In order to study the impact of climate change on the Earth's hydrologic cycle, global information about snowfall is needed. To achieve global measurements of snowfall over both land and ocean, satellites are necessary. While satellites provide the best option for making measurements on a global scale, the task of estimating snowfall rate from these measurements is a complex problem. Satellite-based radar, for example, measures effective radar reflectivity, Ze, which can be converted to snowfall rate, S, via a Ze-S relation. Choosing the appropriate Ze-S relation to apply is a complicated problem, however, because quantities such as particle shape, size distribution, and terminal velocity are often unknown, and these quantities directly affect the Ze-S relation. Additionally, it is important to correctly classify the phase of precipitation. A misclassification can result in order-of-magnitude errors in the estimated precipitation rate. Using global ground-based observations over multiple years, the influence of different geophysical parameters on precipitation phase is investigated, with the goal of obtaining an improved method for determining precipitation phase. The parameters studied are near-surface air temperature, atmospheric moisture, low-level vertical temperature lapse rate, surface skin temperature, surface pressure, and land cover type. To combine the effects of temperature and moisture, wet-bulb temperature, instead of air temperature, is used as a key parameter for separating solid and liquid precipitation. Results show that in addition to wet-bulb temperature, vertical temperature lapse rate also affects the precipitation phase. For example, at a near-surface wet-bulb temperature of 0°C, a lapse rate of 6°C km-1 results in an 86 percent conditional probability of solid precipitation, while a lapse rate of -2°C km-1 results in a 45 percent probability. For near-surface wet-bulb temperatures less than 0°C, skin temperature affects precipitation phase, although the effect appears to be minor. Results also show that surface pressure appears to influence precipitation phase in some cases, however, this dependence is not clear on a global scale. Land cover type does not appear to affect precipitation phase. Based on these findings, a parameterization scheme has been developed that accepts available meteorological data as input, and returns the conditional probability of solid precipitation. Ze-S relations for various particle shapes, size distributions, and terminal velocities have been developed as part of this research. These Ze-S relations have been applied to radar reflectivity data from the CloudSat Cloud Profiling Radar to calculate the annual mean snowfall rate. The calculated snowfall rates are then compared to surface observations of snowfall. An effort to determine which particle shape best represents the type of snow falling in various locations across the United States has been made. An optimized Ze-S relation has been developed, which combines multiple Ze-S relations in order to minimize error when compared to the surface snowfall observations. Additionally, the resulting surface snowfall rate is compared with the CloudSat standard product for snowfall rate. / A Dissertation submitted to the Department of Earth, Ocean and Atmospheric Science in partial fulfillment of the Doctor of Philosophy. / Spring Semester 2017. / March 31, 2017. / Atmospheric Science, Hydrology, Meteorology, Remote Sensing, Snowfall / Includes bibliographical references. / Guosheng Liu, Professor Directing Dissertation; Anke Meyer-Baese, University Representative; Mark A. Bourassa, Committee Member; Ming Cai, Committee Member; Philip G. Sura, Committee Member.

Satellite observed Outgoing Longwave Radiation (OLR) is used to initialize the clouds and radiation of the Florida State University Global Spectral Model. A one-parameter method and two different six-parameter methods of initialization are formulated. The one-parameter method is shown to be the most efficient and produce the best results. / The effects of the cloud and radiation initialization on a five day forecast are presented. The initialization procedure produces a better forecast of OLR than the control experiment by such a large extent that the five day forecast of the initialization experiment has approximately the same verification score as the initial data of the control experiment. The cloud forecasts (high, middle, and low) of the initialization experiment prominently show the cloud signatures of the monsoon, the Pacific and Atlantic Ocean ITCZs, and the African rainbelt, but the cloud signatures of the control experiment are weak or nonexistent. The moist static stability budgets show that the initialization procedure had a large impact on the forecast after five days of integration by producing a monsoon and typhoon that were stronger and better defined. Additionally, radiative destabilization forcing budgets of the initialization experiment were an order of magnitude greater than the control experiment for the Atlantic Ocean ITCZ. The effect of initialization on precipitation forecasts was also investigated. It was found that the model precipitation decreased after initialization. This is attributed to the model formulation of precipitation, and a new formulation is suggested for further investigation. / Source: Dissertation Abstracts International, Volume: 51-12, Section: B, page: 5925. / Major Professor: T. N. Krishnamurti. / Thesis (Ph.D.)--The Florida State University, 1990.

