Abstract In this study, an extreme rainfall event that occurred mainly on October 2, 2018, in the Phoenix metropolitan area, southwestern United States, is simulated with the Weather Research and Forecasting (WRF) model, version 4.0, with multiple microphysics and boundary layer schemes. Paired simulations are conducted by running the model first using a realistic land cover and land‐use dataset (URB run), and second, by replacing the urban land cover with open shrubland cover (called NOURB run). The model simulations are first evaluated against radar Stage IV (hereafter Stage IV) data and multiple radar and multiple sensors (MRMS) rainfall to validate the structure, amplitude, and location (SAL) of the rainfall field for both URB runs and NOURB runs. These evaluation results indicate that the model performance varies considerably depending on the physics schemes employed. Based on SAL values, the average of model simulations captures the main features (values of S, A, and L are less than 0.1) both for URB runs and for NOURB runs. Comparisons of hourly rainfall rates show that modeled rainfall maximums occurred about 1 hr earlier than both Stage IV and MRMS data over the Phoenix metro area, while the maximum rainfall amounts from the model are close to MRMS data but lower than Stage IV data. The differences of rainfall and other meteorological variables between URB runs and NOURB runs are analyzed. The differences of rainfall between URB and NOURB vary depending on the physics scheme employed. Our study also indicates that urbanization‐induced rainfall changes are small within the urban area but are significant at some locations outside of it; an explanation for this pattern needs further investigation. This result implies that using a single physics scheme to address the effects of urbanization on extreme rainfall events may result in inappropriate conclusions.

In this study, the Noah-Multiparameterization with Crop land surface model in the Weather Research and Forecasting (WRF) with Chemistry (WRF/Chem) model is modified to include the effects of chronic ozone exposure (COE) on plant conductance and photosynthesis (PCP) found from field experiments. Based on the modified WRF/Chem, the effects of COE on regional hydrometeorology and crop productivity have been investigated over the central United States. Our results indicate that the model in its current configuration can reproduce the rainfall and temperature patterns of the observations and reanalysis data, although it overestimates rainfall. The model underestimates daily maximum 8-hour average ozone concentrations by 4-7 ppb compared with ozone observations from the Clean Air Status and Trend Network. The experimental tests on the effects of COE include setting different thresholds of ambient ozone concentrations ([O-3]) and using linear regressions to quantify PCP against the COE. Compared with the WRF/Chem control run (i.e., without considering the effects of COE), the modified model at different experimental setups consistently improves the simulated estimates of rainfall and temperatures. The simulations in June, July, August, and September of 2009-2014 show that, over crop lands, surface [O-3] decrease latent heat fluxes (LH) by 9 to 11 W/m(2), increase surface air temperatures (T-2) by 0.6 to 0.7 degrees C with the daily maximum temperature increasing up to 1 degrees C, and decrease rainfall by 0.15 to 0.21 mm per day by mostly reducing convective rainfall. Additionally, surface [O-3] decrease crop yields by 18-23%, decrease Gross Primary Productivity (GPP) by 30%-38% in a domain average and up to 50% in some areas, and decrease crop yields by 30-45%, all of which highly depends on the precise experimental setup, especially the [O-3] threshold. The mechanism producing these results is also discussed. Employing this modified WRF/Chem model in any high [O-3] region can more precisely elucidate the interactions of vegetation, meteorology, chemistry/emissions, and crop productivity.

Nine dust storms in south-central Arizona were simulated with the Weather Research and Forecasting with Chemistry model (WRF-Chem) at 2 km resolution. The windblown dust emission algorithm was the Air Force Weather Agency model. In comparison with ground-based PM10 observations, the model unevenly reproduces the dust-storm events. The model adequately estimates the location and timing of the events, but it is unable to precisely replicate the magnitude and timing of the elevated hourly concentrations of particles 10 mu m and smaller ([PM10]).Furthermore, the model underestimated [PM10] in highly agricultural Pinal County because it underestimated surface wind speeds and because the model's erodible fractions of the land surface data were too coarse to effectively resolve the active and abandoned agricultural lands. In contrast, the model overestimated [PM10] in western Arizona along the Colorado River because it generated daytime sea breezes (from the nearby Gulf of California) for which the surface-layer speeds were too strong. In Phoenix, AZ, the model's performance depended on the event, with both under- and overestimations partly due to incorrect representation of urban features. Sensitivity tests indicate that [PM10] highly relies on meteorological forcing. Increasing the fraction of erodible surfaces in the Pinal County agricultural areas improved the simulation of [PM10] in that region. Both 24-hr and 1-hr measured [PM10] were, for the most part, and especially in Pinal County, extremely elevated, with the former exceeding the health standard by as much as 10-fold and the latter exceeding health-based guidelines by as much as 70-fold. Monsoonal thunderstorms not only produce elevated [PM10], but also cause urban flash floods and disrupt water resource deliveries. Given the severity and frequency of these dust storms, and conceding that the modeling system applied in this work did not produce the desired agreement between simulations and observations, additional research in both the windblown dust emissions model and the weather research/physicochemical model is called for.Implications: While many dust storms can be considered to be natural, in semi-arid climates such storms often have an anthropogenic component in their sources of dust. Applying the natural, exceptional events policy to these storms with strong signatures of anthropogenic sources would appear not only to be misguided but also to stifle genuine regulatory efforts at remediation. Those dust storms that have resulted, in part, from passage over abandoned farm land should no longer be considered natural; policymakers and lawmakers need to compel the owners of such land to reduce its potential for windblown dust.

