5.11 Radiation Fog: Ups Airlines Conceptual Models and Forecast Methods
Total Page:16
File Type:pdf, Size:1020Kb

Load more
Recommended publications
-
Water Vapour Transport to the High Latitudes: Mechanisms And
Water vapour transport to the high latitudes : mechanisms and variability from reanalyses and radiosoundings Ambroise Dufour To cite this version: Ambroise Dufour. Water vapour transport to the high latitudes : mechanisms and variability from reanalyses and radiosoundings. Earth Sciences. Université Grenoble Alpes, 2016. English. NNT : 2016GREAU042. tel-01562031 HAL Id: tel-01562031 https://tel.archives-ouvertes.fr/tel-01562031 Submitted on 13 Jul 2017 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. THÈSE Pour obtenir le grade de DOCTEUR DE L’UNIVERSITÉ DE GRENOBLE Spécialité : Sciences de la Terre et de l’Environnement Arrêté ministériel : 7 août 2006 Présentée par Ambroise DUFOUR Thèse dirigée par Olga ZOLINA préparée au sein du Laboratoire de Glaciologie et Géophysique de l’Environnement et de l’École Doctorale Terre, Univers, Environnement Transport de vapeur d’eau vers les hautes latitudes Mécanismes et variabilité d’après réanalyses et radiosondages Soutenue le 24 mars 2016, devant le jury composé de : M. Christophe GENTHON Directeur de recherche, LGGE, Président M. Stefan BRÖNNIMANN Professeur, Université de Berne, Rapporteur M. Richard P. ALLAN Professeur, Université de Reading, Rapporteur Mme Valérie MASSON-DELMOTTE Directrice de recherche, LSCE, Examinatrice M. -
Wildland Fire Incident Management Field Guide
A publication of the National Wildfire Coordinating Group Wildland Fire Incident Management Field Guide PMS 210 April 2013 Wildland Fire Incident Management Field Guide April 2013 PMS 210 Sponsored for NWCG publication by the NWCG Operations and Workforce Development Committee. Comments regarding the content of this product should be directed to the Operations and Workforce Development Committee, contact and other information about this committee is located on the NWCG Web site at http://www.nwcg.gov. Questions and comments may also be emailed to [email protected]. This product is available electronically from the NWCG Web site at http://www.nwcg.gov. Previous editions: this product replaces PMS 410-1, Fireline Handbook, NWCG Handbook 3, March 2004. The National Wildfire Coordinating Group (NWCG) has approved the contents of this product for the guidance of its member agencies and is not responsible for the interpretation or use of this information by anyone else. NWCG’s intent is to specifically identify all copyrighted content used in NWCG products. All other NWCG information is in the public domain. Use of public domain information, including copying, is permitted. Use of NWCG information within another document is permitted, if NWCG information is accurately credited to the NWCG. The NWCG logo may not be used except on NWCG-authorized information. “National Wildfire Coordinating Group,” “NWCG,” and the NWCG logo are trademarks of the National Wildfire Coordinating Group. The use of trade, firm, or corporation names or trademarks in this product is for the information and convenience of the reader and does not constitute an endorsement by the National Wildfire Coordinating Group or its member agencies of any product or service to the exclusion of others that may be suitable. -
Moisture Transport in Observations and Reanalyses As a Proxy for Snow
Moisture transport in observations and reanalyses as a proxy for snow accumulation in East Antarctica Ambroise DufourIGE, Claudine CharrondièreIGE, and Olga ZolinaIGE, IORAS IGEInstitut des Géosciences de l’Environnement, CNRS/UGA, Grenoble, France IORASP. P. Shirshov Institute of Oceanology, Russian Academy of Science, Moscow, Russia Correspondence to: Ambroise DUFOUR ([email protected]) Abstract. Atmospheric moisture convergence on ice-sheets provides an estimate of snow accumulation which is critical to quantify sea level changes. In the case of East Antarctica, we computed moisture transport from 1980 to 2016 in five reanalyses and in radiosonde observations. Moisture convergence in reanalyses is more consistent than net preciptation but still ranges from 72 5 to 96 mm per year in the four most recent reanalyses, ERA Interim, NCEP CFSR, JRA 55 and MERRA 2. The representation of long term variability in reanalyses is also inconsistent which justified the resort to observations. Moisture fluxes are measured on a daily basis via radiosondes launched from a network of stations surrounding East Antarc- tica. Observations agree with reanalyses on the major role of extreme advection events and transient eddy fluxes. Although assimilated, the observations reveal processes reanalyses cannot model, some due to a lack of horizontal and vertical resolu- 10 tion especially the oldest, NCEP DOE R2. Additionally, the observational time series are not affected by new satellite data unlike reanalyses. We formed pan-continental estimates of convergence by aggregating anomalies from all available stations. We found statistically significant trends neither in moisture convergence nor in precipitable water. 1 Introduction East Antarctica stores the equivalent of 53.3 m of sea level rise out of the 58.3 for the whole continent (Fretwell et al., 2013). -
