Atmospheric Propagation at 100 and 300 GHz: Assessment of a Method to Identify Rainy Conditions during Radiosoundings

The influence of atmospheric gases and tropospheric phenomena becomes more relevant at frequencies within the THz band (100 GHz to 10 THz), severely affecting the propagation conditions. The use of radiosoundings in propagation studies is a well established measurement technique in order to collect information about the vertical structure of the atmosphere, from which gaseous and cloud attenuation can be estimated with the use of propagation models. However, some of these prediction models are not suitable to be used under rainy conditions. In the present study, a method to identify the presence of rainy conditions during radiosoundings is introduced, with the aim of filtering out these events from yearly statistics of predicted atmospheric attenuation. The detection procedure is based on the analysis of a set of parameters, some of them extracted from synoptical observations of weather (SYNOP reports) and other derived from radiosonde observations (RAOBs). The performance of the method has been evaluated under different climatic conditions, corresponding to three locations in Spain, where colocated rain gauge data were available. Rain events detected by the method have been compared with those precipitations identified by the rain gauge. The pertinence Received 26 June 2012, Accepted 31 July 2012, Scheduled 15 August 2012 * Corresponding author: Gustavo Adolfo Siles Soria (gsiles@grc.ssr.upm.es). 258 Siles et al. of the method is discussed on the basis of an analysis of cumulative distributions of total attenuation at 100 and 300 GHz. This study demonstrates that the proposed method can be useful to identify events probably associated to rainy conditions. Hence, it can be considered as a suitable algorithm in order to filter out this kind of events from annual attenuation statistics.

Comparasion of road traffic emission models in Madrid (Spain)

Many cities in Europe have difficulties to meet the air quality standards set by the European legislation, most particularly the annual mean Limit Value for NO2. Road transport is often the main source of air pollution in urban areas and therefore, there is an increasing need to estimate current and future traffic emissions as accurately as possible. As a consequence, a number of specific emission models and emission factors databases have been developed recently. They present important methodological differences and may result in largely diverging emission figures and thus may lead to alternative policy recommendations. This study compares two approaches to estimate road traffic emissions in Madrid (Spain): the COmputer Programme to calculate Emissions from Road Transport (COPERT4 v.8.1) and the Handbook Emission Factors for Road Transport (HBEFA v.3.1), representative of the ‘average-speed’ and ‘traffic situation’ model types respectively. The input information (e.g. fleet composition, vehicle kilometres travelled, traffic intensity, road type, etc.) was provided by the traffic model developed by the Madrid City Council along with observations from field campaigns. Hourly emissions were computed for nearly 15 000 road segments distributed in 9 management areas covering the Madrid city and surroundings. Total annual NOX emissions predicted by HBEFA were a 21% higher than those of COPERT. The discrepancies for NO2 were lower (13%) since resulting average NO2/NOX ratios are lower for HBEFA. The larger differences are related to diesel vehicle emissions under “stop & go” traffic conditions, very common in distributor/secondary roads of the Madrid metropolitan area. In order to understand the representativeness of these results, the resulting emissions were integrated in an urban scale inventory used to drive mesoscale air quality simulations with the Community Multiscale Air Quality (CMAQ) modelling system (1 km2 resolution). Modelled NO2 concentrations were compared with observations through a series of statistics. Although there are no remarkable differences between both model runs, the results suggest that HBEFA may overestimate traffic emissions. However, the results are strongly influenced by methodological issues and limitations of the traffic model. This study was useful to provide a first alternative estimate to the official emission inventory in Madrid and to identify the main features of the traffic model that should be improved to support the application of an emission system based on “real world” emission factors.

Atmospheric modelling of tritium forms transport: review of capabilities and R&D needs for the assessment of fusion facilities environmental impact

One of the key scrutiny issues of new coming energy era would be the environmental impact of fusion facilities managing one kg of tritium. The potential change of committed dose regulatory limits together with the implementation of nuclear design principles (As Low as Reasonably achievable - ALARA -, Defense in Depth -D-i-D-) for fusion facilities could strongly impact on the cost of deployment of coming fusion technology. Accurate modeling of environmental tritium transport forms (HT, HTO) for the assessment of fusion facility dosimetric impact in Accidental case appears as of major interest. This paper considers different short-term releases of tritium forms (HT and HTO) to the atmosphere from a potential fusion reactor located in the Mediterranean Basin. This work models in detail the dispersion of tritium forms and dosimetric impact of selected environmental patterns both inland and in-sea using real topography and forecast meteorological data-fields (ECMWF/FLEXPART). We explore specific values of this ratio in different levels and we examine the influence of meteorological conditions in the HTO behavior for 24 hours. For this purpose we have used a tool which consists on a coupled Lagrangian ECMWF/FLEXPART model useful to follow real time releases of tritium at 10, 30 and 60 meters together with hourly observations of wind (and in some cases precipitations) to provide a short-range approximation of tritium cloud behavior. We have assessed inhalation doses. And also HTO/HT ratios in a representative set of cases during winter 2010 and spring 2011 for the 3 air levels.

