Ground-based profiling networks for improving weather forecasts
Workshop, 3 September 2017, Dublin
Martial Haeffelin (SIRTA, France),
Ewan O’Connor (FMI, Finland/Univ of Reading, U.K.),
Domenico Cimini (CNR-IMAA, Italy),
Ulrich Loehnert (Univ Cologne, Germany)
A new generation of high-resolution (1km) forecast models promises to revolutionise the predictions of hazardous weather such as wind-storms, flash floods, and poor air quality. To realize this promise, a dense observing network, focusing on the lower few km of the atmosphere, is required to verify these new forecast models, with the ultimate goal of assimilating the data.
This session is intended to give an introduction to the current status of ground-based profiling networks in Europe and to demonstrate recent developments in providing calibrated and quality controlled data in near real time to national weather forecast centres so the data can be assimilated into the forecast models. The workshop will focus on the following European networks of a) automatic lidars and ceilometers, b) Doppler lidars, and c) microwave radiometers.
The final programme will be tailored to the experience (and interest) of registered participants.
|10:00 – 12:00||Automatic lidars and ceilometers (Introduction and practice)|
|12:00 – 13:00||Lunch|
|13:00 – 15:00||Doppler wind lidars (Introduction and practice)|
|15:00 – 17:00||Microwave radiometers (Introduction and practice)|
|Target group:||Anyone interested in atmospheric ground-based observations and their value for NWP validation and data assimilation. A basic knowledge of atmospheric observations and data assimilation is required.|
|Min. number of participants:||12|
|Max. number of participants:||30|
To register for the workshop, please send a message to email@example.com by 7 July 2017, with the following information:
- Name and affiliation;
- Primary interest
There is no cost for the registration for and attendance of the workshop, but participation is by pre-registration only. The workshop may be cancelled in the case of low registration numbers.