Seasonal Cycle of Idealized Polar Clouds: Large Eddy
ESSOAr | https://doi.org/10.1002/essoar.10503204.1 | CC_BY_NC_ND_4.0 | First posted online: Tue, 9 Jun 2020 15:35:29 | This content has not been peer reviewed. manuscript submitted to Journal of Advances in Modeling Earth Systems 1 Seasonal cycle of idealized polar clouds: large eddy 2 simulations driven by a GCM 1 2;3 2 4 3 Xiyue Zhang , Tapio Schneider , Zhaoyi Shen , Kyle G. Pressel , and Ian 5 4 Eisenman 1 5 National Center for Atmospheric Research, Boulder, Colorado, USA 2 6 California Institute of Technology, Pasadena, California, USA 3 7 Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA 4 8 Pacific Northwest National Labratory, Richland, Washington, USA 5 9 Scripps Institution of Oceanography, University of California, San Diego, California, USA 10 Key Points: 11 • LES driven by time-varying large-scale forcing from an idealized GCM is used to 12 simulate the seasonal cycle of Arctic clouds 13 • Simulated low-level cloud liquid is maximal in late summer to early autumn, and 14 minimal in winter, consistent with observations 15 • Large-scale advection provides the main moisture source for cloud liquid and shapes 16 its seasonal cycle Corresponding author: X. Zhang, [email protected] {1{ ESSOAr | https://doi.org/10.1002/essoar.10503204.1 | CC_BY_NC_ND_4.0 | First posted online: Tue, 9 Jun 2020 15:35:29 | This content has not been peer reviewed. manuscript submitted to Journal of Advances in Modeling Earth Systems 17 Abstract 18 The uncertainty in polar cloud feedbacks calls for process understanding of the cloud re- 19 sponse to climate warming. -
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. -
Lecture 18 Condensation And
Lecture 18 Condensation and Fog Cloud Formation by Condensation • Mixed into air are myriad submicron particles (sulfuric acid droplets, soot, dust, salt), many of which are attracted to water molecules. As RH rises above 80%, these particles bind more water and swell, producing haze. • When the air becomes supersaturated, the largest of these particles act as condensation nucleii onto which water condenses as cloud droplets. • Typical cloud droplets have diameters of 2-20 microns (diameter of a hair is about 100 microns). • There are usually 50-1000 droplets per cm3, with highest droplet concentra- tions in polluted continental regions. Why can you often see your breath? Condensation can occur when warm moist (but unsaturated air) mixes with cold dry (and unsat- urated) air (also contrails, chimney steam, steam fog). Temp. RH SVP VP cold air (A) 0 C 20% 6 mb 1 mb(clear) B breath (B) 36 C 80% 63 mb 55 mb(clear) C 50% cold (C)18 C 140% 20 mb 28 mb(fog) 90% cold (D) 4 C 90% 8 mb 6 mb(clear) D A • The 50-50 mix visibly condenses into a short- lived cloud, but evaporates as breath is EOM 4.5 diluted. Fog Fog: cloud at ground level Four main types: radiation fog, advection fog, upslope fog, steam fog. TWB p. 68 • Forms due to nighttime longwave cooling of surface air below dew point. • Promoted by clear, calm, long nights. Common in Seattle in winter. • Daytime warming of ground and air ‘burns off’ fog when temperature exceeds dew point. • Fog may lift into a low cloud layer when it thickens or dissipates. -
Chapter 4: Fog
CHAPTER 4: FOG Fog is a double threat to boaters. It not only reduces visibility but also distorts sound, making collisions with obstacles – including other boats – a serious hazard. 1. Introduction Fog is a low-lying cloud that forms at or near the surface of the Earth. It is made up of tiny water droplets or ice crystals suspended in the air and usually gets its moisture from a nearby body of water or the wet ground. Fog is distinguished from mist or haze only by its density. In marine forecasts, the term “fog” is used when visibility is less than one nautical mile – or approximately two kilometres. If visibility is greater than that, but is still reduced, it is considered mist or haze. It is important to note that foggy conditions are reported on land only if visibility is less than half a nautical mile (about one kilometre). So boaters may encounter fog near coastal areas even if it is not mentioned in land-based forecasts – or particularly heavy fog, if it is. Fog Caused Worst Maritime Disaster in Canadian History The worst maritime accident in Canadian history took place in dense fog in the early hours of the morning on May 29, 1914, when the Norwegian coal ship Storstadt collided with the Canadian Pacific ocean liner Empress of Ireland. More than 1,000 people died after the Liverpool-bound liner was struck in the side and sank less than 15 minutes later in the frigid waters of the St. Lawrence River near Rimouski, Quebec. The Captain of the Empress told an inquest that he had brought his ship to a halt and was waiting for the weather to clear when, to his horror, a ship emerged from the fog, bearing directly upon him from less than a ship’s length away. -
