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Sea salt aerosols play an important role in the radiation budget and atmospheric composition over the Arctic, where the climate is rapidly changing. Previous observational studies have shown that Arctic sea ice leads are an important source of sea salt aerosols, and modeling efforts have also proposed blowing snow sublimation as a source. In this study, size-resolved atmospheric particle number concentrations and chemical composition were measured at the Arctic coastal tundra site of Utqiaġvik, Alaska, during spring (3 April–7 May 2016). Blowing snow conditions were observed during 25 % of the 5-week study period and were overpredicted by a commonly used blowing snow parameterization based solely on wind speed and temperature. Throughout the study, open leads were present locally. During periods when blowing snow was observed, significant increases in the number concentrations of 0.01–0.06 µm particles (factor of 6, on average) and 0.06–0.3 µm particles (67 %, on average) and a significant decrease (82 %, on average) in 1–4 µm particles were observed compared to low wind speed periods. These size distribution changes were likely caused by the generation of ultrafine particles from leads and/or blowing snow, with scavenging of supermicron particles by blowing snow. At elevated wind speeds, both submicron and supermicron sodium and chloride mass concentrations were enhanced, consistent with wind-dependent local sea salt aerosol production. At moderate wind speeds below the threshold for blowing snow as well as during observed blowing snow, individual sea spray aerosol particles were measured. These individual salt particles were enriched in calcium relative to sodium in seawater due to the binding of this divalent cation with organic matter in the sea surface microlayer and subsequent enrichment during seawater bubble bursting. The chemical composition of the surface snowpack also showed contributions from sea spray aerosol deposition. Overall, these results show the contribution of sea spray aerosol production from leads on both aerosols and the surface snowpack. Therefore, if blowing snow sublimation contributed to the observed sea salt aerosol, the snow being sublimated would have been impacted by sea spray aerosol deposition rather than upward brine migration through the snowpack. Sea spray aerosol production from leads is expected to increase, with thinning and fracturing of sea ice in the rapidly warming Arctic.

Harsh winters and hazards such as blizzards are synonymous with the northern Great Plains of the United States. Studying these events is difficult; the juxtaposition of cold temperatures and high winds makes microphysical observations of both blowing and falling snow challenging. Historically, these observations have been provided by costly hydrometeor imagers that have been deployed for field campaigns or at select observation sites. This has slowed the development and validation of microphysics parameterizations and remote sensing retrievals of various properties. If cheaper, more mobile instrumentation can be developed, this progress can be accelerated. Further, lowering price barriers can make deployment of instrumentation feasible for education and outreach purposes. The Blowing Snow Observations at the University of North Dakota: Education through Research (BLOWN-UNDER) Campaign took place during the winter of 2019/20 to investigate strategies for obtaining microphysical measurements in the harsh North Dakota winter. Student led, the project blended education, outreach, and scientific objectives. While a variety of in situ and remote sensing instruments were deployed for the campaign, the most novel aspect of the project was the development and deployment of OSCRE, the Open Snowflake Camera for Research and Education. Images from this instrument were combined with winter weather educational modules to describe properties of snow to the public, K–12 students, and members of indigenous communities through a tribal outreach program. Along with an educational deployment of a Doppler on Wheels mobile radar, nearly 1,000 individuals were reached during the project.

This paper presents the results from a series of hypersonic double cone experiments, conducted in the X3 expansion tunnel at the University of Queensland (UQ). The model was previously used and produced at California Institute of Technology (Caltech) and is a scaled model of the double cone model used by the Calspan-University of Buffalo Research Center (CUBRC). The data presented is a full transient set of surface heat flux, and surface pressure measurements along the axial length of the model as well as a full suite of tunnel data. These aim to help characterize the flow over the geometry by providing surface conditions, an accurate location of the boundary layer separation, the flow reattachment points and the general structure of the shockwave boundary layer interaction. The geometry of the X3 facility is also provided to give a more complete picture of the test conditions, an important consideration for numerical models. The outcome of this paper is to provide the most detailed experimental data to date and improve validation attempts of existing non-equilibrium thermochemistry codes.