The Noah-Multiparameterization land surface model in the Weather Research and Forecasting (WRF) with Chemistry (WRF/Chem) is modified to include the effects of chronic ozone exposure (COE) on plant conductance and photosynthesis (PCP) found from field experiments. Based on the modified WRF/Chem, the effects of COE on regional hydroclimate have been investigated over the continental United States. Our results indicate that the model with/without modification in its current configuration can reproduce the rainfall and temperature patterns of the observations and reanalysis data, although it underestimates rainfall in the central Great Plains and overestimates it in the eastern coast states. The experimental tests on the effects of COE include setting different thresholds of ambient ozone concentrations ([O-3]) and using different linear regressions to quantify PCP against the COE. Compared with the WRF/Chem control run (i.e., without considering the effects of COE), the modified model at different experiment setups improves the simulated estimates of rainfall and temperatures in Texas and regions to the immediate north. The simulations in June, July and August of 2007-2012 show that surface [O-3] decrease latent heat fluxes (LH) by 10-27 W m(-2), increase surface air temperatures (T-2) by 0.6 degrees C-2.0 degrees C, decrease rainfall by 0.9-1.4 mm d(-1), and decrease runoff by 0.1-0.17 mm d(-1) in Texas and surrounding areas, all of which highly depends on the precise experiment setup, especially the [ O3] threshold. The mechanism producing these results is that COE decreases the LH and increases sensible heat fluxes, which in turn increases the Bowen ratios and air temperatures. This lowering of the LH also results in the decrease of convective potential and finally decreases convective rainfall. Employing this modifiedWRF/Chem model in any high [ O3] region can improve the understanding of the interactions of vegetation, meteorology, chemistry/emissions, and crop productivity.

In this study, a realistic irrigation method is incorporated into a model called Weather Research and Forecasting with Chemistry (WRF-Chem) to determine the impacts of irrigation on ozone and other pollutants over the Central Valley of California and thereafter throughout the contiguous United States. In comparison with observations, model simulations at current configurations underestimate ozone (O-3) and volatile organic compound (VOC) concentrations, especially during the elevated episode periods. The model results, however, are generally comparable with previous studies and the simulations adequately capture the spatial and temporal variability of these pollutant concentrations. In comparison with observations and model control runs, model simulations with irrigation runs are slightly improved. Our results show that irrigation increases primary pollutant concentrations in the irrigated areas under cloudless conditions, consistent with previous studies. In July and August, 2005, for instance, with clear skies, irrigation increases hourly surface [CO] up to 40 ppb with an irrigated grid average of 16 ppb or 8.3%; [VOC] up to 10 ppb with an irrigated grid average of 4.6 ppb or 21.4%; and [NOx] up to 4 ppb with irrigated grid average of 0.72 ppb or 12.6%, especially near urban areas during daytime. On the other hand, irrigation marginally decreases ground-level ozone concentrations ([O-3]) over irrigated land: it decreases hourly [O-3] by -0.14 ppb or -0.45% and decreases daily 8 h maximum average (DMA8) [O-3] by -0.39 ppb or -0.76%. In contrast, irrigation increases hourly [O-3] up to 5 ppb over the surrounding unirrigated areas of the San Joaquin Valley, during both daytime and nighttime. Furthermore, except for the Pacific Northwest, similar patterns of ozone variation are simulated by the model in other irrigated regions in the continental United States. The explanation is that irrigation results in cooler ground surfaces, which first decreases instability and turbulence, leading to a weakening of the vertical mixing of primary pollutants. Therefore, irrigation increases surface primary pollutant concentrations within the irrigation zone. Irrigation also results in daytime local circulations that enhance horizontal transport of ozone and other pollutants from irrigated to unirrigated areas near the ground surface. Because our results are based on two months in 2005 and on national anthropogenic emission inventories, the magnitude of these ozone changes could vary somewhat year to year; nonetheless, the methods and results from this study can be applied to any heavily polluted and irrigated agricultural area. (C) 2016 Elsevier B.V. All rights reserved.

