Evaluation of the Near-Surface Variables in the HRRR Weather Model Using Observations from the ARM SGP Site

He, Siwei; Turner, David D.; Benjamin, Stanley G.; Olson, Joseph B.; Smirnova, Tatiana G.; Meyers, Tilden

doi:10.1175/JAMC-D-23-0003.1

Title: Evaluation of the Near-Surface Variables in the HRRR Weather Model Using Observations from the ARM SGP Site

Full Record
Other Related Research

Abstract

Abstract The performance of version 4 of the NOAA High-Resolution Rapid Refresh (HRRR) numerical weather prediction model for near-surface variables, including wind, humidity, temperature, surface latent and sensible fluxes, and longwave and shortwave radiative fluxes, is examined over the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) region. The study evaluated the model’s bias and bias-corrected mean absolute error relative to the observations on different time scales. Forecasts of near-surface geophysical variables at five SGP sites (HRRR at 3-km scale) were found to agree well with observations, but some consistent observation–forecast differences also occurred. Sensible and latent heat fluxes are the most challenging variables to be reproduced. The diurnal cycle is the main temporal scale affecting observation–forecast differences of the near-surface variables, and almost all of the variables showed different biases throughout the diurnal cycle. Results show that the overestimation of downward shortwave and the underestimation of downward longwave radiative flux are the two major biases found in this study. The timing and magnitude of downward longwave flux, wind speed, and sensible and latent heat fluxes are also different with contributions from model representations, data assimilation limitations, and differences in scales between HRRR and SGP sites. The positive bias inmore »« less

Authors:

^[1]; Turner, David D. ^[2]; Benjamin, Stanley G. ^[1]; Olson, Joseph B. ^[2]; Smirnova, Tatiana G. ^[1]; Meyers, Tilden ^[3]

a Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, Colorado, b NOAA/Global Systems Laboratory, Boulder, Colorado
b NOAA/Global Systems Laboratory, Boulder, Colorado
c NOAA/Air Resources Laboratory, Oak Ridge, Tennessee

Publication Date:: Thu Jun 01 00:00:00 EDT 2023

Sponsoring Org.:: USDOE

OSTI Identifier:: 1986568

Resource Type:: Published Article

Journal Name:: Journal of Applied Meteorology and Climatology

Additional Journal Information:: Journal Name: Journal of Applied Meteorology and Climatology Journal Volume: 62 Journal Issue: 6; Journal ID: ISSN 1558-8424

Publisher:: American Meteorological Society

Country of Publication:: United States

Language:: English

Citation Formats


                    He, Siwei, Turner, David D., Benjamin, Stanley G., Olson, Joseph B., Smirnova, Tatiana G., and Meyers, Tilden. Evaluation of the Near-Surface Variables in the HRRR Weather Model Using Observations from the ARM SGP Site.  United States: N. p., 2023. 
Web.  doi:10.1175/JAMC-D-23-0003.1.

Copy to clipboard


                    He, Siwei, Turner, David D., Benjamin, Stanley G., Olson, Joseph B., Smirnova, Tatiana G., & Meyers, Tilden. Evaluation of the Near-Surface Variables in the HRRR Weather Model Using Observations from the ARM SGP Site.  United States.  https://doi.org/10.1175/JAMC-D-23-0003.1

Copy to clipboard


                    He, Siwei, Turner, David D., Benjamin, Stanley G., Olson, Joseph B., Smirnova, Tatiana G., and Meyers, Tilden. Thu .  
"Evaluation of the Near-Surface Variables in the HRRR Weather Model Using Observations from the ARM SGP Site".  United States.  https://doi.org/10.1175/JAMC-D-23-0003.1.

Copy to clipboard


                    
@article{osti_1986568,

  title        = {Evaluation of the Near-Surface Variables in the HRRR Weather Model Using Observations from the ARM SGP Site},

  author       = {He, Siwei and Turner, David D. and Benjamin, Stanley G. and Olson, Joseph B. and Smirnova, Tatiana G. and Meyers, Tilden},

  abstractNote = {Abstract The performance of version 4 of the NOAA High-Resolution Rapid Refresh (HRRR) numerical weather prediction model for near-surface variables, including wind, humidity, temperature, surface latent and sensible fluxes, and longwave and shortwave radiative fluxes, is examined over the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) region. The study evaluated the model’s bias and bias-corrected mean absolute error relative to the observations on different time scales. Forecasts of near-surface geophysical variables at five SGP sites (HRRR at 3-km scale) were found to agree well with observations, but some consistent observation–forecast differences also occurred. Sensible and latent heat fluxes are the most challenging variables to be reproduced. The diurnal cycle is the main temporal scale affecting observation–forecast differences of the near-surface variables, and almost all of the variables showed different biases throughout the diurnal cycle. Results show that the overestimation of downward shortwave and the underestimation of downward longwave radiative flux are the two major biases found in this study. The timing and magnitude of downward longwave flux, wind speed, and sensible and latent heat fluxes are also different with contributions from model representations, data assimilation limitations, and differences in scales between HRRR and SGP sites. The positive bias in downward shortwave and negative bias in longwave radiation suggests that the model is underestimating cloud fraction in the study domain. The study concludes by showing a brief comparison with version 3 of the HRRR and shows that version 4 has better performance in almost all near-surface variables. Significance Statement A correct representation of the near-surface variables is important for numerical weather prediction models. This study investigates the capability of the latest NOAA High-Resolution Rapid Refresh (HRRRv4) model in simulating the near-surface variables by comparing against the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) in situ observations. Among others, we find that the surface heat fluxes, such as sensible and latent heat fluxes, are the most difficult variables to be reproduced. This study also shows that the diurnal cycle has the dominant impact on the model’s performance, which means the majority of the outputted near-surface variables have the strong diurnal cycle in their bias errors.},

