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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Object-Based Evaluation of Tropical Precipitation Systems in DYAMOND Simulations over the Maritime Continent

The present study uses an object-based evaluation metric to examine the precipitation bias over the Maritime Continent in the global cloud-resolving models. We specifically focus on the difference between the models that directly resolve convection and those using convection parameterization. The 40-day hindcast experiments of the DYnamics of the Atmospheric general circulation Modeled On Non-hydrostatic Domain (DYAMOND) intercomparison project are evaluated against the high-resolution satellite rainfall products. The hindcast of the Central Weather Bureau Global Forecast System (CWBGFS) under the DYAMOND protocol is also included. The results indicate that most models simulate insufficient numbers of large precipitation system [object-based precipitation system (OPS), > 370 km in scale], indicating weaker convection organization. The observation indicates that the maximum precipitation within the OPS intensifies with increasing object size. All of the models capture this positive relationship, but most of them overestimate the sensitivity. Most of the models overestimate both the frequency and intensity of small OPS (< 160 km), except for the models with convection parameterization [i.e., CWBGFS, European Centre for Medium-Range Weather Forecasts Integrated Forecasting System (IFS)-9 km]. Although most of the models can reproduce the observed peak time of diurnal precipitation over the land area in the Maritime Continent, the simulated fractional contribution of different sizes of OPS to the total precipitation varies from model to model, and their peak times do not follow the observed ones with delayed peak times as the size of OPS increases from small, mid-size, to large categories. Most of the models reasonably capture the mean diurnal cycle peak time, but only the models with convection parameterization and Model for Prediction Across Scales (MPAS) can represent the diurnal evolution of fractional contribution from different OPSs. The implications of the current results to the upscale processes of the tropical convection systems in the global models are also discussed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Diurnal cycle of precipitation over the tropics and central United States: intercomparison of general circulation models

Diurnal precipitation is a fundamental mode of variability that climate models have difficulty in accurately simulating. Here, in this work, the diurnal cycle of precipitation (DCP) in participating climate models from the Global Energy and Water Exchanges' DCP project is evaluated over the tropics and central United States. Common model biases such as excessive precipitation over the tropics, too frequent light-to-moderate rain, and the failure to capture propagating convection in the central United States still exist. Over the central United States, the issues of too weak rainfall intensity in climate runs is well improved in their hindcast runs with initial conditions from numerical weather prediction analyses. But the improvement is minimal over the central Amazon. Incorporating the role of the large-scale environment in convective triggering processes helps resolve the phase-locking issue in many models where precipitation often incorrectly peaks near noon due to maximum insolation over land. Allowing air parcels to be lifted above the boundary layer improves the simulation of nocturnal precipitation which is often associated with the propagation of mesoscale systems. Including convective memory in cumulus parameterizations acts to suppress light-to-moderate rain and promote intense rainfall; however, it also weakens the diurnal variability. Simply increasing model resolution (with cumulus parameterizations still used) cannot fully resolve the biases of low-resolution climate models in DCP. The hierarchy modeling framework from this study is useful for identifying the missing physics in models and testing new development of model convective processes over different convective regimes.

58 GEOSCIENCES↗
DOE OSTI2021

A Parameterization of the Cloud Scattering Polarization Signal Derived From GPM Observations for Microwave Fast Radative Transfer Models

