Skew-T Diagrams and Thermodynamic Properties for Fu
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Impact of Air Pollution Controls on Radiation Fog Frequency in the Central Valley of California
RESEARCH ARTICLE Impact of Air Pollution Controls on Radiation Fog 10.1029/2018JD029419 Frequency in the Central Valley of California Special Section: Ellyn Gray1 , S. Gilardoni2 , Dennis Baldocchi1 , Brian C. McDonald3,4 , Fog: Atmosphere, biosphere, 2 1,5 land, and ocean interactions Maria Cristina Facchini , and Allen H. Goldstein 1Department of Environmental Science, Policy and Management, Division of Ecosystem Sciences, University of 2 Key Points: California, Berkeley, CA, USA, Institute of Atmospheric Sciences and Climate ‐ National Research Council (ISAC‐CNR), • Central Valley fog frequency Bologna, BO, Italy, 3Cooperative Institute for Research in Environmental Science, University of Colorado, Boulder, CO, increased 85% from 1930 to 1970, USA, 4Chemical Sciences Division, NOAA Earth System Research Laboratory, Boulder, CO, USA, 5Department of Civil then declined 76% in the last 36 and Environmental Engineering, University of California, Berkeley, CA, USA winters, with large short‐term variability • Short‐term fog variability is dominantly driven by climate Abstract In California's Central Valley, tule fog frequency increased 85% from 1930 to 1970, then fluctuations declined 76% in the last 36 winters. Throughout these changes, fog frequency exhibited a consistent • ‐ Long term temporal and spatial north‐south trend, with maxima in southern latitudes. We analyzed seven decades of meteorological data trends in fog are dominantly driven by changes in air pollution and five decades of air pollution data to determine the most likely drivers changing fog, including temperature, dew point depression, precipitation, wind speed, and NOx (oxides of nitrogen) concentration. Supporting Information: Climate variables, most critically dew point depression, strongly influence the short‐term (annual) • Supporting Information S1 variability in fog frequency; however, the frequency of optimal conditions for fog formation show no • Table S1 • Table S2 observable trend from 1980 to 2016. -
Influence of the North Atlantic Oscillation on Winter Equivalent Temperature
INFLUENCE OF THE NORTH ATLANTIC OSCILLATION ON WINTER EQUIVALENT TEMPERATURE J. Florencio Pérez (1), Luis Gimeno (1), Pedro Ribera (1), David Gallego (2), Ricardo García (2) and Emiliano Hernández (2) (1) Universidade de Vigo (2) Universidad Complutense de Madrid Introduction Data Analysis Overview Increases in both troposphere temperature and We utilized temperature and humidity data WINTER TEMPERATURE water vapor concentrations are among the expected at 850 hPa level for the 41 yr from 1958 to WINTER AVERAGE EQUIVALENT climate changes due to variations in greenhouse gas 1998 from the National Centers for TEMPERATURE 1958-1998 TREND 1958-1998 concentrations (Kattenberg et al., 1995). However Environmental Prediction–National Center for both increments could be due to changes in the frecuencies of natural atmospheric circulation Atmospheric Research (NCEP–NCAR) regimes (Wallace et al. 1995; Corti et al. 1999). reanalysis. Changes in the long-wave patterns, dominant We calculated daily values of equivalent airmass types, strength or position of climatological “centers of action” should have important influences temperature for every grid point according to on local humidity and temperatures regimes. It is the expression in figure-1. The monthly, known that the recent upward trend in the NAO seasonal and annual means were constructed accounts for much of the observed regional warming from daily means. Seasons were defined as in Europe and cooling over the northwest Atlantic Winter (January, February and March), Hurrell (1995, 1996). However our knowledge about Spring (April, May and June), Summer (July, the influence of NAO on humidity distribution is very August and September) and Fall (October, limited. In the three recent global humidity November and December). -
Forecasting of Thunderstorms in the Pre-Monsoon Season at Delhi
