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Wind and solar energy are expected to play a major role, in the near future electricity generation mix. However, wind and solar energy-based generation are intermittent and non-dispatchable, not being suitable to supply base-load electric power. Their greater penetration and grid integration are critical issues due to their inherent intermittency and variability. Moreover, there are strong evidence that wind and solar energy are showing complementary over appropriate time and space scales. This work investigates such spatiotemporal complementarity and variability as a means by which electricity planners, developers, and grid operators might advance uses and grid integration of wind energy. Over 14 years of synchronous wind velocity and solar radiation measurements at several sites, located in complex terrain of Nevada are used in this study. To do so we used auto-correlations and cross-correlations in wind speed and solar radiation time series, by applying detrended fluctuation analysis and detrended cross-correlation analysis.

This study presents the first global-scale comprehensive climatology of marine fog and is based on ICOADS ship present weather observations for the period 1950–2007. In general, the median marine fog occurrence away from the polar oceans is low (0.2%). Substantially greater marine fog occurrences are limited to four regions, not including the polar region. Fog occurrence maxima along the western side of the sub-polar ocean gyre occur during the warm season and over the shelf, which includes one centred over the Northwest Pacific Kuril Islands (60%) and one over the Northwest Atlantic Grand Banks (45%), while a third lies over the Argentinean shelf break. Fog maxima over seven marginal seas occur over the Okhotsk Sea, Sea of Japan, Yellow Sea, South China Sea, Nova Scotia, North Sea, and Baltic Sea. Five wind-driven coastal upwelling zone maxima comprise the California-Oregon, Namibia-South Africa, Peru, Morocco and Arabian regions. Maximum upwelling fog occurrence during the warm season was found to be inversely proportional to the minimum sea surface temperature (SST). Most fog maximum occurrence locations lie over SST minima in shallow water during the warm season and are capped by a stable lower atmosphere. Positive correlations (up to 0.79) were found between 5-year moving averages of fog in the Kuril Islands and the North Pacific Oscillation. Five-year moving averages of fog in the Grand Banks were positively and significantly correlated (up to 0.56), with the North Atlantic Oscillation represented by the sea level pressure difference between Gibraltar and Reykjavik. In contrast, 5-year moving averages of fog in the Grand Banks and the Atlantic Multidecadal Oscillation index were negatively and signifi- cantly correlated (up to −0.75).

International audience; A severe air quality degradation event occurred in the Santiago Metropolitan Area (SMA), Chile, in June 2014. Meteorological and air quality measurements from 11 stations in the area as well as numerical simulations using the Weather and Research Forecasting (WRF) model were used to explain the main reasons for the occurrence of elevated particulate matter (PM) concentrations. The conditions were characterized with formation of a coastal low in central Chile between the southeastern anticyclone and a high-pressure system over Argentina. At a local scale, these conditions generated a depression at the base of the inversion layer, an increase in the vertical thermal stability, lower humidity and low-wind conditions, which were conducive to a decrease in pollutant dispersion and insufficient ventilation of the polluted air. Measurements and simulations using the WRF model revealed a vertical structure of the boundary layer during these stagnant conditions and provided a basis for a trajectory analysis. The back-trajectory calculation showed that the transport of air parcels was contained in the valley during the highest concentrations. The analysis also enabled the definition of the threshold values of a simple indicator of air pollution (ventilation coefficient, VC), which confirmed the evolution of the episode and divided the observed daily concentrations into two groups, with one including values above the limits prescribed by the national air quality standards (NAQS) and the other including values below these limits. For the SMA, the daily PM concentrations above the NASQ limits were associated with an overall mean threshold value of VC below 500 m2 s−1 (for PM2.5) and 300 m2 s−1 (for PM10). To apply the VC analysis to other pollutants and different geographic locations, different threshold values should be evaluated.

An objective method for tracking pathways of cyclone centres over Europe was developed and applied to the ERA-Interim reanalysis atmospheric data (1979-2014). The method was used to determine trajectories of those Mediterranean cyclones which generated extreme sea levels along the northern and the eastern Adriatic coast during the period from 1979 to 2014. Extreme events were defined as periods during which sea level was above 99.95 percentile value of time series of hourly sea-level data measured at the Venice (northern Adriatic), Split (middle eastern Adriatic) and Dubrovnik (south-eastern Adriatic) tide-gauge stations. The cyclone pathways were tracked backwards from the moment closest to the moment of maximum sea level up to the cyclone origin time, or at most, up to 72 hours prior the occurrence of the sea-level maximum.Our results point out that extreme sea levels in Venice normally appear during synoptic situations in which a cyclone centre is located to the south-west and north-west of Venice, i.e., when it can be found over the Gulf of Genoa, or the Alps. On the contrary, extreme sea levels in Dubrovnik are usually associates with cyclone centres above the middle Adriatic, whereas floods in Split seem to appear during both above-described types of situations.Occurrence times and intensity of cyclones and extreme sea-levels was further associated with the NAO index. It has been shown that the deepest cyclones and corresponding extreme floods tend to occur during the negative NAO phase.

Climate predictions of air temperature in coastal regions represent a great challenge due to the complex interactions among the atmosphere, sea, and land. With approximately 1,200 islands, the Adriatic is a region with a strong land‐sea contrast, land‐atmosphere feedback, and intense air‐sea interaction. Because the Mediterranean has been regarded as a “hot spot” for climate change, regional climate models can be used to provide insight into a more realistic representation of small‐scale weather and climate structure and variability. This advantage is due to the better representation of complex topography, developed coastlines, and land‐sea contrasts, which are important for investigating air temperature trends. The use of regional climate models together with high‐resolution reanalyses and observations in assessments of climate variability and climate change is highly valuable for understanding climate processes at regional scales. The present study focused on air temperature and its trends calculated from measurements and simulated by eight regional climate models from the EURO‐CORDEX database; these data were represented by UERRA reanalyses and E‐OBS gridded data. In the evaluation period (1989–2008), the models' RMSEs were fairly small, in the range 0.5–1.5°C, compared to the historical period (1961–2005), with RMSEs greater than 1.75°C. However, the models showed small absolute trend differences (up to 0.12°C·decade−1 for the historical period). The ensemble means in both periods showed an accuracy improvement of 15–20% compared to the individual models. The models exhibited more success in terms of representing the main statistics and variability of the air temperature structure than in reproducing the temperature trends over 45 years, especially in the northern Adriatic, where there is complex coastal topography and significant seasonal variability in the wind regime. The reanalyses well represented temperature structure but showed less success in explaining the temperature trends than the results from the measured data.