This study analyzes the frequency of strong, isolated convective cells in the vicinity of Louisville, Kentucky. Data from the Severe Weather Data Inventory are used to compare the frequency of convective activity over Louisville with the observed frequency at nearby rural locations from 2003 to 2019. The results show that Louisville experiences significantly more isolated convective activity than do the rural locations. The difference in convective activity between Louisville and the rural locations is strongest during summer, with peak differences occurring between May and August. Relative to the rural locations, Louisville experiences more isolated convective activity in the afternoon and early evening but less activity after midnight and into the early morning. Isolated convective events over Louisville are most likely during quiescent synoptic conditions, whereas rural events are more likely during active synoptic patterns. To determine whether these differences can be attributed primarily to urban effects, two additional cities are shown for comparison—Nashville, Tennessee, and Cincinnati, Ohio. Both Nashville and Cincinnati experience more isolated convective activity than all five of their nearby rural comparison areas, but the results for both are statistically significant at four of the five rural locations. In addition, the analysis of Cincinnati includes a sixth comparison site that overlaps the urbanized area of Columbus, Ohio. For that location, differences in convective activity are not statistically significant.

On 24 February 2019, strong winds behind an Arctic cold front led to widespread blowing snow across the northern Great Plains including areas in eastern North/South Dakota and western Minnesota. Impacts of the event ranged from blizzard conditions in northwest Minnesota to sporadic, minor reductions in visibility across the region. This study documents the event using remotely sensed observations from platforms including geostationary and polar-orbiting satellites, an S-band radar, and time-lapse images from a camera located at the University of North Dakota in Grand Forks, North Dakota. Blowing snow is observed as plumes that resemble horizontal convective rolls (HCRs). Variations in near-infrared imagery are documented, and supporting observations suggest this is due to the occurrence or absence of clouds on top of the blowing snow layer. While lack of in situ observations preclude further investigation of physical differences between plumes, the utility of the Geostationary Operational Environmental Satellite-16 (GOES-16) satellite to operational forecasters is discussed. Improvements to spatial, radiometric, and temporal resolution courtesy of the Advanced Baseline Imager (ABI) on board GOES-16 allows for daytime detection of blowing snow events that previously, was only possible with instruments on board polar-orbiting satellites. This has improved Impact-Based Decision Support Services (IDSS) at National Weather Service offices that deal with the hazard of blowing snow.

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) Model 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 (SR) 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 the 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 precipitation bias exists for classes featuring upscale growth of convection.

A photoswitchable ligand and palladium(II) ions form a dynamic mixture of self-assembled metallosupramolecular structures. The photoswitching ligand is an ortho-fluoroazobenzene with appended pyridyl groups. The E-isomer is combined with palladium(II) salts affords a double-walled triangle with composition [Pd3L6]6+ and a distorted tetrahedron [Pd4L8]8+(1:2 ratio at 298 K). Irradiation with 410 nm light generates a photostationary state with ~80% of the E-isomer of the ligand which results in the selective disassembly of the tetrahedron, the more thermodynamically stable structure, and the formation of the triangle, the kinetic product. The triangle is then slowly transformed back into the tetrahedron over 2 days at 333 K. The Z-isomer of the ligand does not form any well-defined structures and has a thermal half-life of 25 days at 298K. This approach shows how a thermodynamically preferred self-assembled structure can be reversibly pumped to a kinetic trap by small perturbations of the isomer distribution using non-destructive visible light.