Observational Study of a Diurnal Mesoscale Convective Complex
CRARP 25-02 OBSERVATIONAL STUDY OF A DIURNAL MESOSCALE CONVECTIVE COMPLEX David Novak1 and Edward Shimon National Weather Service Forecasting Office Duluth, Minnesota 1. Introduction The Mesoscale Convective Complex (MCC) has been described by Maddox (1980), Bosart and Sanders (1981), and Maddox (1983) as a unique class of intense convection organized on the meso-alpha scale, which occurs primarily in the central United States during the convective season. Fritsch et al. (1981) and Wetzel (1983) showed that MCCs produce locally intense rainfall, large hail, and damaging winds, requiring coincident operational warnings of both severe weather and flash flooding. In contrast to most convection MCCs are distinctly nocturnal (Maddox 1983). Augustine and Caracena (1994) as well as McAnelly and Cotton (1986) have shown that MCCs commence from afternoon thunderstorms in the western Great Plains that organize and continue to develop at night into MCCs. Physically, this nocturnal preference has been linked to the Low Level Jet (LLJ) (Maddox 1980; Wetzel 1983; Maddox 1983), which enhances warm air advection and the flux of moist, unstable air into the system. Composites from Maddox (1983) confirm that MCCs reach maximum size around local midnight, corresponding with the strongest LLJ, and weaken during the morning hours when the LLJ typically weakens (Fig. 1). In contrast to this widely confirmed characteristic of MCCs, convection on 14 August 2000 organized during the early morning in eastern North Dakota, reached maximum intensity and organization during the day across northern Minnesota and northern Wisconsin, and then slowly weakened during the early morning hours of 15 August in central Wisconsin. -
Oak Lawn Tornado 1967 an Analysis of the Northern Illinois Tornado Outbreak of April 21St, 1967 Oak Lawn History
Oak Lawn Tornado 1967 An analysis of the Northern Illinois Tornado Outbreak of April 21st, 1967 Oak Lawn History • First Established in the 1840s and 1850s • School house first built in 1860 • Immigrants came after the civil war • Post office first established in 1882 • Innovative development plan began in 1927 • Population exponentially grew up to the year of 1967 Path of the Storm • The damage path spanned from Palos, Illinois (about 2-3 miles west/southwest of Oak Lawn) to Lake Michigan. With the worst damage occurring in the Oak Lawn area. Specifically, the corner of 95th street and Southwest Highway • Other places that received Significant damage includes: 89th and Kedzie Avenue 88th and California Avenue Near the Dan Ryan at 83rd Street Timing (All times below are in CST on April 21st, 1967) • 8 AM – Thunderstorms develop around the borders of Iowa, Nebraska, Kansas, and Missouri • 4:45 PM – Cell is first indicated on radar (18 miles west/northwest of Joliet) • 5:15 PM – rotating clouds reported 10 miles north of Joliet • 5:18 PM – Restaurant at the corner of McCarthy Road and 127th Street had their windows blown out • 5:24 PM – Funnel Cloud reported (Touchdown confirmed) • 5:26 PM – Storm continues Northeast and crosses Roberts Road between 101st and 102nd streets • 5:32 PM - Tornado cause worst damage at 95th street and Southwest highway • 5:33 PM – Tornado causes damage to Hometown City Hall • 5:34 PM – Tornado passes grounds of Beverly Hills Country Club • 5:35 PM – Tornado crosses Dan Ryan Expressway and flips over a semi • 5:39 PM – Tornado passes Filtration plant at 78th and Lake Michigan and Dissipates Notable Areas Destroyed •Fairway Supermarket •Sherwood Restaurant •Shoot’s Lynwood Tavern •Suburban Bus Terminal •Airway Trailer Park •Oak Lawn Roller Rink •Plenty More! Pictures to the right were taken BEFORE the twister 52 Total Tornadoes in 6 states (5 EF4’s including the Oak Lawn, Belvidere, and Lake Zurich Tornadoes) Oak Lawn Tornado Blue = EF4 Next several slides summary • The next several slides, I will be going through different parameters. -
B-100063 Cloud-Seeding Activities Carried out in the United States
