JPRS ID: 9050 USSR REPORT METEORLOGY AND HYDROLOGY NO.12, FEBRUARY 1980

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~ JPRS L/9050
22 ARril ~980
USSR Re ort
p _
METEOROLOGY AND HYDROL~?GY
No. 2~ February 19~0
_ FBIS FOREIGN BROADCAST INFORMATION SERVICE
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JPFcS L/9050
22 April 1980 "
USSR REPORT
METEOROLOGY AND HYDROLOGY
No. 2, February 1980
_ Translation of the Russian-language monthly journal METEOROLOGIYA
I GIDROLOGIYA published in ~Ioscow by Gidrometeoizdat.
CONTENTS
Decree of the CPSU Central Committee and USSR Council of Ministers on
- Awarding of USSR State Prizes for 1979 in the Field of Science and
Technology 1
- Statistical Prediction of Radiation and Radiation-Advective Fogs -
~ (P. K. Dushkin) 4
The Boundary Condition in Prablems of Atmospheric Diffusion of an
Admixture
- (N. L. Byzova, et al.) 15
Prediction of Air Contamination Over the ~paheron Peninsula
(A. A. Gc~rchiyev and R. M. Ra.fiyev) 25
Influence of Direction of Transport of Air Masses on the Content of
Organic Microadmixtures in Precipitati4n
. (L. A. Volokitina and V. S. Shuklin) 33 -
Objective Analysis of the Tropopause
(V. A. Gordin and Ye. A. Loktionova) 40 .
Checking of the Applicability of Balance Equations on the Basis of
Empirical Data
(A. M. Babaliyev, et al.) 50 -
Some Methods for Parameterization of Subgrid Proceases
(Ye. Ye. Kalenkovich, et al.) 60 ~
- a- IIII - USSR - 33 S& T FOIIO]
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rVit urrl~tru, uar. VL\~l
~
CONTENTS (Continued) PaBe
Hurricanes of Middle-Latitude Seas and Their Consequences _
(L. N. Ikonnikova) 68
Some Features of Water Circulation in the North Atlantic
(A. A. Kutalo 79
Comparison of Satellite and Shipboard Data on Temperature
Measurements of the Water Surface in the Equatorial Atlantic
(A. D. Kirichek, A. F. Lyashenko) 89
Accuracy in Computing the Mont.hly Water Balance of the Aral Sea
(V. N. Bortnik) 95
Use of the Residual Method of Statisti~al Analysis for the
Investigation of Hydrometeorological Processes
- (A. R. Kostantinov, N. M. Khimin) 104
Calculation of Length of Sand Ridges in Open Flows
(B. F. Snishchenko) 115
Inventory of Agroclimatic Resources in the Specialization of
Agricultural Production in the Ukraine
(V. P. Dmitrenko, et al.) 126
Conservative Value in a Barotropic Model With Allawance for Relief
(Z. V. Khvedelidze) 137
Ozone Content in an Urban Atmosphere in Dependence on Meteorological
- Conditions
(V. A. Popov, et al.) 140
C~lculations of Air Hum~dity Over the Sea from thp Water-Air
Temperature Differences
(V. G. Snopkov) 145
- Atmospheric Investigations Abroad Using A~rcraft Laboratories
(Yu. V. Mel'nichuk, et al.) 149
Sixtieth Birthday of Ivan Pavlovich Vetlov 162 -
Seventieth Birthday of Aleksey Aleksandrovich Sokolov 164
Professor Ye. G. Popov Honored 168
At the USSR State Committee on Hydrometeorology and Environmental
Monitoring
(V. N. Drozdov) 169
_ b _
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CONTENTS (Continued) page
Conferencea, Meetings and Sem3nars '
(Yu. G. Slatinskiy, et al.) 171
- Notes From Abroad
(V. I. Silkin) 180 -
- - c -
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.
PUBLICATION DATA
Bnglieh title ~ METEOROLOGY AND HYDROLOGY
- Russian title ~ METEOROLOGIYA i ~IDROLOGIYA
Author (s) ~
Editor (s) : Ye. I. Tolstikov ~
Publishing House : Gidrometeoizdat
Place of Publicati4n : Moscow
Date of Publication : February 1980 -
~
Signed to pres3 ' : 24 Jan 80
_ Copies ~ 3760
COPYRIGHT : "Meteorologiya i gidrologiya",
1980
- d -
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DECREE OF THE CPSU CENTRAL COMMITTEE AND USSR COUNCIL OF MINISTERS ON
AWARDII~G OF USSR STATE PRIZES FOR 1979 IN THE FIELD OF SCIENCE AND
TECHNOi,OGY -
- Moscow METEOROLOGIYA I GIDROLOGIYA in Russian No 2, Feb 80 pp 4-5
[Unsigned decree] -
[Text] The CPSU Central Committee and the USSR Council of Ministers, after
examining the proposals of the Committee on the Lenin and State Prizes
USSR in the Field of Science and Technology of the USSR Council of Min-
iaters, decrees the awarding of the USSR State Prizes for 1979:
II. In the Field of Technology _
2. To Petr Vasil'yevich Babkin, Doctor of Geological and Mineralogical
Sciences,
to Igor' Mikhaylovich Nazarov, Candidate of Technical Sciences, Deputy
Director of the Institute of Applied Geophysics,
to Lidiya Ivanovna Boltneva, Candidate of Physical and Mathematical Sci-
ences, senior scientific speciaYist, worker at this same institute,
~ to Vlad~mir Aleksandrovich Ionov, Candidate of Physical and Mathemat-
ical Sciences, senior scientif ic specialist, woxker at this same insti-
- tute,
to Shepa Davidovich Fridman, Candidate of Technical Sciences, deputy
division head, worker at this same iustitute, -
to Aleksandr Vladimirovich Matveyev, Candidate of Technical Sciencea,
Deputy Director of the All-Union Scientific Research Institute of Explor- ~
atory Geophyaics, _
to Vya~heslav Borisovich Stepanov, chief engineer, worker at this same
institute,
~
1
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~ ~ a.
