in each of 4 seasons from 2016 to 2018. During each campaign, flights originate from the Armstrong Flight Research Center in Palmdale, California, fly north to the western Arctic, south to the South Pacific, east to the Atlantic, north to Greenland, and return to California across central North America. ATom establishes a single, contiguous, global-scale dataset. One intended use of this flight track data is to facilitate to mapping model results from global models onto the precise ATom flight tracks for comparison.

WELDLCLUC.015 was decommissioned on December 2, 2019. The Web-Enabled Landsat Data (WELD) 5-year Land Cover Land Use Change (LCLUC) is a composite of 30 meter (m) land use land change product for the contiguous United States (CONUS). The data were generated from five years of consecutive growing season WELD weekly composite inputs from April 15, 2006, to November 17, 2010. WELD data are created using Landsat Thematic Mapper Plus (ETM+) Terrain Corrected data. This product includes data about tree cover loss and bare ground gain, which are composited over the five year period. WELD LCLUC is distributed in Hierarchical Data Format 4 (HDF4).The WELD project is funded by the National Aeronautics and Space Administration (NASA) and is a collaboration between the United States Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center and the South Dakota State University (SDSU) Geospatial Sciences Center of Excellence (GSCE). Known Issues WELD Version 1.5 known issues can be found in the WELD Version 1.5 User Guide.Improvements/Changes from Previous Version Version 1.5 is the original version.

This data set includes full resolution electric and magnetic wave spectra obtained during a ~ 15-minute period of contiguous observations near the closest approach to Ida and a number of non-contiguous spectra taken prior to closest approach. In addition waveform survey data (uncalibrated) and all instrument housekeeping data are included. The parameters provided for both the electric and magnetic field spectral data are uncalibrated data numbers. Software and calibration tables provided as part of this data set allow for fully calibrated data for the electric field measurements in raw data numbers, voltage at the antenna inputs (V), electric field (V/m), electric field spectral density (V2/m2/Hz), or power flux (W/m2/Hz). The magnetic field measurements can be provided in units of magnetic field spectral density (nT2/Hz). The sources of these data are the High Frequency Receiver, Sweep Frequency Receiver, and Spectrum Analyzer which make up the Low Rate Science portion of the PWS. During the included time interval, the instrument cycled between the electric and magnetic antennas for the Sweep Frequency Receiver and the Spectrum Analyzer. The High Frequency Receiver is always connected to the electric antenna. Hence, for the Sweep Frequency Receiver and the Spectrum Analyzer, the temporal resolution for electric measurements is basically one spectrum per 37.33 seconds. For the High Frequency Receiver, the time between spectra is 18.67 seconds. Note that the lowest 14 High Frequency Receiver channels are sampled twice per 18.67 seconds while the remaining channels are sampled only once. The four Spectrum Analyzer channels are each sampled 7 times in an 18.67 sec interval.

Citation: If using this dataset please cite the following in your work: @misc{VotDasNemSri2010 , author = "Petr Votava and Kamalika Das and Rama Nemani and Ashok N. Srivastava", year = "2010", title = "MODIS surface reflectance data repository", url = "https://c3.ndc.nasa.gov/dashlink/resources/331/", institution = "NASA Ames Research Center" } Petr Votava, Kamalika Das, Rama Nemani, Ashok N. Srivastava. (2010). MODIS surface reflectance data repository. NASA Ames Research Center. Data Description: The California satellite dataset using the MODerate-resolution Imaging Spectroradiometer (MODIS) product MCD43A4 provides reflectance data adjusted using a bidirectional reflectance distribution function (BRDF) to model the values as if they were taken from nadir view. Both Terra and Aqua data are used in the generation of this product, providing the highest probability for quality input data. More information at: https://lpdaac.usgs.gov/lpdaac/products/modis_products_table/nadir_brdf_adjusted_reflectance/16_day_l3_global_500m/v5/combined Data Organization: The nine data folders correspond to three years of data.Under this top level directory structure are separate files for each band (1 - 7) and each 8-day period of the particular year. Within the period the best observations were selected for each location. File Naming Conventions: Each of the files represent a 2D dataset with the naming conventions as follows: MCD43A4.CA_1KM.005.. .flt32 where is the beginning year-day of the period that where YYYY = year and DDD = day of year (001 - 366) represents the observations in particular (spectral) band (band 1 - band 7) - since the indexing is 0-based, the range of indexes on the files is from 0 - 6 (where 0 = band 1, and 6 = band 7) The spectral band frequencies for the MODIS acquisitions are as follows: BAND1 620 - 670 nm BAND2 841 - 876 nm BAND3 459 - 479 nm BAND4 545 - 565 nm BAND5 1230 - 1250 nm BAND6 1628 - 1652 nm BAND7 2105 - 2155 nm File Specifications: Each file is a single 2D dataset. DATA TYPE: 32-bit floating point (IEEE754) with little-Endian byte ordering NUMBER OF ROWS: 1203 NUMBER OF COLUMNS: 738 FILL VALUES (observations that are either not valid or not on land, such as ocean etc.): -999.0 Overview: DATASET: MODIS 8-day Surface Reflectance BRDF-adjusted from Terra and Aqua COLLECTION: 5 DATA TYPE: IEEE754 float (32-bit float) BYTE ORDER: LITTLE ENDIAN (Intel) DIMS: 1203 rows x 738 columns FILL VALUE: -999.0 SPATIAL RESOLUTION: 1km PROJECTION: Lambert Azimuthal Equal Area