Comparing linear and non linear wind flow models

Assessing wind conditions on complex terrain has become a hard task as terrain complexity increases. That is why there is a need to extrapolate in a reliable manner some wind parameters that determine wind farms viability such as annual average wind speed at all hub heights as well as turbulence intensities. The development of these tasks began in the early 90´s with the widely used linear model WAsP and WAsP Engineering especially designed for simple terrain with remarkable results on them but not so good on complex orographies. Simultaneously non-linearized Navier Stokes solvers have been rapidly developed in the last decade through CFD (Computational Fluid Dynamics) codes allowing simulating atmospheric boundary layer flows over steep complex terrain more accurately reducing uncertainties. This paper describes the features of these models by validating them through meteorological masts installed in a highly complex terrain. The study compares the results of the mentioned models in terms of wind speed and turbulence intensity.

Statistical models for short-term wind power ramp forecasting

La predicción de energía eólica ha desempeñado en la última década un papel fundamental en el aprovechamiento de este recurso renovable, ya que permite reducir el impacto que tiene la naturaleza fluctuante del viento en la actividad de diversos agentes implicados en su integración, tales como el operador del sistema o los agentes del mercado eléctrico. Los altos niveles de penetración eólica alcanzados recientemente por algunos países han puesto de manifiesto la necesidad de mejorar las predicciones durante eventos en los que se experimenta una variación importante de la potencia generada por un parque o un conjunto de ellos en un tiempo relativamente corto (del orden de unas pocas horas). Estos eventos, conocidos como rampas, no tienen una única causa, ya que pueden estar motivados por procesos meteorológicos que se dan en muy diferentes escalas espacio-temporales, desde el paso de grandes frentes en la macroescala a procesos convectivos locales como tormentas. Además, el propio proceso de conversión del viento en energía eléctrica juega un papel relevante en la ocurrencia de rampas debido, entre otros factores, a la relación no lineal que impone la curva de potencia del aerogenerador, la desalineación de la máquina con respecto al viento y la interacción aerodinámica entre aerogeneradores. En este trabajo se aborda la aplicación de modelos estadísticos a la predicción de rampas a muy corto plazo. Además, se investiga la relación de este tipo de eventos con procesos atmosféricos en la macroescala. Los modelos se emplean para generar predicciones de punto a partir del modelado estocástico de una serie temporal de potencia generada por un parque eólico. Los horizontes de predicción considerados van de una a seis horas. Como primer paso, se ha elaborado una metodología para caracterizar rampas en series temporales. La denominada función-rampa está basada en la transformada wavelet y proporciona un índice en cada paso temporal. Este índice caracteriza la intensidad de rampa en base a los gradientes de potencia experimentados en un rango determinado de escalas temporales. Se han implementado tres tipos de modelos predictivos de cara a evaluar el papel que juega la complejidad de un modelo en su desempeño: modelos lineales autorregresivos (AR), modelos de coeficientes variables (VCMs) y modelos basado en redes neuronales (ANNs). Los modelos se han entrenado en base a la minimización del error cuadrático medio y la configuración de cada uno de ellos se ha determinado mediante validación cruzada. De cara a analizar la contribución del estado macroescalar de la atmósfera en la predicción de rampas, se ha propuesto una metodología que permite extraer, a partir de las salidas de modelos meteorológicos, información relevante para explicar la ocurrencia de estos eventos. La metodología se basa en el análisis de componentes principales (PCA) para la síntesis de la datos de la atmósfera y en el uso de la información mutua (MI) para estimar la dependencia no lineal entre dos señales. Esta metodología se ha aplicado a datos de reanálisis generados con un modelo de circulación general (GCM) de cara a generar variables exógenas que posteriormente se han introducido en los modelos predictivos. Los casos de estudio considerados corresponden a dos parques eólicos ubicados en España. Los resultados muestran que el modelado de la serie de potencias permitió una mejora notable con respecto al modelo predictivo de referencia (la persistencia) y que al añadir información de la macroescala se obtuvieron mejoras adicionales del mismo orden. Estas mejoras resultaron mayores para el caso de rampas de bajada. Los resultados también indican distintos grados de conexión entre la macroescala y la ocurrencia de rampas en los dos parques considerados. Abstract One of the main drawbacks of wind energy is that it exhibits intermittent generation greatly depending on environmental conditions. Wind power forecasting has proven to be an effective tool for facilitating wind power integration from both the technical and the economical perspective. Indeed, system operators and energy traders benefit from the use of forecasting techniques, because the reduction of the inherent uncertainty of wind power allows them the adoption of optimal decisions. Wind power integration imposes new challenges as higher wind penetration levels are attained. Wind power ramp forecasting is an example of such a recent topic of interest. The term ramp makes reference to a large and rapid variation (1-4 hours) observed in the wind power output of a wind farm or portfolio. Ramp events can be motivated by a broad number of meteorological processes that occur at different time/spatial scales, from the passage of large-scale frontal systems to local processes such as thunderstorms and thermally-driven flows. Ramp events may also be conditioned by features related to the wind-to-power conversion process, such as yaw misalignment, the wind turbine shut-down and the aerodynamic interaction between wind turbines of a wind farm (wake effect). This work is devoted to wind power ramp forecasting, with special focus on the connection between the global scale and ramp events observed at the wind farm level. The framework of this study is the point-forecasting approach. Time series based models were implemented for very short-term prediction, this being characterised by prediction horizons up to six hours ahead. As a first step, a methodology to characterise ramps within a wind power time series was proposed. The so-called ramp function is based on the wavelet transform and it provides a continuous index related to the ramp intensity at each time step. The underlying idea is that ramps are characterised by high power output gradients evaluated under different time scales. A number of state-of-the-art time series based models were considered, namely linear autoregressive (AR) models, varying-coefficient models (VCMs) and artificial neural networks (ANNs). This allowed us to gain insights into how the complexity of the model contributes to the accuracy of the wind power time series modelling. The models were trained in base of a mean squared error criterion and the final set-up of each model was determined through cross-validation techniques. In order to investigate the contribution of the global scale into wind power ramp forecasting, a methodological proposal to identify features in atmospheric raw data that are relevant for explaining wind power ramp events was presented. The proposed methodology is based on two techniques: principal component analysis (PCA) for atmospheric data compression and mutual information (MI) for assessing non-linear dependence between variables. The methodology was applied to reanalysis data generated with a general circulation model (GCM). This allowed for the elaboration of explanatory variables meaningful for ramp forecasting that were utilized as exogenous variables by the forecasting models. The study covered two wind farms located in Spain. All the models outperformed the reference model (the persistence) during both ramp and non-ramp situations. Adding atmospheric information had a noticeable impact on the forecasting performance, specially during ramp-down events. Results also suggested different levels of connection between the ramp occurrence at the wind farm level and the global scale.