  doi          = {10.1175/JAMC-D-23-0003.1},

  journal      = {Journal of Applied Meteorology and Climatology},

  number       = 6,

  volume       = 62,

  place        = {United States},

  year         = {Thu Jun 01 00:00:00 EDT 2023},

  month        = {Thu Jun 01 00:00:00 EDT 2023}

}

Copy to clipboard

Journal Article:

Free Publicly Available Full Text

Publisher's Version of Record
https://doi.org/10.1175/JAMC-D-23-0003.1

Other availability

Search WorldCat to find libraries that may hold this journal

Save / Share:

Export Metadata

Save to My Library

Similar Records in DOE PAGES and OSTI.GOV collections:

A Public-Private-Academic Partnership to Advance Solar Power Forecasting

Technical Report Marquis, Melinda ; Benjamin, Stan ; James, Eric ; ...

Executive Summary NOAA is making major contributions to the solar forecasting project in three areas. First, it is improving its forecasts of solar irradiance, clouds, and aerosols in its numerical weather prediction models. Second, it is providing advanced satellite products for DOE's FOA awardees to use in their forecast systems. Third, it is using high-quality ground-based measurements from SURFRAD and ISIS stations to verify and validate forecast model output. This reports covers results from all three areas for the period May 1, 2014 - April 30, 2015. Modeling In its modeling effort, NOAA continues work to improve the skill ofmore »« less
https://doi.org/10.2172/1422824

Full Text Available
CAUSES: On the Role of Surface Energy Budget Errors to the Warm Surface Air Temperature Error Over the Central United States

Journal Article Ma, H. -Y. ; Klein, S. A. ; Xie, S. ; ... - Journal of Geophysical Research: Atmospheres

Many weather forecasting and climate models simulate a warm surface air temperature (T2m) bias over mid-latitude continents during the summertime, especially over the Great Plains. We present here one of a series of papers from a multi-model intercomparison project (CAUSES: Cloud Above the United States and Errors at the Surface), which aims to evaluate the role of cloud, radiation, and precipitation biases in contributing to T2m bias using a short-term hindcast approach with observations mainly from the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site during the period of April to August 2011. The present study examines the contributionmore »« less
https://doi.org/10.1002/2017JD027194
NOAA Air Resources Laboratory Atmospheric Turbulence and Diffusion Division Contribution to LAFE Field Campaign Report

Program Document Meyers, Tilden ; Lee, Temple R ; Baker, C Bruce ; ...

The Land-Atmosphere Feedback Experiment (LAFE) was conducted between 1 and 31 August, 2017 at the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) observatory. The main scientific goal of LAFE was to investigate feedbacks occurring between different land surface types and the overlying atmosphere to improve turbulence parameterizations in numerical weather prediction models. LAFE collaborators included scientists from the following institutions: Cleveland State University Cooperative Institute for Meteorological Satellite Studies (CIMSS) Cooperative Institute for Research in Environmental Sciences (CIRES) Leibniz University Institute of Meteorology and Climatology Marquette University NASA Goddard Space Flight Center National Center formore »« less
Full Text Available
Exploring Sources of Surface Bias in HRRR Using New York State Mesonet

Journal Article Min, Lanxi ; Fitzjarrald, David R. ; Du, Yuyi ; ... - Journal of Geophysical Research: Atmospheres

Abstract In recent years, there has been increasing demand for applications of short‐term forecasting of renewable energy potential and assessments of the likelihood of extreme weather events using the High‐Resolution Rapid Refresh (HRRR) model. Examining the biases in the newest version of HRRR is necessary to promote further model development. Using data from one of the most comprehensive and dense monitoring networks, New York State Mesonet (NYSM), we evaluate the HRRR version 3 meteorological fields for an entire year. In this work, the land‐atmosphere‐cloud coupling system is evaluated as an integrated whole. We investigate the physical processes influencing the soilmore »« less
https://doi.org/10.1029/2021jd034989

Full Text Available
Gulf of Mexico Turbulence Intensity Model Data in Observational Space

Dataset Tai, Sheng-Lun ; Berg, Larry

The dataset archives model-simulated turbulence intensity and meteorological profiles and timeseries at the Shell Exploration and Production Corporation's Tension Leg Platforms Ursa and Mars. The simulated data are interpolated in time and/or space according to observed quantities. The simulations were carried out for the Gulf of Mexico region using the revised Weather Research and Forecasting (WRF) model version 4.2 that incorporates the implementation of online turbulence intensity (TI) calculations (Tai et al. 2023). The simulated atmospheric profiles near the Shell Exploration and Production Corporation's Tension Leg Platforms Ursa and Mars are archived. Physics parameterizations chosen for the simulations include themore »« less
https://doi.org/10.21947/2279184

View Dataset

Similar Records