Model Output Statistics QUalified by Intercomparison with True Observations

Project aims

The first phase of MiKlip has shown that MiKlip model simulations generally produce lower than observed temperatures in the troposphere. The model atmosphere is also too moist and vertical stability is less than observed. Depending on the model initialization, the simulated temperatures also drift over some of the simulation years. The aims of project MOSQUITO are a comprehensive characterization of these biases and drifts, using high quality radiosonde data as a reference. In addition, representation errors of large scale model simulations and meteorological reanalysis fields will be investigated by comparison with the small scale structure present in the radiosonde data.

Project Structure

MOSQUITO belongs to Module E and is a work-package of the Module-E DWD project. MOSQUITO will be carried out at DWD’s Meteorological Observatory Hohenpeissenberg

Tasks of the project

MOSQUITO will extend the high quality observational database from radiosondes. The observations will be used to estimate systematic errors and uncertainties of meteorological re-analyses and MiKlip global and regional simulations. After characterization, calibration procedures for simulated temperature and humidity profiles will be implemented. Among other things, this should improve probability forecasts for severe weather events.

Fig 1. Systematic temperature bias of MiKlip simulated temperature against radiosonde observations over Germany. B0-lR, B1-LR, B1-MR and P-LR are baseline 0, baseline 1 and prototype low resolution (LR) and mixed resolution (MR) experiments. B1-LR-reg is the COSMO regional simulations for B1-LR.
Fig 2. Same as in 1. but showing the change of temperature bias at 500 hPa as a function of forecast year. The full-field initialization in P-LR results in a large drift. (Click to enlarge)


The project will deliver data-sets to the central MiKlip server and the Central Evaluation System. Calibration procedures will be integrated into the general model post-processing.

Progress so far

Validation of the MiKlip prediction system with radiosonde observations

The MOSQUITO2 work package started in January 2017. Initial validation of the pre-operational MiKlip II model runs indicates that the previous substantial temperature and humidity biases of the model (see Figs. 1 and 2 above) have been reduced substantially. As an example, Fig. 3 now shows good agreement between observed and modelled probability density functions for upper air temperature (left) and relative humidity (right) above Central Europe. In previous model versions, the simulated temperatures were often too low, and the simulated humidity distribution showed an unrealistic large peak at high humidities. In these areas, the MiKlip2 MPIESM 1.2 pre-operational hindcasts give a large improvement.

Fig. 3: Probability density functions for modelled and observed 500 hPa temperature (left) and 700 hPa relative humidity near the Payerne radiosonde station. Model results are for MiKlip II MPIESM 1.2 pre-operational hindcasts.

Comparison between reanalyses and radiosonde observations

Another question to be addressed was how smaller scale variability observed by the radiosondes is captured by global reanalyses. As an example, Fig. 4 shows the standard deviation between radiosonde-measured temperatures and two reanalyses. Generally, the reanalyses (NCEP, MERRA, ERA-Interim, JRA25, 20CR) capture the radiosonde observed temperatures quite well. Over the European continent, the standard deviation of differences, e.g. at the 300 hPa level and for ERA-Interim, is generally smaller than 0.3 K – comparable to the accuracy of the temperature sensors. Near the coast, however, larger differences are seen, with standard deviations reaching or exceeding 1 K. For the 20th century reanalysis, however, larger differences, of the order of 1 K, are seen everywhere. This is not surprising, because the 20th century reanalysis is constrained only by surface pressure observations, whereas all the other reanalyses also consider upper-air information.

Fig. 4: Standard deviation between radiosonde measured temperatures and reanalysis temperatures at 300 hPa, for European stations, and for ERA-Interim (left) and 20th century reanalysis (right).


Deutscher Wetterdienst - German Weather Service
Wolfgang Steinbrecht
+49-(0) 69-8062-9772

Bias and drift of the mid-range decadal climate prediction system (MiKlip) validated by European radiosonde data

2016 - Met. Z., Vol. 25 No. 6 (2016), p. 709 - 720

Pattantyús-Ábrahám, M. | C. Kadow, S. Illing, W. Müller, H. Pohlmann, W. Steinbrecht

Temperature Trends over Germany from Homogenized Radiosonde Data

2015 - J. Climate, Vol. 28 (14), pp. 5699-5715

Pattantyús-Ábrahám M. | W. Steinbrecht