How do various maize crop models vary in their responses to climate change factors?

Comments This article is a U.S. government work, and is not subject to copyright in the United States. Abstract Potential consequences of climate change on crop production can be studied using mechanistic crop simulation models. While a broad variety of maize simulation models exist, it is not known whether different models diverge on grain yield responses to changes in climatic factors, or whether they agree in their general trends related to phenology, growth, and yield. With the goal of analyzing the sensitivity of simulated yields to changes in temperature and atmospheric carbon dioxide concentrations [CO2], we present the largest maize crop model intercomparison to date, including 23 different models. These models were evaluated for four locations representing a wide range of maize production conditions in the world: Lusignan (France), Ames (USA), Rio Verde (Brazil) and Morogoro (Tanzania). While individual models differed considerably in absolute yield simulation at the four sites, an ensemble of a minimum number of models was able to simulate absolute yields accurately at the four sites even with low data for calibration, thus suggesting that using an ensemble of models has merit. Temperature increase had strong negative influence on modeled yield response of roughly 0.5 Mg ha 1 per °C. Doubling [CO2] from 360 to 720 lmol mol 1 increased grain yield by 7.5% on average across models and the sites. That would therefore make temperature the main factor altering maize yields at the end of this century. Furthermore, there was a large uncertainty in the yield response to [CO2] among models. Model responses to temperature and [CO2] did not differ whether models were simulated with low calibration information or, simulated with high level of calibration information.

