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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Quantitative Precipitation Estimation of Extremes in CONUS With Radar Data

When constructing an accurate, continental, in-situ-based, kilometer-scale, long-term record of the precipitation field and its spatiotemporal changes remains a significant challenge. Here, we determine the extreme-value behavior of the NEXRAD Stage IV radar-based quantitative precipitation estimate. We find that the climatology of 5-year daily return values in the contiguous United States East of the Rocky Mountains shows only slight variability on spatial scales smaller than (Formula presented.) ~100 km. In light of this finding, we test whether rain-gauge-only daily precipitation data sets can produce accurate extreme-value behavior at spatial scales finer than the spacing between gauges. We find that the 5-year daily return values are accurate at locations far from rain gauges only if the interpolation between gauges is carried out appropriately for extremes. Precipitation statistics derived from in-situ rain gauge data are therefore of sufficient spatial resolution to faithfully capture daily extremes over much of the eastern United States.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Southern Ocean Precipitation Characteristics Observed From CloudSat and Ground Instrumentation During the Macquarie Island Cloud & Radiation Experiment (MICRE): April 2016 to March 2017

A 1-year blended surface precipitation data set using Parsivel disdrometer, surface W-band radar, and tipping bucket measurements is produced for the Macquarie Island Cloud and Radiation Experiment (MICRE) and compared with retrievals from CloudSat (spaceborne 94 GHz radar). Surface precipitation was observed 44% ± 4% of the time between April 2016 and March 2017. Precipitation composed primarily of small particles (diameter <1 mm) occurred about 36% ± 2% of the time, constituting 10% of total accumulation. Remaining precipitation contained enough large particles such that the disdrometer could be used to identify the precipitation type as rain, ice, snow or wet snow. Seasonal and annual statistics on frequency of occurrence and accumulation for each precipitation type observed during MICRE are presented. Most ice and mixed phase precipitation was shallow, originating at a height of 3 km or lower, and occurred most often when Macquarie Island was to the northwest of the nearest cyclonic low-pressure center. In contrast, rain was more often deep and occurred most frequently when the island was to the southeast of cyclonic lows. A weak diurnal cycle in frequency and mean rate was present with a minimum between 12:00 and 14:00 local time and maximum between 03:00 and 06:00 local time. The CloudSat 2C-Precip-Column product missed the lightest precipitation (because the near-surface reflectivity is <-15 dBZ) and overestimated total liquid precipitation and occurrence of mixed phase precipitation, but captured reasonably well the distribution of rain rates for rates >0.5 mm/hr.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Precipitation–Moisture Coupling Over Tropical Oceans: Sequential Roles of Shallow, Deep, and Mesoscale Convective Systems

We report Precipitation over tropical oceans rapidly increases when the environmental column saturation fraction (CSF) increases past a critical value of ~0.7. Past studies suggested that increased stratiform rainfall greatly contributes to the rapid rainfall enhancement. In this study, the sequential roles of non-deep convection, deep convection, and mesoscale convective system (MCS) in precipitation-moisture interactions are examined using 19 years of satellite observations. When CSF is below ~0.5, non-deep convection dominates total rainfall, and predominantly contributes to moistening of the environment. Between the CSF range of 0.5–0.7, transition to deep convective rainfall begins. Meanwhile, MCS contribution to total rain rapidly increases, and the environment is further moistened. MCS becomes the major rainfall type above the critical CSF value (~0.7), with the rapid increase of total rain mostly explained by the rapid increase in MCS rain area. Rainfall reduction at high CSF values is jointly contributed by MCS and non-deep convection.

