DOC

Thesis format auto Table of figures, contents, etc

By Zachary Spencer,2014-11-24 21:55
6 views 0
Thesis format auto Table of figures, contents, etc

    AN IMPROVED MODEL FOR THE MICROWAVE

    BRIGHTNESS TEMPERATURE SEEN FROM SPACE

    OVER CALM OCEAN

    by

    Your name here

    A thesis submitted in partial fulfillment of the requirements for the degree of

    MASTER OF SCIENCE

    in

    ELECTRICAL ENGINEERING

    UNIVERSITY OF PUERTO RICO

    MAYAGÜEZ CAMPUS

    2005

    Approved by:

    ________________________________ __________________ Sandra L. Cruz-Pol, PhD Date Member, Graduate Committee

    ________________________________ __________________ Sandra L. Cruz-Pol, PhD Date Member, Graduate Committee

    ________________________________ __________________ Sandra L. Cruz-Pol, PhD Date President, Graduate Committee

    ________________________________ __________________ Sandra L. Cruz-Pol, PhD Date Representative of Graduate Studies

    ________________________________ __________________ Sandra L. Cruz-Pol, PhD Date Chairperson of the Department

This thesis was auto-formatted by Dr. Sandra Cruz-Pol, please use to ease your life.;

    ABSTRACT

    This thesis was formatted so that you only type on top of it your material and it should redo the Table of contents automatically by the command on the Menu: Insert>Reference>Index

    and Tables. IMPORTANT: when pasting material to this file be sure to use

    Edit>Paste_Special> Unformatted_Text, so that the format given by this file is not changed.

    When pasting figures, be sure to use Paste_special>Picture(JPEG) to make the size of your

    thesis file as much as 10 times smaller than using just Paste, which usually utilized the BMP format for figure, making your file huge and not portable. The figures should be inserted using Insert>References>Caption>(Figure), and typing the caption of the figure. In some

    versions of Word? you can skip the Reference part. The tables should be inserted using

    Insert>References>Caption>(Table). Word? should automatically increase the figure

    number and add the chapter number to it. The equations are also set so that you do Insert>References>Caption>(Equation) and the equation number increases automatically.

    You can delete the word Equation later, if you prefer to display only the equation number. Good luck! Hope this saves you a lot of work and time. SCP

    This work presents models that predict extinction rates due to atmospheric gases for 35 GHz and 95 GHz radars as a function of elevation angle. The minimum detectable radar reflectivity (dBZ) is computed for both wavelengths using radiosonde and microwave emin

    radiometer measurements. In general, sensitivity decreases with elevation angle mostly because water vapor and their corresponding highest extinction rates propagate through the lower portion of the atmosphere.

     ii

This thesis was auto-formatted by Dr. Sandra Cruz-Pol, please use to ease your life.;

    RESUMEN

    Este trabajo presenta un modelo que predice la razón de extinción para señales de 33 y 95 GHz debido a los gases atmosféricos en función del ángulo de elevación. Se computo la mínima reflectividad detectable por el radar (dBZ) para ambas frecuencias usando emin

    medidas de radiosonda y radiómetro de microondas. En general la sensitividad decrece con el ángulo de elevación debido principalmente a que el vapor de agua y su correspondiente alta extinción suceden en la porción baja de la atmósfera.

    .

     iii

This thesis was auto-formatted by Dr. Sandra Cruz-Pol, please use to ease your life.;

To my family . . .

iv

This thesis was auto-formatted by Dr. Sandra Cruz-Pol, please use to ease your life.;

    ACKNOWLEDGEMENTS

    During the development of my graduate studies in the University of Puerto Rico several persons and institutions collaborated directly and indirectly with my research. Without their support it would be impossible for me to finish my work. That is why I wish to dedicate this section to recognize their support.

    I want to start expressing a sincere acknowledgement to my advisor, Dr. Sandra Cruz-Pol because she gave me the opportunity to research under her guidance and supervision. I received motivation; encouragement and support form her during all my studies. With her, I have learned writing papers for conferences and sharing my ideas to the public. I also want to thank the example, motivation, inspiration and support I received from Dr. José Colom. From these two persons, I am completely grateful. Special thanks I owe Dr. Stephen M. Sekelsky for the opportunity of researching under his supervision, his support, guidance, and transmitted knowledge for the completion of my work.