To diagnose the influence of sea surface temperature (SST) biases on temperature and precipitation patterns in the western United States, we analysed atmospheric and coupled global climate model (GCM) simulated output from the Coupled and Atmospheric Model Intercomparison Project versions 3 and 5 (CMIP3, AMIP3, CMIP5, and AMIP5). We further analyse the impact of SST biases in regional cli- mate modelling simulations. CMIP3 and CMIP5 multi-model ensembles show sys- tematic warm SST biases offshore of California and the Baja California Peninsula (Baja) region, with ensemble mean SST biases of the order of ~3–5 C. Through- out the western United States, 75% of all models in CMIP3 and CMIP5 exhibit wet precipitation biases and corresponding cold biases in surface temperature. The CMIP5 ensemble shows on average a stronger and more consistent relationship between Baja SST biases and precipitation over the west compared to the CMIP3 ensemble. We attempted to isolate the atmospheric response to regional SST biases using a regional climate model (RCM) based on the Weather Research and Fore- casting model with a 36 km grid size. The RCM was driven with the CMIP3-CCSM3 as boundary conditions with and without corrections of simulated SSTs. Results from RCM simulations further confirm that SST biases impact cli- mate regionally and propagate over the western United States and can explain up to 80% of wet precipitation biases. Our regional GCM comparison and RCM experiment assess the robustness of model estimates of climate mean states and constitute an often neglected prerequisite for characterizing how errors transfer from GCM to regional downscaling modelling frameworks and how they could potentially affect downscaling application and impact studies.

Wind energy is a weather and climate-dependent energy resource with natural spatio-temporal variabilities at time scales ranging from fraction of seconds to seasons and years, while at spatial scales it is strongly affected by the terrain and vegetation. To optimize wind energy systems and maximize the energy extraction, wind measurements on various time scales as well as wind energy forecasts are required and needed. This study focuses on spatio-temporal characteristics of the wind velocity in complex terrain, relevant to wind energy assessment, operation, and grid integration, using data collected at 11 towers ranging from 40 to 80 m tall over a 12-year period in complex terrain of western-central and northern Nevada, USA. The autocorrelation analysis, Detrended Fluctuation Analysis (DFA) and Detrended Cross-Correlation Analysis (DCCA) showed strong coherence between the wind speed and direction with slowly decreasing amplitude of the multi-day periodicity with increasing lag periods. Besides pronounced diurnal periodicity at all locations, statistical analysis and DFA also showed significant seasonal and annual periodicities, long-memory persistence with similar characteristics at all sites and towers with a relatively narrow range of the Weibull parameters. The DCCA indicates similar wind patterns at each tower, and strong correlations between measurement sites in spite of separations of about 300 km across the towers’ setup. The northern Nevada area exhibits higher wind resource potential and higher wind persis-tence compared to the western-central region. Overall, the DFA and DCCA results suggest higher degree of complementarity among wind data at measure-ment sites compared to previous standard statistical analysis., Energija vjetra je energetski resurs ovisan o vremenu i klimi s prirodnim prostorno-vremenskim promjenjivostima koje u vremenskim razmjerima idu od sekundi do sezona i godina, dok su na prostornim razmjerima promjenjivosti snažno uvjetovane složenošću terena i vegetacije. Kako bi se optimizirali sustavi energije vjetra i maksimizirala ekstrak-cija energije, potrebna su mjerenja vjetra u različitim vremenskim razmacima, kao i prognoze energije vjetra. Ova studija se fokusira na prostorno-vremenske karakteristike brzine vjetra u složenom terenu, relevantne za procjenu energije vjetra i integraciju u elektrodistribucijske mreže, koristeći podatke prikupljene na 11 tornjeva s rasponima visina od 40 do 80 m tijekom 12-godišnjeg razdoblja u složenoj topografiji zapadno-središnje i sjeverne Nevade, SAD. Autokorelacijska analiza, analiza poremećaja fluktu-acija (DFA) i višestruka korelacijska analiza (DCCA) pokazale su snažnu koherenciju između brzine i smjera vjetra sa sporo opadajućom amplitudom višednevne periodičnosti s povećanjem razdoblja pomaka. Osim izražene dnevne periodičnosti na svim lokacijama, spektralna analiza i DFA također su pokazale značajne sezonske i godišnje periodičnosti, postojanost dugog pamćenja sa sličnim karakteristikama na svim mjestima i tornjevima s relativno uskim rasponom Weibullovih parametara. DCCA ukazuje na slične uzorke vjetra na svakom tornju i jake korelacije između mjernih mjesta unatoč udaljenostima između tornjeva čak do 300 km. Područje sjeverne Nevade pokazuje veći potencijal vjetra i veću postojanost vjetra u usporedbi sa zapadno-središnjom regijom. Sveukupno, rezul-tati DFA i DCCA ukazuju na veći stupanj komplementarnosti među podacima o vjetru na različitim mjestima u usporedbi s prethodnim standardnim statističkim analizama.

A dense fog with the visibility less than 100 m over 6 hours occurred around the Hangzhou Bay off the coast of the western part of the East China Sea on 24–25 June 2013 during Meiyu period. This study focuses on the physical mechanism involved in the fog process by using in-situ observations and model. The analysis indicates that the land-sea thermal contrast played an essential role in the fog episode, while a weak low-pressure wedge associated with the Meiyu front and the diurnal variation in temperature provided background conditions. Induced by the strong land-sea thermal contrast, a secondary circulation formed in the lower levels of the atmospheric boundary layer (ABL) over the coast. The southeasterly wind and subsiding motion associated with the secondary circulation contributed to the moisture supply and the lowering of the boundary layer, favoring fog formation and maintenance. The fog maintained until the weakening of the temperature gradient between land and sea, when the downward flow was replaced by upward motion controlled by the approaching of a low-pressure center. These results are helpful for improving coastal fog forecast in Meiyu period and for our understanding of mechanisms involved in coastal fog processes.

