Sue Ellen Haupt

• PhD, Atmospheric Science, UMi
• Senior Scientist at NSF National Center for Atmospheric Research

Wind energy harvesting from the atmosphere takes place in the atmospheric boundary layer. The boundary layer shear and buoyancy create three-dimensional turbulent eddies spanning a range of scales that form a continuous forward cascade of kinetic energy to the smallest scales of motion where energy is dissipated. Large-scale atmospheric circulation...

The large spatial scale of global Earth system models (ESMs) is often cited as an obstacle to using the output by water resource managers in localized decisions. Recent advances in computing have improved the fidelity of hydrological responses in ESMs through increased connectivity between model components. However, the models are seldom evaluated...

The coastal low-level jet, or coastal low-level jet (CLLJ), is a synoptically-forced meteorological feature frequently present offshore the western United States (U.S.). Characterized by a wind speed maximum that resides at the top of the marine boundary layer, the CLLJ is largely controlled by the location and strength of the North Pacific High (N...

To meet US goals of deploying additional wind energy as part of the decarbonization strategy, wind plants are being planned for the deep water offshore the western US. The wind flow in that region is complex due to the proximity to the coast, cold water upwelling, and persistent stratiform clouds that interact with radiation in ways that have the p...

Energy poverty in Missouri was analyzed using the four quadrant approach using both state and county level data sets for two separate definitions of the grid. Predictions used machine learning techniques including decision trees, random forest, extreme gradient boosting and support vector machines. It was determined that the extreme gradient boosti...

To meet the Biden-Harris administration's goal of deploying 30 GW of offshore wind power by 2030 and 110 GW by 2050, expansion of wind energy into U.S. territorial waters prone to tropical cyclones (TCs) and extratropical cyclones (ETCs) is essential. This requires a deeper understanding of cyclone-related risks and the development of robust, resil...

Optimization of wind energy integration requires knowing the relationship between weather patterns and winds they cause. For a region with less-studied weather such as the Middle East, climatology becomes more vital. The Shagaya Renewable Energy Park in development in Kuwait experiences regional wind regimes that affect wind power production. Weath...

The large spatial scale of global Earth system models (ESM) is often cited as an obstacle to using the output by water resource managers in localized decisions. Recent advances in computing have improved the fidelity of hydrological responses in ESMs through increased connectivity between model components. However, the models are seldom evaluated f...

The Mesoscale to Microscale Coupling team, part of the U.S. Department of Energy Atmosphere to Electrons (A2e) initiative, has studied various important challenges related to coupling mesoscale models to microscale models for the use case of wind energy development and operation. Several coupling methods and techniques for generating turbulence at...

The Mesoscale to Microscale Coupling team, part of the U.S. Department of Energy Atmosphere to electrons (A2e) initiative, has studied various important challenges related to coupling mesoscale models to microscale models for the use case of wind energy development and operation. Several coupling methods and techniques for generating turbulence at...

Flows in the atmospheric boundary layer are turbulent, characterized by a large Reynolds number, the existence of a roughness sublayer and the absence of a well-defined viscous layer. Exchanges with the surface are therefore dominated by turbulent fluxes. In numerical models for atmospheric flows, turbulent fluxes must be specified at the surface;...

Artificial intelligence (AI) and machine learning (ML) have become important tools for environmental scientists and engineers, both in research and in applications. Although these methods have become quite popular in recent years, they are not new. The use of AI methods began in the 1950s and environmental scientists were adopting them by the 1980s...

Generating accurate weather forecasts of planetary boundary layer (PBL) properties is challenging in many geographical regions, oftentimes due to complex topography or horizontal variability in, for example, land characteristics. While recent advances in high-performance computing platforms have led to an increase in the spatial resolution of numer...

This study provides performance analysis results obtained from the 10-MW (five 2-MW turbines) Shagaya pilot wind farm located in a desert area of Kuwait, where hot and dusty conditions prevail and constitute engineering challenges. The 2-year operational data analyzed here provide unique results that elucidate the effects of such conditions on turb...

