Error and bias in snow data assimilation system (snodas) estimates, and their consequences for hydrologic modeling and water supply forecasts, have been explored by numerous authors

The Bureau of Reclamation awarded RTI International a cooperative agreement valued at $399,149 under the SECURE Water Act – Research Agreements program (CFDA 15.560) on August 6, 2025, with completion targeted for September 30, 2027. The award funds research to improve the Snow Data Assimilation System (SNODAS) spatial interpolation methodology, which supports hydrologic modeling and water supply forecasting across western U.S. water systems. RTI will implement and test alternative spatial analysis methods for SNODAS error and bias assessment, including stepwise multiple regression and ordinary kriging techniques. The project leverages existing open-source code repositories and technical expertise from the National Weather Service Office of Water Prediction to efficiently integrate improved interpolation methods into SNODAS operations.

The research will advance SNODAS assimilation capabilities with particular focus on mountainous regions of the Western United States, where current inverse distance weighted analysis demonstrates significant interpolation skill gaps. Deliverables include validation of alternative spatial analysis techniques, streamlined processes for station selection and outlier detection, and enhanced capacity to assimilate new data sources such as airborne light detection and ranging (LiDAR) measurements. The improved interpolation methodology is designed for integration into SNODAS operations with minimal modification to existing operational frameworks, thereby supporting more accurate snow water equivalent estimates critical to federal and regional water resource management.

Guided in part by their findings, we seek to improve the skill of the spatial interpolation used in snodas assimilation. We have recently developed a cross-validation method to assess the skill of the inverse distance weighted (IDW) analysis. This method provides a mechanism for objectively selecting IDW parameters, identifying potential outlier observations, and estimating the accuracy of the IDW result. Its recent use demonstrates significant potential for improvement in the skill of the interpolation method, particularly in mountainous regions of the western united states. We seek to implement and test several alternative methods for spatial analysis of snodas error and bias, including but not limited to stepwise multiple regression and ordinary kriging. Both techniques have been tested in other office of water prediction (OWP) efforts including the national snowfall analysis and the national oceanic and atmospheric administration atlas 14 15 precipitation frequency analysis. The associated code repositories are open source, and the OWP staff who developed them would serve as primary collaborators on this project, providing an efficient pathway to implementing and testing these methods in snodas assimilation. Improved assimilation and interpolation techniques also provide a clearer path to test and implement new data sources, such as airborne lidar measurements. An improved interpolation method could be integrated into snodas operations with little modification of the current operational framework, and the development thereof is also expected to provide US withopportunities to streamline and improve the process of preparing snodas assimilation (including station selection, enhanced abilities to assimilate datasets from aerial snow observation systems, outlier detection, region delineation, and the transfer of information between geographic information systems workstations and operational systems). Finally, the error estimates produced through cross validation of the analysis technique will be used to generate visualizations and statistics that can be shared publicly these will provide a quantitative description of uncertainty that has to date been lacking in many snodas products.