This dissertation describes a procedure for estimating surface heat and moisture fluxes based on the use of remotely sensed satellite measurements and a biosphere model. Data collection and processing procedures are presented along with the measurement analysis for the entire 1987 FIFE (First ISLSCP) Field Experiment). Two FSU stations measured surface layer gradients of temperature and moisture. These data were used in the determination of evapotranspiration based on the Bowen ratio method. / A procedure is developed for filtering the flux time series. The filter, based on a two-dimensional Fourier transform, preserves the basic diurnal features and longer time scales while removing high frequency noise which cannot be attributed to site-induced variation. A filtering procedure is desirable before the measurements are utilized as input with a biosphere model both to prevent numerical instabilities and to insure that model-based intercomparisons at multiple sites are uncontaminated by input variance not related to true site behaviour. / The design and formulation of an experimental biosphere model (Ex-BATS) is described. Ex-BATS has been designed to incorporate in situ measurements and satellite parameterizations of certain canopy variables (surface albedo, leaf area index and stomatal resistance). Model validation inter-comparison shows that Ex-BATS produces realistic diurnal behavior of surface fluxes. The results of a series of numerical experiments using Ex-BATS are presented. These simulations have been performed in order to assess how the model performs when remotely sensed data are used to estimate the canopy variables. Results indicate that Ex-BATS is not sensitive to small variations of surface albedo or leaf area index within the range of estimation uncertainty. Simulations using remotely retrieved stomatal resistance produced significantly reduced RMS differences for latent and sensible heat fluxes over the model using a hypothetical formulation. / Source: Dissertation Abstracts International, Volume: 52-03, Section: B, page: 1498. / Major Professor: Eric A. Smith. / Thesis (Ph.D.)--The Florida State University, 1991.

<p> In cities, microclimates are created by local mixtures of vegetation, constructed materials, vertical structure, and moisture, with significant consequences for human health, air quality, and resource use. Vegetation can moderate microclimates through evapotranspiration, however this function is dependent on local conditions so its effect may vary over space and time. This dissertation used hyperspectral and thermal remote sensing imagery to derive key observations of urban physical and biophysical properties and model urban microclimates across the megacity of Los Angeles. In Chapter 1, I used Multiple Endmember Spectral Mixture Analysis (MESMA) to map sub-pixel fractions of different vegetation types, as well as other types of urban cover, at 4 m and 18 m resolution over Santa Barbara, California (Wetherley et al., 2017). Fractional estimates correlated with validation fractions at both scales (mean R² = 0.84 at 4 m and R² = 0.76 at 18 m), with accuracy affected by image spatial resolution, endmember spatial resolution, and class spectral (dis)similarity. Accuracy was improved by using endmembers measured at multiple spatial resolutions, likely because they incorporated additional spectral variability that occurred across spatial scales. In Chapter 2, I applied this methodology to derive sub-pixel cover for the greater Los Angeles metropolitan area (4,466 km²) (Wetherley et al., 2018). Further improvement in quantifying sub-pixel vegetation types was achieved by modifying the MESMA shade parameter. Land surface temperature (LST), derived from thermal imagery, was used to model temperature change along vegetation fractional gradients, with slopes of LST change showing significant differences between trees and turfgrass (p &lt; 0.001). Expected per-pixel LST was derived from these gradients based on sub-pixel composition, and when compared to measured LST was found to deviate with a standard deviation of 3.5 &deg;C across the scene. These deviations were negatively related to irrigation and income, while building density was observed to affect tree LST more than it affected turfgrass LST. In Chapter 3, I used the map of Los Angeles landcover, along with data from LiDAR, GIS, and WRF climate variables, to parameterize an urban climate model (Surface Urban Energy and Water Balance Scheme: SUEWS) for 2,123 neighborhoods (each 1 km²) across Los Angeles. Modeled latent fluxes were correlated with remote sensing LST (R² = 0.39) collected over a period of 5 hours, with an overall diurnal pattern modified by irrigation timing. Spatial variability across the study area was related to local landcover, with albedo and vegetation fraction strongly influencing latent and sensible fluxes. A strong regional climatic gradient was observed to affect latent fluxes based on coastal proximity. Overall, this dissertation quantifies the key drivers of urban vegetation function in a large city, and further demonstrates the potential of hyperspectral and thermal imagery for observing city scale surface and microclimate variability.</p><p>