High temperatures and dust accumulation can impact wind turbine performance and increase the maintenance burden and operating cost of wind turbines in desert regions. A novel, detailed analysis method is presented to quantify the separate and total effects of these adverse conditions on the performance of each 2-MW turbine of the 10-MW Shagaya wind...

The most mature aspect of applying artificial intelligence (AI)/machine learning (ML) to problems in the atmospheric sciences is likely post-processing of model output. This article provides some history and current state of the science of post-processing with AI for weather and climate models. Deriving from the discussion at the 2019 Oxford worksh...

The chaotic characteristics of the tall tower wind speed data within Missouri was investigated using both quantitative and qualitative methodologies. The phase space diagrams were constructed using the method of time delay. The two parameters needed in the construction of the attractor are the embedding dimension and the time delay. The former was...

The overall goal of the Mesoscale-to-Microscale Coupling (MMC) project is to improve coupling between mesoscale and microscale simulations via improved guidance and new strategies for setting up simulations and for the development of new tools that can be used across the community. Including the mesoscale forcing is critical to modeling the full en...

Promising new opportunities to apply artificial intelligence (AI) to the Earth and environmental sciences are identified, informed by an overview of current efforts in the community. Community input was collected at the first National Oceanic and Atmospheric Administration (NOAA) workshop on “Leveraging AI in the Exploitation of Satellite Earth Obs...

Generation forecasts are considered as one of the most cost-effective tools to integrate high levels of photovoltaic (PV) capacity in power systems. This work aims to briefly discuss the maturation of this research field and the apparent lack of outreach for works that address the end-use of solar forecasts and the associated technical-economic gai...

Wind and solar energy sources are climate and weather dependent, therefore susceptible to a changing climate. We quantify the impacts of climate change on wind and solar electricity generation under high concentrations of greenhouse gases in Texas. We employ mid-twenty-first century climate projections and a high-resolution numerical weather predic...

The need for open science has been recognized by the communities of meteorology and climate science. While these domains are mature in terms of applying digital technologies, the implementation of open science methodologies is less advanced. In a session on “Weather and Climate Science in the Digital Era” at the 14th IEEE International eScience Con...

Electrical system operators utilizing wind energy production need accurate wind power forecasts to prepare for changes in power production. To understand the forecast problem and sources of forecast uncertainty, a climatology of the region of interest is needed. For Shagaya Renewable Energy Park in Kuwait, seasonal and diurnal wind patterns and the...

A modern renewable energy forecasting system blends physical models with artificial intelligence to aid in system operation and grid integration. This paper describes such a system being developed for the Shagaya Renewable Energy Park, which is being developed by the State of Kuwait. The park contains wind turbines, photovoltaic panels, and concent...

The National Center for Atmospheric Research (NCAR) recently updated the comprehensive wind power forecasting system in collaboration with Xcel Energy addressing users’ needs and requirements by enhancing and expanding integration between numerical weather prediction and machine-learning methods. While the original system was designed with the prim...

This work compares the solar power forecasting performance of tree-based methods that include implicit regime-based models to explicit regime separation methods that utilize both unsupervised and supervised machine learning techniques. Previous studies have shown an improvement utilizing a regime-based machine learning approach in a climate with di...

The purpose of the US DOE’s Mesoscale to Microscale Coupling (MMC) Project is to develop, verify, and validate physical models and modeling techniques that bridge the most important atmospheric scales that determine wind plant performance and reliability. The project seeks to create a new predictive numerical simulation capability that represents a...

Wind energy applications including wind resource assessment, wind power forecasting, and wind plant optimization require high-resolution mesoscale simulations. High resolution mesoscale simulations are essential for accurate characterization of atmospheric flows over heterogeneous land use and complex terrain. Under such conditions, the assumption...

