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

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

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

Identification of tower-wake distortions using sonic anemometer and lidar measurements

The eXperimental Planetary boundary layer Instrumentation Assessment (XPIA) field campaign took place in March through May 2015 at the Boulder Atmospheric Observatory, utilizing its 300 m meteorological tower, instrumented with two sonic anemometers mounted on opposite sides of the tower at six heights. This allowed for at least one sonic anemometer at each level to be upstream of the tower at all times and for identification of the times when a sonic anemometer is in the wake of the tower frame. Other instrumentation, including profiling and scanning lidars aided in the identification of the tower wake. Here we compare pairs of sonic anemometers at the same heights to identify the range of directions that are affected by the tower for each of the opposing booms. The mean velocity and turbulent kinetic energy are used to quantify the wake impact on these first- and second-order wind measurements, showing up to a 50 % reduction in wind speed and an order of magnitude increase in turbulent kinetic energy. Comparisons of wind speeds from profiling and scanning lidars confirmed the extent of the tower wake, with the same reduction in wind speed observed in the tower wake, and a speed-up effect around the wake boundaries. Wind direction differences between pairs of sonic anemometers and between sonic anemometers and lidars can also be significant, as the flow is deflected by the tower structure. Comparisons of lengths of averaging intervals showed a decrease in wind speed deficit with longer averages, but the flow deflection remains constant over longer averages. Furthermore, asymmetry exists in the tower effects due to the geometry and placement of the booms on the triangular tower. An analysis of the percentage of observations in the wake that must be removed from 2 min mean wind speed and 20 min turbulent values showed that removing even small portions of the time interval due to wakes impacts these two quantities. However, a vast majority of intervals have no observations in the tower wake, so removing the full 2 or 20 min intervals does not diminish the XPIA dataset

3D wind and turbulence characteristics of the atmospheric boundary layer

The 3D Wind experiment integrates model simulations and measurements from remote sensing, traditional, and unmanned aerial vehicle platforms to quantify wind components over the area of a large wind farm to heights of 200 m. The 3D wind and turbulence characteristics of the atmospheric boundary layer experiment focus on collection and integration of data from remote sensing and in situ instruments to develop precise and accurate characterization of wind and turbulence in the lowest 200 m of the atmospheric boundary layer (ABL). This research is conducted within the context of applications to wind resource characterization, wind farm optimization, and wind farm aerodynamics for power production and fatigue load quantification. These end uses require data over a range of temporal scales from individual turbines through wind farms and clusters of wind farms