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How to Read a METAR
How to read a METAR A METAR will look something like this: PHNY 202124Z AUTO 27009KT 1 1/4SM BR BKN016 BKN038 22/21 A3018 RMK AO2 Let’s decipher what each bit of the METAR means. PHNY The first part of the METAR is the airport identifier for the facility which produced the METAR. In this case, this is Lanai Airport in Hawaii. 202124Z Next comes the time and date of issue. The first two digits correspond to the date of the month, and the last 4 digits correspond to the time of issue (in Zulu time). In the example, the METAR was issued on the 20th of the month at 21:24 Zulu time. AUTO This part indicates that the METAR was generated automatically. 27009KT Next comes the wind information. The first 3 digits represent the heading from which the wind is blowing, and the next digits indicate speed in knots. In this case, the wind is coming from a heading of 270 relative to magnetic north, and the speed is 9 knots. Some other wind-related notation you might see: • 27009G15KT – the G indicates gusting. In this case, the wind comes from 270 at 9 knots, and gusts to 15 knots. • VRB09KT – the VRB indicates the wind direction is variable; the wind speed is 9 knots. 1 1/4SM This section of the METAR indicates visibility in statute miles. In this case, visibility is 1 ¼ statute miles. Note that the range is typically limited to 10 statute miles, so a report with 10 statute mile visibility could well indicate a situation with more than 10 statute miles of visibility. -
Forecasting Tropical Cyclones
Forecasting Tropical Cyclones Philippe Caroff, Sébastien Langlade, Thierry Dupont, Nicole Girardot Using ECMWF Forecasts – 4-6 june 2014 Outline . Introduction . Seasonal forecast . Monthly forecast . Medium- to short-range forecasts For each time-range we will see : the products, some elements of assessment or feedback, what is done with the products Using ECMWF Forecasts – 4-6 june 2014 RSMC La Réunion La Réunion is one of the 6 RSMC for tropical cyclone monitoring and warning. Its responsibility area is the south-west Indian Ocean. http://www.meteo.fr/temps/domtom/La_Reunion/webcmrs9.0/# Introduction Seasonal forecast Monthly forecast Medium- to short-range Other activities in La Réunion TRAINING Organisation of international training courses and workshops RESEARCH Research Centre for tropical Cyclones (collaboration with La Réunion University) LACy (Laboratoire de l’Atmosphère et des Cyclones) : https://lacy.univ-reunion.fr DEMONSTRATION SWFDP (Severe Weather Forecasting Demonstration Project) http://www.meteo.fr/extranets/page/index/affiche/id/76216 Introduction Seasonal forecast Monthly forecast Medium- to short-range Seasonal variability • The cyclone season goes from 1st of July to 30 June but more than 90% of the activity takes place between November and April • The average number of named cyclones (i.e. tropical storms) is 9. • But the number of tropical cyclones varies from year to year (from 3 to 14) Can the seasonal forecast systems give indication of this signal ? Introduction Seasonal forecast Monthly forecast Medium- to short-range Seasonal Forecast Products Forecasts of tropical cyclone activity anomaly are produced with ECMWF Seasonal Forecast System, and also with EUROSIP models (union of UKMO+ECMWF+NCEP+MF seasonal forecast systems) Other products can be informative, for example SST plots. -
Improving Lightning and Precipitation Prediction of Severe Convection Using of the Lightning Initiation Locations
PUBLICATIONS Journal of Geophysical Research: Atmospheres RESEARCH ARTICLE Improving Lightning and Precipitation Prediction of Severe 10.1002/2017JD027340 Convection Using Lightning Data Assimilation Key Points: With NCAR WRF-RTFDDA • A lightning data assimilation method was developed Haoliang Wang1,2, Yubao Liu2, William Y. Y. Cheng2, Tianliang Zhao1, Mei Xu2, Yuewei Liu2, Si Shen2, • Demonstrate a method to retrieve the 3 3 graupel fields of convective clouds Kristin M. Calhoun , and Alexandre O. Fierro using total lightning data 1 • The lightning data assimilation Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information method improves the lightning and Science and Technology, Nanjing, China, 