Latest Results on Variational Soil Moisture Initialisation Martin Lange and Christoph Schraff martin.lange@dwd.de christoph.schraff@dwd.de 05.08.2005 - 1 -

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’ - - 05.08.2005 - 2 -

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) 05.08.2005 - 3 -

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 05.08.2005 - 4 -

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 05.08.2005 - 5 -

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 05.08.2005 - 6 -

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 05.08.2005 - 7 -

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 05.08.2005 - 8 -

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 05.08.2005 - 9 -