DOCSLIB.ORG

Principles of Aerodrome Weather Observations

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Principles of Aerodrome Weather Observations PRINCIPLES OF AERODROME WEATHER OBSERVATIONS Course Notes BUREAU OF METEOROLOGY TRAINING CENTRE 08AUG2018 Contents Cloud Observations ................................................................................................................ 1 Definition of a cloud ........................................................................................................................1 Common terms used when making cloud observations .................................................................2 Cloud Classification ........................................................................................................................3 Table of Classification of Clouds ....................................................................................................4 The 11 basic cloud types ................................................................................................................5 Observing conditions to which the cloud descriptions apply ..........................................................6 Cirrus: Ci ........................................................................................................................................7 Cirrocumulus: Cc ...........................................................................................................................8 Cirrostratus: Cs ..............................................................................................................................9 Altocumulus: Ac .......................................................................................................................... 10 Altostratus: As ............................................................................................................................. 11 Nimbostratus: Ns ........................................................................................................................ 12 Stratocumulus: Sc ....................................................................................................................... 13 Stratus: St ................................................................................................................................... 14 Cumulus: Cu ............................................................................................................................... 15 Towering Cumulus: TCu ............................................................................................................. 16 Cumulonimbus: Cb ..................................................................................................................... 17 Performing a cloud observation ................................................................................................... 18 Identifying the types of cloud present .......................................................................................... 18 Cloud levels – the height range of clouds .................................................................................... 19 Cloud composition ....................................................................................................................... 21 Optical phenomena associated with clouds ................................................................................ 22 Clouds and precipitation .............................................................................................................. 22 Factors affecting the appearance of clouds ................................................................................. 23 Determining cloud types at night ................................................................................................. 24 Estimation of cloud amount ......................................................................................................... 25 Estimation of the height of the cloud base ................................................................................... 26 Laser Ceilometer – Cloud Height and Amount ............................................................................ 32 Cloud observations - Further considerations ............................................................................... 34 Further reading ............................................................................................................................ 34 Visibility Observations .......................................................................................................... 35 Definition ...................................................................................................................................... 35 Factors affecting visibility ............................................................................................................. 35 Weather phenomena and visibility ............................................................................................... 36 Selection of visibility markers ....................................................................................................... 36 Procedure for making visibility observations ............................................................................... 38 Visibility terminology .................................................................................................................... 39 Visibility Meter .............................................................................................................................. 40 Weather Observations .......................................................................................................... 42 Definitions .................................................................................................................................... 42 Precipitation phenomena ............................................................................................................. 43 Precipitation Intensity - TBRG ..................................................................................................... 44 Drizzle DZ .................................................................................................................................. 44 Rain RA..................................................................................................................................... 46 Freezing Rain FZRA and Freezing Drizzle FZDZ............................................................... 46 Snow SN ................................................................................................................................... 47 Hail GR ..................................................................................................................................... 47 Small Hail/Snow Pellets GS...................................................................................................... 48 Snow Grains SG ....................................................................................................................... 48 Ice Pellets PL ............................................................................................................................ 48 Weather watch radar and precipitation identification ................................................................... 49 Approximate rainfall rates ............................................................................................................ 49 Rain bands from Altostratus and Nimbostratus ........................................................................... 49 Showers from cumuliform clouds ................................................................................................ 50 i Heavy precipitation from thunderstorms ...................................................................................... 50 Obscuration phenomena ............................................................................................................. 51 Fog FG ...................................................................................................................................... 51 Fog Patches BCFG ................................................................................................................... 51 Partial Fog (Fog Bank) PRFG .................................................................................................. 51 Shallow Fog MIFG .................................................................................................................... 51 Freezing Fog FZFG .................................................................................................................. 52 Mist BR ..................................................................................................................................... 52 Smoke FU ................................................................................................................................. 52 Haze HZ .................................................................................................................................... 52 Dust DU .................................................................................................................................... 52 Drifting Dust DRDU ................................................................................................................... 52 Drifting Sand DRSA .................................................................................................................. 53 Blowing Dust BLDU .................................................................................................................. 53 Blowing Sand BLSA .................................................................................................................