A suite of modern atmospheric reanalyses is analyzed to determine how they represent North American supercell environments. This analysis is performed by comparing a database of Rapid Update Cycle (RUC-2) proximity soundings with profiles derived from the nearest grid point in each reanalysis. Parameters are calculated using the Sounding and Hodograph Analysis and Research Program in Python (SHARPpy), an open-source Python sounding-analysis package. Representation of supercell environments varies across the reanalyses, and the results have ramifications for climatological studies that use these datasets. In particular, thermodynamic parameters such as the convective available potential energy (CAPE) show the widest range in biases, with reanalyses falling into two camps. The North American Regional Reanalysis (NARR) and the Japanese 55-year Reanalysis (JRA-55) are similar to RUC-2, but other reanalyses have a substantial negative bias. The reasons for these biases vary and range from thermodynamic biases at the surface to evidence of convective contamination. Overall, it is found that thermodynamic biases feed back to other convective parameters that incorporate CAPE directly or indirectly via the effective layer. As a result, significant negative biases are found for indices such as the supercell composite parameter. These biases are smallest for NARR and JRA-55. Kinematic parameters are more consistent across the reanalyses. Given the issues with thermodynamic properties, better segregation of soundings by storm type is found for fixed-layer parameters than for effective-layer shear parameters. Although no reanalysis can exactly reproduce the results of earlier RUC-2 studies, many of the reanalyses can broadly distinguish between environments that are significantly tornadic versus nontornadic.

One primary goal of annual Spring Forecasting Experiments (SFEs), which are coorganized by NOAA’s National Severe Storms Laboratory and Storm Prediction Center and conducted in the National Oceanic and Atmospheric Administration’s (NOAA) Hazardous Weather Testbed, is documenting performance characteristics of experimental, convection-allowing modeling systems (CAMs). Since 2007, the number of CAMs (including CAM ensembles) examined in the SFEs has increased dramatically, peaking at six different CAM ensembles in 2015. Meanwhile, major advances have been made in creating, importing, processing, verifying, and developing tools for analyzing and visualizing these large and complex datasets. However, progress toward identifying optimal CAM ensemble configurations has been inhibited because the different CAM systems have been independently designed, making it difficult to attribute differences in performance characteristics. Thus, for the 2016 SFE, a much more coordinated effort among many collaborators was made by agreeing on a set of model specifications (e.g., model version, grid spacing, domain size, and physics) so that the simulations contributed by each collaborator could be combined to form one large, carefully designed ensemble known as the Community Leveraged Unified Ensemble (CLUE). The 2016 CLUE was composed of 65 members contributed by five research institutions and represents an unprecedented effort to enable an evidence-driven decision process to help guide NOAA’s operational modeling efforts. Eight unique experiments were designed within the CLUE framework to examine issues directly relevant to the design of NOAA’s future operational CAM-based ensembles. This article will highlight the CLUE design and present results from one of the experiments examining the impact of single versus multicore CAM ensemble configurations.

Stretching along the border of North Dakota and Minnesota, The Red River Valley (RRV) of the North has the highest frequency of reported blizzards within the contiguous United States. Despite the numerous impacts these events have, few systematic studies exist that discuss the meteorological properties of blizzards. As a result, forecasting these events and lesser blowing snow events is an ongoing challenge. This study presents a climatology of atmospheric patterns associated with RRV blizzards for the winter seasons of 1979&ndash, 1980 and 2017&ndash, 2018. Patterns were identified using subjective and objective techniques using meteorological fields from the North American Regional Re-analysis (NARR). The RRV experiences, on average, 2.6 events per year. Blizzard frequency is bimodal, with peaks occurring in December and March. The events can largely be typed into four meteorological categories dependent on the forcing that drives the blizzard: Alberta Clippers, Arctic Fronts, Colorado Lows, and Hybrids. The objective classification of these blizzards using a competitive neural network known as the Self-Organizing Map (SOM) demonstrates that gross segregation of the events can be achieved with a small (eight-class) map. This implies that objective analysis techniques can be used to identify these events in weather and climate model output that may aid future forecasting and risk assessment projects.

