[Personnel: Last_Name='RITZ', Middle_Name='A.', First_Name='SCOTT']
The Second Airborne Arctic Stratospheric Expedition (AASE-II) on CD-ROMEntry ID: AASE9192
Abstract: This CD-ROM contains the final data pertaining to the Airborne Arctic Stratospheric Expedition II (AASE-II) which was based in Bangor, Maine between October 1991 and March 1992, with ER-2 flights from Ames Research Center, Moffett Field (California), Fairbanks (Alaska), and Bangor (Maine), along with and DC-8 flights from Ames, Bangor, Anchorage (Alaska), Stavanger (Norway), and Tahiti. The data ... consist of measurements collected onboard the NASA ER-2 and DC-8 aircraft, ozonesonde soundings from six Canadian stations, global grid point values of Nimbus 7 TOMS ozone, and selected radiosonde soundings from stations in the region of the experiment. Theory teams provided calculations of potential vorticity, temperature, geopotential, horizontal winds, parcel back trajectories, and concentrations of short lived species along the aircraft flight tracks; and northern hemispheric analyses of potential vorticity, temperature, geopotential, horizontal winds, and radiative heating rates.
All files within this release are standard ASCII files with variable length records terminated by a carriage return/line feed pair (
In general, the file naming convention uses a two-character prefix to identify the measurement, followed by a six digit number (yymmdd) giving the year, month, and day (UT) of the flight, balloon launch, or model result. To identify the measurement platform, a three character extension of EA1, DA1, Bhh, or Ghh is used to denote the data is from the ER-2, DC-8, balloon, or grid point model output (hh denotes the UT hour of balloon launch or model result). Exceptions to this convention are the SFyymmdd.Enn files which use the extensions E00, E01, E05, E30 to denote aerosol loading factors; the TOMS data files which have the extensions N7; and the chemical modelling result MA911006.H00.
The NASA ER-2 and DC-8 aircraft and balloons were employed to carry a suite of instruments into the lower stratosphere. Many of these were used in the 1989 AASE-I. New instruments included a gas chromatograph to measure CFC-11, a diode laser spectrometer to measure CH4, N2O, and HCL, and spectrometers to measure in situ CO, CH4, N2O, and CO2. The ER-2 was deployed from Ames Research Center, California (37N, 122W), to Fairbanks, Alaska (65N, 147W) in October 1991, so that air could be observed at the highest possible latitudes before it was incorporated into the forming polar vortex. In November 1991, the ER-2 was moved to Bangor, Maine (45N, 69W) where it would have a high probability of encountering air in the arctic vortex, but could still have acceptable weather for aircraft operations. The ER-2 missions involved flights to Greenland, 20N, 25N, 55N, 65N, 70N, 85N, and 90N. The DC-8 missions involved flights to Tahiti, Norway, Wyoming, 15N, 20N, 65N, and 90N.
On March 11, 1992, a balloon launched from Greenland (67.0N, 50.6W) carried instruments to measure O3, NO, and CLO. The objective of the balloon launch was to extend the aircraft measurements to higher latitudes. The Canadian Ozone Experiment (CANOZE-7) launched balloons from December 1991 to March 1992 from Alert, Canada (82N, 62W). The nitric acid measurements made from these balloons are also reported here.