This data set contains Galileo trajectory data in moon (Amalthea, Io, Europa, Ganymede, Callisto) centered coordinates for all of the near satellite encounters. Ephemeris data are provided every two seconds for approximately one hour about closest approach.

This resource area contains descriptions of actual electronic systems failure scenarios with an emphasis on the diversity of failure modes and effects that can befall dependable systems. Introductory pages begin here. The descriptions begin here. These pages are separated into sections. Each section starts with a List of failure scenarios. In between the List slides are slides that give more information on those scenarios which warrant more than a bullet or two of explanation. Some references are listed here. A list of acronyms and initialisms is here. If you would like to add a story to this list or add additional significant details to an existing story, please contact Kevin Driscoll at For a not-quite-working wiki subset of this Resource area, click on the Wiki link just to the left of this Summary or go to the URL https://c3.nasa.gov/dashlink/projects/79/wiki/test_stories_split. Also, those who log in can add comments to the Discussions at the bottom of this page.

This paper takes an empirical approach to identify operational factors at busy airports that may predate go-around maneuvers. Using four years of data from San Francisco International Airport, we begin our investigation with a statistical approach to investigate which features of airborne, ground operations (e.g., number of inbound aircraft, number of aircraft taxiing from gate, etc.) or weather are most likely to fluctuate, relative to nominal operations, in the minutes immediately preceding a missed approach. We analyze these findings both in terms of their implication on current airport operations and discuss how the antecedent factors may affect NextGen. Finally, as a means to assist air traffic controllers, we draw upon techniques from the machine learning community to develop a preliminary alert system for go-around prediction.

GOES-17 (G17) is the second satellite in the US NOAA's GOES-R series. It was launched on 1 Mar 2018 in an interim position at 89.5-deg W for initial Cal/Val, moved to its nominal position at 137.2-deg W in Nov 2018, and declared NOAA operational GOES-West satellite on 12 Feb 2019. Advanced Baseline Imager (ABI) is a 16 channel sensor, of which five (3.9, 8.4, 10.3, 11.2, 12.3 um) are suitable for SST. From altitude 35,800km, G17/ABI maps SST in a Full Disk (FD) area from 163E-77W and 60S-60N, with spatial resolution 2km/nadir to 15km/VZA 67-deg, and 10-min temporal sampling. The ABI L2P SST is derived at the native sensor resolution using NOAA ACSPO system. ACSPO processes every 10-min FD, identifies good-quality ocean pixels (Petrenko et al., 2010) and derives SST using Non-Linear SST (NLSST) algorithm (Petrenko et al., 2014). Unfortunately, the G17 ABI loop heat pipe (LHP) that should maintain the ABI at its intended temperature, is not operating at its designed capacity, which required mitigations to the ACSPO algorithms and releasing an updated ACSPO version 2.71 (Pennybacker et al, 2019). In particular, band 11.2um, most subject to calibration problems, is not used leading to a 3-band (8.4, 10.3, and 12.3um) NLSST, and increased calibration problems prevent SST retrievals at night. As a result, the G17 SST is only reported for 13 out of 24hrs/day, from 20UTC to 08UTC. The 10-min FD data are subsequently collated in time, to produce 1-hr product, with improved coverage and reduced cloud leakages and image noise. The collation algorithm also reduces G17 excessive sensor noise and striping to levels similar to G16. The collated SSTs are only reported over clear-sky water pixels. All pixels with valid SSTs are recommended for use. The L2P is reported in NetCDF4 GDS2 format, 13 granules per day, with a total data volume 0.3GB/day. ACSPO files also report sun-sensor geometry, wind speed and l2p_flags (day/night, land, ice, twilight, glint flags). Per GDS2 specifications, two Sensor-Specific Error Statistics (bias and standard deviation) are reported in each pixel (Petrenko et al., 2016). Pixel earth locations are not reported in the granules, as they remain unchanged from granule to granule. Those can be obtained using a flat lat/lon file or a Python script (see Documentation page). The ACSPO G17 ABI SSTs are continuously validated in SQUAM (Dash et al, 2010). A reduced size (0.1GB/day), 0.02-deg equal-angle gridded L3C product is available at https://podaac.jpl.nasa.gov/dataset/ABI_G17-STAR-L3C-v2.71.