Microwave cloud polarized observations have shown the potential to improve precipitation retrievals since they are linked to the orientation and shape of ice habits. Stratiform clouds show larger brightness temperature (TB) polarization differences (PDs), defined as the vertically polarized TB (TBV) minus the horizontally polarized TB (TBH), with ~10 K PD values at 89 GHz due to the presence of horizontally aligned snowflakes, while convective regions show smaller PD signals, as graupel and/or hail in the updraft tend to become randomly oriented. The launch of the global precipitation measurement (GPM) microwave imager (GMI) has extended the availability of microwave polarized observations to higher frequencies (166 GHz) in the tropics and midlatitudes, previously only available up to 89 GHz. This study analyzes one year of GMI observations to explore further the previously reported stable relationship between the PD and the observed TBs at 89 and 166 GHz, respectively. The latitudinal and seasonal variability is analyzed to propose a cloud scattering polarization parameterization of the PD-TB relationship, capable of reconstructing the PD signal from simulated TBs. Given that operational radiative transfer (RT) models do not currently simulate the cloud polarized signals, this is an alternative and simple solution to exploit the large number of cloud polarized observations available. Finally, the atmospheric radiative transfer simulator (ARTS) is coupled with the weather research and forecasting (WRF) model, in order to apply the proposed parameterization to the RT simulated TBs and hence infer the corresponding PD values, which show to reproduce the observed GMI PDs well.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Limitations of Bin and Bulk Microphysics in Reproducing the Observed Spatial Structure of Light Precipitation

Abstract Coarse-gridded atmospheric models often account for subgrid-scale variability by specifying probability distribution functions (PDFs) of process rate inputs such as cloud and rainwater mixing ratios ( q c and q r , respectively). PDF parameters can be obtained from numerous sources: in situ observations, ground- or space-based remote sensing, or fine-scale modeling such as large-eddy simulation (LES). LES is appealing to constrain PDFs because it generates large sample sizes, can simulate a variety of cloud regimes/case studies, and is not subject to the ambiguities of observations. However, despite the appeal of using model output for parameterization development, it has not been demonstrated that LES satisfactorily reproduces the observed spatial structure of microphysical fields. In this study, the structure of observed and modeled microphysical fields are compared by applying bifractal analysis, an approach that quantifies variability across spatial scales, to simulations of a drizzling stratocumulus field that span a range of domain sizes, drop concentrations (a proxy for mesoscale organization), and microphysics schemes (bulk and bin). Simulated q c closely matches observed estimates of bifractal parameters that measure smoothness and intermittency. There are major discrepancies between observed and simulated q r properties, though, with bulk simulated q r consistently displaying the bifractal properties of observed clouds (smooth, minimally intermittent) rather than rain while bin simulations produce q r that is appropriately intermittent but too smooth. These results suggest fundamental limitations of bulk and bin schemes to realistically represent higher-order statistics of the observed rain structure.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

ARM Data-Oriented Metrics and Diagnostics Package for Climate Model Evaluation

A Python-based metrics and diagnostics package is currently being developed by the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Infrastructure Team at Lawrence Livermore National Laboratory (LLNL) to facilitate the use of long-term, high-frequency measurements from the ARM Facility in evaluating the regional climate simulation of clouds, radiation, and precipitation. This metrics and diagnostics package computes climatological means of targeted climate model simulation and generates tables and plots for comparing the model simulation with ARM observational data. The Coupled Model Intercomparison Project (CMIP) model data sets are also included in the package to enable model intercomparison as demonstrated in Zhang et al. (2017). The mean of the CMIP model can serve as a reference for individual models. Basic performance metrics are computed to measure the accuracy of mean state and variability of climate models. The evaluated physical quantities include cloud fraction, temperature, relative humidity, cloud liquid water path, total column water vapor, precipitation, sensible and latent heat fluxes, and radiative fluxes, with plan to extend to more fields, such as aerosol and microphysics properties. Process-oriented diagnostics focusing on individual cloud- and precipitation-related phenomena are also being developed for the evaluation and development of specific model physical parameterizations. The version 1.0 package is designed based on data collected at ARM’s Southern Great Plains (SGP) Research Facility, with the plan to extend to other ARM sites. The metrics and diagnostics package is currently built upon standard Python libraries and additional Python packages developed by DOE (such as CDMS and CDAT). The ARM metrics and diagnostic package is available publicly with the hope that it can serve as an easy entry point for climate modelers to compare their models with ARM data. In this report, we first present the input data, which constitutes the core content of the metrics and diagnostics package in section 2, and a user's guide documenting the workflow/structure of the version 1.0 codes, and including step-by-step instruction for running the package in section 3.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

