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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Long‐Term Large‐Scale Atmospheric Forcing Data From Three‐Dimensional Constrained Variational Analysis for the ARM SGP Site

Here, this study presents a long‐term three‐dimensional large‐scale forcing data set (VARANAL3D) derived from the three‐dimensional constrained variational analysis (3DCVA) method at the Atmospheric Radiation Measurement (ARM) program Southern Great Plains (SGP) site from 2004 to 2018. Building on the same input data sets as the conventional continuous forcing data set (VARANAL), VARANAL3D maintains overall consistency in domain‐averaged fields while introducing spatial variability, offering critical insights into the influence of mesoscale synoptic systems on cloud‐related processes. Evaluations are conducted across four cloud and precipitation regimes: Clear‐sky, Shallow‐clouds, Afternoon‐precipitation, and Nocturnal‐precipitation, presenting high consistency of the domain‐mean forcing data sets while emphasizing the role of subdomain forcing variability particularly in precipitating regimes. Single column model (SCM) simulations demonstrate that subdomain VARANAL3D forcing improves cloud and precipitation representation, with the ensemble outperforming domain‐mean forcing in three cloudy and precipitating regimes. Overall, these results highlight VARANAL3D's value for investigating the impacts of spatial variability of large‐scale forcing on atmospheric processes. The VARANAL3D data set provides new opportunities for evaluating model physics, advancing the development of scale‐aware parameterizations and deepening our understanding of cloud and precipitation dynamics.

Environmental sciences↗
DOE OSTI2021

Developing High-Resolution Constrained Variational Analysis of Vertical Velocity and Advective Tendencies within the Range of ARM Scanning Radars at the SGP

Research progress has been made in two areas. One is about the incorporation of the ARM variationally constrained objective analysis method into the WRF GSI data assimilation system. The other is the development of high resolution ARM data and its applications. Specially, we developed a new data assimilation algorithm by adding dynamical constraints to the WRF GSI data assimilation system using hybrid ensemble variational system to derive 3-D fields of atmospheric dynamics and thermodynamics over the ARM SGP sites. We also developed 4x4 km high-resolution constrained variational analysis data over the SGP during the PECAN and made them available to the community. Details are in the attached report.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Description of the Three-Dimensional Large-Scale Forcing Data from the 3D Constrained Variational Analysis (VARANAL3D)

This technical report introduces a Three-Dimensional Constrained Variational Analysis (3DCVA) (Tang and Zhang 2015) and its product of three-dimensional large-scale forcing data to drive single-column models (SCM), cloud-resolving models (CRM), and large-eddy simulation (LES) models, and to evaluate model results. The 3DCVA algorithm is an extension of the original 1D constrained variational analysis (1DCVA) (Zhang and Lin 1997, Zhang et al. 2001). The three-dimensional structure of the forcing data allows studies of spatial variation of the large-scale forcing fields and tests of physical parameterizations across scales. In the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility, the 3D forcing data are assigned the datastream name varanal3d. In this technical report, 3DCVA will be used to refer to the algorithm, while VARANAL3D will be used to refer to the data product.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Description of the Three-Dimensional Large-Scale Forcing Data from the 3D Constrained Variational Analysis (VARANAL3D)

This technical report introduces a Three-Dimensional Constrained Variational Analysis (3DCVA) (Tang and Zhang 2015) and its product of three-dimensional large-scale forcing data to drive single-column models (SCM), cloud-resolving models (CRM), and large-eddy simulation (LES) models, and to evaluate model results. The 3DCVA algorithm is an extension of the original 1D constrained variational analysis (1DCVA) (Zhang and Lin 1997, Zhang et al. 2001). The three-dimensional structure of the forcing data allows studies of spatial variation of the large-scale forcing fields and tests of physical parameterizations across scales. In the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility, the 3D forcing data are assigned the datastream name varanal3d. In this technical report, 3DCVA will be used to refer to the algorithm, while VARANAL3D will be used to refer to the data product.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

