Capacity Building and Knowledge Transfer

In the following, we define capacity building as activities related to education of students on B.Sc. and M.Sc. levels, PhD students, and scientists on the postdoc level. Knowledge transfer is considered as the sum of measures to advance the expertise of staff and scientists working at NCMS, research institutes, universities, and industry in the operation and application of observing systems, weather and climate research, weather forecasting, and the understanding and forecasting of cloud seeding activities. Here, an important cross-cutting issue between observations and forecasting is data assimilation (DA), which is a key methodology for improving the skill of forecast models so that DA gets special attention. Of course, there are many links between the areas of knowledge transfer and capacity building, which are also outlined in the following proposed activities. Particularly, this holds for the joint performance and coordination of two proposed workshops and spring schools. In both areas, we see our contributions primarily in four topics:

1. Peparation and performance of operational forecasts: This topic includes presentations on required computing and data storage infrastructure, DA methodology, modeling over multiple scales, and model verification. Our vision is to support efforts of the UAE to set up and operate its own forecast system with improved representation of CI and cloud seeding efforts.
2. Remote sensing methodologies: This topic is dealing with passive and active systems, lidar and radar, beam propagation at different wavelengths, forward operators for data assimilation, observing system design for cloud seeding. Our expertise in clear air active remote sensing will be especially valuable because although radar tells us where it is raining and how much it is raining only lidar tells us why clouds and precipitation are forming and how much it will rain. Our goal is to develop with NCMS, MI, and other research institutes an advanced observing system synergy contributing to cloud seeding research and forecasting.
3. Process understanding: This topic is related to key processes such as LSA interaction and CI as well as their representation in models and the parameterization of sub-gridscale processes. We are convinced that our expertise in these areas will contribute to a better understanding and simulation of cloud seeding, which can only be achieved, if the pre-convective environment and CI are extremely well represented in models.
4. Data handling: The handling of data formats, data archiving, storage, transfer and processing of very large data sets is essential for error and scientific analyses.

These activities are strongly supported by the President of the UHOH. We are aiming to perform these activities in strong collaboration with NCMS, MI, and other interested and suitable parties in the UAE.

For communication platforms and methods, we consider meetings and workshops of several days to be most useful. Furthermore, we will realize an exchange of scientists and students, which will be prepared and performed with this project. Both efforts provide the time for information exchange, training, and discussions. We will also set up internet tools for intensifying discussions such as video conferences, wiki discussion fora, and ftp servers.