This dataset provides the annual date of snowpack seasonal beginning melt (i.e., main melt onset date, MMOD) across northwest Canada; Alaska, U.S.; and parts of far eastern Russia at 6.25 km resolution for the period 1988-2018. MMOD was derived from the daily 19V (K-band) and 37V (Ka-band) GHz bands from the Making Earth Science Data Records for Use in Research Environments (MEaSUREs) Calibrated Enhanced-Resolution Passive Microwave (PMW) EASE-Grid Brightness Temperature (Tb) Earth System Data Record (ESDR). The PMW MMOD dataset was validated using the transition date from Freeze Degree Days (FDD) to Thaw Degree Days (TDD) from in situ air temperature observations from 31 SNOw TELemetry network (SNOTEL) observations, and compared to the established Freeze-Thaw ESDR (FT-ESDR) spring onset date. The resulting MMOD data record is suitable for documenting the spatial-temporal impacts of MMOD variability in ecosystem services, wildlife movements, and hydrologic processes across the ABoVE domain. The data from 1988-2016 included a coastal mask removing coastal pixels due to potential water contamination from coarse brightness temperature observations (Dersken et al., 2012). There is not a coastal mask for the 2017-2018 data. The full data are included, and data users should be aware that coastal values can be adversely affected by adjacent water bodies.

This dataset is derived under the Cross-Calibrated Multi-Platform (CCMP) project and contains a value-added monthly mean ocean surface wind and pseudostress to approximate a satellite-only climatological data record. The CCMP datasets combine cross-calibrated satellite winds obtained from Remote Sensing Systems (REMSS) using a Variational Analysis Method (VAM) to produce a high-resolution (0.25 degree) gridded analysis. The CCMP data set includes cross-calibrated satellite winds derived from SSM/I, SSMIS, AMSR-E, TRMM TMI, QuikSCAT, SeaWinds, WindSat and other satellite instruments as they become available from REMSS. REMSS uses a cross-calibrated sea-surface emissivity model function which improves the consistency between wind speed retrievals from microwave radiometers (i.e., SSM/I, SSMIS, AMSR, TMI, WindSat) and those from scatterometers (i.e., QuikSCAT and SeaWinds). The VAM combines these data with in situ measurements and a starting estimate (first guess) of the wind field. The European Center for Medium-Range Weather Forecasts (ECMWF) ERA-40 Reanalysis is used as the first-guess from 1987 to 1998. The ECMWF Operational analysis is used from January 1999 onward. All wind observations and analysis fields are referenced to a height of 10 meters. The ERA-40 can be obtained from the Computation and Information Systems Laboratory (CISL) at the National Center for Atmospheric Research (NCAR): http://rda.ucar.edu/datasets/ds117.0/. The ECMWF Operational analysis can also be obtained from CISL at NCAR: http://rda.ucar.edu/datasets/ds111.1/. Three products are distributed to complete the CCMP dataset series. L3.0 product contains high-resolution analyses every 6-hours. These data are then time averaged over monthly and 5-day periods to derive the L3.5 product. Directions from the L3.0 product are then assigned to the time and location of the passive microwave satellite wind speed observations to derive the L2.5 product. All datasets are distributed on a 0.25 degree cylindrical coordinate grid. This dataset is one in a series of First-Look (FLK) CCMP datasets and is a continuation and expansion of the SSM/I surface wind velocity project that began under the NASA Pathfinder Program. Refinements and upgrades to the FLK version will be incorporated under a new release (date to be determined) known as Late-look (LLK) and may include additional satellite datasets. All satellite surface wind data are obtained from REMSS under the DISCOVER project: Distributed Information Services: Climate/Ocean Products and Visualizations for Earth Research (http://www.discover-earth.org/index.html). The CCMP project is the result of an investigation funded by the NASA Making Earth Science data records for Use in Research Environments (MEaSUREs) program (http://community.eosdis.nasa.gov/measures/). In accordance with the MEaSUREs program, the CCMP datasets are also known as Earth System Data Records (ESDRs). In collaboration with private and government institutions, a team led by Dr. Robert Atlas (PI; proposal originally solicited by REASoN, and currently funded by MEaSURES) has created the CCMP project to provide multi-instrument ocean surface wind velocity ESDRs, with wide ranging research applications in meteorology and oceanography.

