Dont Mess with Texas: Getting the Lone Star State to Net-Zero by 2050

The world is decarbonizing. Many countries, companies, and financial institutions have committed to cutting their emissions. Decarbonization commitments have been issued by: 136 countries including Canada, China, and the UK, at least 16 U.S. states including New York, Louisiana, and Virginia, and a third of the largest 2,000 publicly traded companies in the world, including Apple, Amazon, and Walmart, and numerous Texas companies like ExxonMobil, American and Southwest Airlines, Baker Hughes, and AT&T.1–9 These decarbonizing countries, states, cities, and companies are Texas's energy customers. If Texas ignores the challenge to decarbonize its economy, it may eventually face the more difficult challenge of selling carbon-intensive products to customers around the world who do not want them. We are already seeing this scenario beginning to play out with France canceling a liquified natural gas deal from Texas gas producers and both U.S. and international automakers announcing shifts to electric vehicles. Proactive net-zero emissions strategies might allow Texas to maintain energy leadership and grow the economy within a rapidly decarbonizing global marketplace.Thankfully, Texas is uniquely positioned to lead the world in the transition to a carbon-neutral energy economy. With the second highest Gross State Product in the US, the Texas economy is on par with countries like Canada, Italy, or Brazil. Thus, Texas's decisions have global implications. Texas also has an abundant resource of low-carbon energy sources to harness and a world-class workforce with technical capabilities to implement solutions at a large-scale quickly and safely. Texas has a promising opportunity to lead the world towards a better energy system in a way that provides significant economic benefits to the state by leveraging our renewable resources, energy industry expertise, and strong manufacturing and export markets for clean electricity, fuels, and products. The world is moving, with or without Texas, but it is likely to move faster--and Texas will be more prosperous--if Texans lead the way.There are many ways to fully decarbonize the Texas economy across all sectors by 2050. In this analysis, we present a Business as Usual (BAU) scenario and four possible pathways to Texas achieving state-wide net-zero emissions by 2050. Figure ES-1 provides a visual comparison of scenario conditions

Spatial and temporal variability of turbulence dissipation rate in complex terrain

To improve parameterizations of the turbulence dissipation rate (ϵ) in numerical weather prediction models, the temporal and spatial variability of ϵ must be assessed. In this study, we explore influences on the variability of ϵ at various scales in the Columbia River Gorge during the WFIP2 field experiment between 2015 and 2017. We calculate ϵ from five sonic anemometers all deployed in a ∼4&thinsp;km2 area as well as from two scanning Doppler lidars and four profiling Doppler lidars, whose locations span a ∼300&thinsp;km wide region. We retrieve ϵ from the sonic anemometers using the second-order structure function method, from the scanning lidars with the azimuth structure function approach, and from the profiling lidars with a novel technique using the variance of the line-of-sight velocity. The turbulence dissipation rate shows large spatial variability, even at the microscale, especially during nighttime stable conditions. Orographic features have a strong impact on the variability of ϵ, with the correlation between ϵ at different stations being highly influenced by terrain. ϵ shows larger values in sites located downwind of complex orographic structures or in wind farm wakes. A clear diurnal cycle in ϵ is found, with daytime convective conditions determining values over an order of magnitude higher than nighttime stable conditions. ϵ also shows a distinct seasonal cycle, with differences greater than an order of magnitude between average ϵ values in summer and winter.</p

Assessment of virtual towers performed with scanning wind lidars and Ka-band radars during the XPIA experiment

During the eXperimental Planetary boundary layer Instrumentation Assessment (XPIA) campaign, which was carried out at the Boulder Atmospheric Observatory (BAO) in spring 2015, multiple-Doppler scanning strategies were carried out with scanning wind lidars and Ka-band radars. Specifically, step–stare measurements were collected simultaneously with three scanning Doppler lidars, while two scanning Ka-band radars carried out simultaneous range height indicator (RHI) scans. The XPIA experiment provided the unique opportunity to compare directly virtual-tower measurements performed simultaneously with Ka-band radars and Doppler wind lidars. Furthermore, multiple-Doppler measurements were assessed against sonic anemometer data acquired from the meteorological tower (met-tower) present at the BAO site and a lidar wind profiler. This survey shows that – despite the different technologies, measurement volumes and sampling periods used for the lidar and radar measurements – a very good accuracy is achieved for both remote-sensing techniques for probing horizontal wind speed and wind direction with the virtual-tower scanning technique

Vertical profiles of the 3-D wind velocity retrieved from multiple wind lidars performing triple range-height-indicator scans

Vertical profiles of 3-D wind velocity are retrieved from triple range-height-indicator (RHI) scans performed with multiple simultaneous scanning Doppler wind lidars. This test is part of the eXperimental Planetary boundary layer Instrumentation Assessment (XPIA) campaign carried out at the Boulder Atmospheric Observatory. The three wind velocity components are retrieved and then compared with the data acquired through various profiling wind lidars and high-frequency wind data obtained from sonic anemometers installed on a 300 m meteorological tower. The results show that the magnitude of the horizontal wind velocity and the wind direction obtained from the triple RHI scans are generally retrieved with good accuracy. However, poor accuracy is obtained for the evaluation of the vertical velocity, which is mainly due to its typically smaller magnitude and to the error propagation connected with the data retrieval procedure and accuracy in the experimental setup