In this study, the impacts of Mexican and southwestern U.S. agricultural and urban irrigation on North American monsoon (NAM) rainfall and other hydrometeorological fields are investigated using the Weather Research and Forecasting (WRF) Model by implementing an irrigation scheme into the WRF land surface model. Taking the 2000-12 monsoon seasons as examples, multiple WRF simulations with irrigation are conducted by designing different crops' maximum allowable water depletions (SWm). In comparison with gridded rainfall observations in urban and rural area, the WRF simulations with/without irrigation generally capture the observations very well, but with underestimation along the western slope of the Sierra Madre Occidental (SMO) and overestimation over southern Mexico. The simulations of WRF with irrigation are slightly improved over those without irrigation, compared with rainfall and sounding observations. Sensitivity studies reveal that the impact of irrigation on rainfall varies with location and NAM rainfall variability. Irrigation increases rainfall in eastern Arizona western New Mexico and in northwestern Mexico because of the irrigation-induced increases of convective available potential energy (CAPE) and precipitable water. Overall, irrigation decreases rainfall in western Arizona, along the western slope of the SMO, and in central Mexico because of irrigation-induced increases of convective inhibition (CIN), decreases of CAPE, and/or large-scale water vapor divergence.

Study region: Morocco (excluding Western Sahara).Study focus: This study evaluated Moroccan precipitation, dynamically down scaled (0.18-degree) from three runs of the studied GCM ECHAM5/MPI-OM, under the present-day (1971-2000/20C3M) and future (2036-2065/A1B) climate scenarios. The spatial and quantitative properties of the downscaled precipitation were evaluated by a verified, fine-resolution reference. The effectiveness of the hydrologic responses, driven by the downscaled precipitation, was further evaluated for the study region over the upstream watershed of Oum er Rbia River located in Central Morocco.New hydrological insights for the region: The raw downscaling runs reasonably featured the spatial properties but quantitatively misrepresented the mean and extreme intensities of present-day precipitation. Two proposed bias correction approaches, namely stationary Quantile-Mapping (QM) and non-stationary Equidistant CDF Matching model (EDCDFm), successfully reduced the system biases existing in the raw downscaling runs. However, both raw and corrected runs projected great diversity in terms of the quantity of future precipitation. Hydrologic simulations performed by a well-calibrated Variable Infiltration Capacity model successfully reproduced the present-day streamfiow. The driven flows were identified highly correlated with the effectiveness of the downscaled precipitation. The future flows were projected to be markedly diverse, mainly due to the varied precipitation projections. Two of the three flow simulation runs projected slight to severe drying scenarios, while another projected an opposite trend for the evaluated future period. (C) 2015 The Authors. Published by Elsevier B.V.

Noah (version 2.7.1), the community land-surface model (LSM) of National Centers for Environmental Predictions-National Center for Atmospheric Research (NCEP-NCAR), which is widely used to describe the land-surface processes either in stand-alone or in coupled land-atmospheric model systems, is recognized to underestimate snow-water equivalent (SWE). Noah's SWE bias can be attributed to its simple snow sub-model, which does not effectively describe the physical processes during snow accumulation and melt period. To improve SWE simulation in the Noah LSM, the Utah Energy Balance (UEB) snow model is implemented in Noah to test alternate snow surface temperature and snowmelt outflow schemes. Snow surface temperature was estimated using the force-restore method and snowmelt event is regulated by accounting for the internal energy of the snowpack. The modified Noah's SWE simulations are compared with the SWE observed at California's NRCS SNOTEL stations for 7 water years: 20022008, while the model's snow surface temperature is verified with observed surface-temperature data at an observation site in Utah. The experiments show that modification in Noah's snow process substantially reduced SWE estimation bias while keeping the simplicity of the Noah LSM. The results suggest that the model did not benefit from the alternate temperature representation but primary improvement can be attributed to the substituted snowmelt process.

