The Effect of Stable Thermal Stratification on Turbulent Boundary
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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. -
Cloud Characteristics, Thermodynamic Controls and Radiative Impacts During the Observations and Modeling of the Green Ocean Amazon (Goamazon2014/5) Experiment
Atmos. Chem. Phys., 17, 14519–14541, 2017 https://doi.org/10.5194/acp-17-14519-2017 © Author(s) 2017. This work is distributed under the Creative Commons Attribution 3.0 License. Cloud characteristics, thermodynamic controls and radiative impacts during the Observations and Modeling of the Green Ocean Amazon (GoAmazon2014/5) experiment Scott E. Giangrande1, Zhe Feng2, Michael P. Jensen1, Jennifer M. Comstock1, Karen L. Johnson1, Tami Toto1, Meng Wang1, Casey Burleyson2, Nitin Bharadwaj2, Fan Mei2, Luiz A. T. Machado3, Antonio O. Manzi4, Shaocheng Xie5, Shuaiqi Tang5, Maria Assuncao F. Silva Dias6, Rodrigo A. F de Souza7, Courtney Schumacher8, and Scot T. Martin9 1Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, USA 2Pacific Northwest National Laboratory, Richland, WA, USA 3National Institute for Space Research, São José dos Campos, Brazil 4National Institute of Amazonian Research, Manaus, Amazonas, Brazil 5Lawrence Livermore National Laboratory, Livermore, CA, USA 6University of São Paulo, São Paulo, Department of Atmospheric Sciences, Brazil 7State University of Amazonas (UEA), Meteorology, Manaus, Brazil 8Texas A&M University, College Station, Department of Atmospheric Sciences, TX, USA 9Harvard University, Cambridge, School of Engineering and Applied Sciences and Department of Earth and Planetary Sciences, MA, USA Correspondence to: Scott E. Giangrande ([email protected]) Received: 12 May 2017 – Discussion started: 24 May 2017 Revised: 30 August 2017 – Accepted: 11 September 2017 – Published: 6 December 2017 Abstract. Routine cloud, precipitation and thermodynamic 1 Introduction observations collected by the Atmospheric Radiation Mea- surement (ARM) Mobile Facility (AMF) and Aerial Facility The simulation of clouds and the representation of cloud (AAF) during the 2-year US Department of Energy (DOE) processes and associated feedbacks in global climate mod- ARM Observations and Modeling of the Green Ocean Ama- els (GCMs) remains the largest source of uncertainty in zon (GoAmazon2014/5) campaign are summarized. -
Laboratory Studies of Turbulent Mixing J.A
Laboratory Studies of Turbulent Mixing J.A. Whitehead Woods Hole Oceanographic Institution, Woods Hole, MA, USA Laboratory measurements are required to determine the rates of turbulent mixing and dissipation within flows of stratified fluid. Rates based on other methods (theory, numerical approaches, and direct ocean measurements) require models based on assumptions that are suggested, but not thoroughly verified in the laboratory. An exception to all this is recently provided by direct numerical simulations (DNS) mentioned near the end of this article. Experiments have been conducted with many different devices. Results span a wide range of gradient or bulk Richardson number and more limited ranges of Reynolds number and Schmidt number, which are the three most relevant dimensionless numbers. Both layered and continuously stratified flows have been investigated, and velocity profiles include smooth ones, jets, and clusters of eddies from stirrers or grids. The dependence of dissipation through almost all ranges of Richardson number is extensively documented. The buoyancy flux ratio is the turbulent kinetic energy loss from raising the potential energy of the strata to loss of kinetic energy by viscous dissipation. It rises from zero at small Richardson number to values of about 0.1 at Richardson number equal to 1, and then falls off for greater values. At Richardson number greater than 1, a wide range of power laws spanning the range from −0.5 to −1.5 are found to fit data for different experiments. Considerable layering is found at larger values, which causes flux clustering and may explain both the relatively large scatter in the data as well as the wide range of proposed power laws. -
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. -
Boundary Layer Verification
