Pan, Linlin, National Center for Atmospheric Research,
Yubao Liu, Yonghui Wu, Dorita Rostkier-Edelstein, Israel Institute for
Biological Research, Ness-Ziona, Israel, and Rong-Shyang Sheu, NCAR
This study focuses on
data quality control (QC) and impact study of the non-conventional datasets
from aircraft and Satellite with the NCAR (National Center for Atmospheric
Research) WRF RTFDDA (Real-time four-dimensional data assimilation) system set
up over the data-sparse Eastern Mediterranean region.
The data investigated in
this research includes AMDAR and AMV datasets. The datasets from radiosondes
and MADIS are also used for comparisons. The RTFDDA system was run continuously
for a 3 days period, with 4 FDDA and forecast cycles a day, 13 h forecasts in
each cycle. The AMDAR data reports pressure-altitudes instead of pressure. Correct
conversion from the height to pressure is very important. It is demonstrated
that improper conversion formula can increase the data error/bias. The AMV data
quality is investigated as a whole as well as in term of different satellite
channel channels (e.g., water vapor channel, visible channel, and infrared
channel). The study shows that AMV data contain some lower quality data and
contains many more outliers. The data from water vapor channel have the largest
bias and root mean squared error (RMSE). More rigorous QC constraints are
required in order to achieve positive impact using the AMV dataset.