With electricity representing around 20% of the global energy demand, and increasing support for renewable sources of electricity, there is also an escalating need to improve solar forecasts to support power management. While considerable research has been directed to statistical methods to improve solar power forecasting, few have employed finite...

Much of the electric system is weather dependent; thus, our ability to forecast the weather contributes to its efficient and economical operation. Climatological forecasts of meteorological variables are used for long-term planning, capturing changing frequencies of extreme events, such as cold and hot periods, and identifying suitable locations fo...

Operating modern multi-modal surface transportation systems are becoming increasingly automated and driven by decision support systems. One aspect necessary for successful, safe, reliable, and efficient operation of any transportation network is real-time and forecasted weather and pavement condition information. Providing such information requires...

Abstract. The need for open science has been recognized by the communities of meteorology and climate science. However, while these domains are mature in terms of applying digital technologies, these are lagging behind where the implementation of open science methodologies is concerned. In a session on Weather and Climate Science in the Digital Era...

Spatio-temporal solar forecasting based on statistical models seldom integrates wind information. An AutoRegressive with eXogenous input (ARX) model was tested using global horizontal irradiation records from a set of pyranometers deployed in Oahu, Hawaii, USA, where northeasterly winds are predominant. When irradiance is forecasted 10-s ahead, int...

Accurately representing flow across the mesoscale to the microscale is a persistent roadblock for completing realistic microscale simulations. The science challenges that must be addressed to coupling at these scales include the following: 1) What is necessary to capture the variability of the mesoscale flow, and how do we avoid generating spurious...

Wind power is a variable generation resource and therefore requires accurate forecasts to enable integration into the electric grid. Generally, the wind speed is forecast for a wind plant and the forecasted wind speed is converted to power to provide an estimate of the expected generating capacity of the plant. The average wind speed forecast for t...

The human population on Earth has increased by a factor of 4.6 in the last 100 years and has become more centered in urban environments. This expansion and migration pattern has resulted in stresses on the environment. Meteorological applications have helped to understand and mitigate those stresses. This chapter describes several applications that...

Deep learning models, such as convolutional neural networks, utilize multiple specialized layers to encode spatial patterns at different scales. In this study, deep learning models are compared with standard machine learning approaches on the task of predicting the probability of severe hail based on upper-air dynamic and thermodynamic fields from...

Wind power is a variable generation resource and therefore requires accurate forecasts to enable integration into the electric grid. Generally, the wind speed is forecast for a wind plant and the forecasted wind speed is converted to power to provide an estimate of the expected generating capacity of the plant. The average wind speed forecast for t...

Coupled mesoscale-microscale simulations are required to provide time-varying weather-dependent inflow and forcing for large-eddy simulations under general flow conditions. Such coupling necessarily spans a wide range of spatial scales (i.e., ~10m to ~10 km). Herein, we use simulations that involve multiple nested domains with horizontal grid spaci...

The use of numerical weather prediction (NWP) models for solar resource evaluation is examined. The theory behind NWP models is described highlighting relevant components for solar energy applications as well as how to use NWP models for mapping the solar resource at the regional scale. Future perspectives are briefly outlined.

Applied meteorology is an important and rapidly growing field. This chapter concludes the three-chapter series of this monograph describing how meteorological information can be used to serve society’s needs while at the same time advancing our understanding of the basics of the science. This chapter continues along the lines of Part II of this ser...

Climate change might impact various components of the bulk electric power system, including electricity demand; transmission; and thermal, hydropower, wind, and solar generators. Most research in this area quantifies impacts on one or a few components and does not link these impacts to effects on power system planning and operations. Here, we advan...

The sensitivities of idealized large-eddy simulations (LESs) to variations of model configuration and forcing parameters on quantities of interest to wind power applications are examined. Simulated wind speed, turbulent fluxes, spectra and cospectra are assessed in relation to variations in two physical factors, geostrophic wind speed and surface r...

Short-range forecasts for periods on the order of hours to days and up to two weeks ahead are necessary to smoothly run transmission and distribution systems, plan maintenance, protect infrastructure and allocate units. In particular, forecasting the renewable energy resources on a day-to-day basis enables integration of increasing capacities of th...