Boundary Layer Verification ECMWF training course April 2015 Maike Ahlgrimm Aim of this lecture • To give an overview over strategies for boundary layer evaluation • By the end of this session you should be able to: – Identify data sources and products suitable for BL verification – Recognize the strengths and limitations of the verification strategies discussed – Choose a suitable verification method to investigate model errors in boundary layer height, transport and cloudiness. smog over NYC Overview • General strategy for process-oriented model evaluation • What does the BL parameterization do? • Broad categories of BL parameterizations • Which aspects of the BL can we evaluate? – What does each aspect tell us about the BL? • What observations are available – What are the observations’ advantages and limitations? • Examples – Clear convective BL – Cloud topped convective BL – Stable BL Basic strategy for model evaluation and improvement: Observations Identify discrepancy Model Output Figure out source of model error Improve parameterization When and where does error occur? Which parameterization(s) is/are involved? What does the BL parameterization do? Attempts to integrate Turbulence transports effects of small scale temperature, moisture and turbulent motion on momentum (+tracers). prognostic variables at grid resolution. Stull 1988 Ultimate goal: good model forecast and realistic BL Broad categories of BL parameterizations Unified BL schemes Specialized BL scheme •Attempt to integrate BL (and •One parameterization for each shallow cloud) effects in one discrete BL type scheme to allow seamless transition •Simplifies parameterization for •Often statistical schemes (i.e. each type, parameterization for making explicit assumptions about each type ideally suited PDFs of modelled variables) using •Limitation: must identify BL type moist-conserved variables reliably, is noisy (lots of if •Limitation: May not work well for statements) mixed-phase or ice •Example: Met-Office (Lock et al. -
On the Computation of Planetary Boundary-Layer Height Using the Bulk Richardson Number Method
Geosci. Model Dev., 7, 2599–2611, 2014 www.geosci-model-dev.net/7/2599/2014/ doi:10.5194/gmd-7-2599-2014 © Author(s) 2014. CC Attribution 3.0 License. On the computation of planetary boundary-layer height using the bulk Richardson number method Y. Zhang1, Z. Gao2, D. Li3, Y. Li1, N. Zhang4, X. Zhao1, and J. Chen1,5 1International Center for Ecology, Meteorology & Environment, Jiangsu Key Laboratory of Agricultural Meteorology, College of Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044, China 2State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China 3Program of Atmospheric and Oceanic Sciences, Princeton University, Princeton, NJ 08540, USA 4School of Atmospheric Sciences, Nanjing University, Nanjing, 210093, China 5Department of Geography, Michigan State University, East Lansing, MI 48824, USA Correspondence to: Z. Gao ([email protected]) Received: 15 March 2014 – Published in Geosci. Model Dev. Discuss.: 24 June 2014 Revised: 12 September 2014 – Accepted: 22 September 2014 – Published: 10 November 2014 Abstract. Experimental data from four field campaigns are 1 Introduction used to explore the variability of the bulk Richardson num- ber of the entire planetary boundary layer (PBL), Ribc, which The planetary boundary layer (PBL), or the atmospheric is a key parameter for calculating the PBL height (PBLH) boundary layer, is the lowest part of the atmosphere that is in numerical weather and climate models with the bulk directly influenced by earth’s surface and has significant im- Richardson number method. First, the PBLHs of three differ- pacts on weather, climate, and the hydrologic cycle (Stull, ent thermally stratified boundary layers (i.e., strongly stable 1988; Garratt, 1992; Seidel et al., 2010). -
Thermo-Convective Instability in a Rotating Ferromagnetic Fluid Layer
Open Phys. 2018; 16:868–888 Research Article Precious Sibanda* and Osman Adam Ibrahim Noreldin Thermo-convective instability in a rotating ferromagnetic fluid layer with temperature modulation https://doi.org/10.1515/phys-2018-0109 Received Dec 18, 2017; accepted Sep 27, 2018 1 Introduction Abstract: We study the thermoconvective instability in a Ferromagnetic fluids are colloids consisting of nanometer- rotating ferromagnetic fluid confined between two parallel sized magnetic particles suspended in a fluid carrier. The infinite plates with temperature modulation at the bound- magnetization of a ferromagnetic fluid depends on the aries. We use weakly nonlinear stability theory to ana- temperature, the magnetic field, and the density of the lyze the stationary convection in terms of critical Rayleigh fluid. The magnetic force and the thermal state of the fluid numbers. The influence of parameters such as the Taylor may give rise to convection currents. Studies on the flow number, the ratio of the magnetic force to the buoyancy of ferromagnetic fluids include, for example, Finalyson [1] force and the magnetization on the flow behaviour and who studied instabilities in a ferromagnetic fluid using structure are investigated. The heat transfer coefficient is free-free and rigid-rigid boundaries conditions. He used analyzed for both the in-phase and the out-of-phase mod- the linear stability theory to predict the critical Rayleigh ulations. A truncated Fourier series is used to obtain a number for the onset of instability when both a magnetic set of ordinary differential equations for the time evolu- and a buoyancy force are present. The generalization of tion of the amplitude of convection for the ferromagnetic Rayleigh Benard convection under various assumption is fluid flow. -
