Tse-Chun Chen;
Daisuke Hotta;
Kriti Bhargava;
Takuma Yoshida;
Eugenia Kalnay
University of Maryland
The 8th EnKF Data Assimilation Workshop
New Applications of DA to improve models and observations
Talk:
Montreal_May2018_TCChen_EKalnay.ppt.pdf
Data assimilation has been traditionally used to obtain accurate initial conditions for forecasting by combining observations with short-range forecasts. We will show that it can also be used to improve the models and the observations. Examples of such new applications of advanced data assimilation are: 1) Use of Ensemble Forecast Sensitivity to Observations (EFSO) to detect flawed observations that make the 6hr forecast worse (Proactive QC), its application to estimate the observations error covariance R, and efficient operational implementation of new observing systems; 2) Correction of model bias through the use of analysis increments; 3) Strongly coupled data assimilation.
Data assimilation has been traditionally used to obtain accurate initial conditions for forecasting by combining observations with short-range forecasts. We will show that it can also be used to improve the models and the observations. Examples of such new applications of advanced data assimilation are: 1) Use of Ensemble Forecast Sensitivity to Observations (EFSO) to detect flawed observations that make the 6hr forecast worse (Proactive QC), its application to estimate the observations error covariance R, and efficient operational implementation of new observing systems; 2) Correction of model bias through the use of analysis increments; 3) Strongly coupled data assimilation.
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