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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Characterizing uncertainties in ground “truth” of precipitation over complex terrain through high-resolution numerical modeling

Ground observation of precipitation over complex terrain is subject to large uncertainties due to inadequate sampling. This study explores a method that combines limited gauge data and a high-resolution numerical simulation to quantify the precipitation uncertainties in central Himalaya. Specifically, the Coefficient of spatial Variability (CV) of precipitation and the minimum Number of Required Stations (NRS) to obtain areal-mean precipitation ground truth values within a 0.25-degree area are investigated using fine-scale meteorological simulation at 1.5 km grid spacing. Evaluation over a densely-gauged area demonstrates comparable CV and NRS values between station observations and simulations. The simulated CV and NRS values in a larger area show a strong and positive dependence on each other and an expected positive (negative) correlation with topographic complexity (temporal scale). The proposed method sheds lights on evaluating precipitation products and holds promise for informing the layout of rain gauge networks in complex terrain.

Ouyang, Lin↗
DOE OSTI2015

The identification of extreme asymptotic giant branch stars and red supergiants in M33 with 24 μm variability

We present the first detection of 24 μm variability in 24 sources in the Local Group galaxy M33. These results are based on 4 epochs of Multiband Imaging Photometer for Spitzer observations, which are irregularly spaced over ∼750 days. We find that these sources are constrained exclusively to the Holmberg radius of the galaxy, which increases their chances of being members of M33. We have constructed spectral energy distributions (SEDs) ranging from the optical to the submillimeter to investigate the nature of these objects. We find that 23 of our objects are most likely heavily self-obscured, evolved stars, while the remaining source is the Giant H ii region, NGC 604. We believe that the observed variability is the intrinsic variability of the central star reprocessed through their circumstellar dust shells. Radiative transfer modeling was carried out to determine their likely chemical composition, luminosity, and dust production rate (DPR). As a sample, our modeling has determined an average luminosity of (3.8±0.9)×10{sup 4} L{sub ⊙} and a total DPR of (2.3±0.1)×10{sup −5} M{sub ⊙} yr{sup −1}. Most of the sources, given the high DPRs and short wavelength obscuration, are likely extreme asymptotic giant branch (XAGB) stars. Five of the sources are found to have luminosities above the classical AGB limit (M{sub bol} <−7.1 mag, L > 54,000 L{sub ⊙}), which classifies them as probable red supergiants (RSGs). Almost all of the sources are classified as oxygen-rich. As also seen in the LMC, a significant fraction of the dust in M33 is produced by a handful of XAGB and RSG stars.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Evidence for multi-decadal fuel buildup in a large California wildfire from smoke radiocarbon measurements

In recent decades, there has been a significant increase in annual area burned in California's Sierra Nevada mountains. This rise in fire activity has prompted the need to understand how historical forest management practices affect fuel composition and emissions. Here we examined the total carbon (TC) concentration and radiocarbon abundance (Δ 14 C) of particulate matter (PM) emitted by the KNP Complex Fire, which occurred during California's 2021 wildfire season and affected several groves of giant sequoia trees in the southern Sierra Nevada. During a 26 h sampling period, we measured concentrations of fine airborne PM (PM 2.5 ), as well as dry air mole fractions of carbon monoxide (CO) and methane (CH 4 ), using a ground-based mobile laboratory. We also collected filter samples of PM 2.5 for analysis of TC concentration and Δ 14 C. High correlation among PM 2.5 , CO, and CH 4 time series confirmed that our PM 2.5 measurements captured variability in wildfire emissions. Using a Keeling plot approach, we determined that the mean Δ 14 C of PM 2.5 was 111.6 ± 7.7‰ (n = 12), which was considerably enriched relative to atmospheric carbon dioxide in the northern hemisphere in 2021 (-3.2 ± 1.4‰). Combining these Δ 14 C data with a steady-state one-box ecosystem model, we estimated that the mean age of fuels combusted in the KNP Complex Fire was 40 years, with a range of 29–57 years. These results provide evidence for emissions originating from woody biomass, larger-diameter fine fuels, and coarse woody debris that have accumulated over multiple decades. This is consistent with independent field observations that indicate high fire intensity contributed to widespread giant sequoia mortality. With the expanded use of prescribed fires planned over the next decade in California to mitigate wildfire impacts, our measurement approach has the potential to provide regionally-integrated estimates of the effectiveness of fuel treatment programs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

