Impact of Radar Reflectivity and Lightning Data Assimilation
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Chapter 9 : Air Mass Air Masses Source Regions
4/29/2011 Chapter 9 : Air Mass • Air masses Contain uniform temperature and humidity characteristics. • Fronts Boundaries between unlike air • Air Masses masses. • Fronts • Fronts on Weather Maps ESS124 ESS124 Prof. Jin-Jin-YiYi Yu Prof. Jin-Jin-YiYi Yu Air Masses • Air masses have fairly uniform temperature and moisture Source Regions content in horizontal direction (but not uniform in vertical). • Air masses are characterized by their temperature and humidity • The areas of the globe where air masses from are properties. called source regions. • The properties of air masses are determined by the underlying • A source region must have certain temperature surface properties where they originate. and humidity properties that can remain fixed for a • Once formed, air masses migrate within the general circulation. substantial length of time to affect air masses • UidilidliliUpon movement, air masses displace residual air over locations above it. thus changing temperature and humidity characteristics. • Air mass source regions occur only in the high or • Further, the air masses themselves moderate from surface low latitudes; middle latitudes are too variable. influences. ESS124 ESS124 Prof. Jin-Jin-YiYi Yu Prof. Jin-Jin-YiYi Yu 1 4/29/2011 Cold Air Masses Warm Air Masses January July January July • The cent ers of cold ai r masses are associ at ed with hi gh pressure on surf ace weath er • The cen ters o f very warm a ir masses appear as sem i-permanentit regions o flf low maps. pressure on surface weather maps. • In summer, when the oceans are cooler than the landmasses, large high-pressure • In summer, low-pressure areas appear over desert areas such as American centers appear over North Atlantic (Bermuda high) and Pacific (Pacific high). -
AN INTRODUCTION to DATA ASSIMILATION the Availability Of
AN INTRODUCTION TO DATA ASSIMILATION AMIT APTE Abstract. This talk will introduce the audience to the main features of the problem of data assimilation, give some of the mathematical formulations of this problem, and present a specific example of application of these ideas in the context of Burgers' equation. The availability of ever increasing amounts of observational data in most fields of sciences, in particular in earth sciences, and the exponentially increasing computing resources have together lead to completely new approaches to resolving many of the questions in these sciences, and indeed to formulation of new questions that could not be asked or answered without the use of these data or the computations. In the context of earth sciences, the temporal as well as spatial variability is an important and essential feature of data about the oceans and the atmosphere, capturing the inherent dynamical, multiscale, chaotic nature of the systems being observed. This has led to development of mathematical methods that blend such data with computational models of the atmospheric and oceanic dynamics - in a process called data assimilation - with the aim of providing accurate state estimates and uncertainties associated with these estimates. This expository talk (and this short article) aims to introduce the audience (and the reader) to the main ideas behind the problem of data assimilation, specifically in the context of earth sciences. I will begin by giving a brief, but not a complete or exhaustive, historical overview of the problem of numerical weather prediction, mainly to emphasize the necessity for data assimilation. This discussion will lead to a definition of this problem. -
Radar Data Assimilation
Radar Data Assimilation David Dowell Assimilation and Modeling Branch NOAA/ESRL/GSD, Boulder, CO Acknowledgment: Warn-on-Forecast project Radar Data Assimilation (for analysis and prediction of convective storms) David Dowell Assimilation and Modeling Branch NOAA/ESRL/GSD, Boulder, CO Acknowledgment: Warn-on-Forecast project Atmospheric Data Assimilation Definition: using all available information – observations and physical laws (numerical models) – to estimate as accurately as possible the state of the atmosphere (Talagrand 1997) Atmospheric Data Assimilation Definition: using all available information – observations and physical laws (numerical models) – to estimate as accurately as possible the state of the atmosphere (Talagrand 1997) Applications: 1. Initializing NWP models NOAA NCEP, NCAR RAL Atmospheric Data Assimilation Definition: using all available information – observations and physical laws (numerical models) – to estimate as accurately as possible the state of the atmosphere (Talagrand 1997) Applications: 1. Initializing NWP models NOAA NCEP, NCAR RAL 2. Diagnosing atmospheric processes (analysis) Schultz and Knox 2009 Assimilating a Radar Observation radar observation (Doppler velocity, reflectivity, …) gridded model fields (wind, temperature, What field(s) should the radar ob. should affect? pressure, humidity, By how much? And how far from the ob.? rain, snow, …) determined by background error covariances (b.e.c.) Various methods have been developed for estimating and using b.e.c.: 3DVar, 4DVar, EnKF, hybrid, … Most -
