Engineering topics

Cline, Joel

Publications and source records attributed to Cline, Joel.

5 records

DOE OSTI2023

Measurements and Model Improvement: Insight into NWP Model Error Using Doppler Lidar and Other WFIP2 Measurement Systems

Abstract Doppler-lidar wind-profile measurements at three sites were used to evaluate NWP model errors from two versions of NOAA’s 3-km-grid HRRR model, to see whether updates in the latest version 4 reduced errors when compared against the original version 1. Nested (750-m grid) versions of each were also tested to see how grid spacing affected forecast skill. The measurements were part of the field phase of the Second Wind Forecasting Improvement Project (WFIP2), an 18-month deployment into central Oregon–Washington, a major wind-energy-producing region. This study focuses on errors in simulating marine intrusions, a summertime, 600–800-m-deep, regional sea-breeze flow found to generate large errors. HRRR errors proved to be complex and site dependent. The most prominent error resulted from a premature drop in modeled marine-intrusion wind speeds after local midnight, when lidar-measured winds of greater than 8 m s −1 persisted through the next morning. These large negative errors were offset at low levels by positive errors due to excessive mixing, complicating the interpretation of model “improvement,” such that the updates to the full-scale versions produced mixed results, sometimes enhancing but sometimes degrading model skill. Nesting consistently improved model performance, with version 1’s nest producing the smallest errors overall. HRRR’s ability to represent the stages of sea-breeze forcing was evaluated using radiation budget, surface-energy balance, and near-surface temperature measurements available during WFIP2. The significant site-to-site differences in model error and the complex nature of these errors mean that field-measurement campaigns having dense arrays of profiling sensors are necessary to properly diagnose and characterize model errors, as part of a systematic approach to NWP model improvement. Significance Statement Dramatic increases in NWP model skill will be required over the coming decades. This paper describes the role of major deployments of accurate profiling sensors in achieving that goal and presents an example from the Second Wind Forecast Improvement Program (WFIP2). Wind-profile data from scanning Doppler lidars were used to evaluate two versions of HRRR, the original and an updated version, and nested versions of each. This study focuses on the ability of updated HRRR versions to improve upon predicting a regional sea-breeze flow, which was found to generate large errors by the original HRRR. Updates to the full-scale HRRR versions produced mixed results, but the finer-mesh versions consistently reduced model errors.

Meteorology & Atmospheric Sciences↗

DOE OSTI2019

On Bridging A Modeling Scale Gap: Mesoscale to Microscale Coupling for Wind Energy

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 rolls within the terra incognita between the scales? 2) Which methods effectively couple the mesoscale to the microscale and capture the correct nonstationary features at the microscale? 3) What are the best methods to initialize turbulence at the microscale? 4) What is the best way to handle the surface-layer parameterizations consistently at the mesoscale and the microscale? 5) How do we assess the impact of improvements in each of these aspects and quantify the uncertainty in the simulations? The U.S. Department of Energy Mesoscale-to-Microscale-Coupling project seeks to develop, verify, and validate physical models and modeling techniques that bridge the most important atmospheric scales determining wind plant performance and reliability, which impacts many meteorological applications. The approach begins with choosing case days that are interesting for wind energy for which there are observational data for validation. The team has focused on modeling nonstationary conditions for both flat and complex terrain. This paper describes the approaches taken to answer the science challenges, culminating in recommendations for best approaches for coupled modeling.

17 WIND ENERGY↗

DOE OSTI2019

Improving Wind Energy Forecasting through Numerical Weather Prediction Model Development

The primary goal of the Second Wind Forecast Improvement Project (WFIP2) is to advance the state-of-the-art of wind energy forecasting in complex terrain. To achieve this goal, a comprehensive 18-month field measurement campaign was conducted in the region of the Columbia River basin. The observations were used to diagnose and quantify systematic forecast errors in the operational High-Resolution Rapid Refresh (HRRR) model during weather events of particular concern to wind energy forecasting. Examples of such events are cold pools, gap flows, thermal troughs/marine pushes, mountain waves, and topographic wakes. WFIP2 model development has focused on the boundary layer and surface-layer schemes, cloud–radiation interaction, the representation of drag associated with subgrid-scale topography, and the representation of wind farms in the HRRR. Additionally, refinements to numerical methods have helped to improve some of the common forecast error modes, especially the high wind speed biases associated with early erosion of mountain–valley cold pools. This study describes the model development and testing undertaken during WFIP2 and demonstrates forecast improvements. Specifically, WFIP2 found that mean absolute errors in rotor-layer wind speed forecasts could be reduced by 5%–20% in winter by improving the turbulent mixing lengths, horizontal diffusion, and gravity wave drag. The model improvements made in WFIP2 are also shown to be applicable to regions outside of complex terrain. Ongoing and future challenges in model development will also be discussed.

17 WIND ENERGY↗

DOE OSTI2019

The Second Wind Forecast Improvement Project (WFIP 2): General Overview

In 2015 the U.S. Department of Energy initiated a four-year study, the second Wind Forecast Improvement Project (WFIP 2), to improve the representation of boundary-layer physics and related processes in mesoscale models for better wind and wind power forecasts. This goal challenges numerical weather prediction models in complex terrain due to inherent assumptions underlying their boundary-layer parameterizations. The WFIP 2 effort involved the wind industry, universities, NOAA, and DOE's national laboratories in an integrated observational and modeling study. Observations spanned 18-months to assure a full annual cycle of continuously recorded observations from remote-sensing and in situ measurement systems. The study area comprised the Columbia Basin of eastern Washington and Oregon, containing more than 4 GW of installed wind capacity; nests of observational systems captured important atmospheric scales from mesoscale to NWP sub-grid scale. Model improvements targeted NOAA's High-Resolution Rapid Refresh (HRRR) model to facilitate transfer of improvements to NWS operational forecast models. This paper describes the general WFIP 2 scope and objectives, the particular scientific challenges of improving wind forecasts in complex terrain, early successes of the project, and an integrated approach to archiving observations and model output. It provides an introduction for a set of more detailed BAMS papers addressing WFIP 2 observational science, modeling challenges and solutions, practical transfer of boundary layer advances to industry use, and advances in coupling improved mesoscale models to microscale models that can represent interactions between wind plants and the atmosphere.

WFIP2, boundary layer, complex terrain, forecastin↗

DOE OSTI2019

The Second Wind Forecast Improvement Project (WFIP2): Observational Field Campaign

The science of wind energy forecasting has taken a leap forward with the unique meteorological observations gathered in complex terrain during the Second Wind Forecast Improvement Project (WFIP2). The Second Wind Forecast Improvement Project (WFIP2) is a U.S. Department of Energy (DOE) and National Oceanic and Atmospheric Administration (NOAA) funded program, with private-sector and university partners, which aims to improve the accuracy of numerical weather prediction (NWP) model forecasts of wind speed in complex terrain for wind energy applications. A core component of WFIP2 was an 18-month field campaign which took place in the U.S. Pacific Northwest between October 2015 and March 2017. A large suite of instrumentation was deployed in a series of telescoping arrays, ranging from 500 km across to a densely instrumented 2 x 2 km area similar in size to a high-resolution NWP model grid cell. Observations from these instruments are being used to improve our understanding of the meteorological phenomena that affect wind energy production in complex terrain, and to evaluate and improve model physical parameterization schemes. We present several brief case studies using these observations to describe phenomena that are routinely difficult to forecast, including wintertime cold pools, diurnally driven gap flows, and mountain waves/wakes. Observing system and data product improvements developed during WFIP2 are also described.

17 WIND ENERGY↗