3D Continuous Forcing Dataset from 3D Constrained Variational Analysis at SGP

The continuous 3D large-scale forcing (VARANAL3D) data set derived from 3D constrained variational analysis (3DCVA) extends the conventional constrained variational analysis method by incorporating multiple sub-columns within the analysis domain. This advancement introduces spatial variability into the large-scale forcing fields, thereby enriching the data set’s applicability. The VARANAL3D data set spans from 2004 to 2018 and covers a region of 5˚×4.5˚ domain around the ARM SGP site. The analysis domain is divided into 10×9 sub-columns with 0.5˚ resolution. The 3D large-scale forcing data provides necessary variables to drive and evaluate single-column models (SCM), cloud-resolving models (CRM) ,and large-eddy simulations (LES), as well as information for testing model sensitivity to spatial variability of the large-scale forcing data, facilitating more rigorous testing and refinement of physical processes in SCM/CRM/LES.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

three-dimensional constrained variational analysis

This is a dataset from three-dimensional constrained variational analysis (3DCVA). It can be used to generate large-scale forcing data for SCM/CRM/LES, or evaluate model results.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Description of the ARM Large-Scale Forcing Data from the Constrained Variational Analysis (VARANAL) Version 2

This technical report represents an update of the previous technical report written by Zhang et al., which described the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility constrained variational analysis (VARANAL) that is used to develop the large-scale forcing data for driving single-column models (SCMs), cloud-resolving models (CRMs), and large-eddy simulation models (LES). The VARANAL algorithm was originally developed by Zhang and Lin and Zhang et al. at Stony Brook University and was migrated to the Lawrence Livermore National Laboratory (LLNL) as the ARM operational objective analysis system in May 1999. Since then, the algorithm has been evolved with time along with the availability of new observations and techniques to meet various modeling needs. Major updates include: 1. The method used to develop multi-year continuous forcing data, 2. The incorporation of eddy correlation flux measurement system (ECOR) turbulent fluxes into the analysis, and 3. Improvements to the workflow (e.g., implementing part of the code into the ARM Data Integrator [ADI]) to increase efficiency. The ARM large-scale forcing data have been widely used for SCM/CRM/LES to understand and improve physical processes in models. Zhang et al. has provided a comprehensive review of the SCM concept, early efforts to derive forcing data for SCM studies, efforts of the ARM constrained variational analysis, and previous SCM studies using ARM cases. This technical report focuses on the constrained variational analysis algorithm and the introduction of the ARM VARANAL products. We also extended the VARANAL algorithm into a three-dimensional constrained variational analysis (3DCVA) and designed an ensemble framework to address the forcing uncertainty. The 3D large-scale forcing data are released as another datastream named “varanal3d”. Please refer to the ARM varanal3d technical report for more information.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Description of the ARM Large-Scale Forcing Data from the Constrained Variational Analysis (VARANAL) Version 2

This technical report represents an update of the previous technical report written by Zhang et al. (2001a) (available at http://www.arm.gov/publications/tech_reports/arm-tr-005.pdf), which described the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility constrained variational analysis (VARANAL) that is used to develop the large-scale forcing data for driving single-column models (SCMs), cloud-resolving models (CRMs), and large-eddy simulation models (LES). The VARANAL algorithm was originally developed by Zhang and Lin (1997) and Zhang et al. (2001b) at Stony Brook University and was migrated to the Lawrence Livermore National Laboratory (LLNL) as the ARM operational objective analysis system in May 1999. Since then, the algorithm has been evolved with time along with the availability of new observations and techniques to meet various modeling needs. Major updates include: (1) The method used to develop multi-year continuous forcing data (Xie et al. 2004), (2) The incorporation of eddy correlation flux measurement system (ECOR) turbulent fluxes into the analysis (Tang et al. 2019), and (3) Improvements to the workflow (e.g., implementing part of the code into the ARM Data Integrator [ADI]) to increase efficiency. The ARM large-scale forcing data have been widely used for SCM/CRM/LES to understand and improve physical processes in models. Zhang et al. (2016) has provided a comprehensive review of the SCM concept, early efforts to derive forcing data for SCM studies, efforts of the ARM constrained variational analysis, and previous SCM studies using ARM cases. This technical report focuses on the constrained variational analysis algorithm and the introduction of the ARM VARANAL products.