A detailed description of the meteorological conditions of the Washoe Valley (Nevada) and simulations that examine the predictability of the westerly high wind event known as the Washoe Zephyr are presented. Numerical weather model prediction skill is computed for day-ahead (24–48 h) forecasts of wind speed at a meteorological tower on the Virginia Hills range relative to a persistence forecast based on a seasonal climatology constructed of hourly mean observations. The model predictions are shown to be more skillful than a climatology based on seasonal and hourly means during winter and less skillful than the seasonal-hourly climatology (SHC) during summer. Overall skill of the forecasted winds tends to increase with finer horizontal grid spacing. Phase errors compose the largest component of the error decomposition and large phase errors are associated with the onset and decay of the diurnally forced Washoe Zephyr during summer and synoptically forced high wind events and valley rotors during winter. The correlation coefficient between forecasts and observations for all forecast horizontal grid spacings considered is shown to depend roughly linearly on the ratio of the integrated power spectral density in the synoptic band to the integrated power spectral density in the combined diurnal and subdiurnal band.

To elucidate the relationship between factors resolved by the positive matrix factorization (PMF) receptor model and actual emission sources and to refine the PMF modeling strategy, speciated PM

Marine fog has a significant impact on human activities and the environment. A substantial portions of all accidents at sea worldwide occur in the presence of dense fog. Fog’s disruption of marine transport, harbor activities, coastal road traffic, and life threatening situations are often in the news. Fog transports droplets and their ions, aerosols, and microorganisms that alter the hydrologic, thermodynamic, nutrient, and toxicological properties of ecosystems in coastal regions. Fog also controls temperature and moisture conditions in coastal areas and consequently affects vegetation and especially forest evolution. Some of the highest degradations of air quality occur during fog under stable and stagnant anticyclonic conditions. In principle, fog forecast models should resolve a huge span of physical processes ranging from the characteristic 10^(-7) m scales of aerosols to synoptic processes with scales of 10^(6) m, which means the range of the smallest to the largest scales is on an order of 10^(13) m. The main variables and processes relevant to fog include aerosols and fog condensation nuclei ; microphysics and precipitation ; advection ; synoptic conditions ; air-sea interaction through surface fluxes of heat, latent heat, moisture, and momentum ; air and sea surface temperature ; ocean currents and upwelling ; land-sea interactions and local circulations ; characteristics of low marine inversion and entrainment ; and coastal topography. The main goal of this book is to provide comprehensive understanding of the major processes leading to formation, evolution, and dissipation of fog by using observations, modeling, and forecasting. Discussed topics include fog observational in situ and remote sensing techniques, worldwide fog climatology, radiation and turbulence properties, microphysics and visibility parameterizations, effects of precipi- tation on fog, and deterministic and probabilistic fog modeling and forecasting.

Herein, an analysis is presented of the world’s marine fog distribution based upon the International Comprehensive Ocean-atmosphere Data Set (ICOADS) ship observations taken during 1950–2007. Fog, shallow fog, and mist are taken from routine weather reports that are encoded in an ICOADS ship observation with the “present weather” code. Occurrence is estimated by the number of observations of a type divided by the total present weather observations in a one-degree area. The bulk of the observations are in the northern temperate and tropical oceans, with decreasing numbers south of 20 °S and large data voids in the polar oceans. Marine fog is infrequent over most of the world’s oceans with the median occurrence 0.2 % while it is in isolated maxima for values larger than about 2 %. In a specific location, either fog or mist are the most frequent, followed with an order of magnitude lower occurrence by shallow fog.

All problems inherent to models’ imperfection and generally insufficient vertical and horizontal resolution, as well as inability to obtain full and accurate initial and boundary conditions, amplify for fog predictions. This is due to a huge span of relevant parameters and processes ranging from aerosols to hemispheric synoptic conditions. Understanding fog characteristics and evolution as well as constructing accurate initial and boundary conditions for forecasting is severely hindered by absence of dense routine and also special measurements over the vast oceans. Fog modeling and forecasting has a long history from early methods based on persistence and synoptic indicators and later through weather analysis to contemporary methods using high-resolution numerical models on regional scales, mesoscales, and microscales. The main issues that are critical to understanding and forecasting marine fog include synoptic conditions and advection, local circulations and flow properties, turbulence, characteristics of inversion and subsidence, longwave and shortwave radiative fluxes, air-sea interaction, aerosols, microphysics, and coastal topography. The two main approaches to fog predictions are statistical and dynamical forecasting methods. Some of the statistical methods are based on various statistical analyses including regressions, correlations, classifications, and tree decision diagrams. Additional statistical methods include artificial neural networks and fuzzy logic, which can successfully treat nonlinear relationships between fog predictors and predictands. The statistical methods are applicable and useful when there is a sufficient archive of fog predictor parameters and fog observations at a location of interest. An advantage of using statistical methods is their computational efficiency allowing for fog nowcasting as an independent tool or in conjunction with operational forecasting models. A disadvantage is that the statistical approach does not take into account the actual three-dimensional weather structure and evolution. Dynamical models for fog forecasting use mathematical representations of basic conservation laws and parameterizations of physical processes including the ones relevant to fog. Early research in numerical studies has used one- (1D) and two-dimensional (2D) models, which can allow for high vertical resolutions and detailed parameterization schemes. Since 1D and 2D models cannot represent the full structure and evolution of atmospheric processes, three-dimensional (3D) models have been used for operational weather forecasts. An important objective of 3D modeling studies is understanding the path history of an air mass transformation that leads to fog or fog-free conditions. Some of the main drawbacks of 3D models include the high computational requirements, which usually result in inadequate horizontal and vertical resolution, and simplifications in physics parameterization schemes. It is obviously best to use the advantages of each of these types of models and to combine them into an integrated modeling system that can improve the accuracy of the forecast. Further improvement in marine fog forecasting is obtained by treating air-sea interaction with coupled atmospheric and ocean models. Besides a deterministic approach with a single fog forecast outcome, probabilistic methods based on an ensemble of solutions are emerging. The probabilistic forecasts are able to reveal uncertainties due to the initial and boundary conditions, physics parameterizations, and model structure and setup. Advanced modeling approaches such as the large-eddy simulation (LES) technique has been also used for fog predictions. LES can simulate high-resolution atmospheric fields including turbulence on limited domains, however, they have limitations in representing realistic synoptic processes. With the rapid development of measurement networks, and especially with satellite data, it has been shown that assimilation of data into the models can significantly improve the forecast accuracy. Although initially fog forecasts were developed and applied to marine areas of North America and Europe, it is encouraging that various fog forecasting methods are being developed and applied to marine areas of other continents. Of definite interest is to estimate projections of fog characteristics using regional climate models that are recently under rapid developments. In spite of their uncertainties in initial and boundary conditions as well as in emissions of aerosols and greenhouse gases, limited resolutions, and generally simplified physics parameterizations, they are valuable tools in assessing meteorological parameters relevant to future fog occurrence and evolution.

