Speaker
Description
Microphysical schemes in numerical weather prediction models are based on the
basic equations that govern the growth and interaction of liquid drops and ice
particles (hydrometeors) in the atmosphere. Unfortunately, our knowledge of
these microphysical processes is still incomplete. In addition, quite drastic
simplifications are necessary to make the problem tractable in numerical
weather prediction. Hence, we need observations to test, validate and improve
the microphysical parameterizations.
Here we give two examples of this validation and development process. First, a
deficiency in the properties of unrimed snowflakes is identified using Doppler
spectra of a vertically pointing radar. The snowflake properties are improved
based on aggregation modeling, and the revised microphysical model is
validated using multi-wavelength radar data. Second, the use of a one- vs
two-moment microphysics is compared for forecasts of deep convection. The
two-moment scheme has several advantages, but shows a bias in the diurnal
cycle of the forward-simulated radar cell-tracking statistics. This can be
traced back to a delayed initiation of convection, which is caused by a bias
in the cloud-radiation interaction of the two-moment microphysics. After this
issue is fixed, several biases of the NWP system are improved, e.g. radar cell
statistics and near-infrared brightness temperature histogram.
