The main scientific goal of this study is to improve our understanding of the small-scale variability of rainfall at scales smaller than the typical resolution of radar-rainfall maps, i.e. below 2 km. This goal is motivated by hydrologic problems of predicting streamflow from ungauged basins. Hydrologic processes leading to flooding often take place at scales of a hillslope and therefore, it is important to understand the behavior of rainfall at smaller scales than have been studied in previous research. Using analytical, computational, and observational tools, the team proposes to address the following scientific questions: What are the physical processes responsible for the statistical and fractal properties of observed rainfall under varying synoptic conditions? What is the scaling structure of rainfall at scales below 2 km and is it consistent with that observed at scales of 2-100 km? If there are changes in rainfall scaling below 2 km, what physical processes are responsible for breaks in the scaling? What physical processes of rainfall are responsible for uncertainty in radar-rainfall estimates? The group will examine these questions through a coordinated program of field measurements, data analysis, and numerical modeling.
The intellectual merit of the proposed work includes: collection of the first simultaneous high-resolution polarization dual radar-lidar measurements directly over the raindrop shape and drop size distribution measuring instruments; prediction of the natural shape of raindrops as a function of the raindrop size distribution; studies of rainfall scaling based on unprecedented spatial resolution data, thus truly connecting the point scale of rain gauge and optical disdrometers and weather radar; and improved understanding of the effects of clustered rain on radar measurements to aid the proper interpretation of remote sensing data.
The broader impacts include advancing discovery while promoting teaching, training and learning; providing research opportunities for underrepresented groups, enhancing the infrastructure for research and education; and broad dissemination of experimental and analytical results. The benefits for the society will be through improvements in remote sensing of rainfall, which will lead to better prediction and control of water resources systems, and timely warnings against natural hazards such as floods, landslides, and hurricanes. |