Load more

Recommended publications
  • Soaring Weather
    Chapter 16 SOARING WEATHER While horse racing may be the "Sport of Kings," of the craft depends on the weather and the skill soaring may be considered the "King of Sports." of the pilot. Forward thrust comes from gliding Soaring bears the relationship to flying that sailing downward relative to the air the same as thrust bears to power boating. Soaring has made notable is developed in a power-off glide by a conven­ contributions to meteorology. For example, soar­ tional aircraft. Therefore, to gain or maintain ing pilots have probed thunderstorms and moun­ altitude, the soaring pilot must rely on upward tain waves with findings that have made flying motion of the air. safer for all pilots. However, soaring is primarily To a sailplane pilot, "lift" means the rate of recreational. climb he can achieve in an up-current, while "sink" A sailplane must have auxiliary power to be­ denotes his rate of descent in a downdraft or in come airborne such as a winch, a ground tow, or neutral air. "Zero sink" means that upward cur­ a tow by a powered aircraft. Once the sailcraft is rents are just strong enough to enable him to hold airborne and the tow cable released, performance altitude but not to climb. Sailplanes are highly 171 r efficient machines; a sink rate of a mere 2 feet per second. There is no point in trying to soar until second provides an airspeed of about 40 knots, and weather conditions favor vertical speeds greater a sink rate of 6 feet per second gives an airspeed than the minimum sink rate of the aircraft.
    [Show full text]
  • Contrail-Cirrus and Their Potential for Regional Climate Change
    Contrail-Cirrus and Their Potential for Regional Climate Change Kenneth Sassen Department of Meteorology, University of Utah, Salt Lake City, Utah ABSTRACT After reviewing the indirect evidence for the regional climatic impact of contrail-generated cirrus clouds (contrail- cirrus), the author presents a variety of new measurements indicating the nature and scope of the problem. The assess- ment concentrates on polarization lidar and radiometric observations of persisting contrails from Salt Lake City, Utah, where an extended Project First ISCCP (International Satellite Cloud Climatology Program) Regional Experiment (FIRE) cirrus cloud dataset from the Facility for Atmospheric Remote Sensing has captured new information in a geographical area previously identified as being affected by relatively heavy air traffic. The following contrail properties are consid- ered: hourly and monthly frequency of occurrence; height, temperature, and relative humidity statistics; visible and in- frared radiative impacts; and microphysical content evaluated from in situ data and contrail optical phenomenon such as halos and coronas. Also presented are high-resolution lidar images of contrails from the recent SUCCESS experiment, and the results of an initial attempt to numerically simulate the radiative effects of an observed contrail. The evidence indicates that the direct radiative effects of contrails display the potential for regional climate change at many midlati- tude locations, even though the sign of the climatic impact may be uncertain. However, new information suggests that the unusually small particles typical of many persisting contrails may favor the albedo cooling over the greenhouse warming effect, depending on such factors as the geographic distribution and patterns in day versus night aircraft usage.
