کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4548746 1627345 2008 16 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Improving assimilation of SeaWiFS data by the application of bias correction with a local SEIK filter
موضوعات مرتبط
مهندسی و علوم پایه علوم زمین و سیارات اقیانوس شناسی
پیش نمایش صفحه اول مقاله
Improving assimilation of SeaWiFS data by the application of bias correction with a local SEIK filter
چکیده انگلیسی

Ocean-biogeochemical models show typically significant errors in the representation of chlorophyll concentrations. The model state can be improved by the assimilation of satellite chlorophyll data with algorithms based on the Kalman filter. However, these algorithms do usually not account for the possibility that the model prediction contains systematic errors in the form of model bias. Accounting explicitly for model biases can improve the assimilation performance. To study the effect of bias estimation on the estimation of surface chlorophyll concentrations, chlorophyll data from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) are assimilated on a daily basis into the NASA Ocean Biogeochemical Model (NOBM). The assimilation is performed by the ensemble-based SEIK filter combined with an online bias correction scheme. The SEIK filter is simplified here by the use of a static error covariance matrix. The performance of the filter algorithm is assessed by comparison with independent in situ data over the 7-year period 1998–2004. The bias correction results in significant improvements of the surface chlorophyll concentrations compared to the assimilation without bias estimation. With bias estimation, the daily surface chlorophyll estimates from the assimilation show about 3.3% lower error than SeaWiFS data. In contrast, the error in the global surface chlorophyll estimate without bias estimation is 10.9% larger than the error of SeaWiFS data.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Marine Systems - Volume 73, Issues 1–2, September 2008, Pages 87–102
نویسندگان
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