The AASE-II data set provides the first seasonal perspective on polar ozone chemistry in the northern hemisphere. It also provides the first detailed investigations of the chemistry occurring on volcanic sulfuric acid aerosols. The data show clearly the important counterbalance between dynamical resupply of ozone in the arctic vortex and ongoing chemical destruction. The aircraft measurements also show the important role of nitrogen oxides, produced by nitric acid photolysis, in destroying CLO after temperatures become too high for polar stratospheric clouds to form. These results highlight the fact that large ozone losses in the arctic spring require either extensive denitrification of the vortex, which could occur if temperatures fell below that at which ice clouds form, or long lasting polar stratospheric clouds, that will occur if temperatures remain low enough for nitric acid to condense for extended periods of time. Neither extremely low temperatures, with extensive denitrification, nor a prolonged period of low temperatures occurred in 1992. Therefore, although very high levels of CLO were present in the arctic vortex in January 1992, the ozone loss that occurred was limited by the rapid decline of CLO in February. Other winters, such as 1992-1993, which had much more persistent low temperatures, may have conditions more conducive to significant ozone loss. It is also clear from the AASE-II data set that volcanic aerosols do significantly perturb stratospheric chemistry. However, some of the reactions are saturated at aerosol surface areas only slightly greater that ambient, and others depend strongly on temperature. Therefore, an assessment of the impact of the Pinatubo eruption on global ozone requires careful treatment of the entire thermal, dynamical, aerosol and chemical evolution of the stratosphere.
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Data Set Citation
Dataset Originator/Creator: Airborne Arctic Stratospheric Expedition II Team (AASE-II)
Dataset Title: The Second Airborne Arctic Stratospheric Expedition (AASE-II) on CD-ROMOnline Resource: http://cloud1.arc.nasa.gov
Start Date: 1991-10-04Stop Date: 1992-03-26
ATMOSPHERE > AEROSOLS > AEROSOL EXTINCTION
ATMOSPHERE > AEROSOLS > AEROSOL PARTICLE PROPERTIES
ATMOSPHERE > AEROSOLS > CLOUD CONDENSATION NUCLEI
ATMOSPHERE > AEROSOLS > PARTICULATE MATTER
ATMOSPHERE > AEROSOLS > SULFATE PARTICLES
ATMOSPHERE > AIR QUALITY > CARBON MONOXIDE
ATMOSPHERE > ALTITUDE > GEOPOTENTIAL HEIGHT
ATMOSPHERE > ATMOSPHERIC CHEMISTRY > TRACE GASES/TRACE SPECIES > BROMINE MONOXIDE
ATMOSPHERE > ATMOSPHERIC PRESSURE > ATMOSPHERIC PRESSURE MEASUREMENTS
ATMOSPHERE > ATMOSPHERIC TEMPERATURE > AIR TEMPERATURE
ATMOSPHERE > ATMOSPHERIC TEMPERATURE > POTENTIAL TEMPERATURE
ATMOSPHERE > ATMOSPHERIC WATER VAPOR > HUMIDITY
ATMOSPHERE > ATMOSPHERIC WATER VAPOR > WATER VAPOR
ATMOSPHERE > ATMOSPHERIC WATER VAPOR > WATER VAPOR PROFILES
ATMOSPHERE > ATMOSPHERIC WINDS > VORTICITY
ATMOSPHERE > CLOUDS > CLOUD TYPES > POLAR STRATOSPHERIC CLOUDS
SOLID EARTH > TECTONICS > VOLCANIC ACTIVITY > ERUPTION DYNAMICS > VOLCANIC GASES
ATMOSPHERE > ATMOSPHERIC CHEMISTRY > CARBON AND HYDROCARBON COMPOUNDS > CARBON DIOXIDE
ATMOSPHERE > ATMOSPHERIC CHEMISTRY > CARBON AND HYDROCARBON COMPOUNDS > CARBON MONOXIDE
ATMOSPHERE > ATMOSPHERIC CHEMISTRY > CARBON AND HYDROCARBON COMPOUNDS > METHANE
ATMOSPHERE > ATMOSPHERIC CHEMISTRY > HALOCARBONS AND HALOGENS > CHLORINE MONOXIDE
ATMOSPHERE > ATMOSPHERIC CHEMISTRY > HALOCARBONS AND HALOGENS > CHLOROFLUOROCARBONS > CFC-11
ATMOSPHERE > ATMOSPHERIC CHEMISTRY > NITROGEN COMPOUNDS > NITRIC ACID
ATMOSPHERE > ATMOSPHERIC CHEMISTRY > NITROGEN COMPOUNDS > NITROGEN DIOXIDE
ATMOSPHERE > ATMOSPHERIC CHEMISTRY > NITROGEN COMPOUNDS > NITROGEN OXIDES > REACTIVE NITROGEN OXIDES
ATMOSPHERE > ATMOSPHERIC CHEMISTRY > NITROGEN COMPOUNDS > NITROUS OXIDE
ATMOSPHERE > ATMOSPHERIC CHEMISTRY > OXYGEN COMPOUNDS > OZONE
Quality Quality indicators for MODIS snow data can be found in three places:
* AutomaticQualityFlag and the ScienceQualityFlag metadata objects and their corresponding explanations: AutomaticQualityFlagExplanation and ScienceQualityFlagExplanation located in the ... CoreMetadata.0 global attributes
* Custom local attributes associated with each Scientific Data Set (SDS), for example snow cover
* Snow Cover Pixel QA data field.
