Hydrogeodesy Training Session Approximately 15 attendees, Three seminars 1. Introduction to Hydrogeodesy 2. Introduction to Data Assimilation 3. Introduction to Earth Observations for Water Resources Management Participants mostly interested in obtaining models for groundwater calculations in their study sites. They asked practical questions on how to apply hydrogeodesy and data assimilation. Several people wanted numerical codes, but we suggested that conceptual understanding and theory needs to obtained first, followed by hands on learning. The main purpose of these seminars is to introduce the audience these concepts and then provide follow up to those truly interested in the applications.

Hydrogeodesy applies geodetic techniques to study and monitor the (terrestrial) hydrology. Geodesy is the science of determining the geometry, gravity field, and rotation of the Earth, and their evolution in time. The geodetic reference frame is crucial for all Earth observations and has hight societal value. GRACE has contributed tremendously to our knowledge about water cycle mass redistributions from global down to 150 km spatial scales and sub-monthly temporal scales. InSAR and GPS provide information on groundwater change; GPS provides soil moisture and snow depth; GRACE data products show differences, depending upon the groups developing them and producing them; community-vetted products are not (yet) available.

Hydrogeodesy has the demonstrated potential to support water management and help address the growing water scarcity. Considerable need for capacity building both in knowledge creation and use of knowledge by decision makers. Hydrogeodesy data products are difficult to understand and apply in disciplines outside of geodesy, particularly hydrology. Obstacles: Gaps in infrastructure need to be addressed. Integrated Earth system models for assimilation of hydrogeodetic observations are needed. User guides, science-policy/public interfaces that provide easier access to hydrogeodesy results.

Data Assimilation Data assimilation provides the initial conditions that produce the best possible model forecast. It must create an analysis consistent with the model numerics, dynamics, physics, and resolution. NWP provides the short-range forecast for the analysis by making a series of small corrections to that forecast based on new information from observations. Analyses is different for different models and will most likely differ from the best estimate of the true state of the atmosphere produced by a hand analysis. Observations are assimilated to correct each short-range forecast that serves as the basis for the next analysis, resulting in a series of small corrections to the model forecast.

The formal approach for data assimilation analysis is to solve for the minimized subspace (X a -X b ) = d x Where, d x is the subspace Xa is the forecast Xb is the background (initial) state.

NWP: Prediction of future atmospheric states U( S x,y,z, t+1) = U( S x,y,z, t) + w u x f[U( S x,y,z,t) - U obs ] T ( S x,y,z, t+1) = T ( S x,y,z, t) + w T x f[T( S x,y,z,t) - T obs ] q( S x,y,z, t+1) = q( S x,y,z, t) + w q x f[q( S x,y,z,t) - q obs ] U - wind direction and magnitude T - atmospheric temperature Q - atmospheric water vapor S x,y,z - space dimensions t - time dimension W u w T w q - assimilation weighting functions for U, T, q f - interpolation function

Marketing Toolkits  International trends and developments in a GEO societal benefit area  Promotion of earth observation applications  How to get funding?  Capacity building  Disaster toolkit  Crop modelling toolkit  Water toolkit 8