58 GEOSCIENCES↗
DOE OSTI2020

Improvement of Atmospheric Objective Analysis Over Sloping Terrain and Its Impact on Shallow-Cumulus Clouds in Large-Eddy Simulations

Surface topography strongly impacts the regional atmospheric circulation dynamically and thermodynamically. Over the Great Plains in the United States, the gently tilted slope is an important factor that impacts clouds, convection and regional circulations. This study enhances an atmospheric constrained variational analysis by using a terrain-following sigma vertical coordinate and applies to the data collected at the Southern Great Plain (SGP) site of the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program. Sensitivity studies are performed to examine the impact of the terrain effects on the derived large-scale atmospheric forcing fields and the simulated shallow-cumulus clouds in large-eddy simulation (LES) driven by the forcing. We found that the terrain impacts on the large-scale forcing fields are mainly at lower levels and are strongly controlled by up/downslope winds. The response of the derived forcing to the slope of the terrain is monotonic, but the response of the simulated shallow-cumulus clouds is more complex and depends on several factors. Overall, the terrain impact is small over SGP due to the small slope angle. However, the flat-surface assumption may cause larger biases in the large-scale forcing fields at locations with steeper terrain. The new sigma-coordinate algorithm, with its consideration of surface slope, should be more suitable to derive large-scale objective analysis over regions with steep terrain for application to force single-column models, cloud resolving models and LES models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Improving Convection Trigger Functions in Deep Convective Parameterization Schemes Using Machine Learning

Abstract Deficiencies in convection trigger functions, used in deep convection parameterizations in General Circulation Models (GCMs), have critical impacts on climate simulations. A novel convection trigger function is developed using the machine learning (ML) classification model XGBoost. The large‐scale environmental information associated with convective events is obtained from the long‐term constrained variational analysis forcing data from the Atmospheric Radiation Measurement (ARM) program at its Southern Great Plains (SGP) and Manaus (MAO) sites representing, respectively, continental mid‐latitude and tropical convection. The ML trigger is separately trained and evaluated per site, and jointly trained and evaluated at both sites as a unified trigger. The performance of the ML trigger is compared with four convective trigger functions commonly used in GCMs: dilute convective available potential energy (CAPE), undilute CAPE, dilute dynamic CAPE (dCAPE), and undilute dCAPE. The ML trigger substantially outperforms the four CAPE‐based triggers in terms of the F 1 score metric, widely used to estimate the performance of ML methods. The site‐specific ML trigger functions can achieve, respectively, 91% and 93% F 1 scores at SGP and MAO. The unified trigger also has a 91% F 1 score, with virtually no degradation from the site‐specific training, suggesting the potential of a global ML trigger function. The ML trigger alleviates a GCM deficiency regarding the overprediction of convection occurrence, offering a promising improvement to the simulation of the diurnal cycle of precipitation. Furthermore, to overcome the black box issue of the ML methods, insights derived from the ML model are discussed, which may be leveraged to improve traditional CAPE‐based triggers.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Comparison of Conventional and Constrained Variational Methods for Computing Large-Scale Budgets and Forcing Fields

Analyses of atmospheric heat and moisture budgets serve as an effective tool to study convective characteristics over a region and to provide large-scale forcing fields for various modeling applications. This paper examines two popular methods for computing large-scale atmospheric budgets: the conventional budget method (CBM) using objectively gridded analyses based primarily on radiosonde data and the constrained variational analysis (CVA) approach which supplements vertical profiles of atmospheric fields with measurements at the top of the atmosphere and at the surface to conserve mass, water, energy, and momentum. Successful budget computations are dependent on accurate sampling and analyses of the thermodynamic state of the atmosphere and the divergence field associated with convection and the large-scale circulation that influences it. Utilizing analyses generated from data taken during Dynamics of the Madden-Julian Oscillation (DYNAMO) field campaign conducted over the central Indian Ocean from October to December 2011, we evaluate the merits of these budget approaches and examine their limitations. While many of the shortcomings of the CBM, in particular effects of sampling errors in sounding data, are effectively minimized with CVA, accurate large-scale diagnostics in CVA are dependent on reliable background fields and rainfall constraints. Finally, for the DYNAMO analyses examined, the operational model fields used as the CVA background state provided wind fields that accurately resolved the vertical structure of convection in the vicinity of Gan Island. However, biases in the model thermodynamic fields were somewhat amplified in CVA resulting in a convective environment much weaker than observed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Sensitivity of the Shallow‐To‐Deep Convective Transition to Moisture and Wind Shear in the Amazon