_
Y
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L. I. Boltneva A. V. Ihnitriyev
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M. V. Nikiforov Sh. D. Fridman
2
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- to Vad3m Valentinovich Filimonov, Candidate of Technical Sciences, div-
is3.on head, worker at this same institute,
to Pavel Nikolayevich Fogt, division head, worker at this same insti-
- tute,
_ to Mikhail Vladimirovich, Candidate of Physical and *4athematical Sci-
- ~ences, laboratory head at the Al~l-Union Scientific Research Institute of
Agricultural Meteorology,
= to Emiliya Yakovlevich Ostrovskiy, Doctor of Technical Sciencea, sen-
- ior scientific specialist at the All-Union Scien~if ic Research Institute
- of Mineral Raw Material,
to Aleksey Viktorovich Dmitriyev, physica?. engineer,
for theoretical and experimental development and introduction into the
national economy of a gamma-spectroti~etric method for remote sensing of
the environment, search for and detection of depoeita of nonferrous,
rare and noble metals.
3
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UDC 551.509.(314+55)
STATISTICAL PREDICTION OF RADIATIOtl AND RADIATION-ADVECTIVE FOGS
Moscow METEOROLOGIYA I GIDROLOGIYA in Russian No 2, FPh 80 pp 6-13
[Article by Candidate of Physical and Mathematical Sciences P. K. Dushkin,
Moscow, submitted for publication 17 May 1979]
Abstract: The author has developed a method -
for statistical alternative prediction of
radiation and radiation-advective foga on
the basie of atandard meteorological measure- ~
menta at a single point. Empirical graphs of -
discriminant functions were constructed for
uae in prediction. None nf the computations
require the use of an electronic computer.
The direct use of long-term observations at
some atation is a prerequisite for taking
regional peculiaritiea into account. The
method was checked by the aiithor using data
from long-term observations at a nwmber of
stations in Mc+skovskaya Oblast, containing
more than 1,000 cases favorable for fog pre-
- diction. On the average, the sum of errors of
the first and second kinds does not exceed 0.40. ~
[TextJ A great number of investigations have been devoted to the physical
and synoptic conditions for fog formation. The list of publications on
the problems involved in fog prediction has lengthened [2]. However, if
we evaluate the purely practical side of solution of the problem, here we
do not note any significant progress. � '
However strange it may seem, neither the investigations of the prediction
- methods no~ the systematic studies carried out in the Hydrometeorological
Service for evaluating the success in predicting fogs make possible a
sufficiently true determination of the attained level. This is attributable
to the fact that in the evaluation of predictiona it is customary to uae
the general guaranteed probability test, representing the ratio of the
number of predictions which have proven correct to the total number -
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of forecasts. The application of this evaluation has already been sub-
jected to criticism in [1]. We recall that the general guaraneeed prob-
ability is dependent not only on the effectiveness of the method, but
also on climatic characteristics. The general guaranteed probability in
essence is an evaluation of the probability of a correct forecast. For
eaample, applicable to fogs it can be represented__in the form -
U=1-[a,P(~)+~ P~~)~~
where U is the general guaranteed probab3lity, oC = p(~f/~) and ~ s , `
p( ~f/C~ ) are errors of the first and second kinda; the symbols ~ and ~ -
are used in denoting a fog and its absence respectively and ~f and ~f
are forecasts of these events.
In accordance with the definition of general guaranteed probability cited
above, it is unthinkable to use it for a comparative evaluation of the
auccess of fore~asts in different climatic zonea.
A necessary and adequate condition for fog formation in the most general
form can be represented in the form of the relationship T5 0,25 0,08 O,ti7 (~1e, y3) 0,20 0,1~ 0,6ri
1X, lll, IV js 0,10 0,15 0.75 KEY:
1. Model
~y; 2. 1f = f (orecast)
1. Sample group
2. N, tenths �
In conclusion we will examine a final variant of the forecasting method.
A distinguishing characteristic of the V3 value is that the significance _
of the velocities Vlg, V20, V22 determining it as predictors increases
with increasing distance from the initial observation time at 1800 hours. _
In order to take this peculiarity into account and smooth the high-
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- frequency velocity fluctuations, we introduce smoothing in the form
~22 = aV22 + abV20 + b2V18, where a+ b= 1, a) b. A numerical experi~- _
ment made it possible to select a suitable value a= 0.8.
_ ~pF. ~
r,
e `
i 6)
6 ~ -
~ ~ 9
; ~
4 ~~`2 ~ ~
. ~ ~ -2 _
. '1
. 2 ~ 1 .
~ ~ -
i ~ m/aec
0 2 4 0 2 4 VH/CeK
ti
Fig. 1. Graph for predicting fogs in March, April, September (August) (a)
and in October, November, December, January and February (b). 1) N~ 5/10, -
2) N < 5/10. _
Table 3 Table 4
Diagnostic Evaluations. Model (~18, Evaluations of Diagnosis and Predic-
V22), 1947-1961 CHS tion on Basis of Independent Observ-
~ ations i962-1971
I'pynna 1 N~ 2 y ~ Q CHS c.. c -
awGopsu I6an.twl I I 1 ~ ~g~ -
- - OnepauiiA ~ ~ 4) ~ G Uo
1?C, II1, 11~' ~ 5 0,17 O,1G (?,67 q2 0~ ,
0,20 U, IU 0,70
`CI, XII, 0,20 0,11 0.6!) 311i~~rtios 0,14U,1~iU,G~S~ U,i-1 0,9U 0.930,53
KEY� ~5 U,21 0,13 U.fi6 ~Y: 4np~ruo~ 0,2UU,140,66i 0,67 U,9'l 0,820,L~.3
~1. Sample group 1. Operation
2. N, tenths ~ 2. 1T = f(orecast) -
3. Diagnosis
4. Forecast
.
The figure shows ~urves for discriminanc functions constructed on the ba-
sis of the (d18, V22) model. It is interesting that attempts to use d,20
or 0 22 ins~ead of Q 18 were unsuccessful. The diagnostic evaluations of
the (Q lg, V22) model are presented j.n Table 3. The~r comparison with the
resulta obtained earlier (Tabi~, 1) indicate a successful chotce of the -
predictor ~22. .
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~ Table 5
Evaluations of Forecasts at Airports and for the Entire Moscow Airport
Complex in a Seriea of Observations 1965-1970
- -
OLLeHKU BHYKOHO AnMOAC- WCpC� fjdK080 Agpaysen
j(OBO NCTbE80
1 2 3 4 5 6
a 0,16 0,23 0,22 0,26 0,21
a 0,23 0,16 0,17 0.20 0,19
Q`, 0,61 Q,fi l 0,61 0,54 0,60
U 0,8? 0,79 0,80 0,76 0,00
Uo 0,67 0,51 0,52 0.52 0,56 _
- ~(mJmQ) ] 0.60 0,53 O,o4 O.f,O 0,60
p(m/mQ) 0,93 0,94 0,91 0,88 0,9? ~
KEY: ~
1. Evaluations
2. Vnukovo
3. Domodedovo _
4. Sheremet'yevo
5. Bykovo '
- 6. Airport complex
7. 1~ = f (orecast)
The data in Table 4 give~ some idea concerning the evaluations obtained
when testing the p 18, V22 model on the basis of independent observational
data. The table includes additional evaluations: U-- general guaranteed
probability, Up guaranteed probability of a random forecast, and also
' evaluations of the probabilities p(~ / c~f) and p(~/ ~ f), introduced for
- the first time by N. A. Bagrov [1]. They determine the soundness of cate- _
gorical formulations of prediction of a fog and its absence.
