Weather Forecasting and Terminology
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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. -
Moons Phases and Tides
Moon’s Phases and Tides Moon Phases Half of the Moon is always lit up by the sun. As the Moon orbits the Earth, we see different parts of the lighted area. From Earth, the lit portion we see of the moon waxes (grows) and wanes (shrinks). The revolution of the Moon around the Earth makes the Moon look as if it is changing shape in the sky The Moon passes through four major shapes during a cycle that repeats itself every 29.5 days. The phases always follow one another in the same order: New moon Waxing Crescent First quarter Waxing Gibbous Full moon Waning Gibbous Third (last) Quarter Waning Crescent • IF LIT FROM THE RIGHT, IT IS WAXING OR GROWING • IF DARKENING FROM THE RIGHT, IT IS WANING (SHRINKING) Tides • The Moon's gravitational pull on the Earth cause the seas and oceans to rise and fall in an endless cycle of low and high tides. • Much of the Earth's shoreline life depends on the tides. – Crabs, starfish, mussels, barnacles, etc. – Tides caused by the Moon • The Earth's tides are caused by the gravitational pull of the Moon. • The Earth bulges slightly both toward and away from the Moon. -As the Earth rotates daily, the bulges move across the Earth. • The moon pulls strongly on the water on the side of Earth closest to the moon, causing the water to bulge. • It also pulls less strongly on Earth and on the water on the far side of Earth, which results in tides. What causes tides? • Tides are the rise and fall of ocean water. -
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. -
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. -
The Error Is the Feature: How to Forecast Lightning Using a Model Prediction Error [Applied Data Science Track, Category Evidential]
The Error is the Feature: How to Forecast Lightning using a Model Prediction Error [Applied Data Science Track, Category Evidential] Christian Schön Jens Dittrich Richard Müller Saarland Informatics Campus Saarland Informatics Campus German Meteorological Service Big Data Analytics Group Big Data Analytics Group Offenbach, Germany ABSTRACT ACM Reference Format: Despite the progress within the last decades, weather forecasting Christian Schön, Jens Dittrich, and Richard Müller. 2019. The Error is the is still a challenging and computationally expensive task. Current Feature: How to Forecast Lightning using a Model Prediction Error: [Ap- plied Data Science Track, Category Evidential]. In Proceedings of 25th ACM satellite-based approaches to predict thunderstorms are usually SIGKDD Conference on Knowledge Discovery and Data Mining (KDD ’19). based on the analysis of the observed brightness temperatures in ACM, New York, NY, USA, 10 pages. different spectral channels and emit a warning if a critical threshold is reached. Recent progress in data science however demonstrates 1 INTRODUCTION that machine learning can be successfully applied to many research fields in science, especially in areas dealing with large datasets. Weather forecasting is a very complex and challenging task requir- We therefore present a new approach to the problem of predicting ing extremely complex models running on large supercomputers. thunderstorms based on machine learning. The core idea of our Besides delivering forecasts for variables such as the temperature, work is to use the error of two-dimensional optical flow algorithms one key task for meteorological services is the detection and pre- applied to images of meteorological satellites as a feature for ma- diction of severe weather conditions. -
Multi-Sensor Improved Sea Surface Temperature (MISST) for GODAE
Multi-sensor Improved Sea Surface Temperature (MISST) for GODAE Lead PI : Chelle L. Gentemann 438 First St, Suite 200; Santa Rosa, CA 95401-5288 Phone: (707) 545-2904x14 FAX: (707) 545-2906 E-mail: [email protected] CO-PI: Gary A. Wick NOAA/ETL R/ET6, 325 Broadway, Boulder, CO 80305 Phone: (303) 497-6322 FAX: (303) 497-6181 E-mail: [email protected] CO-PI: James Cummings Oceanography Division, Code 7320, Naval Research Laboratory, Monterey, CA 93943 Phone: (831) 656-5021 FAX: (831) 656-4769 E-mail: [email protected] CO-PI: Eric Bayler NOAA/NESDIS/ORA/ORAD, Room 601, 5200 Auth Road, Camp Springs, MD 20746 Phone: (301) 763-8102x102 FAX: ( 301) 763-8572 E-mail: [email protected] Award Number: NNG04GM56G http://www.ghrsst-pp.org http://www.usgodae.org LONG-TERM GOALS The Multi-sensor Improved Sea Surface Temperatures (MISST) for the Global Ocean Data Assimilation Experiment (GODAE) project intends to produce an improved, high-resolution, global, near-real-time (NRT), sea surface temperature analysis through the combination of satellite observations from complementary infrared (IR) and microwave (MW) sensors and to then demonstrate the impact of these improved sea surface temperatures (SSTs) on operational ocean models, numerical weather prediction, and tropical cyclone intensity forecasting. SST is one of the most important variables related to the global ocean-atmosphere system. It is a key indicator for climate change and is widely applied to studies of upper ocean processes, to air-sea heat exchange, and as a boundary condition for numerical weather prediction. The importance of SST to accurate weather forecasting of both severe events and daily weather has been increasingly recognized over the past several years. -
