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At least 1,387 records · Page 77

Sharp decline of dust events induces regional wetting over arid and semi-arid Northwest China in the NCAR Community atmosphere model

Abstract Multiple lines of observational evidence have indicated a significant wetting over the arid and semi-arid Northwest China (NWC) during recent decades, coinciding with a simultaneous sharp decline of dust events. Although recent studies have attributed NWC wetting to different anthropogenic and natural forcings, the mechanisms are not definitive and the regional wetting has been greatly underestimated in the Coupled Model Intercomparison Project historical simulations. Based on sensitivity experiments with different dust emission amounts using the NCAR Community Atmospheric Model version 5 (CAM5), here we find that decreasing dusts exert significant impacts on mixed-phase clouds through reducing the concentration of ice nucleating particles, increase the NWC precipitation and thus induce regional wetting through enhancing convection precipitation. A possible convection invigoration mechanism whereby the atmospheric vertical temperature gradient and convective instability are strengthened by reduced dusts, leading to convection invigoration and increased precipitation. These results are reinforced by simulations over the dust region in North Africa where mixed-phase and ice clouds are rare and reduced dusts do not increase precipitation. This study highlights the possible mechanism of dust-ice cloud interactions in recent NWC wetting and future regional climate change.

54 ENVIRONMENTAL SCIENCES↗

Resolution Dependence and Rossby Wave Modulation of Atmospheric Rivers in an Aquaplanet Model

Atmospheric rivers (ARs) are examined in a set of aquaplanet simulations using the Model for Prediction Across Scales dynamical core run at multiple horizontal resolutions, namely, 240, 120, and 60 km. As the resolution is increased, there is an increase in the occurrence of long—lasting ARs. At the same time there is also an increase in the local finite–amplitude wave activity (LWA) of upper–tropospheric absolute vorticity, a measure for Rossby wave phase and amplitude that is closely linked with wave breaking. Consistent with the notion that changes in ARs are driven by midlatitude dynamics, a strong relationship is identified between ARs and the equatorward component of LWA. Here, a logistic regression model is used to quantify the probability of AR occurrence based solely on LWA and explains most of the change in AR frequency with resolution. LWA is a diagnostic that may be easily applied to the broadly available output of phase 6 of the Coupled Model Intercomparison project and other model simulations, thus enabling scientists to infer AR and Rossby wave characteristics. AR characteristics, in particular, require higher–resolution moisture and winds at multiple levels that are not always easily available.

54 ENVIRONMENTAL SCIENCES↗

Systematic Benchmarking of Climate Models: Methodologies, Applications, and New Directions

As climate models become increasingly complex, there is a growing need to comprehensively and systematically assess model performance with respect to observations. Given the increasing number and diversity of climate model simulations in use, the community has moved beyond simple model intercomparison and toward developing methods capable of benchmarking a large number of simulations against a suite of climate metrics. Here, we present a detailed review of evaluation and benchmarking methods and approaches developed in the last decade, focusing primarily on scientific implications for Coupled Model Intercomparison Project (CMIP) simulations and CMIP6 results that contributed to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6). Based on this review, we explain the resulting contemporary philosophy of model benchmarking, and provide clear distinctions and definitions of the terms model verification, process validation, evaluation, and benchmarking. While significant progress has been made in model development based on systematic evaluation and benchmarking efforts, some climate system biases still remain. The development of open‐source community software packages has played a fundamental role in identifying areas of significant model improvement and bias reduction. We review the key features of several software packages that have been commonly used over the past decade to evaluate and benchmark global and regional climate models. Additionally, we discuss best practices for the selection of evaluation and benchmarking metrics and for interpreting the obtained results, the importance of selecting suitable sources of reference data and accurate uncertainty quantification.

