Graduate Exam Abstract
December 10, 2014, 3:00 pm - 5:00 pm
Dean's Conference Room B214
Retrieval Techniques and Information Content Analysis to Improve Remote Sensing of Atmospheric Water Vapor, Liquid Water and Temperature from Ground-based Microwave Radiometer Measurements
Abstract: Observation of profiles of temperature, humidity and winds with sufficient accuracy and fine vertical and temporal resolution are needed to improve mesoscale
weather prediction, track conditions in the lower to mid-troposphere, predict winds for renewable energy, inform the public of severe weather and improve
transportation safety. In comparing these thermodynamic variables, the absolute atmospheric temperature varies only by 15%; in contrast, total water vapor may
change by up to 50% over several hours. In addition, numerical weather prediction (NWP) models are initialized using water vapor profile information, so
improvements in their accuracy and resolution tend to improve the accuracy of NWP. Current water vapor profile observation systems are expensive and have
insufficient spatial coverage to observe humidity in the lower to mid-troposphere. To address this important scientific need, the principal objective of this
dissertation is to improve the accuracy, vertical resolution and revisit time of tropospheric water vapor profiles retrieved from microwave and millimeter-wave
brightness temperature measurements.
Ground-based microwave and millimeter-wave brightness temperature measurements from radiometers operating at frequencies near the 22.235 and 183.31
GHz water vapor absorption lines have been used extensively for retrieval of water vapor profiles. Such microwave radiometers have the advantages of relatively
low cost, potential for future network deployment, and frequent revisit times for sensing dynamic changes as well as gradients in water vapor profiles. To retrieve
water vapor profiles from microwave brightness temperature measurements, Bayesian optimal estimation is commonly used, requiring a water vapor background
data set. Microwave brightness temperature measurements provide information on water vapor at the location and time of measurement, while background data
sets provide statistics on the general behavior and variability of water vapor. Brightness temperature measurements at multiple frequencies contribute information
to profile retrieval, although the information at multiple frequencies may be highly correlated due to similar sensitivities to changes in atmospheric pressure,
temperature and water vapor mixing ratio as a function of altitude. To retrieve profiles with optimal vertical resolution and minimum retrieval error, as many
independent measurements as possible need to be obtained, within the limitations of available resources. To this end, an analysis is performed to determine the
amount of independent information about water vapor and temperature available from the microwave and millimeter-wave frequency spectrum. For this, a
feature selection algorithm based on weighting function analysis is used to determine sets of frequencies between 10 and 200 GHz that have the greatest
number of degrees of freedom for water vapor and temperature retrieval. Another analysis is performed to determine the optimal background data set size and
layer thickness to yield maximum information about water vapor variability to sense dynamic changes in water vapor profiles at a particular location and a
particular time of year. To explore the retrieval technique’s capability and performance, the HUMidity EXperiment 2011 (HUMEX11) was conducted at the U.S.
Department of Energy’s (DOE) Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site. The radiometer-retrieved profiles are compared
with Raman lidar-retrieved profiles to determine their accuracy.
In addition to water vapor, clouds and precipitation also strongly affect microwave and millimeter-wave brightness temperature measurements. Since the
presence of liquid water reduces the accuracy of water vapor retrievals, it is important to distinguish between clear and cloudy sky conditions and to estimate the
amount of liquid water in the atmosphere. To address this need, a technique has been developed based on the ratio of the ground-based brightness temperature
at 23.8 GHz to that at 30.0 GHz, known as the vapor liquid water ratio (VLWR). During clear sky conditions, the VLWR is much greater than unity, but when
sufficient liquid water is present, the VLWR approaches unity. This sensitivity of the VLWR is used to develop an algorithm to retrieve integrated water vapor and
liquid water in the atmosphere over a wide range of elevation angles. Measured brightness temperatures are obtained from the University of Miami radiometer
during the DYNAmics of the Madden-Julian Oscillation (DYNAMO) experiment. The water vapor and liquid water retrieved from microwave brightness
temperatures are compared to those retrieved from radar measurements by the National Center for Atmospheric Research S-PolKa (dual-wavelength S- and Ka-
band) radar, which was collocated with the radiometer.
This dissertation advances the state of knowledge of retrieval of atmospheric water vapor from microwave brightness temperature measurements. It focuses on
optimizing two information sources of interest for water vapor profile retrieval, i.e. independent measurements and background data set size. From a theoretical
perspective, it determines sets of frequencies in the ranges of 20–23, 85–90 and 165–200 GHz that are optimal for water vapor retrieval from each of ground-
based and airborne radiometers. The maximum number of degrees of freedom for the selected frequencies for ground-based radiometers is 5-6, while the
optimum vertical resolution is 0.5 to 1.5 km. On the other hand, the maximum number of degrees of freedom for airborne radiometers is 8-9, while the optimum
vertical resolution is 0.2 to 0.5 km. From an experimental perspective, brightness temperature data sets from the HUMEX11 and DYNAMO field experiments
have been used to improve knowledge of the impact of the background information on retrieval of water vapor profiles and estimation of water vapor and liquid
water using low elevation angle data sets. HUMEX11 measurements have been used to improve retrieval performance by choosing optimal atmospheric a-priori
statistics of 35-55 profiles and layer thickness of 100-m to detect dynamic changes and gradients. DYNAMO measurements have been used to retrieve slant
water path and slant liquid water with estimated error of less than 10% and 25%, respectively, for all elevation angles of interest.
These theoretical and experimental advances improve understanding of retrievals using microwave brightness temperature and extend them to more challenging
applications, including sudden atmospheric gradients and slant path delay retrieval for elevation angles as low as 5º.
Adviser: Prof. Steven C. Reising
Non-ECE Member: Prof. Steven A. Rutledge, Atmospheric Science
Member 3: Prof. Branislav Notaros
Addional Members: Dr. Jothiram Vivekanandan
1) Sahoo, S., X. Bosch-Lluis, S. C. Reising, and J. Vivekanandan, "Radiometric Information Content for Water Vapor and Temperature Profiling in Clear Skies between 10 and 200 GHz," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, accepted for publication, Oct. 2014.
2) Sahoo, S., X. Bosch-Lluis, S. C. Reising, and J. Vivekanandan, "Optimization of Background Information and Layer Thickness for Improved Accuracy of Water-Vapor Profile Retrieval from Ground-Based Microwave Radiometer Measurements at K-band," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, accepted for publication, Oct. 2014.
3) Sahoo, S., X. Bosch-Lluis, S. C. Reising, S. M. Ellis, J. Vivekanandan and P. Zuidema, "Retrieval of Slant Water Path and Slant Liquid Water from Microwave Radiometer Measurements from Zenith to Low Elevation Angles during the DYNAMO Experiment," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, under review.
Program of Study:
ECE 642-001 Time Harmonic Electromagnetics
ECE 562-001 Power Electronics I
STAT 540-001 Data Analysis and Regression
ECE 512-001 Digital Signal Processing
ATS 755-001 Theoretical and Applied Climatology