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At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
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↗
NASA NTRS2003

Westerly Wind Events in the Eastern Indian Ocean as a Precursor to El Nino: A Case Study for the 2002-03 El Nino

This paper extends the work of our previous study, which showed the potential of using precipitation in the eastern Indian Ocean to predict when an El Nino would begin. The paper begins by showing the successful prediction of the 2002-03 El Nino. However, precipitation is really used as a substitute for wind (storms are usually accompanied by heavy wind), because a popular hypothesis is that winds (especially % winds out of the West) stir up the ocean surface in the western Pacific sending currents of warm waters to the east Pacific where El Ninos form. This paper shows that it is typical for storms that produce strong winds in the western Pacific to have traveled from the Indian Ocean. We begin in the Indian Ocean looking at strong bursts of wind over several days. The number of windy days seems to increase in the months prior to El Nino. We examined these relationships in detail for November 2001 to April 2002, before the recent El Nino, using NASA's TRMM and QuikSCAT data. We found in one case that a warming of the eastern Indian Ocean occurred about 25 days before heavy rainfall formed. As the stormed moved eastward it was followed (6 days later) by strong winds out of the West. The entire storm system (and warming of the sea) moved eastward through a small strip of water between Indonesia and Australia, before reaching the western Pacific. Thus, this paper increases our understanding of the physical processes leading to the formation of El Nino.

Curtis, Scott↗
NASA NTRS2004

GPM Mission, its Scientific Agenda, and its Ground Validation Program

The GPM mission is currently planned for start in the late 2010 time frame. From the perspective of NASA s Earth Science Enterprise (ESE) and within the framework of ESE's global water and energy cycle (GWEC) research program, its main scientific goal is to help answer pressing scientific problems concerning how global and regional water cycle processes and precipitation fluctuations and trends influence the variability intrinsic to climate, weather, and hydrology. These problems cut across a hierarchy of space-time scales and include improving understanding of climate-water cycle interactions, developing better techniques for incorporating satellite precipitation measurements into weather and climate predictions, and demonstrating that more accurate, more complete, and better sampled observations of precipitation and other water budget variables used as inputs can improve the ability of prognostic hydrometeorological models in the prediction of hazardous flood-producing storms, seasonal flood/draught conditions, and fresh water resource stores. The GPM mission will expand the scope of precipitation measurement through the use of a constellation of some 9 satellites, one of which will be an advanced TRMM-like core satellite carrying a dual-frequency Ku-Ka band precipitation radar (DPR) and an advanced, multifrequency passive microwave radiometer with vertical-horizontal polarization discrimination (GMI). The other constellation members will include a combination of new dedicated satellites and co-existing operational/research satellites carrying similar (but not identical) passive microwave radiometers. The goal of the constellation is to achieve 3-hour sampling at any spot on the globe -- continuously. The constellation s orbit architecture will consist of a mix of sun-synchronous and non-sun-synchronous satellites with the core satellite providing measurements of calibration-quality rainrates, plus cloud-precipitation microphysical processes, to be used in conjunction with more basic rain retrievals from the other constellation satellites to ensure bias-free constellation coverage.

Smith Eric A.↗
DOE OSTI2024

Weakened Orographic Influence on Cool-Season Precipitation in Simulations of Future Warming Over the Western US

High-resolution regional climate model (RCM) simulations of global warming consistently predict larger percentage increases in precipitation in the lee of midlatitude mountain ranges than on their windward slopes, indicating a weakening of the orographic rain shadow. This redistribution of precipitation could have profound consequences for water resources and ecosystems, but its underlying mechanisms are unknown. Here we show that rain-shadow weakening is just one manifestation of a more general decrease in the influence of orography on precipitation under global warming. We introduce a simple model of precipitation change based on this principle, and find that it agrees well with an ensemble of high-resolution simulations performed over the western United States. We argue that diminished orographic influence can be explained by the unique vertical structure of orographically forced ascent, which tends to maximize in the lower atmosphere where condensation is thermodynamically less sensitive to warming.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1993

The morphology of flare phenomena, magnetic fields, and electric currents in active regions. III - NOAA active region 6233 (1990 August)

