M.Sc. in Meteorology Physical Meteorology Part 2 Atmospheric
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Chapter 8 Atmospheric Statics and Stability
Chapter 8 Atmospheric Statics and Stability 1. The Hydrostatic Equation • HydroSTATIC – dw/dt = 0! • Represents the balance between the upward directed pressure gradient force and downward directed gravity. ρ = const within this slab dp A=1 dz Force balance p-dp ρ p g d z upward pressure gradient force = downward force by gravity • p=F/A. A=1 m2, so upward force on bottom of slab is p, downward force on top is p-dp, so net upward force is dp. • Weight due to gravity is F=mg=ρgdz • Force balance: dp/dz = -ρg 2. Geopotential • Like potential energy. It is the work done on a parcel of air (per unit mass, to raise that parcel from the ground to a height z. • dφ ≡ gdz, so • Geopotential height – used as vertical coordinate often in synoptic meteorology. ≡ φ( 2 • Z z)/go (where go is 9.81 m/s ). • Note: Since gravity decreases with height (only slightly in troposphere), geopotential height Z will be a little less than actual height z. 3. The Hypsometric Equation and Thickness • Combining the equation for geopotential height with the ρ hydrostatic equation and the equation of state p = Rd Tv, • Integrating and assuming a mean virtual temp (so it can be a constant and pulled outside the integral), we get the hypsometric equation: • For a given mean virtual temperature, this equation allows for calculation of the thickness of the layer between 2 given pressure levels. • For two given pressure levels, the thickness is lower when the virtual temperature is lower, (ie., denser air). • Since thickness is readily calculated from radiosonde measurements, it provides an excellent forecasting tool. -
Comparison Between Observed Convective Cloud-Base Heights and Lifting Condensation Level for Two Different Lifted Parcels
AUGUST 2002 NOTES AND CORRESPONDENCE 885 Comparison between Observed Convective Cloud-Base Heights and Lifting Condensation Level for Two Different Lifted Parcels JEFFREY P. C RAVEN AND RYAN E. JEWELL NOAA/NWS/Storm Prediction Center, Norman, Oklahoma HAROLD E. BROOKS NOAA/National Severe Storms Laboratory, Norman, Oklahoma 6 January 2002 and 16 April 2002 ABSTRACT Approximately 400 Automated Surface Observing System (ASOS) observations of convective cloud-base heights at 2300 UTC were collected from April through August of 2001. These observations were compared with lifting condensation level (LCL) heights above ground level determined by 0000 UTC rawinsonde soundings from collocated upper-air sites. The LCL heights were calculated using both surface-based parcels (SBLCL) and mean-layer parcels (MLLCLÐusing mean temperature and dewpoint in lowest 100 hPa). The results show that the mean error for the MLLCL heights was substantially less than for SBLCL heights, with SBLCL heights consistently lower than observed cloud bases. These ®ndings suggest that the mean-layer parcel is likely more representative of the actual parcel associated with convective cloud development, which has implications for calculations of thermodynamic parameters such as convective available potential energy (CAPE) and convective inhibition. In addition, the median value of surface-based CAPE (SBCAPE) was more than 2 times that of the mean-layer CAPE (MLCAPE). Thus, caution is advised when considering surface-based thermodynamic indices, despite the assumed presence of a well-mixed afternoon boundary layer. 1. Introduction dry-adiabatic temperature pro®le (constant potential The lifting condensation level (LCL) has long been temperature in the mixed layer) and a moisture pro®le used to estimate boundary layer cloud heights (e.g., described by a constant mixing ratio. -
Aerodrome Actual Weather – METAR Decode