The objective of this review is to discuss physical processes over a wide range of spatial scales that govern the formation, evolution, and dissipation of marine fog. We consider marine fog as the collective combination of fog over the open sea along with coastal sea fog and coastal land fog. The review includes a history of sea fog research, field programs, forecasting methods, and detection of sea fog via satellite observations where similarity in radiative properties of fog top and the underlying sea induce further complexity. The main thrust of the study is to provide insight into causality of fog including its initiation, maintenance, and destruction. The interplay between the various physical processes behind the several stages of marine fog is among the most challenging aspects of the problem. An effort is made to identify this interplay between processes that include the microphysics of fog formation and maintenance, the influence of large-scale circulations and precipitation/clouds, radiation, turbulence (air–sea interaction), and advection. The environmental impact of marine fog is also addressed. The study concludes with an assessment of our current knowledge of the phenomenon, our principal areas of ignorance, and future lines of research that hold promise for advances in our understanding.

A high ozone (O3) concentrations episode during a heat wave event in the Northeastern Mediterranean was investigated using the WRF/Chem model. To understand the major model uncertainties and errors as well as the impacts of model inputs on the model accuracy, an ensemble modelling experiment was conducted. The 51-member ensemble was designed by varying model physics parameterization options (PBL schemes with different surface layer and land-surface modules, and radiation schemes); chemical initial and boundary conditions; anthropogenic and biogenic emission inputs; and model domain setup and resolution. The main impacts of the geographical and emission characteristics of three distinct regions (suburban Mediterranean, continental urban, and continental rural) on the model accuracy and O3 predictions were investigated. In spite of the large ensemble set size, the model generally failed to simulate the extremes; however, as expected from probabilistic forecasting the ensemble spread improved results with respect to extremes compared to the reference run. Noticeable model nighttime overestimations at the Mediterranean and some urban and rural sites can be explained by too strong simulated winds, which reduce the impact of dry deposition and O3 titration in the near surface layers during the nighttime. Another possible explanation could be inaccuracies in the chemical mechanisms, which are suggested also by model insensitivity to variations in the nitrogen oxides (NOx) and volatile organic compounds (VOC) emissions. Major impact factors for underestimations of the daytime O3 maxima at the Mediterranean and some rural sites include overestimation of the PBL depths, a lack of information on forest fires, too strong surface winds, and also possible inaccuracies in biogenic emissions. This numerical experiment with the ensemble runs also provided guidance on an optimum model setup and input data.

[1] Although buoy and aircraft measurements, as well as numerical simulations, have shown intense over-shelf and slope dynamics of the west coast of the United States in the summer upwelling season, satellite footprint limitations of approximately 25 km resolution have thus far precluded long term, spatially extended monitoring of the near-coastline dynamics. However, recent advancements in satellite data processing have allowed a finer footprint, of approximately 12 km resolution, to investigate further the properties of coastal winds and consequent upwelling. This improved satellite data analysis has confirmed the intense coastal winds over the shelf and slope and revealed their spatial extensions and inhomogeneities on event and multiday scales. The inhomogeneities are dominated by the along-coast pressure gradient modulated by the synoptic effects and topographical forcing of the five major capes, which also generate upwelling wind stress and curl pattern inhomogeneities. Synoptic forcing of the coastal flow was evidenced by high correlation coefficients, in excess of 0.8, between the buoy-measured pressure differences and wind speeds; wind speeds greater than 11 m s−1 occurred only when the along-coast pressure gradient was greater than 0.8 hPa/100 km. Based on Bernoulli flow principles, the observed upper limit of the wind speed on the downwind sides of the major capes is explained by using characteristic values of atmospheric marine layer parameters. Numerical simulations at a similar resolution (12 km) as the new satellite data footprint for June 2001, completed as part of multiyear regional climate modeling efforts, were able to reproduce the main characteristics of the flow.

The main objective of the study was to investigate spatial and temporal characteristics of the wind speed and direction in complex terrain that are relevant to wind energy assessment and development, as well as to wind energy system operation, management, and grid integration. Wind data from five tall meteorological towers located in Western Nevada, USA, operated from August 2003 to March 2008, used in the analysis. The multiannual average wind speeds did not show significant increased trend with increasing elevation, while the turbulence intensity slowly decreased with an increase were the average wind speed. The wind speed and direction were modeled using the Weibull and the von Mises distribution functions. The correlations show a strong coherence between the wind speed and direction with slowly decreasing amplitude of the multiday periodicity with increasing lag periods. The spectral analysis shows significant annual periodicity with similar characteristics at all locations. The relatively high correlations between the towers and small range of the computed turbulence intensity indicate that wind variability is dominated by the regional synoptic processes. Knowledge and information about daily, seasonal, and annual wind periodicities are very important for wind energy resource assessment, wind power plant operation, management, and grid integration.

