Proceedings - C-SRNWP Project

The setup is based on a new numerical kernel, 2-timelevel 3rd order Runge-Kutta, with a
main time step of about 15 seconds. It uses improved physics with e.g. new schemes for
graupel, improved turbulence and shallow convection (the deep convection being resolved),
as well as with implementation of the topographic effects on radiation. A new multi-layer soil
model with 8 layers for energy and 6 for moisture is used; moisture is updated every 24h form
IFS in the lowest levels. A measurement driven soil moisture analysis is being implemented,
as well as a mosaic approach will enable to use the same soil for aLMo and aLMo2. A snow
analysis at 2.2 km refined with the HRV channel of MSG will be used.
It will have its own assimilation cycle with a rapid update cycle having a short cut-off of 60
min. Additional measurements will be assimilated like radar data using the 2-dimension latent
heat nudging scheme and the analysis of the boundary layer will be improved through a better
usage of surface measurements and of the lower part of wind profiles.
Validation of aLMo2 winds (P. Kaufmann and O. Marchand)
The local dispersion modeling for the Swiss emergency response system for nuclear hazards
is currently based on a set of pre-determined wind fields (WINDBANK). The project
“Centrale Nucléaire et Météorologie” (CN-MET) has the purpose to replace the current
system with a system based directly on aLMo2. The comparison between aLMo2 and a
temporal, dense wind measurement network during the WINDBANK campaigns conducted
by the Paul Scherrer Institute is a first step to establish the validity of this concept. The main
advantage of aLMo2 over the current system, to deliver fully three-dimensional wind
information over the whole volume of interest, does however not come into play in this
comparison.
The mean error and standard deviation of the error were calculated for hourly values of 72
days. In addition, histograms and scatter-plots for direction and speed serve to compare model
and measurements. Directions at speeds smaller than 3 ms -1 were filtered out. When looking
at the results, one should keep in mind that the WINDBANK observations were pointobservations,
whereas the aLMo2 forecasts represent a spatial mean over a whole grid cell,
and thus a perfect match cannot be expected.
The wind direction bias (ME) is well below 10° and the wind speed bias far below 1 ms -1 for
all model versions. The direction bias is significantly larger for the coarser-resolution aLMo
analysis than for the others (below 5°, see Table 1). In regards of standard deviation of the
error (STDE), the wind direction of the high-resolution aLMo2 analysis is clearly better. The
wind speed standard deviation however does not show a significant difference (Table 1).
wind direction (°) wind speed (ms -1 )
ME STDE N ME STDE N
aLMo analysis 8.26 53. 15326 0.36 1.743 44155
aLMo2 analysis 3.23 47. 23032 0.17 1.749 64280
aLMo2 19-24h 3.53 57. 5424 0.09 1.789 16347
Table 1: Bias and standard deviation of the difference model minus measurement, for wind direction and wind
speed.
The wind direction histograms for the WINDBANK measurement show a channeling of the
flow over the Swiss plateau (e.g. Fig. 2a). This channeling is underestimated by aLMo2 at
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