We investigate the spatial relationship between vertical electric currents and flare phenomena in NOAA Active Region 6233, which was observed 1990, August 28-31 at Mees Solar Observatory. The two flares studied are the 1N/M1.8 flare on August 28, 22:30 UT and the 1N/M1.6 flare on August 29, 20:35 UT. Using Stokes polarimetry we make magnetograms of the region and compute the vertical current density. Using H-alpha imaging spectroscopy we identify sites of intense nonthermal electron precipitation or of high coronal pressure. The precipitation in these flares is barely strong enough to be detectable. We find that both precipitation and high pressure tend to occur near vertical currents, but that neither phenomenon is cospatial with current maxima. In contrast with the conclusion of other authors, we argue that these observations do not support a current-interruption model for flares, unless the relevant currents are primarily horizontal. The magnetic morphology and temporal evolution of these flares suggest that an erupting filament model may be relevant, but this model does not explicitly predict the relationship between precipitation, high pressure, and vertical currents.

De La Beaujardiere, J.-F.↗
DOE OSTI2024

Can we rely on drought–ending “miracles” in the Colorado River Basin?

Unexpected and large spring precipitation events in the Colorado River Basin (CRB) that significantly alleviated an otherwise severe water shortage have been observed for over a century, such as the “Miracle May” of 2015. Although these events are often termed as “drought-busting” or “miracle events” by water managers and the media, they have not been extensively researched or characterized. In this collaborative study with water managers across the CRB, we propose a definition for these hard-to-predict, ultra-high precipitation events occurring during the late-snow or snowmelt season. This characterization provides a framework for quantifying the frequency and intensity of extreme dry-to-wet springtime transitions. Despite limitations of climate model simulations due to uncertainties and the inhomogeneous qualities, our findings suggest that such transitions may become less frequent and less intense in a warming climate. Finally, in view of the potentially wetter but less-snowy climate in the basin, the need for future research to more quantitatively assess these “miracle events” is emphasized.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2006

Evaluating Clouds in Long-Term Cloud-Resolving Model Simulations with Observational Data

Two 20-day, continental midlatitude cases are simulated with a three-dimensional (3D) cloud-resolving model (CRM) and compared to Atmospheric Radiation Measurement (ARM) data. This evaluation of long-term cloud-resolving model simulations focuses on the evaluation of clouds and surface fluxes. All numerical experiments, as compared to observations, simulate surface precipitation well but over-predict clouds, especially in the upper troposphere. The sensitivity of cloud properties to dimensionality and other factors is studied to isolate the origins of the over prediction of clouds. Due to the difference in buoyancy damping between 2D and 3D models, surface precipitation fluctuates rapidly with time, and spurious dehumidification occurs near the tropopause in the 2D CRM. Surface fluxes from a land data assimilation system are compared with ARM observations. They are used in place of the ARM surface fluxes to test the sensitivity of simulated clouds to surface fluxes. Summertime simulations show that surface fluxes from the assimilation system bring about a better simulation of diurnal cloud variation in the lower troposphere.