The planetary boundary layer turbulence and moist convection parameterizations have been modified recently in the NASA Goddard Institute for Space Studies (GISS) Model E2 atmospheric general circulation model (GCM; post-CMIP5, hereafter P5). In this study, single column model (SCM P5) simulated cloud fractions (CFs), cloud liquid water paths (LWPs) and precipitation were compared with Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) groundbased observations made during the period 2002–08. CMIP5 SCM simulations and GCM outputs over the ARM SGP region were also used in the comparison to identify whether the causes of cloud and precipitation biases resulted from either the physical parameterization or the dynamic scheme. The comparison showed that the CMIP5 SCM has difficulties in simulating the vertical structure and seasonal variation of low-level clouds. The new scheme implemented in the turbulence parameterization led to significantly improved cloud simulations in P5. It was found that the SCM is sensitive to the relaxation time scale. When the relaxation time increased from 3 to 24 h, SCM P5-simulated CFs and LWPs showed a moderate increase (10%–20%) but precipitation increased significantly (56%), which agreed better with observations despite the less accurate atmospheric state. Annual averages among the GCM and SCM simulations were almost the same, but their respective seasonal variations were out of phase. This suggests that the same physical cloud parameterization can generate similar statistical results over a long time period, but different dynamics drive the differences in seasonal variations. This study can potentially provide guidance for the further development of the GISS model.

A 19-month record of total and single-layered low (6 km) cloud fractions (CFs) and the single-layered marine boundary layer (MBL) cloud macrophysical and microphysical properties was generated from ground-based measurements at the Atmospheric Radiation Measurement Program (ARM) Azores site between June 2009 and December 2010. This is the most comprehensive dataset of marine cloud fraction and MBL cloud properties. The annual means of total CF and single-layered low, middle, and high CFs derived from ARM radar and lidar observations are 0.702, 0.271, 0.01, and 0.106, respectively. Greater total and single-layered high (>6 km) CFs occurred during the winter, whereas single-layered low (The seasonal variations of cloud heights and thickness are also associated with the seasonal synoptic patterns. The MBL cloud layer is low, warm, and thin with large liquid water path (LWP) and liquid water content (LWC) during summer, whereas during winter it is higher, colder, and thicker with reduced LWP and LWC. The cloud LWP and LWC values are greater at night than during daytime. The monthly mean daytime cloud droplet effective radius re values are nearly constant, while the daytime droplet number concentration Nd basically follows the LWC variation. There is a strong correlation between cloud condensation nuclei (CCN) concentration NCCN and Nd during January–May, probably due to the frequent low pressure systems because upward motion brings more surface CCN to cloud base (well-mixed boundary layer). During summer and autumn, the correlation between Nd and NCCN is not as strong as that during January–May because downward motion from high pressure systems is predominant. Compared to the compiled aircraft in situ measurements during the Atlantic Stratocumulus Transition Experiment (ASTEX), the cloud microphysical retrievals in this study agree well with historical aircraft data. Different air mass sources over the ARM Azores site have significant impacts on the cloud microphysical properties and surface CCN as demonstrated by great variability in NCCN and cloud microphysical properties during some months.

The Atmospheric Radiation Measurement (ARM) Program Southern Great Plains (SGP) site has a rich history of actively sensed cloud observations. Fourteen years (1997–2010) of observations from the Millimeter Cloud Radar (MMCR), Micropulse Lidar (MPL), and Belfort/Vaisala Ceilometers are used to understand how instrument selection and sampling impacts estimates of Cloud Fraction (CF) at this location. Although all instruments should be used in combination for the best estimates of CF, instrument downtime limits available samples and increases observational errors, demanding that users make sacrifices when calculating CF at longer intervals relevant to climate studies. Selection of MMCR or MMCR + MPL cloud masks changes very little in the overall understanding of total CF. Addition of the MPL increases the 14-year average CF by 9 %, mainly through an increase in optically thin high clouds year-round, and mid-level clouds during the summer months. Splitting the period into two equal 7-year periods reveals negligible change in MMCR + MPL CF. For the MMCR, however, CF deceases by 6.1 %. This sudden change in CF occurs around the time the radar was upgraded, suggesting that this decrease is tied to hardware sensitivity or scanning strategy changes. Users must be cognizant of this and other issues when calculating CF from the variety of observations available at the ARM SGP site.

