Offline Assessment of NESDIS OSCAT data Li Bi 1 Sid Boukabara 2 1 RTI/STAR/JCSDA 2 STAR/JCSDA American Meteorological Society 94 th Annual Meeting 02/06/2014

Outline Motivation Introduction of statistics used in this study Preliminary results – Raw data – Filtered data Conclusion Future work

Motivation Optimize the usage of OSCAT data by performing offline assessment before the assimilation. Based on the offline assessment statistics of wind speed, direction, u/v component bias and STDV comparing with GDAS analysis, suggest optimal QC methods. Data usage: – NESDIS OSCAT 50km data June 2012 HDF5/BUFR data. Selected wind vector cell quality flags: – Land-sea boundary flag – Land flag – Ice flag – Rain impact flag – Wind retrieval flag Fig.1. Selected wind vector cell quality flags

Statistics for raw data Calculated O-B bias and STDV w.r.t. GDAS analysis – Wind speed, direction, U/V components Geographic stats By observation bins By SST range By cell index – Ascending, descending separated. – Histogram of counts in each bins – Latitude band profile

speed bias raw data speed bias filtered data speed STDV raw data speed STDV filtered data

direction bias raw data direction bias filtered data direction STDV raw data direction STDV filtered data

Raw data ascendingRaw data descendingRaw data all Filtered data ascendingFiltered data descendingFiltered data all

Raw data ascendingRaw data descendingRaw data all Filtered data ascendingFiltered data descendingFiltered data all

Summary finding after applying basic retrieval filtering The basic retrieval filtering effectively remove land/sea boundary flag, ice flag, etc. Slightly reduce latitude band wind speed STDV. Direction latitude profile remains similar except for high latitudes. Latitude band cut off (60 o S-60 o N) is suggested. Further assessment for optimal filtering is very necessary.

Raw data ascending (speed, dir, u/v) Raw data descending (speed, dir, u/v) Raw data all (speed, dir, u/v) Filtered data ascending (speed, dir, u/v) Filtered data ascending (speed, dir, u/v) Filtered data all (speed, dir, u/v) Layout of OSCAT vs. GDAS stats bias and STDV

Wind Speed - Raw data Wind Speed Filtered data

Speed Relative Bias - Raw data Speed Relative Bias - Filtered data 5m/s cut off for wind speed is suggested

Wind Direction - Raw data Wind Direction - Filtered data

Count Histogram - Raw data Count Histogram - Filtered data Basic filter + 5m/s cut off for wind speed + high wind speed cut off for U/V components

Raw data Filtered data 15m/s cut off for U-Comp is suggested

Raw data Filtered data 15m/s cut off for V-Comp is suggested

Raw data ascendingRaw data descendingRaw data all Filtered data ascendingFiltered data descendingFiltered data all

Wind Speed - Raw data Wind Speed - Filtered data

Wind Direction - Raw data Wind Direction - Filtered data

Raw data all Filtered data all

Wind Speed - Raw data Wind Speed - Filtered data

Wind Direction - Raw data Wind Direction - Filtered data

Count Histogram - Raw data Count Histogram - Filtered data

Conclusion The optimal filtering effectively reduced large STDV for wind speed, as well as wind direction in the observation bins. Suggested new filtering includes: – Old filtering provided during the retrieval to remove ice/rain flag etc. – 5m/s cut off for wind speed – Latitude band cut off (60 o S-60 o N only) – 15m/s cut off for high u and v components of the winds Wind speed biases generated in the observation bins. Largely reduce wind direction STDV in scan position which is mainly due to low winds.

Future Work Test the optimal observation error Possible bias correction based on the preliminary results Revisit thinning methods Test with high resolution OSCAT data (25km) Exploring assessing and assimilating the scatterometer data in rainy conditions (pending maturity of the products) Perform the data assimilation experiment with the optimal filtering, bias correction and observation error.