Zeng, Xiping↗
NASA NTRS2008

The Global Precipitation Measurement (GPM) Project

The GIobd Precipitation hleasurement (GPM) mission is an international cooperati~ee ffort to advance weather, climate, and hydrological predictions through space-based precipitation measurements. The Core Observatory will be a reference standard to uniform11 calibrate data from a constellatism of spacecraft with passive microuave sensors. GP3l mission data will be used for scientific research as well as societal applications. GPM is being developed under a partnership between the United States (US) National .Aeronautics and Space Administration (XASA) and the Japanese Aerospace and Exploration Agency (JAYA). NASA is developing the Core Observatory, a Low-Inclination Constellation Observatory, two GPM Rlicrowave Imager (GXII) instruments. Ground Validation System and Precipitation Processing System for the GPRl mission. JAXA will provide a Dual-frequency Precipitation Radar (DPR) for installation on the Core satellite and launch services for the Core Observatory. Other US agencies and international partners contribute to the GPkf mission by providing precipitation measurements obtained from their own spacecraft and,'or providing ground-based precipitation measurements to support ground validation activities. The GPM Core Observatory will be placed in a low earth orbit (-400 krn) with 65-degree inclination, in order to calibrate partner instruments in a variety of orbits. The Core Observatory accommodates 3 instruments. The GkfI instrument provides measurements of precipitation intensity and distribution. The DPR consists of Ka and Ku band instruments, and provides threedimensional measurements of cloud structure, precipitation particle size distribution and precipitation intensitj and distribution. The instruments are key drivers for GPM Core Observatory overall size (1 1.6m x 6.5m x 5.0m) and mass (3500kg), as well as the significant (-1 950U.3 power requirement. The Core Spacecraft is being built in-house at Goddard Space Flight Center. The spacecraft structure consists of an aluminum lower bus structure. composite upper bus structure, '-axis steerable High Gain Antenna System on a dual-hinged boom, and two deploy able solar arraq s. The propulsion system features twelve thrusters and a single Composite Overlv~apP ressure Vessel tank. The GPhl Core spacecraft is one of the first large spacecraft developed to be demiseable (i.e. burn up upon atmospheric reentry j. The spacecraft dernissable components-- structure. propulsion tank, lithium-ion battery, sotar array md reaction wheels. are a unique fcature.

Azarbarazin, Ardeshir Art↗
DOE OSTI2026

Subseasonal Forecasting and MJO Teleconnections in Machine Learning Weather Prediction Models

Abstract In recent years, machine‐learning (ML) models trained on reanalysis data have rivaled physics‐based forecast models in terms of performance skill for global weather forecasting. With increased rollout stability, the question of how these models perform for subseasonal to seasonal (S2S, week 3–8) forecasting has emerged. In this study we run a large set of subseasonal hindcasts over 2004–2023 to evaluate two ML weather forecast models at the S2S time scale, SFNO‐HENS (Nvidia, fully ML) and NeuralGCM (Google Research, hybrid). Corresponding hindcasts from the European Centre for Medium‐Range Weather Forecasts (ECMWF) are used as a baseline for comparison to a physics‐based model. Because our focus is on predicting moisture transport over the Western United States between October and March, we evaluate the models' prediction skill for the Madden‐Julian Oscillation (MJO) and its associated teleconnections in the North Pacific. We find that both ML models are competitive with the ECWMF model, with comparable skill in predicting the North Pacific large‐scale circulation and the MJO at week 3 and beyond. Even though overall the mid‐latitude subseasonal prediction skill remains low, the ML models exhibit interesting behavior such as a realistic propagation of the MJO across the Maritime Continent and realistic teleconnections. A SFNO‐HENS sensitivity experiment with altered initial conditions in the tropics demonstrates the stability of the model, and it illustrates the capability of ML models to represent important physical processes of the atmosphere at the S2S time scale. Plain Language Summary Predicting weather patterns and precipitation a few weeks in advance (subseasonal time scale) is of great interest for stakeholders such as water managers in the Southwest United States (US), where arid conditions prevail. Subseasonal forecasts from traditional weather forecast models exhibit low skill in the region, limiting their applicability. Here we examine whether the recent breakthrough in weather forecasting made with machine learning/artificial intelligence models can translate to improved subseasonal forecasts. Recently‐developed machine learning models exhibit comparable skill to a state‐of‐the‐art physics‐based model for predicting weather patterns in the North Pacific/North America region, and associated moisture transport. The same applies to their skill in predicting the tropical pattern, the Madden‐Julian Oscillation, and its important remote perturbations over the midlatitude East Pacific and Southwest US. Additionally, a perturbation experiment carried out with one of the machine learning models illustrates their ability to not only predict the evolution of atmospheric fields, but also to learn and represent physical processes such as tropics‐extratropics Rossby wave propagation. Key Points Two machine learning weather forecast models exhibit state‐of‐the‐art prediction skill at the subseasonal time scale in the Pacific sector The models equal ECWMF in terms of Madden‐Julian oscillation (MJO) prediction skill, and they accurately predict the MJO propagation and associated teleconnections The two machine‐learning models represent key physical processes for subseasonal prediction, despite being trained for weather forecasting