The two volume set LNCS 10072 and LNCS 10073 constitutes the refereed proceedings of the 12th International Symposium on Visual Computing, ISVC 2016, held in Las Vegas, NV, USA in December 2016. The 102 revised full papers and 34 poster papers presented in this book were carefully reviewed and selected from 220 submissions. The papers are organized in topical sections: Part I (LNCS 10072) comprises computational bioimaging; computer graphics; motion and tracking; segmentation; pattern recognition; visualization; 3D mapping; modeling and surface reconstruction; advancing autonomy for aerial robotics; medical imaging; virtual reality; computer vision as a service; visual perception and robotic systems; and biometrics. Part II (LNCS 9475): applications; visual surveillance; computer graphics; and virtual reality.

An urban heat island (UHI) effect is identified in Reno, Nevada by analyzing regional temperature trends calculated from seven long- term observation stations for the summer and winter seasons between 1950 and 2014. The UHI is maximized during summer (JuneeAugust) and characterized by asymmetric in- creases in minimum (~1.1 C/decade, p < 0.01) versus maximum temperature (~0.1 C/decade, p < 0.01) trends in excess of trends from regional climate stations. Comparisons of historical Reno temperatures with an ensemble of 66 bias- corrected and downscaled global climate model (GCM) outputs spanning 1950e2014 demonstrates cold biases of 1.5e4.5 C during summer with minimum temperature having the largest bias. We show that a secondary bias correction step utilizing the statistical downscaling method of quantileequantile mapping (QQM) can reduce biases in future climate projections assuming no changes to the UHI. The QQM results in an additional total warming of ensemble mean temperatures by ~3 C for downscaled GCM output and ~4 C for re-gridded 1 grid resolution GCM output for 2030 e2049 under the RCP8.5 emissions scenario. These temperature differences produce additional increases in summer potential evapotranspiration of 10% compared to non-QQM bias-corrected GCM output. It was shown that the QQM method represents a useful and computationally efficient method for bias correction of temperature projections for cities where UHI effects exist. Planning and impacts studies of urban water resources can benefit from these improved climate projections, particularly in regions where downscaled GCM output is unavailable.

Arctic sea ice decline is expected to continue throughout the twenty-first century as a result of increased greenhouse gas concentrations. Here we investigate the impact of a strong Arctic sea ice decline on the atmospheric circulation and low pressure systems in the Northern Hemisphere through numerical experimentation with a coupled climate model. More specifically, a large ensemble of 1-yr-long integrations, initialized on 1 June with Arctic sea ice thickness artificially reduced by 80%, is compared to corresponding unperturbed control experiments. The sensitivity experiment shows an ice-free Arctic from July to October; during autumn the largest near-surface temperature increase of about 15 K is found in the central Arctic, which goes along with a reduced meridional temperature gradient, a decreased jet stream, and a southward shifted Northern Hemisphere storm track; and the near-surface temperature response in winter and spring reduces substantially due to relatively fast sea ice growth during the freezing season. Changes in the maximum Eady growth rate are generally below 5% and hardly significant, with reduced vertical wind shear and reduced vertical stability counteracting each other. The reduced vertical wind shear manifests itself in a decrease of synoptic activity by up to 10% and shallower cyclones while the reduced vertical stability along with stronger diabatic heating due to more available moisture may be responsible for the stronger deepening rates and thus faster cyclone development once a cyclone starts to form. Furthermore, precipitation minus evaporation decreases over the Arctic because the increase in evaporation outweighs that for precipitation, with implications for the ocean stratification and hence ocean circulation.

This paper implements an individual station-based downscaled approach based on a quantil–quantile bias correction mapping to further downscale regional gridded simulated output into individual station locations. We describe and propose a framework to optimize the usefulness of this downscaling approach over small watersheds in mountain regions, where downscale gridded data (∼10km) is still too coarse for use in sub-regional hydrologic simulation studies. Two regional downscaled data sets were downscaled to individual station locations: the Desert Research Institute-Regional Climate Model based on the Weather Research and Forecasting model (DRI-RCM; 12 km resolution); and the US Bureau of Reclamation Bias-Corrected Spatial Disaggregation (BCSD; 12 km resolution). We applied the station-based downscaled data in the integrated hydrologic model, GSFLOW, as adapted to the Incline Creek watersheds in the northeastern Lake Tahoe basin. Hydrologic model results showed that the proposed downscaled approach provides reasonable downscaled climate data that can be used to evaluate sub-regional scale hydrologic processes.

The main objective of this study was to investigate the causes as well as the temporal and spatial dynamics of ozone pollution during three high ozone episodes in the northeastern Mediterranean Basin using surface measurements and WRF/Chem model simulations. The simulation results revealed that the analyzed episodes were mainly caused by the impact of accumulation of pollution rich in ozone over the northern Adriatic. Two mechanisms were simulated: (1) the accumulated pollutants occasionally encroaching and retreating, impacting the coastal areas; and (2) broad advection impacting areas further inland in the northeastern Mediterranean Basin including Slovenia. The simulations also show that in some specific conditions the elevated ozone levels over continental Slovenia can be separated from the direct Mediterranean influences by the Alpine-Dinaric mountain ridge. The simulations show a trend of underestimating maximum and overestimating minimum ozone concentrations, but the overall statistics agree fairly well with the statistics of the measured ozone concentrations.

To elucidate the relationship between factors resolved by the positive matrix factorization (PMF) receptor model and actual emission sources and to refine the PMF modeling strategy, speciated PM2.5 (particulate matter with aerodynamic diameter 0.95). Retaining more factors in the model does not help resolve minor sources, unless temporal resolution of the data is increased, thus allowing more information to be used by the model. If guided with a priori knowledge of source markers and/or special events, rotation of factors leads to more interpretable PMF factors. The choice of uncertainty weighting coefficients greatly influences the PMF modeling results, but it cannot usually be determined for simulated or real-world data. A simple test is recommended to check whether the weighting coefficients are suitable. However, uncertainties in the data divert PMF solutions even when the optimal weighting coefficients and number of factors are in place.

