Zeszyty Naukowe 13(85) 29
Scientific Journals
Zeszyty Naukowe
Maritime University of Szczecin
Akademia Morska w Szczecinie
2008, 13(85) pp. 29‐32 2008, 13(85) s. 29‐32
Analyzing and forecasting of atmospheric fronts
development and dissipation areas for meteorological
support of maritime navigation
Wyznaczanie i prognozowanie obszarów powstawania
i rozpadu frontów atmosferycznych dla potrzeb
meteorologicznego zabezpieczenia żeglugi
Janusz M. Jasiński, Dariusz Chaładyniak
Wojskowa Akademia Techniczna, Wydział Inżynierii Lądowej i Geodezji 00-908 Warszawa, ul. S. Kaliskiego 2, tel. 022 683 99 22,
e-mail: janusz.jasinski@wat.edu.pl, dchaladyniak@wat.edu.pl
Key words: frontogenesis, frontolysis, satellite images, meteorological elements Abstract
Efficiency and safety of maritime navigation depends very much on weather conditions. Since, in majority of cases, severe weather conditions are related with atmospheric fronts, it is crucial for meteorological support to provide a profound analysis and precise forecast of atmospheric fronts development and dissipation areas. The paper presents a method of determining the areas of frontogenetic and frontolytic zones worked out with the use of the results of research concerning the frontogenesis and frontolysis processes. The method is based on extensive analysis of the values of the frontogenetic function and the Q-vectors’ divergence.
Słowa kluczowe: frontogeneza, frontoliza, zdjęcia satelitarne, elementy meteorologiczne Abstrakt
Skuteczność i bezpieczeństwo żeglugi morskiej zależy w dużym stopniu od warunków atmosferycznych. W większości przypadków niebezpieczne zjawiska pogody są związane z frontami atmosferycznymi, stąd dla prawidłowego wsparcia meteorologicznego niezwykle istotnym jest zapewnienie głębokiej analizy i precy-zyjnej prognozy obszarów rozwoju i rozpadu frontów atmosferycznych. Artykuł przedstawia metodę wyzna-czania obszarów występowania stref frontogenetycznych i frontolitycznych opracowaną z wykorzystaniem wyników badań dotyczących procesów frontogenezy i frontolizy. Metoda opiera się na kompleksowej anali-zie wartości funkcji frontogenetycznej i dywergencji Q-wektorów.
Introduction
The issue of atmospheric fronts forecasting is still open despite many years of research. It is no longer strange when we observe satellite imagery and the miscellaneous forms of clouds. That is why all results of investigating atmospheric states are of considerable value and are basis for better understanding of the processes that determine the weather.
Innovation of contemporary atmospheric re-search consists in using numerous available sources
of information and methods of processing. Com-prehensive diagnosing and forecasting of frontoge-netic and frontolytic areas uses both remote sensing and numeric methods.
Among remote sensing methods applied in me-teorology, photo-interpretation of cloud systems plays a special role. The importance of the method results from the fact that the satellite images enable to observe the evolution of organized structures and systems of clouds at the same time and they are a scene for a standard synoptic analysis using con-ventional meteorological data. Numerical methods
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30 Scientific Journals 13(85)
using mathematical approach to analysis and as-sessment of large-scale weather processes enable objective determination of the probable areas of initiating and developing of cyclones, atmospheric fronts and weather phenomena [1].
Traditional methods of determining the fronto-genetic and frontolytic areas used so far are not a fully reliable source of information for research or practical operational applications, even if require-ments concerning precision are not high.
Application of the frontogenetic function and Q-vectors divergence to diagnosing and forecasting of the frontogenesis and frontolysis processes
The diagnosis of frontogenesis and frontolysis processes consists in determining zones with ap-propriate values of the frontogenetic function and Q-vectors divergence and analyzing them along with existing fronts observed in satellite images [2, 3].
Forecasting of frontogenesis and frontolysis processes, however, consists in application of the frontogenetic function and Q-vectors divergence values calculated with the use of forecast values of meteorological elements. These were obtained from GRID data. Numerical values of forecasted charts are available every 6 hours (from Bracknell) or every 12 hours (from Offenbach). Calculated values of the frontogenetic function and Q-vectors diver-gence are forecasts for 6, 12, 18 etc. hours. The calculated values are used to determine frontogene-sis and frontolyfrontogene-sis areas for comparison with satel-lite images of cloud systems [3, 4, 5].
