Description of the experiments

Based on the data assimilation algorithms described in the preceding sections, different combinations of algorithms for assimilating conventional and precipitation observations are possible. The LETKF and LHN can be combined by assimilating conventional observations via KENDA and by using LHN to assimilate radar derived surface precipitation rates. LHN is applied to each ensemble member and the deterministic run individually. This setup is the current reference system at DWD and is scheduled for pre-operational testing. In a full LETKF system, all available observations are assimilated via KENDA, i.e. radar reflectivities are included via EMVORADO.

The combination of nudging and LHN is the current operational deterministic data as- similation system at DWD. The system assimilates conventional observations via nudging and radar derived surface precipitation rates via LHN. This setup is however not investi- gated in this study.

Experiment 1: Dependency of the update frequency

Radar data is available at a high temporal resolution. Thus, an obvious assumption is that frequent assimilation updates improve the analysis and subsequent forecasts. This hypothesis is examined in this first set of experiments. A one-day case study within the experimental period, 26 May 2014, with varying update frequency is performed and the quality of the analysis and forecast is studied. The experimental setup is as follows: The ensemble with 40 members is initialized at 00 UTC based on downscaled fields from the global model ICON. Prior to data assimilation, the ensemble is propagated for 12 hours to ensure that the ensemble develops a realistic approximation of the convective scale forecast error structures (following Zhang et al. 2009). In the assimilation cycle from 12 to 15 UTC, conventional and radar reflectivity observations are assimilated every 5, 15, 30 or 60 minutes with the full LETKF system. The setups are called CONV+RAD 5, CONV+RAD 15, CONV+RAD 30, and CONV+RAD 60. In each analysis step, all observations collected during the previous first guess window are assimilated by the 4D-LETKF. This means, for example, CONV+RAD 15 assimilates observations from three time steps: observations

Table 5.2.: Summary of the experimental setups used in experiments 1 and 2. Conventional (AMDAR,

SYNOP, TEMP, Wind profiler)

Reflectivity Rain rate (2D)

Update fre- quency

4D-weighting

CONV X 60 not applicable

CONV+RAD

X Entire volume 60 X

( ˆ= CONV+RAD 60)

CONV+RAD 30 X Entire volume 30 X

CONV+RAD 15 X Entire volume 15 X

CONV+RAD 5 X Entire volume 5 not applicable

CONV+RAD 1ELE X Lowest elevation 60 X

CONV+RAD 4ELE X Every third elevation 60 X

CONV+RAD 3DLETKF X Entire volume 60

CONV+LHN X X 60 not applicable

from the analysis time, and 5 and 10 minutes prior to the analysis. For verification, a reference simulation named CONV assimilates only conventional observations with hourly updates. The last analysis at 15 UTC provides the initial conditions for a 6 hour ensemble forecast and an additional deterministic forecast for the period 15 to 21 UTC.

Experiment 2: Evaluation over one week

This experiment is not restricted to a few test cases but assesses the impact of radar reflectivity assimilation on deterministic forecasts based on a simulation of seven days. The basic cycle of the experiments is as follows: The assimilation cycle is initialized on 21 May 2014 at 12 UTC by interpolating the global model fields provided by ICON to the COSMO-DE grid. With hourly update intervals, the model is continuously cycled until 29 May 2014, 00 UTC. Throughout this period, 29 deterministic forecasts with a lead time of 24 hours are initialized in 6 hourly intervals. The first forecast is initialized after a 12 hour spinup on 22 May, 00 UTC, the last forecast is initialized on 29 May, 00 UTC.

Firstly, the full LETKF system is compared with DWD’s current reference system that uses the LETKF for conventional observations and LHN for radar derived precipitation rates, called CONV+LHN. As in experiment 1, a system CONV is set up that assimilates only conventional observations.

Secondly, this experiment assesses the impact of the amount of radar information assim- ilated. Intuitively, more observational data might improve analysis and forecast, however, the handling of large amounts of data requires many computational resources. Thus, for the full LETKF system, the value of the dense vertical information and the 4D-formulation of the LETKF (c.f. Sec. 3.1.4) is investigated in the following setups:

• CONV+RAD: As in CONV+RAD 60, entire radar reflectivity volumes are assimilated in addition to conventional observations. The trajectory of the entire first guess window is included.

• CONV+RAD 1ELE: Only the lowest PPI scan at 0.5◦ _{elevation angle is assimilated.}

Due to the propagation of the radar beam, there is still information on different vertical levels available in areas of station overlap (c.f. Section 4.1, Eq. 4.4). The trajectory of the entire first guess window is included.

• CONV+RAD 4ELE: Here, more and higher elevation scans are considered, namely every third PPI scan (elevations at 0.5◦_{, 3.5}◦_{, 8.0}◦_{, and 25.0}◦_{). The trajectory of}

the entire first guess window is included.

• CONV+RAD 3DLETKF: Full radar reflectivity volumes are assimilated, but only measurements that are valid at the exact analysis time. Prior measurements during

the first guess window are not used.

A summary of the setups used in experiments 1 and 2 is given in Table 5.2.