WASHINGXJN. O.C. 205.48 13-100063 Schweikcr: LM096545 This is in response to your request of September 22, .2-o 1971, for certain background informatio-n on cloud-seeding activities carried out -...-in _-..T---*the .Unitc.b_S~.~-~,.under programs supported-by the Federal agencies. Pursuant to the specific xz2- questions contained in your request, we directed our:review toward developing information-----a-=v-~ .,- , L-..-”on- .-cloud-seeding ,__ ._ programs sup- ported by Federal agencies, on the cost- ‘and purposes of such progrys, on the impact of cloud seeding on precipitation and severe storms, and on the types of chemicals used for seeding and their effect on the--environment. We also ob- tained dafa cdncerning the extent of cloud seeding conducted over Pennsylvania. Our review was conducted at various Federal departments ’ and agencies headquartered in Washington, D.C., and at cer- tain of their field offices in Colorado and Montana. We in- terviewed cognizant agency officials and reviewed appropriate records and files of the agencies. In addition, we reviewed pertinent reports and documentation of the Federal Council for Science and Technology, the National Academy of Sciences, and the National Water Commission. BACKGROUND AND COST DATA Several Federal agencies support weather modification programs which involve cloud-seeding activities. Major re- search programs include precipitation modification, fog and cloud modification, hail suppression, and lightning and hur- ricane modification. Statistics compiled by the Interdepartmental Committee for Atmospheric Sciences showed that costs for federally spon- sored weather modification rograms during fiscal years 1959 through 1970 totaled about %‘74 million; estimated costs for fiscal years 1971 and 1972 totaled about $35 million. -
A Simple Moisture Advection Model of Specific Humidity
1NOVEMBER 2016 C HADWICK ET AL. 7613 A Simple Moisture Advection Model of Specific Humidity Change over Land in Response to SST Warming ROBIN CHADWICK,PETER GOOD, AND KATE WILLETT Met Office Hadley Centre, Exeter, United Kingdom (Manuscript received 24 March 2016, in final form 25 July 2016) ABSTRACT A simple conceptual model of surface specific humidity change (Dq) over land is described, based on the effect of increased moisture advection from the oceans in response to sea surface temperature (SST) warming. In this model, future q over land is determined by scaling the present-day pattern of land q by the fractional increase in the oceanic moisture source. Simple model estimates agree well with climate model projections of future Dq (mean spatial correlation coefficient 0.87), so Dq over both land and ocean can be viewed primarily as a thermodynamic process controlled by SST warming. Precipitation change (DP) is also affected by Dq, and the new simple model can be included in a decomposition of tropical precipitation change, where it provides increased physical understanding of the processes that drive DP over land. Confidence in the thermodynamic part of extreme precipitation change over land is increased by this improved understanding, and this should scale approximately with Clausius–Clapeyron oceanic q increases under SST warming. Residuals of actual climate model Dq from simple model estimates are often associated with regions of large circulation change, and can be thought of as the ‘‘dynamical’’ part of specific humidity change. The simple model is used to explore intermodel uncertainty in Dq, and there are substantial contributions to uncertainty from both the thermodynamic (simple model) and dynamical (residual) terms. -
OBSERVATIONAL ANALYSIS of the PREDICTABILITY of MESOSCALE CONVECTIVE SYSTEMS by Israel L
National Science Foundation Graduate Fellowship DGE-0234615 and Grant ATM-0324324 OBSERVATIONAL ANALYSIS OF THE PREDICTABILITY OF MESOSCALE CONVECTIVE SYSTEMS by Israel L. Jirak William R. Cotton, P.I. OBSERVATIONAL ANALYSIS OF THE PREDICTABILITY OF MESOSCALE CONVECTIVE SYSTEMS by Israel L. Jirak Department of Atmospheric Science Colorado State University Fort Collins, Colorado 80523 Research Supported by National Science Foundation under a Graduate Fellowship DGE-0234615 and Grant ATM-0324324 October 27, 2006 Atmospheric Science Paper No. 778 ABSTRACT OBSERVATIONAL ANALYSIS OF THE PREDICTABILITY OF MESOSCALE CONVECTIVE SYSTEMS Mesoscale convective systems (MCSs) have a large influence on the weather over the central United States during the warm season by generating essential rainfall and severe weather. To gain insight into the predictability of these systems, the precursor environment of several hundred MCSs were thoroughly studied across the U.S. during the warm seasons of 1996-98. Surface analyses were used to identify triggering mechanisms for each system, and North American Regional Reanalyses (NARR) were used to examine dozens of parameters prior to MCS development. Statistical and composite analyses of these parameters were performed to extract valuable information about the environments in which MCSs form. Similarly, environments that are unable to support organized convective systems were also carefully investigated for comparison with MCS precursor environments. The analysis of these distinct environmental conditions led to the discovery of significant differences between environments that support MCS development and those that do not support convective organization. MCSs were most commonly initiated by frontal boundaries; however, such features that enhance convective initiation are often not sufficient for MCS development, as the environment needs to lend additional support for the development and organization oflong-lived convective systems.