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BEAT - The Basic ENVISAT Atmospheric Toolbox

BEAT - The Basic ENVISAT Atmospheric Toolbox. Sander Niemeijer <niemeijer@stcorp.nl>. Introducing the BEAT software. BEAT : the B asic E NVISAT A tmospheric T oolbox Goal: to provide the necessary software to support scientific usage of atmospheric data One of the ESA ENVISAT Toolboxes

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BEAT - The Basic ENVISAT Atmospheric Toolbox

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  1. BEAT - The Basic ENVISAT Atmospheric Toolbox Sander Niemeijer <niemeijer@stcorp.nl>

  2. Introducing the BEAT software BEAT: the Basic ENVISAT Atmospheric Toolbox Goal: to provide the necessary software to support scientific usage of atmospheric data • One of the ESA ENVISAT Toolboxes • Open Source and freely available • Cross Platform (Linux, Windows, Mac OS X, etc.)

  3. BEAT components • BEAT package • BEAT-I direct product interface • BEAT-II simplified data interface • VISAN visualization & analysis application • Scientific Tutorial • Scientific Modules (GeoFit) • Sample data

  4. BEAT-I Overview • BEAT Layer-I provides a ‘direct product interface’ to all data contained in a product file. • Support for: • GOMOS, MIPAS, SCIAMACHY (ENVISAT) • GOME (ERS-2) • OMI, TES, MLS (Aura) • GOME-2, IASI (MetOp) • Any HDF4/5-based data (e.g. ground-based observations) • Most XML files (new since BEAT v4.2, future use for e.g. Aeolus) • Directly ingest data from C, Fortran, Python, IDL, MATLAB • Command-line tools: beatcheck, beatdump, beatfind

  5. BEAT-I: How does it work? (1) Single Interface to all product data 4 back-ends ASCII/BIN XML HDF4 HDF5 BEAT data dictionary XML data dictionary MetOp (EPS) netCDF HDF5, HDF-EOS5 (Aura) ENVISAT (PDS) XML (Earth Explorer) HDF4, HDF-EOS2 (Cal/Val data) GOME (binary, ASCII) …

  6. BEAT-I: How does it work? (2) Both self-describing and non-self-describing products are hierarchically structured (i.e., like a tree): • records have fields, identified by name • arrays have entries, identified by index BEAT-I understands this tree structure, and allows you to gointo branches by specifying… • field names - e.g. “mph”, “dsr_time”, … • array indices - e.g. [1,2,3], [17], or just 17.

  7. BEAT-I: How does it work? (3) • BEAT-I includes a data dictionary containing electronic descriptions of all data formats that are not ‘self-describing’ and uses this to dynamically interpret a product • Documentationof data dictionarydescription (i.e. fullformat descriptions)included with BEAT

  8. The BEAT-I interface - examples pf = beat_open(“SCI_OL__2P_…….N1”) product = beat_fetch(pf) mph = beat_fetch(pf, “mph”) abs_orbit = beat_fetch(pf, “mph”, “abs_orbit”) time = beat_fetch(pf, “nad_uv0_o3”, [12], “dsr_time”) time = beat_fetch(pf, “nad_uv0_o3”, 12, “dsr_time”) all_times = beat_fetch(pf, “nad_uv0_o3”, -1, “dsr_time”) result = beat_close(pf)

  9. BEAT-II Overview Why a second layer? • Many data products have a very complicated structure that reflect the way the data-processors work, not the way end-users look at the data. • Correct matching of measured spectra/retrievals to geolocation (“ground pixels”) can be hard to get right. • For typical analysis work, only a fraction of the data within the file is useful. • It is difficult to compare data from different instruments. BEAT-II addresses these issues by providing a simplified (but still valid!) view on the data.

  10. BEAT-II Features (1) • Only one call needed to ingest the most important data from a single product or multiple products; • Ingest product data into ‘flat’ BEAT-II records; • BEAT-II records use native data type for each interface (e.g. ‘struct’ for MATLAB/IDL, ‘object’ for Python); • Each field corresponds with a specific scientific parameter; • Fields use standardized naming convention and provide data in standardized units, thus allowing easy comparison;

  11. BEAT-II Features (2) • Powerful ingestion filter options to determine which parameters to ingest and to put restrictions on time, location, etc.; • Provides useful algorithms for analysis: • filter data; • find co-located data; • import/export record to ASCII, binary, HDF4, and HDF5; • Support for IDL, MATLAB, C, Fortran, and Python; • Command-line tool: beatl2dump; • BEAT-II is build on top of BEAT-I, but also supports: • ACE-FTS, ODIN/OSIRIS

  12. The ingested SCIAMACHY data is returned to you using the same structure as you would get for GOME or OMI data, even though those data was stored very differently in the data files. Also, the call to the ingestion function is almost identical to the one you would use for GOME or OMI. >>> scia = beatl2.ingest(‘SCI_OL__2P*’, ‘data=nadir_uv_0, longitude_max=-120, latitude_min=50, latitude_max=75, scan_direction=forward, convert_to_DU=1’) >>> print scia type: ’SCIAMACHY_L2_Offline_nadir_uv_0' time: [2132 double] latitude: [2132 double] longitude: [2132 double] corner_latitude: [2132 x4 double] corner_longitude: [2132 x4 double] o3_column: [2132 double] o3_column_unit: 'DU' o3_column_error: [2132 double] o3_column_error_unit: ’%' integration_time: [2132 double] scan_direction: [2132 string] >>> beat.time_to_string(scia.time[0]) ’16-SEP-2005 05:27:49.339949' Reading SCIAMACHY data with BEAT-II

  13. The VISAN program • Ingest data using BEAT-I and BEAT-II; • Python language for command input and performing calculations and manipulations; • Product Browser to inspect product structure and contents; • With one function call, create interactive 2D and World plot visualizations of your data; • Open Source and Cross-Platform: supports Windows, Linux, and Mac OS X.

  14. Main Window & Product Browser

  15. Plotting SCIAMACHY data >>> wplot(scia, colorrange=[150,500]) • Only a single command is needed to turn the ingested SCIAMACHY data into a visualization

  16. VISAN - different projections

  17. VISAN - GOMOS Level 1

  18. Co-locations can be performed based on time, earth surface distance, and/or altitude (the last three parameters of the beatl2.find_colocated_data function). After ingestion of SCIAMACHY and OMI data as show before, we us a distance parameter of 100 km to co-locate the data. >>> [scia2, omi2] = beatl2.find_colocated_data(scia, omi, -1, 100,-1) Example: Co-locating data

  19. Example: VISAN Overplot >>> w = wplot(scia2, colorrange=[150,500]) >>> wplot(omi2, colorrange=[150,500], window=w) • VISAN overplots can be very helpful when you want to perform a quick visual comparison of data

  20. Future of BEAT More missions! Aeolus EarthCARE … But your feedback counts as well!

  21. More Information? http://www.stcorp.nl/beat/ beat@stcorp.nl

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