Land–atmosphere (L–A) interactions play a critical role in determining the diurnal evolution of land surface and planetary boundary layer (PBL) temperature and moisture states and fluxes. In turn, these interactions regulate the strength of the connection between surface moisture and precipitation in a coupled system. To address model deficiencies, recent studies have focused on development of diagnostics to quantify the strength and accuracy of the land–PBL coupling at the process level. In this paper, a diagnosis of the nature and impacts of local land–atmosphere coupling (LoCo) during dry and wet extreme conditions is presented using a combination of models and observations during the summers of 2006 and 2007 in the U.S. southern Great Plains. A range of diagnostics exploring the links and feedbacks between soil moisture and precipitation is applied to the dry/wet regimes exhibited in this region, and in the process, a thorough evaluation of nine different land–PBL scheme couplings is conducted under the umbrella of a high-resolution regional modeling test bed. Results show that the sign and magnitude of errors in land surface energy balance components are sensitive to the choice of land surface model, regime type, and running mode. In addition, LoCo diagnostics show that the sensitivity of L–A coupling is stronger toward the land during dry conditions, while the PBL scheme coupling becomes more important during the wet regime. Results also demonstrate how LoCo diagnostics can be applied to any modeling system (e.g., reanalysis products) in the context of their integrated impacts on the process chain connecting the land surface to the PBL and in support of hydrological anomalies.

With continual advancements in data assimilation systems, new observing systems, and improvements in model parameterizations, several new atmospheric reanalysis datasets have recently become available. Before using these new reanalyses it is important to assess the strengths and underlying biases contained in each dataset. A study has been performed to evaluate and compare cloud fractions (CFs) and surface radiative fluxes in several of these latest reanalyses over the Arctic using 15 years (1994–2008) of high-quality Baseline Surface Radiation Network (BSRN) observations from Barrow (BAR) and Ny-Alesund (NYA) surface stations. The five reanalyses being evaluated in this study are (i) NASA's Modern-Era Retrospective analysis for Research and Applications (MERRA), (ii) NCEP's Climate Forecast System Reanalysis (CFSR), (iii) NOAA's Twentieth Century Reanalysis Project (20CR), (iv) ECMWF's Interim Reanalysis (ERA-I), and (v) NCEP–Department of Energy (DOE)'s Reanalysis II (R2). All of the reanalyses show considerable bias in reanalyzed CF during the year, especially in winter. The large CF biases have been reflected in the surface radiation fields, as monthly biases in shortwave (SW) and longwave (LW) fluxes are more than 90 (June) and 60 W m−2 (March), respectively, in some reanalyses. ERA-I and CFSR performed the best in reanalyzing surface downwelling fluxes with annual mean biases less than 4.7 (SW) and 3.4 W m−2 (LW) over both Arctic sites. Even when producing the observed CF, radiation flux errors were found to exist in the reanalyses suggesting that they may not always be dependent on CF errors but rather on variations of more complex cloud properties, water vapor content, or aerosol loading within the reanalyses.

Atmospheric states from the Modern-Era Retrospective analysis for Research and Applications (MERRA) and the North American Regional Reanalysis (NARR) are compared with data from the Atmospheric Radiation Measurement Program (ARM) Southern Great Plains (SGP) site, including the ARM continuous forcing product and Cloud Modeling Best Estimate (CMBE) soundings, during the period 1999–2001 to understand their validity for single-column model (SCM) and cloud-resolving model (CRM) forcing datasets. Cloud fraction, precipitation, and radiation information are also compared to determine what errors exist within these reanalyses. For the atmospheric state, ARM continuous forcing and the reanalyses have good agreement with the CMBE sounding information, with biases generally within 0.5 K for temperature, 0.5 m s−1 for wind, and 5% for relative humidity. Larger disagreements occur in the upper troposphere (p < 300 hPa) for temperature, humidity, and zonal wind, and in the boundary layer (p > 800 hPa) for meridional wind and humidity. In these regions, larger errors may exist in derived forcing products. Significant differences exist for vertical pressure velocity, with the largest biases occurring during the spring upwelling and summer downwelling periods. Although NARR and MERRA share many resemblances to each other, ARM outperforms these reanalyses in terms of correlation with cloud fraction. Because the ARM forcing is constrained by observed precipitation that gives the adequate mass, heat, and moisture budgets, much of the precipitation (specifically during the late spring/early summer) is caused by smaller-scale forcing that is not captured by the reanalyses. While reanalysis-based forcing appears to be feasible for the majority of the year at this location, it may have limited usage during the late spring and early summer, when convection is common at the ARM SGP site. Both NARR and MERRA capture the seasonal variation of cloud fractions (CFs) observed by ARM radar–lidar and Geostationary Operational Environmental Satellite (GOES) with high correlations (0.92–0.78) but with negative biases of 14% and 3%, respectively. Compared to the ARM observations, MERRA shows better agreement for both shortwave (SW) and longwave (LW) fluxes except for LW-down (due to a negative bias in water vapor): NARR has significant positive bias for SW-down and negative bias for LW-down under clear-sky and all-sky conditions. The NARR biases result from a combination of too few clouds and a lack of sufficient extinction by aerosols and water vapor in the atmospheric column. The results presented here represent only one location for a limited period, and more comparisons at different locations and longer periods are needed.

