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Title: Sensitivity Study for Forecasting Variables of WRF-Solar Using a Tangent Linear Approach

Abstract

Integrating solar generation in recent years has highlighted the need for improved accuracy in predicting solar power. Confidence in solar power forecasting can be achieved by designing an ensemble that provides reliable probabilistic information for solar radiation with reduced uncertainty and error. Ideally, ensemble members are created through the optimized perturbation of the initial conditions in numerical weather prediction (NWP) models. Tangent linear models are capable of efficiently investigating the sensitivity of solar radiation to model input parameters because they do not require individual perturbation of each variable. This sensitivity study using tangent linear models provide us the capability to identify the right variables to perturb in an ensemble prediction system. In this study, we developed tangent linear models for WRF-Solar modules that directly impact the computation of solar radiation and the simulation of cloud formation and dissipation including the Fast All-sky Model for Solar Applications (FARMS), the Noah land surface model (LSM), the Thompson microphysics, the Mello-Yamada-Nakanishi-Niino (MYNN) boundary layer parameterization, and the Deng scheme for a shallow-convection parameterization. A sensitivity analysis was conducted under various scenarios based on satellite observations and model simulations from the National Solar Radiation Data Base (NSRDB) and WRF-Solar, respectively. Critical forecasting variables thatmore » are highly sensitive to the forecasting of global horizontal irradiance (GHI), direct normal irradiance (DNI), cloud mixing ratio, cloud tendency, cloud fraction, and sensible and latent heat fluxes were determined using the relevant WRF-Solar module. This study will be used as a guidance on future research leading to high-quality probabilistic solar forecasting. In this presentation, we discuss the validation of tangent linear approach for WRF-Solar modules and illustrate how the sensitivity results are valuable in the improvement of probabilistic solar prediction.« less

Authors:
 [1]; ORCiD logo [1];  [1];  [2];  [2]
  1. National Renewable Energy Laboratory (NREL), Golden, CO (United States)
  2. National Center for Atmospheric Research
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Solar Energy Technologies Office (EE-4S)
OSTI Identifier:
1598139
Report Number(s):
NREL/PR-5D00-75802
DOE Contract Number:  
AC36-08GO28308
Resource Type:
Conference
Resource Relation:
Conference: Presented at AMS 2020: American Meteorological Society 100th Annual Meeting, 12-16 January 2020, Boston, Massachusetts
Country of Publication:
United States
Language:
English
Subject:
24 POWER TRANSMISSION AND DISTRIBUTION; 29 ENERGY PLANNING, POLICY, AND ECONOMY; WRF-Solar; tangent linear; sensitivity analysis; probabilistic solar prediction; ensemble

Citation Formats

Yang, Jaemo, Sengupta, Manajit, Xie, Yu, Jimenez, Pedro A., and Kim, Ju-Hye. Sensitivity Study for Forecasting Variables of WRF-Solar Using a Tangent Linear Approach. United States: N. p., 2020. Web.
Yang, Jaemo, Sengupta, Manajit, Xie, Yu, Jimenez, Pedro A., & Kim, Ju-Hye. Sensitivity Study for Forecasting Variables of WRF-Solar Using a Tangent Linear Approach. United States.
Yang, Jaemo, Sengupta, Manajit, Xie, Yu, Jimenez, Pedro A., and Kim, Ju-Hye. Thu . "Sensitivity Study for Forecasting Variables of WRF-Solar Using a Tangent Linear Approach". United States. https://www.osti.gov/servlets/purl/1598139.
@article{osti_1598139,
title = {Sensitivity Study for Forecasting Variables of WRF-Solar Using a Tangent Linear Approach},
author = {Yang, Jaemo and Sengupta, Manajit and Xie, Yu and Jimenez, Pedro A. and Kim, Ju-Hye},
abstractNote = {Integrating solar generation in recent years has highlighted the need for improved accuracy in predicting solar power. Confidence in solar power forecasting can be achieved by designing an ensemble that provides reliable probabilistic information for solar radiation with reduced uncertainty and error. Ideally, ensemble members are created through the optimized perturbation of the initial conditions in numerical weather prediction (NWP) models. Tangent linear models are capable of efficiently investigating the sensitivity of solar radiation to model input parameters because they do not require individual perturbation of each variable. This sensitivity study using tangent linear models provide us the capability to identify the right variables to perturb in an ensemble prediction system. In this study, we developed tangent linear models for WRF-Solar modules that directly impact the computation of solar radiation and the simulation of cloud formation and dissipation including the Fast All-sky Model for Solar Applications (FARMS), the Noah land surface model (LSM), the Thompson microphysics, the Mello-Yamada-Nakanishi-Niino (MYNN) boundary layer parameterization, and the Deng scheme for a shallow-convection parameterization. A sensitivity analysis was conducted under various scenarios based on satellite observations and model simulations from the National Solar Radiation Data Base (NSRDB) and WRF-Solar, respectively. Critical forecasting variables that are highly sensitive to the forecasting of global horizontal irradiance (GHI), direct normal irradiance (DNI), cloud mixing ratio, cloud tendency, cloud fraction, and sensible and latent heat fluxes were determined using the relevant WRF-Solar module. This study will be used as a guidance on future research leading to high-quality probabilistic solar forecasting. In this presentation, we discuss the validation of tangent linear approach for WRF-Solar modules and illustrate how the sensitivity results are valuable in the improvement of probabilistic solar prediction.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2020},
month = {1}
}

Conference:
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