    [Show full text]
  • Cloud Atlas? 5
    Episode 9 Teacher Resource 4th April 2017 Clouds 1. Briefly explain how clouds form. Students will develop an 2. Why are clouds an important part of the earth’s atmosphere? understanding of how clouds form and the different types of clouds. 3. What does a meteorologist study? 4. What is a Cloud Atlas? 5. When was it first published? 6. What does a cumulus cloud look like? 7. What is the name of the cloud that brings rain and lightning? 8. The Cloud Atlas has special clouds that are defined by the unusual Science – Year 4 ways they form. Give an example of one. Represent and communicate observations, ideas and findings 9. Describe the cloud that Gary helped get into the Cloud Atlas. using formal and informal 10. What do you understand more clearly since watching the BTN representations (ACSIS071) story? Science – Year 5 Solids, liquids and gases have different observable properties a nd behave in different ways (ACSSU077) Science – Year 7 Class Discussion Some of Earth’s resources are Watch the BTN Cloud Atlas story and discuss the information raised as a renewable, including water that cycles through the environment, class. What questions do students have (what are the gaps in their but others are non-renewable knowledge)? The following questions may help guide the discussion: (ACSSU116) What are clouds? Come up with a definition. What have you noticed about clouds? How do clouds form? What are the main types of clouds? What is the Cloud Atlas? The following KWLH organiser provides students with a framework to explore their knowledge on this topic and consider what they would like to know and learn.
    [Show full text]
  • Manuscript Was Written Jointly by YL, CK, and MZ
    A simplified method for the detection of convection using high resolution imagery from GOES-16 Yoonjin Lee1, Christian D. Kummerow1,2, Milija Zupanski2 1Department of Atmospheric Science, Colorado state university, Fort Collins, CO 80521, USA 5 2Cooperative Institute for Research in the Atmosphere, Colorado state university, Fort Collins, CO 80521, USA Correspondence to: Yoonjin Lee ([email protected]) Abstract. The ability to detect convective regions and adding heating in these regions is the most important skill in forecasting severe weather systems. Since radars are most directly related to precipitation and are available in high temporal resolution, their data are often used for both detecting convection and estimating latent heating. However, radar data are limited to land areas, 10 largely in developed nations, and early convection is not detectable from radars until drops become large enough to produce significant echoes. Visible and Infrared sensors on a geostationary satellite can provide data that are more sensitive to small droplets, but they also have shortcomings: their information is almost exclusively from the cloud top. Relatively new geostationary satellites, Geostationary Operational Environmental Satellites-16 and -17 (GOES-16 and GOES-17), along with Himawari-8, can make up for some of this lack of vertical information through the use of very high spatial and temporal 15 resolutions. This study develops two algorithms to detect convection at vertically growing clouds and mature convective clouds using 1-minute GOES-16 Advanced Baseline Imager (ABI) data. Two case studies are used to explain the two methods, followed by results applied to one month of data over the contiguous United States.
    [Show full text]
  • Assessing and Improving Cloud-Height Based Parameterisations of Global Lightning Flash Rate, and Their Impact on Lightning-Produced Nox and Tropospheric Composition
    https://doi.org/10.5194/acp-2020-885 Preprint. Discussion started: 2 October 2020 c Author(s) 2020. CC BY 4.0 License. Assessing and improving cloud-height based parameterisations of global lightning flash rate, and their impact on lightning-produced NOx and tropospheric composition 5 Ashok K. Luhar1, Ian E. Galbally1, Matthew T. Woodhouse1, and Nathan Luke Abraham2,3 1CSIRO Oceans and Atmosphere, Aspendale, 3195, Australia 2National Centre for Atmospheric Science, Department of Chemistry, University of Cambridge, Cambridge, UK 3Department of Chemistry, University of Cambridge, Cambridge, UK 10 Correspondence to: Ashok K. Luhar ([email protected]) Abstract. Although lightning-generated oxides of nitrogen (LNOx) account for only approximately 10% of the global NOx source, it has a disproportionately large impact on tropospheric photochemistry due to the conducive conditions in the tropical upper troposphere where lightning is mostly discharged. In most global composition models, lightning flash rates used to calculate LNOx are expressed in terms of convective cloud-top height via the Price and Rind (1992) (PR92) 15 parameterisations for land and ocean. We conduct a critical assessment of flash-rate parameterisations that are based on cloud-top height and validate them within the ACCESS-UKCA global chemistry-climate model using the LIS/OTD satellite data. While the PR92 parameterisation for land yields satisfactory predictions, the oceanic parameterisation underestimates the observed flash-rate density severely, yielding a global average of 0.33 flashes s-1 compared to the observed 9.16 -1 flashes s over the ocean and leading to LNOx being underestimated proportionally. We formulate new/alternative flash-rate 20 parameterisations following Boccippio’s (2002) scaling relationships between thunderstorm electrical generator power and storm geometry coupled with available data.