58 GEOSCIENCES↗
DOE OSTI2023

Drivers of Cloud Condensation Nuclei in the Eastern North Atlantic as Observed at the ARM Site

Abstract Seven years of data collected at the Atmospheric Radiation Measurement's Eastern North Atlantic (ENA) site are analyzed to understand the controls of Cloud Condensation Nuclei (CCN) concentrations in the region. Day‐night differences in the aerosol data as segregated by wind direction demonstrate the aerosol observations to be impacted by local emissions when the wind direction (wdir) is between 90° and 310° (measured clockwise from the North where air is coming from). Data collected during marine conditions (wdir <90° or wdir >310°) show the CCN concentrations to be higher in the summer months as compared to the winter months. CCN budget analysis revealed advection and precipitation scavenging being primarily responsible for modulating the CCN concentrations at the site on monthly timescales, with rain rates driving the precipitation scavenging term. High (greater than 75th percentile) and low (lower than 25th percentile) CCN events were identified for each month to characterize the sub‐monthly variability of CCN concentrations. Low CCN events had thicker clouds, stronger rain rates, and lower reanalysis reported free‐tropospheric aerosol pseudo number concentration at the ENA site as compared to the high CCN events. Analysis of satellite data of air‐parcels 48 hr prior to their arrival at the ENA site demonstrated the air parcels during low CCN events to encounter higher cloudiness, stronger rain rates, and higher cloud top heights as compared to the high CCN events. The results presented herein provide key constraints for model evaluation studies and climatological studies conducted at the ENA site.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Leveraging Groundwater Dynamics to Improve Predictions of Summer Low-Flow Discharges

Summer streamflow predictions are critical for managing water resources; however, warming-induced shifts from snow to rain regimes impact low-flow predictive models. Additionally, reductions in snowpack drive earlier peak flows and lower summer flows across the western United States increasing reliance on groundwater for maintaining summer streamflow. However, it remains poorly understood how groundwater contributions vary interannually. Here, we quantify recession limb groundwater (RLGW), defined as the proportional groundwater contribution to the stream during the period between peak stream flow and low flow, to predict summer low flows across three diverse western US watersheds. We ask (a) how do snow and rain dynamics influence interannual variations of RLGW contributions and summer low flows?; (b) which watershed attributes impact the effectiveness of RLGW as a predictor of summer low flows? Linear models reveal that RLGW is a strong predictor of low flows across all sites and drastically improves low-flow prediction compared to snow metrics at a rain-dominated site. Results suggest that strength of RLGW control on summer low flows may be mediated by subsurface storage. Subsurface storage can be divided into dynamic (i.e., variability saturated) and deep (i.e., permanently saturated) components, and we hypothesize that interannual variability in dynamic storage contribution to streamflow drives RLGW variability. In systems with a higher proportion of dynamic storage, RLGW is a better predictor of summer low flow because the stream is more responsive to dynamic storage contributions compared to deep-storage-dominated systems. Overall, including RLGW improved low-flow prediction across diverse watersheds.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Opportunistic Short‐Term Water Uptake Dynamics by Subalpine Trees Observed via In Situ Water Isotope Measurements

Abstract Variations in tree water sources are important to understand in semi‐arid ecosystems because climatic shifts towards lower snowpack and increased drought affect water availability in subalpine forests of the western US. Here, we use daily in situ measurements of stable isotopes ( 2 H & 18 O) in soil and tree stem water, soil matric potential and sap flow to study tree water uptake dynamics. We instrumented three soil profiles down to 90 cm, as well as three aspen and engelmann spruce trees near Gothic, Colorado, in the East River watershed. We observed the fate of natural isotopic variations in rainfall, soil, and plants from June to October 2022, and in August 2023 we conducted a 2 H labeled irrigation experiment. Our observations showed that all studied aspen trees compensated for water scarcity in the shallow soil by shifting the dominant water source at 60(±20) cm to ⅔ of uptake from 90 cm within a few days of a dry period. Both species relied on snowmelt stored in the subsoil to sustain transpiration. Intense rainfall caused the plant water uptake to shift partially to top soil layers within 2 days. Spruce transpiration was lower and relied more on snowmelt, because rainfall infiltration was low in the spruce stand due to high canopy interception. Our findings highlight the important role of snowmelt stored in the deep soil layers for subalpine forest drought response and the dominant fate of monsoonal rainfall to become transpiration rather than recharging groundwater and streams in the Upper Colorado River. Plain Language Summary There is a need to understand how trees in mountainous regions respond to dry conditions that lead to water scarcity, because climate projections suggest that such conditions will become more frequent in the future. Here we present a novel data set of measurements of daily stable isotopes of water across soil profiles and in tree stems of aspen and spruce. Our data show that when the upper soil dried out, aspen trees shifted to using water from deeper layers (beneath 60 cm) to keep transpiring. For spruce trees the uptake pattern is less clear, but both types of trees mainly used snowmelt stored in the deeper soil layers to survive the dry summer. After heavy rain, aspen and spruce trees switched to using water from the top 20 cm of soil. However, for spruce, only some rain reached the soil because the dense tree canopy intercepted it, so spruce trees stayed more dependent on snowmelt and used less water overall. This study shows how important deep snowmelt water is for helping forests survive dry periods and suggests that most summer rain is quickly used by trees rather than replenishing streams and groundwater in the headwaters of the Colorado River. Key Points Tree water resources changed within a few days from snow dominated to higher share of rainfall as soils wetted up after a dry period Compensatory plant water uptake by aspen from the deep layer (90 cm), while uptake from soil depths that became drier (60 cm) declined Strong differences between water sources and availability beneath aspen and spruce, respectively