The wind potential in western Nevada was assessed by using wind, temperature, and pressure data over a period of four and half years from four 50 m tall towers. The seasonal wind patterns for all towers show a maximum during the spring season. Diurnal wind speed patterns for all seasons and months showed a minimum during the late morning and a maximum during the late afternoon. The highest values are during the spring season with multi-annual hourly wind speeds at or above 8 m/s and relative frequency of the wind speed in the optimum turbine range (5–25 m/s) of 70% or higher for the Tonopah tower, with lower values for the other three towers. The monthly power law index values are lower than the standard value 0.147 (in general 0.13 or lower). The hourly turbulence intensities were higher at lower elevations, with values of about 0.35 or higher at the 10 m level and at lower wind speed range (5.0 m/s or less). Higher turbulence intensities were found for all towers and heights during the spring and summer seasons and lower values during the rest of the year. The daily gust factor for the 2003–2007 composite data sets shows low probabilities (2% or less) of the wind gusts exceeding 25 m/s.

Significant uncertainties in the prediction of pollutant transport and dispersion limit the accuracy of air quality in areas with complex terrain, such as along the California coastline, which suffers from elevated air pollutant concentrations. Typical Lagrangian air quality models treat the dispersion of plumes better than Eulerian models but the chemical interactions induced by the mixing of intersecting plumes are ignored. In contrast, Eulerian models treat the emissions as well mixed within each grid box. To address these limitations, an air quality model with in-line chemistry and meteorology that combines the advantages of the Eulerian and Lagrangian approach to air quality modeling has been developed. In order to evaluate the model, simulation results of ozone concentrations were compared against a commonly used photochemical model (CAMx) and with airborne data from a field study made in the San Diego area of southwestern California.

A summer wind speed maximum extending more than 200 km occurs over water around Point Conception, California, the most extreme bend along the U.S. West Coast. The following several causes were investigated for this wind speed maximum: 1) synoptic conditions, 2) marine layer hydraulic flow effects, 3) diurnal variations, 4) mountain leeside downslope flow, 5) sea surface temperature structure, and 6) island influence. Synoptic conditions set the general wind speed around Point Conception, and these winds are classified as strong, moderate, or weak. The strong wind condition extends about Point Conception, reaching well offshore toward the southwest, and the highest speeds are within 20 km to the south. Moderate wind cases do not extend as far offshore, and they have a moderate maximum wind speed that occurs over a smaller area in the western mouth of the Santa Barbara Channel. The weak wind speed case consists of light and variable winds about Point Conception. Each category occurs about one-third of the time. Atmospheric marine layer hydraulic dynamics dominate the situation after the synoptic condition is set. This includes an expansion fan on the south side of the point and a compression bulge on the north side. The expansion fan significantly increases the wind speeds over a large area that extends to the southwest, south, and east of Point Conception, and the maximum wind speed is increased for the strong and moderate synoptic cases as well. The horizontal sea surface temperature pattern contributes to the sea surface wind maximum through the Froude number, which links the potential temperature difference between the sea surface temperature and the capping inversion temperature with marine layer acceleration in an expansion fan. A greater potential temperature difference across the top of the marine layer also causes more energy to be trapped in the marine layer, instead of escaping upward. The thermally driven flow resulting from differential heating over land in the greater Los Angeles, California, coastal and elevated area to the east is not directly related to the wind speed maximum, either in the Santa Barbara Channel or in the open ocean extending farther offshore. The effects of the thermally driven flow extend only to the east of the Santa Barbara Channel. The downslope flow on the south side of the Santa Ynez Mountains that is generated by winds crossing the Santa Ynez Mountain ridge contributes neither to the high-speed wind maximum in the Santa Barbara channel nor to that extending farther offshore. Fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) simulations do support a weak leeside flow in the upper portions of the Santa Ynez Mountains. The larger Channel Islands have a significant effect on the marine layer flow and the overwater wind structure. One major effect of the Santa Barbara Channel Islands is the extension of the zone of high-speed winds farther to the south than would otherwise be the case.

A numerical study using the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) was performed to assess the impact of initial and boundary conditions, the parameterization of turbulence transfer and its coupling with cloud-driven radiation, and cloud microphysical processes on the accuracy of mesoscale predictions and forecasts of the cloud-capped marine boundary layer. Aircraft, buoy, and satellite data and the large eddy simulation (LES) results during the Dynamics and Chemistry of Marine Stratocumulus field experiment (DYCOMS II) in July 2001 were used in the assessment. Three of the tested input fields (Eta, NCEP, and ECMWF) show deficiencies, mainly in the thermodynamic structure of the lowest 1500 m of the marine atmosphere. On a positive note, the simulated marine-layer depth showed good agreement with aircraft observations using the Eta fields, while using the NCEP and ECMWF datasets underestimated the marine-layer depth by about 20%–30%. The predicted turbulence kinetic energy (inversion strength) was about 50% of that obtained from the LES results (aircraft observed). As a consequence of moisture overprediction, the predicted liquid water path was twice the observed by 1–2 g kg−1. The sensitivity tests have shown that the selections of turbulence and cloud microphysical schemes significantly influence the turbulence estimates and cloud parameters. Two of the tested turbulence schemes (Eta PBL and Burk–Thompson) did not exhibit the coupling with radiation. The significant differences in the simulated turbulence estimates appear to be a consequence of the use of water-conserving potential temperature variables. The microphysical parameterization, which uses the number concentration of cloud drops in the autoconversion process, simulates a realistic evolution of precipitable hydrometeors in the cloudy marine layer on the positive side, but on the other hand enhances the decoupling in the turbulence structure. This study can provide guidance to operational forecasters concerning accuracy issues of the commonly used large-scale analyses for model initialization, and optimal selection of model parameterizations in order to simulate and forecast the cloudy atmospheric boundary layer over the ocean.