SOFIA OBSERVATIONS OF SN 2010jl: ANOTHER NON-DETECTION OF THE 9.7 μm SILICATE DUST FEATURE

We present photometric observations from the Stratospheric Observatory for Infrared Astronomy (SOFIA) at 11.1 μm of the Type IIn supernova (SN IIn) 2010jl. The SN is undetected by SOFIA, but the upper limits obtained, combined with new and archival detections from Spitzer at 3.6 and 4.5 μm, allow us to characterize the composition of the dust present. Dust in other SN IIn has been shown in previous works to reside in a circumstellar shell of material ejected by the progenitor system in the few millenia prior to explosion. Our model fits show that the dust in the system shows no evidence for the strong, ubiquitous 9.7 μm feature from silicate dust, suggesting the presence of carbonaceous grains. The observations are best fit with 0.01–0.05 M{sub ⊙} of carbonaceous dust radiating at a temperature of ∼550–620 K. The dust composition may reveal clues concerning the nature of the progenitor system, which remains ambiguous for this subclass. Most of the single star progenitor systems proposed for SNe IIn, such as luminous blue variables, red supergiants, yellow hypergiants, and B[e] stars, all clearly show silicate dust in their pre-SN outflows. However, this post-SN result is consistent with the small sample of SNe IIn with mid-IR observations, none of which show signs of emission from silicate dust in their IR spectra.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

TESS cycle 1 observations of roAp stars with 2-min cadence data

ABSTRACT We present the results of a systematic search for new rapidly oscillating Ap (roAp) stars using the 2-min cadence data collected by the Transiting Exoplanet Survey Satellite (TESS) during its Cycle 1 observations. We identify 12 new roAp stars. Amongst these stars we discover the roAp star with the longest pulsation period, another with the shortest rotation period, and six with multiperiodic variability. In addition to these new roAp stars, we present an analysis of 44 known roAp stars observed by TESS during Cycle 1, providing the first high-precision and homogeneous sample of a significant fraction of the known roAp stars. The TESS observations have shown that almost 60 per cent (33) of our sample of stars are multiperiodic, providing excellent cases to test models of roAp pulsations, and from which the most rewarding asteroseismic results can be gleaned. We report four cases of the occurrence of rotationally split frequency multiplets that imply different mode geometries for the same degree modes in the same star. This provides a conundrum in applying the oblique pulsator model to the roAp stars. Finally, we report the discovery of non-linear mode interactions in α Cir (TIC 402546736, HD 128898) around the harmonic of the principal mode – this is only the second case of such a phenomenon.

Holdsworth, D. L.↗
DOE OSTI2024

A graphics processing unit accelerated sparse direct solver and preconditioner with block low rank compression

We present the GPU implementation efforts and challenges of the sparse solver package STRUMPACK. The code is made publicly available on github with a permissive BSD license. STRUMPACK implements an approximate multifrontal solver, a sparse LU factorization which makes use of compression methods to accelerate time to solution and reduce memory usage. Multiple compression schemes based on rank-structured and hierarchical matrix approximations are supported, including hierarchically semi-separable, hierarchically off-diagonal butterfly, and block low rank. Here, in this paper, we present the GPU implementation of the block low rank (BLR) compression method within a multifrontal solver. Our GPU implementation relies on highly optimized vendor libraries such as cuBLAS and cuSOLVER for NVIDIA GPUs, rocBLAS and rocSOLVER for AMD GPUs and the Intel oneAPI Math Kernel Library (oneMKL) for Intel GPUs. Additionally, we rely on external open source libraries such as SLATE (Software for Linear Algebra Targeting Exascale), MAGMA (Matrix Algebra on GPU and Multi-core Architectures), and KBLAS (KAUST BLAS). SLATE is used as a GPU-capable ScaLAPACK replacement. From MAGMA we use variable sized batched dense linear algebra operations such as GEMM, TRSM and LU with partial pivoting. KBLAS provides efficient (batched) low rank matrix compression for NVIDIA GPUs using an adaptive randomized sampling scheme. The resulting sparse solver and preconditioner runs on NVIDIA, AMD and Intel GPUs. Interfaces are available from PETSc, Trilinos and MFEM, or the solver can be used directly in user code. We report results for a range of benchmark applications, using the Perlmutter system from NERSC, Frontier from ORNL, and Aurora from ALCF. For a high frequency wave equation on a regular mesh, using 32 Perlmutter compute nodes, the factorization phase of the exact GPU solver is about 6.5× faster compared to the CPU-only solver. The BLR-enabled GPU solver is about 13.8× faster than the CPU exact solver. For a collection of SuiteSparse matrices, the STRUMPACK exact factorization on a single GPU is on average 1.9× faster than NVIDIA’s cuDSS solver.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Analysis of Inlier and Outlier Compounds with Respect to Artificial Neural Network Cetane Number Prediction Accuracy

