Freezing Fog Formation in a Supercooled Boundary Layer Solving the Winter Fog Forcasting Challenge for Elmendorf Air Force Base, Alaska
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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. -
Impact of Air Pollution Controls on Radiation Fog Frequency in the Central Valley of California
RESEARCH ARTICLE Impact of Air Pollution Controls on Radiation Fog 10.1029/2018JD029419 Frequency in the Central Valley of California Special Section: Ellyn Gray1 , S. Gilardoni2 , Dennis Baldocchi1 , Brian C. McDonald3,4 , Fog: Atmosphere, biosphere, 2 1,5 land, and ocean interactions Maria Cristina Facchini , and Allen H. Goldstein 1Department of Environmental Science, Policy and Management, Division of Ecosystem Sciences, University of 2 Key Points: California, Berkeley, CA, USA, Institute of Atmospheric Sciences and Climate ‐ National Research Council (ISAC‐CNR), • Central Valley fog frequency Bologna, BO, Italy, 3Cooperative Institute for Research in Environmental Science, University of Colorado, Boulder, CO, increased 85% from 1930 to 1970, USA, 4Chemical Sciences Division, NOAA Earth System Research Laboratory, Boulder, CO, USA, 5Department of Civil then declined 76% in the last 36 and Environmental Engineering, University of California, Berkeley, CA, USA winters, with large short‐term variability • Short‐term fog variability is dominantly driven by climate Abstract In California's Central Valley, tule fog frequency increased 85% from 1930 to 1970, then fluctuations declined 76% in the last 36 winters. Throughout these changes, fog frequency exhibited a consistent • ‐ Long term temporal and spatial north‐south trend, with maxima in southern latitudes. We analyzed seven decades of meteorological data trends in fog are dominantly driven by changes in air pollution and five decades of air pollution data to determine the most likely drivers changing fog, including temperature, dew point depression, precipitation, wind speed, and NOx (oxides of nitrogen) concentration. Supporting Information: Climate variables, most critically dew point depression, strongly influence the short‐term (annual) • Supporting Information S1 variability in fog frequency; however, the frequency of optimal conditions for fog formation show no • Table S1 • Table S2 observable trend from 1980 to 2016. -
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. -
Forecasting of Thunderstorms in the Pre-Monsoon Season at Delhi
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Publications of the IAS Fellows Meteorol. Appl. 6, 29–38 (1999) Forecasting of thunderstorms in the pre-monsoon season at Delhi N Ravi1, U C Mohanty1, O P Madan1 and R K Paliwal2 1Centre for Atmospheric Sciences, Indian Institute of Technology, New Delhi 110 016, India 2National Centre for Medium Range Weather Forecasting, Mausam Bhavan Complex, Lodi Road, New Delhi 110 003, India Accurate prediction of thunderstorms during the pre-monsoon season (April–June) in India is essential for human activities such as construction, aviation and agriculture. Two objective forecasting methods are developed using data from May and June for 1985–89. The developed methods are tested with independent data sets of the recent years, namely May and June for the years 1994 and 1995. The first method is based on a graphical technique. Fifteen different types of stability index are used in combinations of different pairs. It is found that Showalter index versus Totals total index and Jefferson’s modified index versus George index can cluster cases of occurrence of thunderstorms mixed with a few cases of non-occurrence along a zone. The zones are demarcated and further sub-zones are created for clarity. The probability of occurrence/non-occurrence of thunderstorms in each sub-zone is then calculated. The second approach uses a multiple regression method to predict the occurrence/non- occurrence of thunderstorms. A total of 274 potential predictors are subjected to stepwise screening and nine significant predictors are selected to formulate a multiple regression equation that gives the forecast in probabilistic terms. -
CHAPTER NO.4 Psychrometry