Currently used dispersion models, such as the AMS/EPA Regulatory Model (AERMOD), process routinely available meteorological observations to construct model inputs. Thus, model estimates of concentrations depend on the availability and quality of meteorological observations, as well as the specification of surface characteristics at the observing site. We can be less reliant on these meteorological observations by using outputs from prognostic models, which are routinely run by the National Oceanic and Atmospheric Administration (NOAA). The forecast fields are available daily over a grid system that covers all of the United States. These model outputs can be readily accessed and used for dispersion applications to construct model inputs with little processing. This study examines the usefulness of these outputs through the relative performance of a dispersion model that has input requirements similar to those of AERMOD. The dispersion model was used to simulate observed tracer concentrations from a Tracer Field Study conducted in Wilmington, California in 2004 using four different sources of inputs: (1) onsite measurements; (2) National Weather Service measurements from a nearby airport; (3) readily available forecast model outputs from the Eta Model; and (4) readily available and more spatially resolved forecast model outputs from the MM5 prognostic model. The comparison of the results from these simulations indicate that comprehensive models, such as MM5 and Eta, have the potential of providing adequate meteorological inputs for currently used short-range dispersion models such as AERMOD.

A method based on a statistical approach of estimating uncertainty in simulating the transport and dispersion of atmospheric pollutants is developed using observations and modeling results from a tracer experiment in the complex terrain of the southwestern USA. The method takes into account the compensating nature of the error components by representing all terms, except dispersion error and variance of stochastic processes. Dispersion error and the variance of the stochastic error are estimated using the maximum likelihood estimation technique applied to the equation for the fractional error. Mesoscale Model 5 (MM5) and a Lagrangian random particle dispersion model with three optional turbulence parameterizations were used as a test bed for method application. Modeled concentrations compared well with the measurements (correlation coefficients on the order of 0.8). The effects of changing two structural components (the turbulence parameterization and the model grid vertical resolution) on the magnitude of the dispersion error also were examined. The expected normalized dispersion error appears to be quite large (up to a factor of three) among model runs with various turbulence schemes. Tests with increased vertical resolution of the atmospheric model (MM5) improved most of the dispersion model statistical performance measures, but to a lesser extent compared to selection of a turbulence parameterization. Method results confirm that structural components of the dispersion model, namely turbulence parameterizations, have the most influence on the expected dispersion error.

The main objectives of this study were to compare three wind-stress algorithms of varying intricacy and estimate the extent to which each method altered computed wind-stress curl. The algorithms included (1) a simple bulk formula for neutral conditions that is dependent only on wind velocity components; (2) a formula that in addition to dependence on wind components includes a simplified effect of thermal stability through differences in air and sea temperatures; and (3) an algorithm that includes full treatment of dynamics and atmospheric stability. Data for the analysis were from a field program that used a special buoy network off Bodega Bay during 28 June–4 August 2001. A diamond-shaped setup of five closely separated buoys in Bodega Bay allowed for one of the first attempts to compute wind-stress curl over the ocean using buoy measurements. Based on an analysis of the available dataset, the marine layer over Bodega Bay is characterized by positive wind-stress curl with a median value around 0.2 Pa (100 km) −1 and maximum values reaching 2.5 Pa (100 km) −1 . Positive wind-stress curl was observed for all wind speed conditions, whereas negative wind-stress curl episodes were associated mostly with low-wind conditions. Comparison of wind-stress curl computed using the three algorithms showed that differences among them can be significant. The first and third algorithms indicated similar stress curl (difference around 10%), but the differences between these two and the second algorithm were much higher (approximately 40%). The reason for the difference is the stability correction, which in the third algorithm strongly decreases with an increase in wind speeds, but stays at a similar level for all wind speeds in the second algorithm. Consequently, for higher wind speeds the variability of wind stress calculated using the second algorithm is greater than for the other two algorithms, causing significant differences in computed wind-stress curl (root mean-square error equal to 0.19 Pa (100 km) −1 ). Despite the apparent biases in computed wind stress and wind-stress curl among the algorithms, all of them show a significant trend of decreasing sea-surface temperature (SST) with increasing wind-stress curl. The bootstrapping analysis has revealed that both the along-shore wind stress and wind-stress curl have noticeable correlation with the changes in the sea-surface temperature as an indirect indication of the upwelling. An additional analysis, based on the low-pass filtered data, showed also significant agreement between the measured divergence in the cross-shore surface transport and the wind-stress curl computed for all three algorithms.

An array of five buoys and three coastal stations is used to characterize the winds, stress, and curl of the wind stress over the shelf off Bodega Bay, California. The wind and wind stress are strong and persistent in the summer and weak in the winter. In the summer, wind and stress decrease strongly across the shelf, toward the coast. Combinations of buoys are used to compute the curl of the wind stress over different portions of the shelf. The mean summer 2001 curl of the wind stress over the array depends upon the area selected, varying between −1.32×10 −6 and +7.80×10 −6 Pa m −1 . The winter 2002 wind-stress curl also depends on location, varying from −2.06×10 −6 to +2.78×10 −6 Pa m −1 . Mean monthly curl of the wind stress is a maximum in the summer and a minimum near zero in the winter. In both the summer and the winter, the correlation between the wind-stress curl for different portions of the shelf varies between moderate negative, though insignificance, to high positive. A wind measurement at a single point can be poorly related to the measured curl of the wind stress at other locations over the shelf. The measurements show that the use of one wind measurement to characterize the curl of the wind stress over the shelf without further investigation of the local wind-stress curl structure is risky.