    The Grant from NSF EIA 99-77071 provided the funding and the resources for the development of this research. At last, but the most important I would like to thank my family, for their unconditional support, inspiration and love.

     v

    This thesis was auto-formatted by Dr. Sandra Cruz-Pol, please use to ease your life.;

    Table of Contents

    ABSTRACT .......................................................................................................................................... II RESUMEN ........................................................................................................................................... III ACKNOWLEDGEMENTS ................................................................................................................. V TABLE OF CONTENTSTABLE LIST................................................................................................ VI TABLE LIST ...................................................................................................................................... VII FIGURE LIST ................................................................................................................................... VIII 1 INTRODUCTION ....................................................................................................................... 2 1.1 MOTIVATION .......................................................................................................................... 2 1.2 LITERATURE REVIEW .............................................................................................................. 3 1.3 SUMMARY OF FOLLOWING CHAPTERS ...................................................................................... 5 2 THEORETICAL BACKGROUND ............................................................................................. 6 2.1 RADIATIVE TRANSFER EQUATIONS .......................................................................................... 6 2.1.1 Equations relating humidity profiles and microwave radiometer data to attenuation ........... 6 2.1.2 Water vapor profile and zenith attenuation statistics at 33 and 95 GHz ............................... 8 2.2 SCAN EQUATIONS ................................................................................................................ 12 2.3 RADAR SYSTEM CHARACTERISTIC AND MCTEX EXPERIMENT LAYOUT .............................. 17 2.3.1 Maritime Continent Thunderstorm Experiment (MCTEX) ................................................. 17 2.3.2 Radar Hardware of Cloud Profiling Radar System (CPRS) ............................................... 17 3 MICROWAVE ATMOSPHERIC ABSORPTION MODEL .................................................. 19 3.1 ATMOSPHERIC ABSORPTION ................................................................................................ 19 3.2 NEW MODEL RETRIEVED PARAMETERS ................................................................................. 21 3.2.1 Bullet and Bullet Rosettes Toolbox for DDSCAT Program ................................................ 22 3.3 BACKSCATTERING AND DWR ANALYZE WITH IDL PROGRAM AND DDSCAT ................... 23 4 CONCLUSIONS AND FUTURE WORK ................................................................................ 26 APPENDIX A. IDL CODES FOR DBZEMIN ................................................................................ 29 APPENDIX B PROGRAMS FOR BULLET AND DWR .......................................................... 31 APPENDIX B1 IDL PROGRAM FOR REFRACTION INDEX ............................................................................ 31

     vi

This thesis was auto-formatted by Dr. Sandra Cruz-Pol, please use to ease your life.;

    Table List

Tables Page

    TABLE 2.1CPRS Parameters ........................................................................................ 10 TABLE 2.2CPRS Operational Models........................................................................... 11 TABLE 2.3 Mean values of the regions for CPRS data collected and dBZ simulated 16 emin

     vii

This thesis was auto-formatted by Dr. Sandra Cruz-Pol, please use to ease your life.;

    Figure List

Figures Page

    Figure 2.1 Passive remote sensing with upward-looking radiometer ................................ 7 Figure 2.2 Mean specific humidity profile ....................................................................... 9 Figure 2.3 Profile of extinction rates (--33 GHz and 95 GHz)Profile of extinction rates (--

    33 GHz and 95 GHz) ................................................................................................. 10 Figure 2.4 Minimum detectable signal for a single zenith pulse at different modes of radar pulse width.(a) Mode 1: ) = 200ns, (b) Mode 2: ) = 500ns, (c) Mode 3: ) = 1,000ns. ..... 11

    Figure 2.5 Flowchart for the IDL routine used for calculating the dBZemin ................... 13 Figure 2.6 Mnimum detectable dBZe in mode 1 ();= 200 ns), (a) 33 GHz, (b) 95 GHz . 14

