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Database Title

9.8 Data products from satellite data screens

Keywords
Type1.
Type 11.
New Zealand EEZ; oceans; physical processes; climate;
Clouds; atmospheric winds; ocean colour;SST;
Abstract Remote sensing data has been archived in original format and used to derive the following products:
  1. Clouds
  2. Sea surface temperature(SST)
  3. Ocean colour
  4. Atmospheric microwave emission
  5. Infrared atmospheric emission
  6. Atmospheric winds.

There are also radar observations.

Geographical Coverage Varying coverage depending on conditions of Australia to the mid Pacific; tropics to Antartica.
Dataset start date. 1993 (NOAA)-1,2,4,5
1998 (GMS)-6
2000 (Seastar Spacecraft)-3
1993-radar
(numbers from abstract)
Dataset end date. Current
Status/currency. In progress
Update frequency. Unknown
Maintenance. NIWA

 

Technical Evaluation

 
Parameters- what is measured
  • Radiances emitted from the Earth's atmosphere
  • Radar reflectivity from hydrometers
Parameters- what is calculated
  • SST
  • Atmospheric winds
  • Ocean colour properties
  • Calibrated radiances
  • Atmospheric microwave and infrared emissions
  • Precipitation
  • Winds
Methods used to measure parameters Measurements are taken at many different spectral intervals from satellite images from NOAA,GMS and Seastar satellites. There are a variety of transformations on the data.
Scale of use. Horizontal:1-50km for satellite and radar
Number of records Radar/GMS/NOAA: 4 terabytes since 1993 includes 200 GB for SST data at 1km resolution. Data for radar/GMS/NOAA comes in at 6GB/day.
GIS compatibility. STT data and atmospheric emission data is geographically located. There may be too much data to use in GIS.
Available formats for users. Multiple proprietry formats.
Published papers
Access constraints. 100% NIWA owned
Measurement Accuracy To be completed by database manager.
Completeness of dataset To be completed by database manager.
Positional accuracy To be completed by database manager.
Database custodian NIWA
Database custodian contact person Mike Uddstrom
Database custodian Contact
Address
Phone
Fax
Email
P.O.Box 14-901
Kilburnie
Wellington
+64 4 386 0365
+64 4 386 2153
m.uddstrom@niwa.co.nz
References E.g. Uddstrom,M.J.; Oien,N.A. 1999. On the use of high-resolution satellite data to describe the spatial and temporal variability of sea surface temperatures in the New Zealand region.
Date metadata record prepared. June 2000
Author of metadata record. Victoria Froude

 

Management Evaluation

 
Original purpose. Research and environmental monitoring.
Eg.: fisheries research, meterology, oceanography.
Relationships with classification systems. N/A
Relationships with other databases These datasets link together. There are also links to fisheries catch datasets.
Known relationships with proposed EPIP indicators. Not known.
Database uses? The derived information is used for a range of purposes relating to research and environmental monitoring.
Public awareness of the database None
Database strengths. SST and atmospheric wind data has been callibrated and vallidated.
Database limitations.
  • The data volumes are too large to allow queries
  • Specialised software is needed to access the data
  • There are callibration and vallidation problems with ocean colour
  • If cloudy can't get SST data
  • There are radar range dependent errors.

 

What are the Current and Emerging Uses of the Database for:

 
Assisting with determining historic state/baseline. This can be done where data sets exist.
There is international data dating back to 1982 but it has a lower spatial resolution and accuracy than present day data.
Assisting with determining current state/baseline. The data is used for this.
Assisting with modeling possible future outcomes. This is possible for SST for several months and for weather predictions for 6-36 hours
Risk assessment. This is possible for weather, especially atmospheric microwave and infrared emissions
Monitoring site selection and sample design. This is possible.
Aggregating and reporting data locally, regionally or nationally. The data can be aggregated in different ways depending on purpose.