Characteristic scales and structures of precipitation particle distributions have generally been explored over dimensions considerably larger than 1 km, yet for several reasons it is important to characterize their variability on far smaller scales. These include the need to: (1) optimize interpretation of observations made by simple rain gages and disdrometers and the translation of such measurements to much larger scales, as needed for validation of numerical weather forecasts and flood warnings; (2) better understand drop interactions and evolution in a multi-dimensional context rather than just the classical time dependent treatment in the vertical for falling precipitation (as has often been considered adequate for representation of processes including raindrop collision, coalescence and break-up); (3) achieve a more complete description of how radar-emitted microwaves interact with rain; and (4) ultimate connection and perhaps translation of 1-D observations of drop size distributions to larger 2-D domains.
The intellectual merit of this project derives from quantification of small-scale precipitation structures via combined use of a 2-D video disdrometer in conjunction with a networked array of optical disdrometers, through which investigators will calculate pair-correlation functions in rain to derive improved measures of any given precipitation field's horizontal geometry. Disdrometer observations (whose horizontal separation may be readily adjusted) will also allow examination a wide range of distances for possible scaling, self-similarity and functional structures of drop size distributions so as to better connect with remote-sensor measurements of precipitation covering large areas. The over-arching objective of this work is to collect and analyze time-series observations of rainfall over horizontal scales ranging from centimeters (as observed using a 2-D video disdrometer) up to several tens to perhaps a hundred meters or more. Broader Impacts will come through potential contributions to improved flood prediction and monitoring as well as better processes relevant to soil erosion, through support of hands-on student involvement in, and through improved observational infrastructure at an undergraduate institution serving underrepresented groups.
