Mountain Scholar :: Browsing by Author "Chandrasekar, V., advisor"

Browsing by Author "Chandrasekar, V., advisor"

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Embargo
Advanced processing of dual polarization weather radar signal
(Colorado State University. Libraries, 2022) Haran, Shweta, author; Chandrasekar, V., advisor; Chen, Haonan, committee member; Siller, Thomas, committee member
This research focuses on processing of radar data in spectral domain and analysis of micro-physical properties of hail and rain in severe convective and stratiform storms. This research also discusses the optimization of a parametric time domain method to separate cloud and drizzle data. The microphysical and kinematic properties of hydrometeors present in a precipitation event can be studied using spectral domain processing and analysis of the radar moments. This study along with polarimetric information is called spectral polarimetry. For this study, the observations made by CSU-CHIVO (Colorado State University - C-band Hydrometeorological Instrument for Volumetric Observation) radar during the RELAMPAGO (Remote sensing of Electrification, Lightning, And Mesoscale/Microscale Processes with Adaptive Ground Observations) campaign is utilized. Features such as the slope in differential reflectivity, spectrum width, and spectral copolar correlation are studied which gives a better understanding of the storm microphysics. In this thesis, microphysical properties of different types of hydrometeors such as hail, rain, and large drops are studied using convective and stratiform storm observations. A parametric time-domain method (PTDM) is utilized for the separation of cloud and drizzle data. To reduce the time latency present in processing the data, the processing code is optimized by deploying on a high-performance computer (HPC). The processing code is tested on an HPC and automated to handle errors in processing. The run time is reduced by approximately 50%, hence increasing the data processing efficiency. This study shows that optimization of the run time using an HPC is an efficient method. Data processing using an HPC can be used to deploy similar time-consuming algorithms, hence increasing the efficiency and performance.
Embargo
Advanced solutions for rainfall estimation over complex terrain in the San Francisco Bay area
(Colorado State University. Libraries, 2023) Biswas, Sounak Kumar, author; Chandrasekar, V., advisor; Cheney, Margaret, committee member; Gooch, Steven, committee member; James, Susan, committee member
Fresh water is an increasingly scarce resource in the western United States and effective management and prediction of flooding and drought have a direct economic impact on almost all aspects of society. Therefore it is critical to monitor and predict water inputs into the hydrological cycle of the Western United States (US). The complex topography of the western US poses a significant challenge in developing physically realistic and spatially accurate estimates of precipitation using remote sensing techniques. The intricate landscape presents a challenging observing environment for weather radar systems. This is further compounded by the complex microphysical processes during the cool season which are influenced by coastal air-sea interactions, as well as orographic effects along the coastal regions of the West. The placement and density of operational National Weather Service (NWS) radars (popularly known as NEXRAD or WSR-88D) pose a challenge in meeting the needs for water resource management in the western US due to the complex terrain of the region. Consequently, areas like the San Francisco Bay Area could use enhanced precipitation monitoring, in terms of amount and type, along watersheds and surrounding rivers and streams. Shorter wavelength radars such as X-Band radar systems are able to augment the WSR-88D network, to observe better the lower atmosphere with higher temporal and spatial resolution. This research investigates and documents the challenges of precipitation monitoring by radars over complex terrain and aims to provide effective and advanced solutions for accurate Quantitative Precipitation Estimation (QPE) using both WSR-88D and the gap-filling X-Band radar systems over the Bay Area on the US West Coast, with a focus on the cool season. Specifically, this study focuses on a precipitation microphysics perspective, aiming to create an algorithm capable of distinguishing orographically enhanced rainfall from cool-season stratiform rainfall using X-Band radar observations. A radar-based rainfall estimator is developed to increase the accuracy of rainfall quantification. Additionally, various other scientific and engineering challenges have been addressed including radar calibration, attenuation correction of the radar beam, radar beam blockage due to terrain, and correction of measurements of the vertical profiles of radar observables. The final QPE product is constructed by merging the X-Band based QPE product with the operational NEXRAD based QPE product, significantly enhancing the overall quality of rainfall mapping within the Bay Area. Case studies reveal that the new product is able to improve QPE accuracy by ~70% in terms of mean absolute error and root mean squared error compared to the operational products. This establishes the overall need for precipitation monitoring by gap-filling X-Band radar systems in the complex terrain of the San Francisco Bay Area.