It is of interest to study the possibilities of using the developed graphs
for predicting fogs in different regions of the country. A solution of
- thia problem involves processing of long-term meteorological observations
at many stations. We have already taken the initial step in this direc-
tion: we carried out checking of effectiveness using data from a 6-year
series of ~bservations at the airports Vnukovo, Domodedovo, Sheremet'yevo,
Bykovo, repre~ented in the form of coded telegrams.
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i Now we will evaluate the quality of the meteorological observationa at the
mentioned airports (1965-1970). In contrast to the observations at the C?iS
(1947-1961), where the humidity measurements were made with a paychrometer
at poaitive temperatures and with a hair hygrameter in the ca8e of negative
temperatures, the data for the Moacow airports contain information on humid-
ity measured year-round using a hair hygrometer. Thus, the humidity meas-
urements at airports were made for the most part with large errors. An ob-
vious shortcoming of the analyzed data is also the rounding-off of air
temperatures and dew point to whole degrees, accomplished during the coding
of ineteorological observations.
The results of checking of the forecasting method on the basis of the model
are presented in Table 5. Their comparison with the data in Table 4 shows
that at airports there is some worsening in the quality of forecasts (a
decrease in Q on the average by 6%), which is a result of the above-men-
tioned errors in observational data. To be sure, it is not impossible that
a decrease in the quality of forecasts is to some degree a result of the
manifestation of local conditions, about which we know nothing. For ex-
ample, the decrease in Q by 12% at Bykovo is evidently caused by the in-
fluence of some local conditions.
Despite some worsening in the evaluations of the forecasts, the main con-
clusion drawn from tpsts of the method is that the developed graphs can be
employed successfully, at least in the Moscow region. We emnhasize that the
fog forecasts made by the method prope~_3 in this study are characterized
on the average by the evaluation Q= 0.60. As a comparison we recall that
for the prediction of thunderstorms by different methods this evaluation
_ falls in the range 0.23-0.35 [lJ.
With respect to the possibilities of using the proposed graphs in other re-
gions of the country, this problem can be solved only after corresponding
- testr~. The following conclusions are possible.
1. The graphs can be used in the particular region in unmodified form.
2. It is necessary to take into account regional characteristics within the =
- framework of the recommended predictors, but the graphs must be recon-
structed, using local meteorological observations far this purpose. The
archival data used for this purpose, depending on the frequency of recur-
rence of fogs in the particular ~egion, must usually incorporate a 10-15-
year series of observations. In some regions with a relatively great fre-
- quency of recurrence of fogs the volume of archival data used in con-
structing the graphs can be limited to a 5-year series of hourly observa-
tions .
3. Necessary allowance for local peculiarities must be made by making use
of additional components of the vector-predictor.
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BIBLIOGRAPHY
1. Bagrov, N. A., "Statietical Analyeis of the Reaulte of Tests of Some
- Methods for Predicting Thunderstorms," METEOROLOGIYA I GIDROLOGIYA
(Meteoralogy and Hydrology), No 8, 1965. -
2. Berlyand, M. Ye., Vorontsov, P. A., et al., TUMANY (Fogs), Leningrad, -
Gidrometeoizdat, 1961.
3. Matveyev, L. T., OSNOVY OBSHCHEY METEOROLOGII (Principles of General
Meteorology), Leningrad, Gidrometeoizdat, 1965.
4. RUKOVODSTv~ PO KRATKOSROCHNYM PROGNOZAM POGODY (Manual on Short-Range
Weather Forecasting), Part II, Leningrad, Gidrometeoizdat, 1965.
~
i
~
~
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UDC 551.551.8
THE BOUNDARY CONDITION IN PROBLEMS OF ATMOSPHERIC DIFFUSION OF AN ADMIXTURE
_ Moscow METEOROLOGIYA I GIDROLOGIYA in Russian No 2, Feb 80 pp 14-20
[Article by Doctor of Physical and Ma.thematical Sciences N. L. Byzpva,
- I. A. Krotova and G. A. Natanzon, Institute of Experimental Meteorologyr
submitted for publication 23 February 1979]
Abstract: In the ~oint solution of the prob-
lem of dynamics and diffusion it is desirable
to formulate boundary conditions for the ad- -
mixture at the dynamic roughness level, rather
than having a common boundary for all equations
in the system. The parameter in the boundary
condition for the concentration of admixture
charar.terizing its entrapment can be obtained
by scaling the experimental values of the rate
- o~ dry precipitation to the level z= z~. The
article gives a review of the experimental
values of the rate of dry precipitation. The
authors analyze the results of numerical solu-
tion of the diffusion equation with a boundary
condition of the mixed type. A region of val-
ues of the rate of dry precipitation, trans-
itional from a total reflection regime to a
total absorption regime,'which corresponds to
the real range of ineasured values, is detected.
[Text] A more precise determination of the boundary condition at the under-
lying surface in problems of the diffusion of an admixture in the atmosphere
- is of. considerable interest in those cases when the contamination of the
soil by the effluent of industrial enterprises is investigated because
even limited fallout of contaminating matter from the atmosphere over a
prolonged period can lead to appreciable concentrations on its surface.
Within the framework of the semiempirical theory this boundary condition,
introduced independently in [9J and [13J, has the following form: '
p~ K ds + wq = vq. (1)
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The ec;uation for computing vertical diffusicn of an admixture from a long- -
= acting source is written in the form:
aQ aq _ a aq
U a~ w ad as K a" � ~ 2~
~ Here P is the flux of admixture onto the underlying surface, K is the co-
efficient of vertical turbulent diffusiori, U is wind velocity, q is the
concentration of admixture, w is the rate of gravitational precipitation
of admixture particles in the calm air, v is a characteristic having the
dimensionality of velocity.