Three years of surface and Geostationary Operational Environmental Satellite (GOES) data from the Department of Energy Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site are used to evaluate the NASA GISS Single Column Model (SCM) simulated clouds from January 1999 to December 2001. The GOES-derived total cloud fractions for both 0.5° and 2.5° grid boxes are in excellent agreement with surface observations, suggesting that ARM point observations can represent large areal observations. Low (6 km) levels of cloud fractions, however, have negative biases as compared to the ARM results due to multilayer cloud scenes that can either mask lower cloud layers or cause misidentifications of cloud tops. Compared to the ARM observations, the SCM simulated most midlevel clouds, overestimated low clouds (4%), and underestimated total and high clouds by 7% and 15%, respectively. To examine the dependence of the modeled high and low clouds on the large-scale synoptic patterns, variables such as relative humidity (RH) and vertical pressure velocity (omega) from North American Regional Reanalysis (NARR) data are included. The successfully modeled and missed high clouds are primarily associated with a trough and ridge upstream of the ARM SGP, respectively. The PDFs of observed high and low occurrence as a function of RH reveal that high clouds have a Gaussian-like distribution with mode RH values of ∼40%–50%, whereas low clouds have a gammalike distribution with the highest cloud probability occurring at RH ∼75%–85%. The PDFs of modeled low clouds are similar to those observed; however, for high clouds the PDFs are shifted toward higher values of RH. This results in a negative bias for the modeled high clouds because many of the observed clouds occur at RH values below the SCM-specified stratiform parameterization threshold RH of 60%. Despite many similarities between PDFs derived from the NARR and ARM forcing datasets for RH and omega, differences do exist. This warrants further investigation of the forcing and reanalysis datasets.

A new three-dimensional reflectivity echo in the rear flank of supercells known as the descending reflectivity core (DRC) has been documented in the literature by Rasmussen et al. The DRC is an enhanced region of reflectivity presumed to occur in the rear-flank downdraft (RFD) of a supercell. In the four cases they studied, this feature descended with time from the rear-echo overhang at 3–6 km in height into the supercell appendage. In addition, the DRC often occurred prior to tornadogenesis. The purpose of this paper is to serve as a more thorough analysis of DRCs using a larger sample of storms. The frequency of DRCs is explored within isolated supercells with persistent rear-flank appendages, and in particular at times preceding reported tornado onset in those supercells. Of the 64 supercells included within this study, 59% produced DRCs, with 30% of these DRCs occurring within 10 min prior to 5 min after tornadogenesis. This study included 89 reported tornadoes and 71 DRCs. Statistical analysis of the dataset reveals that while DRCs are sometimes associated with tornadoes, they presently have limited usefulness for tornado nowcasting. Improvements to Weather Surveillance Radar-1988 Doppler (WSR-88D) resolution and further classification of DRCs may help discriminate between tornadic and nontornadic appendages in the future, however.