These quality indicators are generated during production or in post-production scientific and quality checks of the data product. The AutomaticQualityFlag is automatically set according to conditions for meeting data criteria in the snow mapping algorithm. In most cases, the flag is set to either Passed or Suspect, and in rare instances, it may be set to Failed. Suspect means that a significant percentage of the data were anomalous and that further analysis should be done to determine the source of anomalies. The AutomaticQualityFlagExplanation contains a brief message explaining the reason for the setting of the AutomaticQualityFlag. The ScienceQualityFlag and the ScienceQualityFlagExplanation maybe updated after production, either after an automated QA program is run or after the data product is inspected by a qualified snow scientist. Content and explanation of this flag are dynamic so it should always be examined if present in the external metadata file. The snow algorithm identifies missing data and reports them in the output product. Certain expected anomalous conditions may exist with the input data such as a few missing lines or unusable data from the MODIS sensor. In these cases, the snow algorithm makes no snow decision for an affected pixel. Summary statistics are calculated for these conditions and reported as Valid EV Obs Band x percent and Saturated EV Obs Band 1 percent local attributes Riggs, Hall, and Salomonson 2006). In addition to these data values, the product contains quality information at the pixel level. The Snow Cover Pixel QA data field provides additional information on algorithm results for each pixel within a MODIS scene and is used as a measure of usefulness for snow-cover data. The QA data are stored as coded integer values and tell if algorithm results were nominal, abnormal, or if other defined conditions were encountered for a pixel (Riggs, Hall, and Salomonson 2006). For example, intermediate checks for theoretical bounding of reflectance data and the NDSI ratio are made in the algorithm. In theory, reflectance values should lie within the 0-100 percent range, and the NDSI ratio should lie within the -1.0 to +1.0 range. Summary statistics are kept for pixels that exceed these theoretical limits; however, the test for snow is done regardless of violations of these limits. The NASA Goddard Space Flight Center: MODIS Land Quality Assessment Web site provides updated quality information for each product.
Data Set Progress
Distribution Media: FTP
Distribution Size: 0.5 - 10.0 MB
Distribution Format: GeoTIFF
Distribution Media: FTP
Distribution Size: 0.5 - 10.0 MB
Distribution Format: HDF-EOS
National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GSFC) Mail stop 614.1
Province or State: MD
Postal Code: 20771
Email: vincent.salomonson at utah.edu
University of Utah Department of Meteorology 135 S 1460 E, Rm 809
City: Salt Lake City
Province or State: UT
Postal Code: 84112
Email: George.A.Riggs at nasa.gov
NASA Goddard Space Flight Center (GSFC) Science Systems and Applications, Inc. Code 614.1
Province or State: MD
Postal Code: 20771
Role: TECHNICAL CONTACT
Phone: +1 (303) 492-6199
Fax: +1 (303) 492-2468
Email: nsidc at nsidc.org
National Snow and Ice Data Center CIRES, 449 UCB University of Colorado
Province or State: CO
Postal Code: 80309-0449
Diner, D.J., J.V. Martonchik, C. Borel, S.A.W. Gerstl, H.R. Gordon, Y. Knyazikhin, R. Myneni, B. Pinty, and M.M. Verstraete. 1999. MISR Level-2 surface retrieval algorithm theoretical basis document. Pasadena, CA: Jet Propulsion Laboratory.