For the purpose ~f a clearer understanding of the physical sense of condi-
tion (1) we will examine a case when near the underlying surface horizon-
tal transfer can be neglected and the vertical flux can be considered
approxitpately constant with altitude. This can occur, in particular, at '
an adequate distance from it. Limiting ourselves to the case of a weight- ,
less admixture (w = 0), assuming
Ksx upz. ~3~
where u* is dynamic velocity, from (2) in this approximation we have
.
9~x~ z) = px 1 n P= y~~ (4 )
Here ln zg is an integration constant, for the time being not determined,
and the value - - - - - - _ .
V ~Z~ ~ X Uy
in (z~zg)
(5)
_ can be considered as not dependent on x.
The experiments show that the vertical flux of admixture near the earth
- in actuality is proportional to the surface concentration. The V(z) g~ir-
- ameter, which is usually called the "rate of dry precipitation," is de-
pendent both on the pro~erties of the admixture and the underlying sur-
face and on meteorological conditions [20]. Usually the rate of dry pre-
cipitation is determined from the measured concentration and the flux of
admixture at some altitude (in the atmosphere about 1 m above the un-
derlying surface). In principle, at ti~is attitude it would also be pos-
- sible to stipulate boundary condition (1), but in a number of cases it
is desirable to relate it to some universal height, for example, to the
roughness level z~, where a boundary condition is also set for wind vel-
ocity. Then, using (1)-(5), we obtain a correlation between zg v and
V(z) _
1~ (zo) = v = ~ (~1
1- ~ ~s~ ln (z;ro) ~ ~6)
_ x u~,
" / ~.u;~l (7)
za=roexp~- v~
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We note that under the total reflection condition (v ~ 0) we ha~~p z~ 0,
whereas with complete absorption (v ~ oo) the zg value tende to z0, which
' also determines the range of its possible values.
We must emphasize again that in the problem of diffusion of an admixture
- the formulation of the boundary condition (1) with z= z0 is no more than
a convenient mathemdtical procedure ensuring a single boundary for problems
of diffusion and dynamics. The q(zp) value figuring in (1) does not coin-
cide with the real concentration at the leve' z~ because expression (3) is -
not applicable in the immediate neighborhood of the underlying surface. In
particular, one must not identify q(z~) with the mean concentration at
the earth's surface qs, which within the framework of the considered for-
mulation does not enter into the problem at all. In this respect this ap-
proach differs from [7, 12], where the concept of near-wall resistance and
diffusion roughness is introduced for different scalars, taking into ac-
count the difference between the concentrations at the level z and at the
underlying surface.
Naturally, the zg values determined using (4)-(7) coincide with those in-
troduced in [12], but with qs = 0. However, expressions (1)-(7) can be
employed, uaing only experimental data on the flux and concentration at
some level z within the effective limits of the logarithmic law. Using
- the V(z) value it is easy to determine the boundary condition parameter
v. The relationship between V(z) with different z for w~ 0 is given in
[14], and between V(z) and v-- in [4].
Taking into account everything said above, we note that study [18] in our
opinion is methodologically incorrect. Its authors, introducing a laminar
sublayer and determining the concentration jump using the empirical for-
mula �or temperature, attempted to correct the v vaiues determined in
[4] on the basis of experimental data in the atmosphere. The negative
values of this parameter obtained in [18] are attributed not to the "in-
accuracy in experimental data," but to the inadmissibility of such use
of them. The authors of [I1] allowed the opposite error, specifically,
they attempted to find a coefficient characterizing the entrapment of
the admixture in the laminar sublayer, correcting measurement data for
- the concentration and fluxes only in the turbulent layer.
Now we will discuss in greater detail the problems involved in precipita-
tion of an aerosol admixture onto the underlying surface. It follows from
general considerations that L-or very heavy particles the flux is determin-
ed entirely by the rate of gravitational settling (v = w). This is also
confirmed by experimental data [2]. For a weightless admixture (w = 0)
it is desirable to assume v= bu,~, where b is a d.imensionless coefficient,
which, speaking in general, can also be dependent on u*. In a general case
the v value is determined as some function of w and u*. As noted in the
review [19], the precipitation r~f particles is determined for the most
part by the properties of the flow near the wall; in this case a very im-
portant role is played by turbulent fluctuations, which penetrate into
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the laminar sublayer. Therefore, for describing the precipitation of par-
ticles it is necessary to have a more detailed knowledge of the near-wall
region than for comFuting the transport of momentum. In the case of very
small particles it is neceaeary to take Brownian diffusion into account,
and in the case of larger particlea their inertia. The influence of
wall roughn~sa is extremely great, although it is known poorly; both the
z~ values and the form of the elements are important.
The principal results of the theoretical studies used in [19] relate to
- smooth walls and the experiments were carried out in wind tunnels. The
authors of [19] for a smooth wall recommend the e~cpression
b - = AS~,
*
where for spherical particles
$,r=0,~5(rtvdlZ p, ~9)
~ 1
(d is particle diameter, o'and f~ are the densities of matter in particles
- and air density, 'v is the kinematic viscosity of the air). Expressions
(8)-(9) can be used with z~u*/y< 0.13, 0.3 < S,~< 8. In the atmosphere the
first of these conditions is satisfied only for such surfaces as ice, _
snow and calm water when there is a weak wind. The numerical coeff icient A
in different theoretical evaluations varies from 1.7�10-4 to 6.7~10-4; in .
experiments it was found that 4.7�10-4.
There are almoat no such recommendations in~the literature for rough sur-
faces. For example, the model of precipitation of particles onto the rough
walls of pipes formulated in [11] ia not generalized directly for an at-
mospheric flow around a surface since it is suitable only for intermediate
Reynolds numbers. The mode'_ computations of the v value cited in [11] re-
veal the presence of a minimum in the region of a diameter of particles of
about several micrometers and its gradual smoothing with an increase in
roughness, its considerable dependence on roughness an increase with an
increase in z0 for all sizes of particles, most conspicuous in the range
of their radii from 10-1 to 5 � m. The attempt at constructing a model for
the precipitation of particles in the vegetation layer in the case of a
real atmosphere [6] cannot be considered completed.
, The results of experimental determination of the V(z) and v values under
different conditions for particles of different sizes with different chem-
ical properties can be found in [2, 14, 20], and for gases and aerosols
under natural conditions also in [15-17, 21J. All the experimental data
- exhibit a great scatter, associated both with the fundamental difficulties
in measuring the sought-for values and with uncontrollable factors, of
which there are a particularly great number in natural experiments. As
follows from [2, 4], for many surfaces which do not very greatly inter-
cept particles, v exceeds the measured V(z) values by not more than 30-40%.