Sprenger, Matthias↗
DOE OSTI2026

Can ERA5 Be Used to Study Mesoscale Convective System Climatological Characteristics?

Mesoscale convective systems (MCSs) produce more than half of tropical rainfall and are central to the global hydrologic cycle. As the climate warms, environments favorable for MCSs may become more common; however, limited observational records hamper understanding of how MCSs respond to variations and changes in their environments. Here, we evaluate how well MCSs are represented in ERA5, a widely used global high‐resolution reanalysis product. Using PyFLEXTRKR, which jointly tracks top‐of‐atmosphere infrared brightness temperature and surface precipitation, we identified MCSs in ERA5 and compared them with those identified in satellite observations using the same detection algorithm. This comparison analysis spans 2007–2020 using hourly data at 0.25° horizontal resolution focusing over the tropics. ERA5 reproduces observed brightness‐temperature statistics and captures the geographic distribution and seasonal and diurnal cycles of MCS cold cloud shields. However, ERA5 precipitation exhibits an intensity bias—too much light rain and too little heavy rain—which shifts the rain‐rate distribution and reduces the frequency of MCSs relative to observations. Within MCSs, ERA5 precipitation exhibits the same pattern of bias, yielding a systematic underestimation of MCS precipitation intensity. Consistent with these biases, ERA5 underestimates the contribution of MCS to tropical rainfall by 25%–34% in key regions. Overall, ERA5 is suitable for studying MCS cold cloud‐shield climatology and evolution, but precipitation‐based MCS characteristics (including event‐level precipitation features and the geospatial distribution of MCS precipitation) should be interpreted with caution. These findings clarify which aspects of MCS behavior are robustly represented in ERA5 for climatological applications.

mesoscale convection↗
DOE OSTI2024

Canopy cover and microtopography control precipitation-enhanced thaw of ecosystem-protected permafrost

Abstract Northern high-latitudes are projected to get warmer and wetter, which will affect rates of permafrost thaw and mechanisms by which thaw occurs. To better understand the impact of rain, as well as other factors such as snow depth, canopy cover, and microtopography, we instrumented a degrading permafrost plateau in south-central Alaska with high-resolution soil temperature sensors. The site contains ecosystem-protected permafrost, which persists in unfavorable climates due to favorable ecologic conditions. Our study (2020–2022) captured three of the snowiest years and three of the four wettest years since the site was first studied in 2015. Average thaw rates along an across-site transect increased nine-fold from 6 ± 5 cm yr −1 (2015–2020) to 56 ± 12 cm yr −1 (2020–2022). This thaw was not uniform. Hummock locations, residing on topographic high points with relatively dense canopy, experienced only 8 ± 9 cm yr −1 of thaw, on average. Hollows, topographic low points with low canopy cover, and transition locations, which had canopy cover and elevation between hummocks and hollows, thawed 44 ± 6 cm yr −1 and 39 ± 13 cm yr −1 , respectively. Mechanisms of thaw differed between these locations. Hollows had high warm-season soil moisture, which increased thermal conductivity, and deep cold-season snow coverage, which insulated soil. Transition locations thawed primarily due to thermal energy transported through subsurface taliks during individual rain events. Most increases in depth to permafrost occurred below the ∼45 cm thickness seasonally frozen layer, and therefore, expanded existing site taliks. Results highlight the importance of canopy cover and microtopography in controlling soil thermal inputs, the ability of subsurface runoff from individual rain events to trigger warming and thaw, and the acceleration of thaw caused by consecutive wet and snowy years. As northern high-latitudes become warmer and wetter, and weather events become more extreme, the importance of these controls on soil warming and thaw is likely to increase.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