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Publications of the IAS Fellows Meteorol. Appl. 6, 29–38 (1999) Forecasting of thunderstorms in the pre-monsoon season at Delhi N Ravi1, U C Mohanty1, O P Madan1 and R K Paliwal2 1Centre for Atmospheric Sciences, Indian Institute of Technology, New Delhi 110 016, India 2National Centre for Medium Range Weather Forecasting, Mausam Bhavan Complex, Lodi Road, New Delhi 110 003, India Accurate prediction of thunderstorms during the pre-monsoon season (April–June) in India is essential for human activities such as construction, aviation and agriculture. Two objective forecasting methods are developed using data from May and June for 1985–89. The developed methods are tested with independent data sets of the recent years, namely May and June for the years 1994 and 1995. The first method is based on a graphical technique. Fifteen different types of stability index are used in combinations of different pairs. It is found that Showalter index versus Totals total index and Jefferson’s modified index versus George index can cluster cases of occurrence of thunderstorms mixed with a few cases of non-occurrence along a zone. The zones are demarcated and further sub-zones are created for clarity. The probability of occurrence/non-occurrence of thunderstorms in each sub-zone is then calculated. The second approach uses a multiple regression method to predict the occurrence/non- occurrence of thunderstorms. A total of 274 potential predictors are subjected to stepwise screening and nine significant predictors are selected to formulate a multiple regression equation that gives the forecast in probabilistic terms. -
How Appropriate They Are/Will Be Using Future Satellite Data Sources?
Different Convective Indices - - - How appropriate they are/will be using future satellite data sources? Ralph A. Petersen1 1 Cooperative Institute for Meteorological Satellite Studies (CIMSS), University of Wisconsin – Madison, Madison, Wisconsin, USA Will additional input from Steve Weiss, NOAA/NWS/Storm Prediction Center ✓ Increasing the Utility / Value of real-time Satellite Sounder Products to fill gaps in their short-range forecasting processes Creating Temperature/Moisture Soundings from Infra-Red (IR) Satellite Observations A Conceptual Tutorial All level of the atmosphere is continually emit radiation toward space. Satellites observe the net amount reaching space. • Conceptually, we can think about the atmosphere being made up of many thin layers Start from the bottom and work up. 1 – The greatest amount of radiation is emitted from the earth’s surface 4 - Remember, Stefan’s Law: Emission ~ σTSfc 2 – Molecules of various gases in the lowest layer of the atmosphere absorb some of the radiation and then reemit it upward to space and back downward to the earth’s surface - Major absorbers are CO2 and H2O 4 - Emission again ~ σT , but TAtmosphere<TSfc - Amount of radiation decreases with altitude Creating Temperature/Moisture Soundings from Infra-Red (IR) Satellite Observations A Conceptual Tutorial All level of the atmosphere is continually emit radiation toward space. Satellites observe the net amount reaching space. • Conceptually, we can think about the atmosphere being made up of many thin layers Start from the bottom and work -
CHAPTER NO.4 Psychrometry
CHAPTER NO.4 Psychrometry C605.4-Explain Psychometric properties & calculate various parameters. Necessity of Air conditioning Purpose of air conditioning is 1) To provide comfort conditions for human comfort. 2) To provide comfort in commercial places like office, restaurant ,shopping complex , banks etc. 3) To provide controlled conditions for industrial process. 4) To provide ultra clean atmosphere for precision work. Dalton’s low of partial pressure • Dalton’s law of partial pressure statures that ‘the total pressure of mixture of gases equal to the sum of the partial pressures exerted by each gas when it occupies the mixture volume at there temperature of mixture’. • According to Dalton’s law of partial pressure, • Pt = Pa + Pb + Pc. Psychometrics properties of air 1. Dry air : It is a mixture of oxygen (20.91%) and nitrogen (79.09 %) by volume. 2. Moist air: It is mixture of dry air and water vapour. 3. Saturated air : Air which have maximum amount of water vapor. 4. Dry bulb temp.(DBT) : It is temperature of air recorded by ordinary thermometer with clean and dry sensing elements.(td or tdb) 5. Wet bulb temp.(WBT) : It is temperature of air recorded by thermometer when its bulb is covered with a wet cloth.(tw or twb) Psychometric properties of air 6. Wet bulb depression : It is the difference between dry bulb temp. and wet bulb temp. 7. Dew point temp.: It is the temperature of air recorded by thermometer when the moisture present in the air begins to condensed. 8. Dew point depression : It is difference between dry bulb temp. -