Environmental sciences↗

Best practices in software development for robust and reproducible geoscientific models based on insights from the Global Carbon Budget's dynamic vegetation models

Computational models play an increasingly vital role in scientific research by enabling the numerical simulation of complex processes. Such models are also fundamental in geosciences. For instance, they offer critical insights into the impacts of global change on the Earth system today and in the future. Beyond their value as research tools, models are also software products and should therefore adhere to certain established software engineering standards. However, scientists are rarely trained as software developers, which can lead to potential deficiencies in software quality like unreadable, inefficient, or erroneous code. The complexity of models, coupled with their integration into broader workflows, also often makes it challenging to reproduce results, evaluate processes, and build upon them. In this paper, we review the state and current practices of the development processes of the state-of-the-art land surface models used by the Global Carbon Budget. We combine the experience of modelers from the respective research groups with the expertise of software engineers from tech companies to outline key principles and tools for improving software quality in research. We explore four main areas: (1) model testing and validation, (2) scientific, technical, and user documentation, (3) version control, continuous integration, and code review, and (4) the portability and reproducibility of workflows. Our review reveals that while modeling communities are incorporating many best practices, significant room for improvement remains in areas such as automated testing, automated documentation, and reproducibility. Therefore, we here identify and promote essential software engineering practices, including numerous examples of practices from within the community that can serve as guidelines for other models and could help streamline processes across the entire community. We conclude with an open-source example implementation of these principles, demonstrating portable and reproducible data flows, a continuous integration setup, and web-based visualizations. This example may serve as a practical resource for model developers, users, and all scientists engaged in scientific programming.

Gregor, Konstantin [Technical Univ. of Munich (Ger↗

One-way coupling of an integrated assessment model and a water resources model: evaluation and implications of future changes over the US Midwest

An integrated model is being developed to advance our understanding of the interactions between human activities, terrestrial system and water cycle, and how system interactions will be affected by a changing climate at the regional scale. As a first step towards that goal, a global integrated assessment model including a waterdemand model is coupled offline with a land surface hydrology – routing – water resources management model. A spatial and temporal disaggregation approach is developed to project the annual regional water demand simulations into a daily time step and subbasin representation. The model demonstrated reasonable ability to represent the historical flow regulation and water supply over the Midwest (Missouri, Upper Mississippi and Ohio). Implications for the future flow regulation, water supply and supply deficit are investigated using a climate change projection with the B1 emission scenario which affects both natural flow and water demand. Over the Midwest, changes in flow regulation are mostly driven by the change in natural flow due to the limited storage capacity over the Ohio and Upper Mississippi river basins. The changes in flow and demand have a combined effect on the Missouri Summer regulated flow. The supply deficit tends to be driven by the change in flow over the region. Spatial analysis demonstrates the relationship between the supply deficit and the change in demand over urban areas not along a main river or with limited storage, and over areas upstream of groundwater dependent fields with therefore overestimated demand.

Environmental Molecular Sciences Laboratory↗

The effects of stimulated star formation on the evolution of the galaxy. III - The chemical evolution of nonlinear systems

The evolution of models for star formation in galaxies with disk and halo components is discussed. Two phases for the halo (gas and stars) and three for the disk (including clouds) are used in these calculations. The star-formation history is followed using nonlinear phase-coupling models which completely determine the populations of the phases as a function of time. It is shown that for a wide range of parameters, including the effects of both spontaneous and stimulated star formation and mass exchange between the spatial components of the system, the observed chemical history of the galaxy can easily be obtained. The most sensitive parameter in the detailed metallicity and star-formation history for the system is the rate of return of gas to the diffuse phase upon stellar death.

Shore, Steven N.↗

Data assimilation empowered neural network parametrizations for subgrid processes in geophysical flows

In the past couple of years, there has been a proliferation in the use of machine learning approaches to represent subgrid-scale processes in geophysical flows with an aim to improve the forecasting capability and to accelerate numerical simulations of these flows. Despite its success for different types of flow, the online deployment of a data-driven closure model can cause instabilities and biases in modeling the overall effect of subgrid-scale processes, which in turn leads to inaccurate prediction. To tackle this issue, we exploit the data assimilation technique to correct the physics-based model coupled with the neural network as a surrogate for unresolved flow dynamics in multiscale systems. In particular, we use a set of neural network architectures to learn the correlation between resolved flow variables and the parametrizations of unresolved flow dynamics and formulate a data assimilation approach to correct the hybrid model during their online deployment. We illustrate our framework in a set of applications of the multiscale Lorenz 96 system for which the parametrization model for unresolved scales is exactly known, and the two-dimensional Kraichnan turbulence system for which the parametrization model for unresolved scales is not known a priori. Our analysis, therefore, comprises a predictive dynamical core empowered by (i) a data-driven closure model for subgrid-scale processes, (ii) a data assimilation approach for forecast error correction, and (iii) both data-driven closure and data assimilation procedures. We show significant improvement in the long-term prediction of the underlying chaotic dynamics with our framework compared to using only neural network parametrizations for future prediction. Moreover, we demonstrate that these data-driven parametrization models can handle the non-Gaussian statistics of subgrid-scale processes, and effectively improve the accuracy of outer data assimilation workflow loops in a modular nonintrusive way.

