Mesoscale Simulations of Australian Direct Normal Irradiance, Featuring an Extreme Dust Event

Mesoscale Simulations of Australian Direct Normal Irradiance, Featuring an Extreme Dust Event AbstractDirect normal irradiance (DNI) is the main input for concentrating solar power (CSP) technologies—an important component in future energy scenarios. DNI forecast accuracy is sensitive to radiative transfer schemes (RTSs) and microphysics in numerical weather prediction (NWP) models. Additionally, NWP models have large regional aerosol uncertainties. Dust aerosols can significantly attenuate DNI in extreme cases, with marked consequences for applications such as CSP. To date, studies have not compared the skill of different physical parameterization schemes for predicting hourly DNI under varying aerosol conditions over Australia. The authors address this gap by aiming to provide the first Weather and Forecasting (WRF) Model DNI benchmarks for Australia as baselines for assessing future aerosol-assimilated models. Annual and day-ahead simulations against ground measurements at selected sites focusing on an extreme dust event are run. Model biases are assessed for five shortwave RTSs at 30- and 10-km grid resolutions, along with the Thompson aerosol-aware scheme in three different microphysics configurations: no aerosols, fixed optical properties, and monthly climatologies. From the annual simulation, the best schemes were the Rapid Radiative Transfer Model for global climate models (RRTMG), followed by the new Goddard and Dudhia schemes, despite the relative simplicity of the latter. These top three RTSs all had 1.4–70.8 W m−2 lower mean absolute error than persistence. RRTMG with monthly aerosol climatologies was the best combination. The extreme dust event had large DNI mean bias overpredictions (up to 4.6 times), compared to background aerosol results. Dust storm–aware DNI forecasts could benefit from RRTMG with high-resolution aerosol inputs. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Applied Meteorology and Climatology American Meteorological Society

Mesoscale Simulations of Australian Direct Normal Irradiance, Featuring an Extreme Dust Event

Loading next page...
 
/lp/ams/mesoscale-simulations-of-australian-direct-normal-irradiance-featuring-7rNawivyEx
Publisher
American Meteorological Society
Copyright
Copyright © American Meteorological Society
ISSN
1558-8432
D.O.I.
10.1175/JAMC-D-17-0091.1
Publisher site
See Article on Publisher Site

Abstract

AbstractDirect normal irradiance (DNI) is the main input for concentrating solar power (CSP) technologies—an important component in future energy scenarios. DNI forecast accuracy is sensitive to radiative transfer schemes (RTSs) and microphysics in numerical weather prediction (NWP) models. Additionally, NWP models have large regional aerosol uncertainties. Dust aerosols can significantly attenuate DNI in extreme cases, with marked consequences for applications such as CSP. To date, studies have not compared the skill of different physical parameterization schemes for predicting hourly DNI under varying aerosol conditions over Australia. The authors address this gap by aiming to provide the first Weather and Forecasting (WRF) Model DNI benchmarks for Australia as baselines for assessing future aerosol-assimilated models. Annual and day-ahead simulations against ground measurements at selected sites focusing on an extreme dust event are run. Model biases are assessed for five shortwave RTSs at 30- and 10-km grid resolutions, along with the Thompson aerosol-aware scheme in three different microphysics configurations: no aerosols, fixed optical properties, and monthly climatologies. From the annual simulation, the best schemes were the Rapid Radiative Transfer Model for global climate models (RRTMG), followed by the new Goddard and Dudhia schemes, despite the relative simplicity of the latter. These top three RTSs all had 1.4–70.8 W m−2 lower mean absolute error than persistence. RRTMG with monthly aerosol climatologies was the best combination. The extreme dust event had large DNI mean bias overpredictions (up to 4.6 times), compared to background aerosol results. Dust storm–aware DNI forecasts could benefit from RRTMG with high-resolution aerosol inputs.

Journal

Journal of Applied Meteorology and ClimatologyAmerican Meteorological Society

Published: Mar 6, 2018

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off