Measurement and characterization of precipitation size and type provides invaluable insight into atmospheric conditions and processes. The data are critical for climate records, scientific research, weather radar, and applications that examine and quantify the effects of precipitation on societal function. As a consequence, the need for routine qualitative and quantitative measurements of precipitation type and size continues in earnest amongst all sectors of society.
Over the last three decades, the availability of all available weather information has undergone a complete transformation with the steady rise of the private weather industry. The redistribution of raw meteorology data as custom decision products has come to define this now lucrative industry. Many companies have grown in large part from the development and timely dissemination of routine weather-based decision products, applications, and forecasts. These products also now underpin the day to day operations and success of major industries in the United States, particularly energy production and distribution, agriculture, transportation and commerce, education, and construction at the local, regional, national scale.
To this end, weather impacting the operation and output of wind farms is one of the top challenges facing the new energy industry today. In addition, weather impacts such as ice damage to electric utilities distributing energy causes hundreds of millions of dollars worth of damage each winter. Current technology routinely measures surface precipitation accumulation at weather stations, usually in rain gauges or tipping buckets. However, information on precipitation type (drizzle, rain, mixed ice, and snow or mixed precipitation) remains unavailable, unaffordable, or highly limited to certain particle size ranges unless augmented by human observation. In addition, measurement of precipitation size (including droplet size distribution and inferred parameters such as rain rate) remains unavailable as part of routine weather and climate records.
The lack of detailed climatology records impedes improvements of numerical weather prediction and Doppler radar spectra calibration. Specifically, parameterization of model microphysical processes and the translation of radar reflectivity into precipitation intensity, respectively. Research using radar to define more accurate droplet size distributions for ground-based precipitation particles (as opposed to in-cloud droplets) is impeded by a dearth of high quality ground-based precipitation particle observations.
To date, few instruments exist for measurement of precipitation type and, those that do, lack either the comprehensive capabilities to differentiate between all precipitation types or do not provide quantitative data for certain types or sub types. In addition, no precipitation instrumentation has been developed successfully for detecting real-time wintry mixed precipitation. Specific data on precipitation types such as sleet, freezing rain, and hail in terms of the particle make-up (e.g., liquid to frozen particle fraction) remain unavailable.
The mission behind this work, which was funded by the National Science Foundation's (NSF) Small Business Innovation Research (SBIR) Phase I program and is currently under review for Phase II funding, aims to combat the longstanding inconsistency associated precipitation sensor technology. We propose to develop a Precipitation Imaging and Characterization System (PICS), an optical imaging sensor with the capability of providing comprehensive measurement of precipitation type (encompassing the full range of particles from small drizzle, rain, mixed ice, and snow) and of particle size (including droplet size distribution and associated parameters such as velocity distribution and volumetric density). As part of work, we also plan to develop a prototype algorithm to define mixed-phase particles (their frozen to liquid fraction).
Figure 1 illustrates the proposed PICS configuration where a camera array and an opposing light source straddle a defined and fixed sampling volume through which precipitation particles fall. The multi-camera imaging technology proposed aims to overcome the key technical problems of existing/competing precipitation characterization systems while managing cost for a large base of potential users. This hardware setup will be integrated with a software system which incorporates data logging capability, object recognition techniques, as well as statistical and diagnostic algorithms for accurate particle characterization and sizing. The principle innovations of PICS are rooted in its novel measurement approach, the comprehensive nature of each measurement, and the competitive cost relative to existing technology. Further, we plan to design PICS technology in a modular format relative to measured precipitation parameters where parameters (data output) will take on an increased level of accuracy and sophistication in relation to cost. This approach would provide the PICS technology at more than one price point and therefore give customers options in terms of measurement sophistication and level of detail. For example, one customer may desire bulk precipitation type detection only (e.g., rain, ice, or snow) whereas another may want to detect drizzle or particle size or even derived statistical parameters. The latter would assume a higher price point due to the nature of the algorithms required.
Figure 1. General conceptual illustration of PICS based PIS (outdoor unit): Imaging System, and PCS (indoor unit): Control box, Data Logging and Processing Computer.
The design and development team of PICS spans multiple disciplines specializing in meteorology; fluid dynamics and multiphase flows; and pattern and object recognition technologies working in business, higher education and non-profit sectors. If the proposed research gets funded by NSF, the working prototype of PICS system and its modular components will be available for testing by interested parties in 2012.
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