42 ENGINEERING↗

An alternate approach to ensemble ENSO forecast spread: Application to the 2014 forecast

Abstract Evaluating the 2014 El Niño forecast as a “bust” may be tapping into a bigger issue, namely that forecast “overconfidence” from single‐model ensembles could affect the retrospective assessment of El Niño–Southern Oscillation (ENSO) predictions. The present study proposes a new approach to quantifying an “expected” spread and uncertainty from noise‐driven processes and supplementing these measures with actual ENSO forecasts. Expanding on a previously developed coupled model framework that isolates noise‐driven ENSO‐like errors, an experimental design is implemented to generate an expected December Niño‐3.4 spread from March initial condition sea surface temperature errors that have similar structure to the 2014 and 2015 observed. Results reveal that the 2014 ENSO forecast falls within the expected uncertainty generated by ENSO‐independent, forecast‐independent, noise‐driven errors.

Larson, Sarah M.↗

Lattice-Distortion-Enhanced Yield Strength in a Refractory High-Entropy Alloy

Severe distortion is one of the four core effects in single-phase high-entropy alloys (HEAs) and contributes significantly to the yield strength. However, the connection between the atomic-scale lattice distortion and macro-scale mechanical properties through experimental verification has yet to be fully achieved, owing to two critical challenges: 1) the difficulty in the development of homogeneous single-phase solid-solution HEAs and 2) the ambiguity in describing the lattice distortion and related measurements and calculations. Here, a single-phase body-centered-cubic (BCC) refractory HEA, NbTaTiVZr, using thermodynamic modeling coupled with experimental verifications, is developed. Compared to the previously developed single-phase NbTaTiV HEA, the NbTaTiVZr HEA shows a higher yield strength and comparable plasticity. The increase in yield strength is systematically and quantitatively studied in terms of lattice distortion using a theoretical model, first-principles calculations, synchrotron X-ray/neutron diffraction, atom-probe tomography, and scanning transmission electron microscopy techniques. These results demonstrate that severe lattice distortion is a core factor for developing high strengths in refractory HEAs.

36 MATERIALS SCIENCE↗

The effect of Mn/Ni on thermodynamic properties of critical nucleus in Fe-Cu-Mn (Ni) ternary alloys

The aging- or radiation-induced hardening of Cu/Mn/Ni precipitates in Fe alloys is one of property degradation mechanisms in structural materials in nuclear reactors. Experiments show that aging or radiation leads the formation of Cu-rich precipitates, and the addition of Mn or Ni elements enhances the precipitation kinetics. In this study, the phase-field model coupled with the constrained string method have been applied to investigate the thermodynamic properties of critical nuclei such as the minimum energy path of Cu/Mn/Ni precipitation in Fe-Cu-Mn and Fe-Cu-Ni ternary alloys. The chemical free energies used in the model are taken from CALPHAD. The simulation results show that the formation of Cu/Mn/Ni clusters needs to overcome an energy barrier, and the precipitate has a Core-Shell structure. The thermodynamic properties of the critical nucleus are influenced by temperature and Cu/Mn/Ni overall concentrations, which are in accordance with the simulation results as well as the experimental observations.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Understanding the Cascade: Removing GCM Biases Improves Dynamically Downscaled Climate Projections

Polarization surrounding bias correction (BC) in creating climate projections arises from its lack of physicality. Here, we perform and analyze 18 dynamical downscaling simulations (with and without BC) to better understand the physical impacts of BC, applied before downscaling, on regional climate output across the western United States. Without BC, downscaled precipitation is systematically and unrealistically wet biased compared to a hierarchy of observationally based datasets over the 1980–2014 period due to cascading mean–state Global Climate Model (GCM) biases: (a) overly strong lower–tropospheric lapse rates (5 K/km), (b) overly cold (2 K) tropospheric temperatures, and (c) anomalous mid–tropospheric cyclonic vorticity advection. With BC, downscaled precipitation (snow) biases are virtually eliminated (halved). Identified GCM biases are common to the broader Coupled Model Intercomparison Project ensemble. Physical effects of BC on the quality of the regionalized projections, pending an evaluation of BC's distortion of the downscaled climate response, may motivate its broader application by dynamical downscalers.