Artificial neural networks (ANNs) are exceptional at forming non-linear correlations between multivariate input and target variables; however, they are often seen as a “black box” approach, since how ANNs form these correlations is somewhat ambiguous. Furthermore, the process underlying how ANNs learn from inlier and outlier samples within the input dataset is not fully understood. Intuitively, it is expected that training ANNs with inlier samples will increase prediction accuracy and training with outlier samples will reduce prediction accuracy; though, in practice, this is not always true. The present work identifies and analyzes inliers and outliers of existing experimental cetane number (CN) data encompassing a variety of compounds and compound groups. It also investigates how ANNs trained to predict CN perform with and without outliers included in the training data, and whether a relationship exists between inliers/outliers and ANN prediction accuracy across the whole dataset and for individual samples. Additionally, individual outlier compounds are analyzed, highlighting how they structurally differ from inlier compounds.

09 BIOMASS FUELS↗
DOE OSTI2019

First Cosmology Results Using Type Ia Supernovae from the Dark Energy Survey: Photometric Pipeline and Light-curve Data Release

We present griz light curves of 251 SNe Ia from the first 3 years of the Dark Energy Survey Supernova Program’s (DES-SN) spectroscopically classified sample. The photometric pipeline described in this paper produces the calibrated fluxes and associated uncertainties used in the cosmological parameter analysis by employing a scene modeling approach that simultaneously models a variable transient flux and temporally constant host galaxy. We inject artificial point sources onto DECam images to test the accuracy of our photometric method. Upon comparison of input and measured artificial supernova fluxes, we find that flux biases peak at 3 mmag. Here, we require corrections to our photometric uncertainties as a function of host galaxy surface brightness at the transient location, similar to that seen by the DES Difference Imaging Pipeline used to discover transients. The public release of the light curves can be found at https://des.ncsa.illinois.edu/releases/sn.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

The Pulsating Helium-atmosphere White Dwarfs. I. New DBVs from the Sloan Digital Sky Survey

Abstract We present a dedicated search for new pulsating helium-atmosphere (DBV) white dwarfs from the Sloan Digital Sky Survey using the McDonald 2.1 m Otto Struve Telescope. In total we observed 55 DB and DBA white dwarfs with spectroscopic temperatures between 19,000 and 35,000 K. We find 19 new DBVs and place upper limits on variability for the remaining 36 objects. In combination with previously known DBVs, we use these objects to provide an update to the empirical extent of the DB instability strip. With our sample of new DBVs, the red edge is better constrained, as we nearly double the number of DBVs known between 20,000 and 24,000 K. We do not find any new DBVs hotter than PG 0112+104, the current hottest DBV is at T eff ≈ 31,000 K, but do find pulsations in four DBVs with temperatures between 27,000 and 30,000 K, improving empirical constraints on the poorly defined blue edge. We investigate the ensemble pulsation properties of all currently known DBVs, finding that the weighted mean period and total pulsation power exhibit trends with effective temperature that are qualitatively similar to the pulsating hydrogen-atmosphere white dwarfs.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Production rate calibration for cosmogenic 10 Be in pyroxene by applying a rapid fusion method to 10 Be-saturated samples from the Transantarctic Mountains, Antarctica