Peings, Yannick↗
NASA NTRS1993

On the response of the tropical atmosphere to large-scale deforestation

Recent studies on the Amazon deforestation problem predict that removal of the forest will result in a higher surface temperature, a significant reduction in evaporation and precipitation, and possibly significant changes in the tropical circulation. Here, we discuss the basic mechanisms contributing to the response of the tropical atmosphere to deforestation. A simple linear model of the tropical atmosphere is used in studying the effects of deforestation on climate. It is suggested that the impact of large-scale deforestation on the circulation of the tropical atmosphere consists of two components: the response of the tropical circulation to the negative change in precipitation (heating), and the response of the same circulation to the positive change in surface temperature. Owing to their different signs, the changes in predicted temperature and precipitation excite competing responses working in opposite directions. The predicted change in tropical circulation determines the change, if any, in atmospheric moisture convergence, which is equivalent to the change in run-off. The dependence of run-off predictions on the relative magnitudes of the predicted changes in precipitation and surface temperature implies that the predictions about run-off are highly sensitive, which explains, at least partly, the disagreement between the different models concerning the sign of the predicted change in Amazonian run-off.

Eltahir, E. A. B.↗
NASA NTRS2014

An Update on Oceanic Precipitation Rate and its Zonal Distribution in Light of Advanced Observations from Space

This study contributes to the estimation of the global mean and zonal distribution of oceanic precipitation rate using complementary information from advanced precipitation measuring sensors and provides an independent reference to assess current precipitation products. Precipitation estimates from the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and CloudSat cloud profiling radar (CPR) were merged, as the two complementary sensors yield an unprecedented range of sensitivity to quantify rainfall from drizzle through the most intense rates. At higher latitudes, where TRMM PR does not exist, precipitation estimates from Aqua's Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) complemented CloudSat CPR to capture intense precipitation rates. The high sensitivity of CPR allows estimation of snow rate, an important type of precipitation at high latitudes, not directly observed in current merged precipitation products. Using the merged precipitation estimate from the CloudSat, TRMM, and Aqua platforms (this estimate is abbreviated to MCTA), the authors' estimate for 3-yr (2007-09) nearglobal (80degS-80degN) oceanic mean precipitation rate is approx. 2.94mm/day. This new estimate of mean global ocean precipitation is about 9% higher than that of the corresponding Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) value (2.68mm/day) and about 4% higher than that of the Global Precipitation Climatology Project (GPCP; 2.82mm/day). Furthermore, MCTA suggests distinct differences in the zonal distribution of precipitation rate from that depicted in GPCPand CMAP, especially in the Southern Hemisphere.

profiling radar↗
DOE OSTI2024

A Gaussian process autoregressive model capturing microstructure evolution paths in a Ni–Mo–Nb alloy

Additive manufacturing is increasingly being employed to produce components of complex geometries in structural alloys because of the expected energy savings associated with the near-net-shape capability and the ability to build in novel internal features that are not possible with many conventional manufacturing approaches. However, because of the extreme thermal conditions encountered, the non-equilibrium microstructures produced during powder bed-based additive manufacturing processes must be subjected to custom post-heat treatment processes to recover the target mechanical properties. Phase-field models and simulation techniques have matured to a state where the microstructure evolution paths, and the morphologies of the resulting precipitate phases can be predicted reasonably accurately, considering alloy-specific thermodynamic and kinetic aspects of the nucleation and growth processes. However, phase-field simulations are computationally intensive, which precludes the ability to apply the simulations directly to the length scale of the entire component. Therefore, it is highly desirable to develop low-computational-cost surrogate models that effectively capture the physics at the microstructural length scale, while facilitating the design of optimized processing conditions resulting in location-specific targeted microstructures at the component scale. The work presented here demonstrates the application of the materials knowledge system framework to develop a surrogate model that effectively captures the microstructural path during annealing of a Ni–Mo–Nb alloy containing different Mo and Nb compositions known to segregate during solidification under additive manufacturing conditions. Specifically, the surrogate model built in this work is based on a Gaussian process autoregressive model informed by statistical representation of simulated microstructures using two-point correlations and dimensionality reduction through principal component analysis. In conclusion, this surrogate model is shown to capture the bifurcation of the microstructural path during precipitation, which yields a microstructure dominated by the $\gamma^{\prime\prime}$ phase at high Nb concentrations and the $\delta$ phase at low Nb concentrations.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Reactive transport modeling of the Aquifer Thermal Energy Storage (ATES) system at Stockton University, New Jersey during seasonal operations