Aerodrome Actual Weather – METAR decode Code element Example Decode Notes 1 Identification METAR — Meteorological Airfield Report, SPECI — selected special (not from UK civil METAR or SPECI METAR METAR aerodromes) Location indicator EGLL London Heathrow Station four-letter indicator 'ten twenty Zulu on the Date/Time 291020Z 29th' AUTO Metars will only be disseminated when an aerodrome is closed or at H24 aerodromes, A fully automated where the accredited met. observer is on duty break overnight. Users are reminded that reports AUTO report with no human of visibility, present weather and cloud from automated systems should be treated with caution intervention due to the limitations of the sensors themselves and the spatial area sampled by the sensors. 2 Wind 'three one zero Wind degrees, fifteen knots, Max only given if >= 10KT greater than the mean. VRB = variable. 00000KT = calm. 31015G27KT direction/speed max twenty seven Wind direction is given in degrees true. knots' 'varying between two Extreme direction 280V350 eight zero and three Variation given in clockwise direction, but only when mean speed is greater than 3 KT. variance five zero degrees' 3 Visibility 'three thousand two Prevailing visibility 3200 0000 = 'less than 50 metres' 9999 = 'ten kilometres or more'. No direction is required. hundred metres' Minimum visibility 'Twelve hundred The minimum visibility is also included alongside the prevailing visibility when the visibility in one (in addition to the 1200SW metres to the south- direction, which is not the prevailing visibility, is less than 1500 metres or less than 50% of the prevailing visibility west' prevailing visibility. A direction is also added as one of the eight points of the compass. -
A User's Guide to Co-Ops Visibility Sensor
A USER’S GUIDE TO CO-OPS VISIBILITY SENSOR OBSERVATIONS The American Meteorological Society defines visibility as “the greatest distance in a given direction at which it is just possible to see and identify with the unaided eye (a) in the daytime, a prominent dark object against the sky at the horizon, and (b) at night, a known, preferably unfocused, moderately intense light source. A wide variety of factors contribute to changing this distance. CO-OPS deploys automated visibility sensors at locations selected in concert with supporting PORTS® partners. The sites must be both acceptable for sensor operation and useful for the user. Because fog can be patchy, it is important for users to understand how to interpret the disseminated data. The sensors used by CO-OPS optically examine a small volume of air to determine the scattering characteristics of the air parcel, the most common technique used to measure visibility. Scatter can be caused by fog, smog, dust – any particles in the air will suffice. When the scatter is small the visibility is good, while large scatter means the visibility at the location of the sensor is poor. It is important to realize that the visibility range provided by the sensor, more correctly known as Meteorological Optical Range (MOR), is a measurement made at a single point. When fog or other scattering particles are uniformly widespread it may be reasonable to presume the MOR applies over large distances, ie. a reading of 5 nautical miles means a person will see sufficiently large objects at that distance. But such a presumption may not be valid, and the sensor has no way of observing MOR except at the deployment site. -
MSE3 Ch14 Thunderstorms
Chapter 14 Copyright © 2011, 2015 by Roland Stull. Meteorology for Scientists and Engineers, 3rd Ed. thunderstorms Contents Thunderstorms are among the most violent and difficult-to-predict weath- Thunderstorm Characteristics 481 er elements. Yet, thunderstorms can be Appearance 482 14 studied. They can be probed with radar and air- Clouds Associated with Thunderstorms 482 craft, and simulated in a laboratory or by computer. Cells & Evolution 484 They form in the air, and must obey the laws of fluid Thunderstorm Types & Organization 486 mechanics and thermodynamics. Basic Storms 486 Thunderstorms are also beautiful and majestic. Mesoscale Convective Systems 488 Supercell Thunderstorms 492 In thunderstorms, aesthetics and science merge, making them fascinating to study and chase. Thunderstorm Formation 496 Convective Conditions 496 Thunderstorm characteristics, formation, and Key Altitudes 496 forecasting are covered in this chapter. The next chapter covers thunderstorm hazards including High Humidity in the ABL 499 hail, gust fronts, lightning, and tornadoes. Instability, CAPE & Updrafts 503 CAPE 503 Updraft Velocity 508 Wind Shear in the Environment 509 Hodograph Basics 510 thunderstorm CharaCteristiCs Using Hodographs 514 Shear Across a Single Layer 514 Thunderstorms are convective clouds Mean Wind Shear Vector 514 with large vertical extent, often with tops near the Total Shear Magnitude 515 tropopause and bases near the top of the boundary Mean Environmental Wind (Normal Storm Mo- layer. Their official name is cumulonimbus (see tion) 516 the Clouds Chapter), for which the abbreviation is Supercell Storm Motion 518 Bulk Richardson Number 521 Cb. On weather maps the symbol represents thunderstorms, with a dot •, asterisk , or triangle Triggering vs. Convective Inhibition 522 * ∆ drawn just above the top of the symbol to indicate Convective Inhibition (CIN) 523 Trigger Mechanisms 525 rain, snow, or hail, respectively. -