ARM data-oriented metrics and diagnostics package for climate model evaluation (ARM-DIAGS-V3) version 3

A Python-based metrics and diagnostics package is currently being developed by the ARM Infrastructure Team at Lawrence Livermore National Laboratory to facilitate the use of long-term high frequency measurements from the ARM program in evaluating the regional climate simulation of clouds, radiation, precipitation, and aerosols. This metrics and diagnostics package computes climatological means of targeted climate model simulation and generates tables and plots for comparing the model simulation with ARM observational data. The CMIP model data sets are also included in the package to enable model inter-comparison as demonstrated in Zhang et al. (2018) and Zhang et al. (2020). The mean of the CMIP model can be served as a reference for individual models. Basic performance metrics are computed to measure the accuracy of mean state and variability of climate models. The evaluated physical quantities include cloud fraction, temperature, relative humidity, cloud liquid water path, total column water vapor, precipitation, sensible and latent heat fluxes, aerosol optical depth, and radiative fluxes, with plan to extend to more fields, such as the evaluation of model simulated aerosol physicochemical properties and cloud microphysics properties. Process-oriented diagnostics focusing on aerosol, cloud, and precipitation-related phenomena are also being developed for the evaluation and development of specific model physical parameterizations. In addition to the Southern Great Plains (SGP), North Slope of Alaska (NSA) and Tropical Western Pacific (TWP) atmospheric observatories in the ARMDIAGS version 2.0, the version 3.0 package have extended to the data collected at the ARM Eastern North Atlantic (ENA) atmospheric observatory and the Observation and Modeling of the Green Ocean Amazon (GOAMAZON) field campaign. The metrics and diagnostics package are currently built upon standard Python libraries and additional Python packages developed by DOE (CDAT). The ARM metrics and diagnostic package is available publicly with the hope that it can serve as an easy entry point for climate modelers to compare their models with ARM data. In this report, we first provide an overview of major metrics in section 2. The input data, which constitutes the core content of the metrics and diagnostics package, is summarized in section 3. A user's guide documenting the workflow/structure of the version 3.0 codes and including step-by-step instruction for running the package is described in section 4.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Frozen Hydrometeor Terminal Fall Velocity Dependence on Particle Habit and Riming as Observed by Vertically Pointing Radars

Vertically pointing Ka-band radar measurements are used to derive fall velocity–reflectivity factor ($V$ t = $aZ$$^{b}_{e}$) relations for frozen hydrometeor populations of different habits during snowfall events observed at Oliktok Point, Alaska, and at the Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC). Case study events range from snowfall with highly rimed particles observed during periods with large amounts of supercooled liquid water path (LWP > 320 g m –2 ) to unrimed snowflakes including instances when pristine planar crystals were the dominant frozen hydrometeor habit. The prefactor a and the exponent b in the observed $V$ t –$Z$ e relations scaled to the sea level vary in the approximate ranges 0.5–1.4 and 0.03–0.13, respectively (reflectivities are in mm 6 m –3 and velocities are in m s –1 ). The coefficient a values are the smallest for planar crystals (a ~ 0.5) and the largest (a > 1.2) for particles under severe riming conditions with high LWP. There is no clear distinction between b values for high and low LWP conditions. The range of the observed $V$ t –$Z$ e relation coefficients is in general agreement with results of modeling using fall velocity–size (υ t = αD β ) relations for individual particles found in literature for hydrometeors of different habits, though there is significant variability in α and β coefficients from different studies even for a same particle habit. Correspondences among coefficients in the $V$ t –$Z$ e relations for particle populations and in the individual particle υ t –$D$ relations are analyzed. Furthermore, these correspondences and the observed $V$ t –$Z$ e relations can be used for evaluating different frozen hydrometeor fall velocity parameterizations in models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Evaluation of present-day extreme precipitation over the United States: an inter-comparison of convection and dynamic permitting configurations of E3SMv1

