12 th JCSDA Workshop Ocean 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

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

Development of the QC procedure was based on Jason-1 NRT Hs data for 2011 obtained via GTS In 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

Example of raw SWH Jason-1 data: Dec. 6, 2011

Developed QC procedure includes: 1. Valid value (range) test 2. Proximity to land test 3. Proximity to ice test 4. De-spiking (statistical outliers)

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

Data that are likely affected by proximity to floating ice

Ice Concentration NCEP operational (5’ grid)

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

 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

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 20 th and 80 th 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

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

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