This effort focuses on planning, conducting, and analyzing data collection events from an extended space-based imaging campaign using an advanced remote-sensing methodology developed at the AFRL Space Vehicles Directorate. This data collection campaign (which leverages space-based imagery of ground events, with ground-based truth sensors) is intended to help validate unique methods in advanced infrared sensing, and as such must take place under carefully-monitored conditions, at specific locations in the world. The field campaign will consist of coordinated data collection events using exquisite sensing assets to observe high-radiance, short-duration events, which necessitates careful advance planning. Coordination of practical details, including timing of events, coordination of assets, collaboration with domestic and international partners, and management of data collection requirements, is important to this effort.

Furthermore, uncertainties associated with the presence of clouds when imaging from space have to be taken into consideration. To accomplish this goal, a diverse network of retrieved measurements must be statistically data mined from both up- and down-looking sensors. These include the following:

(1) AERONET (Aerosol Robotic Network), a network of globally distributed instruments which operate in a cloud-mode acquisition and measure a variety of atmospheric and cloud parameters, including cloud optical depths between 10 and 90.

(2) Micro Pulse Lidar Network (MPLNET) which measures aerosol cloud vertical structure continuously, day and night.

(3) Ceilometers at major airports which provide cloud base heights.

(4) MODIS Cloud Product, a global product that provides cloud particle-phase, effective cloud particle radius, and cloud optical thickness.

(5) Other global satellite cloud datasets as needed.

The various datasets are needed to resolve the point source measurements (AERONET, MIPLNET and ceilometers) to the satellite kilometer-scale footprints. Researching and resolving the highlighted issues requires a solid background in programming and atmospheric physics to perform data mining techniques and develop algorithms appropriate for effective cloud optical depth retrievals. Additionally, skills in data analysis, including implementing advanced analysis routines and conducting comparative analyses, are important, as is an ability to communicate technical material in writing or orally. Key technical background areas include electro-optical sensors, infrared imaging, cloud physics and/or atmospheric transmission, as well as hands-on experience. Additionally, an ability to travel to field locations is desirable.

 

Keywords:

Advanced imaging; Space-based imaging; Cloud statistics; Cloud optical depth; Atmospheric transmission;

Citizenship:  Open to U.S. citizens

Level:  Open to Postdoctoral and Senior applicants