Effect of Deep Convection on the TTL Composition Over the Southwest Indian Ocean During Austral Summer
https://doi.org/10.5194/acp-2019-1072 Preprint. Discussion started: 22 January 2020 c Author(s) 2020. CC BY 4.0 License. Effect of deep convection on the TTL composition over the Southwest Indian Ocean during austral summer. Stephanie Evan1, Jerome Brioude1, Karen Rosenlof2, Sean. M. Davis2, Hölger Vömel3, Damien Héron1, Françoise Posny1, Jean-Marc Metzger4, Valentin Duflot1,4, Guillaume Payen4, Hélène Vérèmes1, 5 Philippe Keckhut5, and Jean-Pierre Cammas1,4 1LACy, Laboratoire de l’Atmosphère et des Cyclones, UMR8105 (CNRS, Université de La Réunion, Météo-France), Saint- Denis de la Réunion, 97490, France 2Chemical Sciences Division, Earth System Research Laboratory, NOAA, Boulder, 80305, CO, USA 3National Center for Atmospheric Research, Boulder, 80301, CO, USA 10 4Observatoire des Sciences de l’Univers de La Réunion, UMS3365 (CNRS, Université de La Réunion, Météo-France), Saint- Denis de la Réunion, 97490, France 5LATMOS, Laboratoire ATmosphères, Milieux, Observations Spatiales-IPSL UMR8190 (UVSQ Université Paris-Saclay, Sorbonne Université, CNRS), Guyancourt, 78280, France Correspondence to: Stephanie Evan ([email protected]) 15 Abstract. Balloon-borne measurements of CFH water vapor, ozone and temperature and water vapor lidar measurements from the Maïdo Observatory at Réunion Island in the Southwest Indian Ocean (SWIO) were used to study tropical cyclones' influence on TTL composition. The balloon launches were specifically planned using a Lagrangian model and METEOSAT 7 infrared images to sample the convective outflow from Tropical Storm (TS) Corentin on 25 January 2016 and Tropical Cyclone (TC) Enawo on 3 March 2017. 20 Comparing CFH profile to MLS monthly climatologies, water vapor anomalies were identified. Positive anomalies of water vapor and temperature, and negative anomalies of ozone between 12 and 15 km in altitude (247 to 121hPa) originated from convectively active regions of TS Corentin and TC Enawo, one day before the planned balloon launches, according to the Lagrangian trajectories. -
Physiological Equivalent Temperature Index Applied
The seventh International Conference on Urban Climate, 29 June - 3 July 2009, Yokohama, Japan PHYSIOLOGICAL EQUIVALENT TEMPERATURE INDEX APPLIED TO WIND TUNNEL EROSION TECHNIQUE PICTURES FOR THE ASSESSMENT OF PEDESTRIAN THERMAL COMFORT Alessandra Rodrigues Prata-Shimomura; Leonardo Marques Monteiro; Anésia Barros Frota * *Laboratório de Conforto Ambiental e Eficiência Energética, Faculdade de Arquitetura e Urbanismo, Universidade de São Paulo - LABAUT/FAUUSP, São Paulo, Brazil Abstract The goal of this research was to verify the influence of the effect of the wind on the pedestrians and the applicability of an index of environmental comfort for external spaces, according to data of wind tunnel simulations. The simulations were performed for the case study of Moema, an upper middle class verticalized residential neighbourhood in Sao Paulo, Brazil. The index used was the Physiological Equivalent Temperature (PET), established specifically for the evaluation of subtropical climates such as the one from the study area. Figures obtained from wind tunnel simulations, using erosion techniques, were used to visualize the field of speed in the level of pedestrian, describing the areas affected by the action of different wind speeds The index was applied on the erosion technique pictures, defining the areas of greater and lesser influence in terms of the effect of wind on the climatic conditions of the site. As a result, one may verify the assessment of the area under study, observing the conditions of comfort and discomfort in light of changes in the values of wind speed. Key words: thermal comfort indices, urban external spaces, wind tunnel simulation 1. INTRODUCTION The metropolitan region of São Paulo has 20 million inhabitants, 11 million of which live within the capital’s boundaries, making it the largest urban agglomeration of South America (Brandão, 2007). -