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Synthetic aperture radar interferometry (InSAR) is a technique that enables generation of Digital Elevation Models (DEMs) and detection of surface motion at the centimetre level using radar signals transmitted from a satellite or an aeroplane. Deformation observations can be performed due to the fact that surface motion, caused by natural and human activities, generates a local phase shift in the resultant interferogram. The magnitude of surface deformation can be estimated directly as a fraction of the wavelength of the transmitted signal. Moreover, differential InSAR (DInSAR) eliminates the phase signal caused by relief to yield a differential interferogram in which the signature of surface deformation can be seen. Although InSAR applications are well established, the improvement of the interferometry technique and the quality of its products is highly desirable to further enhance its capabilities. The application of InSAR encounters problems due to noise in the interferometric phase measurement, caused by a number of decorrelation factors. In addition, the interferogram contains biases owing to satellite orbit errors and atmospheric heterogeneity These factors dramatically reduce the stlectiveness of radar interferometry in many applications, and, in particular, compromise detection and analysis of small-scale spatial deformations. The research presented in this thesis aim to apply radar interferometry processing to detect small-scale surface deformations, improve the quality of the interferometry products, determine the minimum and maximum detectable deformation gradient and enhance the analysis of the interferometric phase image. The quality of DEM and displacement maps can be improved by various methods at different processing levels. One of the methods is filtering of the interferometric phase. / However, while filtering reduces noise in the interferogram, it does not necessarily enhance or recover the signal. Furthermore, the impact of the filter can significantly change the structure of the interferogram. A new adaptive radar interferogram filter has been developed and is presented herein. The filter is based on a modification to the Goldstein radar interferogram filter making the filter parameter dependent on coherence so that incoherent areas are filtered more than coherent areas. This modification minimises the loss of signal while still reducing the level of noise. A methodology leading to the creation of a functional model for determining minimum and maximum detectable deformation gradient, in terms of the coherence value, has been developed. The sets of representative deformation models have been simulated and the associated phase from these models has been introduced to real SAR data acquired by ERS-1/2 satellites. A number of cases of surface motion with varying magnitudes and spatial extent have been simulated. In each case, the resultant surface deformation has been compared with the 'true' surface deformation as defined by the deformation model. Based on those observations, the functional model has been developed. Finally, the extended analysis of the interferometric phase image using a wavelet approach is presented. The ability of a continuous wavelet transform to reveal the content of the wrapped phase interferogram, such as (i) discontinuities, (ii) extent of the deformation signal, and (iii) the magnitude of the deformation signal is examined. The results presented represent a preliminary study revealing the wavelet method as a promising technique for interferometric phase image analysis.

The concentration of phytoplankton in waters off the Western Australian coastline contrast with the coastal waters west of southern Africa and South America. The lack of favourable upwelling conditions results in the majority of the southeastern Indian Ocean surface waters being nutrient poor. Which is reflected in their low productivity. Several areas either on or in close proximity to the coastline are notably more productive. The associated forcing terms generating phytoplankton favourable conditions differ between areas. as do the seasons in which they occur. Measurements of chlorophyll a concentration. the major photosynthetic pigment contained in phytoplankton, may be directly related to oceanic bioproductivity. Using data collected by the Coastal Zone Color Seamier between 1979-86, this work aims to improve the understanding of the spatial and temporal changes that occurred in chlorophyll a abundance in the southeastern Indian Ocean. The highest seasonal mean concentrations occur in Summer (January-March) and Autumn (April-June); the former occurring in waters of the North West Shelf and the latter in close coastal areas of Western Australia south of North West. Cape. Concentrations observed in the offshore oceanic regions are mostly poor. Exceptions to this occur in proximity to the adjacent Indonesian islands and directly south of Albany (possibly due to northwards flow of subantarctic nutrient-rich waters). A considerable interannual variation was also noted, with the highest mean chlorophyll concentrations occurring in 1981. 1982 and 1983. / The influence of the forcing terms on chlorophyll a appears to vary significantly among the waters of North West Shelf, Western and southern Western Australian coastline. This is most notable in the interseasonal variations. The changes observed interannually and their influence on chlorophyll a are not easily discernible. but there may be some connection with the La Nina/El Nino related changes in both currents and winds.