ESSENTIALS of METEOROLOGY (7Th Ed.) GLOSSARY
ESSENTIALS OF METEOROLOGY (7th ed.) GLOSSARY Chapter 1 Aerosols Tiny suspended solid particles (dust, smoke, etc.) or liquid droplets that enter the atmosphere from either natural or human (anthropogenic) sources, such as the burning of fossil fuels. Sulfur-containing fossil fuels, such as coal, produce sulfate aerosols. Air density The ratio of the mass of a substance to the volume occupied by it. Air density is usually expressed as g/cm3 or kg/m3. Also See Density. Air pressure The pressure exerted by the mass of air above a given point, usually expressed in millibars (mb), inches of (atmospheric mercury (Hg) or in hectopascals (hPa). pressure) Atmosphere The envelope of gases that surround a planet and are held to it by the planet's gravitational attraction. The earth's atmosphere is mainly nitrogen and oxygen. Carbon dioxide (CO2) A colorless, odorless gas whose concentration is about 0.039 percent (390 ppm) in a volume of air near sea level. It is a selective absorber of infrared radiation and, consequently, it is important in the earth's atmospheric greenhouse effect. Solid CO2 is called dry ice. Climate The accumulation of daily and seasonal weather events over a long period of time. Front The transition zone between two distinct air masses. Hurricane A tropical cyclone having winds in excess of 64 knots (74 mi/hr). Ionosphere An electrified region of the upper atmosphere where fairly large concentrations of ions and free electrons exist. Lapse rate The rate at which an atmospheric variable (usually temperature) decreases with height. (See Environmental lapse rate.) Mesosphere The atmospheric layer between the stratosphere and the thermosphere. -
Impact of Meteorology and Atmospheric Pollutants on Visibility
Atmos. Chem. Phys., 17, 2085–2101, 2017 www.atmos-chem-phys.net/17/2085/2017/ doi:10.5194/acp-17-2085-2017 © Author(s) 2017. CC Attribution 3.0 License. 60 years of UK visibility measurements: impact of meteorology and atmospheric pollutants on visibility Ajit Singh, William J. Bloss, and Francis D. Pope School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, B15 2TT, UK Correspondence to: Francis D. Pope ([email protected]) Received: 16 August 2016 – Discussion started: 26 August 2016 Revised: 6 January 2017 – Accepted: 17 January 2017 – Published: 13 February 2017 Abstract. Reduced visibility is an indicator of poor air qual- in aerosol particle properties. This approach may help con- ity. Moreover, degradation in visibility can be hazardous to strain global model simulations which attempt to generate human safety; for example, low visibility can lead to road, aerosol fields for time periods when observational data are rail, sea and air accidents. In this paper, we explore the com- scarce or non-existent. Both the measured visibility and the bined influence of atmospheric aerosol particle and gas char- modelled aerosol properties reported in this paper highlight acteristics, and meteorology, on long-term visibility. We use the success of the UK’s Clean Air Act, which was passed in visibility data from eight meteorological stations, situated in 1956, in cleaning the atmosphere of visibility-reducing pol- the UK, which have been running since the 1950s. The site lutants. locations include urban, rural and marine environments. Most stations show a long-term trend of increasing visi- bility, which is indicative of reductions in air pollution, es- pecially in urban areas. -
P2.55 Visibility Versus Precipitation Rate and Relative Humidity