Work at the nexus between energy and meteorology aims at integrating meteorological information into operational risk management and strategic planning for the energy sector, at all timescales, from long-term climate change and climate variability to shorter term local weather. Weather and climate risk management can be a powerful instrument for de...

The interplay between energy and meteorology (based on its broad meaning of weather, water and climate) has been steadily growing. For this relationship to continue flourishing, a formal structure for stakeholders to interact effectively is required. The process of formation of the World Energy & Meteorology Council (WEMC), an organisation aimed at...

Numerical weather prediction (NWP) models are important tools in the process of generating forecasts of wind and solar power output from a farm. Before running an NWP model or being able to interpret its output, however, modelers and forecasters ought to develop an understanding of several foundational principles that undergird a successful NWP for...

Utilities consider having accurate electric load forecasts to be critical to their day-to-day operations. But the growing penetration of distributed photovoltaic (DPV) solar power production “behind the meter” makes it more difficult to predict load because it is hard to distinguish between increased solar power generation and decreased power consu...

It is in the nature of chaotic atmospheric processes that weather forecasts will never be perfectly accurate. This natural fact poses challenges not only for private life, public safety, and traffic but also for electrical power systems with high shares of weather-dependent wind and solar power production. To facilitate a secure and economic grid a...

The sensitivities of idealized Large-Eddy Simulations (LES) to variations of model configuration and forcing parameters on quantities of interest to wind power applications are examined. Simulated wind speed, turbulent fluxes, spectra and cospectra are assessed in relation to variations of two physical factors, geostrophic wind speed and surface ro...

Forecasting severe hail accurately requires predicting how well atmospheric conditions support the development of thunderstorms, the growth of large hail, and the minimal loss of hail mass to melting before reaching the surface. Existing hail forecasting techniques incorporate information about these processes from proximity soundings and numerical...

The field of atmospheric science has been enhanced by its long-standing collaboration with entities with specific needs. This chapter and the two subsequent ones describe how applications have worked to advance the science at the same time that the science has served the needs of society. This chapter briefly reviews the synergy between the applica...

Gridded forecasts of solar irradiance are increasingly needed to integrate power into the electric grid from distributed solar installations and newer large-scale installations that don’t have long records of observed irradiance. We evaluate different combinations of statistical learning models and aggregations of weather data from observed sites t...

As integration of solar power into the national electric grid rapidly increases, it becomes imperative to improve forecasting of this highly variable renewable resource. Thus, a team of researchers from public, private, and academic sectors partnered to develop and assess a new solar power forecasting system, Sun4Cast®. The partnership focused on i...

One of the key enabling technologies for integrating solar energy into the grid is short-range forecasting. Two issues have emerged in the literature. The first has to do with the relative merits of physics-based versus time series models. The second is how to parameterize short-term variability. One promising approach is time-varying parameter mod...

A genetic algorithm is paired with a Lagrangian puff atmospheric model to reconstruct the source characteristics of an atmospheric release. Observed meteorological and ground concentration measurements from the real-world Dipole Pride controlled release experiment are used to test the methodology. A sensitivity study is performed to quantify the re...

High-impact weather events such as severe thunderstorms, tornadoes, and hurricanes, cause significant disruptions to infrastructure, property loss, and even fatalities. High-impact events can also positively impact society, such as the impact on savings through renewable energy. Prediction of these events has improved substantially with greater obs...

Modeling the downwind hazard area resulting from the unknown release of an atmospheric contaminant requires estimation of the source characteristics of a localized source from concentration or dosage observations and use of this information to model the subsequent transport and dispersion of the contaminant. This source term estimation problem is m...

The Sun4CastTM solar power forecasting system, designed to predict solar irradiance and power generation at solar farms, is comprised of several component models operating on both the nowcasting (0-6 h) and day-ahead forecast horizons. The different nowcasting models include a statistical forecasting model (“StatCast”), two satellite-based forecast...

