Results from a cloud-resolving model are systematically compared to various satellite and ground-based observations in order to better understand the mean background errors and their correlations. When exploring model biases in temperature, precipitable water vapor, and liquid water path, a warm, moist bias at night and a cool, dry bias during the day is observed. Values for the background error decorrelation length of water variables are determined. In addition, a dynamic cloud mask is presented to permit more control in the assimilation of cloudy satellite radiances, allowing different cloud types to be excluded from the assimilation and establishing values for the maximum residuals to be considered.
Assimilation of cloud-affected infrared radiances is then performed using a 4DVAR data assimilation system. Several experiments are performed to determine the sensitivity of the assimilation to factors including the maximum-allowed residual in the cloud mask, the magnitude of the background error decorrelation length for water variables, the length of the assimilation window, and the inclusion of ground-based data. Additionally, visible and near-infrared satellite data are included in a separate experiment. The assimilation results are validated using independent ground-based data. The introduction of the cloud mask where large residuals are allowed has the greatest positive impact on the assimilation. Extending the length of the assimilation window in conjunction with the use of the cloud mask results in a better-conditioned minimization, as well as a smoother response of the model state to the assimilation.
The assimilation results of each sensitivity experiment are then validated against data from three NEXRAD radar systems as well as ISCCP DX data. These studies show that while the analysis increases the cloud thickness as expected, it does not improve the location skill of the model. Cloud-related diagnostics including changes in variables such as incoming longwave radiation, outgoing longwave radiation, incoming shortwave radiation, precipitable water vapor, liquid water path, ice water path, and optical depth are computed for each experiment in order to see the impact of the assimilation on these variables. The differences between each experiment demonstrate the large sensitivity of the assimilation to the manner in which it is performed.
|Commitee:||Evans, K. Franklin, Friedrich, Katja, Pilewskie, Peter, Weiss, Jeffrey|
|School:||University of Colorado at Boulder|
|Department:||Atmospheric and Oceanic Sciences|
|School Location:||United States -- Colorado|
|Source:||DAI-B 71/06, Dissertation Abstracts International|
|Keywords:||Cloud data assimilation, Cloud-affected radiances, Infrared satellites|
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