Abstract Accurate simulation of the present-day characteristics of mean and extreme precipitation at regional scales remains a challenge for Earth system models, which is due in part to deficiencies in model physics such as convective parameterization (CP), and coarse resolution. High horizontal resolution (HR, ∼25 km) and multiscale modeling framework (MMF, i.e. replacing conventional CP with embedded km-scale cloud-resolving models) are two promising directions that could help improve the interaction between subgrid-scale physical processes and large-scale climate. Here, we evaluate simulated extreme precipitation over the United States (US) across three configurations (i.e. low-resolution [LR], HR, and MMF) of the Energy Exascale Earth System Model (E3SMv1) and intercompare them against two gridded observation datasets (climate prediction center daily US precipitation and integrated multi-satellite retrievals for global precipitation measurement). We assess the model’s ability to simulate very heavy seasonal precipitation (illustrated by the difference between the 99th and 90th percentile values) as well as the spatial distributions of several extreme precipitation indices defined by the expert team on climate change detection and indices. Our results show that both the dry (i.e. consecutive dry days (CDD)) and wet (i.e. consecutive wet days, maximum 5 day precipitation, and very wet days) extremes evaluated herein show some improvement as well as degradation with MMF and HR relative to LR. These results vary across seasons and US subregions. For instance, only the very heavy precipitation of winter is improved with MMF and HR. Both configurations alleviate the well-known drizzling bias evident in LR across both winter and summer in many parts of the US, largely due to the overall improvement in intensity and frequency of precipitation. Additionally, our results suggest that while E3SMv1-MMF has higher intensity rates when it does rain, it has too many CDD during the summer, contributing to a low mean precipitation bias.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

One Stomatal Model to Rule Them All? Toward Improved Representation of Carbon and Water Exchange in Global Models

Abstract Stomatal conductance schemes that optimize with respect to photosynthetic and hydraulic functions have been proposed to address biases in land‐surface model (LSM) simulations during drought. However, systematic evaluations of both optimality‐based and alternative empirical formulations for coupling carbon and water fluxes are lacking. Here, we embed 12 empirical and optimization approaches within a LSM framework. We use theoretical model experiments to explore parameter identifiability and understand how model behaviors differ in response to abiotic changes. We also evaluate the models against leaf‐level observations of gas‐exchange and hydraulic variables, from xeric to wet forest/woody species spanning a mean annual precipitation range of 361–3,286 mm yr −1 . We find that models differ in how easily parameterized they are, due to: (a) poorly constrained optimality criteria (i.e., resulting in multiple solutions), (b) low influence parameters, (c) sensitivities to environmental drivers. In both the idealized experiments and compared to observations, sensitivities to variability in environmental drivers do not agree among models. Marked differences arise in sensitivities to soil moisture (soil water potential) and vapor pressure deficit. For example, stomatal closure rates at high vapor pressure deficit range between −45% and +70% of those observed. Although over half the new generation of stomatal schemes perform to a similar standard compared to observations of leaf‐gas exchange, two models do so through large biases in simulated leaf water potential (up to 11 MPa). Our results provide guidance for LSM development, by highlighting key areas in need for additional experimentation and theory, and by constraining currently viable stomatal hypotheses.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Robustness of the Stochastic Parameterization of Subgrid-Scale Wind Variability in Sea Surface Fluxes

Abstract High-resolution numerical models have been used to develop statistical models of the enhancement of sea surface fluxes resulting from spatial variability of sea surface wind. In particular, studies have shown that flux enhancement is not a deterministic function of the resolved state. Previous studies focused on single geographical areas or used a single high-resolution numerical model. This study extends the development of such statistical models by considering six different high-resolution models, four different geographical regions, and three different 10-day periods, allowing for a systematic investigation of the robustness of both the deterministic and stochastic parts of the data-driven parameterization. Results indicate that the deterministic part, based on regressing the unresolved normalized flux onto resolved-scale normalized flux and precipitation, is broadly robust across different models, regions, and time periods. The statistical features of the stochastic part of the model (spatial and temporal autocorrelation and parameters of a Gaussian process fit to the regression residual) are also found to be robust and not strongly sensitive to the underlying model, modeled geographical region, or time period studied. Best-fit Gaussian process parameters display robust spatial heterogeneity across models, indicating potential for improvements to the statistical model. These results illustrate the potential for the development of a generic, explicitly stochastic parameterization of sea surface flux enhancements dependent on wind variability.