Open Access
An integrated retrieval framework for multiple polarization, multiple frequency radar networks
(Colorado State University. Libraries, 2015) Hardin, Joseph C., author; Chandrasekar, V., advisor; Jayasumana, Anura P., committee member; Mielke, Paul, committee member; Cheney, Margaret, committee member
Radar networks form the backbone of severe weather and remote sensing in throughout most of the world. These networks provide diverse measurements of weather phenomenon, but ultimately are measuring indirect parameters rather than detecting the physics of the situation. One of the long standing goals of weather remote sensing is to relate the measurements from the various instruments to the physics that give rise to the measurements. Weather radar networks give both a better spatial coverage than single radars, as well as providing multiple looks at the environment. Newly developed radar networks have started to incorporate multiple frequencies and multiple polarizations to take advantage of attributes of different radar frequencies. Raindrops occupy different scattering regimes based on the frequency of the radar being used. Based on this, multiple radars at different wavelengths provide unique information about the microphysical characteristics of the atmosphere. Nonetheless, very little work has been conducted on fusing multiple radar measurements at heterogeneous frequencies to improve microphysical retrievals. This work presents a forward variational algorithm for multiple radar fusion that retrieves microphysical parameters from the atmosphere. The single radar case and the multiple radar case will both be addressed. Ground instrumentation will be used for verification, and the spatial and temporal variability of precipitation microphysics will be discussed.
Open Access
Application of the variational method for correction of wet ice attenuation for X-band dual-polarized radar
(Colorado State University. Libraries, 2011) Tolstoy, Leonid, author; Bringi, V. N., advisor; Chandrasekar, V., advisor; Notaros, B., committee member; Kummerow, C. D., committee member
In recent years there has been a huge interest in the development and use of dual-polarized radar systems operating at X-band (~10 GHz) region of the electromagnetic spectrum. This is due to the fact that these systems are smaller and cheaper allowing for a network to be built, for example, for short range (typically < 30-40 km) hydrological applications. Such networks allow for higher cross-beam spatial resolutions while cheaper pedestals supporting a smaller antenna also allows for higher temporal resolution as compared with large S-band (long range) systems used by the National Weather Service. Dual-polarization radar techniques allow for correction of the strong attenuation of the electromagnetic radar signal due to rain at X-band and higher frequencies. However, practical attempts to develop reliable correction algorithms have been cumbered by the need to deal with the rather large statistical fluctuations or "noise" in the measured polarization parameters. Recently, the variational method was proposed, which overcomes this problem by using the forward model for polarization variables, and uses iterative approach to minimize the difference between modeled and observed values, in a least squares sense. This approach also allows for detection of hail and determination of the fraction of reflectivity due to the hail when the precipitation shaft is composed of a mixture of rain and hail. It was shown that this approach works well with S-band radar data. The purpose of this research is to extend the application of the variational method to the X-band dual-polarization radar data. The main objective is to correct for attenuation caused by rain mixed with wet ice hydrometeors (e.g., hail) in deep convection. The standard dual-polarization method of attenuation-correction using the differential propagation phase between H and V polarized waves cannot account for wet ice hydrometeors along the propagation path. The ultimate goal is to develop a feasible and robust variational-based algorithm for rain and hail attenuation correction for the Collaborate Adaptive Sensing of the Atmosphere (CASA) project.
Open Access
Application-aware transport services for sensor-actuator networks
(Colorado State University. Libraries, 2007) Banka, Tarun, author; Jayasumana, Anura P., advisor; Chandrasekar, V., advisor
Many emerging mission-critical sensor actuator network applications rely on the best-effort service provided by the Internet for data dissemination. This dissertation investigates the paradigm of application-aware networking to meet the QoS requirements of the mission-critical applications over best-effort networks that do not provide end-to-end QoS support. An architecture framework is proposed for application-aware data dissemination using overlay networks. The application-aware architecture framework enables application-aware processing at overlay nodes in the best-effort network to meet the QoS requirements of the heterogeneous end users of mission-critical sensor-actuator network applications. An application-aware congestion control protocol performs data selection and real-time scheduling of data for transmission while considering different bandwidth and data quality requirements of heterogeneous end users. A packet-marking scheme is proposed that enables application-aware selective drop and forwarding of packets at intermediate overlay nodes during network congestion to further enhance the QoS received by the end users under dynamic network conditions. Effectiveness of the transport services based on application-aware architecture framework is demonstrated by one-to-many high-bandwidth time-series radar data dissemination protocol for CASA (Collaborative Adaptive Sensing of the Atmosphere) application. Experiment results demonstrate that under similar network conditions and available bandwidth, application-aware processing at overlay nodes significantly improves the quality of the time-series radar data delivered to the end users compared to case when no such application-aware processing is performed. Moreover, it is shown that application-aware congestion control protocol is friendly to the already existing TCP cross-traffic on the network as long as bandwidth requirements of the mission-critical applications are met. Scalability analysis of application-aware congestion control protocol shows that it is able to schedule data at cumulative rates of more than 700M bps without degrading the QoS received by multiple end users.