Hydrogeochemical processes associated with Aquifer Thermal Energy Storage (ATES) operations can often impact the system performance owing to mineral precipitation either at the wellbore or in the aquifer owing to changes in temperature and fluid disequilibria. Although failure of ATES systems due to mineral precipitation ("fouling") is common, predictive reactive-transport models have rarely been applied to plan their design and operation. Here, the objective of this study is to develop a reactive-transport model by coupling thermal, hydrological, and chemical (THC) processes to evaluate effects of introduced atmospheric oxygen on water chemistry, mineral precipitation/dissolution, porosity, and permeability changes associated with an ATES system at Stockton University (New Jersey, USA). The THC model builds on a Thermal-Hydrological-Mechanical (THM) model of the site that evaluated system failure owing to possible fracturing in the caprock or around the wellbore. The causes of the system failure are not known – potential causes include hydraulic fracturing owing to elevated pump pressures that took place, a flow pathway created by one of the boreholes, or a pre-existing natural hydrologic connection between the upper unconfined aquifer and the ATES aquifer, any of which could have led to oxygenated water entering the reservoir and causing the observed Fe-oxide fouling on well screens. The THC model is used to evaluate some of the hypotheses and observations regarding system failure owing to geochemical processes. The reactive-transport code TOUGHREACT V4 was used to model the THC processes during seasonal heating and cooling operations at the Stockton ATES site over 6 years of operation. In the THC simulations, the primary effects on geochemistry were observed when the injection water is saturated with atmospheric oxygen. Simulations show greater precipitation of goethite near the cold wells as compared to the warm wells. Although volume fractions of Fe-hydroxides were relatively small, the model was aimed at processes in the aquifer at the scale of meters and larger rather than at the scale of mm or cm (i.e., a well screen). Kaolinite is the dominant precipitating phase, also around the cold wells. Illite dissolves near the cold wells and precipitates near the warm wells. There is a net decrease in the porosity near the cold wells and increase near the warm wells, although a slight amount of thermal contraction near the cold wells and expansion near the warm wells is responsible for a significant proportion of the porosity change. Owing to the coarse discretization of the numerical grid near the wells (compared to the screen thickness) the magnitude of permeability changes at the wellbore are likely underestimated. The reactive transport model in this study can be used for characterization of aquifers, optimizing the operational parameters (temperature, pressure, pH etc.), and planning of mitigation strategies for ATES systems.

15 GEOTHERMAL ENERGY↗
NASA NTRS1993

Effects of precipitation on the thermodynamic structure of the trade wind boundary layer

A model of the thermodynamic structure of the trade wind boundary layer is formulated to include the parameterization of precipitation in relatively shallow clouds. Although the area-averaged simulated precipitation rates are relatively small (less than 1 mm/day), the inclusion of precipitation has an appreciable effect on the predicted thermodynamic structure. The cloud layer structure simulated with precipitation is warmer, drier, and more stable than that simulated without precipitation. The simulated inversion height is lowered by as much as 60 mbar when precipitation is included.

Albrecht, Bruce A.↗
DOE OSTI2022

Identifying Hydrometeorological Factors Influencing Reservoir Releases Using Machine Learning Methods

Simulation of reservoir releases plays a critical role in social-economic functioning and our nation's security. How-ever, it is challenging to predict the reservoir release accurately because of many influential factors from natural environments and engineering controls such as the reservoir inflow and storage. Moreover, climate change and hydrological intensification causing the extreme precipitation and temperature make the accurate prediction of reservoir releases even more challenging. Machine learning (ML) methods have shown some successful applications in simulating reservoir releases. However, previous studies mainly used inflow and storage data as inputs and only considered their short-term influences (e.g, previous one or two days). In this work, we use long short-term memory (LSTM) networks for reservoir release prediction based on four input variables including inflow, storage, precipitation, and temperature and consider their long-term influences. We apply the LSTM model to 30 reservoirs in Upper Colorado River Basin, United States. We analyze the prediction performance using six statistical metrics. More importantly, we investigate the influence of the input hydrometeorological factors, as well as their temporal effects on reservoir release decisions. Results indicate that inflow and storage are the most influential factors but the inclusion of precipitation and temperature can further improve the prediction of release especially in low flows. Additionally, the inflow and storage have a relatively long-term effect on the release. These findings can help optimize the water resources management in the reservoirs.

