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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Regime-specific Cloud Vertical Overlap Characteristics from Radar and Lidar Observations at the ARM sites

Climate and numerical weather prediction models require assumptions to represent the vertical distribution of subgrid-scale clouds, which have radiative transfer implications. In this study, nearly 25 years of ground-based radar and lidar observations of vertical cloud profiles at the Atmospheric Radiation Measurement Program (ARM) Southern Great Plains (SGP) site are utilized to derive cloud vertical overlap characteristics from the Cloud Type (CLDTYPE) data product. Here, the cloud vertical overlap characteristics are further separated by cloud regime by considering seven cloud types (i.e., low cloud, congestus, deep convection, altocumulus, altostratus, cirrostratus, and cirrus) as well as periods of shallow cumulus. The decorrelation length scale (i.e., exponential transition from maximum to random overlap with layer separation) is found to vary by cloud regime, ranging between 0.04 km for cirrostratus paired with cirrus to 4.58 km for low cloud paired with cirrus at SGP. Cloud vertical overlap characteristics are also considered for other ARM sites including the Tropical Western Pacific (TWP), North Slope of Alaska (NSA), and Eastern North Atlantic (ENA) sites amongst other shorter term ARM deployments globally. The decorrelation length scale ranged globally from 1.03 km in the Arctic Ocean to 3.06 km in Manacapuru, Brazil. Globally, the decorrelation length scale by cloud regime exhibited similarities (e.g., for cirrus paired with cirrus) and differences (e.g., congestus paired with cirrus). The results could help inform development of cloud vertical overlap assumptions within operational numerical weather prediction models and potentially improve prediction of radiative fluxes for weather, climate, and renewable energy forecasting.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

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

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.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Experimental Report: Multi-Instrument Comparison of AAF Condensation Particle Counters

Condensation particle counters (CPCs), also known as condensation nucleus counters (CNCs) are sophisticated instruments designed to measure the concentration of aerosol particles in the atmosphere. These devices are pivotal in environmental monitoring, industrial applications, and scientific research, particularly in atmospheric studies. CPCs are vital for understanding the role of aerosols in climate systems. Aerosols influence cloud formation, radiative forcing, and atmospheric chemistry. By providing accurate measurements of particle concentrations and distributions, CPCs contribute to models that predict atmospheric impact and weather patterns (Mei et al. 2021). CPCs operate by enlarging submicron particles, including those as small as a few nanometers, to sizes detectable by optical methods. This report delves into the mechanisms, importance, and contributions of CPCs to atmospheric research at the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) Aerial Facility (AAF), highlighting their role in advancing air quality assessment, pollution control, and atmospheric studies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Rapid SACR Observations of Convection at Bankhead National Forest (RAPID) Field Campaign Report

Improving our representation of convective cell processes requires better quantification of convective clouds throughout their entire life cycle. This includes gaining a clearer understanding of the controls on key convective cloud properties, such as updraft intensity, particle size distributions, rainfall rates, and hydrometeor species. Our inability to improve convective cloud process modeling stems, in part, from a limited understanding of convective cell properties, particularly given how rapidly these storms evolve. This lack of detailed observations in the most intense and organized convective storms is especially significant, as large errors remain in representing these clouds, which are critical for severe weather prediction and Earth system model performance. Cloud and precipitation radars are essential tools for studying cloud microphysics and dynamics, particularly in deeper convective clouds. The recent U.S. Department of Energy Atmospheric Radiation Measurement (ARM) User Facility’s third Mobile Facility (AMF3) Bankhead National Forest (BNF) deployment provides a unique opportunity to investigate important land–atmosphere interactions, as well as the environmental controls on deep convective cloud processes, in a location favorable for frequent convection. We operate the X/Ka-band Scanning ARM Cloud Radar (X/Ka SACR) using a scan strategy optimized to capture these rapidly evolving clouds and their properties, thereby improving studies of deep convective cloud processes. This effort is strengthened by a complementary and coordinated partnership with ongoing university and multi-agency radar activities collocated in north Alabama—a unique opportunity to examine clouds and precipitation from a lifetime-centric perspective.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Probabilistic Forecast of All-Sky Solar Radiation Using Enhanced WRF-Solar: Preprint