Open Access
Cross validation of observations from the GPM dual-frequency precipitation radar and dual-polarization S-band ground radars
(Colorado State University. Libraries, 2018) Biswas, Sounak Kumar, author; Chandrasekar, V., advisor; Cheney, Margaret, committee member; Mielke, Paul W., committee member
This research presents a comparative study of observations and various products of the Global Precipitation Measurement (GPM) Mission Satellite with dual polarization S-Band Ground Radars. The GPM mission is a joint venture by the NASA and the JAXA. The radar on board the core observatory is a dual-frequency precipitation radar (DPR) capable of simultaneously operating at 13.6 GHz (Ku band) and 35.5 GHz (Ka band). The DPR is expected to revolutionize the way precipitation is measured from space through its dual-frequency observations. Ground Validation is one of the most critical aspects of the GPM mission. The best way of doing this is by direct comparison of the space-based observations with well calibrated dual polarization ground radar measurements. Before any direct comparisons can be made, volume matching of the data is necessary due to the difference in observation geometry and resolution volume of both the system. In this study, a methodology developed by Bolen and Chandrasekar (2001) for aligning TRMM satellite data with ground radar data is followed. This technique was extended by Schwaller and Morris (2011). Radar reflectivity and rainfall rate product comparison study have been performed in detail. Vertical profiles have been studied thoroughly. Various case studies of simultaneous GPM-DPR and ground radar observations have been carefully chosen. Ground validation operational NEXRAD sites have been considered from all over the USA. Comparison studies with research radars such as CSU-CHILL and NASA N-POL have also been conducted. The GPM satellite's profile classification module's products are also evaluated. Results from Hydrometeor classification method by Bechini and Chandrasekar (2015) for ground radars have been extensively used for validating DPR's melting layer detection capability in different types of precipitation system. In this study, a new method developed by Le et al (2017) for identification of snow falling on the ground has been considered. Ground validation comparisons have been performed with observations from ground radars and the results are presented.
Open Access
Deep neural network based rain/no-rain classification and rain rate estimation
(Colorado State University. Libraries, 2022) Potnis, Jay U., author; Chandrasekar, V., advisor; Cheney, Margaret, committee member; Siller, Thomas, committee member
Quantitative Precipitation Estimation is the process of computing rainfall rate or rainfall accumulation based on the state of the atmosphere. Atmospheric conditions can be described by using observations from meteorological instruments. Extreme weather events caused due to high rainfall can be dangerous in terms of loss of property and life. To prevent such disasters, accurate QPE algorithms that analyze and estimate the amount of rainfall observed in a region are critical. Moreover, rain rate estimates are crucial products in making management decisions in water, energy, construction infrastructure, and many other institutions. Researching state-of-the-art rainfall estimation techniques that make use of reliable remote sensing equipment such as satellites and radars is important as deploying rain gauges everywhere is not possible and is not a viable option. As rain precipitation is a complicated phenomenon, depending on multiple factors in the atmosphere, research is being done in this domain for many decades and the goal is to improve the accuracy of estimation by using new state-of-the-art methods. Weather radars are reliable remote sensing instruments that are used to capture the different properties of weather in form of products called moments. The goal of this work is to use weather radars in conjunction with Deep Neural Networks to provide solutions to multiple tasks in the QPE domain. Neural networks can be used for precipitation flagging such as classifying rain and no rain events. They can also be used for estimating the rain rates at specific coordinates or along regions. Though multiple empirical relationships between radar moments and rain rate already exist, this work provides good state-of-the-art alternatives to these equations and can even achieve comparable accuracy.
Open Access
Design, deployment, and cost considerations for DARMA; a low-cost and lightweight FMCW radar
(Colorado State University. Libraries, 2022) Bruner, Marshall, author; Chandrasekar, V., advisor; Cheney, Margaret, committee member; Gooch, Ryan, committee member
The capability of frequency-modulated continuous wave (FMCW) radar to operate in low-power environments has made it a good choice for many mobile systems including automobile radars. While specialized FMCW radars have seen an increase in production recently, there is a lack of general-purpose FMCW radars with the ability to be used in a multitude of applications, especially for volume targets such as precipitation. This thesis presents design considerations for the Dual-polarization phased Array Radar for Measurement of the Atmosphere (DARMA), a low-cost, medium range (km) radar with the versatility to operate mounted on an unmanned aircraft system (UAS) or ground platform. The radar features modular subsystems which allow for easy swapping to support different application requirements as well as upgrades due to rapidly changing technology. Signal processing methods are also introduced, and implemented on COTS systems, to allow for noise mitigation, target detection, and estimation of weather products.
Embargo
Interpolating RGB radar images based on machine learning
(Colorado State University. Libraries, 2023) Yi, Chenke, author; Chandrasekar, V., advisor; Chen, Haonan, advisor; Siller, Thomas, committee member; Gooch, Steven, committee member
Weather radar interpolation is the process of estimating and predicting rainfall data in areas that are not directly observed by radar. This technique is commonly used in weather forecasting, flood prediction, and agricultural planning. The main goal of weather radar interpolation is to produce accurate and reliable precipitation maps in areas with limited radar coverage or where the radar data is incomplete. The interpolation methods can be categorized into two main groups: deterministic and stochastic. Deterministic methods use mathematical equations and physical models to estimate the rainfall, while stochastic methods rely on statistical algorithms to analyze the correlations between the radar measurements and ground observations. In recent years, machine learning algorithms have also been applied to weather radar interpolation, showing promising results in accuracy and robustness. In this paper, we mainly propose a radar image interpolation method based on spatio-temporal convolutional networks. The experiments are mainly compared and analyzed for different combinations of networks, connection methods, and different loss functions.