P2.55 VISIBILITY VERSUS PRECIPITATION RATE AND RELATIVE HUMIDITY I. Gultepe1,2, and G. A. Isaac1 1Cloud Physics and Severe Weather Research Section, Science and Technology Branch, Environment Canada Toronto, Ontario, M3H 5T4, Canada 1. INTRODUCTION 2. OBSERVATIONS The purpose of this work is to analyze the The main observations used in the analysis were relationships between visibility and precipitation the precipitation rates for rain and snow from the rate (PR), and visibility and relative humidity Desert Research Institute (DRI) manufactured with respect to water (RHw), obtained from hot plates (Rasmussen et al., 2002), the surface measurements. Precipitation Occurrence Sensor System (POSS) (Sheppard, 1990), and from the Visalia FD12P, Presently, visibility parameterizations related to ice and liquid particle characteristics from the precipitation type in forecast models are not optical probes, visibility from the Belfort adequate because they represent mid-latitude visibility meter, the Vaisala FD12P and from the cloud systems (Stoelinga, 1999). Some previous fog measuring device (FMD), and relative works have shown that particle phase is an humidity and temperature from the Campbell important factor in the visibility calculation but Scientific instruments. Details on these usually it is ignored (Rasmussen et al., 1999). instruments can be found in Isaac et al. (2005) The relative humidity with respect to water in and Gultepe et al. (2006). forecast models is used for visibility parameterization but changes have been 3. ANALYSIS AND RESULTS continuously made because of the uncertainty in The data were collected during the Alliance Icing accurate RHw measurements (Smirnova et al., Research Study (AIRS 2) which was conducted 2000). -
Thunderstorm Predictors and Their Forecast Skill for the Netherlands
Atmospheric Research 67–68 (2003) 273–299 www.elsevier.com/locate/atmos Thunderstorm predictors and their forecast skill for the Netherlands Alwin J. Haklander, Aarnout Van Delden* Institute for Marine and Atmospheric Sciences, Utrecht University, Princetonplein 5, 3584 CC Utrecht, The Netherlands Accepted 28 March 2003 Abstract Thirty-two different thunderstorm predictors, derived from rawinsonde observations, have been evaluated specifically for the Netherlands. For each of the 32 thunderstorm predictors, forecast skill as a function of the chosen threshold was determined, based on at least 10280 six-hourly rawinsonde observations at De Bilt. Thunderstorm activity was monitored by the Arrival Time Difference (ATD) lightning detection and location system from the UK Met Office. Confidence was gained in the ATD data by comparing them with hourly surface observations (thunder heard) for 4015 six-hour time intervals and six different detection radii around De Bilt. As an aside, we found that a detection radius of 20 km (the distance up to which thunder can usually be heard) yielded an optimum in the correlation between the observation and the detection of lightning activity. The dichotomous predictand was chosen to be any detected lightning activity within 100 km from De Bilt during the 6 h following a rawinsonde observation. According to the comparison of ATD data with present weather data, 95.5% of the observed thunderstorms at De Bilt were also detected within 100 km. By using verification parameters such as the True Skill Statistic (TSS) and the Heidke Skill Score (Heidke), optimal thresholds and relative forecast skill for all thunderstorm predictors have been evaluated. -
A Review of Ocean/Sea Subsurface Water Temperature Studies from Remote Sensing and Non-Remote Sensing Methods
water Review A Review of Ocean/Sea Subsurface Water Temperature Studies from Remote Sensing and Non-Remote Sensing Methods Elahe Akbari 1,2, Seyed Kazem Alavipanah 1,*, Mehrdad Jeihouni 1, Mohammad Hajeb 1,3, Dagmar Haase 4,5 and Sadroddin Alavipanah 4 1 Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran 1417853933, Iran; [email protected] (E.A.); [email protected] (M.J.); [email protected] (M.H.) 2 Department of Climatology and Geomorphology, Faculty of Geography and Environmental Sciences, Hakim Sabzevari University, Sabzevar 9617976487, Iran 3 Department of Remote Sensing and GIS, Shahid Beheshti University, Tehran 1983963113, Iran 4 Department of Geography, Humboldt University of Berlin, Unter den Linden 6, 10099 Berlin, Germany; [email protected] (D.H.); [email protected] (S.A.) 5 Department of Computational Landscape Ecology, Helmholtz Centre for Environmental Research UFZ, 04318 Leipzig, Germany * Correspondence: [email protected]; Tel.: +98-21-6111-3536 Received: 3 October 2017; Accepted: 16 November 2017; Published: 14 December 2017 Abstract: Oceans/Seas are important components of Earth that are affected by global warming and climate change. Recent studies have indicated that the deeper oceans are responsible for climate variability by changing the Earth’s ecosystem; therefore, assessing them has become more important. Remote sensing can provide sea surface data at high spatial/temporal resolution and with large spatial coverage, which allows for remarkable discoveries in the ocean sciences. The deep layers of the ocean/sea, however, cannot be directly detected by satellite remote sensors. -