To blend growing amounts of power from renewable resources into utility operations requires accurate forecasts. For both day ahead planning and real-time operations, the power from the wind and solar resources must be predicted based on real-time observations and a series of models that span the temporal and spatial scales of the problem, using the...

WRF-Solar is a specific configuration and augmentation of the Weather Research and Forecasting (WRF) model designed for solar energy applications. Recent upgrades to the WRF model contribute to making the model appropriate for solar power forecasting, and comprise 1) developments to diagnose internally relevant atmospheric parameters required by th...

The shortwave radiative impacts of unresolved cumulus clouds are investigated using 6-h ensemble simulations performed with the WRF-Solar Model and high-quality observations over the contiguous United States for a 1-yr period. The ensembles use the stochastic kinetic energy backscatter scheme (SKEBS) to account for implicit model uncertainty. Resul...

This paper describes the development and testing of a cloud-regime-dependent short-range solar irradiance forecasting system for predictions of 15-min-average clearness index (global horizontal irradiance). This regime-dependent artificial neural network (RD-ANN) system classifies cloud regimes with a k-means algorithm on the basis of a combination...

Solar power can provide substantial power supply to the grid; however, it is also a highly variable energy source due to changes in weather conditions, i.e. clouds, that can cause rapid changes in solar power output. Independent systems operators (ISOs) and regional transmission organizations (RTOs) monitor the demand load and direct power generati...

The goal of ensemble down selection is to retain the subset of ensemble members that span the uncertainty space of the forecast while eliminating those that are most redundant. There are hundreds of combinations of physics schemes that can be used in typical numerical weather prediction (NWP) models. Limited computational resources, however, force...

This technical note summarizes metrics that have been defined to assess the quality of forecasts of solar irradiance and solar power production forecasting. A set of six base metrics is proposed to meet the needs of users of solar power forecasts. Four of these metrics-Mean Absolute Error, Root Mean Square Error, Distribution of Forecast Errors, an...

The National Center for Atmospheric Research (NCAR) led a partnership to advance the state-of-the-science of solar power forecasting by designing, developing, building, deploying, testing, and assessing the SunCast Solar Power Forecasting System. The project included cutting-edge research, testing in several geographically and climatologically dive...

To blend growing amounts of renewable energy into utility grids requires accurate estimates of the power from those resources for both day ahead planning and real-time operations. This requires predicting the wind and solar resource on those timescales. Accurate prediction of these meteorological variables is a big data problem that requires a mult...

The deployment of solar-based electricity generation, especially in the form of photovoltaics (PVs), has increased markedly in recent years due to a wide range of factors including concerns over greenhouse gas emissions, supportive government policies, and lower equipment costs. Still, a number of challenges remain for reliable, efficient integrati...

Wind energy. It's clean. It's renewable. Its potential is enormous. But to draw energy from the wind and send it to people's homes reliably and efficiently, you have to know when the wind will blow and when it won't. When it stops or changes rapidly, you have to be ready to substitute power from another source. And because such sources aren't alway...

As the penetration of solar power increases, the variable generation from this renewable resource will necessitate solar irradiance forecasts for utility companies to balance the energy grid. In this study, the temporal irradiance variability is calculated by the temporal standard deviation of the Global Horizontal Irradiance (GHI) at eight sites i...

The National Center for Atmospheric Research and the National Renewable Energy Laboratory (NREL) collaborated to develop a method to assess the interannual variability of wind and solar power over the contiguous United States under current and projected future climate conditions, for use with NREL's Regional Energy Deployment System (ReEDS) model....

The U.S. Department of Energy (DOE) Atmosphere to Electrons (A2e) program seeks to better model the flow physics that affect the energy produced by wind farms. To that end, beginning in FY2015, A2e initiated a Mesoscale to Microscale Coupling (MMC) project to look at the best methods to model the interface between the mesoscale systems and the micr...