Basic Features on a Skew-T Chart
Skew-T Analysis and Stability Indices to Diagnose Severe Thunderstorm Potential Mteor 417 – Iowa State University – Week 6 Bill Gallus Basic features on a skew-T chart Moist adiabat isotherm Mixing ratio line isobar Dry adiabat Parameters that can be determined on a skew-T chart • Mixing ratio (w)– read from dew point curve • Saturation mixing ratio (ws) – read from Temp curve • Rel. Humidity = w/ws More parameters • Vapor pressure (e) – go from dew point up an isotherm to 622mb and read off the mixing ratio (but treat it as mb instead of g/kg) • Saturation vapor pressure (es)– same as above but start at temperature instead of dew point • Wet Bulb Temperature (Tw)– lift air to saturation (take temperature up dry adiabat and dew point up mixing ratio line until they meet). Then go down a moist adiabat to the starting level • Wet Bulb Potential Temperature (θw) – same as Wet Bulb Temperature but keep descending moist adiabat to 1000 mb More parameters • Potential Temperature (θ) – go down dry adiabat from temperature to 1000 mb • Equivalent Temperature (TE) – lift air to saturation and keep lifting to upper troposphere where dry adiabats and moist adiabats become parallel. Then descend a dry adiabat to the starting level. • Equivalent Potential Temperature (θE) – same as above but descend to 1000 mb. Meaning of some parameters • Wet bulb temperature is the temperature air would be cooled to if if water was evaporated into it. Can be useful for forecasting rain/snow changeover if air is dry when precipitation starts as rain. Can also give -
A Meteorological and Blowing Snow Data Set (2000–2016) from a High-Elevation Alpine Site (Col Du Lac Blanc, France, 2720 M A.S.L.)
Earth Syst. Sci. Data, 11, 57–69, 2019 https://doi.org/10.5194/essd-11-57-2019 © Author(s) 2019. This work is distributed under the Creative Commons Attribution 4.0 License. A meteorological and blowing snow data set (2000–2016) from a high-elevation alpine site (Col du Lac Blanc, France, 2720 m a.s.l.) Gilbert Guyomarc’h1,5, Hervé Bellot2, Vincent Vionnet1,3, Florence Naaim-Bouvet2, Yannick Déliot1, Firmin Fontaine2, Philippe Puglièse1, Kouichi Nishimura4, Yves Durand1, and Mohamed Naaim2 1Univ. Grenoble Alpes, Université de Toulouse, Météo-France, CNRS, CNRM, Centre d’Etudes de la Neige, Grenoble, France 2Univ. Grenoble Alpes, IRSTEA, UR ETNA, 38042 St-Martin-d’Hères, France 3Centre for Hydrology, University of Saskatchewan, Saskatoon, SK, Canada 4Graduate School of Environmental Studies, Nagoya University, Nagoya, Japan 5Météo France, DIRAG, Point à Pitre, Guadeloupe, France Correspondence: Florence Naaim-Bouvet (fl[email protected]) Received: 12 June 2018 – Discussion started: 25 June 2018 Revised: 8 October 2018 – Accepted: 9 November 2018 – Published: 11 January 2019 Abstract. A meteorological and blowing snow data set from the high-elevation experimental site of Col du Lac Blanc (2720 m a.s.l., Grandes Rousses mountain range, French Alps) is presented and detailed in this pa- per. Emphasis is placed on data relevant to the observations and modelling of wind-induced snow transport in alpine terrain. This process strongly influences the spatial distribution of snow cover in mountainous terrain with consequences for snowpack, hydrological and avalanche hazard forecasting. In situ data consist of wind (speed and direction), snow depth and air temperature measurements (recorded at four automatic weather stations), a database of blowing snow occurrence and measurements of blowing snow fluxes obtained from a vertical pro- file of snow particle counters (2010–2016). -
NOAA Technical Memorandum NWS WR-221 UTILIZATION of the BULK RICHARDSON NUMBER, HELICITY and SOUNDING MODIFICATION in the ASSESS
