Guide to Understanding the Forecast
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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. -
Chapter 7.0 – Determining Wind Direction Section 7.1 Overview Of
Chapter 7.0 – Determining Wind Direction Section 7.1 Overview of Wind Direction The wind direction is a measure or indication of where the air movement originated from. The wind direction can be measured through the use of a wind sock, wind vane, or a light object attached to a pole and string (example: A ping pong ball attached to a string which is tied to a stick). Wind direction is generally reported in either Azimuth degrees or Cardinal direction. Azimuth uses a circle with the northern most position indicating 0 degrees. The Cardinal direction system gives an Azimuth degree value a name. For example, 180 degrees is South(S) and 270 degrees is West (W) (See Figure 18 - A basic compass rose). Figure 18 - A basic compass rose Section 7.2 Overview of the homemade Wind Vane The wind vane used in this design was a homemade wind vane using a miniature absolute magnetic shaft encoder. The encoder chosen for use was the MA3 produced by US Digital. The purpose for choosing this specific encoder in regards to this design was that the MA3 met four (4) critical objectives. First, the MA3 was the correct size for the application. Second, the MA3 uses an analog output of 0 volts to 5 volts with respect to the current positions (See Figure 19 – MA3 Output behaviour) Figure 19 – MA3 Output behaviour Third, the MA3 uses a 5 volt input. This was a major consideration when choosing an encoder as a 5 volt input allowed for a more simple integration. Fourth, and final, the MA3 met the requirements of being able to function in an adverse environment, having an operational temperature of -40 ºC to +125 ºC. -
NWS Unified Surface Analysis Manual
Unified Surface Analysis Manual Weather Prediction Center Ocean Prediction Center National Hurricane Center Honolulu Forecast Office November 21, 2013 Table of Contents Chapter 1: Surface Analysis – Its History at the Analysis Centers…………….3 Chapter 2: Datasets available for creation of the Unified Analysis………...…..5 Chapter 3: The Unified Surface Analysis and related features.……….……….19 Chapter 4: Creation/Merging of the Unified Surface Analysis………….……..24 Chapter 5: Bibliography………………………………………………….…….30 Appendix A: Unified Graphics Legend showing Ocean Center symbols.….…33 2 Chapter 1: Surface Analysis – Its History at the Analysis Centers 1. INTRODUCTION Since 1942, surface analyses produced by several different offices within the U.S. Weather Bureau (USWB) and the National Oceanic and Atmospheric Administration’s (NOAA’s) National Weather Service (NWS) were generally based on the Norwegian Cyclone Model (Bjerknes 1919) over land, and in recent decades, the Shapiro-Keyser Model over the mid-latitudes of the ocean. The graphic below shows a typical evolution according to both models of cyclone development. Conceptual models of cyclone evolution showing lower-tropospheric (e.g., 850-hPa) geopotential height and fronts (top), and lower-tropospheric potential temperature (bottom). (a) Norwegian cyclone model: (I) incipient frontal cyclone, (II) and (III) narrowing warm sector, (IV) occlusion; (b) Shapiro–Keyser cyclone model: (I) incipient frontal cyclone, (II) frontal fracture, (III) frontal T-bone and bent-back front, (IV) frontal T-bone and warm seclusion. Panel (b) is adapted from Shapiro and Keyser (1990) , their FIG. 10.27 ) to enhance the zonal elongation of the cyclone and fronts and to reflect the continued existence of the frontal T-bone in stage IV. -
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. -
Impact of Cloud Analysis on Numerical Weather Prediction in the Galician Region of Spain