2National Center for Atmospheric Research, Boulder, CO, USA, 3Cooperative convective precipitation short-term Institute for Mesoscale Meteorological Studies (CIMMS), NOAA/National Severe Storms Laboratory, University of Oklahoma forecasts (OU), Norman, OK, USA Abstract In this study, a lightning data assimilation (LDA) scheme was developed and implemented in the Correspondence to: Y. Liu, National Center for Atmospheric Research Weather Research and Forecasting-Real-Time Four-Dimensional [email protected] Data Assimilation system. In this LDA method, graupel mixing ratio (qg) is retrieved from observed total lightning. To retrieve qg on model grid boxes, column-integrated graupel mass is first calculated using an Citation: observation-based linear formula between graupel mass and total lightning rate. Then the graupel mass is Wang, H., Liu, Y., Cheng, W. Y. Y., Zhao, distributed vertically according to the empirical qg vertical profiles constructed from model simulations. … T., Xu, M., Liu, Y., Fierro, A. O. (2017). Finally, a horizontal spread method is utilized to consider the existence of graupel in the adjacent regions Improving lightning and precipitation prediction of severe convection using of the lightning initiation locations. -
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. -
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. -
Wind Energy Forecasting: a Collaboration of the National Center for Atmospheric Research (NCAR) and Xcel Energy
Wind Energy Forecasting: A Collaboration of the National Center for Atmospheric Research (NCAR) and Xcel Energy Keith Parks Xcel Energy Denver, Colorado Yih-Huei Wan National Renewable Energy Laboratory Golden, Colorado Gerry Wiener and Yubao Liu University Corporation for Atmospheric Research (UCAR) Boulder, Colorado NREL is a national laboratory of the U.S. Department of Energy, Office of Energy Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC. S ubcontract Report NREL/SR-5500-52233 October 2011 Contract No. DE-AC36-08GO28308 Wind Energy Forecasting: A Collaboration of the National Center for Atmospheric Research (NCAR) and Xcel Energy Keith Parks Xcel Energy Denver, Colorado Yih-Huei Wan National Renewable Energy Laboratory Golden, Colorado Gerry Wiener and Yubao Liu University Corporation for Atmospheric Research (UCAR) Boulder, Colorado NREL Technical Monitor: Erik Ela Prepared under Subcontract No. AFW-0-99427-01 NREL is a national laboratory of the U.S. Department of Energy, Office of Energy Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC. National Renewable Energy Laboratory Subcontract Report 1617 Cole Boulevard NREL/SR-5500-52233 Golden, Colorado 80401 October 2011 303-275-3000 • www.nrel.gov Contract No. DE-AC36-08GO28308 This publication received minimal editorial review at NREL. NOTICE This report was prepared as an account of work sponsored by an agency of the United States government. Neither the United States government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. -
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. -
3.2 the Forecast Icing Potential (Fip) Algorithm
3.2 THE FORECAST ICING POTENTIAL (FIP) ALGORITHM Frank McDonough *, Ben C. Bernstein, Marcia K. Politovich, and Cory A. Wolff National Center for Atmospheric Research, Boulder, CO 1. Introduction The 70% threshold was chosen to allow for expected model moisture errors and to allow for In-flight icing is a serious hazard to the cold clouds to be found without a model forecast aviation community. It occurs when subfreezing of water saturation. The use of a combination of liquid cloud and/or precipitation droplets freeze to relative humidity with respect to both water and ice the exposed surfaces of an aircraft in flight. Icing may allow for the use of a higher threshold in conditions can occur on large scales or in small future versions of FIP. volumes where conditions may change quickly. Cloud base is found by working upward in the Because of the nature of icing conditions, the need same column until the first level with RH≥80% is for a forecast product with high spatial and found. Since the model boundary layer often has temporal resolution is apparent. RH>80%, even in cloud-free air, FIP begins its The FIP (Forecast Icing Potential) algorithm search for a cloud base at 1000ft (305m) AGL. was developed at the National Center for The threshold of 80% for cloud base identification Atmospheric Research under the Federal Aviation is used to also allow for model moisture errors and Administration’s Aviation Weather Research the expectation that cloud base is found at warmer Program. It uses 20-km resolution Rapid Update temperatures where RH and RHi are more Cycle (RUC) model output to determine the equivalent. -