54 ENVIRONMENTAL SCIENCES↗

Theoretical analysis of the S{sub 2}←S{sub 0} vibronic spectrum of the 2-pyridone dimer

The interplay between excitonic and vibronic coupling in hydrogen-bonded molecular dimers leads to complex spectral structures and other intriguing phenomena such as a quenching of the excitonic energy splitting. We recently extended our analysis from that of the quenching mechanism to the theoretical investigation of the complete vibronic spectrum for the ortho-cyanophenol dimer. We now apply the same approach to the vibronic spectrum of the 2-pyridone dimer and discuss the assignment of vibronic lines to gain insight into the underlying coupling mechanism. This is based on potential energy surfaces obtained at the RI-CC2/aug-cc-pVTZ level. They are used for the dynamical analysis in the framework of a multi-mode vibronic coupling approach. The theoretical results based on the quadratic vibronic coupling model are found to be in good agreement with the experimental resonant two-photon ionization spectrum.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Improved Predictability of the Indian Ocean Dipole Using a Stochastic Dynamical Model Compared to the North American Multimodel Ensemble Forecast

This study assesses the predictive skill of eight North American Multimodel Ensemble (NMME) models in predicting the Indian Ocean dipole (IOD). We find that the forecasted ensemble-mean IOD–El Niño–Southern Oscillation (ENSO) relationship deteriorates away from the observed relationship with increasing lead time, which might be one reason that limits the IOD predictive skill in coupled models. We are able to improve the IOD predictive skill using a recently developed stochastic dynamical model (SDM) forced by forecasted ENSO conditions. The results are consistent with the previous result that operational IOD predictability beyond persistence at lead times beyond one season is mostly controlled by ENSO predictability and the signal-to-noise ratio of the Indo-Pacific climate system. The multimodel ensemble (MME) investigated here is found to be of superior skill compared to each individual model at most lead times. Importantly, the skill of the SDM IOD predictions forced with forecasted ENSO conditions were either similar or better than those of the MME IOD forecasts. Moreover, the SDM forced with observed ENSO conditions exhibits significantly higher IOD prediction skill than the MME at longer lead times, suggesting the large potential skill increase that could be achieved by improving operational ENSO forecasts. We find that both cold and warm biases of the predicted Niño-3.4 index may cause false alarms of negative and positive IOD events, respectively, in NMME models. Many false alarms for IOD forecasts at lead times longer than one season in the original forecasts disappear or are significantly reduced in the SDM forced by forecasted ENSO conditions.

54 ENVIRONMENTAL SCIENCES↗

Modeling central metabolism and energy biosynthesis across microbial life

Here, automatically generated bacterial metabolic models, and even some curated models, lack accuracy in predicting energy yields due to poor representation of key pathways in energy biosynthesis and the electron transport chain (ETC). Further compounding the problem, complex interlinking pathways in genome-scale metabolic models, and the need for extensive gapfilling to support complex biomass reactions, often results in predicting unrealistic yields or unrealistic physiological flux profiles. As a result, to overcome this challenge, we developed methods and tools to build high quality core metabolic models (CMM) representing accurate energy biosynthesis based on a well studied, phylogenetically diverse set of model organisms. We compare these models to explore the variability of core pathways across all microbial life, and by analyzing the ability of our core models to synthesize ATP and essential biomass precursors, we evaluate the extent to which the core metabolic pathways and functional ETCs are known for all microbes. 6,600 (80 %) of our models were found to have some type of aerobic ETC, whereas 5,100 (62 %) have an anaerobic ETC, and 1,279 (15 %) do not have any ETC. Using our manually curated ETC and energy biosynthesis pathways with no gapfilling at all, we predict accurate ATP yields for nearly 5586 (70 %) of the models under aerobic and anaerobic growth conditions. This study revealed gaps in our knowledge of the central pathways that result in 2,495 (30 %) CMMs being unable to produce ATP under any of the tested conditions. We then established a methodology for the systematic identification and correction of inconsistent annotations using core metabolic models coupled with phylogenetic analysis. In conclusion, we predict accurate energy yields based on our improved annotations in energy biosynthesis pathways and the implementation of diverse ETC reactions across the microbial tree of life. We highlighted missing annotations that were essential to energy biosynthesis in our models. We examine the diversity of these pathways across all microbial life and enable the scientific community to explore the analyses generated from this large-scale analysis of over 8000 microbial genomes.

