DOCSLIB.ORG

Basic Features on a Skew-T Chart

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
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 a rough idea of temperature within the cold pools of thunderstorms • Equivalent Potential temperature – gives an idea of total energy in the atmosphere (sensible heat plus latent heat) Conserved quantities • For unsaturated lift, potential temperature and mixing ratio are conserved (which is why you move along dry adiabats and mixing ratios when lifting/sinking parcels on a skew-T chart) • For satured lift, θE or θW are conserved so parcels follow a moist adiabat (line of constant θE or θW ), and thus dew point drops faster than for unsaturated lift, and temperature cools more slowly due to latent heat release Other parameters to compute • Tc – Convective temperature – temperature air has to be heated to for it to rise on its own and form deep convection • CCL – Convective condensation level – level at which air becomes positively buoyant when Tc is reached • LCL – Lifted Condensation Level – level where air first becomes saturated after lifting • LFC – Level of Free Convection – level where air becomes positively buoyant after being lifted past LCL More parameters • EL – Equilibrium Level – level where parcel no longer is positively buoyant and acceleration would cease • MCL – Mixing Condensation Level – level where saturation occurs due to mixing of layer near ground • CAPE – Convective Available Potential Energy – positive area in region where parcel is warmer than environment • CIN – Convective Inhibition – negative energy where parcel is cooler than environment More… • Cap – informal term for warm layer of air, usually between 700 and 850 mb, which creates CIN and prevents convection from easily forming • Overshooting top – go beyond EL to the point where it appears you’ve created enough negative area to balance the positive area in the CAPE region Overshooting top Negative area EL CAPE CIN LFC LCL Stability changes • Diabatic (radiation, evaporation, condensation) • Advection of different lapse rate • Differential advection due to wind shear • Vertical motion (“potential instability” can exist if currently stable lapse rates would become unstable due to lift – this usually happens if lower part of layer is moist and upper part is dry) Lift this layer 100 mb and see how lapse rate changes Stability Indices • LI (Lifted Index) = T(env@500mb) – T(parcel@500 mb). •Lift an air parcel from the surface (requires use of skew-T) • LI < 0 significant cu convection possible • -4 < LI < 0 showers • -6 < LI < -4 thunderstorms •LI < -6 severe thunderstorms (SPC has -6 to -9 is very unstable; < -9 extremely unstable) LI here is about -4 since parcel temp at 500 is -12 and environ. Temp is -16. SI (Showalter Index) • Same as LI but lift the parcel from 850 mb •Index is mostly used in Midwest but not so much elsewhere. Why? (elevated convection at night dominates the Midwest – so lifting from 850 makes more sense than from surface) • SI 0 – 3 Thunderstorm possible but need trigger • SI -3 to 0 unstable, T.storm probable • SI -4 to -6 very unstable, svr.storm potential • SI < -6 extremely unstable, svr.storms likely K Index •K = (T850 –T500) + Td850 –DD700 • Because DD (dew point depression is subtracted out at 700 mb, and dry air there is helpful for severe weather (thus big DD), this index works best for general thunderstorm prediction and NOT severe storm prediction K guidelines K value Thunderstorm probability < 15 0% 15-20 <20% 21-25 20-40% 26-30 40-60% 31-35 60-80% 36-40 80-90% >40 Near 100% TT (Total Totals) East Central/ Wx • TT = T 850 West +Td - 850 44 48 Iso. Storms (winds < 35 2*(T500) knts, hail < .5 inch) •Works for 46 52 Scatt. Storms (winds severe storm 35-50 knts, hail .5-1 in.) prediction 48 55 Scatt. MDT, iso. SVR 50 58 Scatt. MDT, few SVR, iso Tornadoes 52 61 Scatt-numerous MDT, Scatt. SVR, few Tornadoes 56 64 Numerous MDT, Scatt. SVR, Scatt. Tornadoes SWEAT (Severe WEAther Threat) • SWI = 12*Td850 +20*(TT-49) +2*fff850 +fff500 +125*(S + 0.2) • fff is wind speed in knots • S = sin (dd500 –dd850) where dd is direction in degrees, and S= -0.2 if dd850 < 130 or > 250 dd500 < 210 or > 310 dd500 -dd850 < 0 Both 850 and 500 speeds < 15 knots SWI Interpretation •This is a good index for helping with rotating storm/tornado forecasting since it includes wind and wind shear information • SWI > 300: high severe storm potential • SWI > 400: high tornado potential HWBZ (Height of the Wet Bulb Zero) •This is a standard procedure for forecasting large hail probability • HWBZ between 2.1 – 2.7 km is ideal • HWBZ between 1.5 – 3.4 km is possible range for large hail •HWBZ > 3.4 km can still yield large hail IF very large CAPE exists HWBZ calculation is an interative procedure. Try a point like 650 mb and compute Tw there. It looks like -8C. So, obviously HWBZ is lower down than that. Try new point, like 700mb. 700mb Tw still is colder than 0 (about -6 C), so we need to try