Atmospheric parameters, spectral indexes and their relation to CPV spectral performance

Air Mass and atmosphere components (basically aerosol (AOD) and precipitable water (PW)) define the absorption of the sunlight that arrive to Earth. Radiative models such as SMARTS or MODTRAN use these parameters to generate an equivalent spectrum. However, complex and expensive instruments (as AERONET network devices) are needed to obtain AOD and PW. On the other hand, the use of isotype cells is a convenient way to characterize spectrally a place for CPV considering that they provide the photocurrent of the different internal subcells individually. Crossing data from AERONET station and a Tri-band Spectroheliometer, a model that correlates Spectral Mismatch Ratios and atmospheric parameters is proposed. Considering the amount of stations of AERONET network, this model may be used to estimate the spectral influence on energy performance of CPV systems close to all the stations worldwide.

A model intercomparison of changes in the Atlantic thermohaline circulation in response to increasing atmospheric CO2 concentration

As part of the Coupled Model Intercomparison Project, integrations with a common design have been undertaken with eleven different climate models to compare the response of the Atlantic thermohaline circulation ( THC) to time-dependent climate change caused by increasing atmospheric CO2 concentration. Over 140 years, during which the CO2 concentration quadruples, the circulation strength declines gradually in all models, by between 10 and 50%. No model shows a rapid or complete collapse, despite the fairly rapid increase and high final concentration of CO2. The models having the strongest overturning in the control climate tend to show the largest THC reductions. In all models, the THC weakening is caused more by changes in surface heat flux than by changes in surface water flux. No model shows a cooling anywhere, because the greenhouse warming is dominant.

Description of atmospheric conditions at the Pierre Auger Observatory using the Global Data Assimilation System (GDAS)

Atmospheric conditions at the site of a cosmic ray observatory must be known for reconstructing observed extensive air showers. The Global Data Assimilation System (GDAS) is a global atmospheric model predicated on meteorological measurements and numerical weather predictions. GDAS provides altitude-dependent profiles of the main state variables of the atmosphere like temperature, pressure, and humidity. The original data and their application to the air shower reconstruction of the Pierre Auger Observatory are described. By comparisons with radiosonde and weather station measurements obtained on-site in Malargue and averaged monthly models, the utility of the GDAS data is shown. (C) 2012 Elsevier B.V. All rights reserved.

Liquid–Liquid, Vapor–Liquid, and Vapor–Liquid–Liquid Equilibrium Data for the Water–n-Butanol–Cyclohexane System at Atmospheric Pressure: Experimental Determination and Correlation

The temperature and the composition of the vapor–liquid–liquid equilibrium (VLLE) and the vapor–liquid equilibrium (VLE) of a ternary mixture of water–n-butanol–cyclohexane were measured at atmospheric pressure (101.32 kPa) in a modified dynamic recirculating still. As found in the literature, the experimental data obtained reveal a ternary azeotrope at 341.86 K with a mole fraction composition of 0.281, 0.034, and 0.685 water, n-butanol, and cyclohexane, respectively. The liquid–liquid equilibrium (LLE) compositions were measured at a constant temperature of 313.15 K and compared with data in the literature collected at other temperatures. Thermodynamic consistency of all the experimental data was demonstrated. The universal quasichemical (UNIQUAC) and the nonrandom two-liquid (NRTL) thermodynamic models were used to correlate the VLE and LLE data, while the original universal functional (UNIFAC) model was used to compare the predicted data.

Validation of the Swiss methane emission inventory by atmospheric observations and inverse modelling

Atmospheric inverse modelling has the potential to provide observation-based estimates of greenhouse gas emissions at the country scale, thereby allowing for an independent validation of national emission inventories. Here, we present a regional-scale inverse modelling study to quantify the emissions of methane (CH₄) from Switzerland, making use of the newly established CarboCount-CH measurement network and a high-resolution Lagrangian transport model. In our reference inversion, prior emissions were taken from the "bottom-up" Swiss Greenhouse Gas Inventory (SGHGI) as published by the Swiss Federal Office for the Environment in 2014 for the year 2012. Overall we estimate national CH₄ emissions to be 196 ± 18 Gg yr⁻¹ for the year 2013 (1σ uncertainty). This result is in close agreement with the recently revised SGHGI estimate of 206 ± 33 Gg yr⁻¹ as reported in 2015 for the year 2012. Results from sensitivity inversions using alternative prior emissions, uncertainty covariance settings, large-scale background mole fractions, two different inverse algorithms (Bayesian and extended Kalman filter), and two different transport models confirm the robustness and independent character of our estimate. According to the latest SGHGI estimate the main CH₄ source categories in Switzerland are agriculture (78 %), waste handling (15 %) and natural gas distribution and combustion (6 %). The spatial distribution and seasonal variability of our posterior emissions suggest an overestimation of agricultural CH₄ emissions by 10 to 20 % in the most recent SGHGI, which is likely due to an overestimation of emissions from manure handling. Urban areas do not appear as emission hotspots in our posterior results, suggesting that leakages from natural gas distribution are only a minor source of CH₄ in Switzerland. This is consistent with rather low emissions of 8.4 Gg yr⁻¹ reported by the SGHGI but inconsistent with the much higher value of 32 Gg yr⁻¹ implied by the EDGARv4.2 inventory for this sector. Increased CH₄ emissions (up to 30 % compared to the prior) were deduced for the north-eastern parts of Switzerland. This feature was common to most sensitivity inversions, which is a strong indicator that it is a real feature and not an artefact of the transport model and the inversion system. However, it was not possible to assign an unambiguous source process to the region. The observations of the CarboCount-CH network provided invaluable and independent information for the validation of the national bottom-up inventory. Similar systems need to be sustained to provide independent monitoring of future climate agreements.