    Figure 2.7 Minimum detectable dBZe in mode in mode 2 ()= 500 ns), (a) 33 GHz, (b) 95

    GHz .............................................................................................................................. 14

    Figure 2.8 The plot on (a) depicts the radar reflectivity measured at 95GHz with CPRS and plot on data at same time than CPRS data was collected at 95GHz. ............................... 15 Figure 2.9 Hill ratio comparison between various atmospheric models showing agreement of the chosen water vapor absorption line shape with the radiometer data. (See text for explanation of models' acronyms). ................................................................................. 16 Figure 3.1 Bullet and Bullet Rosettes with different angles of junction .......................... 20 Figure 3.2 Wind speed model relating ( to wind speed for the MCW algorithm as calibrated 0

    for Topex altimeter. ....................................................................................................... 22

    Figure 3.3 Methodology used to create a bullet formed by an array of N dipoles separated by, (a) General process, (b) bullet 3D-view, and (c) Bullet rosette with 3 bullets. ............... 23 Figure 3.4 Backscattering (10 log() of different indexes of refraction, (a) Backscattering in b

    dB to 33GHz with 652 dipoles array, (b) Backscattering in dB to 95GHz . .................... 24 Figure 3.5 Variation of the number of raob profiles used depending on the limits in space and time separation imposed on the data .............................................................................. 25

     viii

    1 INTRODUCTION

    Knowledge of the state of the ocean plays a vital role in weather and ocean wave forecasting models [Wilheit, 1979a] as well as in ocean-circulation models [Dobson et al., 1987]. One approach to measuring the state of the ocean is by remote sensing of the ocean’s surface emission. Microwave radiometers on satellites can completely cover the earth’s oceans. Satellite radiometry offers numerous advantages over ship and buoy data. Some of these advantages include the vast coverage of global seas, including locations where radiosonde or buoys cannot be afforded, relatively low power consumption, no maintenance and continuous operation under a wide range of weather conditions.

    Measurements of the microwave brightness seen from the sea are used in the retrieval of physical parameters such as wind speed, cloud liquid water and path delay. A suitable model for these measurements includes contributions from atmospheric emission, mainly water vapor and oxygen, and from ocean emission.

1.1 Motivation

    The need to improve the calibration of existing models for atmospheric and ocean emission is motivated by several current and upcoming satellite remote sensing missions. In the case of TMR, an improved atmospheric model would enhance the inversion algorithm used to retrieve path delay information. Another case is the JASON satellite, a joint NASA/CNES radiometer and altimeter scheduled to be launched in 2000 [JPL, 1998]. For JASON, absolute calibration is performed by occasionally looking at calm water. This type of

    2

    calibration reduces the cost in hardware, complexity, size and power. However, the quality of the calibration depends strongly on the accuracy of a model for the calm water emission. In contrast, for the TMR an absolute calibration is performed using hot and cold references carried by the satellite [Ruf et al., 1995].

    In this document, a section is devoted to each of these models. In Part I, the development of an improved microwave atmospheric absorption model is presented. Part II is dedicated to ocean microwave emission. In both cases, a model is developed and iteratively adjusted to fit a carefully calibrated set of measurements.

    1.2 Literature Review

    Seasat was the first satellite designed for remote sensing of the Earth's oceans. It was launched in 1978 by the National Aeronautic and Space Administration (NASA). The mission was designed to demonstrate the feasibility of global satellite monitoring of oceanographic phenomena and to help determine the requirements for an operational ocean remote sensing satellite system. It included the Scanning Multichannel Microwave Radiometer (SMMR) which measured vertical and horizontal linearly polarized brightness temperatures at 6.6, 10.7, 18, 21 and 37 GHz. The SMMR was used to retrieve surface wind speed, ocean surface temperature, atmospheric water vapor content, rain rate, and ice coverage. Unfortunately, the mission only lasted approximately 100 days due to a failure of the vehicle's electric power system [Njoku et al.,1980].

    , cloud water content, and ocean surface wind speeds [Hollinger et al., 1990].

     3

Report this document

For any questions or suggestions please email
cust-service@docsford.com