A Hybrid Method to Estimate Specific Differential Phase and Rainfall With Linear Programming and Physics Constraints

A hybrid method of combining linear programming (LP) and physical constraints is developed to estimate specific differential phase (K DP ) and to improve rain estimation. Moreover, the hybrid K DP estimator and the existing estimators of LP, least squares fitting, and a self-consistent relation of polarimetric radar variables are evaluated and compared using simulated data. Our simulation results indicate the new estimator's superiority, particularly in regions where backscattering phase (δ hv ) dominates. Further, a quantitative comparison between auto-weather-station rain-gauge observations and K DP -based radar rain estimates for a Meiyu event also demonstrate the superiority of the hybrid K DP estimator over existing methods.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

A Retrieval of Tropical Latent Heating Using the 3D Structure of Precipitation Features

Radar-based latent heating retrievals typically apply a lookup table (LUT) derived from model output to surface rain amounts and rain type to determine the vertical structure of heating. In this study, a method has been developed that uses the size characteristics of precipitating systems (i.e., area and mean echo-top height) instead of rain amount to estimate latent heating profiles from radar observations. This technique [named the convective–stratiform area (CSA) algorithm] leverages the relationship between the organization of convective systems and the structure of latent heating profiles and avoids pitfalls associated with retrieving accurate rainfall information from radars and models. The CSA LUTs are based on a high-resolution regional model simulation over the equatorial Indian Ocean. The CSA LUTs show that convective latent heating increases in magnitude and height as area and echo-top heights grow, with a congestus signature of midlevel cooling for less vertically extensive convective systems. Stratiform latent heating varies weakly in vertical structure, but its magnitude is strongly linked to area and mean echo-top heights. The CSA LUT was applied to radar observations collected during the DYNAMO/Cooperative Indian Ocean Experiment on Intraseasonal Variability in the Year 2011 (CINDY2011)/ARM MJO Investigation Experiment (AMIE) field campaign, and the CSA heating retrieval was generally consistent with other measures of heating profiles. Finally, the impact of resolution and spatial mismatch between the model and radar grids is addressed, and unrealistic latent heating profiles in the stratiform LUT, namely, a low-level heating peak, an elevated melting layer, and net column cooling, were identified. These issues highlight the need for accurate convective–stratiform separations and improvement in PBL and microphysical parameterizations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Turbulence in The Marine Boundary Layer and Air Motions Below Stratocumulus Clouds at the ARM Eastern North Atlantic Site