Fields of measured values of geopotential Φ and temperature T on the same isobaric surface, ob-tained from GRID data sets, have been used for analysis. The following figures present examples of cases in which air temperature has decreased in the direction of the North.
Figure 1 presents a model field of geopotential with two highs and a low in a slightly disturbed westerly thermal wind. In the vicinity of the low center, the change of the geostrophic wind direction to east along isotherms takes place from north to south through cold air in accordance with the circu-lation in the low. Expression ∇Q describes de-scending currents in the area of cold air advection on the western side of the trough and ascending currents in the area of warm air advection on the eastern side [6].
The upper trough and wedge system in figure 2a indicates that, in this case, there is no temperature advection (parallel isotherms and isobars). Across
the trough, the Q-vector direction is westerly be-cause the wind direction changes from north-west to south-west along the isotherm. However, across the wedge the appropriate changes of wind are re-versed and the Q-vector’s direction is easterly. We can conclude that ascending currents occur down-stream the trough while descending currents exist in the downstream part of the wedge, which is also confirmed by analysis based on vorticity advection.
Descending currents
Ascending currents Cold air
Warm air
Fig. 1. Distribution of Q-vectors in case of a model field of geopotential (solid lines) and temperature (dashed lines); W – high, N – low [6]
Rys. 1. Rozkład Q-wektorów w modelowym przypadku pola geopotencjału (linie ciągłe) i temperatury (linie przerywane); W – wyż, N – niż [6]
A model case of frontogenesis process in a con-vergent flow zone corresponding to an area of en-trance to a jet stream in upper layers of the atmos-phere is presented in figures 2b and 3a. Q-vectors
Descending currents Ascending currents Cold air Warm air Cold air Warm air Descending currents Ascending currents b) a)
Fig. 2. a) A model case of Q-vectors layout in the fields of isobars (solid lines) and isotherms (dashed lines) on upper levels of the atmosphere for a system of troughs and wedges; b) a model case of frontogenesis process in a convergent flow [6]
Rys. 2. a) Modelowy przypadek rozkładu Q-wektorów w po-lach izobar (linie ciągłe) i izoterm (linie przerywane) na gór-nych poziomach atmosfery dla systemu zatok i klinów; b) modelowy przypadek procesu frontogenezy w zbieżnym przepływie [6]
Analyzing and forecasting of atmospheric fronts development and dissipation areas...
Zeszyty Naukowe 13(85) 31
are directed across the isotherms to the warm air with ascending currents while the cold air descends. This situation is in harmony with traditional obser-vations of relative vertical movements of air masses on an atmospheric front. An important observation here is that it is rather the frontogenetic forcing than the front itself that causes such a layout of vertical currents. Various parts of the baroclinic zone may have various forms of frontogenetic or frontolytic forcing [6]. Descending currents Stable state Cold air Warm air Cold air Warm air Descending currents Ascending currents Descending currents Ascending currents Ascending currents Frontogenesis Frontolysis b) a)
Fig. 3. a) Q-vectors layout in a convergent flow; b) a model case of baroclinic zone with frontogenesis and frontolysis processes and a stable state [6]
Rys. 3. a) Rozkład Q-wektorów w polu zbieżności; b) mode-lowy przypadek strefy baroklinowej z procesami frontogenezy, frontolizy i stanem stabilnym [6]
In figure 3b the area on the left shows the same orientation of isotherms and Q-vectors as in figure 2b which may lead to the development of an inten-sive frontal surface. However, the area on the right side shows something totally different, simething that would occur if the location of highs and lows in figure 2b had been exchanged. Q-vectors are now directed to the cold air which indicates a fron-tolysis process. Between the areas there is a zone with parallel Q-vectors and isotherms. In this case the baroclinic zone is not active and the vertical currents are not intensive. These are areas of stable atmosphere [6].