further down – maybe 740 mb? Tw at 740 mb looks like exactly 0C, so this is HWBZ level. Need to convert to a height value to apply the rule. Usually, 740 mb will be around 2.6 km, so this is probably a good large hail sounding BRN (Bulk Richardson Number) •BRN is used to predict storm type, and in particular supercell potential • BRN = CAPE/(.5*∆U2) where ∆U = Ave U in 0-6km layer – Ave U in 0-500 m layer ( U here is full wind, so components are needed for calculation) BRN < 10 – too much shear and storms are ripped apart BRN 15-35 – Severe weather likely with supercells favored BRN > 50 – Multicells likely (less tornado potential) EHI (Energy-Helicity Index) • EHI = CAPE * SRH /166000 where SRH is storm relative helicity EHI > 2.5 supercells with tornado potential Sounding from around time of tornadic thunderstorms Sounding from near tornadic storms that hit Waterloo area.
Load more

Recommended publications
  • 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.
    [Show full text]
  • 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).
    [Show full text]
  • Laboratory Studies of Turbulent Mixing J.A
    Laboratory Studies of Turbulent Mixing J.A. Whitehead Woods Hole Oceanographic Institution, Woods Hole, MA, USA Laboratory measurements are required to determine the rates of turbulent mixing and dissipation within flows of stratified fluid. Rates based on other methods (theory, numerical approaches, and direct ocean measurements) require models based on assumptions that are suggested, but not thoroughly verified in the laboratory. An exception to all this is recently provided by direct numerical simulations (DNS) mentioned near the end of this article. Experiments have been conducted with many different devices. Results span a wide range of gradient or bulk Richardson number and more limited ranges of Reynolds number and Schmidt number, which are the three most relevant dimensionless numbers. Both layered and continuously stratified flows have been investigated, and velocity profiles include smooth ones, jets, and clusters of eddies from stirrers or grids. The dependence of dissipation through almost all ranges of Richardson number is extensively documented. The buoyancy flux ratio is the turbulent kinetic energy loss from raising the potential energy of the strata to loss of kinetic energy by viscous dissipation. It rises from zero at small Richardson number to values of about 0.1 at Richardson number equal to 1, and then falls off for greater values. At Richardson number greater than 1, a wide range of power laws spanning the range from −0.5 to −1.5 are found to fit data for different experiments. Considerable layering is found at larger values, which causes flux clustering and may explain both the relatively large scatter in the data as well as the wide range of proposed power laws.
    [Show full text]
  • 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.
    [Show full text]
  • Boundary Layer Verification
    Boundary Layer Verification ECMWF training course April 2015 Maike Ahlgrimm Aim of this lecture • To give an overview over strategies for boundary layer evaluation • By the end of this session you should be able to: – Identify data sources and products suitable for BL verification – Recognize the strengths and limitations of the verification strategies discussed – Choose a suitable verification method to investigate model errors in boundary layer height, transport and cloudiness. smog over NYC Overview • General strategy for process-oriented model evaluation • What does the BL parameterization do? • Broad categories of BL parameterizations • Which aspects of the BL can we evaluate? – What does each aspect tell us about the BL? • What observations are available – What are the observations’ advantages and limitations? • Examples – Clear convective BL – Cloud topped convective BL – Stable BL Basic strategy for model evaluation and improvement: Observations Identify discrepancy Model Output Figure out source of model error Improve parameterization When and where does error occur? Which parameterization(s) is/are involved? What does the BL parameterization do? Attempts to integrate Turbulence transports effects of small scale temperature, moisture and turbulent motion on momentum (+tracers). prognostic variables at grid resolution. Stull 1988 Ultimate goal: good model forecast and realistic BL Broad categories of BL parameterizations Unified BL schemes Specialized BL scheme •Attempt to integrate BL (and •One parameterization for each shallow cloud) effects in one discrete BL type scheme to allow seamless transition •Simplifies parameterization for •Often statistical schemes (i.e. each type, parameterization for making explicit assumptions about each type ideally suited PDFs of modelled variables) using •Limitation: must identify BL type moist-conserved variables reliably, is noisy (lots of if •Limitation: May not work well for statements) mixed-phase or ice •Example: Met-Office (Lock et al.