Atmospheric Moisture
Name_____________________________________Date___________________Period________ Atmospheric Moisture Background Whether in solid, liquid or gaseous form, water is the most important component of the atmosphere and essentially helps control all other aspects of weather. However, in order to truly understand the atmosphere, simply describing moisture is not enough. In meteorology, several different moisture measurements are used to determine the overall stability and characteristics of the air. All of these “moisture variables” can be broken down into two categories. The first are those that are solely dependent upon the physical amount of water contained in the air and are known as absolute measurements. Relative measurements are those variables that not only depend upon the amount of water present but also the temperature of the air. Vapor Pressure (e) and Saturation Vapor Pressure (es) The total atmospheric pressure at any time is actually the sum of many small pressures for each of the atmosphere’s components. For example, oxygen, nitrogen, etc. all exert their own individual pressures that are all added together to create the overall atmospheric pressure. Vapor pressure is the portion of total pressure contributed by water. This amount is very small which is why huge differences in water vapor content in the atmosphere yield only small fluctuations in the overall atmospheric pressure. This measure is also an absolute measurement since it is solely dependent upon the actual amount of water vapor in the air. However, there is a maximum amount of pressure that water vapor can exert in the atmosphere before the water is squeezed out into a liquid again and condenses. This limit is known as the saturation vapor pressure and is dependent upon both water content and temperature. -
Basic Features on a Skew-T Chart
Skew-T Analysis and Stability Indices to Diagnose Severe Thunderstorm Potential Mteor 417 – Iowa State University – Week 6 Bill Gallus Basic features on a skew-T chart Moist adiabat isotherm Mixing ratio line isobar Dry adiabat Parameters that can be determined on a skew-T chart • Mixing ratio (w)– read from dew point curve • Saturation mixing ratio (ws) – read from Temp curve • Rel. Humidity = w/ws More parameters • Vapor pressure (e) – go from dew point up an isotherm to 622mb and read off the mixing ratio (but treat it as mb instead of g/kg) • Saturation vapor pressure (es)– same as above but start at temperature instead of dew point • Wet Bulb Temperature (Tw)– lift air to saturation (take temperature up dry adiabat and dew point up mixing ratio line until they meet). Then go down a moist adiabat to the starting level • Wet Bulb Potential Temperature (θw) – same as Wet Bulb Temperature but keep descending moist adiabat to 1000 mb More parameters • Potential Temperature (θ) – go down dry adiabat from temperature to 1000 mb • Equivalent Temperature (TE) – lift air to saturation and keep lifting to upper troposphere where dry adiabats and moist adiabats become parallel. Then descend a dry adiabat to the starting level. • Equivalent Potential Temperature (θE) – same as above but descend to 1000 mb. Meaning of some parameters • Wet bulb temperature is the temperature air would be cooled to if if water was evaporated into it. Can be useful for forecasting rain/snow changeover if air is dry when precipitation starts as rain. Can also give -
DEPARTMENT of GEOSCIENCES Name______San Francisco State University May 7, 2013 Spring 2009
DEPARTMENT OF GEOSCIENCES Name_____________ San Francisco State University May 7, 2013 Spring 2009 Monteverdi Metr 201 Quiz #4 100 pts. A. Definitions. (3 points each for a total of 15 points in this section). (a) Convective Condensation Level --The elevation at which a lofted surface parcel heated to its Convective Temperature will be saturated and above which will be warmer than the surrounding air at the same elevation. (b) Convective Temperature --The surface temperature that must be met or exceeded in order to convert an absolutely stable sounding to an absolutely unstable sounding (because of elimination, usually, of the elevated inversion characteristic of the Loaded Gun Sounding). (c) Lifted Index -- the difference in temperature (in C or K) between the surrounding air and the parcel ascent curve at 500 mb. (d) wave cyclone -- a cyclone in which a frontal system is centered in a wave-like configuration, normally with a cold front on the west and a warm front on the east. (e) conditionally unstable sounding (conceptual definition) –a sounding for which the parcel ascent curve shows an LFC not at the ground, implying that the sounding is unstable only on the condition that a surface parcel is force lofted to the LFC. B. Units. (2 pts each for a total of 8 pts) Provide the units used conventionally for the following: θ ____ Ko * o o Td ______C ___or F ___________ PGA -2 ( )z ______m s _______________** w ____ m s-1_____________*** *θ = Theta = Potential Temperature **PGA = Pressure Gradient Acceleration *** At Equilibrium Level of Severe Thunderstorms € 1 C. Sounding (3 pts each for a total of 27 points in this section). -
Modeling of Heat Stress in Sows—Part 1: Establishment of the Prediction Model for the Equivalent Temperature Index of the Sows
animals Article Modeling of Heat Stress in Sows—Part 1: Establishment of the Prediction Model for the Equivalent Temperature Index of the Sows Mengbing Cao 1,2, Chao Zong 1,2,*, Xiaoshuai Wang 3, Guanghui Teng 1,2, Yanrong Zhuang 1,2 and Kaidong Lei 1,2 1 College of Water Resources and Civil Engineering, China Agricultural University, Beijing 100083, China; [email protected] (M.C.); [email protected] (G.T.); [email protected] (Y.Z.); [email protected] (K.L.) 2 Key Laboratory of Agricultural Engineering in Structure and Environment, Ministry of Agriculture and Rural Affairs, Beijing 100083, China 3 College of Biosystems Engineering and Food Science, Zhejiang University, No. 866 Yuhangtang Road, Hangzhou 310058, China; [email protected] * Correspondence: [email protected] Simple Summary: Sows are susceptible to heat stress. Various indicators can be found in the literature assessing the level of heat stress in pigs, but none of them is specific to assess the sows’ thermal condition. Moreover, previous thermal indices have been developed by considering only partial environment parameters, and the interaction between the index and the animal’s physiological response are not always included. Therefore, this study aims to develop and assess a new thermal index specified for sows, called equivalent temperature index for sows (ETIS), with a comprehensive consideration of the influencing factors. An experiment was conducted, and the experimental Citation: Cao, M.; Zong, C.; Wang, data was applied for model development and validation. The equivalent temperatures have been X.; Teng, G.; Zhuang, Y.; Lei, K. transformed on the basis of equal effects of air velocity, relative humidity, floor heat conduction and Modeling of Heat Stress in indoor radiation on the thermal index, and used for the ETIS combination. -
Thermodynamics of Power Generation
THERMAL MACHINES AND HEAT ENGINES Thermal machines ......................................................................................................................................... 1 The heat engine ......................................................................................................................................... 2 What it is ............................................................................................................................................... 2 What it is for ......................................................................................................................................... 2 Thermal aspects of heat engines ........................................................................................................... 3 Carnot cycle .............................................................................................................................................. 3 Gas power cycles ...................................................................................................................................... 4 Otto cycle .............................................................................................................................................. 5 Diesel cycle ........................................................................................................................................... 8 Brayton cycle .....................................................................................................................................