Impact of Cloud Analysis on Numerical Weather Prediction in the Galician Region of Spain M. J. SOUTO, C. F. BALSEIRO AND V. P…REZ-MU—UZURI Group of Nonlinear Physics, Faculty of Physics, University of Santiago de Compostela, Santiago de Compostela, Spain Ming Xue University of Oklahoma, School of Meteorology, and CAPS Oklahoma, USA Keith BREWSTER Center for Analysis and Prediction of Storms, Oklahoma, USA April, 2001 Revised December, 2001 Corresponding author address: Dra. M. J. Souto, Group of Nonlinear Physics Faculty of Physics, University of Santiago de Compostela E-15706, Santiago de Compostela, Spain e-mail: [email protected] ABSTRACT The Advanced Regional Prediction System (ARPS) is applied to operational numerical weather forecast in Galicia, northwest Spain. A 72-hour forecast at a 10-km horizontal resolution is produced dta for the region. Located on the northwest coast of Spain and influenced by the Atlantic weather systems, Galicia has a high percentage (almost 50%) of rainy days per year. For these reasons, the precipitation processes and the initialization of moisture and cloud fields are very important. Even though the ARPS model has a sophisticated data analysis system (ADAS) that includes a 3D cloud analysis package, due to operational constraint, our current forecast starts from 12-hour forecast of the NCEP AVN model. Still, procedures from the ADAS cloud analysis are being used to construct the cloud fields based on AVN data, and then applied to initialize the microphysical variables in ARPS. Comparisons of the ARPS predictions with local observations show that ARPS can predict quite well both the daily total precipitation and its spatial distribution. -
Analysis of Lightning and Precipitation Activities in Three Severe Convective Events Based on Doppler Radar and Microwave Radiometer Over the Central China Region
atmosphere Article Analysis of Lightning and Precipitation Activities in Three Severe Convective Events Based on Doppler Radar and Microwave Radiometer over the Central China Region Jing Sun 1, Jian Chai 2, Liang Leng 1,* and Guirong Xu 1 1 Hubei Key Laboratory for Heavy Rain Monitoring and Warning Research, Institute of Heavy Rain, China Meteorological Administration, Wuhan 430205, China; [email protected] (J.S.); [email protected] (G.X.) 2 Hubei Lightning Protecting Center, Wuhan 430074, China; [email protected] * Correspondence: [email protected]; Tel.: +86-27-8180-4905 Received: 27 March 2019; Accepted: 23 May 2019; Published: 1 June 2019 Abstract: Hubei Province Region (HPR), located in Central China, is a concentrated area of severe convective weather. Three severe convective processes occurred in HPR were selected, namely 14–15 May 2015 (Case 1), 6–7 July 2013 (Case 2), and 11–12 September 2014 (Case 3). In order to investigate the differences between the three cases, the temporal and spatial distribution characteristics of cloud–ground lightning (CG) flashes and precipitation, the distribution of radar parameters, and the evolution of cloud environment characteristics (including water vapor (VD), liquid water content (LWC), relative humidity (RH), and temperature) were compared and analyzed by using the data of lightning locator, S-band Doppler radar, ground-based microwave radiometer (MWR), and automatic weather stations (AWS) in this study. The results showed that 80% of the CG flashes had an inverse correlation with the spatial distribution of heavy rainfall, 28.6% of positive CG (+CG) flashes occurred at the center of precipitation (>30 mm), and the percentage was higher than that of negative CG ( CG) − flashes (13%). -
Weather & Climate
Weather & Climate July 2018 “Weather is what you get; Climate is what you expect.” Weather consists of the short-term (minutes to days) variations in the atmosphere. Weather is expressed in terms of temperature, humidity, precipitation, cloudiness, visibility and wind. Climate is the slowly varying aspect of the atmosphere-hydrosphere-land surface system. It is typically characterized in terms of averages of specific states of the atmosphere, ocean, and land, including variables such as temperature (land, ocean, and atmosphere), salinity (oceans), soil moisture (land), wind speed and direction (atmosphere), and current strength and direction (oceans). Example of Weather vs. Climate The actual observed temperatures on any given day are considered weather, whereas long-term averages based on observed temperatures are considered climate. For example, climate averages provide estimates of the maximum and minimum temperatures typical of a given location primarily based on analysis of historical data. Consider the evolution of daily average temperature near Washington DC (40N, 77.5W). The black line is the climatological average for the period 1979-1995. The actual daily temperatures (weather) for 1 January to 31 December 2009 are superposed, with red indicating warmer-than-average and blue indicating cooler-than-average conditions. Departures from the average are generally largest during winter and smallest during summer at this location. Weather Forecasts and Climate Predictions / Projections Weather forecasts are assessments of the future state of the atmosphere with respect to conditions such as precipitation, clouds, temperature, humidity and winds. Climate predictions are usually expressed in probabilistic terms (e.g. probability of warmer or wetter than average conditions) for periods such as weeks, months or seasons. -