Measurements of multiple cosmogenic nuclides in a single sample are valuable for various applications of cosmogenic nuclide exposure dating and allow for correcting exposure ages for surface weathering and erosion and establishing exposure–burial history. Here we provide advances in the measurement of cosmogenic 10 Be in pyroxene and constraints on the production rate that provide new opportunities for measurements of multi-nuclide systems, such as 10 Be/ 3 He, in pyroxene-bearing samples. We extracted and measured cosmogenic 10 Be in pyroxene from two sets of Ferrar Dolerite samples collected from the Transantarctic Mountains in Antarctica. One set of samples has 10 Be concentrations close to saturation, which allows for the production rate calibration of 10 Be in pyroxene by assuming production–decay equilibrium. The other set of samples, which has a more recent exposure history, is used to determine if a rapid fusion method can be successfully applied to samples with Holocene to Last Glacial Maximum exposure ages. From measured 10 Be concentrations in the near-saturation sample set we find the production rate of 10 Be in pyroxene to be 3.74 ± 0.10 atoms g -1 yr -1 , which is consistent with 10 Be/ 3 He paired nuclide ratios from samples assumed to have simple exposure. Given the high 10 Be concentration measured in this sample set, a sample mass of ~ 0.5 g of pyroxene is sufficient for the extraction of cosmogenic 10 Be from pyroxene using a rapid fusion method. However, for the set of samples that have low 10 Be concentrations, measured concentrations were higher than expected. We attribute spuriously high 10 Be concentrations to failure in removing all meteoric 10 Be and/or a highly variable and poorly quantified procedural blank background correction.

58 GEOSCIENCES↗
DOE OSTI2022

Physics-Informed Machine Learning of Dynamical Systems for Efficient Bayesian Inference

Although the no-u-turn sampler (NUTS) is a widely adopted method for performing Bayesian inference, it requires numerous posterior gradients which can be expensive to compute in practice. Recently, there has been a significant interest in physics-based machine learning of dynamical (or Hamiltonian) systems and Hamiltonian neural networks (HNNs) is a noteworthy architecture. But these types of architectures have not been applied to solve Bayesian inference problems efficiently. We propose the use of HNNs for performing Bayesian inference efficiently without requiring numerous posterior gradients. We introduce latent variable outputs to HNNs (L-HNNs) for improved expressivity and reduced integration errors. We integrate L-HNNs in NUTS and further propose an online error monitoring scheme to prevent sampling degeneracy in regions where L-HNNs may have little training data. We demonstrate L-HNNs in NUTS with online error monitoring consider several complex high-dimensional posterior densities and compare its performance to NUTS.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Update on the Current R&D Activity in the U.S.

This presentation is to fulfil DOE commitment to GIF VHTR Materials PMB. Notched creep rupture testing: Weldment V-notch specimen test finished. 800C 60MPa, 15,716 hour rupture life. Two double V-notch specimen tests have finished. Crack growth tests: System modification was required for creep and creep-fatigue testing, Epsilon 3548COD Extensometer has been ordered, to allow load-line displacement measurements, to be paired with direct current potential drop already used to measure crack length, Sample redesigned in accordance to ASTM E2760 and E1457 to allow for both creep and creep-fatigue crack growth rate testing. Alloy 800H Weldments (1/2) and Alloy 800H Weldments (2/2), Variable Fatigue Testing, and Multi=axial Stress Relaxation. Continue work to finish 800H model, examine ?universal? constitutive model form.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