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For strongly intercepting surfaces, such as moist grass, fabric, artificial
grass, the v value can exceed V(z) by an order of magnitude or more. In a
number of cases, with finite V(z) values it becomes virtually infinitely
large; in other words, these surfaces completely absorb the tiny particles -
falling on them.
The most complete experimental results of determination of V(z), both un-
_ der field conditions and in a wind tunnel, are given in [14]. The depen-
dence of the V(z) value obtained here, virtually coinciding in this case
with v, on particle size d shows that in the range of diameters greater
than 10 � m with suff icient accuracy it can be assumed that v= bu,t + w; b
is virtually not dependent on d and u*. With a decrease in d to l� m the
b value decreases by more than an order of magnitude; in the region d N 1
�m there is a minimum, after which with a further decrease in particle .
diameter b increases as a result af Brownian diffusion (the dependence
qualitatively coincides with [11]).
4o Hu.~4 �P H/Q
Qo
- 10Z
Q) Po 1'
2
3 -
10y
.
- \ _
\
10s 1
~ Z ~.S
~ 4. 1;~
Xo/H
~!0 '
b) ~ ~ ~i
~30 ~
\
~ ~ 3
ZO Z
~ S
\ ~
10
7
6
D ' '
J
l0"~` 10"s 10"2 10'~ 10� 10~ lOZ 10~ d
Fig. 1. Dimensionless maximum concentrations and flux of admixture
distance of the concentration maximum to proj ection of the source (b))innd _
dependence on b= v/u,~. 1-4) under condition (3), H/zp = 100, 500, 1000,
5000 respectively, 5) with U and K not dependent on z; 6) total range of
� experimental values b; 7) same for grass
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We will note the general properties of the V(z), v and b parameters
which make it possible to be oriented in the ranges of their valuea
cited in [2, 4, 14-17, 20, 21]. In the case of a horizontally uniform
- surface they are not dependent on the diatance to the source. Under or-
dinary conditions the b value can be coneidered nondependent on wind
_ velocity, so that v in actuality is linearly related to u*; but when
there is a considerable wind and a dry surface there is a tendency to a
decrease in b with an increase in u,~, evidently in connection with the
process of deflation of particles from surface elements~ In this proce$s
partieles less than 20 � m in diameter lie within the viscoue aublayer
and are deflated to a lesaer degree. All other conditiona being equal,
the v and V(z) ;-alues are always greater for chemically active particles
than for inactive particles, for a surface covered by vegetation, than ,
for a bare surface, for a moist surface than for a dry surface, with
an unstable stratification than with a stable stratification. For all -
the investigated surfaces v> w, although it is possible to visualize
a very smooth surface near which the heavy particles are accumulated,
but are not entrapped by it (see [22]).
In general, the minimum V(z) values, according to data in the literature, i
are 0.02-0.03 cm/sec for particles with a diameter less than 1-2 � m and
0.1-0.2 m/aec for particles with a diameter more t:zan 2 � m(under condi-
Cions of a weak wind, stability, slightly and moderately entrapping sur-
face). The maximum V(z) values with w< 2 cm/sec are 30-36 cm/sec (artif-
icial grass, moist canvas). For natural grass and soil in nature we give
the V(z) values from 0.2 to 8 cm/sec, in dependence on zp, u and stabil- -
ity. The ratio of the V(z) values for moist and dry surfaces is 1.1-2.8. ~
- The range of the value B(z) _[V(z) - w]/u* is somewhat narrower. For
natural dry grass and particles greater than 5 �m in diameter it is, for
example, from 0.01 to 0.08. The minimum v and b values coincide with the
- minimum V(z) and B(z) values, but in the dire.ction of maximum values
the range of v and b values widens virtually without limit.
The influence of dYy p~acipitation on the field of concentration of an
admixture in the atmosphere in the case of its modeling by the boundary
condition (1) is examined in [3, 8, 10, 22). For this use is made of
solutions of equation (2) with a linear source of the intensity Q at
the level H. Source [3J analyzes the expression for thz concentration -
and flux obtained in [10] with the parameters K and U not dependent on
z. It was shown that in the case v= 0, w~ 0 the concentration at the
earth's surface with x-~c~o tends to some constant value, which is asso-
ciated with the accumulation of particles precipitating from the atmo-
sphere near a complEtely reflecting discontinuity. However small v may
be, the admizture is expelled from the atmosphere and such accumulation `
does not occur. Sources [8, 22] gfve the results of numerical solution
of equation (2) with a logarithmic wind profile and with stipulation
of K in accord.lnce with (3); the boundary conditior~ is set at the level
z= z0. The cases v= 0 and v= w are considered in [22]. It was found
that with H~ 0 and v= 0 particle~ are accumulated near the boundary, but
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only under the condition w] xu,~. With some combinations of parameters
the surface concentration increases monotonically with an increase in .
x, completely n~t having a maximum.
Source [8] gives some special results of r_omputations with w= 0 and var-
iation of the v parameter Cn a broad range. Using theae, and also a num-
ber of additional results ~f computations, we will examine the dependence
of the characterist{.cs of the field of concentration of the admixture
near the underlying surfac.e on the boundary condition parameter v. Fig-
ure 1 gives the values of the normalized maximum (subscript 0) concentra-
tions and the coordinates of the concentration maximum as a function of
b= v/u* and H/zp. Here we have also plotted the full range of experi-
mentally determined values b and the range of values of this parameter
for natural grass. As mi~ht be expected, in the case of small b the con-
centrations, and in the case of gr�eat b-- the flux are virtually not de-
pendent on b. The mentioned limiting cases correspond to substantially
diiferent patterns of formation of the maximum surface concentration
and the maximum flux. With an increase in entrapment the maximum flf the
surface concentration is displaced toward the source.
r;
Q ~ ~ �
~ ~
/ \ 2
/ \
;J� ~ ~ _
- / \
_ / \ ,
' \ J
\
i ~
1C' / \ 1
/ \
_ / \
~ \
/
~ ? cm/sec
~a 9 ~ ~ ~
10 " 101 JO� ' f01 Vc.^r'cr,r
Fig. 2. Vertical flux of matter onto underlying surface in dependence on -
v at different distances. 1) 300 m; 2,) 1000 m and 3) 10 000 m; 1' and 2'
same as 1 and 2 for a model with constant U and K.
We note that the right-hand sides of the curves (b.> 5), in contrasr to
the left-hand sides, being constructed in nonnormalized coordinates,
merge for different u,~. The similar dependences for a model of diffusion
with a wind velocity and turbulent diffusion coefficient (here b= vH/K)
- constant with height, plotted in Fig. 1, show that qualitatively the -
dependence of PQ, q~ and :c~ on b remains the same with changeover to
- another model. However, x~ with constant K and U, is dependent on b
more strongly, which in this case is associated with the absence of a
layer poorly conducting the admir.ture.