Marine stratocumulus clouds are intimately coupled to the turbulence in the boundary layer and drizzle is known to be ubiquitous within them. Six years of data collected at the Atmospheric Radiation Measurement (ARM)’s Eastern North Atlantic site are utilized to characterize turbulence in the marine boundary layer and air motions below stratocumulus clouds. Profiles of variance of vertical velocity binned by wind direction (wdir) yielded that the boundary layer measurements are affected by the island when the wdir is between 90° and 310° (measured clockwise from North where air is coming from). Data collected during the marine conditions (wdir<90 or wdir>310) showed that the variance of vertical velocity was higher during the winter months than during the summer months due to higher cloudiness, wind speeds, and surface fluxes. Furthermore, during marine conditions the variance of vertical velocity and cloud fraction exhibited a distinct diurnal cycle with higher values during the nighttime than during the daytime. Detailed analysis of 32 cases of drizzling marine stratocumulus clouds showed that for a similar amount of radiative cooling at the cloud top, within the sub-cloud layer 1) drizzle increasingly falls within downdrafts with increasing rain rates, 2) the strength of the downdrafts increases with increasing rain rates, and 3) the correlation between vertical air motion and rain rate is highest in the middle of the sub-cloud layer. The results presented herein have implications for climatological and model evaluation studies conducted at the ENA site, along with efforts of accurately representing drizzle-turbulence interactions in a range of atmospheric models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Evaluation of Subgrid-scale Hydrometeor Transport Schemes using a High-resolution Cloud-resolving Model

Potential ways of parameterizing vertical turbulent fluxes of hydrometeors are examined using a high-resolution cloud-resolving model. The cloud-resolving model uses the Morrison microphysics scheme, which contains prognostic variables for rain, graupel, ice, and snow. A benchmark simulation with a horizontal grid spacing of 250 m of a deep convection case carried out to evaluate three different ways of parameterizing the turbulent vertical fluxes of hydrometeors: an eddy-diffusion approximation, a quadrant-based decomposition, and a scaling method that accounts for within-quadrant (subplume) correlations. Results show that the down-gradient nature of the eddy-diffusion approximation tends to transport mass away from concentrated regions, whereas the benchmark simulation indicates that the vertical transport tends to transport mass from below the level of maximum to aloft. Unlike the eddy-diffusion approach, the quadri-modal decomposition is able to capture the signs of the flux gradient but underestimates the magnitudes. The scaling approach is shown to perform the best by accounting for within-quadrant correlations, and improves the results for all hydrometeors except for snow. A sensitivity study is performed to examine how vertical transport may affect the microphysics of the hydrometeors. The vertical transport of each hydrometeor type is artificially suppressed in each test. Results from the sensitivity tests show that cloud-droplet-related processes are most sensitive to suppressed rain or graupel transport. In particular, suppressing rain or graupel transport has a strong impact on the production of snow and ice aloft. Lastly, a viable subgrid-scale hydrometeor transport scheme in an assumed probability density function parameterization is discussed.

cloud-resolving model↗
DOE OSTI2022

Evaluating Tropical Precipitation Relations in CMIP6 Models with ARM Data

Abstract A set of diagnostics based on simple, statistical relationships between precipitation and the thermodynamic environment in observations is implemented to assess phase 6 of the Coupled Model Intercomparison Project (CMIP6) model behavior with respect to precipitation. Observational data from the Atmospheric Radiation Measurement (ARM) permanent field observational sites are augmented with satellite observations of precipitation and temperature as an observational baseline. A robust relationship across observational datasets between column water vapor (CWV) and precipitation, in which conditionally averaged precipitation exhibits a sharp pickup at some critical CWV value, provides a useful convective onset diagnostic for climate model comparison. While a few models reproduce an appropriate precipitation pickup, most models begin their pickup at too low CWV and the increase in precipitation with increasing CWV is too weak. Convective transition statistics compiled in column relative humidity (CRH) partially compensate for model temperature biases—although imperfectly since the temperature dependence is more complex than that of column saturation. Significant errors remain in individual models and weak pickups are generally not improved. The conditional-average precipitation as a function of CRH can be decomposed into the product of the probability of raining and mean precipitation during raining times (conditional intensity). The pickup behavior is primarily dependent on the probability of raining near the transition and on the conditional intensity at higher CRH. Most models roughly capture the CRH dependence of these two factors. However, compensating biases often occur: model conditional intensity that is too low at a given CRH is compensated in part by excessive probability of precipitation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

How Frequent Will the Rarest Daily Rainfall Records of Hurricane Ida’s Remnants Be in the Future?