    [Show full text]
  • Comment Chapter Team Response Id Page Line Page Line 2257 2 0 0 0 0 Excellent Comprehensive and Rich Report
    SROCC Second Order Draft Government and Expert Review Comments - Chapter 2 Comment Chapter From From To To Comment Chapter Team Response id page line page line 2257 2 0 0 0 0 Excellent comprehensive and rich report. I have only few rather technical remarks. A more Taken into account – sentence included in permafrost section general recommendation relates to the new landscapes which are rapidli forming in deglaciating mountain areas and need comprehensive anticipation/modeling/treatment. This is an emerging research field (Haeberli, W. (2017): Integrative modelling and managing new landscapes and environments in de-glaciating mountain ranges: An emerging trans- disciplinary research field. Forestry Research and Engineering: International Journal 1(1). doi:10.15406/freij.2017.01.00005). This could be more strongly emphasized, for instance, in section 2.4 on page 47 an don page 5, line 53. Such new landscapes will be characterized by strong and long-lasting disequilibria, especially concerning slope stability, sediment cascades or eco-systems. One important factor thereby is the strongly different response time of cryosphere components: snow = almost immediate, mountain glaciers = years to decades, mountain permafrost =decades to centuries to even millennia. As an example, in many mountain chains, permafrost inside high peaks will probably continue to exist (far out of thermal equilibrium) when glaciers will have already long disappeared. [Wilfried Haeberli, Switzerland] 2259 2 0 0 0 0 The figures are interesting but rather overloaded and not easy to read and understand. Accepted - The figures have been revised to improve readability. [Wilfried Haeberli, Switzerland] 2415 2 0 0 0 It is puzzling to see that chapter authors have consciously chosen to ignore the pre- Rejected - Pre-industrial changes are not part of the government industrial, pre-Little-Ice-Age palaeoclimatic context.
    [Show full text]
  • Cloud-Base Height Derived from a Ground-Based Infrared Sensor and a Comparison with a Collocated Cloud Radar
    VOLUME 35 JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY APRIL 2018 Cloud-Base Height Derived from a Ground-Based Infrared Sensor and a Comparison with a Collocated Cloud Radar ZHE WANG Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, CMA Key Laboratory for Aerosol–Cloud–Precipitation, and School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, and Training Center, China Meteorological Administration, Beijing, China ZHENHUI WANG Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, CMA Key Laboratory for Aerosol–Cloud–Precipitation, and School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, China XIAOZHONG CAO,JIAJIA MAO,FA TAO, AND SHUZHEN HU Atmosphere Observation Test Bed, and Meteorological Observation Center, China Meteorological Administration, Beijing, China (Manuscript received 12 June 2017, in final form 31 October 2017) ABSTRACT An improved algorithm to calculate cloud-base height (CBH) from infrared temperature sensor (IRT) observations that accompany a microwave radiometer was described, the results of which were compared with the CBHs derived from ground-based millimeter-wavelength cloud radar reflectivity data. The results were superior to the original CBH product of IRT and closer to the cloud radar data, which could be used as a reference for comparative analysis and synergistic cloud measurements. Based on the data obtained by these two kinds of instruments for the same period (January–December 2016) from the Beijing Nanjiao Weather Observatory, the results showed that the consistency of cloud detection was good and that the consistency rate between the two datasets was 81.6%. The correlation coefficient between the two CBH datasets reached 0.62, based on 73 545 samples, and the average difference was 0.1 km.