Open Access
Linking system cost model to system optimization using a cost sensitivity algorithm
(Colorado State University. Libraries, 2022) Polidi, Danny Israel, author; Chandrasekar, V., advisor; Borky, Mike, committee member; Bradley, Thomas, committee member; Popat, Ketul, committee member
Lack of adequate cost analysis tools early in the design life cycle of a system contributes to non-optimal system design choices both in performance and cost. Modern software packages exist that perform complex physics-based simulations. Physics based simulations alone typically do not consider cost as a factor or input variable. Modern software packages exist which calculate cost and can aid in determining the cost sensitivity to a chosen design solution. It should be possible to combine the system sensitivity to cost with the system sensitivity to performance. Methods and algorithms are needed to determine which components in a system would most significantly contribute towards the impact to the overall cost and which design alternatives provide the best value to the system. These methods and algorithms are needed during concept development to aid in system scoping and cost estimation. In the bidding phase of a system design, most of the time is typically spent determining cost. System design trades are either seldomly done or abbreviated. This has not been preferable because the system design becomes locked into place long before significant trades have been performed. And the solution may not be optimal for either cost or performance. This paper reviews the research performed and includes work in creating a cost model based on a set of questions & answers to drive system design, electronic design work applicable to the specific subsystem element FLO (Frequency Locked Oscillator), development of a standardized modular diagram and Work Breakdown Structure (WBS) for a RADAR System applied to military aerospace applications in the aerospace industry, and the development of a cost sensitivity algorithm. The goal of the research and cost sensitivity algorithm was to allow the system designer the ability to optimize for both cost and performance early in the system design cycle.
Open Access
Machine learning models applied to storm nowcasting
(Colorado State University. Libraries, 2020) Cuomo, Joaquin M., author; Anderson, Chuck, advisor; Chandrasekar, V., advisor; Pallickara, Sangmi Lee, committee member; Suryanarayanan, Sid, committee member
Weather nowcasting is heavily dependent on the observation and estimation of radar echoes. There are many different types of deployed nowcasting systems, but none of them based on machine learning, even though it has been an active area of research in the last few years. This work sets the basis for considering machine learning models as real alternatives to current methods by proposing different architectures and comparing them against other nowcasting systems, such as DARTS and STEPS. The methods proposed here are based on residual convolutional encoder-decoder architectures, and they reach the state of the art performance and, in certain scenarios, even outperform them. Different experiments are presented on how the model behaves when using recurrent connections, different loss functions, and different prediction lead times.
Embargo
Microphysical retrieval in severe storms from ground-based and space-borne radar network: application to La Plata region in South America
(Colorado State University. Libraries, 2023) Arias Hernández, Iván D., author; Chandrasekar, V., advisor; Cheney, Margaret, committee member; Ray, Indrakshi, committee member; Chávez, José, committee member
The microphysics of severe weather is studied using a network approach from multiple platform observations. Observations acquired near the foothills of the Andes in Argentina are used in this investigation. La Plata region in Argentina is known for having some of the tallest storms on Earth. During the Austral summer of 2018, a network of radars was deployed in this region to study these storms as part of the RELAMPAGO field experiment. This network of ground-based radars, in addition to satellite and in-situ observations, is used to understand the microphysics of severe storms in this part of the world. The knowledge gained from studying the microphysics of these storms in South America is applied to understand convection more broadly. In addition, these multiple platform observations are used to understand how the storms in South America may differ from storms in other regions. The analysis from simultaneous radar observations is used to self-calibrate the radar network. In this investigation, first, an extensive calibration of the radar network measurements was performed to obtain high-quality data for this study. The ground-based radars' dual-polarization measurements were calibrated using a network-based approach. In addition, satellite measurements from GPM radar were used as a common platform for calibrating the ground-based radars in the network. A new parameterization for the attenuation correction is developed for ground-based radar in this region as an outcome of the network calibration exercise. After careful calibration, the radar measurements in the network were used to obtain observational statistics over the RELAMPAGO campaign domain. These statistics are applied to understand the connection between the radar retrievals and to select the severe weather cases to study. For the severe weather cases identified in the radar statistics, spectral polarimetric decomposition from radar signal samples in updraft environments is derived. First, updrafts are identified using dual Doppler analysis. Subsequently, the reflectivity, differential reflectivity, and coherence spectra are computed from radar signal samples. Practical considerations about the computation of the spectrum in updraft are also presented. The spectral analysis revealed that bimodalities in the spectrum can be found in updraft conditions. In addition, a technique to quantify the attenuation of C-band radar signals in melting ice was developed using multiple radar observations. The attenuation estimates are used to parameterize the specific attenuation in melting ice to explain the enhanced attenuation. Finally, convective permitting high-resolution simulations are compared with the radar network observations for a representative severe weather case. This comparison is conducted to test the effectiveness of downscaling to resolve better convective processes that lead to severe weather.