This study presents enhancements of the Weather Research and Forecasting model with solar extensions (WRF-Solar) to provide probabilistic forecasts of solar radiation. Our approach builds ensemble WRF-Solar runs by introducing stochastic perturbations of variables that produce the largest uncertainties in predicting surface irradiance and clouds. The key variables are identified using tangent linear sensitivity analysis of six physics packages responsible for all-sky irradiance variability. An optimal strategy to stochastically perturb the selected variables is developed and applied to WRF-Solar to generate ensemble members for day-ahead solar prediction. The National Solar Radiation Database (NSRDB) is used to validate the ensemble forecast at arbitrary locations on the model grid. Preliminary results indicate that the proposed technique can potentially produce WRF-Solar ensembles providing reliable information of solar prediction uncertainty. This study describes the implemented methodology and initial results as well as future research to improve ensemble-based probabilistic forecasts with WRF-Solar.

all-sky↗
DOE OSTI2024

Sensitivity of thermodynamic profiles retrieved from ground-based microwave and infrared observations to additional input data from active remote sensing instruments and numerical weather prediction models

Accurate and continuous estimates of the thermodynamic structure of the lower atmosphere are highly beneficial to meteorological process understanding and its applications, such as weather forecasting. In this study, the Tropospheric Remotely Observed Profiling via Optimal Estimation (TROPoe) physical retrieval is used to retrieve temperature and humidity profiles from various combinations of input data collected by passive and active remote sensing instruments, in situ surface platforms, and numerical weather prediction models. Among the employed instruments are microwave radiometers (MWRs), infrared spectrometers (IRSs), radio acoustic sounding systems (RASSs), ceilometers, and surface sensors. TROPoe uses brightness temperatures and/or radiances from MWRs and IRSs, as well as other observational inputs (virtual temperature from the RASS, cloud-base height from the ceilometer, pressure, temperature, and humidity from the surface sensors) in a physical iterative retrieval approach. This starts from a climatologically reasonable profile of temperature and water vapor, with the radiative transfer model iteratively adjusting the assumed temperature and humidity profiles until the derived brightness temperatures and radiances match those observed by the MWR and/or IRS instruments within a specified uncertainty, as well as within the uncertainties of the other observations, if used as input. In this study, due to the uniqueness of the dataset that includes all the above-mentioned sensors, TROPoe is tested with different observational input combinations, some of which also include information higher than 4 km above ground level (a.g.l.) from the operational Rapid Refresh numerical weather prediction model. These temperature and humidity retrievals are assessed against independent collocated radiosonde profiles under non-cloudy conditions to assess the sensitivity of the TROPoe retrievals to different input combinations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Assessment of Cloud Mask Forecasts from the WRF-Solar Ensemble Prediction System: Preprint

Numerical weather prediction (NWP) models are important tools used by government agencies and the renewable energy enterprise to forecast solar radiation. Cloud prediction, a key process of NWP models, has the highest impact on the accuracy of solar forecasting. This study uses satellite observations from the National Solar Radiation Data Base (NSRDB) to evaluate the cloud mask forecast by the WRF-Solar Ensemble Prediction System (WRF-Solar EPS). Preliminary analysis of the data in 2018 demonstrates the need of further improvement in predicting thin and low-level clouds. The information obtained from this work will be used to enhance the WRF-Solar EPS in reproducing the cloud field over the contiguous U.S. and reducing solar forecasting errors.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2022

Evaluation of WRF-Solar Cloud Forecast Using the NSRDB: Preprint

Cloud forecast is a crucial component in predicting solar irradiance from numerical weather prediction (NWP) models. Assessing cloud properties from the NWP models requires significant work due to the need for high-quality data, spatial analysis covering model extent, and detailed analysis of model performance for different types of clouds. This study presents an evaluation of the WRF-Solar cloud forecast using the National Solar Radiation Database (NSRDB). We propose an evaluation framework applied to a single model prediction as well as ensemble-based forecasts. Various cloud detection metrics are calculated when comparing with the satellite-derived dataset. The mismatched clouds from the WRF-Solar model are quantified using nine cloud types classified by cloud top height and cloud optical depth. The results based on the WRF-Solar forecasts covering the entire U.S. for the full year of 2018 shows mismatched cloud frequency in the range of 8% - 46% for thick and high-level (deep convective) to thin and low-level (cumulus) clouds.

cloud mask forecast↗
DOE OSTI2024

Using MARCUS, MICRE, and COMBLE data to improve understanding and modeling of cloud, aerosol, and boundary layer processes at high-latitudes

Because it is believed general circulation (GCM) and numerical weather prediction (NWP) models underestimate shortwave radiation over the Southern Ocean (SO) due to inadequate representations of boundary layer (BL) and cloud processes, it is critical to improve our understanding of key aerosol, cloud, precipitation, and BL processes. Over the north Atlantic Ocean (NA), cold-air outbreaks are common, yet few studies of the aerosol and environmental controls of the associated convective BL clouds exist as needed to develop and evaluate GCM representations. Although processes cannot be observed, cloud and aerosol properties can be measured in-situ or remotely retrieved, which when combined with numerical simulations enable process level understanding required to improve model representations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