Deep convection is the primary influence on weather and climate in tropical regions. However, understanding and simulating the shallow‐to‐deep (STD) convective transition has long been challenging. Here, we conduct high‐resolution numerical simulations to assess the environmental controls on the evolution of isolated convection in the Amazon during the wet season. The large‐scale forcing derived through a constrained variational analysis approach for the GoAmazon2014/5 Experiment is used in the simulations. Through sensitivity experiments, we examine the relative importance of moisture and wind shear in controlling the shallow‐to‐deep convective transition for isolated convective events. Convection exhibits the greatest sensitivity to humidity within the lowest 1.5 km, where a 4 mm reduction in column water vapor nearly suppresses ice water formation on deep convective days. In contrast, a reduction in column water vapor in the free troposphere by a factor of two or more is necessary to produce a comparable impact on convection. Increasing low‐level wind speed from 6 to 9 m s -1 enhances afternoon deep convection, raising the cloud ice mixing ratio by approximately 25%. Conversely, upper‐level wind shear reveals the weakest correlation with daytime convection in our simulations. Our results help characterize the role of moisture and wind shear on the STD transition and our understanding of the underlying mechanisms.

environmental controls↗
DOE OSTI2024

Development of the ARM Lagrangian Large-Scale Forcing Data (ARMLAGTRAJ) Value-Added Product Based on the lagtraj Framework

The Atmospheric Radiation Measurement (ARM) large-scale forcing data developed based on the constrained variational analysis (VARANAL) value-added product (VAP) (Zhang and Lin 1997, Zhang et al. 2001, Xie et al. 2004, Tang et al. 2019) has been widely used for single-column models (SCMs), cloud-resolving models (CRMs), and large-eddy simulation models (LESs) to understand and improve physical processes in models. Recently, the U.S. Department of Energy (DOE) ARM user facility conducted several major field campaigns using ship-based moving observational platforms. For example, the Marine ARM GPCI Investigation of Clouds (MAGIC) field campaign focused on the role of subtropical marine-boundary layer (MBL) clouds, and the Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC) field campaign aimed to improve understanding of the coupled climate systems in the Arctic. Observations from moving platforms are critical to provide a comprehensive characterization of coupled-system processes associated with all stages of the cloud and/or sea-ice life cycle. Traditional ARM large-scale forcing data have been developed at fixed locations. They need to be extended to include these moving platforms to address data needs for ship-based field campaigns or to support LES modeling in a Lagrangian framework. With these considerations in mind, we develop ARM-type Lagrangian large-scale forcing data sets based on the lagtraj framework (Boeing et al. 2020) with notable enhancements in generating forcings that are more suitable for ARM field campaigns. The lagtraj is a novel tool that generates forcings for LES and SCM simulation in both Lagrangian and Eulerian perspective. This technical report focuses on the major changes we performed on the lagtraj algorithm and provides an overview of the ARM Lagrangian Large-Scale Forcing Data (ARMLAGTRAJ) value-added products.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Environmental controls on isolated convection during the Amazonian wet season

The Amazon rainforest is a vital component of the global climate system, influencing the hydrological cycle and tropical circulation. However, understanding and modeling the evolution of convection in this region remain a scientific challenge. Here, we assess the environmental conditions associated with shallow, congestus, and isolated deep convection days during the wet season (December to April), employing measurements from the Green Ocean Amazon 2014–2015 (GoAmazon2014/5) experiment and large-scale wind fields from the constrained variational analysis. Composites of deep days show moister than average conditions below 3 km early in the morning. Analyzing the water budget at the surface through observations only, we estimated the water vapor convergence term as a residual of the water balance closure. Convergence remains nearly zero during the deep days until early afternoon (13:00 LST), when it becomes a dominant factor in the water budget. At 14:00 LST, the deep days experience a robust upward large-scale vertical velocity, especially above 4 km, which supports the shallow-to-deep convective transition occurring around 16:00–17:00 LST. In contrast, shallow and congestus days exhibit drier pre-convective conditions, along with diurnal water vapor divergence and large-scale subsidence that extend from the surface to the lower free troposphere. Moreover, afternoon precipitation exhibits the strongest linear correlation (0.6) with large-scale vertical velocity, nearly double the magnitude observed for other environmental factors, even moisture, at different levels and periods of the day. Precipitation also exhibits a moderate increase with low-level wind shear, while upper-level shear has a relatively minor negative impact on convection.