Weather Numbers Multiple Choices I
Weather Numbers Answer Bank A. 1 B. 2 C. 3 D. 4 E. 5 F. 25 G. 35 H. 36 I. 40 J. 46 K. 54 L. 58 M. 72 N. 74 O. 75 P. 80 Q. 100 R. 910 S. 1000 T. 1010 U. 1013 V. ½ W. ¾ 1. Minimum wind speed for a hurricane in mph N 74 mph 2. Flash-to-bang ratio. For every 10 second between lightning flash and thunder, the storm is this many miles away B 2 miles as flash to bang ratio is 5 seconds per mile 3. Minimum diameter of a hailstone in a severe storm (in inches) A 1 inch (formerly ¾ inches) 4. Standard sea level pressure in millibars U 1013.25 millibars 5. Minimum wind speed for a severe storm in mph L 58 mph 6. Minimum wind speed for a blizzard in mph G 35 mph 7. 22 degrees Celsius converted to Fahrenheit M 72 22 x 9/5 + 32 8. Increments between isobars in millibars D 4mb 9. Minimum water temperature in Fahrenheit for hurricane development P 80 F 10. Station model reports pressure as 100, what is the actual pressure in millibars T 1010 (remember to move decimal to left and then add either 10 or 9 100 become 10.0 910.0mb would be extreme low so logic would tell you it would be 1010.0mb) Multiple Choices I 1. A dry line front is also known as a: a. dew point front b. squall line front c. trough front d. Lemon front e. Kelvin front 2. -
5B.2 4-Dimensional Variational Data Assimilation for the Weather Research and Forecasting Model
5B.2 4-Dimensional Variational Data Assimilation for the Weather Research and Forecasting Model Xiang-Yu Huang*1, Qingnong Xiao1, Xin Zhang2, John Michalakes1, Wei Huang1, Dale M. Barker1, John Bray1, Zaizhong Ma1, Tom Henderson1, Jimy Dudhia1, Xiaoyan Zhang1, Duk-Jin Won3, Yongsheng Chen1, Yongrun Guo1, Hui-Chuan Lin1, Ying-Hwa Kuo1 1National Center for Atmospheric Research, Boulder, Colorado, USA 2University of Hawaii, Hawaii, USA 3Korean Meteorological Administration, Seoul, South Korea 1. Introduction The 4D-Var prototype was built in 2005 and has under continuous refinement since then. Many single observation experiments have been carried out to The 4-dimensional variational data assimilation validate the correctness of the 4D-Var formulation. A (4D-Var) (Le Dimet and Talagrand, 1986; Lewis and series of real data experiments have been conducted to Derber, 1985) has been pursued actively by research assess the performance of the 4D-Var (Huang et al. community and operational centers over the past two th 2006). Another year of fast development of 4D-Var has decades. The 5 generation Pennsylvania State led to the completion of a basic system, which will be University – National Center for Atmospheric Research described in section 3. mesoscale model (MM5) based 4D-Var (Zou et al. 1995; Ruggiero et al. 2006), for example, has been widely used for more than 10 years. There are also 2. The WRF 4D-Var Algorithm successful operational implementations of 4D-Var (e.g. Rabier et al. 2000). The WRF 4D-Var follows closely the incremental The 4D-Var technique has a number of advantages 4D-Var formulation of Courtier et al. -
Assimilation of GOES-16 Radiances and Retrievals Into the Warn-On-Forecast System
MAY 2020 J O N E S E T A L . 1829 Assimilation of GOES-16 Radiances and Retrievals into the Warn-on-Forecast System THOMAS A. JONES,PATRICK SKINNER, AND NUSRAT YUSSOUF Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, and National Severe Storms Laboratory, and University of Oklahoma, Norman, Oklahoma Downloaded from http://journals.ametsoc.org/mwr/article-pdf/148/5/1829/4928277/mwrd190379.pdf by NOAA Central Library user on 11 August 2020 KENT KNOPFMEIER AND ANTHONY REINHART Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, and National Severe Storms Laboratory, Norman, Oklahoma XUGUANG WANG University of Oklahoma, Norman, Oklahoma KRISTOPHER BEDKA AND WILLIAM SMITH JR. NASA Langley Research Center, Hampton, Virginia RABINDRA PALIKONDA Science Systems and Applications, Inc., Hampton, Virginia (Manuscript received 14 November 2019, in final form 28 January 2020) ABSTRACT The increasing maturity of the Warn-on-Forecast System (WoFS) coupled with the now operational GOES-16 satellite allows for the first time a comprehensive analysis of the relative impacts of assimilating GOES-16 all-sky 6.2-, 6.9-, and 7.3-mm channel radiances compared to other radar and satellite observations. The WoFS relies on cloud property retrievals such as cloud water path, which have been proven to increase forecast skill compared to only assimilating radar data and other conventional observations. The impacts of assimilating clear-sky radiances have also been explored and shown to provide useful information on midtropospheric moisture content in the near-storm environment. Assimilation of all-sky radiances adds a layer of complexity and is tested to determine its effectiveness across four events occurring in the spring and summer of 2019. -