1 Module 4 Water Vapour in the Atmosphere 4.1 Statement of The
Module 4 Water Vapour in the Atmosphere 4.1 Statement of the General Meteorological Problem D. Brunt (1941) in his book Physical and Dynamical Meteorology has stated, “The main problem to be discussed in connection to the thermodynamics of the moist air is the variation of temperature produced by changes of pressure, which in the atmosphere are associated with vertical motion. When damp air ascends, it must eventually attain saturation, and further ascent produces condensation, at first in the form of water drops, and as snow in the later stages”. This statement of the problem emphasizes the role of vertical ascends in producing condensation of water vapour. However, several text books and papers discuss this problem on the assumption that products of condensation are carried with the ascending air current and the process is strictly reversible; meaning that if the damp air and water drops or snow are again brought downwards, the evaporation of water drops or snow uses up the same amount of latent heat as it was liberated by condensation on the upward path of the air. Another assumption is that the drops fall out as the damp air ascends but then the process is not reversible, and Von Bezold (1883) termed it as a pseudo-adiabatic process. It must be pointed out that if the products of condensation are retained in the ascending current, the mathematical treatment is easier in comparison to the pseudo-adiabatic case. There are four stages that can be discussed in connection to the ascent of moist air. (a) The air is saturated; (b) The air is saturated and contains water drops at a temperature above the freezing-point; (c) All the water drops freeze into ice at 0°C; (d) Saturated air and ice at temperatures below 0°C. -
NATIONAL WEATHER SERVICE INSTRUCTION 10-813 NOVEMBER 18, 2020 Operations and Services Aviation Weather Services, NWSPD 10-8 TERMINAL AERODROME FORECASTS
Department of Commerce • National Oceanic & Atmospheric Administration • National Weather Service NATIONAL WEATHER SERVICE INSTRUCTION 10-813 NOVEMBER 18, 2020 Operations and Services Aviation Weather Services, NWSPD 10-8 TERMINAL AERODROME FORECASTS NOTICE: This publication is available at: http://www.nws.noaa.gov/directives/. OPR: W/AFS24 (M. Graf) Certified by: W/AFS24 (B. Entwistle) Type of Issuance: Routine SUMMARY OF REVISIONS: This directive supersedes NWS Instruction 10-813, Terminal Aerodrome Forecasts, dated November 21, 2016. Changes made include: • Section 3 – updated links and removed unnecessary footnote • Section 4 – updated ICAO and WMO manual and regulation numbers • Section 4 – allowed up to 8 FM groups for 30 hour TAF locations • Section 4.1 – updated coordination section to include the AWC (including NAMs) and the AAWU. Also included the 10-803 link. • Section 4.2 – Introduced topic of Digital Aviation Services (DAS) • Section 4.2 – added the word “specifically” to the definition of vicinity as defined by the FAA • Section 4.3 – updated to include the link for ASOS/AWOS limitations. • Section 4.9 – added ICAO verbiage to address the grey window for 00/06/12/18 UTC issuances • Section 4.11 – updated to include the link to the FAA’s list of core airports. • Section 4.13 – revised TAF examples to clearly show format of AMD NOT SKED • Section 6 – updated to include the link to 10-2003. • Section 7 – updated the Performance and Evaluation Branch’s verification link. • Appendix A – updated the definitions of Hail (GR) and Snow Pellets (GS) in the table. • Appendix B – renumbered sections • Appendix B Section 1 – the IWXXM TAF is explained (LT) • Appendix B Section 2.4.2 – updated wording to add more detail for low winds • Appendix B Section 2.6 – added verbiage to use 3 winter weather types judiciously • Appendix B Section 2.8 – LLWS section rewritten to improve clarity • Appendix B Section 2.9.1 – moved NDFD wording from TCF discussion to section 2.6 • Appendix B Section 2.9.1 – updated CDM/CCFP to TFM/TCF.