The CLoud–Aerosol–Radiation Interaction and Forcing: Year 2017 (CLARIFY-2017) measurement campaign

The representations of clouds, aerosols, and cloud–aerosol–radiation impacts remain some of the largest uncertainties in climate change, limiting our ability to accurately reconstruct past climate and predict future climate. The south-east Atlantic is a region where high atmospheric aerosol loadings and semi-permanent stratocumulus clouds are co-located, providing an optimum region for studying the full range of aerosol–radiation and aerosol–cloud interactions and their perturbations of the Earth's radiation budget. While satellite measurements have provided some useful insights into aerosol–radiation and aerosol–cloud interactions over the region, these observations do not have the spatial and temporal resolution, nor the required level of precision to allow for a process-level assessment. Detailed measurements from high spatial and temporal resolution airborne atmospheric measurements in the region are very sparse, limiting their use in assessing the performance of aerosol modelling in numerical weather prediction and climate models. CLARIFY-2017 was a major consortium programme consisting of five principal UK universities with project partners from the UK Met Office and European- and USA-based universities and research centres involved in the complementary ORACLES, LASIC, and AEROCLO-sA projects. The aims of CLARIFY-2017 were fourfold: (1) to improve the representation and reduce uncertainty in model estimates of the direct, semi-direct, and indirect radiative effect of absorbing biomass burning aerosols; (2) to improve our knowledge and representation of the processes determining stratocumulus cloud microphysical and radiative properties and their transition to cumulus regimes; (3) to challenge, validate, and improve satellite retrievals of cloud and aerosol properties and their radiative impacts; (4) to improve the impacts of aerosols in weather and climate numerical models. This paper describes the modelling and measurement strategies central to the CLARIFY-2017 deployment of the FAAM BAe146 instrumented aircraft campaign, summarizes the flight objectives and flight patterns, and highlights some key results from our initial analyses.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Self-Supervised Cloud Classification

Abstract Low-level marine clouds play a pivotal role in Earth’s weather and climate through their interactions with radiation, heat and moisture transport, and the hydrological cycle. These interactions depend on a range of dynamical and microphysical processes that result in a broad diversity of cloud types and spatial structures, and a comprehensive understanding of cloud morphology is critical for continued improvement of our atmospheric modeling and prediction capabilities moving forward. Deep learning has recently accelerated our ability to study clouds using satellite remote sensing, and machine learning classifiers have enabled detailed studies of cloud morphology. A major limitation of deep learning approaches to this problem, however, is the large number of hand-labeled samples that are required for training. This work applies a recently developed self-supervised learning scheme to train a deep convolutional neural network (CNN) to map marine cloud imagery to vector embeddings that capture information about mesoscale cloud morphology and can be used for satellite image classification. The model is evaluated against existing cloud classification datasets and several use cases are demonstrated, including training cloud classifiers with very few labeled samples, interrogation of the CNN’s learned internal feature representations, cross-instrument application, and resilience against sensor calibration drift and changing scene brightness. The self-supervised approach learns meaningful internal representations of cloud structures and achieves comparable classification accuracy to supervised deep learning methods without the expense of creating large hand-annotated training datasets. Significance Statement Marine clouds heavily influence Earth’s weather and climate, and improved understanding of marine clouds is required to improve our atmospheric modeling capabilities and physical understanding of the atmosphere. Recently, deep learning has emerged as a powerful research tool that can be used to identify and study specific marine cloud types in the vast number of images collected by Earth-observing satellites. While powerful, these approaches require hand-labeling of training data, which is prohibitively time intensive. This study evaluates a recently developed self-supervised deep learning method that does not require human-labeled training data for processing images of clouds. We show that the trained algorithm performs competitively with algorithms trained on hand-labeled data for image classification tasks. We also discuss potential downstream uses and demonstrate some exciting features of the approach including application to multiple satellite instruments, resilience against changing image brightness, and its learned internal representations of cloud types. The self-supervised technique removes one of the major hurdles for applying deep learning to very large atmospheric datasets.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Improving Prediction of Surface Solar Irradiance Variability by Integrating Observed Cloud Characteristics and Machine Learning

