12 th jcsda workshop ocean data assimilation development of a gsi-based da interface for operational...
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12th JCSDA WorkshopOcean Data Assimilation
Development of a GSI-based DA interface for operational wave forecasting systems at
NOAA/NCEP
Vladimir Osychny, Hendrik Tolman, Henrique Alves, Arun Chawla
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• The main objective of the project:
to develop a GSI-based module in WAVEWATCH III for assimilation of total significant wave height (Hs) from altimeter missions
• Completed work:
- developed a quality-control (QC) module for Near-Real-Time (NRT) Hs data from satellites
- developed a strategy to adapt the GSI for Hs assimilation using RTMA 2D approach (in collaboration with RTMA team: Manuel Pondeca, Steven Levine )
- modified the GSI code (RTMA 2DVAR) to include the new variable - significant wave height
- determined that current RTMA prepbufr has enough wave-height data to start preliminary tests
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Development of the QC procedure was based on
Jason-1 NRT Hs data for 2011 obtained via GTSIn principle:
254 passes
~10 days exact repeat cycle
~ 6 km (1 sec) sampling rate
3-10 km Hs footprint
In NRT GTS reality:
Not quite exact repeat passes
Not quite regular alongtrack sampling
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Example of raw SWH Jason-1 data: Dec. 6, 2011
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Developed QC procedure includes:
1. Valid value (range) test
2. Proximity to land test
3. Proximity to ice test
4. De-spiking (statistical outliers)
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Data rejected based on proximity to land test
• For each data location: a data is flagged as being likely “bad”,
if a land point is found within the area with radius approx 20 km
• Test is based on ETOPO-1 data set, which is also used in operational
wave model
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Data that are likely affected by proximity to floating ice
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Ice ConcentrationNCEP operational (5’ grid)
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For each data location: → a data is rejected, if ice is found within the area with radius approx. 20 km -- same as for the “land” test;
→ this search radius seems to be too small in the case of ice
Details of the Proximity to Ice Test
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Results of the proximity to ice test are more accurate with a larger search radius – 40 km
Also shown are “spikes” identified by the de-spiking procedure
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An iterative de-spiking procedure
• Iterative core:
1. A low-pass signal is obtained by using an order-statistic filter: Approx. 10 sec. (~60 km, ~11 points; 5 minimum) data window Mean is calculated for values between 20th and 80th percentile
2. Estimate STD based on the high-pass residue for the same data window and the same data selection
3. Flag outliers (> 3STD)
4. Additional constraints at each iteration: - test differences between neighboring data values for original data and for high-pass portion- introduce lower limit on RMS
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The next step:
use the Real-Time Mesoscale Analysis (RTMA) 2DVAR approach to adapt the GSI for Hs assimilation
What is RTMA?• operational hourly analysis of atmospheric surface data• based on GSI- and an atmospheric forecast model
RTMA is the best choice of development framework for our purposes because:
• similar set up (although different models, grids, etc.)• relatively simpler case to start with• substantial existing expertise• opportunity to add a valuable (for forecasters) new analysis variable
to an existing operational system (RTMA) while developing a data assimilation module for a wave model
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Summary:
Concluded development of a working version of the QC procedure
In progress:- Transfer the QC procedure to FORTRAN or Python (currently in Matlab)- Test the QC procedure on real time GTS data (Jason-2)- Start pre-operational cycling on WCOSS
In progress:- modify RTMA to include analysis of Significant Wave Height- work with EMC obsproc group to include altimeter wave height data into
RTMA prepbufr- further modify RTMA code to build the GSI-based data assimilation module
for the operational wave model