Abstract Gaining continued insights into the impact of global warming on the occurrence of hurricane-associated intense record downpours is essential for building climate resilient communities. This study investigates projected future changes in extreme rainfall over the Northeast United States, as represented by extreme daily amounts during Hurricane Ida in 2021. We used historical control simulations of Weather Research and Forecasting (WRF) Model generated from 40 years of weather events (1980–2014, 12 km) forced by the fifth generation European Centre for Medium-Range Weather Forecasts atmospheric reanalysis. These simulations are thermodynamically modified (2060–2100) via an imposed warming for the high-emission scenario of shared socioeconomic pathway (SSP585) from a range of general circulation models. Ground observations from the Global Historical Climatology Network (1950–2014) and WRF simulations (historical, 1980–2014, and future, 2060–2100) are integrated into a nonstationary generalized extreme value (GEV) framework to assess the frequency of Ida’s heaviest daily rain rates under the SSP585 scenario. Results show that Ida’s daily maximum rainfall recorded at different observation locations was higher than the single highest September daily maximum observed (1950–2014) for 5 out of 17 stations (∼30% of the stations). Ida-like extreme daily rain rates are projected to be, on average, more than 2 times more likely to occur at the end of the century in the simulations (with some regions as high as 5 times). This work demonstrates that integrating a high-resolution atmospheric model’s present-day and thermodynamically modified future simulations along with ground observations, within a nonstationary statistical framework, is crucial for understanding changing characteristics of extreme weather events. Significance Statement Daily scale extreme precipitation is expected to become more frequent and severe, as evidenced by observations and model simulations. While it is important to investigate how these intensifying heavy rainfall events affect current engineering standards, fewer studies have contextualized how warming impacts the most extreme rainfall from a single storm event relative to historical heavy downpours. In this study, we focused on the daily extreme rainfall associated with the extratropical transition of Hurricane Ida (2021), particularly over the northeastern United States—some of which exceeded the commonly used hydrologic design criteria for a 100-yr storm. Using a high-resolution atmospheric model simulation, we investigated how continued warming may influence the frequency of such daily rain rates. Under a high-emission scenario, these events are projected to become up to 5 times more likely at the end of the twenty-first century.

Dollan, Ishrat J↗
DOE OSTI2021

Equatorial waves triggering extreme rainfall and floods in southwest Sulawesi, Indonesia

On the basis of detailed analysis of a case study and long-term climatology, it is shown that equatorial waves and their interactions serve as precursors for extreme rain and flood events in the central Maritime Continent region of southwest Sulawesi, Indonesia. Meteorological conditions on January 22, 2019, leading to heavy rainfall and devastating flooding in this area were studied. It is shown that a convectively coupled Kelvin wave (CCKW) and a convectively coupled Rossby wave (CCERW) embedded within the larger-scale envelope of the Madden-Julian Oscillation (MJO) enhanced convective phase, contributed to the onset of a mesoscale convective system which developed over the Java Sea. Low-Level convergence from the CCKW forced mesoscale convective organization and orographic ascent of moist air over the slopes of southwest Sulawesi. Climatological analysis shows that 92 % of December-January-February floods and 76% of extreme rain events in this region were immediately preceded by positive low-level westerly wind anomalies. It is estimated that both CCKWs and CCERWs propagating over Sulawesi double the chance of floods and extreme rain event development, which the probability of such hazardous events occurring during their combined activity is eight times greater than on a random day. While the MJO is a key component shaping tropical atmospheric variability, it is shown that its usefulness as a single factor for extreme weather-driven hazard prediction is limited.