A 5-year, 1-minute resolution observational dataset of clouds and solar radiation was produced that includes two metrics of the variability in surface solar irradiance due to cloud type and fractional sky cover. Multiple regression models were trained to fit observations of surface solar irradiance variability from those two cloud property predictors. We found that ensemble tree-based methods, Random Forest and Gradient Boosting Machine, have the least overfitting issues and showed the best performance with an R2 of 0.42. While the observational data trained in this study was only from one site, the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site in Oklahoma, initial comparisons of the seasonality of the statistics suggest that these results are relatively weather regime independent; the generality of such a finding across sites will be tested in future work. The observational data and developed machine learning model are being used to create a numerical weather prediction model parameterization to enable day-ahead solar variability prediction in a computationally efficient way. This is a first step towards creating a new paradigm of predicting day-ahead variability with the potential to provide a new tool to improve grid operation, planning, and resilience.

Riihimaki, Laura↗
DOE OSTI2022

Evaluating WRF-Solar EPS cloud mask forecast using the NSRDB

Improving the accuracy of day-ahead solar forecasts using numerical weather prediction models requires improving the forecasting of cloud occurrence and properties. Validating cloud forecasts is challenging because this requires evaluating different types of clouds over a wide variety of regions. This study analyzes the cloud occurrence (or cloud mask) over the contiguous United States (CONUS), predicted by the Weather Research and Forecasting-Solar Ensemble Prediction System (WRF-Solar EPS), to identify the strengths and limitations of the model in reproducing cloud fields. To enable the in-depth analysis of cloud mask forecasts covering CONUS, we use satellite observations from the National Solar Radiation Database (NSRDB). Two evaluation methods are implemented to consider all clouds and partially removed clouds from the 2-km NSRDB in evaluating the 9-km WRF-Solar EPS cloud mask. Cloud detection metrics as well as the frequency of cloud occurrence are used to quantify the monthly performance of WRF-Solar EPS. Mismatched cloud frequency (MCF) is used to assess the model's capability to predict different types of clouds, which are classified using three levels of cloud optical depth (COD) and cloud top height (CTH). The day-ahead forecasts covering the full year of 2018 demonstrate that WRF-Solar EPS produces MCFs ranging from 27%-46%, 13%-34%, and 8%-19% for thin, mid-thickness, and thick clouds, respectively. For three CTH levels, the model shows MCFs ranging from 19%-46%, 16%-33%, and 8%-27% for low-level, middle-level, and high-level clouds, respectively. This comprehensive characterization of model performance helps identify model weakness and will eventually lead to improvements in cloud and solar radiation forecasting.

14 SOLAR ENERGY↗
DOE OSTI2023

Vertical Structure and Ice Production Processes of Shallow Convective Postfrontal Clouds over the Southern Ocean in MARCUS. Part II: Modeling Study

Abstract Part I of this series presented a detailed overview of postfrontal mixed-phase clouds observed during the Measurements of Aerosols, Radiation, and Clouds over the Southern Ocean (MARCUS) field campaign. In Part II, we focus on a multiday (23–26 February 2018) case with the aim of understanding ice production as well as model sensitivity to ice process parameterizations using the Weather Research and Forecasting (WRF) Model. The control simulation with the Predicted Particle Properties (P3) microphysics scheme underestimates the ice content and overestimates the supercooled liquid water content, contrary to the bias common in global climate models. The simulations targeted at ice production processes show negligible sensitivity to cloud droplet number concentrations. Further, neither increasing ice nuclei particle (INP) concentrations to an unrealistic level nor adjusting it to MARCUS field estimations alone guarantees more ice production in the model. However, the simulated clouds are found to be highly sensitive to the implementation of immersion freezing, the thresholding of condensation/deposition freezing initiation, and the rime splintering process. By increasing immersion freezing of cloud droplets, relaxing thresholds for condensation/deposition freezing, or removing rime splintering thresholds, the model significantly improves its performance in producing ice. The relaxation of the immersion freezing temperature threshold to the observed cloud-top temperature suggests an in-cloud seeder–feeder mechanism. The results of this work call for an increase in observations of INP, especially over the remote Southern Ocean and at relatively high temperatures, and measurements of ice particle size distributions to better constrain ice nucleating processes in models.

