1. Latest Results on Variational Soil Moisture Initialisation
Martin Lange and Christoph Schraff

2. ELDAS experiments for May – December 2000
model setup
LM version 3.15
ELDAS configurations, 2-layer soil model
continuous assimilation cycle, 00-UTC forecasts
parameters setup related to soil moisture initialisation (SMA) for 4 experiments
variables in cost function precipitation fields used to update
(for which prediction should soil moisture from one day to next
be improved by design) (evaporation always from model)
‘SMA-T2m’ T2m precipitation of model forecast
‘SMA-T2m+Rh2m’ T2m + RH2m precipitation of model forecast
‘SMA-T2m+Rubel prec.’ T2m observed (‘Rubel’) precipitation
‘SMA-T2m+Rh2m+Rubel’ T2m + RH2m observed (‘Rubel’) precipitation
‘no SMA’

3. time series of running monthly mean
ELDAS domain average of soil moisture increments in bottom layer
model increments
SMA increments
Again, hourly accumulated precipitation height is plotted in the figures.
The LHN run with diagnostic precipitation shows precip patterns, which are in good agreement both in position and amplitude with the radar observations.
When we look at the run with prognostic precipitation, we detect, that too much precipitation is inserted during the assimilation and that further on the horizontal structure of precipitation no longer fits so well to the Radar.
evaporation > precipitation in summer
SMA increments have more variability
top-layer mean increments are much smaller (not shown)

4. time series of running monthly mean
root mean square over ELDAS domain of bottom-layer soil moisture increments
model increments
SMA increments
Again, hourly accumulated precipitation height is plotted in the figures.
The LHN run with diagnostic precipitation shows precip patterns, which are in good agreement both in position and amplitude with the radar observations.
When we look at the run with prognostic precipitation, we detect, that too much precipitation is inserted during the assimilation and that further on the horizontal structure of precipitation no longer fits so well to the Radar.
SMA increments >> model increments,
further increased if RH2m in cost function

5. time series of running monthly mean
root mean square over ELDAS domain of top-layer soil moisture increments
model increments
SMA increments
Again, hourly accumulated precipitation height is plotted in the figures.
The LHN run with diagnostic precipitation shows precip patterns, which are in good agreement both in position and amplitude with the radar observations.
When we look at the run with prognostic precipitation, we detect, that too much precipitation is inserted during the assimilation and that further on the horizontal structure of precipitation no longer fits so well to the Radar.
( model increments smaller if
observed precipitation used )
SMA increments < model increments,
yet increased if RH2m in cost function

6. time series of running monthly mean
T2m forecasts for 12 and 15 UTC (ELDAS domain and time average for 00-UTC LM runs)
bias
r m s e
Again, hourly accumulated precipitation height is plotted in the figures.
The LHN run with diagnostic precipitation shows precip patterns, which are in good agreement both in position and amplitude with the radar observations.
When we look at the run with prognostic precipitation, we detect, that too much precipitation is inserted during the assimilation and that further on the horizontal structure of precipitation no longer fits so well to the Radar.
SMA reduces bias in warm season
SMA reduces rmse by ≥10% in warm season,
use of RH2m slightly beneficial
use of observed precip slightly beneficial

7. time series of running monthly mean
RH2m forecasts for 12 and 15 UTC (ELDAS domain and time average for 00-UTC LM runs)
bias
r m s e
Again, hourly accumulated precipitation height is plotted in the figures.
The LHN run with diagnostic precipitation shows precip patterns, which are in good agreement both in position and amplitude with the radar observations.
When we look at the run with prognostic precipitation, we detect, that too much precipitation is inserted during the assimilation and that further on the horizontal structure of precipitation no longer fits so well to the Radar.
use of RH2m reduces bias in May - July
SMA reduces rmse by 10 – 30 %,
use of RH2m beneficial
use of observed precip beneficial

8. time series of running monthly mean
6- to 30-hour precipitation forecasts (ELDAS domain average for 00-UTC LM runs)
frequency bias
5 mm threshold
TSS
Again, hourly accumulated precipitation height is plotted in the figures.
The LHN run with diagnostic precipitation shows precip patterns, which are in good agreement both in position and amplitude with the radar observations.
When we look at the run with prognostic precipitation, we detect, that too much precipitation is inserted during the assimilation and that further on the horizontal structure of precipitation no longer fits so well to the Radar.
SMA strongly reduced bias, increases TSS by about 5 – 10 %
use of RH2m : neutral impact
use of observed precip : beneficial

9. Conclusions Note Further Plan
current implementation of soil moisture initialisation is strongly beneficial for prediction of daytime T2m and RH2m, and also beneficial for precipitation forecasts
inclusion of RH2m in addition to T2m in cost function further improves predicted RH2m
use of observed precipitation to update soil moisture in time further improved prediction of daytime RH2m and of precipitation
best results with both modifications
Note
the soil moisture initialisation adapted to the multi-layer soil model is running in the pre-operational LME suite
Further Plan
investigation to replace the variationally derived relationship between 2-m temperature (+ 2-m humidity) and soil moisture by a parameterized regression.
this would render obsolete the extra model forecast integrations required in the current SMI implementation