The Wind Events and Shelf Transport (WEST) program was an interdisciplinary study of coastal upwelling off northern California in 2000-03. WEST was comprised of modeling and field observations. The primary goal of WEST was to better describe and understand the competing influences of wind forcing on planktonic productivity in coastal waters. While increased upwelling-favorable winds lead to increased nutrient supply, they also result in reduced light exposure due to deeper surface mixed layers and increased advective loss of plankton from coastal waters. The key to understanding high levels of productivity, amidst these competing responses to wind forcing, is the temporal and spatial structure of upwelling. Temporal fluctuations and spatial patterns allow strong upwelling that favors nutrient delivery to be juxtaposed with less energetic conditions that favor stratification and plankton blooms. Observations of winds, ocean circulation, nutrients, phytoplankton and zooplankton off Bodega Bay and Point Reyes (38 degrees N) were combined with model studies of winds, circulation and productivity. This overview of the WEST program provides an introduction to the WEST special issue of Deep-Sea Research, including the motivation for WEST, a summary of study components, an integrative synthesis of major research results to-date, and background on conditions during field studies in May-June 2001 (the upwelling period on which this special issue is focused). (c) 2006 Elsevier Ltd. All. rights reserved.

Evolution of sea fog has been investigated using three-dimensional Mesoscale Model 5 (MM5) simulations. The study focused on widespread fog-cloud layers advected along the California coastal waters during 14–16 April 1999. According to analysis of the simulated trajectories, the intensity of air mass modification during this advection significantly depended on whether there were clouds along the trajectories and whether the modification took place over the land or ocean. The air mass, with its trajectory endpoint in the area where the fog was observed and simulated, gradually cooled despite the gradual increase in sea-surface temperature along the trajectory. Modelling results identified cloud-top cooling as a major determinant of marine-layer cooling and turbulence generation along the trajectories. Scale analysis showed that the radiative cooling term in the thermodynamic equation overpowered surface sensible and latent heat fluxes, and entrainment terms in cases of the transformation of marine clouds along the trajectories. Transformation of air masses along the trajectories without clouds and associated cloud-top cooling led to fog-free conditions at the endpoints of the trajectories over the ocean. The final impact on cloud-fog transition was determined by the interaction of synoptic and boundary-layer processes. Dissipation of sea fog was a consequence of a complex interplay between advection, synoptic evolution, and development of local circulations. Movement of the high-pressure system over land induced weakening of the along-shore advection and synoptic-pressure gradients, and allowed development of offshore flows that facilitated fog dissipation.

Ten air-quality samplings flights were performed over the San Diego metropolitan area during July 2003. One of the objectives of these flights was to assess the contribution of the offshore pollution sources (civilian and military vessels) to the air quality in the region. The flights, which took place during the mid-day hours in the lower levels of the boundary layer, originated offshore and proceeded eastwards through a series of concentric arcs covering an area of approximately 600 km2. The data from the continuous gas analyzers were translated to image plots that enabled the determination of the impact of the various sources on air quality. The results revealed that the offshore sources could often be detected (at least during five of the 10 flights) from their SO2 plume. However, the area covered by these plumes and the concentrations were significantly lower than those measured over other parts of the domain due to sources south of the region. It was further observed that vehicular traffic contributed in a major source way to the formation of ozone. During most flights the pollution cloud, originating over the high traffic area in the vicinity of the downtown area, could be tracked ‘downwind’ until the completion of the photochemical processing. By then, most of the NOX had been converted to NOZ, and peak O3 levels caused by the local emissions had started to ebb. Also, during most flights boundary layer peak levels of O3 exceeded 100 ppbv, and in one case were above 140 ppbv.

Month-long simulations using the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) with a horizontal resolution of 9 km have been used to investigate perturbations of topographically forced wind stress and wind stress curl during upwelling-favorable winds along the California and Baja California coasts during June 1999. The dominant spatial inhomogeneity of the wind stress and wind stress curl is near the coast. Wind and wind stress maxima are found in the lees of major capes near the coastline. Positive wind stress curl occurs in a narrow band near the coast, while the region farther offshore is characterized by a broad band of weak negative curl. Curvature of the coastline, such as along the Southern California Bight, forces the northerly flow toward the east and generates positive wind stress curl even if the magnitude of the stress is constant. The largest wind stress curl is simulated in the lees of Point Conception and the Santa Ba...

A historical review of research on sea fog is presented. The period of interest is essentially the twentieth century, beginning with the celebrated work of G. I. Taylor in the aftermath of the Titanic tragedy. It has been argued that relative maxima in fog frequency over the North Atlantic (including the Brit-ish Isles and the Grand Banks of Newfoundland) and the North Pacific (including the U.S. West Coast) has led to major contributions by scientists in England and the United States. The early work (pre–World War II) tended to be phenomenological—that is, conceptual with broad inference from statistical summaries. Yet, this early work laid the foundation for the numerical modeling that came with the advent of computers in the postwar period. The subtleties associated with sea fog formation and maintenance are explored by analyzing some of the results from the numerical simulations. The essay ends with a speculative view on our prospects for a more complete understanding of sea fog in light of t...

Simulation of the transport and dispersion of chemical tracer in complex terrain has been performed using artificial neural networks (ANN). The ANN method has been applied to relatively high temporal resolution data (hourly averages—long time series), and lower-resolution data (daily averages—short time series). The meteorological input consisting of surface and upper-air data was selected in such a way that it optimally represents the spatial inhomogeneity of the flow field, atmospheric stability, and synoptic conditions. In both cases, the inclusion of previous tracer concentrations as input has significantly improved the ANN performance. For the daily average case, several isolated single-point sharp peaks that were recorded in the series of daily concentrations were not resolved by the ANN. An improved correlation with measurements (from 0.946 to 0.997) was obtained after simple smoothing of the tracer concentrations. Because the number of data samples was small, a “leave-one-out” method was used. The hourly averages provided more cases and thus more significant input for ANN training; however, it brought more uncertainty into the selection of appropriate inputs because of the transport time due to the separation between the source and receptor. Here, training was performed using the first 85% of cases; the rest was used for testing. The ANN-simulated hourly concentrations agreed well with the measured concentrations and yielded correlation coefficients for the training and testing sets of 0.844 and 0.896, respectively. The sensitivity analysis revealed that previous concentration data contributed to resolving peaks in simulated concentrations while meteorological data provided more information on the temporal characteristics of the simulated tracer concentrations. A rudimentary comparison with traditional statistical methods revealed that the ANN performed better and showed fewer limitations as a tool for tracer modeling, especially for long-term prediction.