Open Access
Microphysics and dynamics retrievals from dual-polarization radar for very short-term forecasting
(Colorado State University. Libraries, 2016) Bechini, Renzo, author; Chandrasekar, V., advisor; Jayasumana, Anura, committee member; Mielke, Paul, committee member; Sun, Juanzhen, committee member
Nowcasting is primarily a description of the near-future forecasted atmospheric state, relying heavily on observations. Besides routine meteorological observations (pressure, temperature, humidity, wind), dual-polarization weather radar provides a large amount of useful information due to the frequent-update (~5 min) and high-resolution (~500 m) three-dimensional sampling of the atmosphere. However, the atmospheric state variables are not readily invertible from radar remote observations, resulting in complexity in the numerical model data assimilation. This problem is normally dealt with by defining observation operators to simulate the radar variables from the model state vector. In this work the dual-polarization radar based retrievals are developed in order to demonstrate their potential for microphysics and dynamics retrievals. In particular the analysis of radar observations in convective storms and in stratiform ice clouds revealed that specific dual-polarization signatures can be successfully related to important dynamic properties such as vertical air motions, both in convective precipitation (strong updrafts, several m s-1) and in stratiform precipitation (large areas of weak updrafts, tenths of m s-1, associated with mid-tropospheric mesoscale forcing). Given the relevance of polarimetric signatures to dynamics retrievals, an improved hydrometeor classification method is developed based on a learn-from-data approach. In this technique, the traditional bin-based classification is replaced with a semi-supervised approach which combines cluster analysis, spatial contiguity, and statistical inference to assign the most likely class to a set of identified connected regions. The hydrometeor classification and relevant dual-polarization signatures establish a starting point to explore new means to improve the analysis of precipitation and near-surface winds, and their subsequent nowcasting. In particular the relevance of a well-known dual-polarization feature associated with deep convection (vertical columns of differential reflectivity) is illustrated by including the microphysics and dynamics-related information into a simple method for the analysis of surface winds. The goal of a physically consistent analysis is further pursued considering the Variational Doppler Radar Analysis System (VDRAS), an advanced four-dimensional data assimilation system based on a cloud-scale model, specifically designed for ingesting Doppler weather radar observations. The typical application using single-polarization observations from long-range S-band or C-band radars is here extended to high frequency (X-band), short range radars and dual-polarization observations. The combination of the hydrometeor classification and dual-polarization rainwater estimation allows to successfully assimilating the X-band observations, otherwise prone to relevant errors when using the reflectivity-based observation operator widely employed in numerical models. The feasibility of X-band data assimilation to contribute building a consistent analysis for nowcasting is demonstrated over the Dalls-Fort Worth test bed, where a dense network of dual-polarization X-band radars is being deployed. Eventually, a novel method for the nowcasting of precipitation and winds is built upon the VDRAS analysis, in an attempt to combine the robustness and consistency of data assimilation and the efficacy of extrapolation techniques for very short-term forecasting.
Open Access
Modeling fuzzy criteria preference to evaluate tradespace of system alternatives
(Colorado State University. Libraries, 2018) White, Wesley Gunnar, author; Chandrasekar, V., advisor; Bradley, Thomas, committee member; Chavez, Jose, committee member; Jayasumana, Anura P., committee member
This dissertation explores techniques for evaluating system concepts using the point of diminishing marginal utility to determine a best value alternative with an optimal combination of risk, performance, reliability, and life cycle cost. The purpose of this research is to address the uncertainty of customer requirements and assess crisp and fuzzy design parameters to determine a best value system. At the time of this research, most commonly used decision analysis (DA) techniques use minimum and maximum values under a specific criterion to evaluate each alternative. These DA methods do not restrict scoring beyond the point of diminished marginal utility resulting in superfluous capabilities and overvalued system alternatives. Using these models, an alternative being evaluated could receive significantly higher scores when reported capabilities are greater than ideal customer requirements. This problem is pronounced whenever weights are applied to criteria where excessive capabilities are recorded. The techniques explored in this dissertation utilize fuzzy membership functions to restrict scoring for alternatives that provide excess capabilities beyond ideal customer requirements. This research investigates and presents DA techniques for evaluating system alternatives that determine an ideal compromise between risk, performance criteria, reliability and life cycle costs.
Open Access
Multi-frequency dual-polarized platform for validation of satellite precipitation measurements
(Colorado State University. Libraries, 2017) Vega, Manuel A., author; Chandrasekar, V., advisor; Jayasumana, Anura, committee member; Cheney, Margaret, committee member; Mielke, Paul, committee member
Satellite missions such as the Tropical Rainfall Measuring Mission (TRMM) and the Global Precipitation Measurement (GPM) mission have demonstrated the value of rainfall measurements at a global scale. Both missions use a multi-frequency, active/passive (i.e. radar/radiometer) suite of instruments to achieve their measurement goals. Calibration and validation of these instruments has a vital role in the success of the mission since quantitative characterization of precipitation is the primary goal. Furthermore, these missions have also extended the understanding of the synergy between radar/radiometer observations within the atmospheric science community. From a ground validation (GV) perspective, active/passive observations are typically achieved with co-located, but independent instruments. In some cases, this has introduced radio frequency interference (RFI) between adjacent active/passive frequencies of operation, asynchronous scanning strategies and unmatched observation volumes. The work presented focuses on the following topics: 1) engineering aspects in the design of an active/passive remote sensing platform, 2) the design of a solid-state, dual-polarized, multi-frequency, Doppler radar system and performance characterization and 3) calibration approach for a ground based, multi-frequency, radar/radiometer system and first calibrated observations in this mode of operation.