Endo, Kota↗
DOE OSTI2022

Estimating Drizzle Parameters by Aircraft Sampling

In-cloud supersaturation is calculated from aircraft derived measurements of the in-cloud vertical gradient of liquid water content, vertical velocity, the pt and 3 rd cloud droplet radius moments and a parameter related to the heat and mass transport during condensation and evaporation of cloud droplets. The aircraft data where taken during the VOCALS 2008 study in marine stratocumulus clouds off the coast of Chile. Approximately 16000 discrete samples, 2.5 meter in length, were used in the present analysis of in-cloud supersaturation and its variability, both of which are indicative of turbulent processes within the cloud and their significant impact on drizzle formation. Two additional drizzle parameters, the eddy persistence time and the variance of fluctuations in in-cloud droplet growth rate were also estimated from this data set. These results will help to constrain parameterizations of underlying turbulent microphysical processes of clouds and thereby advance the future development of warm cloud and precipitation models for climate simulation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Machine learning based algorithms for uncertainty quantification in numerical weather prediction models

Complex numerical weather prediction models incorporate a variety of physical processes, each described by multiple alternative physical schemes with specific parameters. The selection of the physical schemes and the choice of the corresponding physical parameters during model configuration can significantly impact the accuracy of model forecasts. There is no combination of physical schemes that works best for all times, at all locations, and under all conditions. It is therefore of considerable interest to understand the interplay between the choice of physics and the accuracy of the resulting forecasts under different conditions. This paper demonstrates the use of machine learning techniques to study the uncertainty in numerical weather prediction models due to the interaction of multiple physical processes. The first problem addressed herein is the estimation of systematic model errors in output quantities of interest at future times, and the use of this information to improve the model forecasts. The second problem considered is the identification of those specific physical processes that contribute most to the forecast uncertainty in the quantity of interest under specified meteorological conditions. In order to address these questions we employ two machine learning approaches, random forests and artificial neural networks. The discrepancies between model results and observations at past times are used to learn the relationships between the choice of physical processes and the resulting forecast errors. Numerical experiments are carried out with the Weather Research and Forecasting (WRF) model. The output quantity of interest is the model precipitation, a variable that is both extremely important and very challenging to forecast. The physical processes under consideration include various micro-physics schemes, cumulus parameterizations, short wave, and long wave radiation schemes. The experiments demonstrate the strong potential of machine learning approaches to aid the study of model errors.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Convective Transition Statistics over Tropical Oceans for Climate Model Diagnostics: GCM Evaluation

To assess deep convective parameterizations in a variety of GCMs and examine the fast-time-scale convective transition, a set of statistics characterizing the pickup of precipitation as a function of column water vapor (CWV), PDFs and joint PDFs of CWV and precipitation, and the dependence of the moisture–precipitation relation on tropospheric temperature is evaluated using the hourly output of two versions of the GFDL Atmospheric Model, version 4 (AM4), NCAR CAM5 and superparameterized CAM (SPCAM). The 6-hourly output from the MJO Task Force (MJOTF)/GEWEX Atmospheric System Study (GASS) project is also analyzed. Contrasting statistics produced from individual models that primarily differ in representations of moist convection suggest that convective transition statistics can substantially distinguish differences in convective representation and its interaction with the large-scale flow, while models that differ only in spatial–temporal resolution, microphysics, or ocean–atmosphere coupling result in similar statistics. Most of the models simulate some version of the observed sharp increase in precipitation as CWV exceeds a critical value, as well as that convective onset occurs at higher CWV but at lower column RH as temperature increases. While some models quantitatively capture these observed features and associated probability distributions, considerable intermodel spread and departures from observations in various aspects of the precipitation–CWV relationship are noted. For instance, in many of the models, the transition from the low-CWV, nonprecipitating regime to the moist regime for CWV around and above critical is less abrupt than in observations. Additionally, some models overproduce drizzle at low CWV, and some require CWV higher than observed for strong precipitation. For many of the models, it is particularly challenging to simulate the probability distributions of CWV at high temperature.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