    [Show full text]
  • New Cloud Types 2019
    UPSC MAIN & PRELIMS NEW CLOUD TYPES 2019 BY : NEETU SINGH This is updated material for New Cloud Types, targeting both upcoming Prelims and Main Exams. Video is attached to provide you with the gist of content. https://youtu.be/01Ciwd9b470 New Cloud Types PRINCIPLES OF CLOUD CLASSIFICATION Useful concepts Height, altitude, vertical extent Clouds continuously evolve and appear in an infinite variety of forms. However, there is a limited number · Height: Vertical distance from the point of of characteristic forms frequently observed all over observation on the Earth's surface to the point the world, into which clouds can be broadly grouped being measured. in a classification scheme. The scheme uses · Altitude: Vertical distance from mean sea level to genera(defined according to their appearance and the point being measured. position in the sky), species(describing shape and · Height/Altitude of cloud base: For surface structure) and varieties(describing transparency and observations, height of the cloud base above arrangement).This is similar to the systems used in ground level; for aircraft observations, altitude of the classification of plants or animals, and similarly the cloud base above mean sea level. uses Latin names. · Vertical extent: Vertical distance from a cloud's There are some intermediate or transitional forms of base to its top. clouds that, although observed fairly frequently, are Levels not described in the classification scheme. The transitional forms are of little interest; they are less Clouds are generally encountered over a range of stable and in appearance are not very different from altitudes varying from sea level to the top of the the definitions of the characteristic forms.
    [Show full text]
  • Cloud-Spotting Game Sheet
    Spot ‘Em Car Games Clouds and Weather Mark down each type of cloud you spot, and see how long it takes you to get them all! 1. Cirrus (2) 2. Altocumulus (2) 3. Cirrocumulus (1) 4. Cirrostratus (3) 5. Cumulus (1) 6. Cirrus fibratus (2) 7. Altostratus (3) 8. Nimbostratus (2) 9. Stratocumulus (1) 10. Stratus (3) 11. Lenticular cloud (10) 12. Funnel cloud (10) 13. Rainbow (5) 14. Airplane contrail (2) 15. Crepuscular rays (10) www.HowToRaiseAHappyGenius.com Printed by Pictish Beast Publications Spot ‘Em Car Games Clouds and Weather More information about how to identify the weather phenomena that are part of this car game 1. Cirrus: Cirrus clouds look like strands of white cotton wool that have been pulled apart and spread across the sky. 2. Altocumulus: Altocumulus clouds form a layer at mid-altitudes that covers much of the sky, and this layer is usually made up of patterns of regularly spaced and shaped patches with bands of blue sky between them. 3. Cirrocumulus: Cirrocumulus clouds are similar to altocumulus, but they are found higher up in the sky and are made up of smaller patches of cloud. 4. Cirrostratus: Cirrostratus clouds form a continuous sheet of cloud high up in the sky that are thin enough for the sun to be able to shine through, creating a halo effect. 5. Cumulus: Cumulus clouds are distinctive fluffy looking clouds that are clearly separated from other clouds in the sky. They are what you would draw if asked to draw a picture of a cloud. 6. Cirrus fibratus: Cirrus fibratus are a type of Cirrus cloud that form very distinctive long, fluffy lines across the sky.