Catastrophic Weather Perils in the United States Climate Drivers Catastrophic Weather Perils in the United States Climate Drivers
Catastrophic Weather Perils in the United States Climate Drivers Catastrophic Weather Perils in the United States Climate Drivers Table of Contents 2 Introduction 2 Atlantic Hurricanes –2 Formation –3 Climate Impacts •3 Atlantic Sea Surface Temperatures •4 El Niño Southern Oscillation (ENSO) •6 North Atlantic Oscillation (NAO) •7 Quasi-Biennial Oscillation (QBO) –Summary8 8 Severe Thunderstorms –8 Formation –9 Climate Impacts •9 El Niño Southern Oscillation (ENSO) 10• Pacific Decadal Oscillation (PDO) 10– Other Climate Impacts 10–Summary 11 Wild Fire 11– Formation 11– Climate Impacts 11• El Niño Southern Oscillation (ENSO) & Pacific Decadal Oscillation (PDO) 12– Other Climate / Weather Variables 12–Summary May 2012 The information contained in this document is strictly proprietary and confidential. 1 Catastrophic Weather Perils in the United States Climate Drivers INTRODUCTION The last 10 years have seen a variety of weather perils cause significant insured losses in the United States. From the wild fires of 2003, hurricanes of 2004 and 2005, to the severe thunderstorm events in 2011, extreme weather has the appearance of being the norm. The industry has experienced over $200B in combined losses from catastrophic weather events in the US since 2002. While the weather is often seen as a random, chaotic thing, there are relatively predictable patterns (so called “climate states”) in the weather which can be used to inform our expectations of extreme weather events. An oft quoted adage is that “climate is what you expect; weather is what you actually observe.” A more useful way to think about the relationship between weather and climate is that the climate is the mean state of the atmosphere (either locally or globally) which changes over time, and weather is the variation around that mean. -
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. -
The Effects of Diabatic Heating on Upper
THE EFFECTS OF DIABATIC HEATING ON UPPER- TROPOSPHERIC ANTICYCLOGENESIS by Ross A. Lazear A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science (Atmospheric and Oceanic Sciences) at the UNIVERSITY OF WISCONSIN - MADISON 2007 i Abstract The role of diabatic heating in the development and maintenance of persistent, upper- tropospheric, large-scale anticyclonic anomalies in the subtropics (subtropical gyres) and middle latitudes (blocking highs) is investigated from the perspective of potential vorticity (PV) non-conservation. The low PV within blocking anticyclones is related to condensational heating within strengthening upstream synoptic-scale systems. Additionally, the associated convective outflow from tropical cyclones (TCs) is shown to build upper- tropospheric, subtropical anticyclones. Not only do both of these large-scale flow phenomena have an impact on the structure and dynamics of neighboring weather systems, and consequently the day-to-day weather, the very persistence of these anticyclones means that they have a profound influence on the seasonal climate of the regions in which they exist. A blocking index based on the meridional reversal of potential temperature on the dynamic tropopause is used to identify cases of wintertime blocking in the North Atlantic from 2000-2007. Two specific cases of blocking are analyzed, one event from February 1983, and another identified using the index, from January 2007. Parallel numerical simulations of these blocking events, differing only in one simulation’s neglect of the effects of latent heating of condensation (a “fake dry” run), illustrate the importance of latent heating in the amplification and wave-breaking of both blocking events. -
Weather Patterns and Weather Types