59 BASIC BIOLOGICAL SCIENCES↗

Modeling Discrete Forest Anisotropic Reflectance Over a Sloped Surface With an Extended GOMS and SAIL Model

Topographic effects on canopy reflectance play a pivotal role in the retrieval of surface biophysical variables over rugged terrain. In this paper, we proposed a new canopy anisotropic reflectance model for discrete forests, Geometric Optical and Mutual Shadowing and Scattering-from-Arbitrarily-Inclined-Leaves model coupled with Topography (GOSAILT), which considers the effects of slope, aspect, geotropic nature of tree growth, multiple scattering, and diffuse skylight. GOSAILT-simulated areal proportions of four scene components (i.e., sunlit crown, shaded crown, sunlit background, and shaded background) were evaluated using the Geometric Optical model for Sloping Terrains (GOST) model. The canopy reflectances simulated by GOSAILT were validated against two reflectance data sets: Discrete anisotropic radiative transfer (DART) simulations and wide-angle infrared dual-model line/area array scanner (WIDAS) observations. Compared with a horizontal surface, the forest canopy reflectance over a steep slope (60°) is significantly distorted with absolute (relative) bias values of 0.048 (79.60%) and 0.056 (12.02%) for the red and near-infrared (NIR) bands, respectively. The GOSAILT-simulated component areal proportions show close agreements with GOST. Moreover, GOSAILT simulations have high overall accuracy (red band: coefficient of determination (R²) = 0.96; root-mean-square error (RMSE) = 0.003; and mean absolute percentage error (MAPE) = 3.91%; and NIR band: R² = 0.78, RMSE = 0.019; MAPE = 3.94%) when compared with the DART simulations. In conclusion, these extensive validations indicate good performances of GOSAILT in canopy reflectance simulations over sloped surfaces.

58 GEOSCIENCES↗

Exploratory Precipitation Metrics: Spatiotemporal Characteristics, Process-Oriented, and Phenomena-Based

Abstract Precipitation sustains life and supports human activities, making its prediction one of the most societally relevant challenges in weather and climate modeling. Limitations in modeling precipitation underscore the need for diagnostics and metrics to evaluate precipitation in simulations and predictions. While routine use of basic metrics is important for documenting model skill, more sophisticated diagnostics and metrics aimed at connecting model biases to their sources and revealing precipitation characteristics relevant to how model precipitation is used are critical for improving models and their uses. This paper illustrates examples of exploratory diagnostics and metrics including 1) spatiotemporal characteristics metrics such as diurnal variability, probability of extremes, duration of dry spells, spectral characteristics, and spatiotemporal coherence of precipitation; 2) process-oriented metrics based on the rainfall–moisture coupling and temperature–water vapor environments of precipitation; and 3) phenomena-based metrics focusing on precipitation associated with weather phenomena including low pressure systems, mesoscale convective systems, frontal systems, and atmospheric rivers. Together, these diagnostics and metrics delineate the multifaceted and multiscale nature of precipitation, its relations with the environments, and its generation mechanisms. The metrics are applied to historical simulations from phases 5 and 6 of the Coupled Model Intercomparison Project. Models exhibit diverse skill as measured by the suite of metrics, with very few models consistently ranked as top or bottom performers compared to other models in multiple metrics. Analysis of model skill across metrics and models suggests possible relationships among subsets of metrics, motivating the need for more systematic analysis to understand model biases for informing model development.