    [Show full text]
  • Indian Monsoon Basic Drivers and Variability
    Indian Monsoon Basic Drivers and Variability GOTHAM International Summer School PIK, Potsdam, Germany, 18-22 Sep 2017 R. Krishnan Indian Institute of Tropical Meteorology, Pune, India The Indian (South Asian) Monsoon Tibetan Plateau India Indian Ocean Monsoon circulation and rainfall: A convectively coupled phenomenon Requires a thermal contrast between land & ocean to set up the monsoon circulation Once established, a positive feedback between circulation and latent heat release maintains the monsoon The year to year variations in the seasonal (June – September) summer monsoon rains over India are influenced internal dynamics and external drivers Long-term climatology of total rainfall over India during (1 Jun - 30 Sep) summer monsoon season (http://www.tropmet.res.in) Interannual variability of the Indian Summer Monsoon Rainfall Primary synoptic & smaller scale circulation features that affect cloudiness & precipitation. Locations of June to September rainfall exceeding 100 cm over the land west of 100oE associated with the southwest monsoon are indicated (Source: Rao, 1981). Multi-scale interactions Low frequency Synoptic Systems:Lows, sub-seasonal Depressions, MTC variability – Large scale Active and monsoon Break monsoon Organized convection, Embedded mesoscale systems , heavy rainfall (intensity > 10 cm/day) Winds at 925hPa DJF West African Monsoon Asian Monsoon Austral Monsoon JJA Courtesy: J.M. Slingo, Univ of Reading Land/Sea Temperature contrasts Nov./Dec. West African Monsoon Asian Monsoon Austral Monsoon May/June Courtesy:
    [Show full text]
  • Precipitation Type Graphic Documentation Page
    Precipitation Type Graphic Documentation Page Purpose: The National Weather Service in Caribou and Gray has created graphics that easily display precipitation types for mixed winter weather events. These graphics can be used by core customers for briefing purposes on upcoming winter events. Product Creation: These graphics are created automatically when each individual weather forecast office updates the official forecast. An individual forecast graphic will be created in 3 hour increments through the first 24 hours, then in 6 hour increments thereafter through 120 hours (5 days). Depending on how many forecast updates will dictate how often these graphics are created, but on average the first 24 hour forecast will be updated 6 times a day and the day 2 to 5 period will be updated twice daily (~ 3 p.m. and 3 a.m.). Product Location: Graphics will be hosted via the NWS Internet pages at the following URL: http://www.weather.gov/car/WeatherTypeCovForecast Note: The URL will be hidden for the first test season (winter 2015-16) and will only be available for a small test group to evaluate the graphics. Precipitation Type Graphic Example: The precipitation type page consists of the main graphic with the toggle commands at the bottom of the page. See image below for number reference locations: 1. Valid Time – Provides you with the graphic valid time in either 3hr or 6hr forecast time periods 2. Legend – Simple legend that explains what the precipitation type graphic colors are representing (a more detailed description of this legend is in the following section of this document).
    [Show full text]
  • Downloaded 09/24/21 05:27 PM UTC Fig
    Using Citizen Science Reports to Evaluate Estimates of Surface Precipitation Type BY SHENG CHEN, JONATHAN J. GOURLEY, YANG HONG, QING CAO, NICHOLAS CARR, PIERRE-EMMANUEL KIRSTETTER, JIAN ZHANG, AND ZAC FLAMIG he Multi-Radar Multi-Sensor (MRMS) system tree logic. To date, there has not yet been a system- uses data from the Next Generation Radar net- atic evaluation of the MRMS surface precipitation T work (NEXRAD) combined with model analyses type products beyond case studies. An opportunity from the Rapid Refresh (RAP) system to provide pre- exists to employ newly collected citizen-scientist (also cipitation rate and type products on a grid with 1-km referred to as “crowdsourced”) reports made possible horizontal resolution every 2 min (Zhang et al. 2011). through the meteorological Phenomena Indication The model analysis fields were found to be useful for Near the Ground (mPING) project to accomplish estimating precipitation type at the surface, which algorithm evaluations (Elmore et al. 2014). differs from the various Hydrometeor Classification Human weather observers have the ability to use Algorithms (HCA) designed to identify hydrometeors multiple clues in order to make a decision about at the height of radar sampling (e.g., Park et al. 2008). a given precipitation type. They typically begin These sampled hydrometeors undergo changes in with eyesight in order to assess the fall speed of the terms of their sizes, shapes, orientation, and phase as hydrometeor, its color, size, and shape. If uncertainty they fall and reach the surface. During the cool-season remains, then they may use other observations such months, these changes often occur below the height as touch to determine its phase (liquid, frozen, or of radar sampling.
    [Show full text]