Browsing by Author "Chandra, Chandrasekar V., advisor"
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Item Open Access Description and evaluation of the CASA dual-Doppler system(Colorado State University. Libraries, 2011) Martinez, Matthew Thomas, author; Chandra, Chandrasekar V., advisor; Notaros, Branislav M., committee member; Mielke, Paul W., committee memberLong range weather surveillance radars are designed for observing weather events for hundreds of kilometers from the radar and operate over a large coverage domain independently of weather conditions. As a result a loss in spatial resolution and limited temporal sampling of the weather phenomenon occurs. Due to the curvature of the Earth, long-range weather radars tend to make the majority of their precipitation and wind observations in the middle to upper troposphere, resulting in missed features associates with severe weather occurring in the lowest three kilometers of the troposphere. The spacing of long-range weather radars in the United States limits the feasibility of using dual-Doppler wind retrievals that would provide valuable information on the kinematics of weather events to end-users and researchers. The National Science Foundation Center for Collaborative Adapting Sensing of the Atmosphere (CASA) aims to change the current weather sensing model by increasing coverage of the lowest three kilometers of the troposphere by using densely spaced networked short-range weather radars. CASA has deployed a network of these radars in south-western Oklahoma, known as Integrated Project 1 (IP1). The individual radars are adaptively steered by an automated system known as the Meteorological Command and Control (MCC). The geometry of the IP1 network is such that the coverage domains of the individual radars are overlapping. A dual-Doppler system has been developed for the IP1 network which takes advantage of the overlapping coverage domains. The system is comprised of two subsystems, scan optimization and wind field retrieval. The scan strategy subsystem uses the DCAS model and the number of dual-Doppler pairs in the IP1 network to minimizes the normalized standard deviation in the wind field retrieval. The scan strategy subsystem also minimizes the synchronization error between two radars. The retrieval itself is comprised of two steps, data resampling and the retrieval process. The resampling step map data collected in radar coordinates to a common Cartesian grid. The retrieval process uses the radial velocity measurements to estimate the northward, eastward, and vertical component of the wind. The error in the retrieval is related to the beam crossing angle. The best retrievals occur at beam crossing angles greater than 30 degrees. During operations statistics on the scan strategy and wind field retrievals are collected in real-time. For the scan strategy subsystem statistics on the beam crossing angels, maximum elevation angle, number of elevation angles, maximum observable height, and synchronization time between radars in a pair are collected by the MCC. These statistics are used to evaluate the performance of the scan strategy subsystem. Observations of a strong wind event occurring on April 2, 2010 are used to evaluate the decision process associated with the scan strategy optimization. For the retrieval subsystem, the normalized standard deviation for the wind field retrieval is used to evaluate the quality of the retrieval. Wind fields from an EF2 tornado observed on May 14, 2009 are used to evaluate the quality of the wind field retrievals in hazardous wind events. Two techniques for visualizing vector fields are available, streamlines and arrows. Each visualization technique is evaluated based on the task of visualizing small and large scale phenomenon. Applications of the wind field retrievals include the computation of the vorticity and divergence fields. Vorticity and divergence for an EF2 tornado observed on May 14, 2009 are evaluated against vorticity and divergence for other observed tornadoes.Item Open Access Electronic scan weather radar: scan strategy and signal processing for volume targets(Colorado State University. Libraries, 2013) Nguyen, Cuong Manh, author; Chandra, Chandrasekar V., advisor; Jayasumana, Anura P., committee member; Mielke, Paul W., committee member; Notaros, Branislav, committee memberFollowing the success of the WSR-88D network, considerable effort has been directed toward searching for options for the next generation of weather radar technology. With its superior capability for rapidly scanning the atmosphere, electronically scanned phased array radar (PAR) is a potential candidate. A network of such radars has been recommended for consideration by the National Academies Committee on Weather Radar Technology beyond NEXRAD. While conventional weather radar uses a rotating parabolic antenna to form and direct the beam, a phased array radar superimposes outputs from an array of many similar radiating elements to yield a beam that is scanned electronically. An adaptive scan strategy and advanced signal designs and processing concepts are developed in this work to use PAR effectively for weather observation. An adaptive scan strategy for weather targets is developed based on the space-time variability of the storm under observation. Quickly evolving regions are scanned more often and spatial sampling resolution is matched to spatial scale. A model that includes the interaction between space and time is used to extract spatial and temporal scales of the medium and to define scanning regions. The temporal scale constrains the radar revisit time while the measurement accuracy controls the dwell time. These conditions are employed in a task scheduler that works on a ray-by-ray basis and is designed to balance task priority and radar resources. The scheduler algorithm also includes an optimization procedure for minimizing radar scan time. In this research, a signal model for polarimetric phased array weather radar (PAWR) is presented and analyzed. The electronic scan mechanism creates a complex coupling of horizontal and vertical polarizations that produce the bias in the polarimetric variables retrieval. Methods for