NOAA Technical Memorandum NWS WR-221 UTILIZATION OF THE BULK RICHARDSON NUMBER, HELICITY AND SOUNDING MODIFICATION IN THE ASSESSMENT OF THE SEVERE CONVECTIVE STORMS OF 3 AUGUST 1992 ~ \ Eric C. Evenson Weather Service Forecast Office Great Falls, Montana November 1993 u.s. DEPARTMENT OF I National Oceanic and National Weather COMMERCE Atmospheric Administration I Service 75 A Study of the Low Level Jet Stream of the San Joaqnin Valley. Ronald A Wlllis and NOAA TECHNICAL MEMORANDA Philip Williams, Jr., May 1972. (COM 72 10707) National Weather Service, Western Region Subseries 76 Monthly Climatological Charta of the Behavior of Fog and Low Stratus at Los Angeles International Airport. Donald M. Gales, July 1972. (COM 72 11140) The National Weather Service (NWS) Western Region (WR) Subseries provi~es an informal 77 A Study of Radar Echo Distribution in Arizona During July and August. John E. Hales, Jr.. medium for the documentation and quick dissemination of results not. appropnate, or not ~et July 1972. (COM 72 11136) ready, for formal publication. The series is used to report. o'! work '-? progreas, to des~ 78 Forecasting Precipitation at Bakersfield, California, Using Preasure Gradient Vectors. Earl technical procedures and practices, or. to relate progre_as ~ a limited. audience. These Techrucal T. Riddiough, July 1972. (COM 72 11146) Memoranda will report on investigations devoted primarily to regtonal and local problems of 79 Climate of Stockton, California. Robert C. Nelson, July 1972. (COM 72 10920) interest mainly to penionnel, and hence will not be widely distributed. 60 Estimation of Number of Days Above or Below Selected Temperatures. Clarence M. -
Thunderstorm Analysis in the Northern Rocky Mountains
This file was created by scanning the printed publication. ^1 / Errors identified by the software have been corrected; ^n/' however, some errors may remain. 4* * THUNDERSTORM ANALYSTS c' Tn the NORTHERN ROCKY MOUNTATNS DeVerColson ENTERMOUNTAEN FOREST AND RANGE EXPEREMENT STATEON FOREST SERVECE UNETED STATES DEPARTMENT OF AGRECULTURE Ogden, Utah Reed W. Bailey, Director RESEARCH PAPER NO. 49 1957 Research Paper No. 49 l957 THUNDERSTORM ANALYSIS IN THE NORTHERN ROCKY MOUNTAINS By DeVer Colson Meteorologist INTERMOUNTAIN FOREST AND RANGE EXPERIMENT STATION Forest Service U.S. Department of Agriculture Ogden, Utah Reed W. Bailey, Director THUNDERSTORM ANALYSIS IN THE NORTHERN ROCKY MOUNTAINS DeVer Col son!' U.S. Weather Bureau Washington, D.C. INTRODUCTION Lightning-caused fires are a continuing serious threat to forests in the northern Rocky Mountain area. More than 70 percent of all forest fires in this area are caused by lightning. In one l0-day period in July l940 the all-time record of l,488 lightning fires started on the national forests in Region l of the U.S. Forest Service.—' Project Skyfire was planned and organized to study the causes and char acteristics of lightning storms and to see what steps could be taken to decrease the great losses caused by lightning fires. One important phase of Project Skyfire is the study and analysis of the weather phenomena associated with the formation and growth of lightning storms. A better understanding of these factors is valuable to both the forester and the meteorologist. In the Project Skyfire research program,analyses are being made of the specific characteristics of individual lightning storms and the general characteristics of storms during an entire fire season. -
Evolution of Slantwise Vertical Motions in NCEP's Mesoscale Eta Model
VOLUME 127 MONTHLY WEATHER REVIEW JANUARY 1999 Evolution of Slantwise Vertical Motions in NCEP's Mesoscale Eta Model MUKUT B. MATHUR AND KEITH F. B RILL National Centers for Environmental Prediction, Washington, D.C. CHARLES J. SEMAN Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey (Manuscript received 18 July 1997, in ®nal form 30 December 1997) ABSTRACT Numerical forecasts from the National Centers for Environmental Prediction's mesoscale version of the h coordinate±based model, hereafter referred to as MESO, have been analyzed to study the roles of conditional symmetric instability (CSI) and frontogenesis in copious precipitation events. A grid spacing of 29 km and 50 layers are used in the MESO model. Parameterized convective and resolvable-scale condensation, radiation physics, and many other physical processes are included. Results focus on a 24-h forecast from 1500 UTC 1 February 1996 in the region of a low-level front and associated deep baroclinic zone over the southeastern United States. Predicted precipitation amounts were close to the observed, and the rainfall in the model was mainly associated with the resolvable-scale condensation. During the forecast deep upward motion ampli®es in a band oriented west-southwest to east-northeast, nearly parallel to the mean tropospheric thermal wind. This band develops from a sloping updraft in the low-level nearly saturated frontal zone, which is absolutely stable to upright convection, but susceptible to CSI. The updraft is then nearly vertical in the middle troposphere where there is very weak conditional instability. We regard this occurrence as an example of model-produced deep slantwise convection (SWC).