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

Author:

Banta Robert M.12,Pichugina Yelena L.12,Brewer W. Alan2,Balmes Kelly A.13,Adler Bianca14,Sedlar Joseph13,Darby Lisa S.5,Turner David D.6,Kenyon Jaymes S.16,Strobach Edward J.12,Carroll Brian J.12,Sharp Justin7,Stoelinga Mark T.8,Cline Joel9,Fernando Harindra J. S.10

Affiliation:

1. a CIRES, University of Colorado Boulder, Boulder, Colorado

2. b NOAA/Chemical Sciences Laboratory, Boulder, Colorado

3. c NOAA/Global Monitoring Laboratory, Boulder, Colorado

4. d NOAA/Physical Systems Laboratory, Boulder, Colorado

5. f LDWX LLC, Boulder, Colorado

6. e NOAA/Global Sciences Laboratory, Boulder, Colorado

7. h Sharply Focused LLC, Portland, Oregon

8. j Vaisala, Seattle, Washington

9. i NOAA Tropical Program Coordinator, Silver Spring, Maryland

10. g University of Notre Dame, Notre Dame, Indiana

Abstract

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.

Funder

Earth System Research Laboratories

NOAA Research

Office of Environmental Management

Office of Science

none

Publisher

American Meteorological Society

Subject

Atmospheric Science

Reference65 articles.

1. Evaluation of a cloudy cold-air pool in the Columbia River basin in different versions of the High-Resolution Rapid Refresh (HRRR) model;Adler, B.,2023

2. A2E, 2017a: Lidar—ESRL WindCube 200s, Wasco Airport—Reviewed Data (wfip2/lidar.z04.b0). A2e Data Archive and Portal for U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, accessed 19 December 2017, https://doi.org/10.21947/1418023.

3. A2E, 2017b: Lidar—ESRL WindCube 200s, Arlington Airport—Reviewed Data (wfip2/lidarz05.b0). A2e Data Archive and Portal for U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, accessed 19 December 2017, https://doi.org/10.21947/1418024.

4. A2E, 2017c: Lidar—ND Halo Scanning Doppler, Boardman—Reviewed Data (wfip2/lidar.z07.b0). A2e Data Archive and Portal for U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, accessed 29 March 2018, https://doi.org/10.21947/1402036.

5. A2E, 2017d: Shortwave, Longwave Radiometer—ESRL SURFRAD, Wasco—Derived Data (wfip2/swlwr.z01.c0). A2e Data Archive and Portal for U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, accessed 15 November 2022, https://doi.org/10.21947/1402039.

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