Summary of METROMEX, Volume 2: Causes of Precipitation Anomalies
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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. -
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. -
Potential Vorticity
POTENTIAL VORTICITY Roger K. Smith March 3, 2003 Contents 1 Potential Vorticity Thinking - How might it help the fore- caster? 2 1.1Introduction............................ 2 1.2WhatisPV-thinking?...................... 4 1.3Examplesof‘PV-thinking’.................... 7 1.3.1 A thought-experiment for understanding tropical cy- clonemotion........................ 7 1.3.2 Kelvin-Helmholtz shear instability . ......... 9 1.3.3 Rossby wave propagation in a β-planechannel..... 12 1.4ThestructureofEPVintheatmosphere............ 13 1.4.1 Isentropicpotentialvorticitymaps........... 14 1.4.2 The vertical structure of upper-air PV anomalies . 18 2 A Potential Vorticity view of cyclogenesis 21 2.1PreliminaryIdeas......................... 21 2.2SurfacelayersofPV....................... 21 2.3Potentialvorticitygradientwaves................ 23 2.4 Baroclinic Instability . .................... 28 2.5 Applications to understanding cyclogenesis . ......... 30 3 Invertibility, iso-PV charts, diabatic and frictional effects. 33 3.1 Invertibility of EPV ........................ 33 3.2Iso-PVcharts........................... 33 3.3Diabaticandfrictionaleffects.................. 34 3.4Theeffectsofdiabaticheatingoncyclogenesis......... 36 3.5Thedemiseofcutofflowsandblockinganticyclones...... 36 3.6AdvantageofPVanalysisofcutofflows............. 37 3.7ThePVstructureoftropicalcyclones.............. 37 1 Chapter 1 Potential Vorticity Thinking - How might it help the forecaster? 1.1 Introduction A review paper on the applications of Potential Vorticity (PV-) concepts by Brian -
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. -
Statistical Single-Station Short-Term Forecasting of Temperature and Probability of Precipitation: Area Interpolation and NWP Combination
APRIL 1999 RAIBLE ET AL. 203 Statistical Single-Station Short-Term Forecasting of Temperature and Probability of Precipitation: Area Interpolation and NWP Combination CHRISTOPH C. RAIBLE,GEORG BISCHOF,KLAUS FRAEDRICH, AND EDILBERT KIRK Meteorologisches Institut, UniversitaÈt Hamburg, Hamburg, Germany (Manuscript received 18 February 1998, in ®nal form 21 October 1998) ABSTRACT Two statistical single-station short-term forecast schemes are introduced and applied to real-time weather prediction. A multiple regression model (R model) predicting the temperature anomaly and a multiple regression Markov model (M model) forecasting the probability of precipitation are shown. The following forecast ex- periments conducted for central European weather stations are analyzed: (a) The single-station performance of the statistical models, (b) a linear error minimizing combination of independent forecasts of numerical weather prediction and statistical models, and (c) the forecast representation for a region deduced by applying a suitable interpolation technique. This leads to an operational weather forecasting system for the temperature anomaly and the probability of precipitation; the statistical techniques demonstrated provide a potential for future ap- plications in operational weather forecasts. 1. Introduction (Wilson 1985; Dallavalle 1996; KnuÈpffer 1996) as well Although, in general, numerical weather prediction as the long-term prediction (more than a month ahead) (NWP) models are hard to beat in very short-term fore- of monthly mean temperature (Nicholls 1980; Navato casting (up to 24 h), they do require a substantial amount 1981; Madden 1981; Norton 1985). Thus, the ®rst pur- of computation time and the model forecasts are not pose of this paper is to set up a statistical model of always stable at this timescale. -
ISSUES : DATA SET Investigating the Footprint of Climate Change On
