Numerical Weather Prediction and Synoptic Meteorology
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Comparing Historical and Modern Methods of Sea Surface Temperature
EGU Journal Logos (RGB) Open Access Open Access Open Access Advances in Annales Nonlinear Processes Geosciences Geophysicae in Geophysics Open Access Open Access Natural Hazards Natural Hazards and Earth System and Earth System Sciences Sciences Discussions Open Access Open Access Atmospheric Atmospheric Chemistry Chemistry and Physics and Physics Discussions Open Access Open Access Atmospheric Atmospheric Measurement Measurement Techniques Techniques Discussions Open Access Open Access Biogeosciences Biogeosciences Discussions Open Access Open Access Climate Climate of the Past of the Past Discussions Open Access Open Access Earth System Earth System Dynamics Dynamics Discussions Open Access Geoscientific Geoscientific Open Access Instrumentation Instrumentation Methods and Methods and Data Systems Data Systems Discussions Open Access Open Access Geoscientific Geoscientific Model Development Model Development Discussions Open Access Open Access Hydrology and Hydrology and Earth System Earth System Sciences Sciences Discussions Open Access Ocean Sci., 9, 683–694, 2013 Open Access www.ocean-sci.net/9/683/2013/ Ocean Science doi:10.5194/os-9-683-2013 Ocean Science Discussions © Author(s) 2013. CC Attribution 3.0 License. Open Access Open Access Solid Earth Solid Earth Discussions Comparing historical and modern methods of sea surface Open Access Open Access The Cryosphere The Cryosphere temperature measurement – Part 1: Review of methods, Discussions field comparisons and dataset adjustments J. B. R. Matthews School of Earth and Ocean Sciences, University of Victoria, Victoria, BC, Canada Correspondence to: J. B. R. Matthews ([email protected]) Received: 3 August 2012 – Published in Ocean Sci. Discuss.: 20 September 2012 Revised: 31 May 2013 – Accepted: 12 June 2013 – Published: 30 July 2013 Abstract. Sea surface temperature (SST) has been obtained 1 Introduction from a variety of different platforms, instruments and depths over the past 150 yr. -
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. -
Winter 2020/2021 Volume 18
NATIONAL WEATHER SERVICE GREEN BAY Winter 2020/2021 Volume 18 Lake Michigan water levels remain near record highs BY: mike cellitti Inside this issue: In December 2012 and January 2013, the Lake Michigan-Huron basin (Lake Michigan and Lake Huron are treated as one lake from a hydrologic perspective) East River Watershed 3 Resiliency Project observed record low water levels, making it the 14th consecutive year of below normal water levels. These record low water levels garnered national attention 2020-21 Winter Forecast 4 raising concerns for the shipping industry, climate impacts, and the long-term future Ambassador of Excellence 6 of the Great Lakes water levels. Since this minimum in water levels 6 years ago, Lake Kotenberg Joins NWS 7 Michigan-Huron has been on the rise, culminating in record high water levels for much of this year (Figure 1). In fact, Lake Michigan-Huron set monthly mean record Severe Weather Spotters 7 high water levels from January to August, peaking in July at greater than 3 inches Thank You Observers! 8 above the previous record. Word Search 9 The water level on the Great Lakes can fluctuate on a monthly, seasonal, and annual basis depending upon a variety of factors including the amount of precipitation, evaporation, and rainfall induced runoff. Precipitation and runoff typically peak in late spring and summer as a result of snowmelt and thunderstorm activity. Although it is difficult to measure, evaporation occurs the most when cold air flows over the relatively warm waters of the Great Lakes during the fall and winter months. East River The record high water levels of Lake Michigan-Huron are largely a result of well Flooding above normal precipitation across the basin over the past 5 years. -
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. -
USING a LIGHTNING SAFETY TOOLKIT for OUTDOOR VENUES Charles C
USING A LIGHTNING SAFETY TOOLKIT FOR OUTDOOR VENUES Charles C. Woodrum Meteorologist National Weather Service, NOAA Pittsburgh, Pennsylvania Donna Franklin Office of Climate, Water, and Weather Services National Weather Service, NOAA Silver Spring, Maryland _________________________ Abstract guidelines that are used as a template for creating a new plan or enhancing an existing plan. The toolkit The threat of fatal lightning strikes at outdoor was developed within the framework of NCAA venues continues to be a pressing concern for event Guideline 1d. The NWS used plans from the managers. Several delays were documented in 2010 University of Tampa, the University of Maryland, and and 2011 in which spectators did not have enough time Vanderbilt University along with guidance from to evacuate, or chose to wait out delays in unsafe emergency management at the University of locations. To address this issue, the National Weather Tennessee and Florida State University as assistance Service (NWS) developed a lightning safety toolkit and to develop the toolkit. recognition program to help meteorologists work with venue officials to encourage sound and proactive Guidelines established for venue decisions when thunderstorms threaten their venue. management include: redundant data reception sources; effective decision support standards; 1. Introduction and Background multiple effective communication methods; a public notification plan; protection program with shelters; and Every year, hundreds of outdoor venue education of staff and patrons. The toolkit template managers are challenged to determine when an event safety plan helps venues meet these guidelines by delay is necessary due to thunderstorm hazards. In providing steps to follow before, during, and after the 2000, “ninety-two percent of National Collegiate Athletic event. -
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. -
CALIFORNIA STATE UNIVERSITY, NORTHRIDGE FORECASTING CALIFORNIA THUNDERSTORMS a Thesis Submitted in Partial Fulfillment of the Re
CALIFORNIA STATE UNIVERSITY, NORTHRIDGE FORECASTING CALIFORNIA THUNDERSTORMS A thesis submitted in partial fulfillment of the requirements For the degree of Master of Arts in Geography By Ilya Neyman May 2013 The thesis of Ilya Neyman is approved: _______________________ _________________ Dr. Steve LaDochy Date _______________________ _________________ Dr. Ron Davidson Date _______________________ _________________ Dr. James Hayes, Chair Date California State University, Northridge ii TABLE OF CONTENTS SIGNATURE PAGE ii ABSTRACT iv INTRODUCTION 1 THESIS STATEMENT 12 IMPORTANT TERMS AND DEFINITIONS 13 LITERATURE REVIEW 17 APPROACH AND METHODOLOGY 24 TRADITIONALLY RECOGNIZED TORNADIC PARAMETERS 28 CASE STUDY 1: SEPTEMBER 10, 2011 33 CASE STUDY 2: JULY 29, 2003 48 CASE STUDY 3: JANUARY 19, 2010 62 CASE STUDY 4: MAY 22, 2008 91 CONCLUSIONS 111 REFERENCES 116 iii ABSTRACT FORECASTING CALIFORNIA THUNDERSTORMS By Ilya Neyman Master of Arts in Geography Thunderstorms are a significant forecasting concern for southern California. Even though convection across this region is less frequent than in many other parts of the country significant thunderstorm events and occasional severe weather does occur. It has been found that a further challenge in convective forecasting across southern California is due to the variety of sub-regions that exist including coastal plains, inland valleys, mountains and deserts, each of which is associated with different weather conditions and sometimes drastically different convective parameters. In this paper four recent thunderstorm case studies were conducted, with each one representative of a different category of seasonal and synoptic patterns that are known to affect southern California. In addition to supporting points made in prior literature there were numerous new and unique findings that were discovered during the scope of this research and these are discussed as they are investigated in their respective case study as applicable. -
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. -
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. -
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.