A New Method for Ice-Ice Aggregation in the Adaptive Habit Model

In this paper, a novel methodology for modeling ice-ice aggregation is presented. This methodology combines a modified hydrodynamic collection algorithm with bulk aggregate characteristic information from an offline simulator that collects ice particles, namely, the Ice Particle and Aggregate Simulator, and has been implemented into the Adaptive Habit Microphysics scheme in the Weather Research and Forecasting Model. Aggregates, or snow, are formed via collection of cloud ice particles, where initial ice characteristics and the resulting geometry determine aggregate characteristics. Upon implementation, idealized squall-line simulations are performed to examine the new methodology in comparison with commonly used bulk microphysics schemes. It is found that the adaptive habit aggregation parameterization develops snow and reduces ice mass and number concentrations compared to other schemes. The development of aggregates through the new methodology cascades into other interesting effects, including enhancements in ice and snow growth, as well as homogeneous freezing. Further microphysical analyses reveal varying sensitivities, where snow processes are most sensitive to the new parameterization, followed by ice, then cloud, rain, and graupel processes. Further, the new scheme results in enhancements in surface precipitation due to the persistence of snow at lower altitudes. This persistence is a result of shape-dependent melting and sublimation, increasing the residence time. Moreover, these low-level enhancements are reflected in increases in radar reflectivity at the surface and its spatial distribution. Finally, the ability to predict snow shape and density allows for the simulation of polarimetric radar quantities, resulting in signature enhancements compared to schemes that do not consider spatial and temporal variations in snow shape and density.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Diagnosis of convective organization and cold pools using ARM datasets and evaluation of a unified convection parameterization (UNICON)

Tropical thunderstorms often cluster together. Studies have suggested that the degree to which the tropical thunderstorms are clustered impacts Earth's energy balance and water cycle, as well as extreme precipitation events. However, the processes controlling the spatial distribution of the thunderstorms are poorly understood and are not properly represented in most computer models for weather and climate prediction. Under the goals of better understanding how convection organizes at the mesoscale and advancing the representation of mesoscale convective organization in global climate models, we i) objectively quantified the degrees of convective organization and diagnosed cold pool processes using ARM field campaign observations, ii) examined the organization processes in storm-resolving model simulations, and iii) evaluated the impacts of mesoscale convective organization in global model simulations. The project yielded a firm reference against which the global model representation of mesoscale convective organization and cold pools can be evaluated against and shed new light into the role of parameterized convective organization in the global model simulation of the basic state and variability. Our results revealed two distinct phases of convective clustering during the two-day rain episodes (Cheng et al. 2018) and a new mechanism through which vertical wind shear in the low-troposphere can aid convective organization over tropical oceans (Cheng et al. 2020). It was demonstrated that the interactive representation of cold pools and mesoscale convective organization is key for global models to successfully simulate both the mean state and intraseasonal variability in the tropics (Ahn et al. 2019; 2020).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Co-Based superalloy morphology evolution: A phase field study based on experimental thermodynamic and kinetic data

Cobalt-based superalloys with two phase $\gamma$/$\gamma '$ microstructures offer great promise as candidates for next-generation high-temperature alloys for applications, such as turbine blades. It is essential to understand the thermodynamic and kinetic factors that influence the microstructural evolution of these alloys in order to optimize the alloy compositions and processing steps with a goal to improve their coarsening, creep and rafting behavior. We are using a continuum phase field approach to study the diffusion process and to predict the equilibrium shapes of Co-Al-W $\gamma '$ precipitates. In order to obtain quantitatively predictive capabilities, we extract chemical free energies for the $\gamma$/$\gamma '$ phases based on CALculation of PHAse Diagrams (CALPHAD) thermodynamic data and diffusion mobilities for Co alloys based on CALPHAD kinetic data. We also use experimental or first-principles data for other quantities, such as misfit strain and interface information, for the parameterization of our model. A particular focus of our study is to understand how different energy balances, misfit strain and kinetics affect the coarsening and rafting behavior of $\gamma '$ precipitates, and the sensitivity of the final precipitate shape to materials parameters. Here, we find that the equilibrium shape of the precipitate results from a delicate competition between chemical, interfacial, and elastic energies, and it is very sensitive to changes in model parameters. Here, we examine how modeling input parameters affect the equilibrium shape of precipitates and relate these parameters to experimentally available values.