Age models for sediment cores off Peru

Planktonic foraminiferal faunas of the southeast Pacific indicate that sea surface temperatures (SST) have varied by as much as 8-10°C in the Peru Current, and by ?5-7°C along the equator, over the past 150,000 years. Changes in SST at times such as the Last Glacial Maximum reflect incursion of high-latitude species Globorotalia inflata and Neogloboquadrina pachyderma into the eastern boundary current and as far north as the equator. A simple heat budget model of the equatorial Pacific shows that observed changes in Peru Current advection can account for about half of the total variability in equatorial SSTs. The remaining changes in equatorial SST, which are likely related to local changes in upwelling or pycnocline depth, precede changes in polar climates as recorded by d18O. This partitioning of processes in eastern equatorial Pacific SST reveals that net ice-age cooling here reflects first a rapid response of equatorial upwelling to insolation, followed by a later response to changes in the eastern boundary current associated with high-latitude climate (which closely resembles variations in atmospheric CO2 as recorded in the Vostok ice core). Although precise mechanisms responsible for the equatorial upwelling component of climate change remain uncertain, one likely candidate that may operate independently of the ice sheets is insolation-driven changes in El Niño/Southern Oscillation (ENSO) frequency. Early responses of equatorial SST detected both here and elsewhere highlight the sensitivity of tropical systems to small changes in seasonal insolation. The scale of tropical changes we have observed are substantially greater than model predictions, suggesting a need for further quantitative assessment of processes associated with long-term climate change.

Age models and stable isotope record of sediment cores from the Atlantic Ocean

The high-resolution delta18O and delta13C records of benthic foraminifera from a 150,000-year long core from the Caribbean Sea indicate that there was generally high delta13C during glaciations and low delta13C during interglaciations. Due to its 1800-m sill depth, the properties of deep water in the Caribbean Sea are similar to those of middepth tropical Atlantic water. During interglaciations, the water filling the deep Caribbean Sea is an admixture of low delta13C Upper Circumpolar Water (UCPW) and high delta13C Upper North Atlantic Deep Water (UNADW). By contrast, only high delta13C UNADW enters during glaciations. Deep ocean circulation changes can influence atmospheric CO2 levels (Broecker and Takahashi, 1985; Boyle, 1988 doi:10.1029/JC093iC12p15701; Keir, 1988 doi:10.1029/PA003i004p00413; Broecker and Peng, 1989 doi:10.1029/GB003i003p00215). By comparing delta13C records of benthic foraminifera from cores lying in Southern Ocean Water, the Caribbean Sea, and at several other Atlantic Ocean sites, the thermohaline state of the Atlantic Ocean (how close it was to a full glacial or full interglacial configuration) is characterized. A continuum of circulation patterns between the glacial and interglacial extremes appears to have existed in the past. Subtracting the deep Pacific (~mean ocean water) delta13C record from the Caribbean delta13C record yields a record which describes large changes in the Atlantic Ocean thermohaline circulation. The delta13C difference varies as the vertical nutrient distribution changes. This new proxy record bears a striking resemblance to the 150,000-year-long atmospheric CO2 record (Barnola et al., 1987 doi:10.1038/329408a0). This favorable comparison between the new proxy record and the atmospheric CO2 record is consistent with Boyle's (1988a) model that vertical nutrient redistribution has driven large atmospheric CO2 changes in the past. Changes in the relative contribution of NADW and Pacific outflow water to the Southern Ocean are also consistent with Broecker and Peng's (1989) recent model for atmospheric CO2 changes.

Models of earth's atmosphere (90 to 2500 km)

Cover title.

Use of environmental satellite data for input to energy balance snowmelt models /

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