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Rice Atmospheric Information Network (RAIN) Project

Meterological disturbances have the power and energy to cause major impacts to life and property. Wind storms, rain, floods, and landslides are responsible for more property damage and loss of life than any other events. The large spatial and temporal variation of rainfall rate and wind velocity has been a long-standing problem for meteorological and hydrological studies. Current environmental monitoring systems are incapable of providing the real-time, high spatial resolution, ground-level maps of rainfall and wind velocity that are critical for predicting, monitoring, and responding to severe environmental events. The RAIN project represents a new direction in weather sensing: replacing sparse, expensive, centralized sensing facilities with many interconnected, rugged, low-cost, battery-powered weather sensing nodes. The RAIN design can locate sensing resources precisely where more coverage, accuracy, and resolution are needed, such as in a complicated urban environment, over an airport runway, or in a flood prone area.

RAIN sensors and nodes will be realized by applying advanced engineering design principles and utilizing state-of-the-art, inexpensive photonic and digital signal processing hardware. Unique sensors use low-power laser beams to measure path-averaged raindrop size distribution, rainfall rate, and wind velocity. These transmission measurements are much more stable and accurate than backscattering techniques, like radar, and provide more data than point measurements. The optical beams used for sensing will also be used for communication between nodes, forming a sensor network that delivers data, via the Internet, to a central computer. Optical communication consumes much less power than radio links.

To date, the RAIN project consists of three primary efforts:

• Undergraduate independent research projects developing a simplified rainfall sensor and signal processing methods. UGProjects
• Development of a rainfall sensor network for flood prediction. RAIN sensors will be deployed in a proof-of-concept testbed in Harris Gully, Houston, a flood-prone area containing Rice University and the Texas Medical Center. RAIN data will be used to develop an improved physics-based, distributed hydrological model that can predict local flooding at areas of critical importance. Rainfall/Flooding Project
• Development of wind velocity sensors to provide data to predict local atmospheric transport of pollution and the dispersal paths of toxic materials from chemical leaks or fires. Amospheric transport models for local and urban scales will be developed to exploit the unique characteristics of RAIN sensor data. Wind/Transport Project

RAIN is supported by a grant from the Enriching Rice through Information Technology (ERIT)(approve sites) program of the Computer and Information Technology Institute. Additional support comes from the George R. Brown School of Engineering, and the Department of Electrical and Computer Engineering.