Objectives (1) To describe obstetrical and neonatal outcomes among a cohort of hepatitis C virus (HCV) infected women, comparing HCV RNA positive to HCV RNA negative women; (2) to characterize virologic and hepatic parameters associated with HCV infection during pregnancy; and (3) to describe the rate of HCV vertical transmission. Methods We prospectively enrolled 145 HCV-positive pregnant women across British Columbia between 2000 and 2003. Participating women were monitored during pregnancy and their infants were followed to assess them for HCV infection. Maternal HCV RNA was assessed close to delivery. Results Seventy percent of women reported injection drug use as their primary risk factor for HCV acquisition. Observed rates of intrauterine fetal death, preterm delivery, small for gestational age, and low birth weight infants were 3.4%, 17.9%, 11.3%, and 12.5%, respectively, without a significant association with maternal HCV RNA status. The rate of cholestasis was 5.6% in the HCV RNA-positive group (6/108) and 2.8% in the HCV RNA-negative group (1/37) ( P =0.496). Serum alanine aminotransferase levels decreased significantly through pregnancy, and were significantly higher in HCV RNA-positive women than in HCV RNA-negative women after controlling for cholestasis, co-infections, and alcohol consumption. Among the HCV RNA-positive women, the median FIB-4 score was 0.67 (IQR 0.56 to 0.76) in the first trimester, 0.74 (IQR 0.52 to 1.18) in the second trimester, and 0.89 (IQR 0.52 to 1.09) in the third trimester ( P =0.02). The median HCV viral load at delivery was 424 561 IU/mL. The vertical transmission rate was 4.7% in HCV RNA-positive women, with no cases in HCV RNA-negative women. Conclusion Because of the high rates of poor obstetrical outcomes found in this prospective cohort, population-level screening for HCV in pregnancy should be considered.

Deep Convective Systems (DCSs) consist of intense convective cores (CC), large stratiform rain (SR) regions, and extensive non-precipitating anvil clouds (AC). This study focuses on the evolution of these three components and the factors that affect convective AC production. An automated satellite tracking method is used in conjunction with a recently developed multi-sensor hybrid classification to analyze the evolution of DCS structure in a Lagrangian framework over the central United States. Composite analysis from 4221 tracked DCSs during two warm seasons (May-August, 2010-2011) shows that maximum system size correlates with lifetime, and longer-lived DCSs have more extensive SR and AC. Maximum SR and AC area lag behind peak convective intensity and the lag increases linearly from approximately 1-hour for short-lived systems to more than 3-hours for long-lived ones. The increased lag, which depends on the convective environment, suggests that changes in the overall diabatic heating structure associated with the transition from CC to SR and AC could prolong the system lifetime by sustaining stratiform cloud development. Longer-lasting systems are associated with up to 60% higher mid-tropospheric relative humidity and up to 40% stronger middle to upper tropospheric wind shear. Regression analysis shows that the areal coverage of thick AC is strongly correlated with the size of CC, updraft strength, and SR area. Ambient upper tropospheric wind speed and wind shear also play an important role for convective AC production where for systems with large AC (radius greater than 120-km) they are 24% and 20% higher, respectively, than those with small AC (radius=20 km).

