Evaluation of surface shortwave downward radiation forecasts by the numerical weather prediction model AROME

Author:

Magnaldo Marie-Adèle,Libois QuentinORCID,Riette SébastienORCID,Lac Christine

Abstract

Abstract. With the worldwide development of the solar energy sector, the need for reliable surface shortwave downward radiation (SWD) forecasts has significantly increased in recent years. SWD forecasts of a few hours to a few days based on numerical weather prediction (NWP) models are essential to facilitate the incorporation of solar energy into the electric grid and ensure network stability. However, SWD errors in NWP models can be substantial. In order to characterize the performances of AROME in detail, the operational NWP model of the French weather service Météo-France, a full year of hourly AROME forecasts is compared to corresponding in situ SWD measurements from 168 high-quality pyranometers covering France. In addition, to classify cloud scenes at high temporal frequency and over the whole territory, cloud products derived from the Satellite Application Facility for Nowcasting and Very Short Range Forecasting (SAF NWC) from geostationary satellites are also used. The 2020 mean bias is positive, with a value of 18 W m−2, meaning that AROME on average overestimates the SWD. The root-mean-square error is 98 W m−2. The situations that contribute the most to the bias correspond to cloudy skies in the model and in the observations, situations that are very frequent (66 %) and characterized by an annual bias of 24 W m−2. Part of this positive bias probably comes from an underestimation of cloud fraction in AROME, although this is not fully addressed in this study due to the lack of consistent observations at kilometer resolution. The other situations have less impact on SWD errors. Missed cloudy situations and erroneously predicted clouds, which generally correspond to clouds with a low impact on the SWD, also have low occurrence (4 % and 11 %). Likewise, well-predicted clear-sky conditions are characterized by a low bias (3 W m−2). When limited to overcast situations in the model, the bias in cloudy skies is small (1 W m−2) but results from large compensating errors. Indeed, further investigation shows that high clouds are systematically associated with a SWD positive bias, while low clouds are associated with a negative bias. This detailed analysis shows that the errors result from a combination of incorrect cloud optical properties and cloud fraction errors, highlighting the need for a more detailed evaluation of cloud properties. This study also provides valuable insights into the potential improvement of AROME physical parametrizations.

Funder

Horizon 2020 Framework Programme

Région Occitanie Pyrénées-Méditerranée

Publisher

Copernicus GmbH

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3