Environmental Sciences & Ecology↗
DOE OSTI2025

DESI DR1 Ly$α$ forest: 3D full-shape analysis and cosmological constraints

We perform an analysis of the full shapes of Lyman-$α$ (Ly$α$) forest correlation functions measured from the first data release (DR1) of the Dark Energy Spectroscopic Instrument (DESI). Our analysis focuses on measuring the Alcock-Paczynski (AP) effect and the cosmic growth rate times the amplitude of matter fluctuations in spheres of $8$$h^{-1}\text{Mpc}$, $fσ_8$. We validate our measurements using two different sets of mocks, a series of data splits, and a large set of analysis variations, which were first performed blinded. Our analysis constrains the ratio $D_M/D_H(z_\mathrm{eff})=4.525\pm0.071$, where $D_H=c/H(z)$ is the Hubble distance, $D_M$ is the transverse comoving distance, and the effective redshift is $z_\mathrm{eff}=2.33$. This is a factor of $2.4$ tighter than the Baryon Acoustic Oscillation (BAO) constraint from the same data. When combining with Ly$α$ BAO constraints from DESI DR2, we obtain the ratios $D_H(z_\mathrm{eff})/r_d=8.646\pm0.077$ and $D_M(z_\mathrm{eff})/r_d=38.90\pm0.38$, where $r_d$ is the sound horizon at the drag epoch. We also measure $fσ_8(z_\mathrm{eff}) = 0.37\; ^{+0.055}_{-0.065} \,(\mathrm{stat})\, \pm 0.033 \,(\mathrm{sys})$, but we do not use it for cosmological inference due to difficulties in its validation with mocks. In $Λ$CDM, our measurements are consistent with both cosmic microwave background (CMB) and galaxy clustering constraints. Using a nucleosynthesis prior but no CMB anisotropy information, we measure the Hubble constant to be $H_0 = 68.3\pm 1.6\;\,{\rm km\,s^{-1}\,Mpc^{-1}}$ within $Λ$CDM. Finally, we show that Ly$α$ forest AP measurements can help improve constraints on the dark energy equation of state, and are expected to play an important role in upcoming DESI analyses.

Cuceu, Andrei [LBL, Berkeley; Chicago U., KICP] (O↗
DOE OSTI2021

Unsupervised machine learning for unbiased chemical classification in X-ray absorption spectroscopy and X-ray emission spectroscopy

Here we report a comprehensive computational study of unsupervised machine learning for extraction of chemically relevant information in X-ray absorption near edge structure (XANES) and in valence-to-core X-ray emission spectra (VtC-XES) for classification of a broad ensemble of sulphorganic molecules. By progressively decreasing the constraining assumptions of the unsupervised machine learning algorithm, moving from principal component analysis (PCA) to a variational autoencoder (VAE) to t-distributed stochastic neighbour embedding (t-SNE), we find improved sensitivity to steadily more refined chemical information. Surprisingly, when embedding the ensemble of spectra in merely two dimensions, t-SNE distinguishes not just oxidation state and general sulphur bonding environment but also the aromaticity of the bonding radical group with 87% accuracy as well as identifying even finer details in electronic structure within aromatic or aliphatic sub-classes. We find that the chemical information in XANES and VtC-XES is very similar in character and content, although they unexpectedly have different sensitivity within a given molecular class. We also discuss likely benefits from further effort with unsupervised machine learning and from the interplay between supervised and unsupervised machine learning for X-ray spectroscopies. Our overall results, i.e., the ability to reliably classify without user bias and to discover unexpected chemical signatures for XANES and VtC-XES, likely generalize to other systems as well as to other one-dimensional chemical spectroscopies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