- 1 - TIEE Teaching Issues and Experiments in Ecology - Volume 10, April 2014 ISSUES : DATA SET Investigating the footprint of climate change on phenology and ecological interactions in north-central North America Kellen M. Calinger Department of Evolution, Ecology, and Organismal Biology, The Ohio State University, Columbus, OH 43210-1293; [email protected] THE ECOLOGICAL QUESTION: Have long-term temperatures changed throughout Ohio? How will these temperature changes impact plant and animal phenology, ecological interactions, and, as a result, species diversity? ECOLOGICAL CONTENT: Climate change, phenology, pollinators, trophic mismatch, species diversity, arrival time, mutualism WHAT STUDENTS DO: o Produce and analyze graphs of temperature change using large, long-term data sets (Synthesis, Analysis) o Develop methods for calculating species-specific shifts in flowering time with temperature increase (Synthesis) o Use these methods to calculate flowering shifts in six plant Aquilegia canadensis (red columbine) species (Application) flowering with open and closed flowers o Describe the ecological consequences of shifting plant and animal phenology (Comprehension) o Understand how interactions between species as well as with their abiotic environment affect community structure and species diversity (Knowledge, Comprehension) o Evaluate data “cherry-picking” as a climate change skeptic tactic (Evaluation) STUDENT-ACTIVE APPROACHES: Open-ended inquiry, guided inquiry, cooperative learning, critical thinking SKILLS: Work with large data sets, create and analyze multiple types of graphs, connect the development of procedures and data analysis to clarifying ecological impacts of climate change ASSESSABLE OUTCOMES: Student-made graphs, calculations of temperature and phenology shifts, answers to short questions, brief paragraphs describing student-generated methods TIEE, Volume 10 © 2014 – Kellen M. -
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. -
Appendix a Gempak Parameters
GEMPAK Parameters APPENDIX A GEMPAK PARAMETERS This appendix contains a list of the GEMPAK parameters. Algorithms used in computing these parameters are also included. The following constants are used in the computations: KAPPA = Poisson's constant = 2 / 7 G = Gravitational constant = 9.80616 m/sec/sec GAMUSD = Standard atmospheric lapse rate = 6.5 K/km RDGAS = Gas constant for dry air = 287.04 J/K/kg PI = Circumference / diameter = 3.14159265 References for some of the algorithms: Bolton, D., 1980: The computation of equivalent potential temperature., Monthly Weather Review, 108, pp 1046-1053. Miller, R.C., 1972: Notes on Severe Storm Forecasting Procedures of the Air Force Global Weather Central, AWS Tech. Report 200. Wallace, J.M., P.V. Hobbs, 1977: Atmospheric Science, Academic Press, 467 pp. TEMPERATURE PARAMETERS TMPC - Temperature in Celsius TMPF - Temperature in Fahrenheit TMPK - Temperature in Kelvin STHA - Surface potential temperature in Kelvin STHK - Surface potential temperature in Kelvin N-AWIPS 5.6.L User’s Guide A-1 October 2003 GEMPAK Parameters STHC - Surface potential temperature in Celsius STHE - Surface equivalent potential temperature in Kelvin STHS - Surface saturation equivalent pot. temperature in Kelvin THTA - Potential temperature in Kelvin THTK - Potential temperature in Kelvin THTC - Potential temperature in Celsius THTE - Equivalent potential temperature in Kelvin THTS - Saturation equivalent pot. temperature in Kelvin TVRK - Virtual temperature in Kelvin TVRC - Virtual temperature in Celsius TVRF - Virtual -
Genesis of Diamond Dust and Thick Cloud Episodes Observed Above Dome C, Antarctica by Ricaud Et Al
Manuscript Title: Genesis of Diamond Dust and Thick Cloud Episodes observed above Dome C, Antarctica by Ricaud et al. RESPONSES TO THE REVIEWERS We would like to thank the reviewers for their insightful comments that were helpful in improving substantially the presentation and contents of the revised manuscript. We have addressed appropriately all issues raised by the reviewers. The reviewers' comments are repeated below in blue and our responses appear in black. The title has been modified into: Genesis of Diamond Dust, Ice Fog and Thick Cloud Episodes observed and modelled above Dome C, Antarctica We have inserted this sentence in the acknowledgments: We finally would like to thank the two anonymous reviewers to their fruitful comments. Changes have been highlighted in yellow in the revised manuscript. 