CHAPTER NO.4 Psychrometry C605.4-Explain Psychometric properties & calculate various parameters. Necessity of Air conditioning Purpose of air conditioning is 1) To provide comfort conditions for human comfort. 2) To provide comfort in commercial places like office, restaurant ,shopping complex , banks etc. 3) To provide controlled conditions for industrial process. 4) To provide ultra clean atmosphere for precision work. Dalton’s low of partial pressure • Dalton’s law of partial pressure statures that ‘the total pressure of mixture of gases equal to the sum of the partial pressures exerted by each gas when it occupies the mixture volume at there temperature of mixture’. • According to Dalton’s law of partial pressure, • Pt = Pa + Pb + Pc. Psychometrics properties of air 1. Dry air : It is a mixture of oxygen (20.91%) and nitrogen (79.09 %) by volume. 2. Moist air: It is mixture of dry air and water vapour. 3. Saturated air : Air which have maximum amount of water vapor. 4. Dry bulb temp.(DBT) : It is temperature of air recorded by ordinary thermometer with clean and dry sensing elements.(td or tdb) 5. Wet bulb temp.(WBT) : It is temperature of air recorded by thermometer when its bulb is covered with a wet cloth.(tw or twb) Psychometric properties of air 6. Wet bulb depression : It is the difference between dry bulb temp. and wet bulb temp. 7. Dew point temp.: It is the temperature of air recorded by thermometer when the moisture present in the air begins to condensed. 8. Dew point depression : It is difference between dry bulb temp. -
Atmospheric Moisture
Name_____________________________________Date___________________Period________ Atmospheric Moisture Background Whether in solid, liquid or gaseous form, water is the most important component of the atmosphere and essentially helps control all other aspects of weather. However, in order to truly understand the atmosphere, simply describing moisture is not enough. In meteorology, several different moisture measurements are used to determine the overall stability and characteristics of the air. All of these “moisture variables” can be broken down into two categories. The first are those that are solely dependent upon the physical amount of water contained in the air and are known as absolute measurements. Relative measurements are those variables that not only depend upon the amount of water present but also the temperature of the air. Vapor Pressure (e) and Saturation Vapor Pressure (es) The total atmospheric pressure at any time is actually the sum of many small pressures for each of the atmosphere’s components. For example, oxygen, nitrogen, etc. all exert their own individual pressures that are all added together to create the overall atmospheric pressure. Vapor pressure is the portion of total pressure contributed by water. This amount is very small which is why huge differences in water vapor content in the atmosphere yield only small fluctuations in the overall atmospheric pressure. This measure is also an absolute measurement since it is solely dependent upon the actual amount of water vapor in the air. However, there is a maximum amount of pressure that water vapor can exert in the atmosphere before the water is squeezed out into a liquid again and condenses. This limit is known as the saturation vapor pressure and is dependent upon both water content and temperature. -
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 -
Properties of Diamond Dust Type Ice Crystals Observed in Summer Season at Amundsen-Scott South Pole Station, Antarctica
180 JournaloftheMeteorological SocietyofJapanVol 57,No.2 Properties of Diamond Dust Type Ice Crystals Observed in Summer Season at Amundsen-Scott South Pole Station, Antarctica By Katsuhiro Kikuchi Department of Geophysics, Hokkaido University, Sapporo and Austin W. Hogan Atmospheric Sciences Research Center, State University of New York at Albany, Albany, New York (Manuscript received 17 November 1977, in revised form 20 May 1978) Abstract The properties of diamond dust type ice crystals were studied from replicas obtained during the 1975 austral summer at South Pole Station, Antarctica. The time variation of the number concentration and shapes of crystals, and the length of the c-axis, the axial ratio (c/a) and the growth mode of columnar type crystal were examined at an air tempera- ture of -35*. Columnar type crystals prevailed, but occasionally more than half the number of ice crystals were plate types, including hexagonal, scalene hexagonal, pentagonal, rhombic, trapezoidal and triangular plates. A time variation of two hour periodicity was found in the number concentration of columnar and plate type crystals. When the number con- centration of columnar type crystals decreased, the length of the c-axis of columnar type crystals also decreased. When the number concentration of columnar type crystals increased, the length of the c-axis of the crystals also increased. There was sufficient water vapor to grow these ice crystals in a supersaturation layer several tens to several hundred meters above the surface. The growth mode of columnar type crystals was different from that of warm and cold region columns reported by Ono (1969). The mass growth rate was 6.0* 10-10 gr*sec-1, and was similar to that obtained in cold room experiments by Mason (1953), but less than that found in field experiments by Isono, et al. -
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. -
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.