In this study, a regional climate model (RCM) is employed to investigate the effect of irrigation on hydrology over California through implementing a “realistic irrigation” scheme. Our results indicate that the RCM with a realistic irrigation scheme commonly practiced in California can capture the soil moisture and evapotranspiration (ET) variation very well in comparison with the available in situ and remote sensing data. The RCM results show significant improvement in comparison with those outputs from the default run and the commonly used runs with fixed soil moisture at field capacity. Furthermore, the model reproduces the observed decreasing trends of the reference ET (i.e., ET0) from the California Irrigation Management Information System (CIMIS). The observed decreasing trend is most likely due to the decreasing trend of downward solar radiation shown by models and CIMIS observations. This issue is fundamental in projecting future irrigation water demand. The deep soil percolation rate changes depending on the irrigation method and irrigation duration. Finally, the model results show that precipitation change due to irrigation in California is relatively small in amount and mainly occurs along the midlatitudes in the western United States.

Soil moisture plays a key role in water and energy exchange in the land hydrologic process. Effective soil moisture information can be used for many applications in weather and hydrological forecasting, water resources, and irrigation system management and planning. However, to accurate modeling of soil moisture variation in the soil layer is still very challenging. In this study, in situ and remote sensing information of near-surface soil moisture is assimilated into the Noah land surface model (LSM) to estimate deep-layer soil moisture variation. The sequential Monte Carlo-Particle Filter technique, being well known for capability of modeling high nonlinear and non-Gaussian processes, is applied to assimilate surface soil moisture measurement to the deep layers. The experiments were carried out over several locations over the semi-arid region of the US. Comparing with in situ observations, the assimilation runs show much improved from the control (non-assimilation) runs for estimating both soil moisture and temperature at 5-, 20-, and 50-cm soil depths in the Noah LSM.

Numerical weather prediction models play a major role in weather forecasting, especially in cases of extreme events. The Weather Research and Forecasting Model (WRF), among others, is extensively used for both research and practical applications. Previous studies have highlighted the sensitivity of this model to microphysics and cumulus schemes. This study investigated the performance of the WRF in forecasting precipitation, hurricane track, and landfall time using various microphysics and cumulus schemes. A total of 20 combinations of microphysics and cumulus schemes were used, and the model outputs were validated against ground-based observations. While the choice of microphysics and cumulus schemes can significantly impact model output, it is not the case that any single combination can be considered "ideal" for modeling all characteristics of a hurricane, including precipitation amount, areal extent, hurricane track, and the time of landfall. For example, the model's ability to simulate precipitation (with the least total bias) is best achieved using Betts-Miller-Janjie (BMJ) cumulus parameterization in combination with the WRF single-moment five-class microphysics scheme (WSM5). It was determined that the WSM5 BMJ, WSM3 (the three-class version of the WSM scheme)-BMJ, and Ferrier microphysics in combination with the Grell-Devenyi cumulus scheme were the best combinations for simulation of the landfall time. However, the hurricane track was best estimated using the Lin et al. and Kessler microphysics options with BMJ cumulus parameterization. Contrary to previous studies, these results indicated that the use of cumulus schemes improves model outputs when the grid size is smaller than 10 km. However, it was found that many of the differences between parameterization schemes may be well within the uncertainty of the measurements.

The agricultural sector is the largest consumer of water in California. The impacts of irrigation on local and/or regional weather and climate have been studied and reported in recent literature. However, because of the lack of observations and realistic irrigation schemes employed in the numerical models, most previous studies fall in the category of sensitivity tests, focusing on temperature variations. The results being reported in this paper are obtained by incorporating into the MM5/Noah land surface model an irrigation method practiced in California's farming sector. The proposed irrigation scheme is based on the principle that irrigation occurs when available soil‐water content is less than the maximum allowable water depletion (SWm), which depends on both soil type and crop type. The study's focus was to evaluate the impact of a more realistic irrigation scheme on surface fluxes, especially evapotranspiration (ET). It is demonstrated that more accurate amounts and patterns of ET in the Central Valley are realized, as compared to ET estimates (in terms of amounts and spatial distribution) obtained from remotely sensed observation as well as in situ ground data. It is demonstrated that significant discrepancies of ET estimates between different irrigation schemes used in regional hydroclimate modeling exist, which may result in erroneous conclusions about the impact of irrigation on regional water balance, especially over and near agricultural areas.