36 MATERIALS SCIENCE↗
DOE OSTI2025

The Impacts of Rotational Mixing on the Precipitation Simulated by a Convection Permitting Model

With increased availability of computational resources, regional and global scale convection-permitting model (CPM, Δx ~ 1–10 km) simulations are becoming more common. CPMs have improved accuracy in their representation of deep convection and mesoscale convective systems (MCSs) compared to coarser resolution models. However, CPMs still exhibit convective cloud and precipitation biases relative to observations, notably a lesser frequency of light precipitation rates and greater frequency of heavy precipitation rates. In this work we hypothesize that these CPM biases are related to under-resolved mixing between convective updrafts and their surrounding environment. To test this hypothesis, we introduce a parameterization to the Weather Research and Forecasting model (WRF) that adds a small angular rotation of the grid-scale flow about the axis perpendicular to the plane of convective drafts. This rotated flow is then allowed to alter advection of moisture and hydrometeors. The effects of such mixing on precipitation characteristics are evaluated in month-long 4-km grid spacing simulations over the Amazon. The enhanced mixing transports moisture and condensate from convective cores to other areas including downdrafts. This increases the frequency of low-precipitable water and light precipitation. It also decreases the frequency of intense precipitation from isolated deep convection and MCSs, increases cloud top temperatures, reduces radar echo-top heights, and increases overall precipitation by altering the relationship of precipitation with precipitable water, in better agreement with observations. The results suggest when optimized using multiple observations, such an approach may provide a path toward more accurate representation of convection and precipitation statistics in convection-permitting simulations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Atmospheric ice nuclei concentration measurements over a high altitude-station in the Western Ghats, India

Mixed-phase and ice clouds play a significant role in modulating the Indian summer monsoon rainfall, and modeling studies suggest that inaccurate representation of ice nucleating particles (INP) is one of the factors, which have resulted in a deficiency in the prediction of cloud properties and hydrological cycle. We do not understand the ice nucleation mechanisms to represent the ice formation in the model, and therefore more INP measurements at various supercooled temperatures are needed. This study reports for the first time real-time INP concentration at a high altitude station in the Western Ghats region of India, and these results will be used to constrain the ice nucleation parameterizations in the model. The variation of INP for any particular cloud condition can have strong microphysical responses of mixed-phase precipitation formation processes. Also, to improve the representation of heterogeneous ice nucleation, INP measurements of various types of aerosols commonly observed over the Indian region are needed. This study presents, the INP concentrations and daily concentration of non-refractory chemical composition (organics, sulfate, nitrate, ammonium, and chloride), cloud condensation nuclei (CCN), and aerosol size distribution. Spectrometer for Ice Nuclei (SPIN) was operated to measure INP concentration at the mountain site from August to December 2018. INP measurements were performed at three different temperatures (-25, -30, and - 34 °C) in the immersion freezing mode relevant for mixed-phase cloud conditions. The average INP concentrations approximately varied from 0.18 to 12.4 L-1, 0.39 to 24 L-1, 1.1 to 40.2 L-1 at -25, -30, and - 34 °C, respectively. These concentrations are within the concentrations reported in the literature from different regions across the globe. The air mass back trajectory analysis showed that continental air mass contains more INP compared to maritime air mass. The information on INP concentration and chemical characteristics of aerosol will help to improve heterogeneous ice nucleation parameterization in numerical models.

Kumar, Anil V.↗