Latos, Beata↗
DOE OSTI2021

Growth of Mesoscale Convective Systems in Observations and a Seasonal Convection-Permitting Simulation over Argentina

Abstract A 6.5-month, convection-permitting simulation is conducted over Argentina covering the Remote Sensing of Electrification, Lightning, And Mesoscale/Microscale Processes with Adaptive Ground Observations and Clouds, Aerosols, and Complex Terrain Interactions (RELAMPAGO-CACTI) field campaign and is compared with observations to evaluate mesoscale convective system (MCS) growth prediction. Observed and simulated MCSs are consistently identified, tracked, and separated into growth, mature, and decay stages using top-of-the-atmosphere infrared brightness temperature and surface rainfall. Simulated MCS number, lifetime, seasonal and diurnal cycles, and various cloud-shield characteristics including growth rate are similar to those observed. However, the simulation produces smaller rainfall areas, greater proportions of heavy rainfall, and faster system propagations. Rainfall area is significantly underestimated for long-lived MCSs but not for shorter-lived MCSs, and rain rates are always overestimated. These differences result from a combination of model and satellite retrieval biases, in which simulated MCS rain rates are shifted from light to heavy, while satellite-retrieved rainfall is too frequent relative to rain gauge estimates. However, the simulation reproduces satellite-retrieved MCS cloud-shield evolution well, supporting its usage to examine environmental controls on MCS growth. MCS initiation locations are associated with removal of convective inhibition more than maximized low-level moisture convergence or instability. Rapid growth is associated with a stronger upper-level jet (ULJ) and a deeper northwestern Argentinean low that causes a stronger northerly low-level jet (LLJ), increasing heat and moisture fluxes, low-level vertical wind shear, baroclinicity, and instability. Sustained growth corresponds to similar LLJ, baroclinicity, and instability conditions but is less sensitive to the ULJ, large-scale vertical motion, or low-level shear. Growth sustenance controls MCS maximum extent more than growth rate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

A Regime-Based Evaluation of Southern and Northern Great Plains Warm-Season Precipitation Events in WRF

A competitive neural network known as the Self-Organizing Map (SOM), is used to objectively identify synoptic patterns in the North American Regional Reanalysis (NARR) for warm-season (April-September) precipitation events over the Southern and Northern Great Plains (SGP/NGP) from 2007 to 2014. Classifications for both regions demonstrate contrast in dominant synoptic patterns ranging from extratropical cyclones to subtropical ridges, all of which have preferred months of occurrence. Precipitation from deterministic Weather Research and Forecasting (WRF) simulations run by the National Severe Storms Laboratory (NSSL) are evaluated against National Centers for Environmental Prediction (NCEP) Stage IV observations. The SGP features larger observed precipitation amount, intensity, and coverage, as well as better model performance than the NGP. Both regions’ simulated convective rain intensity and coverage have good agreement with observations, whereas the stratiform rain is more problematic with weaker intensity and larger coverage. Further evaluation based on SOM regimes shows that WRF bias varies with the type of meteorological forcing, which can be traced to differences in the diurnal cycle and properties of stratiform and convective rain. The higher performance scores are generally associated with extratropical cyclone condition than the subtropical ridge. Of the six SOM classes over both regions, the largest precipitation oversimulation is found for SR dominated classes, whereas a nocturnal negative bias for precipitation exists for classes featuring upscale growth of convection.

self-organizing maps, synoptic pattern, Extratropi↗
DOE OSTI2018

Wear behavior of Al 0.6 CoCrFeNi high-entropy alloys: Effect of environments

Environment can impact the wear behavior of metals and alloys substantially. The tribological properties of Al 0.6 CoCrFeNi high-entropy alloys (HEAs) were investigated in ambient air, deionized water, simulated acid rain, and simulated seawater conditions at frequencies of 2–5 Hz. The as-cast alloy was composed of simple face-centered cubic and body-centered cubic phases. The wear rate of the as-cast HEA in the ambient air condition was significantly higher than that in the liquid environment. The wear resistance in seawater was superior to that in ambient air, deionized water, and acid rain. Both the friction coefficient and wear rate in seawater were the lowest due to the formation of oxidation film, lubrication, and corrosion action in solution. The dominant wear mechanism in the ambient air condition and deionized water was abrasive wear, delamination wear, and oxidative wear. Here by contrast, the wear mechanism in acid rain and seawater was mainly corrosion wear, adhesive wear, abrasive wear, and oxidative wear.

36 MATERIALS SCIENCE↗