Open Access
Nowcasting for a high-resolution weather radar network
(Colorado State University. Libraries, 2010) Ruzanski, Evan, author; Chandrasekar, V., advisor; Jayasumana, Anura P., committee member; Mielke, Paul W., committee member; Notaros, Branislav M., committee member
Short-term prediction (nowcasting) of high-impact weather events can lead to significant improvement in warnings and advisories and is of great practical importance. Nowcasting using weather radar reflectivity data has been shown to be particularly useful. The Collaborative Adaptive Sensing of the Atmosphere (CASA) radar network provides high-resolution reflectivity data amenable to producing valuable nowcasts. The high-resolution nature of CASA data requires the use of an efficient nowcasting approach, which necessitated the development of the Dynamic Adaptive Radar Tracking of Storms (DARTS) and sinc kernel-based advection nowcasting methodology. This methodology was implemented operationally in the CASA Distributed Collaborative Adaptive Sensing (DCAS) system in a robust and efficient manner necessitated by the high-resolution nature of CASA data and distributed nature of the environment in which the nowcasting system operates. Nowcasts up to 10 min to support emergency manager decision-making and 1-5 min to steer the CASA radar nodes to better observe the advecting storm patterns for forecasters and researchers are currently provided by this system. Results of nowcasting performance during the 2009 CASA IP experiment are presented. Additionally, currently state-of-the-art scale-based filtering methods were adapted and evaluated for use in the CASA DCAS to provide a scale-based analysis of nowcasting. DARTS was also incorporated in the Weather Support to Deicing Decision Making system to provide more accurate and efficient snow water equivalent nowcasts for aircraft deicing decision support relative to the radar-based nowcasting method currently used in the operational system. Results of an evaluation using data collected from 2007-2008 by the Weather Service Radar-1988 Doppler (WSR-88D) located near Denver, Colorado, and the National Center for Atmospheric Research Marshall Test Site near Boulder, Colorado, are presented. DARTS was also used to study the short-term predictability of precipitation patterns depicted by high-resolution reflectivity data observed at microalpha (0.2-2 km) to mesobeta (20-200 km) scales by the CASA radar network. Additionally, DARTS was used to investigate the performance of nowcasting rainfall fields derived from specific differential phase estimates, which have been shown to provide more accurate and robust rainfall estimates compared to those made from radar reflectivity data.
Open Access
Phase coding and frequency diversity for weather radars
(Colorado State University. Libraries, 2020) Kumar, Mohit, author; Chandrasekar, V., advisor; Cheney, Margaret, committee member; James, Susan, committee member; Jayasumana, Anura, committee member
This thesis has developed three main ideas: 1) Polyphase coding to achieve orthogonality between successive pulses leading to second trip suppression abilities, 2) Frequency diversity on a pulse to pulse basis to achieve second trip suppression and retrieval capability in a weather radar, 3) a multiple input, multiple output (MIMO) configuration using the orthogonality features obtained using ideas in 1 and 2. It is shown in this thesis that this configuration for a radar leads to better spatial resolution by the formation of a bigger virtual array. It is also demonstrated that orthogonality is a big requirement to get this improvement from a MIMO configuration. This thesis addresses this issue with a new polyphase code pair and mismatched filter based framework which gives excellent orthogonal features compared to a matched filter processor. The MIMO platform is a long term goal (technologically) and therefore the polyphase codes were used to demonstrate second trip suppression abilities that uses orthogonal features of these codes to reduce range and velocity ambiguity. These are called as Intra-pulse phase coding techniques. The thesis also demonstrates another technique to achieve orthogonality between pulses by coding them on different frequencies. This is termed as Inter-pulse frequency diversity coding. In the beginning, design and implementation of Intra-pulse polyphase codes and algorithms to generate these codes with good correlation properties are discussed. Next, frequency diversity technique is introduced and compared with other inter-pulse techniques. Other Inter-pulse coding schemes like that based on Chu codes are widely used for second trip suppression or cross-polarization isolation. But here, a novel technique is discussed taking advantage of frequency diverse waveforms. The simulations and tests are accomplished on D3R weather radar system. A new method is described to recover velocity and spectral width due to incoherence in samples from change of frequency pulse to pulse. It is shown that this technique can recover the weather radar moments over a much higher dynamic range of the other trip contamination as compared with the popular systematic phase codes, for second trip suppression and retrieval. For these new features to be incorporated in the D3R radar, it went through upgrade of the IF sections and digital receivers. The NASA dual-frequency, dual-polarization, Doppler radar (D3R) is an important ground validation tool for the global precipitation measurement (GPM) mission's dual-frequency precipitation radar (DPR). It has undergone extensive field trials starting in 2011 and continues to provide observations that enhance our scientific knowledge. This upgrade would enable more research frontiers to be explored with enhanced performance. In the thesis, this upgrade work is also discussed.