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. -
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. -
Lecture 18 Condensation And
Lecture 18 Condensation and Fog Cloud Formation by Condensation • Mixed into air are myriad submicron particles (sulfuric acid droplets, soot, dust, salt), many of which are attracted to water molecules. As RH rises above 80%, these particles bind more water and swell, producing haze. • When the air becomes supersaturated, the largest of these particles act as condensation nucleii onto which water condenses as cloud droplets. • Typical cloud droplets have diameters of 2-20 microns (diameter of a hair is about 100 microns). • There are usually 50-1000 droplets per cm3, with highest droplet concentra- tions in polluted continental regions. Why can you often see your breath? Condensation can occur when warm moist (but unsaturated air) mixes with cold dry (and unsat- urated) air (also contrails, chimney steam, steam fog). Temp. RH SVP VP cold air (A) 0 C 20% 6 mb 1 mb(clear) B breath (B) 36 C 80% 63 mb 55 mb(clear) C 50% cold (C)18 C 140% 20 mb 28 mb(fog) 90% cold (D) 4 C 90% 8 mb 6 mb(clear) D A • The 50-50 mix visibly condenses into a short- lived cloud, but evaporates as breath is EOM 4.5 diluted. Fog Fog: cloud at ground level Four main types: radiation fog, advection fog, upslope fog, steam fog. TWB p. 68 • Forms due to nighttime longwave cooling of surface air below dew point. • Promoted by clear, calm, long nights. Common in Seattle in winter. • Daytime warming of ground and air ‘burns off’ fog when temperature exceeds dew point. • Fog may lift into a low cloud layer when it thickens or dissipates. -
Cloud and Precipitation Radars
Sponsored by the U.S. Department of Energy Office of Science, the Atmospheric Radiation Measurement (ARM) Climate Research Facility maintains heavily ARM Radar Data instrumented fixed and mobile field sites that measure clouds, aerosols, Radar data is inherently complex. ARM radars are developed, operated, and overseen by engineers, scientists, radiation, and precipitation. data analysts, and technicians to ensure common goals of quality, characterization, calibration, data Data from these sites are used by availability, and utility of radars. scientists to improve the computer models that simulate Earth’s climate system. Storage Process Data Post- Data Cloud and Management processing Products Precipitation Radars Mentors Mentors Cloud systems vary with climatic regimes, and observational DQO Translators Data capabilities must account for these differences. Radars are DMF Developers archive Site scientist DMF the only means to obtain both quantitative and qualitative observations of clouds over a large area. At each ARM fixed and mobile site, millimeter and centimeter wavelength radars are used to obtain observations Calibration Configuration of the horizontal and vertical distributions of clouds, as well Scan strategy as the retrieval of geophysical variables to characterize cloud Site operations properties. This unprecedented assortment of 32 radars Radar End provides a unique capability for high-resolution delineation Mentors science users of cloud evolution, morphology, and characteristics. One-of-a-Kind Radar Network Advanced Data Products and Tools All ARM radars, with the exception of three, are equipped with dual- Reectivity (dBz) • Active Remotely Sensed Cloud Locations (ARSCL) – combines data from active remote sensors with polarization technology. Combined -60 -40 -20 0 20 40 50 60 radar observations to produce an objective determination of hydrometeor height distributions and retrieval with multiple frequencies, this 1 μm 10 μm 100 μm 1 mm 1 cm 10 cm 10-3 10-2 10-1 100 101 102 of cloud properties.