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. -
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. -
Chapter 4: Fog
CHAPTER 4: FOG Fog is a double threat to boaters. It not only reduces visibility but also distorts sound, making collisions with obstacles – including other boats – a serious hazard. 1. Introduction Fog is a low-lying cloud that forms at or near the surface of the Earth. It is made up of tiny water droplets or ice crystals suspended in the air and usually gets its moisture from a nearby body of water or the wet ground. Fog is distinguished from mist or haze only by its density. In marine forecasts, the term “fog” is used when visibility is less than one nautical mile – or approximately two kilometres. If visibility is greater than that, but is still reduced, it is considered mist or haze. It is important to note that foggy conditions are reported on land only if visibility is less than half a nautical mile (about one kilometre). So boaters may encounter fog near coastal areas even if it is not mentioned in land-based forecasts – or particularly heavy fog, if it is. Fog Caused Worst Maritime Disaster in Canadian History The worst maritime accident in Canadian history took place in dense fog in the early hours of the morning on May 29, 1914, when the Norwegian coal ship Storstadt collided with the Canadian Pacific ocean liner Empress of Ireland. More than 1,000 people died after the Liverpool-bound liner was struck in the side and sank less than 15 minutes later in the frigid waters of the St. Lawrence River near Rimouski, Quebec. The Captain of the Empress told an inquest that he had brought his ship to a halt and was waiting for the weather to clear when, to his horror, a ship emerged from the fog, bearing directly upon him from less than a ship’s length away. -
Investigating the Climate System Precipitationprecipitation “The Irrational Inquirer”
Educational Product Educators Grades 5–8 Investigating the Climate System PrecipitationPrecipitation “The Irrational Inquirer” PROBLEM-BASED CLASSROOM MODULES Responding to National Education Standards in: English Language Arts ◆ Geography ◆ Mathematics Science ◆ Social Studies Investigating the Climate System PrecipitationPrecipitation “The Irrational Inquirer” Authored by: CONTENTS Mary Cerullo, Resources in Science Education, South Portland, Maine Grade Levels; Time Required; Objectives; Disciplines Encompassed; Key Terms; Key Concepts . 2 Prepared by: Stacey Rudolph, Senior Science Prerequisite Knowledge . 3 Education Specialist, Institute for Global Environmental Strategies Additional Prerequisite Knowledge and Facts . 5 (IGES), Arlington, Virginia Suggested Reading/Resources . 5 John Theon, Former Program Scientist for NASA TRMM Part 1: How are rainfall rates measured? . 6 Editorial Assistance, Dan Stillman, Truth Revealed after 200 Years of Secrecy! Science Communications Specialist, Pre-Activity; Activity One; Activity Two; Institute for Global Environmental Activity Three; Extensions. 8 Strategies (IGES), Arlington, Virginia Graphic Design by: Part 2: How is the intensity and distribution Susie Duckworth Graphic Design & of rainfall determined? . 9 Illustration, Falls Church, Virginia Airplane Pilot or Movie Critic? Funded by: Activity One; Activity Two. 9 NASA TRMM Grant #NAG5-9641 Part 3: How can you study rain? . 10 Give us your feedback: Foreseeing the Future of Satellites! To provide feedback on the modules Activity One; Activity Two . 10 online, go to: Activity Three; Extensions . 11 https://ehb2.gsfc.nasa.gov/edcats/ educational_product Unit Extensions . 11 and click on “Investigating the Climate System.” Appendix A: Bibliography/Resources . 12 Appendix B: Assessment Rubrics & Answer Keys. 13 NOTE: This module was developed as part of the series “Investigating the Climate Appendix C: National Education Standards.