26: Weather Patterns and Weather Types IAN G MCKENDRY Department of Geography, The University of British Columbia, Vancouver, BC, Canada In popular usage, the terms “weather pattern” and “weather type” are used variously and often imprecisely to describe states of the atmosphere. An understanding of weather processes and patterns has important applications in such diverse areas as air-quality management, hydrology, water management, human health, forestry, agriculture, energy demand, transportation safety, the insurance industry, economics, and tourism. These terms are formally defined and the various classification techniques that form the basis of synoptic climatology are described. In summarizing the various methods used to identify weather patterns and types, it is abundantly clear that underlying such approaches are significant assumptions about the state of the atmosphere together with varying degrees of subjectivity inherent in the techniques themselves. It is important that these constraints are adequately acknowledged in all applications of the techniques outlined. Finally, the impacts of computing advances, new techniques, and enhanced datasets on synoptic climatology are described. INTRODUCTION has been invested in identifying the linkages between par- ticular states of the atmosphere (as manifested by weather “Weather” is defined as the instantaneous state of the patterns and types) and the environment. This field of atmosphere at a particular location and is typically charac- investigation, characterized by a variety of classification terized by a range of meteorological variables (or weather techniques, forms a significant part of the subdiscipline of elements) such as pressure, temperature, humidity, wind, synoptic climatology (Yarnal, 1993) and will be the focus cloudiness, and precipitation (Barry and Chorley, 1995). -
Recent Results of Observation Data Denial Experiments
Recent results of observation data denial experiments Weather Science Technical Report 641 24th February 2021 Brett Candy, James Cotton and John Eyre www.metoffice.gov.uk © Crown Copyright 2021, Met Office Contents Contents ............................................................................................................................... 1 1 Introduction .................................................................................................................... 2 2 Operational NWP configuration ...................................................................................... 3 3 Data Denial Experiments ............................................................................................... 6 3.1 Introduction ......................................................................................................... 6 3.2 Results ................................................................................................................ 8 3.3 Continued Impact of POES............................................................................... 12 3.4 Verification of Tropical Cyclone Tracks .............................................................. 15 3.5 A Data Denial Experiment including withdrawal from the ensemble ................... 16 4 FSOI Results ............................................................................................................... 18 5 Conclusions ................................................................................................................. 21 Acknowledgements -
Convective Scale Data Assimilation and Nowcasting
Convective Scale Data Assimilation and Nowcasting Susan P Ballard1, Bruce Macpherson2, Zhihong Li1, David Simonin1, Jean-Francois Caron1, Helen Buttery1, Cristina Charlton-Perez1, Nicolas Gaussiat2, Lee Hawkness-Smith1 ,Chiara Piccolo2, Graeme Kelly1, Robert Tubbs1, Gareth Dow2 and Richard Renshaw2 1 Met Office, Dept of Meteorology, University of Reading, RG6 6BB [email protected] 2 Met Office, FitzRoy Road, Exeter, EX31 3PB Abstract Increasing availability of computer power and nonhydrostatic models has made limited area NWP at convective scales, 1-4km resolution, a reality for National Met Services in the past few years. At this time around the world nudging, variational data assimilation and ensemble Kalman filters are being used or developed for high resolution data assimilation in research centres and weather services, and are already operational in some Weather Services, for high resolution models in the range 1-10km. This paper reviews some of the issues relating to convective scale limited area data assimilation and in particular their application in NWP-based nowcasting. 1. Introduction Increasing availability of computer power and nonhydrostatic models has made limited area NWP at convective scales, 1-4km resolution, a reality for National Met Services in the past few years. Many services are already using these systems operationally for short-range forecasting up to about T+36hours every 3 or 6hours (Honda et al 2005, Saito et al 2006, Stephan et al 2008, Seity et al 2011, Brousseau et al 2011, Bauer et al 2011). These forecasts are also used for merging with traditional nowcasting techniques to extend the skilful forecast range e.g.