Tools for use of Predictive Rainfall within Irrigation Decision Aids, Downscaling of Soil Moisture, and Non-Gaussian Data Assimilation for Agricultural/Military Applications and Analysis of Atmospheric River Events

At Colorado State University (CSU), several tools are under development that bring several synergies within the hydrometeorological domain. This includes: a) a USDA-NIFA funded Ogallala Water Coordinated Agricultural Project (OWCAP) irrigation phone app that makes use of numerical weather prediction-based (NWP) quantitative precipitation forecasts (QPF) of rainfall patterns within the irrigation decision aid system at field-scales, b) an advanced soil moisture downscaling tool, and c) non-Gaussian data assimilation methodologies applied to 1DVAR water vapor fields for satellite retrieval and NWP performance improvements. Of the above systems, several have unique delivery methods which enables us to scale the outreach of these systems toward more users. The first, is the CSU Water Irrigation Scheduler for Efficient (WISE) Application tool which is a cloud-based phone app that is feed data via cloud-based data. This irrigation application is used by farmers to maximize their agricultural productivity while conserving their water resources. The second is a GIS-based application that enables a detailed fine-scale regional analysis of soil moisture from coarse large-scale soil moisture estimates. This has numerous military and agricultural applications, as well as potential hydrology cal/val implications regarding in situ field study data sets, e.g., to spread the potential impact of in situ measurements and mitigate micro-scale representation errors. Also, error distributions are being explicitly represented within the soil moisture downscaling tool. Lastly, the non-Gaussian distributed errors have been shown to impact data assimilation and satellite retrieval systems. New methods (initially focused on skewed water vapor distributions) are being deployed and tested within the new NSF-funded CIRA Data Assimilation Testbed (CDAT), a website where near-real-time results and software tools will be shared to the educational community that is interested in learning about these effects. CDAT will be used as an educational tool for understanding interactions between atmospheric dynamics and non-Gaussian variable distributions for use with atmospheric rivers and dynamically-driven moisture- events. We plan to discuss future collaboration and educational opportunities.