Includes bibliography.

This thesis describes the research undertaken for a degree of Master of Science in a retrospective study of airborne remotely sensed data registered in 1990 and 1993, and field captured data of aquatic humus concentrations for ~ 45 lakes in Tasmania. The aim was to investigate and describe the relationship between the remotely sensed data and the field data and to test the hypothesis that the remotely sensed data would establish further evidence of a limnological corridor of change running north-west to south- east. The airborne remotely sensed data consisted of data captured by the CSIRO Ocean Colour Scanner (OCS) and a newly developed Canadian scanner, a compact airborne spectrographic imager (CASI). The thesis investigates the relationship between the two kinds of data sources. The remotely sensed data was collected with the OCS scanner in 1990 (during one day) and with both the OCS and the CASI in 1993 (during three days). The OCS scanner registers data in 9 wavelength bands between 380 nm and 960 nm with a 10-20 nm bandwidth, and the CASI in 288 wavelength bands between 379.57 nm and 893.5 nm (ie. spectral mode) with a spectral resolution of 2.5 nm. The remotely sensed data were extracted from the original tapes with the help of the CSIRO and supplied software and digital sample areas (band value means) for each lake were subsequently extracted for data manipulation and statistical analysis. Field data was captured concurrently with the remotely sensed data in 1993 by lake hopping using a light aircraft with floats. The field data used for analysis with the remotely sensed data were the laboratory determined g440 values from the 1993 water samples collated with g440 values determined from earlier years. No spectro-radiometric data of the lakes, data of incoming irradiance or ancillary climatic data were captured during the remote sensing missions. The sections of the background chapter in the thesis provide a background to the research both in regards to remote sensing of water quality and the relationship between remotely sensed spectral data and water quality parameters, as well as a description of the Tasmanian lakes flown. The lakes were divided into four groups based on results from previous studies and optical parameters, especially aquatic humus concentrations as measured from field captured data. The four groups consist of the ‘green” clear water lakes mostly situated on the Central Plateau, the ‘brown” highly dystrophic lakes in western Tasmania, the ‘corridor” lakes situated along a corridor of change lying approximately between the two lines denoting the Jurassic edge and 1200 mm isohyet, and the ‘eastern, turbid” lakes make up the fourth group. The analytical part of the research work was mostly concerned with manipulating and analysing the CASI data because of its higher spectral resolution. The research explores methods to apply corrections to this data to reduce the disturbing effects of varying illumination and atmospheric conditions. Three different methods were attempted. In the first method two different standardisation formulas are applied to the data as well as ‘day correction” factors calculated from data from one of the lakes, Lake Rolleston, which had data captured for all three days of the remote sensing operations. The standardisation formulas were also applied to the OCS data. In second method an attempt to reduce the effects of the atmosphere was performed using spectro-radiometric captured in 1988 for one of the lakes flown, Great Lake. All the lake sample data were time normalised using general irradiance data obtained from the University of Tasmania and the sky portion as calculated from Great Lake upwelling irradiance data was then subtracted. The last method involved using two different band ratios to eliminate atmospheric effects. Statistical analysis was applied to the data resulting from the three methods to try to describe the relationship between the remotely sensed data and the field captured data. Discriminant analysis, cluster analysis and factor analysis using principal component analysis (pea) were applied to the remotely sensed data and the field data. The factor scores resulting from the pca were regressed against the field collated data of g440 as were the values resulting from last method. The results from the statistical analysis of the data from the first method show that the lakes group well (100%) against the predetermined groups using discriminant analysis applied to the remotely sensed CASI data. Most variance in the data are contained in the first factor resulting from pca regardless of data manipulation method. Regression of the factor scores against g440 field data show a strong non- linear relationship and a one-sided linear regression test is therefore considered an inappropriate analysis method to describe the dataset relationships. The research has shown that with the available data, correction and analysis methods, and within the scope of the Masters study, it was not possible to establish the relationships between the remotely sensed data and the field measured parameters as hoped. The main reason for this was the failure to retrieve remotely sensed lake signatures adequately corrected for atmospheric noise for comparison with the field data. This in turn is a result of the lack of detailed ancillary information needed to apply available established methods for noise reduction - to apply these methods we require field spectroradiometric measurements and environmental information of the varying conditions both within the study area and within the time frame of capture of the remotely sensed data.