Evaluation of single and multiple Doppler lidar techniques to measure complex flow during the XPIA field campaign

Accurate three-dimensional information of wind flow fields can be an important tool in not only visualizing complex flow but also understanding the underlying physical processes and improving flow modeling. However, a thorough analysis of the measurement uncertainties is required to properly interpret results. The XPIA (eXperimental Planetary boundary layer Instrumentation Assessment) field campaign conducted at the Boulder Atmospheric Observatory (BAO) in Erie, CO, from 2 March to 31 May 2015 brought together a large suite of in situ and remote sensing measurement platforms to evaluate complex flow measurement strategies. In this paper, measurement uncertainties for different single and multi-Doppler strategies using simple scan geometries (conical, vertical plane and staring) are investigated. The tradeoffs (such as time–space resolution vs. spatial coverage) among the different measurement techniques are evaluated using co-located measurements made near the BAO tower. Sensitivity of the single-/multi-Doppler measurement uncertainties to averaging period are investigated using the sonic anemometers installed on the BAO tower as the standard reference. Finally, the radiometer measurements are used to partition the measurement periods as a function of atmospheric stability to determine their effect on measurement uncertainty. It was found that with an increase in spatial coverage and measurement complexity, the uncertainty in the wind measurement also increased. For multi-Doppler techniques, the increase in uncertainty for temporally uncoordinated measurements is possibly due to requiring additional assumptions of stationarity along with horizontal homogeneity and less representative line-of-sight velocity statistics. It was also found that wind speed measurement uncertainty was lower during stable conditions compared to unstable conditions

Internet of Things for Environmental Sustainability and Climate Change

Our world is vulnerable to climate change risks such as glacier retreat, rising temperatures, more variable and intense weather events (e.g., floods, droughts, and frosts), deteriorating mountain ecosystems, soil degradation, and increasing water scarcity. However, there are big gaps in our understanding of changes in regional climate and how these changes will impact human and natural systems, making it difficult to anticipate, plan, and adapt to the coming changes. The IoT paradigm in this area can enhance our understanding of regional climate by using technology solutions, while providing the dynamic climate elements based on integrated environmental sensing and communications that is necessary to support climate change impacts assessments in each of the related areas (e.g., environmental quality and monitoring, sustainable energy, agricultural systems, cultural preservation, and sustainable mining). In the IoT in Environmental Sustainability and Climate Change chapter, a framework for informed creation, interpretation and use of climate change projections and for continued innovations in climate and environmental science driven by key societal and economic stakeholders is presented. In addition, the IoT cyberinfrastructure to support the development of continued innovations in climate and environmental science is discussed

Initial Results from the Experimental Measurement Campaign (XMC) for Planetary Boundary Layer (PBL) Instrument Assessment (XPIA) Experiment

The Experimental Measurement Campaign (XMC) for Planetary Boundary Layer (PBL) Instrument Assessment (XPIA) is a DOE funded study to develop and validate methods of making three dimensional measurements of wind fields. These techniques are of interest to study wind farm inflows and wake flows using remote sensing instrumentation. The portion of the experiment described in this presentation utilizes observations from multiple Doppler wind lidars, soundings, and an instrumented 300m tower, the Boulder Atmospheric Observatory (BAO) in Erie, Colorado

Initial Results from the Experimental Measurement Campaign (XMC) for Planetary Boundary Layer (PBL) Instrument Assessment (XPIA) Experiment

The Experimental Measurement Campaign (XMC) for Planetary Boundary Layer (PBL) Instrument Assessment (XPIA) is a DOE funded study to develop and validate methods of making three dimensional measurements of wind fields. These techniques are of interest to study wind farm inflows and wake flows using remote sensing instrumentation. The portion of the experiment described in this presentation utilizes observations from multiple Doppler wind lidars, soundings, and an instrumented 300m tower, the Boulder Atmospheric Observatory (BAO) in Erie, Colorado

Lidar Uncertainty Measurement Experiment (LUMEX) – Understanding Sampling Errors

Coherent Doppler LIDAR (Light Detection and Ranging) has been widely used to provide measurements of several boundary layer parameters such as profiles of wind speed, wind direction, vertical velocity statistics, mixing layer heights and turbulent kinetic energy (TKE). An important aspect of providing this wide range of meteorological data is to properly characterize the uncertainty associated with these measurements. With the above intent in mind, the Lidar Uncertainty Measurement Experiment (LUMEX) was conducted at Erie, Colorado during the period June 23rd to July 13th, 2014. The major goals of this experiment were the following: Characterize sampling error for vertical velocity statistic