Meteorology & Atmospheric Sciences↗
DOE OSTI2022

Development of a triple-moment ice-phase cloud microphysics scheme and its application to the Single Column Atmosphere Model

Parameterization of cloud microphysics is critical for accurate simulation of weather and climate, in which the characteristics of cloud particle spectrum inevitably further affect climate simulation by changing the cloud evolution and cloud radiation effects. The popular currently used double-moment cloud microphysics schemes in numerical models can predict the intercept (N 0 ) and slope (λ) parameters of cloud particle spectrum but cannot predict the spectral shape parameter (μ), which hinders accurate description of cloud physical processes in climate models. Therefore, in the present study, we built upon the ideas of previously developed triple-moment cloud microphysics scheme, considered radar reflectivity factor as the third predictor in addition to number and mass concentration, and introduced its related prediction equation to the Single Column Atmosphere Model Version 5.3 (SCAM5.3). Moreover, the relevant microphysical process formulas in the model were revised, and a triple-moment ice-cloud microphysics scheme was constructed to predict the μ of ice particle spectrum. Based on this model, a 29-day case of the Atmospheric Radiation Measurement Program in the summer of 1997 (ARM97) was simulated, and the differences in cloud fraction and radiation simulations between the double- and triple-moment schemes were analysed. The μ of ice particle spectrum predicted by the triple-moment scheme mainly ranged from 0 to 4; the peak value was around 2, and at least 85% of the μ values were greater than the default 0. Therefore, the μ=0 setting in the double-moment scheme is unreasonable. Moreover, the developed triple-moment ice-cloud microphysics scheme yielded a narrower ice particle spectrum, which was closer to the observation results of previous studies, than the double-moment cloud microphysics scheme (e.g., height=233 hPa). Furthermore, compared with the double-moment scheme, the triple-moment scheme achieved closer simulations of the cloud fraction observations, particularly for ice clouds in the upper levels. Considering the close association between cloud fraction and radiation, the error with the triple-moment scheme was smaller than that with the double-moment scheme regardless of the shortwave (surface downward shortwave radiation, net downward shortwave radiation at the top of the atmosphere, and net shortwave radiation at the surface) or longwave (surface downward longwave radiation and net longwave radiation at the surface) flux density. Finally, the improvement mechanism of the triple-moment scheme on cloud fraction and radiation simulations was explored. The major reason is that the triple-moment scheme weakens the autoconversion of ice crystal to form snow and enhances the growth process of ice crystal deposition, thereby increasing the mass concentration of ice crystals and ice cloud fraction. Overall, the developed triple-moment ice-cloud microphysics scheme can help improve the simulation ability of models for cloud-climate feedback and other processes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Evaluation of the Rapid Refresh Numerical Weather Prediction Model over Arctic Alaska

Abstract Despite a need for accurate weather forecasts for societal and economic interests in the U.S. Arctic, thorough evaluations of operational numerical weather prediction in the region have been limited. In particular, the Rapid Refresh Model (RAP), which plays a key role in short-term forecasting and decision-making, has seen very limited assessment in northern Alaska, with most evaluation efforts focused on lower latitudes. In the present study, we verify forecasts from version 4 of the RAP against radiosonde, surface meteorological, and radiative flux observations from two Arctic sites on the northern Alaskan coastline, with a focus on boundary layer thermodynamic and dynamic biases, model representation of surface inversions, and cloud characteristics. We find persistent seasonal thermodynamic biases near the surface that vary with wind direction, and may be related to the RAP’s handling of sea ice and ocean interactions. These biases seem to have diminished in the latest version of the RAP (version 5), which includes refined handling of sea ice, among other improvements. In addition, we find that despite capturing boundary layer temperature profiles well overall, the RAP struggles to consistently represent strong, shallow surface inversions. Further, while the RAP seems to forecast the presence of clouds accurately in most cases, there are errors in the simulated characteristics of these clouds, which we hypothesize may be related to the RAP’s treatment of mixed-phase clouds.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

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

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.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

The Impact of Circumsolar Diffuse Radiation on the Numerical Computation and Forecasting of DNI

Weather Research and Forecasting model with solar extensions (WRF-Solar) uses the Fast All-sky Radiation Model for Solar applications (FARMS) to predict solar radiation from the forecasted atmospheric gases, aerosols, and clouds. The Beer-Bouguer-Lambert law utilized by FARMS as well as other radiative transfer models assumes Direct Normal Irradiance (DNI) is a narrow beam only covering the sun disk. This assumption is interpreted differently in solar observations by pyrheliometers where the circumsolar diffuse radiation is accounted. This study precisely computes circumsolar diffuse radiation in all-sky conditions using a radiative transfer model based on a parameterization of precomputed cloud transmittance and reflectance. The model is implemented in WRF-Solar to provide day-ahead DNI forecast in the north America. The computation and forecasting of DNI is compared with the Beer-Bouguer-Lambert law to understand the impact of circumsolar diffuse radiation.

aerosols↗