Embargo
Precipitation mapping at local, regional and global scale
(Colorado State University. Libraries, 2022) Joshil, Shashank S., author; Chandrasekar, V., advisor; Cheney, Margaret, committee member; Gooch, S. Ryan, committee member; James, Susan P., committee member
It is well established that the Earth's water cycle is accelerating, and extreme precipitation events are becoming more common. While we cannot avoid this issue, we can be better prepared to handle it if we can obtain accurate observations of precipitation for use in short-term and long-term prediction models. Various remote sensing instruments are available to obtain precipitation data. In this research work, mapping precipitation at local, regional, and global scales is studied. The technology of precipitation mapping at these scales is very different and elaborated. Examples of precipitation measurements from these scales are discussed. At the local scale, rain gauges and disdrometers are two prominent instruments that are utilized for precipitation measurement. Precipitation observations captured from these two instruments are introduced. Millimeter wave radars have been previously used in various domains, and extensive research is currently in progress to improve this technology. This research will present the potential of using automobile class radars to obtain local surface precipitation. Since the maximum range of an automobile radar is within a few hundred meters, we can consider the observations to be at a local scale. With the help of signal modeling, methods to obtain the rainfall rate at the millimeter wave band by using radar parameters, such as reflectivity and attenuation are discussed. A simulation tool is developed that generates the radar signals at the millimeter wave frequency band. The various parameters which are used in the signal simulations are explained in detail, and the simulation results are presented. Experiments for mapping precipitation using a current state-of-the-art automobile radar are carried out, and the results are discussed. The reflectivity value obtained from the experiment using automobile radar is compared to the NWS reflectivity mosaic, and the results match within a couple of decibels (dB). Weather radars are remote sensing instruments that provide precipitation observations at a regional scale. They provide data at a large spatial extent. Weather radar observations obtained at various frequency bands for mapping precipitation is discussed with examples. The current networks of instruments and system architectures that provide precipitation information at a regional scale are discussed. The precipitation data obtained from individual automobile radars is considered as a local data point, and precipitation maps at the regional scale are constructed. The system analysis of using a network of automobile radars for mapping precipitation is discussed with the help of simulations. The Dallas-Fort-Worth urban region is considered for the simulation study, and the potential of using millimeter wave radars to create precipitation maps is presented. Three different interpolation techniques, linear, nearest-neighbor, and natural are explored to study the reconstruction of precipitation maps. A system architecture for precipitation mapping using automobile radars is also discussed. The attenuation of radar signals has to be addressed and corrected to obtain accurate precipitation information from radar data. The attenuation correction in weather radars for rain hydrometeors is well studied in the literature, but attenuation correction for snow is limited. This is due to the fact that snow does not attenuate much at lower frequency bands like S and C bands and because the snow particles vary in their particle size distributions and have complex shapes. Theoretical relationships between specific phase and attenuation are developed using signal simulations. This research will introduce a new algorithm that corrects radar signal attenuation in rain and snow. The attenuation correction method developed is applied to X-band and Ku-band radar data, and the results are discussed. It was observed from the data for snow cases that the path integrated attenuation at the X-band reached up to 2 dB and, at the Ku-band, it reached up to 8 dB. Mapping precipitation at a global scale is a challenging task. The Dual Precipitation Radar (DPR) is a spaceborne instrument providing valuable precipitation information at the global scale, but the observations from this instrument suffer from poor spatial and range resolutions. Synthetic Aperture Radars (SAR) are well known for providing high spatial resolution data. In the past, SARs have been deployed on airborne and spaceborne platforms for mapping land cover and constructing surface elevation models. The potential of using SAR for mapping precipitation is not widely explored. In this research, SAR signal simulations are carried out to observe precipitation from spaceborne platforms. The mathematical framework for monostatic and bistatic SAR is discussed. The simulation results for two specific spaceborne SAR architectures are discussed in detail. The variation of precipitation parameters such as velocity and spectral width are studied using simulations. This dissertation presents the roles and challenges of observing precipitation at the three scales, with suggestions for future research.