54 ENVIRONMENTAL SCIENCES↗

Collaborative Proposal: Improving Decadal Prediction of Arctic Climate Variability and Change Using a Regional Arctic System Model (RASM)

RASM is a multi-disciplinary project, which brings together researchers from six state universities, one military postgraduate school, and one DoE laboratory to address the core modeling objectives of the arctic research community articulated in the Arctic System Modeling report by Roberts et al. (2010b). This report advocates the construction of a regional downscaling tool to generate probabilistic decadal projections of Greenland ice sheet retreat, evolution of arctic sea ice cover, changes in land surface vegetation, and regional processes leading to arctic amplification. Unified coupled models such as RASM are ideal for this purpose because they simulate fine-scale physics, essential for the realistic representation of intra-annual variability, in addition to processes fundamental to long term climatic shifts (Hurrell et al. 2009). By using RASM with boundary conditions from a global model, we can generate many-member ensembles essential for understanding uncertainty in regional climate projections (Hawkins and Sutton 2009). This probabilistic approach is computationally prohibitive for high-resolution global models in the foreseeable future, and also for regional models interactively nested within global simulations. Yet it is fundamental for quantifying uncertainty in decadal forecasts to make them useful for decision makers (Doherty et al. 2009). For this reason, we have targeted development of ensemble generation techniques as a core project task (Task 4.5). Environmental impact assessment specialists need high-fidelity regional ensemble projections to improve the accuracy of their work (Challinor et al. 2009; Moss et al. 2010). This is especially true of the Arctic, where economic, social and national interests are rapidly reshaping the high north in step with regional climate change. During the next decade, considerable oil and gas discoveries are expected across many parts of the marine and terrestrial Arctic (Gautier et al. 2009), the economics of the Northern Sea Route will steadily improve (Arctic Council 2009), and sovereign claims over the Arctic Ocean will increasingly be subject to international negotiations (Proelss 2009). Issues such as these have led to an expanding demand for Arctic climate projections to aid national and commercial decisions. However, detailed information from existing models is lacking. RASM will enhance the existing Arctic system modeling capabilities and align them with the scientific and societal needs outlined above. The science involved in the development of RASM will be integrated with teaching and training at a few different levels. RASM PIs will supervise at least six postdoctoral and doctoral students with additional involvement of undergraduate research assistants. RASM postdoctoral fellows and graduate trainees will benefit from being a part of a large, collaborative, and multi-disciplinary research projects employing state-of-the-art modeling and computational tools. They will also have the opportunity to contribute to the UTEP-led education and outreach effort developed specifically for RASM. The UTEP team will use RASM as a platform for education and outreach. First, we will develop products for public dissemination, such as curriculum units, lesson plans and other materials (simulations, movies, and images) for use by students and teachers in high school and university classrooms. Second, we will facilitate bringing RASM PIs, postdocs, and graduate students into the classroom, through electronic mentorship and by contributing content via online lectures and presentations. Third, by the use of problem-based learning (PBL) approaches, we will provide real-world scenarios and problems enabling students to do research and develop position papers or presentations on topics related to RASM. (Problem-based learning is a student-centered, inquiry-based approach in which students work in teams to solve challenging, open-ended problems.) Fourth, we will develop teacher-training materials that will be developed into workshops (face-to-face or online) to help teachers understand how to use the materials we supply. Overall, we will provide a broad, and long-term RASM program for education and outreach. RASM education and outreach activities will target underrepresented populations - in particular, Hispanic students and teachers. For example, the UTEP student body is more than 70 percent Hispanic and 55 percent female, and the majority of students are the first in their families to attend college. Students from underrepresented groups will be given the chance to expand their understanding of the different scientific knowledge and processes associated with RASM. We will develop an eye-catching RASM website to include information about the project, model results and educational materials, and lists of RASM publications and presentations.

54 ENVIRONMENTAL SCIENCES↗

High-fidelity Simulation of Jet Noise from Rectangular Nozzles

This Phase II project validated a state-of-the-art LES model, coupled with a Ffowcs Williams-Hawkings (FW-H) far-field acoustic solver, to support the development of advanced engine concepts. These concepts include innovative flow control strategies to attenuate jet noise emissions. The end-to-end LES/ FW-H noise prediction model was demonstrated and validated by applying it to rectangular nozzle designs with a high aspect ratio. The model also was validated against acoustic and flow-field data from a realistic jet-pylon experiment, thereby significantly advancing the state of the art for LES.

Sinha, Neeraj↗