CSAPR2 cell-tracking data collected during TRACER

One of the challenges of analyzing convective cell properties is quick evolution of the individual convective cells. While the operational radar data provide great a data set to analyze the evolution of radar observables of convective precipitation clouds statistically, previous studies also suggested that, because of the quick evolution of cell life cycle, conventional radar volume scan strategies taking ~5-7 minutes might not capture the detailed evolution. The TRACER campaign deployed CSAPR2, which performed frequent update of RHI and sector PPI scans to track convective cells every < 2 minutes guided by a new cell-tracking framework, Multisensor Agile Adaptive Sampling (MAAS; Kollias et al. 2020). This allows for capturing fast-evolving radar observables. The submitted data files are CSAPR2 data in CfRadial format collected during the TRACER field campaign from June to September 2020. The data files include processed radar variables including: noise-masked reflectivity and differential reflectivity corrected for rain attenuation and systematic biases, noise-masked dealiased radial velocity, specific differential phase, locations of target cells (latitude, longitude, radar range), and radar-echo classification.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Relating Texture and Thermomechanical Processing Variables in Mg–Zn–Ca Alloys

It is well known that the strong basal texture commonly produced in magnesium alloy sheets leads to poor formability at room temperature. A sizable body of work has explored how changing the alloy composition and rolling conditions can yield more desirable textures; however important thermomechanical variables, such as the feed rate during rolling, are often not included in the literature, making it difficult to correlate how changes in processing affect the final crystallographic texture. This work explores the texture evolution and grain refinement in Mg-Zn-Ca alloys during plane strain compression (PSC) using a Gleeble thermomechanical simulator. This instrument allows for precise control and capture of the thermomechanical history of the sample. The texture and grain morphology of the compressed samples were characterized using Electron Backscatter Diffraction (EBSD). The texture results will be used to identify which alloys and processing conditions should be scaled up for future rolling studies.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Conversions between gas-phase metallicities in MaNGA

ABSTRACT We present polynomial conversions between each of 11 different strong line gas-phase metallicity calibrations, each based on ∼1.1 million star-forming spaxels in the public Sloan Digital Sky Survey (SDSS) Data Release 15 (DR15) Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey. For this sample, which is ∼20 times larger than previous works, we present fifth-order polynomial fits for each of 110 possible calibration conversions, for both Small Magellanic Cloud-type and Milky Way-type dust corrections. The typical 2σ scatter around our polynomial fits is 0.1 dex; we present the range over which the metallicities are valid. Conversions between metallicities which rely on the same set of line ratios, or a heavily shared set of emission lines, have reduced scatter in their conversions relative to those conversions with little overlap in required emission lines. Calibration conversions with less consistent sets of emission lines also have increased galaxy-to-galaxy variability, and this variability can account for up to 35 per cent of the total scatter. We also compare our conversions to previous work with the single fibre SDSS DR7 spectra along with higher spatial resolution data from the TYPHOON Integral Field Spectroscopy survey, resulting in comparison samples with spatial resolutions from several kpc down to ∼100 pc. Our metallicity conversions, obtained with the large sample of MaNGA, are robust against the influence of diffuse ionized gas, redshift, effective radius, and spatial blurring, and are therefore consistent across both integrated spectra and the high-resolution integral field spectroscopy data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

FSVPy: A python-based package for fluorescent streak velocimetry (FSV)

Predictive constitutive equations that connect easy-to-measure transport properties (e.g., viscosity and conductivity) with system performance variables (e.g., power consumption and efficiency) are needed to design advanced thermal and electrical systems. In this work, we explore the use of fluorescent particle-streak analysis to directly measure the local velocity field of a pressure-driven flow, introducing a new Python package (FSVPy) to perform the analysis. Fluorescent streak velocimetry combines high-speed imaging with highly fluorescent particles to produce images that contain fluorescent streaks, whose length and intensity can be related to the local flow velocity. By capturing images throughout the sample volume, the three-dimensional velocity field can be quantified and reconstructed. We demonstrate this technique by characterizing the channel flow profiles of several non-Newtonian fluids: micellar Cetylpyridinium Chloride solution, Carbopol 940, and Polyethylene Glycol. We then explore more complex flows, where significant acceleration is created due to microscale features encountered within the flow. We demonstrate the ability of FSVPy to process streaks of various shapes and use the variable intensity along the streak to extract position-specific velocity measurements from individual images. Thus, we demonstrate that FSVPy is a flexible tool that can be used to extract local velocimetry measurements from a wide variety of fluids and flow conditions.

