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Project Description

Purdue University

Contact Info

Purdue University

550 Stadium Mall Drive
West Lafayette, IN 47907-2051

Dr Jennifer Haase

Phone: (765) 494-1643
Fax: 765-496-1210
Email: jhaase@purdue.edu
Website: http://web.ics.purdue.edu/~jhaase

Fields of Interest

Remote Sensing; Atmospheric water vapor; Assimilation systems

Description of Scientific Projects

Research investigating atmospheric properties using GPS signals recorded from an airborne platform. GPS navigation signals that pass near horizontally through the atmosphere from satellites that are setting behind the Earth are significantly delayed due to the varying index of refractivity of the atmosphere, which in turn depends on the atmospheric state ˘ pressure, temperature, and humidity. We are developing a new airborne system for atmospheric remote sensing based on this concept. The research requires a broad range of interests beyond atmospheric science, in particular electromagnetic wave propagation and theory of optics. The candidate should have a strong quantitative/mathematical background in atmospheric science, physics, geophysics, mathematics, or engineering and prior experience in programming in C or Fortran; Research using the new high resolution MODIS sensor on the Terra and Aqua Earth Observation Satellites. The MODIS sensor is able to image precipitable water vapor with an unprecedented resolution of 1 kilometer. The research project involves preparation of the data for assimilation into weather prediction models for improving the forecasts of events such as hurricane Lili in 2002. Hurricane Lili grew in intensity category 2 to category 4 in 24 hours, then decreased in intensity even more rapidly, from category 4 to category 1 in 13 hours, a phenomena that is still currently unexplained. The candidate will compare MODIS data with GPS precipitable water vapor data and develop algorithms for evaluating data quality, and will learn to use the NCL language for manipulating satellite data. The candidate should have a strong interest in atmospheric remote sensing, a relatively quantitative mathematical background and prior experience in programming. Experience with NWP modeling, particularly WRF, is a particular advantage.

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