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It is of interest to study the behavior of the concentration, and in
particular, the flux of the admixture onto the underlying surface far
from the source. With v= 0 it is absent, but however small v may be, it
ia greater than zero, and accordingly there can be an accumulation of -
admixture matter on the eoil. Figure 2 givea the resulte of numerical
c4mputation of the value P= vq with z~ zp, H/z0 = 100, u* ~~.2 m/sec
and different x values in dependence on the v value. It is intereating
that at any distance the flux behaves the same as at the point with the
maximum value: in the case of amall v it increases linearly with an in-
crease in v, whereas with large v it tends to a constant value and ceases
to be dependent on v. This is attributable to the fact that with small v
- precipitation onto the surface is limited specifically by this parameter, _
whereas in `'�~e case of 1;arge v-- by that limiting conductivi*_y which
can be en~ured by a surface layer with small values of the coefficient -
of turbulent diffusion. As indicated by Eig. 2, in a case wh~n K and U _
are stipulated as not dependent on z, with small v the dependence of
the P parameter on it is the same, although with a change in x the drop-
off occurs considerably more rapidly. However, in the case of large v
the flux at near distances is not limited and therefore at great dis-
tances (x~ x~) it decreases with an incre.sse in v as 1/v. In this sense
the result in [SJ, relating to the role of entrapment of an admixture ~
by the underlying surface at great distances, obtained when using a model
with a turbulent diffusion coefficient not dependent on z, is possibly ;
in need of correction. i
_
,
~
- i
!
_ 22
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BIBLIOGRAPHY
1. Bakulin, V. N., "Theoretical Analyeis ~f the Reasona for Poasible
_ Errors in Experimental Det~rmination of the Rate of Dry Precipita-
tion of Aerosols," METEOROLOGICHESKIYE ASPEKTY RADIATSIONNOGO ZAGRYA-
ZNENIYA ATMOSFERY. TRUDY MEZI~IDUNARODNOGO SIMPOZIUMA V G. TBILISI
(Meteorological Aspects of Radiatioa Contamination of the Atmoephere.
Transactions of the International Sympoeium at Tbiliai)(15-20 October
1973), Leningrad, 1976.
2. Byzova, N. L., RASSEYANIYE PRIl~iESI V POGRANICHNOM SLOYE ATMOSFERY
(Scattering of an Admixture in the Atmospheric Boundary Layer), Gidro-
meteoizdat, 1974.
3. Byzova, N. L., Kutsenogiy, K. P., "Influence of Structure of the Sur- _
face Atmospheric Layer and Boundary Conditions on the Dose Value and
. Density of Deposita," TRUDY IEM (Transactions of the Institute of Ex-
perimental Meteorology), Na 15(60), 1976.
- 4. Byzova, N. L., Makhon'ko, K. P., "Interaction Between an Aerosol and -
the L'nderlying Surface," IZV. AN SSSR, FIZIKA ATMOSFERY I OKEANA (News
~ of the USSR Academy of Sciences, Physics of the Atmosphere and Ocean),
Vol 4, No 9, 1968.
5. Vel'tishcheva, N. S., "Numerical Solution of the Turbulent Diffusion
Equation in the Variable Wind Field," METEOROLOGICHESKIYE ASPEKTY
RADIATSIONNOGO ZAGRYAZNENIYA ATMOSFERY. TRUDY MEZFIDUNARODNOGO SIMPOZ-
- IUMA V TBILISI, 15-20 OKTYABRYA 1973, Leningrad, 1976.
- 6. Dunskiy, V. F., "Inertial Mechanism of Precipitation of a Coarsely
Disperae Aerosol on the Earth's Vegetation Cover," DOKLADY AN SSSR -
(Reporta of the USSR Academy of Sciences), Vol 159, No 6, 1964.
7. Zilitinkevich, S. S., DINAMIKA POGRANICHNOGO SLOYA ATMOSFERY (Dynamics
of the Atmospheric Boundary Layer), Leningrad, Gidrometeoizdat, 1973.
8. Krotova, I. A., Natanzon, G. A., "Influence of the Underlying Surface
on the Propagation of a Weightless Admixture in the Atmospheric Sur-
~ face Layer," TRUDY IEM, No 21(80), 1978.
9. Monin, A. S., "Atmospheric Diffusion," USPEKHI FIZICHESKIKH NAUK (Ad-
vances in the Physical Sciences), 67, No 1, 1969.
10. Monin, A. S., "The Boundary Con~dition at the Earth's Surface for a Dif-
fusing Admixture," ATMOSFERNAYA DIFFUZIYA I ZAGRYAZNENIYE VOZDUKHA
(Atmospheric Diffusion and Air Contamination), tranalated from English,
Moscow, IL, 1962.
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11. Brown, L. W., "Deposition of Particles on Rough Surfacea During Tur-
bulent Gas-Flow in a Pipe," ATMOS. ENVIRON., Vol 8, No 8, 1974.
12. Bruteaert, W., "The Roughneas Length for Water Vapour, Sensible Heat
and Other Scalars," Jo ATMOS. SCI., Vol 32, No 10, 1975.
13. Calder, K. L., "Atmospheric Diffusion of Particulate Material, Con-
aidered as a Boundary Value Problem," J. METEOROL., Vol 78, No 3,
1961. ~
- 1-
14. Chamberlain, A. C., "Transport of Lycop~dium Spores and Other Par-
ticles to ltough Surfaces," PROC. ROY. SOC., A, Vol 296, No 1444, 1967.
15. Garland, J. A., Atkins, D. H. F., Readings, C. J., Caughey, 5. J.,
"Deposit~.on of Gaseous Sulphur Dioxide to the Ground," ATMOS. ENVIRON.,
_ Vol 8, No 1, 1974.
, 16. Dropps, J. G., "Field Measurements of Dry Removal Rates of Air Pol- .
lutants Over Vegetati~n Surfaces," ATMOSPHERE, Vol 15, No 140 (abatr.)
1977.
17. Garland, J. A., "The Dry Deposition of Sulphur Dioxide to Land and
Water Surfaces," PROC. ROY. SOC., London, A. 354, No 1678, 1977.
18. Jordanov, D. L., D3olov, J. D., "On the Interaction of Diffusing Ad-
. mixture With Earth Surface," DOKLADY BOLGARSKOY AN (Reports of the
Bulgarian Academy of Sciences), 29, No 6, 1976.
