Today's weather satellites provide scientists with a unique opportunity to evaluate the abilities of various reanalysis datasets to depict multilayer tropospheric water vapor. A research team undertook a study to assess multilayer water vapor depiction in six representative reanalysis datasets against the measurements from the Advanced Himawari Imager over East Asia.
is important in the formation of clouds and precipitation, it is vital for scientists to better understand water vapor and the biases among various datasets. Their work was published in the journalScientists produce reanalysis datasets when they blend historical observations over a period of time using modern weather forecasting models.
The team used data collected by the Advanced Himawari Imager in 2016. The imager flew onboard the Himawari-8 satellite, collecting daily measurements over East Asia. The team studied the bias features of multilayer water vapor from six reanalysis datasets. These six datasets from major meteorological forecast centers included ERA-Interim, ERA5, CFSR, MERRA-2, JRA-55, and CRA.
The team's study indicated that the six widely-used reanalysis datasets show consistent wet biases in the upper troposphere in comparison to satellite water vapor observations. Wet bias describes how some forecasters overestimate the probability of precipitation. The bias is smaller in the mid than upper troposphere.
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