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S. Crewell, H. Bloemink, A. Feijt, S. G. García, D. Jolivet, O. A. Krasnov, A. van Lammeren, U. Löhnert, E. van Meijgaard, J. Meywerk, M. Quante, K. Pfeilsticker, S. Schmidt, T. Scholl, C. Simmer, M. Schröder, T. Trautmann, V. Venema, M. Wendisch, and U. Willén

Clouds cause uncertainties in the determination of climate sensitivity to either natural or anthropogenic changes. Furthermore, clouds dominate our perception of the weather, and the relatively poor forecast of cloud and precipitation parameters in numerical weather prediction (NWP) models is striking. In order to improve modeling and forecasting of clouds in climate and NWP models the BALTEX BRIDGE Campaign (BBC) was conducted in the Netherlands in August/September 2001 as a contribution to the main field experiment of the Baltic Sea Experiment (BALTEX) from April 1999 to March 2001 (BRIDGE). The complex cloud processes, which involve spatial scales from less than 1 mm (condensation nuclei) to 1000 km (frontal systems) require an integrated measurement approach. Advanced remote sensing instruments were operated at the central facility in Cabauw, Netherlands, to derive the vertical cloud structure. A regional network of stations was operated within a 100 km × 100 km domain to observe solar radiation, cloud liquid water path, cloud-base temperature, and height. Aircraft and tethered balloon measurements were used to measure cloud microphysical parameters and solar radiation below, in, and above the cloud. Satellite measurements complemented the cloud observations by providing the spatial structure from above. In order to better understand the effect of cloud inhomogeneities on the radiation field, three-dimensional radiative transfer modeling was closely linked to the measurement activities. To evaluate the performance of dynamic atmospheric models for the cloudy atmosphere four operational climate and NWP models were compared to the observations. As a first outcome of BBC we demonstrate that increased vertical resolution can improve the representation of clouds in these models.

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Continuous Evaluation of Cloud Profiles in Seven Operational Models Using Ground-Based Observations

A. J. Illingworth, R. J. Hogan, E.J. O'Connor, D. Bouniol, M. E. Brooks, J. Delanoé, D. P. Donovan, J. D. Eastment, N. Gaussiat, J. W. F. Goddard, M. Haeffelin, H. Klein Baltink, O. A. Krasnov, J. Pelon, J.-M. Piriou, A. Protat, H. W. J. Russchenberg, A. Seifert, A. M. Tompkins, G.-J. van Zadelhoff, F. Vinit, U. Willén, D. R. Wilson, and C. L. Wrench

The Cloudnet project aims to provide a systematic evaluation of clouds in forecast and climate models by comparing the model output with continuous ground-based observations of the vertical profiles of cloud properties. In the models, the properties of clouds are simplified and expressed in terms of the fraction of the model grid box, which is filled with cloud, together with the liquid and ice water content of the clouds. These models must get the clouds right if they are to correctly represent both their radiative properties and their key role in the production of precipitation, but there are few observations of the vertical profiles of the cloud properties that show whether or not they are successful. Cloud profiles derived from cloud radars, ceilometers, and dual-frequency microwave radiometers operated at three sites in France, Netherlands, and the United Kingdom for several years have been compared with the clouds in seven European models. The advantage of this continuous appraisal is that the feedback on how new versions of models are performing is provided in quasi-real time, as opposed to the much longer time scale needed for in-depth analysis of complex field studies. Here, two occasions are identified when the introduction of new versions of the ECMWF and Météo-France models leads to an immediate improvement in the representation of the clouds and also provides statistics on the performance of the seven models. The Cloudnet analysis scheme is currently being expanded to include sites outside Europe and further operational forecasting and climate models.

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