19. Kneen, T., Strauas, W., "Deposition of Dust from ~rbulent Gas
Streams," ATMOS. ENVIRON., Vol 3, No 1, 1969.
20. METEOROLOGY AND ATOMIC ENERGY, 1968. U. S. Atomic Energy Commission.
Division of Technical Information. Translated: METEOROLOGIYA I ATOM-
~ NAYA ENERGIYA, Leningrad, Gidrometeoizdat, 1971. ~
21. Owers, M. J., Powell, A. W., "Deposition Velocity of Sulphur Dioxide
on Land and Wa.'ter Surfaces Using a 35 S Tracer Method," A1'MOS. ENVIR- .
oN., Vol 8, N~ 1, 1974.
22. Shreffler, I. H., "Numerical Experimentation With Particles Having
Non-zero Terminal Velocity in the Atmospheric Surface Layer," BOUN-
DARY-LAYER METEOROL., Vol 9, No 2, 1975.
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. UDC 551.510.42
PREDICTION OF AIR CONTAMINATION OVER THE APSHFRON PENINSULA
Moacow METEOROLOGIYA I GIDROLOGIYA in Russian No 2, Feb 80 pp 21-26
[Article by Candidate of Physical and Mathematical Sciences A. A. Gorchi-
yev and R. M. Rafiyev, Institute of ~pace Investigations of Natural Re-
sources Academy of Sciences Azerbaydzhan Academy of Sciences, submitted
for publication 11 June 1979]
Abstract: The author proposes a method for the
ahort-range forecasting of the mean concentra-
tion of S02 and N02 by means of the method of
expansion of variables in natural orthogonal
functions. In contrast to other studies, here
- it is proposed that use be made of the coef-
ficients of expansion of the fielde of concen-
tration of S02 and N02, wind velocity and the
vertical temperature profile as pr~dictors. The
probability of success of such a prediction for
S02 and N02 is 58.2~ and 60.4X respectively. -
_ [Text] At the present time a short-range forecast of air contamination for
several days in advance is of practical interest. Interest in short-range
forecasting can be attributed to the fact that in many cities and indus-
trial centers the di~charge of harmful substances into the atmosphere and
their concentration in the air are very high. However, it is not always
possible to move large aources of air contamination far beyond the limite
of a city. The need therefore arises of lesaening the discharge into the
atmosphere during periods of time when unfavorable meteorological condi-
tions arise and a high level of air contamination can be created in resi-
dential areas as well. The prediction of contamination of the air basin -
on the basis of data on meteoxological conditions makes possible the
timely adoption of ineasures for the purpose of preventing negative con-
sequences, or at least, limitation of their scales. For example, in the
- case of prediction of a dangerous concentration, exceeding the sanitary-
hygienic norme, it is possible to shift to the use of a different kind of
fuel (from mazu't to gass etc.).or temporarily shut down functioning appar-
atus.
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The problem of the influence of ineteorological conditions on the concentra-
tion of harmful admixtures in the atmosphere has been examined both in
theoretical investigations [3, 4] and in studies of analysis of actual ob-
servational data [2, 7].
The authore of thie article have devoted their main attention to the depen-
dence betwPen weather conditions and air contamination in general over a _
major city, Baku. For this purpoae we use the firat coefficients of expan-
aion in natural orthogonal functions (NOF) of the fields of concentrations
of S02 and N02, as was first done in [5].
Taking into acco~tnt the meteorological characteristics of the Apsheron Pen-
insula, as predictors we included individual meteorological elements which
in the ma~ority of cases are characteristic for a stable state of the atmo-
aphere in the presence of temperature inversions. It follows from [6] that
the Apsheron Peninsula is characterized by high frequencies of recurrence
" of temperature inversions, which in the lower kilometer layer of the atmo-
sphere constitute almost 75y per year. It is also important to take the
nature of the wind regime into aceount. The mean annual wind velocity
here, according to long-term data, is 6.3 m/sec, wherezs the number of days
with a wind velocity greater than 15 m/sec averages 67 per year.
For tl:e analysis and prediction of the degree of air contamination as the
initial information we used five-year data for the su~er season (1971-1975)
on the concentration of harmful admixturea (S02 and NOZ) at 17 observation
points in Baku and also air temperature data from aerological stations at -
ten standard levela ~0.03; 0.20; 0.33; 0.50; 0.63; 0.75; 0.93; 1.00; 1.50
and 2.00 km) over the Apsheron Peninsula and wind velocity data for nine
~ stations situated around Baku (Astara, Baku, Mashtagi, Neftechala, Neft-
yanyye Kamni, Artem Island, Zhiloy Island, Svinoy Island and Sumgait).
In contrast to [5], in preaicting air contamination we did not use the
meteorological elements themselves, but their coefficients of expansion in
NOF, vertical temperature profiles and wind velocity fields.
It must be noted that the rate of convergence of the expansion for tempera-
ture is considerably greater than for the SOZ and N02 concentrations. Thus,
in the mentioned season the first term Qf the SOZ concentration expansion
describes 55-63X, the first two terms 65-70% of the total variability,
= and N02 37-40~ and 53-55% respectively. For the vertical temperature
- profile the first term of tt~e expaneion describes 92Y and the first two
terms 979~ of the total variability. The first eigenvector usually well
describes the main vertical variation of temperature deviations from the
mean profile, which corresponds to a definite character of the interlevel
_ co~relation between the temperature variations at the ground and its varia-
tiona at higher-lying levels. The second vector describes the variation of
the additional correction to the temperature variations caused by the in-
fluence of the underlying surface.
~ 26
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Table 1
Correlation Coefficient Ri Between q and Coefficients of Expansion in NOF
a, I as I aa ai I a'r I aj ( a2 I /r~
,
9so~ 0,96 0,11 0,05 -0,17 -0,03 -O,19 0,03 1,00
~N o~ 0,98 0,06 0,04 -0, l 1 -0~01 -0,19 0,02 1,00
For wind velocity the first term of the expansion describP~ 59%, and the
first two terms 75y of the tntal dispersion; the first E~DF character-
izes the principal characterist,.cs of the spatial behavior of the wind
velocity field, its aimultaneous changes in general over the Apsheron Pen-
inaula.
