Improving GPS positioning estimates during extreme weather situations by the help of fine-mesh numerical weather models

Space geodetic applications require to model troposphere delays as good as possible in order to achieve highly accurate positioning estimates. However, these models are not capable to consider complex refractivity fields which are likely to occur during extreme weather situations like typhoons, storms, heavy rain-fall, etc. Thus it has been investigated how positioning results can be improved if information from numerical weather models is taken into account. It will be demonstrated that positioning errors can be significantly reduced by the usage of ray-traced slant delays. Therefore, meso-scale and fine-mesh numerical weather models are utilized and their impact on the positioning results will be measured. The approach has been evaluated during a typhoon passage using global positioning service (GPS) observations of 72 receivers located around Tokyo, proving the usefulness of ray-traced slant delays for positioning applications. Thereby, it is possible reduce virtual station movements as well as improve station height repeatabilities by up to 30% w.r.t. standard processing techniques. Additionally the advantages and caveats of numerical weather models will be discussed and it will be shown how fine-mesh numerical weather models, which are restricted in their spatial extent, have to be handled in order to provide useful corrections.