Earth Science Data and Information System (ESDIS). 1996. EOS Ground System (EGS) systems and operations concept. Greenbelt, MD: Goddard Space Flight ... Center.
Hall, D.K., G.A. Riggs, and V.V. Salomonson. September 2001a. Algorithm Theoretical Basis Document (ATBD) for the MODIS Snow-, Lake Ice- and Sea Ice-Mapping Algorithms. Greenbelt, MD: Goddard Space Flight Center.
Hall, D.K., G.A. Riggs, V.V. Salomonson, N.E. DiGirolamo, and K.J. Bayr. 2002. MODIS snow-cover products. Remote Sensing of the Environment 83: 181-194. Hall, D.K. and J. Martinec. 1985. Remote sensing of ice and snow. London: Chapman and Hall.
Hall, D.K., J.L. Foster, D.L. Verbyla, A.G. Klein, and C.S. Benson. 1998. Assessment of snow cover mapping ac
curacy in a variety of vegetation cover densities in central Alaska. Remote Sensing of the Environment 66: 129-137.
Hall, D.K., J.L. Foster, V.V. Salomonson, A.G. Klein, and J.Y.L. Chien. 2001b. Development of a technique to assess snow-cover mapping accuracy from space. IEEE Transactions on Geoscience and Remote Sensing 39(2): 232-238.
Hall, D.K., G.A. Riggs. 2006. Assessment of errors in the MODIS suite of snow-cover products. Hydrological Processes, in press.
Hapke, B. 1993. Theory of reflectance and emittance spectroscopy. Cambridge: Cambridge University Press. Klein, A. MODIS Snow Albedo Prototype. 2003.
Klein, A.G., and J. Stroeve. 2002. Development and validation of a snow albedo algorithm for the MODIS instrument. Annals of Glaciology 34: 45-52.
Klein, A.G., D.K. Hall, and G.A. Riggs. 1998. Improving snow-cover mapping in forests through the use of a canopy reflectance model. Hydrologic Processes 12(10-11): 1723-1744.
Markham, B.L. and J.L. Barker. 1986. Landsat MSS and TM post-calibration dynamic ranges, exoatmospheric reflectances and at-satellite temperatures. EOSAT Technical Notes 1:3-8.
MODIS Characterization and Support Team (MCST). 2000. MODIS Level-1B product user's guide for Level-1B Version 2.3.x Release 2. MCST Document #MCM-PUG-01-U-DNCN.
Pearson II, F. 1990. Map projections: theory and applications. Boca Raton, FL: CRC Press, Inc.
Riggs, G.A., D.K. Hall, and V.V. Salomonson. January 2006. MODIS snow products user guide for collection 4 data products.
Salomonson, V. and I. Appel. 2006. Development of the Aqua MODIS NDSI Fractional Snow Cover Algorithm and Validation Results. Transactions on Geoscience and Remote Sensing 44(7):1747-1756.
Salomonson, V. and I. Appel. 2004. Estimating fractional snow cover from MODIS using the normalized difference snow index (NDSI). Remote Sensing of the Environment 89:351-360.
Wiscombe, W.J. and S.G. Warren. 1980. A model for the spectral albedo of snow I: pure snow. Journal of the Atmospheric Sciences 37: 2712-2733.
Extended Metadata Properties
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Creation and Review Dates
DIF Creation Date: 2006-11-09
Last DIF Revision Date: 2012-05-04
Future DIF Review Date: 2008-09-01