Mechanics↗
DOE OSTI2023

Functional Predictor Variables for the Leaching Potential of Arsenic and Selenium from Coal Fly Ash

The release of leachates from intact coal ash impoundments is a concern due to the enrichment and mobilization of toxic elements such as arsenic (As) and selenium (Se). This study aims to explore the intrinsic properties of coal fly ash that correlate with the relative leachability of As and Se. We performed leaching experiments with 52 fly ash samples collected from 15 different U.S. power plants and representing coal feedstocks from the three major domestic regions. We assessed the mobilization potential of As and Se in fly ash based on standardized leaching protocols and performed multivariate and lasso regression analyses to explore correlations of leachable As and Se contents with characteristics such as major element contents, loss on ignition, and pH. The results of regression models indicated that major elements (Fe, Ca, and Al) for a wide range of fly ashes can serve as predictor variables for the leaching potential of As but not for Se. LOI and pH were not important predictive variables in the models. Both regression approaches resulted in relatively strong fits for leachable As (correlation coefficient R 2 = 0.78 for both models) compared to models for leachable Se (R 2 = 0.49). Overall, these results suggest that correlation models combined with on-site elemental analysis with portable analyzers may enable a screening method for leachable As in coal ash.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2018

Evaluating high-resolution forecasts of atmospheric CO and CO 2 from a global prediction system during KORUS-AQ field campaign

Accurate and consistent monitoring of anthropogenic combustion is imperative because of its significant health and environmental impacts, especially at city-to-regional scale. Here, we assess the performance of the Copernicus Atmosphere Monitoring Service (CAMS) global prediction system using measurements from aircraft, ground sites, and ships during the Korea-United States Air Quality (KORUS-AQ) field study in May to June 2016. Our evaluation focuses on CAMS CO and CO 2 analyses as well as two higher-resolution forecasts (16 and 9km horizontal resolution) to assess their capability in predicting combustion signatures over east Asia. Our results show a slight overestimation of CAMS CO 2 with a mean bias against airborne CO 2 measurements of 2.2, 0.7, and 0.3ppmv for 16 and 9km CO 2 forecasts, and analyses, respectively. The positive CO 2 mean bias in the 16km forecast appears to be consistent across the vertical profile of the measurements. In contrast, we find a moderate underestimation of CAMS CO with an overall bias against airborne CO measurements of -19.2 (16km), -16.7 (9km), and -20.7 ppbv (analysis). This negative CO mean bias is mostly seen below 750 hPa for all three forecast/analysis configurations. Despite these biases, CAMS shows a remarkable agreement with observed enhancement ratios of CO with CO 2 over the Seoul metropolitan area and over the West (Yellow) Sea, where east Asian outflows were sampled during the study period. More efficient combustion is observed over Seoul (dCO dCO 2 Combining double low line 9 ppbvppmv -1 ) compared to the West Sea (dCO dCO 2 Combining double low line 28 ppbvppmv -1 ). This "combustion signature contrast" is consistent with previous studies in these two regions. CAMS captured this difference in enhancement ratios (Seoul: 8-12 ppbvppmv -1 , the West Sea: ~ 30 ppbvppmv- 1 ) regardless of forecast/analysis configurations. The correlation of CAMS CO bias with CO2 bias is relatively high over these two regions (Seoul: 0.64-0.90, the West Sea: ~ 0.80) suggesting that the contrast captured by CAMS may be dominated by anthropogenic emission ratios used in CAMS. However, CAMS shows poorer performance in terms of capturing local-to-urban CO and CO 2 variability. Along with measurements at ground sites over the Korean Peninsula, CAMS produces too high CO and CO 2 concentrations at the surface with steeper vertical gradients (~ 0.4 ppmvhPa-1 for CO 2 and 3.5 ppbvhPa -1 for CO) in the morning samples than observed (~ 0.25 ppmvhPa -1 for CO 2 and 1.7 ppbvhPa -1 for CO), suggesting weaker boundary layer mixing in the model. Lastly, we find that the combination of CO analyses (i.e., improved initial condition) and use of finer resolution (9km vs. 16km) generally produces better forecasts.

54 ENVIRONMENTAL SCIENCES↗