    [Show full text]
  • On the Computation of Planetary Boundary-Layer Height Using the Bulk Richardson Number Method
    Geosci. Model Dev., 7, 2599–2611, 2014 www.geosci-model-dev.net/7/2599/2014/ doi:10.5194/gmd-7-2599-2014 © Author(s) 2014. CC Attribution 3.0 License. On the computation of planetary boundary-layer height using the bulk Richardson number method Y. Zhang1, Z. Gao2, D. Li3, Y. Li1, N. Zhang4, X. Zhao1, and J. Chen1,5 1International Center for Ecology, Meteorology & Environment, Jiangsu Key Laboratory of Agricultural Meteorology, College of Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044, China 2State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China 3Program of Atmospheric and Oceanic Sciences, Princeton University, Princeton, NJ 08540, USA 4School of Atmospheric Sciences, Nanjing University, Nanjing, 210093, China 5Department of Geography, Michigan State University, East Lansing, MI 48824, USA Correspondence to: Z. Gao ([email protected]) Received: 15 March 2014 – Published in Geosci. Model Dev. Discuss.: 24 June 2014 Revised: 12 September 2014 – Accepted: 22 September 2014 – Published: 10 November 2014 Abstract. Experimental data from four field campaigns are 1 Introduction used to explore the variability of the bulk Richardson num- ber of the entire planetary boundary layer (PBL), Ribc, which The planetary boundary layer (PBL), or the atmospheric is a key parameter for calculating the PBL height (PBLH) boundary layer, is the lowest part of the atmosphere that is in numerical weather and climate models with the bulk directly influenced by earth’s surface and has significant im- Richardson number method. First, the PBLHs of three differ- pacts on weather, climate, and the hydrologic cycle (Stull, ent thermally stratified boundary layers (i.e., strongly stable 1988; Garratt, 1992; Seidel et al., 2010).
    [Show full text]
  • 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
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • Atmospheric Instability Parameters Derived from Msg Seviri Observations
    P3.33 ATMOSPHERIC INSTABILITY PARAMETERS DERIVED FROM MSG SEVIRI OBSERVATIONS Marianne König*, Stephen Tjemkes, Jochen Kerkmann EUMETSAT, Darmstadt, Germany 1. INTRODUCTION K-Index: Convective systems can develop in a thermo- (Tobs(850)–Tobs(500)) + TDobs(850) – (Tobs(700)–TDobs(700)) dynamically unstable atmosphere. Such systems may quickly reach high altitudes and can cause severe Lifted Index: storms. Meteorologists are thus especially interested to Tobs - Tlifted from surface at 500 hPa identify such storm potentials when the respective system is still in a preconvective state. A number of Maximum buoyancy: obs(max betw. surface and 850) obs(min betw. 700 and 300) instability indices have been defined to describe such Qe - Qe situations. Traditionally, these indices are taken from temperature and humidity soundings by radiosondes. As (T is temperature, TD is dewpoint temperature, and Qe radiosondes are only of very limited temporal and is equivalent potential temperature, numbers 850, 700, spatial resolution there is a demand for satellite-derived 300 indicate respective hPa level in the atmosphere) indices. The Meteorological Product Extraction Facility (MPEF) for the new European Meteosat Second and the precipitable water content as additionally Generation (MSG) satellite envisages the operational derived parameter. derivation of a number of instability indices from the brightness temperatures measured by certain SEVIRI 3. DESCRIPTION OF ALGORITHMS channels (Spinning Enhanced Visible and Infrared Imager, the radiometer onboard MSG). The traditional 3.1 The Physical Retrieval physical approach to this kind of retrieval problem is to infer the atmospheric profile via a constrained inversion An iterative solution to the inversion equation (e.g. and compute the indices then directly from the obtained Ma et al.
    [Show full text]
  • 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).
    [Show full text]
  • 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.
    [Show full text]