1 Anonymous Referee #1 This manuscript intends to study of cold weather conditions (over Antarctica). It focuses on clouds and diamond dust, and various observational platforms and model simulations over more than 1 month of observations. There are several issues with this manuscript and need to be improved significantly before goes to publication. Because of above I see that paper needs to be improved significantly before making a decision if it is appropriate for this ACP. ® Specific changes have been made in response to the reviewers' comments and are described below. Major/minor issues: 1. Objectives are not clearly set up. Lots of information but nothing to do with objectives. ® We have clarified this crucial point. The objectives of the paper are mainly to investigate the processes that cause the presence of thick cloud and diamond dust/ice fog episodes above the Dome C station based on observations and verify whether operational models can evaluate them. -
Predicting Extremes Presenter: Gil Compo
Predicting Extremes Presenter: Gil Compo Subject Matter Experts: Tom Hamill, Marty Hoerling, Matt Newman, Judith Perlwitz NOAA Physical Sciences Laboratory Review November 16-20, 2020 Physical Science for Predicting Extremes Observe Initial focus in What extreme events have happened? How can we measure the important processes? Predicting Extremes is on Users & Stakeholders Subseasonal to Understand Seasonal (S2S) Physical Sciences LaboratoryHow predictable is an extreme event? What are the physical laws governing the processes? Predict Generate improved predictions, consistent with our understanding of the physical laws. By design, PSL’s activities are “predicting Predict the expected forecast skill in advance the nation’s path through a varying and changing climate” consistent with NOAA’s encompassing mission to “understand Communicate and predict changes in climate, weather, Convey what is known and not known about extremes in oceans, and coasts, and to share that ways that facilitate effective decision making. knowledge and information with others.” 2 Goals for Predicting Extremes • Observe Extreme Events Advanced observations Data assimilation Reanalysis • Understand conditional and unconditional (climatological) distributions and their tails What is predictable at what leads? • Improve predictions of these distributions at all leads Even the mean is hard! Only some forecasts may have useful skill – Need to identify “Forecasts of Opportunity” • Communicate new understanding and improved predictions to stakeholders and decision makers -
2017 Edition of ‘Blue Ridge Thunder’ the Biannual Newsletter of the National Weather Service (NWS) Office in Blacksburg, VA
Welcome to the spring 2017 edition of ‘Blue Ridge Thunder’ the biannual newsletter of the National Weather Service (NWS) office in Blacksburg, VA. In this issue you will find articles of interest on the weather and climate of our region and the people and technologies needed to bring accurate forecasts and warnings to the public. Weather Highlight Spring Storms produce tornadoes and flooding Peter Corrigan, Senior Service Hydrologist Inside this Issue: 1: Weather Highlight Late April and early May of 2017 saw a return to a more active Spring storms produce weather pattern across the Blacksburg County Warning Area (CWA). tornadoes and flooding Two tornadoes developed during the overnight hours of May 4-5 as an upper level low tracked across the Tennessee Valley and pushed a 3: GOES 16 Debut complex frontal system through the region overnight. Low level southeast flow led to atmospheric saturation and around 3 AM a line 4: Advances in Lightning of convective storms entered Rockingham County, NC. An embedded Detection cell within this line became briefly tornadic around 312 AM and 5: The new EHWO persisted for six minutes according to the NWS damage assessment survey conducted later that day. The survey team confirmed an EF1 6: Winter 2016-2017 tornado (on the enhanced Fujita scale) with winds as high as 110 Breaks Records mph along a 3.3 mile track through the western portions of Eden. 7: Summer 2017 Outlook 8: Tropical Forecast 9: Heat Safety and Recent WFO Staff Changes Damage from EF1 tornado in Eden, NC – May 5, 2017 Blue Ridge Thunder – Spring 2017 Page 1 While damage was significant, with 25 homes and In late April the story was flooding which occurred nine businesses sustaining damage, there were no as a result of several days of heavy rainfall from injuries or fatalities.