Open Access
Precipitation observations from high frequency spaceborne polarimetric synthetic aperture radar and ground-based radar: theory and model validation
(Colorado State University. Libraries, 2010) Fritz, Jason P., author; Chandrasekar, V., advisor; Jayasumana, Anura P., committee member; Notaros, Branislav M., committee member; Mielke, Paul W., committee member
Global weather monitoring is a very useful tool to better understand the Earth's hydrological cycle and provide critical information for emergency and warning systems in severe cases. Developed countries have installed numerous ground-based radars for this purpose, but they obviously are not global in extent. To address this issue, the Tropical Rainfall Measurement Mission (TRMM) was launched in 1997 and has been quite successful. The follow-on Global Precipitation Measurement (GPM) mission will replace TRMM once it is launched. However, a single precipitation radar satellite is still limited, so it would be beneficial if additional existing satellite platforms can be used for meteorological purposes. Within the past few years, several X-band Synthetic Aperture Radar (SAR) satellites have been launched and more are planned. While the primary SAR application is surface monitoring, and they are heralded as "all weather'' systems, strong precipitation induces propagation and backscatter effects in the data. Thus, there exists a potential for weather monitoring using this technology. The process of extracting meteorological parameters from radar measurements is essentially an inversion problem that has been extensively studied for radars designed to estimate these parameters. Before attempting to solve the inverse problem for SAR data, however, the forward problem must be addressed to gain knowledge on exactly how precipitation impacts SAR imagery. This is accomplished by simulating storms in SAR data starting from real measurements of a storm by ground-based polarimetric radar. In addition, real storm observations by current SAR platforms are also quantitatively analyzed by comparison to theoretical results using simultaneous acquisitions by ground radars even in single polarization. For storm simulation, a novel approach is presented here using neural networks to accommodate the oscillations present when the particle scattering requires the Mie solution, i.e., particle diameter is close to the radar wavelength. The process of transforming the real ground measurements to spaceborne SAR is also described, and results are presented in detail. These results are then compared to real observations of storms acquired by the German TerraSAR-X satellite and by one of the Italian COSMO-SkyMed satellites both operating in co-polar mode (i.e., HH and VV). In the TerraSAR-X case, two horizontal polarization ground radars provided simultaneous observations, from which theoretical attenuation is derived assuming all rain hydrometeors. A C-band fully polarimetric ground radar simultaneously observed the storm captured by the COSMO-SkyMed SAR, providing a case to begin validating the simulation model. While previous research has identified the backscatter and attenuation effects of precipitation on X-band SAR imagery, and some have noted an impact on polarimetric observations, the research presented here is the first to quantify it in a holistic sense and demonstrate it using a detailed model of actual storms observed by ground radars. In addition to volumetric effects from precipitation, the land backscatter is altered when water is on or near the surface. This is explored using TRMM, Canada's RADARSAT-1 C-band SAR and Level 3 NEXRAD ground radar data. A weak correlation is determined, and further investigation is warranted. Options for future research are then proposed.
Open Access
Quantitative precipitation estimation for an X-band weather radar network
(Colorado State University. Libraries, 2013) Chen, Haonan, author; Chandrasekar, V., advisor; Notaros, Branislav M., committee member; Mielke, Paul W., committee member
Currently, the Next Generation (NEXRAD) radar network, a joint effort of the U.S. Department of Commerce (DOC), Defense (DOD), and Transportation (DOT), provides radar data with updates every five-six minutes across the United States. This network consists of about 160 S-band (2.7 to 3.0 GHz) radar sites. At the maximum NEXRAD range of 230 km, the 0.5 degree radar beam is about 5.4 km above ground level (AGL) because of the effect of earth curvature. Consequently, much of the lower atmosphere (1-3 km AGL) cannot be observed by the NEXRAD. To overcome the fundamental coverage limitations of today's weather surveillance radars, and improve the spatial and temporal resolution issues, the National Science Foundation Engineering Center (NSF-ERC) for Collaborative Adaptive Sensing of the Atmosphere (CASA) was founded to revolutionize weather sensing in the lower atmosphere by deploying a dense network of shorter-range, low-power X-band dual-polarization radars. The distributed CASA radars are operating collaboratively to adapt the changing atmospheric conditions. Accomplishments and breakthroughs after five years operation have demonstrated the success of CASA program. Accurate radar quantitative precipitation estimation (QPE) has been pursued since the beginning of weather radar. For certain disaster prevention applications such as flash flood and landslide forecasting, the rain rate must however be measured at a high spatial and temporal resolution. To this end, high-resolution radar QPE is one of the major research activities conducted by the CASA community. A radar specific differential propagation phase (Kdp)-based QPE methodology has been developed in CASA. Unlike the rainfall estimation based on the power terms such as radar reflectivity (Z) and differential reflectivity (Zdr), Kdp-based QPE is less sensitive to the path attenuation, drop size distribution (DSD), and radar calibration errors. The CASA Kdp-based QPE system is also immune to the partial beam blockage and hail contamination. The performance of the CASA QPE system is validated and evaluated by using rain gauges. In CASA's Integrated Project 1 (IP1) test bed in Southwestern Oklahoma, a network of 20 rainfall gauges is used for cross-comparison. 40 rainfall cases, including severe, multicellular thunderstorms, squall lines and widespread stratiform rain, that happened during years 2007 - 2011, are used for validation and evaluation purpose. The performance scores illustrate that the CASA QPE system is a great improvement compared to the current state-of-the-art. In addition, the high-resolution CASA QPE products such as instantaneous rainfall rate map and hourly rainfall amount measurements can serve as a reliable input for various distributed hydrological models. The CASA QPE system can save lived and properties from hazardous flash floods by incorporating hydraulic and hydrologic models for flood monitoring and warning.