Proceedings - C-SRNWP Project

scaling factor α and of the latent heat nudging temperature increments often occur. To
mitigate this, the upper limit for α is reduced to 2 and the lower limit increased accordingly to
0.5. Furthermore, the linear scaling is replaced by a logarithmic scaling, leading to effective
limits of 1.7 and 0.3, respectively. This adaptation reduces the simulated precipitation
amounts during the LHN.
• In downdraft regions further upstream in convective cells, high precipitation rates occur
often where latent heating is weak or even negative in most vertical layers. In order to avoid
negative LHN temperature increments and cooling where the precipitation rate should be
increased (and vice versa), only the vertical model layers with positive simulated latent
heating are used to compute and insert the LHN increments. This modification tends to render
the increments more coherent and the scheme more efficient.
• Precipitation produced by the prognostic scheme will take some time to reach the ground
where it is compared to the radar-derived surface precipitation rate. Thus, the conventional
LHN scheme can notice only with some temporal delay when it has already initiated
precipitation aloft. Therefore, a more immediate information on the precipitation rate already
initialised is required, i.e. a sort of undelayed 'reference precipitation' which is used merely to
replace the delayed prognostic model precipitation in the computation of the scaling factor α.
One choice to work reasonably well is found to be the vertically averaged precipitation flux.
a) b)
Figure 1. Mean equitable threat score (a) and mean frequency bias (b) for hourly precipitation (threshold 0.1 mm) for the
overall (combined 0 and 12 UTC forecast) runs as a function of forecast time. The thin vertical purple lines indicate the
starting time of the free forecasts. The “mean” scores were obtained by adding up the contingency table over a 45-day period.
Assimilation cycles: nudging without LHN (blue dashed, label “LMK TS 2.6 (5784)”) and nudging with LHN (green solid,
label “LMK TS 3.3b (5730)”). The red bars at the bottom of the panels show the number of observed precipitation events
above the considered threshold value.
3. VERIFICATION OF MODELLED AGAINST RADAR-OBSERVED
PRECIPITATION
For an assessment of the overall performance of the revised LHN during assimilation and
especially for an evaluation of LHN's forecast impact, two experiments using the COSMO
model in the 2.8km version (LM-K) of the German weather service (DWD) have been carried
out for a 45-day period in June and July 2006. A LHN experiment, comprising of an LM-K
assimilation run with LHN and two daily 24-hour forecasts starting at 0 and 12 UTC, is
compared to a control experiment, which has been set up in the same way but does not use
LHN during assimilation. The particular comparison measures are “Equitable Threat Score”
(ETS) and “Frequency Bias” (FBI) for hourly precipitation.
On average, the positive impact of radar data is visible in the combined 0 and 12 UTC
forecasts’ ETS for up to 5 hours for a threshold value of 0.1 mm (fig. 1a). This impact time
decreases slightly for higher threshold values (not shown). Whether the rapid decrease of ETS
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