Methodology of analyzing and forecasting the frontogenesis and frontolysis
processes
The following methodology of analyzing and forecasting the frontogenesis and frontolysis
proc-esses is a result of the research using remote sens-ing and numerical methods. It is composed of the following phases [3, 7, 8]:
– acquisition, archiving and analysis of satellite images of cloud systems in various spectral channels (IR, VIS, WV) and in various areas (D1, D2, D3);
– decoding numerical data from GRID data sets, which comprise meteorological elements charts (geopotential, air temperature, relative humidity, wind seed and direction) in regular angular geo-graphical grid;
– projecting satellite images contents and GRID data to a common cartographic system for paral-lel analysis of remote sensing and numerical da-ta;
– superimposing meteorological elements infor-mation (GRID data) above satellite images; – determining the frontogenetic function and
Q-vectors divergence values in meteorological elements fields, gradient fields and in satellite images;
– analysis of the frontogenetic function and Q-vectors divergence values in the fields of geopotential, air temperature, relative humidity, wind speed and direction as well as in the fields of air temperature advection, relative vorticity and in satellite images;
– determining the areas of frontogenesis (positive values of the frontogenetic function and Q-vectors divergence) and frontolysis (negative values), as well as forecasting of creation, evolu-tion and dissipaevolu-tion of atmospheric fronts; – verifying the forecasted areas of frontogenesis
and frontolysis by comparing them with satellite images analysis.
Summary of results
The research indicates that the best results of frontogenesis forecasting are obtained in high value fields of horizontal geopotential and air temperature gradients. In these cases, both the frontogenetic function and the Q-vectors divergence have posi-tive values.
Creation and development of atmospheric fronts are also observed in fields of significant values of wind speed and convergence which is accompanied by positive values of the frontogenetic function and the Q-vectors divergence. A slightly weaker con-formity of the frontogenetic function and the Q-vectors divergence is observed in the fields of relative humidity, air temperature advection and relative vorticity. Forecasting of frontolysis yields the best results in low-gradient fields of pressure
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and air temperature as well as in zones of wind divergence. The frontogenetic function and the Q-vectors divergence values are negative here. The research indicates also that forecasting of the fron-togenesis and frontolysis processes is more accurate when more meteorological elements and character-istics are considered (temperature advection, rela-tive vorticity) [3, 7, 9].
Some of the analyzed synoptic situations have given slightly different results from the general observations concerning forecasting of the fronto-genesis and frontolysis processes. Due to variety of processes in the atmosphere, in some cases the frontogenetic function and the Q-vectors diver-gence values have opposite signs. The proposed methodology still requires modification and consid-eration of the new sources of information and new achievements in data processing [3, 7, 9].
References
1. JASIŃSKI J., PIETREK S.: Numerical models results application
to mesoscale weather forecasting. 18th Conference on Weather Analysis and Forecasting, Fort Lauderdale, USA, 30.07–02.08 2001.
2. CHAŁADYNIAK D.: Combined analysis of satellite image and
GRID data to forecasting the frontogenesis and frontolysis areas. 18th International Conference on Interactive informa-tion processing Systems for Meteorology, Oceanography, and Hydrology, Orlando, Florida, 13–17.01.2002.
3. CHAŁADYNIAK D.: Analysis of cloud systems by means of Q-vector fields and satellite images. 31st Conference on Broad-cast Meteorology, Williamsburg, Virginia, 24–28.06.2002. 4. CHAŁADYNIAK D., WINNICKI I. A.: Application of numerical
data and satellite images to analysis of frontogenesis and frontolysis processes. 18th Conference on Weather Analysis and Forecasting, Fort Lauderdale, Florida, 30.07–2.08.2001. 5. JASIŃSKI J., KROSZCZYŃSKI K., PIETREK S.: Non-standard
synoptic materials preparation based on mesoscale models data and satellite imagery. V European Conference on Applications of Meteorology ECAM 2001, Budapest, 24– 28.09.2001.
6. SANDERS F., HOSKINS B. J.: An easy method for estimation of Q-vectors from weather maps. Weather Forecasting, 1990, 5, 2, 346–353.
7. CHAŁADYNIAK D.: Analysis and forecasting of frontogenesis
and frontolysis by means of Q-vector diagnostics combined with satellite images. P. J. Envir. Stud. 2006, 15, 3c.
8. JASIŃSKI J., KROSZCZYŃSKI K., PIETREK S.: Nowcasting of
temporal and spatial distributions of meteorological elements values in severe weather. VI European Conference on Appli-cations of Meteorology ECAM 2003, Rome, 15–19.09.2003. 9. CHAŁADYNIAK D.: Analysis and forecasting of frontogenesis
and frontolysis processes by means of Q-vectors method. XIV International Scientific and Technical Conference, The Part of Navigation in Support of Human Activity on the Sea, Gdynia 2004.
Recenzent: prof. dr hab. inż. Bernard Wiśniewski Akademia Morska w Szczecinie
