Model Output Statistics QUalified by Intercomparison with True Observations
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.
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
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.
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.
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.
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.
Deutscher Wetterdienst - German Weather Service
Pattantyús-Ábrahám, M. | C. Kadow, S. Illing, W. Müller, H. Pohlmann, W. Steinbrecht
Pattantyús-Ábrahám M. | W. Steinbrecht