The Public Use Microdata Samples (PUMS) are computer-accessible files containing records for a sample of housing Units, with information on the characteristics of each housing Unit and the people in it for 1940-1990. Within the limits of sample size and geographical detail, these files allow users to prepare virtually any tabulations they require. Each datafile is documented in a codebook containing a data dictionary and supporting appendix information. Electronic versions for the codebooks are only available for the 1980 and 1990 datafiles. Identifying information has been removed to protect the confidentiality of the respondents. PUMS is produced by the United States Census Bureau (USCB) and is distributed by USCB, Inter-university Consortium for Political and Social Research (ICPSR), and Columbia University Center for International Earth Science Information Network (CIESIN).

This data set contains two versions of three different data file types. One version is a corrected form of the other ('C'). The corrected data are adjusted for minute gaps in between the detector cycles. This data ranges from the Io flyby (December 1995) to end of mission. All available motor positions are provided. Step 0, which looks behind the background shield, is included for an indication of the background penetration of the detector. The rates reported are in units of (counts/sec), pitch and phase angles are in (radians). The sectors are ordered in time

The version 4.0 SMAP-SSS level 3, 8-Day running mean gridded product is based on the fourth release of the validated standard mapped sea surface salinity (SSS) data from the NASA Soil Moisture Active Passive (SMAP) observatory, produced operationally by Remote Sensing Systems (RSS). Enhancements with this release include: use of an improved 0.125 degree land correction table with land emission based on SMAP TB; replacement of the previous NCEP sea-ice mask with one based on RSS AMSR-2 and implementing a sea-ice threshold of 0.3% (gain weighted sea-ice fraction); revised solar flagging that depends on glint angle and wind speed; inclusion of estimated SSS-uncertainty; consolidation of both 40KM and 70KM SMAP-SSS datasets as variable fields in a single data product. Daily data files for this product are based on SSS averages spanning an 8-day moving time window. SMAP data begins on April 1,2015 and is ongoing. L3 products are global in extent and gridded at 0.25degree x 0.25degree with a default spatial feature resolution of approximately 70KM. Note that while a SSS 40KM variable is also included in the product, for most open ocean applications, the default SSS variable (70KM) is best used as they are significantly less noisy than the 40KM data. The SMAP satellite is in a near-polar orbit at an inclination of 98 degrees and an altitude of 685 km. It has an ascending node time of 6 pm and is sun-synchronous. With its 1000km swath, SMAP achieves global coverage in approximately 3 days, but has an exact orbit repeat cycle of 8 days. On board instruments include a highly sensitive L-band radiometer operating at 1.41GHz and an L-band 1.26GHz radar sensor providing complementary active and passive sensing capabilities. Malfunction of the SMAP scatterometer on 7 July, 2015, has necessitated the use of collocated wind speed, primarily from WindSat, for the surface roughness correction required for the surface salinity retrieval.

MI1AENG1_2 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 1A Engineering Data file Type 1 version 2. It is the Reformatted Annotated Level 1A product for the camera Engineering data, which represents indicators of sampled measurements for that MISR instrument. This product provides all of the data needed to describe the state of the instrument for Level 1 processing and analysis at a later date. These data are composed primarily of temperatures, voltages and currents of each camera, the optical bench, calibration diodes, and system electronics. Verification and reporting flags for latches and limit-switches on the cover/goniometer and the calibration diffuser panels are also incorporated into these data. The MISR instrument consists of nine pushbroom cameras which measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four cameras pointing forward, and four cameras pointing aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid, are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm.

The ACSPO G17/ABI L3C (Level 3 Collated) product is a gridded version of the ACSPO G17/ABI L2P product available at https://podaac.jpl.nasa.gov/dataset/ABI_G17-STAR-L2P-v2.71. The L3C output files are 1hr granules in NetCDF4 format, compliant with the GHRSST Data Specification version 2 (GDS2). Due to the loop heat pipe (LHP) issue on G17 ABI, there are only 13 granules available per 24hr interval, from 20UTC to 08UTC, followed by a break from 09UTC to 19UTC, with a total data volume of 0.1GB/day. Valid SSTs are found over oceans, sea, lakes or rivers, with fill values reported elsewhere. The following additional layers are also reported: SST, ACSPO clear-sky mask (ACSM; provided in each grid as part of l2p_flags, which also includes day/night, land, ice, twilight, and glint flags), NCEP wind speed and ACSPO SST minus reference (Canadian Met Centre 0.1deg L4 SST; available at https://podaac.jpl.nasa.gov/dataset/CMC0.1deg-CMC-L4-GLOB-v3.0 ). All valid SSTs in L3C are recommended for users, although data over internal waters may not have enough in situ data to be adequately validated. Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (bias and standard deviation) are reported in each pixel with valid SST. The ACSPO VIIRS L3U product is monitored and validated against iQuam in situ data (Xu and Ignatov, 2014) in SQUAM (Dash et al, 2010).