Engineering topics

Darby, Lisa S.

Publications and source records attributed to Darby, Lisa S..

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 OSTI2021

Doppler-Lidar Evaluation of HRRR-Model Skill at Simulating Summertime Wind Regimes in the Columbia River Basin during WFIP2

Complex-terrain locations often have repeatable near-surface wind patterns, such as synoptic gap flows and local thermally forced flows. An example is the Columbia River Valley in east-central Oregon-Washington, a significant wind-energy-generation region and the site of the Second Wind-Forecast Improvement Project (WFIP2). Data from three Doppler lidars deployed during WFIP2 define and characterize summertime wind regimes and their large-scale contexts, and provide insight into NWP model errors by examining differences in the ability of a model [NOAA’s High-Resolution Rapid-Refresh (HRRR-version1)] to forecast wind-speed profiles for different regimes. Seven regimes were identified based on daily time series of the lidar-measured rotor-layer winds, which then suggested two broad categories. First, in three regimes the primary dynamic forcing was the large-scale pressure gradient. Second, in two regimes the dominant forcing was the diurnal heating-cooling cycle (regional sea-breeze-type dynamics), including the marine intrusion previously described, which generates strong nocturnal winds over the region. The other two included a hybrid regime and a non-conforming regime. For the large-scale pressure-gradient regimes, HRRR had wind-speed biases of ~1 m s -1 and RMSEs of 2-3 m s -1 . Errors were much larger for the thermally forced regimes, owing to the premature demise of the strong nocturnal flow in HRRR. Thus, the more dominant the role of surface heating in generating the flow, the larger the errors. Major errors could result from surface heating of the atmosphere, boundary-layer responses to that heating, and associated terrain interactions. Finally, measurement/modeling research programs should be aimed at determining which modeled processes produce the largest errors, so those processes can be improved and errors reduced.

17 WIND ENERGY↗

DOE OSTI2020

Characterizing NWP Model Errors Using Doppler-Lidar Measurements of Recurrent Regional Diurnal Flows: Marine-Air Intrusions into the Columbia-River Basin

Ground-based Doppler-lidar instrumentation provides atmospheric wind data at dramatically improved accuracies and spatial/temporal resolutions. These capabilities have provided new insights into atmospheric flow phenomena, but they also should have a strong role in NWP model improvement. Insight into the nature of model errors can be gained by studying recurrent atmospheric flows, here a regional summertime diurnal sea breeze and subsequent marine-air intrusion into the arid interior of Oregon-Washington, where these winds are an important windenergy resource. These marine intrusions were sampled by three scanning Doppler lidars in the Columbia-River Basin as part of the Second Wind Forecast Improvement Project (WFIP2), using data from summer 2016. Lidar time-height cross sections of wind speed identified eight days when the diurnal-flow cycle, (peak wind speeds at midnight, afternoon minima) was obvious and strong. Eight-day composite time-height cross sections of lidar wind speeds are used to validate those generated by the operational NCEP-HRRR model. HRRR simulated the diurnal wind cycle, but produced errors in the timing of onset and significant errors due to a premature nighttime demise of the intrusion flow, producing low-bias errors of 6 m s-1 . Day-to-day and in the composite, whenever a marine intrusion occurred, HRRR made these same errors. The errors occurred under a range of gradient wind conditions indicating that they resulted from the misrepresentation of physical processes within a limited region around the measurement locations. Because of their generation within a limited geographical area, field-measurement programs can be designed to find and address the sources of these NWP errors.

Banta, Robert M.↗

NASA NTRS1999

The Multi-Center Airborne Coherent Atmospheric Wind Sensor: Recent Measurements and Future Applications

The coherent Doppler lidar, when operated from an airborne platform, offers a unique measurement capability for study of atmospheric dynamical and physical properties. This is especially true for scientific objectives requiring measurements in optically-clear air, where other remote sensing technologies such as Doppler radar are at a disadvantage in terms of spatial resolution and coverage. Recent experience suggests airborne coherent Doppler lidar can yield unique wind measurements of--and during operation within--extreme weather phenomena. This paper presents the first airborne coherent Doppler lidar measurements of hurricane wind fields. The lidar atmospheric remote sensing groups of National Aeronautics and Space Administration (NASA) Marshall Space Flight Center, National Oceanic and Atmospheric Administration (NOAA) Environmental Technology Laboratory, and Jet Propulsion Laboratory jointly developed an airborne lidar system, the Multi-center Airborne Coherent Atmospheric Wind Sensor (MACAWS). The centerpiece of MACAWS is the lidar transmitter from the highly successful NOAA Windvan. Other field-tested lidar components have also been used, when feasible, to reduce costs and development time. The methodology for remotely sensing atmospheric wind fields with scanning coherent Doppler lidar was demonstrated in 1981; enhancements were made and the system was reflown in 1984. MACAWS has potentially greater scientific utility, compared to the original airborne scanning lidar system, owing to a factor of approx. 60 greater energy-per-pulse from the NOAA transmitter. MACAWS development was completed and the system was first flown in 1995. Following enhancements to improve performance, the system was re-flown in 1996 and 1998. The scientific motivation for MACAWS is three-fold: obtain fundamental measurements of subgrid scale (i.e., approx. 2-200 km) processes and features which may be used to improve parameterizations in hydrological, climate, and general/regional circulation models; obtain similar datasets to improve understanding and predictive capabilities for similarly-scaled processes and features; and simulate and validate the performance of prospective satellite Doppler lidars for global tropospheric wind measurement.

Rothermel, Jeffry↗

NASA NTRS1999

The Multi-Center Airborne Coherent Atmospheric Wind Sensor: Recent Measurements and Future Applications

The atmospheric lidar remote sensing groups of NOAA Environmental Technology Laboratory, Jet Propulsion Laboratory and NASA Marshall Space Flight Center jointly developed an airborne scanning coherent Doppler Lidar. We describe the system, present recent measurement (including the first wind fields measured within a hurricane using Doppler lidar), and describe prospective instrument improvements and research applications.

Rothermel, Jeffry↗