Fan, Ming↗
DOE OSTI2020

Impacts of urbanization, antecedent rainfall event, and cyclone tracks on extreme floods at Houston reservoirs during Hurricane Harvey

The objective of this study is to evaluate the effects of urbanization, antecedent rainfall event (ARE), and varying cyclone tracks on the streamflow—and thus the subsequent reservoir status—during the floods caused by Hurricane Harvey in August-September 2017. Through a hydrological modeling approach, we examined how these factors influenced the inflows, peak pool elevations and outflows of the two most important detention reservoirs in the Houston region, the Addicks and Barker Reservoirs. A high-resolution rainfall reanalysis dataset for extreme storm events, along with a suite of synthetic rainfall values from a variety of storm tracks, were adopted to represent both the truth and the maximum possible rainfall during the Hurricane Harvey period. Results showed the following: Urbanization only led to slight increases in peak inflows, not necessarily leading to an increase in peak pool elevations. ARE contributed to the peak inflow and pool elevation slightly. In contrast, if the cyclone had followed the most adverse track consistent with earlier forecasts (all else being equal), the total volumetric flow into the two reservoirs could have been significantly larger (37.00% and 48.70% respectively), thus increasing the peak pool elevations by 1.06 and 1.37 meters respectively. These results suggest that large uncertainties exist for flood management at a watershed scale during hurricanes, because of the uncertainties with regard to the cyclone track. This is true even if storm-relative precipitation rates could be predicted perfectly.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Exploiting Commonly-Reported Age-Hardening Data and Discovering Systematics Across Metallic Alloys and Alloy Systems to Identify Corrosion’s Most Influential Factors

This work explored ways to data mine legacy literature and predict solid-solid precipitation in support of simpler assessment of corrosion propensity in metallic alloys. Of interest was locating the peak age watershed (maximum hardness or strength), beyond which lies the regime termed “overaging.” A diligent search of literature and reference books, discussions with SMEs, and application of various algorithms showed that none of the premises going in held up to scrutiny.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Assessing Secondary Ice Production in Continental Clouds Based on AMF Synergistic Remote Sensing Observations

Ice crystals play an important role in radiation and precipitation formation. However, the prediction of ice number concentration has proven to be problematic, because the predicted concentration of ice nucleating particles is often smaller than the observed by several orders of magnitude. This difference between the observed and the predicted number concentration has been explained by so-called secondary ice production (SIP), which includes hypothesized mechanisms like rime splintering, shattering of large frozen drops, and ice-ice collision breakup. To better observe SIP and understand its frequency, production rate, and impact on cloud evolution, we developed a new method for retrieving microphysical properties of concurrent pristine ice and snow aggregates from X-band polarimetric radar observations. Compared to aircraft cloud probe measurements, our retrieved total number concentration and ice water content are overestimated by 98% and 44%, respectively, which outperforms other empirical relationships. The capability of quantifying pristine ice number concentration, which is the most essential observable in SIP studies, opens a new opportunity to build a long-term record of SIP from surface-based radar measurements. We have also conducted simulations using a cloud-resolving model to evaluate the representativeness of the parameterizations of three SIP mechanisms and for studying the impact of SIP on a frontal system. We found a surprisingly good agreement between the observed and retrieved pristine ice number concentrations. The good performance is attributed to the rime splintering process in the temperature zone between–3° and –8°C, and to the ice-ice collision breakup process at higher altitudes. The impact of the shattering of large frozen drops appears minimal. Apart from the enhancement of ice number concentration and increased horizontal coverage of pristine ice, the addition of three SIP processes does not change cloud evolutions dramatically in our simulations.

58 GEOSCIENCES↗