bias correction for simultaneous and alternating transmission modes are proposed. It is shown that the bias can be effectively removed; however, data quality degradation occurs at far off boresight directions. The effective range for the bias correction methods is suggested by using radar simulation. The pulsing scheme used in PAWR requires a new ground clutter filtering method. The filter is designed to work with a signal covariance matrix in the time domain. The matrix size is set to match the data block size. The filter's design helps overcome limitations of spectral filtering methods and make efficient use of reducing ground clutter width in PAWR. Therefore, it works on modes with few samples. Additionally, the filter can be directly extended for staggered PRT waveforms. Filter implementation for polarimetric retrieval is also successfully developed and tested for simultaneous and alternating staggered PRT. The performance of these methods is discussed in detail. It is important to achieve high sensitivity for PAWR. The use of low-power solid state transmitters to keep costs down requires pulse compression technique. Wide-band pulse compression filters will partly reduce the system sensitivity performance. A system for sensitivity enhancement (SES) for pulse compression weather radar is developed to mitigate this issue. SES uses a dual-waveform transmission scheme and an adaptive pulse compression filter that is based on the self-consistency between signals of the two waveforms. Using SES, the system sensitivity can be improved by 8 to 10 dB.Item Open Access Frequency diversity wideband digital receiver and signal processor for solid-state dual-polarimetric weather radars(Colorado State University. Libraries, 2012) Mishra, Kumar Vijay, author; Chandra, Chandrasekar V., advisor; Jayasumana, Anura P., committee member; Mielke, Paul W., committee memberThe recent spate in the use of solid-state transmitters for weather radar systems has unexceptionably revolutionized the research in meteorology. The solid-state transmitters allow transmission of low peak powers without losing the radar range resolution by allowing the use of pulse compression waveforms. In this research, a novel frequency-diversity wideband waveform is proposed and realized to extenuate the low sensitivity of solid-state radars and mitigate the blind range problem tied with the longer pulse compression waveforms. The latest developments in the computing landscape have permitted the design of wideband digital receivers which can process this novel waveform on Field Programmable Gate Array (FPGA) chips. In terms of signal processing, wideband systems are generally characterized by the fact that the bandwidth of the signal of interest is comparable to the sampled bandwidth; that is, a band of frequencies must be selected and filtered out from a comparable spectral window in which the signal might occur. The development of such a wideband digital receiver opens a window for exciting research opportunities for improved estimation of precipitation measurements for higher frequency systems such as X, Ku and Ka bands, satellite-borne radars and other solid-state ground-based radars. This research describes various unique challenges associated with the design of a multi-channel wideband receiver. The receiver consists of twelve channels which simultaneously downconvert and filter the digitized intermediate-frequency (IF) signal for radar data processing. The product processing for the multi-channel digital receiver mandates a software and network architecture which provides for generating and archiving a single meteorological product profile culled from multi-pulse profiles at an increased data date. The multi-channel digital receiver also continuously samples the transmit pulse for calibration of radar receiver gain and transmit power. The multi-channel digital receiver has been successfully deployed as a key component in the recently developed National Aeronautical and Space Administration (NASA) Global Precipitation Measurement (GPM) Dual-Frequency Dual-Polarization Doppler Radar (D3R). The D3R is the principal ground validation instrument for the precipitation measurements of the Dual Precipitation Radar (DPR) onboard the GPM Core Observatory satellite scheduled for launch in 2014. The D3R system employs two broadly separated frequencies at Ku- and Ka-bands that together make measurements for precipitation types which need higher sensitivity such as light rain, drizzle and snow. This research describes unique design space to configure the digital receiver for D3R at several processing levels. At length, this research presents analysis and results obtained by employing the multi-carrier waveforms for D3R during the 2012 GPM Cold-Season Precipitation Experiment (GCPEx) campaign in Canada.Item Open Access Machine learning-based fusion studies of rainfall estimation from spaceborne and ground-based radars(Colorado State University. Libraries, 2019) Tan, Haiming, author; Anderson, Charles W., advisor; Chandra, Chandrasekar V., advisor; Ray, Indrajit, committee member; Chavez, Jose L., committee memberPrecipitation measurement by satellite radar plays a significant role in researching the water circle and forecasting extreme weather event. Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) has capability of providing a high-resolution vertical profile of precipitation over the tropics regions. Its successor, Global Precipitation Measurement (GPM) Dual-frequency Precipitation Radar (DPR), can provide detailed information on the microphysical properties of precipitation particles, quantify particle size distribution and quantitatively measure light rain and falling snow. This thesis presents a novel Machine Learning system for ground-based and space borne radar rainfall estimation. The system first trains ground radar data for rainfall estimation using rainfall measurements from gauges and subsequently uses the ground radar based rainfall estimates to train spaceborne radar data in order to get space based rainfall product. Therein, data alignment between spaceborne and ground radar is conducted using the methodology proposed by Bolen and Chandrasekar (2013), which can minimize the effects of potential geometric distortion of spaceborne radar observations. For demonstration purposes, rainfall measurements from three rain gauge networks near Melbourne, Florida, are used for training and validation purposes. These three gauge networks, which are located in Kennedy Space Center (KSC), South Florida Water Management District (SFL), and St. Johns Water Management District (STJ), include 33, 46, and 99 rain gauge stations, respectively. Collocated ground radar observations from the National Weather Service (NWS) Weather Surveillance Radar – 1988 Doppler (WSR-88D) in Melbourne (i.e., KMLB radar) are trained with the gauge measurements. The trained model is then used to derive KMLB radar based rainfall product, which is used to train both TRMM PR and GPM DPR data collected from coincident overpasses events. The machine learning based rainfall product is compared against the standard satellite products, which shows great potential of the machine learning concept in satellite radar rainfall estimation. Also, the local rain maps generated by machine learning system at KMLB area are demonstrate the application potential.Item Open Access Rainfall estimation from spaceborne and ground based radars using neural networks(Colorado State University. Libraries, 2009) Alqudah, Amin, author; Chandra, Chandrasekar V., advisorRainfall observed on the ground is dependent on the four dimensional radar observations. However it is difficult to express this in a simple form. A simple Z-R relation is not sufficient and has large uncertainty and it needs to be adaptively adjusted. Prior research has shown that neural networks can be used to estimate ground rainfall from radar measurements. Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) is the first space borne observation platform for mapping precipitation over the tropics. TRMM measured rainfall is important in order to study the precipitation distribution all over the globe in the tropics. TRMM ground validation is a critical important component to ensure the measurement accuracy. However, this ground validation has quite different characteristics from TRMM in terms of resolution, scale, viewing aspect, and uncertainties. This makes the use of ground radar rainfall information to correct TRMM rainfall estimates a very challenging task. In this dissertation, rainfall estimation using neural networks is investigated in order to improve rainfall estimation based on measurements taken by ground radars and TRMM-PR. Ground Radar measurements will be used to estimate rainfall using adaptive neural networks. Improvements are also suggested and performed including the use of Principal Components Analysis, ensemble average neural network, and the use of Bayesian Neural Networks. For TRMM-PR purposes a single neural network is not efficient to extract the relation between TRMM-PR measurements and the rain gauges; this is because of the resolution differences between TRMM-PR profile and the rain gauges and the low number of TRMM overpasses over these gauges which will make the training data set to have less number of profiles and not be able to generalize. Therefore, a novel hybrid Neural Network model is presented to train ground radars for rainfall estimate using rain gauge data and subsequently the trained ground radar rain estimates to train TRMM-PR based Neural Networks for rainfall estimation. This hybrid neural network model will derive the relation between rain gauges and ground radar measurements, and transfer this relation to adaptive rainfall estimation for TRMM-PR in order to estimate rainfall and generate global rainfall maps.Item Open Access Simulation of space-based radar observations of precipitations(Colorado State University. Libraries, 2008) Khajonrat, Direk, author; Chandra, Chandrasekar V., advisorThe Tropical Rainfall Measurement Mission (TRMM) will soon be followed on by the Global Precipitation Measurement (GPM). The GPM satellite will be the next generation observation of precipitation from space. The GPM will carry a dual-frequency precipitation radar (DPR) operating at 13.6 GHz (Ku-band) and 35.6 GHz (Ka-band), as opposed to a single-frequency 13.8 GHz (Ku-band) precipitation radar (PR) in TRMM. A greater degree of accuracy of precipitation measurements can be achieved by a dual-frequency radar using measurements from the two channels. The DPR on the GPM will be the first space-based dual-frequency precipitation radar. Since spaceborne precipitation observations have never been done in Ka-band before, extensive research on dual-frequency radar, including electromagnetic wave propagation characteristics from space and retrieval algorithms are essential for system development and system evaluations. Because the DPR is the first of its kind, a simulation-based study can provide significant assessment of the GPM system which is presented here. The research reported here focuses on developing methodologies for simulating the precipitation characteristics that would be observed from space by DPR using current space-based radar observations and earth-based radar measurements. The underlying microphysics of precipitation structures are important for developing a simulation model and a realistic model of precipitation is desired for representative simulation results. In this research; a microphysical model of precipitation is developed based on airborne radar measurements. The simulation of precipitation observations in Ku- and Ka-band are performed using both TRMM-PR observations and ground-based radar measurements. The simulation of a wide variety of precipitation regimes reveals the characteristics of the precipitation observed in Ku- and Ka-band, and allows testing of different retrieval algorithms-either the single-frequency (TRMM-like algorithm) or dual-frequency techniques. A significant degradation of signal in the Ka-band channel in intense precipitation such as an intense convective storm and tropical storms directly affect the retrieval algorithms that can be used. Vertical reflectivity profiles classification and drop size distribution parameters estimation of tropical storms are studied and results are presented here.