Background: To investigate the diagnostic performance of dual-energy subtraction CTA in evaluating intracranial aneurysms by comparison with DSA. Methods: Ninety-seven patients with suspected intracranial aneurysms were included into our study and completed both 64-section dual-energy subtraction CTA and DSA examinations. Two independent readers retrospectively reviewed all subtraction CTA images in a blinded manner. Sensitivity, specificity, positive predictive value and negative predictive value of subtraction CTA and DSA were calculated on a per-patient and per-aneurysm basis. Results: According to the reference standard, 96 aneurysms were present in 81 patients and no aneurysm was found in 16 patients. The overall sensitivity, specificity, positive predictive value and negative predictive value of subtraction CTA on a per-aneurysm basis were 98.9, 100, 100 and 94.1%, respectively. DSA prospectively detected 88 aneurysms in 79 of 81 patients. On a per-aneurysm basis, the sensitivity, specificity, positive predictive value and negative predictive value of DSA were 91.7, 100, 100 and 66.7%, respectively. Conclusion: The diagnostic accuracy of 64-section dual-energy subtraction CTA is promising in detection and characterization of intracranial aneurysms. In most cases, it may substitute for conventional DSA as the primary imaging method in the diagnostic work-up of intracranial aneurysms.

The circle of Willis is a major collateral circulation that has an important role in ischemic events. The purpose of our study was to investigate the collateral circulation in a Chinese population with 64-section multidetector CT angiography (CTA). A total of 170 patients who underwent 64-section CT angiography at The First Affiliated Hospital of Chongqing Medical University were included in our study. The morphological variations in the anterior and posterior circle of Willis were assessed in each patient. A total of 160 patients were included in the final analysis, of whom 126 (79%) demonstrated a complete anterior circle of Willis, and 50 (31%) had a complete posterior circle of Willis. A complete circle of Willis was seen in 43 of 160 participants (27%). A fetal-type posterior circle of Willis was seen in 15 (9.4%) patients. This is the first report of a CTA study of collateral circulation in a Chinese population. A higher prevalence of compromised posterior collaterals was observed in this Chinese population compared to Western and Japanese populations.

[1] The effect of irrigation on regional climate has been studied over the years. However, in most studies, the model was usually set at coarse resolution, and the soil moisture was set to field capacity at each time step. We reinvestigated this issue over the Central Valley of California's agricultural area by: (1) using the regional climate model at different resolutions down to the finest resolution of 4 km for the most inner domain, covering California's Central Valley, the central coast, the Sierra Nevada Mountains, and water; (2) using a more realistic irrigation scheme in the model through the use of different allowable soil water depletion configurations; and (3) evaluating the simulated results against satellite and in situ observations available through the California Irrigation Management Information System (CIMIS). The simulation results with fine model resolution and with the more realistic irrigation scheme indicate that the surface meteorological fields are noticeably improved when compared with observations from the CIMIS network and Moderate Resolution Imaging Spectroradiometer data. Our results also indicate that irrigation has significant impacts on local meteorological fields by decreasing temperature by 3°–7°C and increasing relative humidity by 9–20%, depending on model resolutions and allowable soil water depletion configurations. More significantly, our results using the improved model show that the effects of irrigation on weather and climate do not extend very far into nonirrigated regions.

Background and Purpose: The purpose of this study was to investigate the clinical value of 3D rotational angiography (3DRA) for evaluation of cerebral vasospasm in patients with aneurysmal subarachnoid hemorrhage (SAH) by comparison with 2D digital subtraction angiography (DSA). Methods: Forty-six patients who had undergone 2D DSA and 3DRA for evaluation of cerebral vasospasm following SAH were retrospectively analyzed. 3DRA was routinely performed after standard 2D DSA. 3D volume rendering images were created from 3DRA dataset and compared with DSA for the detection and characterization of vasospasm. Results: Of the 46 patients investigated, 25 had vasospasm on 2D DSA images. No vasospasm was observed in 21 patients with aneurysmal SAH. According to the reference standard of DSA, 46 spastic segments were found in 25 patients with vasospasms. A total of 51 spastic segments were found on 3DRA volume rendering angiograms. The sensitivity, specificity, positive and negative predictive values of 3DRA for detecting vasospasm were 100, 76, 90, 100%, respectively. Conclusion: The pseudo-spasm phenomenon was frequently observed on 3DRA volume rendering images. 3DRA was less useful than 2D DSA for evaluation of vasospasm after SAH.

This paper re-examines the classic question of how a household should optimally allocate its portfolio between risky stocks and risk-free bonds over its lifecycle.We show that allowing for the wage indexation of social security benefits fundamentally alters the optimal decisions.Moreover, the optimal allocation is close to observed empirical behavior.Households, therefore, do not appear to be making large "mistakes," as sometimes believed.In fact, traditional financial planning advice, as embedded in "target date" funds -whose enormous recent growth has been encouraged by new government policy -often leads to even relatively larger "mistakes" and welfare losses.