Hydrological years 2006 (HY06, 10/2005-09/2006) and 2007 (HY07, 10/2006-09/2007) provide a unique opportunity to examine hydrological extremes in the central US because there are no other examples of two such highly contrasting precipitation extremes occurring in consecutive years at the Southern Great Plains (SGP) in recorded history. The HY06 annual precipitation in the state of Oklahoma, as observed by the Oklahoma Mesonet, is around 61% of the normal (92.84 cm, based on the 1921-2008 climatology), which results in HY06 the second-driest year in the record. In particular, the total precipitation during the winter of 2005-06 is only 27% of the normal, and this winter ranks as the driest season. On the other hand, the HY07 annual precipitation amount is 121% of the normal and HY07 ranks as the seventh-wettest year for the entire state and the wettest year for the central region of the state. Summer 2007 is the second-wettest season for the state. Large-scale dynamics play a key role in these extreme events. During the extreme dry period (10/2005-02/2006), a dipole pattern in the 500-hPa GH anomaly existed where an anomalous high was over the southwestern U.S. region and an anomalous low was over the Great Lakes. This pattern is associated with inhibited moisture transport from the Gulf of Mexico and strong sinking motion over the SGP, both contributing to the extreme dryness. The precipitation deficit over the SGP during the extreme dry period is clearly linked to significantly suppressed cyclonic activity over the southwestern U.S., which shows robust relationship with the Western Pacific (WP) teleconnection pattern. The precipitation events during the extreme wet period (May-July 2007) were initially generated by active synoptic weather patterns, linked with moisture transport from the Gulf of Mexico by the northward low level jet, and enhanced by the mesoscale convective systems. Although the drought and pluvial conditions are dominated by large-scale dynamic patterns, we have demonstrated that the two positive feedback processes during the extreme dry and wet periods found in this study play a key role to maintain and reinforce the length and severity of existing drought and flood events. For example, during the extreme dry period, with less clouds, LWP, PWV, precipitation, and thinner Cu cloud thickness, more net radiation was absorbed and used to evaporate water from the ground. The evaporated moisture, however, was removed by low-level divergence. Thus, with less precipitation and removed atmospheric moisture, more absorbed incoming solar radiation was used to increase surface temperature and to make the ground drier.

This study investigates the impact of snow, graupel, and hail processes on the simulated squall lines over the Southern Great Plains in the United States. Weather Research and Forecasting (WRF) model is used to simulate two squall line events in May 2007, and the results are validated against radar and surface observations in Oklahoma. Several microphysics schemes are tested in this study, including WRF 5-Class Microphysics Scheme (WSM5), WRF 6-Class Microphysics Scheme (WSM6), Goddard Three Ice scheme (Goddard 3-ice) with graupel, Goddard Two Ice scheme (Goddard 2-ice), and Goddard 3-ice hail scheme. The simulated surface precipitation is sensitive to the microphysics scheme, and especially to whether graupel or hail category is included. All of the three ice (3-ice) schemes overestimated the total precipitation, within which WSM6 has the highest overestimation. Two ice (2-ice) schemes, missing a graupel/hail category, produced less total precipitation than 3-ice schemes. By applying a radar-based convective/stratiform partitioning algorithm, we find that by including the graupel/hail processes, there is an increase in areal coverage, precipitation intensity, updraft and downdraft intensity in convective region and a reduction of areal coverage and its precipitation intensity in stratiform region. For vertical structures, all the bulk schemes, especially 2-ice schemes, havemore » the highest reflectivity located at upper levels (~8 km), which is unrealistic compared to observations. In addition, this study shows the radar-based convective/stratiform partitioning algorithm can reasonably identify WRF simulated precipitation, wind and microphysics fields in both convective and stratiform regions.« less

Instrument downtime leads to uncertainty in the monthly and annual record of cloud fraction (CF), making it difficult to perform time series analyses of cloud properties and perform detailed evaluations of model simulations. As cloud occurrence is partially controlled by the large-scale atmospheric environment, this knowledge is used to reduce uncertainties in the instrument record. Synoptic patterns diagnosed from the North American Regional Reanalysis (NARR) during the period 1997–2010 are classified using a competitive neural network known as the self-organizing map (SOM). The classified synoptic states are then compared to the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) instrument record to determine the expected CF. A number of SOMs are tested to understand how the number of classes and the period of classifications impact the relationship between classified states and CFs. Bootstrapping is utilized to quantify the uncertainty of the instrument record when statistical information from the SOM is included. Although all SOMs significantly reduce the uncertainty of the CF record calculated in Kennedy et al. (Theor Appl Climatol 115:91–105, 2014), SOMs with a large number of classes and separated by month are required to produce the lowest uncertainty and best agreement with the annual cycle of CF. This result may be due to a manifestation of seasonally dependent biases in NARR. With use of the SOMs, the average uncertainty in monthly CF is reduced in half from the values calculated in Kennedy et al. (Theor Appl Climatol 115:91–105, 2014).