<div>Global Navigation Satellite System Reflectometry (GNSS-R) is a remote sensing technique that uses reflected satellite navigation signals from the Earth surface in a bistatic radar configuration. GNSS-R observations have been collected using receivers on stationary, airborne and spaceborne platforms. The delay-Doppler map (DDM) is the fundamental GNSS-R measurement from which ocean surface wind speed can be retrieved. GNSS-R observations can be assimilated into numerical weather prediction models to improve weather analyses and forecasts. The direct assimilation of DDM observations shows potential superiority over the assimilation of wind retrievals.</div><div><br></div><div>This dissertation demonstrates the direct assimilation of GNSS-R DDMs using a two-dimensional variational analysis method (VAM). First, the observation forward model and its Jacobian are developed. Then, the observation's bias correction, quality control, and error characterization are presented. The DDM assimilation was applied to a global and a regional case. </div><div><br></div><div>In the global case, DDM observations from the NASA Cyclone Global Navigation Satellite System (CYGNSS) mission are assimilated into global ocean surface wind analyses using the European Centre for Medium-Range Weather Forecasts (ECMWF) 10-meter winds as the background. The wind analyses are improved as a result of the DDM assimilation. VAM can also be used to derive a new type of wind vector observation from DDMs (VAM-DDM).</div><div><br></div><div>In the regional case, an observing system experiment (OSE) is used to quantify the impact of VAM-DDM wind vectors from CYGNSS on hurricane forecasts, in the case of Hurricane Michael (2018). It is found that the assimilation of VAM-DDM wind vectors at the early stage of the hurricane improves the forecasted track and intensity.</div><div><br></div><div>The research of this dissertation implies potential benefits of DDM assimilation for future research and operational applications.</div>

An algorithm has been developed that enables improved the resolution wind estimates from SeaWinds data. This thesis presents the development of three key portions of the high resolution wind retrieval algorithm: Compositing individual σ-0 measurements and Kp, Retrieved wind bias correction, and ambiguity selection for high resolution winds. The high resolution winds produced by this algorithm are expected to become a useful resource for scientists and engineers studying the ocean winds. The high resolution wind retrieval algorithm allows wind to be retrieved much closer to land than is available from the low resolution winds estimated from the same scatterometer by the Jet Propulsion Laboratory. The high resolution winds allow features such as the eye of hurricanes to be seen with much greater detail than was previously possible.

The Imaging Wind and RAin Profiler (IWRAP) is an airborne scatterometer system built and operated by University of Massachusetts Amherst's Microwave Remote Sensing Laboratory (MIRSL). The radar is seasonally deployed aboard one of the two National Oceanic and Atmospheric Administration (NOAA) WP-3D Orion ``Hurricane Hunter'' aircraft based out of MacDill AFB in Tampa, Florida. IWRAP is a dual-frequency, Ku- and C-band, scatterometer that uses two conically scanning antennas to estimate the ocean surface wind vectors as well as intervening rain profiles. Data that is gathered with IWRAP is used to improve current Geophysical Model Functions (GMF) or to help derive new GMFs for other undocumented incidence angles. This thesis outlines the improvements and changes made to the IWRAP system from 2009-2011. Chapter Two describes the IWRAP instrument including a description of the instrument status as of Fall 2009, and a summary of instrument operations in 2010 and 2011. Chapter Three describes hardware and software modifications to support dual-polarization. It also describes hardware-based and flight-based attempts to observe at large incidence angles. Chapter Four is an analysis of the stability of the internal calibration both during flights and over a season. System documentation is consolidated into a single technical manual in Appendix A.

Representations of Boundary Layer Cloudiness and Surface Wind Probability Distributions in Subtropical Marine Stratus and Stratocumulus Regions Yanping He 153 pages Directed by Dr. Robert E. Dickinson A simple low cloud cover scheme is developed for the subtropical marine stratus and stratocumulus (MSC) regions. It is based on a modified CIN concept named the Lower Troposphere Available Dry Inhibition Energy (ADIN). The e-folder time for the local change of ADIN is found to be approximately 6 to 7 hours. On monthly and longer timescales, local productions of ADIN are balanced by local destructions of ADIN within lower troposphere. Dynamical transport of environmental dry static energy and surface evaporation lead to the variations of cloud top radiative cooling, which is a linear function of low cloud cover. Data analysis suggests that total ADIN dynamical transport plays the most important role in determining the seasonal variations and spatial variations of low cloud amounts¡£ The new scheme produces realistic seasonal and spatial variations of both EECRA ship observation and satellite observations in all MSC regions. It explains 25% more covariance than that using Klein-Hartmann (KH) scheme for monthly ISCCP low cloud amount near the Peruvian and Canarian region during the period from 1985 to 1997£¬it better represents the relationship between ENSO index and low cloud cover variations near the Peruvian region. When implemented into NCAR CAM3.1, it systematically reduces the model biases in the summertime spatial variations of low cloud amount and downward solar radiation in the Peruvian, California, and Canarian regions. Model simulated summertime cloud liquid water path, large scale precipitation, and surface fluxes are also significantly changed. A single predictor named Lower troposphere available thermal inhibition energy (ATIN) is also shown to be more skillful than the lower tropospheric stability in diagnosing low cloud stratiform clouds in the monthly and seasonal timescales. On synoptic timescale, dynamical transport of available dry inhibition energy and surface evaporation are better correlated with marine low cloud amount variations than ATIN and lower troposphere stability. The influence of boundary layer clouds, ocean surface SST, and large scale divergence on the stochastic dynamics of local ocean surface winds are addressed using QuikSCAT and AIRS satellite observations and a simple conceptual model in the southeast Pacific. The ocean surface pressure gradient depends on both the boundary layer height and temperature inversion strength. Marine boundary clouds are diagnosed using the cloud cover scheme developed in Chapter 2. The model successfully reproduces the observed mean state, the standard deviation, and skewness of local surface wind speeds in the southeast Pacific.