Light, Color, and Atmospheric Optics
Light, Color, and Atmospheric Optics GEOL 1350: Introduction To Meteorology 1 2 • During the scattering process, no energy is gained or lost, and therefore, no temperature changes occur. • Scattering depends on the size of objects, in particular on the ratio of object’s diameter vs wavelength: 1. Rayleigh scattering (D/ < 0.03) 2. Mie scattering (0.03 ≤ D/ < 32) 3. Geometric scattering (D/ ≥ 32) 3 4 • Gas scattering: redirection of radiation by a gas molecule without a net transfer of energy of the molecules • Rayleigh scattering: absorption extinction 4 coefficient s depends on 1/ . • Molecules scatter short (blue) wavelengths preferentially over long (red) wavelengths. • The longer pathway of light through the atmosphere the more shorter wavelengths are scattered. 5 • As sunlight enters the atmosphere, the shorter visible wavelengths of violet, blue and green are scattered more by atmospheric gases than are the longer wavelengths of yellow, orange, and especially red. • The scattered waves of violet, blue, and green strike the eye from all directions. • Because our eyes are more sensitive to blue light, these waves, viewed together, produce the sensation of blue coming from all around us. 6 • Rayleigh Scattering • The selective scattering of blue light by air molecules and very small particles can make distant mountains appear blue. The blue ridge mountains in Virginia. 7 • When small particles, such as fine dust and salt, become suspended in the atmosphere, the color of the sky begins to change from blue to milky white. • These particles are large enough to scatter all wavelengths of visible light fairly evenly in all directions. -
An Operational Marine Fog Prediction Model
U. S. DEPARTMENT OF COMMERCE NATIONAL OCEANIC AND ATMOSPHERIC ADMINISTRATION NATIONAL WEATHER SERVICE NATIONAL METEOROLOGICAL CENTER OFFICE NOTE 371 An Operational Marine Fog Prediction Model JORDAN C. ALPERTt DAVID M. FEIT* JUNE 1990 THIS IS AN UNREVIEWED MANUSCRIPT, PRIMARILY INTENDED FOR INFORMAL EXCHANGE OF INFORMATION AMONG NWS STAFF MEMBERS t Global Weather and Climate Modeling Branch * Ocean Products Center OPC contribution No. 45 An Operational Marine Fog Prediction Model Jordan C. Alpert and David M. Feit NOAA/NMC, Development Division Washington D.C. 20233 Abstract A major concern to the National Weather Service marine operations is the problem of forecasting advection fogs at sea. Currently fog forecasts are issued using statistical methods only over the open ocean domain but no such system is available for coastal and offshore areas. We propose to use a partially diagnostic model, designed specifically for this problem, which relies on output fields from the global operational Medium Range Forecast (MRF) model. The boundary and initial conditions of moisture and temperature, as well as the MRF's horizontal wind predictions are interpolated to the fog model grid over an arbitrarily selected coastal and offshore ocean region. The moisture fields are used to prescribe a droplet size distribution and compute liquid water content, neither of which is accounted for in the global model. Fog development is governed by the droplet size distribution and advection and exchange of heat and moisture. A simple parameterization is used to describe the coefficients of evaporation and sensible heat exchange at the surface. Depletion of the fog is based on droplet fallout of the three categories of assumed droplet size. -
Tropical-Cyclone Forecasting: a Worldwide Summary of Techniques
John L. McBride and Tropical-Cyclone Forecasting: Greg J. Holland Bureau of Meteorology Research Centre, A Worldwide Summary Melbourne 3001, of Techniques and Australia Verification Statistics Abstract basis for discussion at particular sessions planned for the work- shop. Replies to this questionnaire were received from 16 of- Questionnaire replies from forecasters in 16 tropical-cyclone warning fices. These are listed in Table 1, grouped according to their centers are summarized to provide an overview of the current state of ocean basins. Encouraged by the high information content of the science in tropical-cyclone analysis and forecasting. Information is tabulated on the data sources and techniques used, on their role and these responses, the authors sent a second questionnaire on the perceived usefulness, and on the levels of verification and accuracy of analysis and forecasting of cyclone position and motion. Re- cyclone forecasting. plies to this were received from 13 of the listed offices. This paper tabulates and syntheses information provided on the following aspects of tropical-cyclone forecasting: 1) the techniques used; 2) the level of verification; and 3) the level 1. Introduction of accuracy of analyses and forecasts. Separate sections cover forecasting of cyclone formation; analyzing cyclone structure Tropical cyclones are the major severe weather hazard for a and intensity; forecasting structure and intensity; analyzing cy- large "slice" of mankind. The Bangladesh cyclones of 1970 and 1985, Hurricane Camille (USA, 1969) and Cyclone Tracy (Australia, 1974) to name just four, would figure prominently TABLE 1. Forecast offices from which unofficial replies were re- in any list of major natural disasters of this century. -
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.