The question as to how many coefficienta of the expansion of each epecific =
field must be included in the forecast is extrem~ly important becauae with
the dropping of a part of the NOF there is a decrease in the accuracy of
Yepreaentation of the initial fields. In selecting the number of NOF, from
the set of which the archives of propoaed predictors is formed (that is, _
the test predictors), it is necessary to be guided first and foremost by
the epatial scale of the considered fields; in each specific case for this
purpose we analyzed the form of the isolinea of the NOF and the behavior of -
_ the differences of the eigenvalues coxreaponding to them [8]. For the pur-
pose of predicting the mean concentration of S02 and N02 for the firat and _
second halves of the day for two days in advance in the set of test-predic-
tors we included the three coefficients of expansion of the concentration
fields �1, ~2 �tg and also the coefficients of expansion of the temper-
ature fields oc L, o[ 2 and wind velocity oC i, ~ 2 in NOF.
The teat-predictors (al, ~'2, a3), (�~1' �C T2) and (ai, �G 2), representing
the coefficients of expansion of different elements, can be related to one
another and are not orthogonal. The latter circumstance required the use
of orthogonalization of predictors. A new orthogonal system of predictors
was obtained by the maximum pro~ections method [1]. In subsequent stages
of prediction the test-predictors were sub~ected to further analysis and
re~ection of unsuccessful predictors. For this purpoae we computed the
correlation coefficients Ri between the S02 and N02 concentrations and the
i-th expansion coefficients. 1'he results of the computations are given in
Table 1, where R2 Ri is the square of the multiple eorrelation coef- -
ficient.
In accordance with the table we carried out a so-called "revision" of all
the predictors. As a result, the predictors ~ 3, ~ T2 and aC~2 were diacard- _
ed. Thus, for predicting the mean concentration of 502 and ft02 for the city
27
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as a whole as the predictors we include two coefficients of expansion in
NOF one coefficient each for expansion of the vertical temperature
profile and the wind velocity field. The first group of predictors (~1,
OC2) takes into account the influence of inertial factors, the second
- (ocT) and third (oC groups in turn take into account the conditions
forlvertical and horizontal ditfusion of harmful admixtures. In combina-
tion these parameters determine the state of local and general circula-
tion procesaea, on which, for the most part, is dependent the distribu-
ticn of harmful admixtLres in the atmosphere. The principal contribution
to the eneral level of atmospheric contamination is from ~1 and in -
part a~ and Ot~l. The correlation between the mean concentration of S02
and N02, atma~nFieric stratification (ali) and wind velocity (oLi) is neg-
ative (0.17; -0.11 and -0.19; -0.19).
Rlr~ Ql
0, B
1
0, 4 1~~
~
?v~~ ~ -
` yc� ~ ~,l~~
0,0 _ ' ~
~ 1 ~
6)
0, B
U4 ~v � 1 Zd
~ ,
~ hours
0 9 B >Z 16 74v
Fig. 1. Autocorrelation fur~.:.tions of first (1), second (2) and third (3)
coefficients of expansion of the fields of concentration of S02 (a) and
N02 (b).
It is known that the first coefficient of expansion of the concentration
of harmf.ul admixtures (ocl) characterizes the level of general contamina-
tion of the atmosphere in a city and is more closely related to the
meteorological situation [5]. The scheme makes use of the oCl values ob-
tained using data from measurements made two days prior to the time for _
which the forecast is made. The degree of coherence of the series of con-
centrations w3s evaluated using correlation functions computed using the
formula
_ 1. .N_s .
1V T~ at a?~-T - ai
- R 1Z) = nr_s t_1 N-: ~1r ~ ~1~
~ 1 j' 2 -2 I ~ 2 -2
N-t ~r a~ -at N-T aj+~ -a2 -
l-1 1=1
Z8
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where ocl and n~ are the mean values of the firat N-Z and last N- t terms
in the aeries re~pectively; ~G is the lag (time shift), 'G m 2, 4, 6,...
da~?e. Theee correlation functione describe large-ecale meteorological
diaturbancea.
Figure 1 ahows the time correlation function of the parameters ~1,
and o~3 during the mentioned period of the summer season for sulfur gas
and N02, computed on a"BESM-6" electronic computer. It followa from an
analyeis of variation of the functions R dl (-G R oc2 ( t) and Ra 3('G) for
S02 and NOZ that the most prolonged correlations are obtained for the
first expansion coefficient; for the aubsequent coefficients, as a rule,
the time correlation radius decreases. Figure la shows that in the city
for al of aulfur gas a high correlation Ro[1(ti) ~ 0.5 is retained to the
interval 'G = 6 days, and for N02 to 'C = 4 days (Fig. lb). Such a var-
iation of R a 1(-~) is evidently attributable to the presence of a stable
- "contamination cap" over the city. After two days the correlation coef- -
f icient for S02 and N02 is 0.67 and 0.54 respectively. On the basis of
these investigations it can be assumed that this information on the state
of the air basin over a city is adequate for the prediction of atmospheric
- contamination.
- The initial data for the element to be predicted for the forecasting sta-
_ tion were the deviations of the mean concentration from the sample mean
separately for each of 17 observation points. The problem involved a de-
termination of the predicted values p qii for two days in advance (anom- -
aliea of the mean concentration for the i-th day for point with the
uae of four orthogonal expansion coefficients. For this purpose we used
the multiple regression method. The anticipated value 4 q~~ i+2 for two
days in advance in this case was determined using the regression coef- -
ficient ~ - ~ ~
- _ z
0 q~, r+~ = bja~ r-}- c~a~ ~ Q~~GCnj. ~2)
n-1
J The weighting coefficients an~, b3 and c~ were computed by the least
- squares method, that is, in such a way as to satisfy the cond~tion
M - - 2
~ ~ 91, r+'= - bl ai C~ al 1-}- ~ a�~ a�i = m i n. ( g)
t-l n=1
The finding of the coefficients an~, b and c~ is~considerably simplified
_ if we take into account the property o~ the orthogonality of the expan- -
sion coefficients. - - _
rxk!'Qf!'=~~ ~1~~~ _
J ~(F ~
where ~ ki and correspond to ~n~ ,�C li and OC ii'
Differentiating equation (3) for an~, b~ and c~ and equating it to zero, -
we obtain a system of normal equations which is solved relative to the
, weighting coefficients. As a result of condition (4) the coefficients ~
29
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an~, b~ and c~ are found from the expressions _
1. 2.
an/ y Q~, i-{-4 ani~ n-~~
�nl 1
1
~ (5)
b~ lai
i~' Q~. i+a ai
i
_ i
~
~l a~~a~ ~z Ql, r+~ ai
i.
- !
In order to check the possibilities of the resulting forecasting schemes
we conatructed a series of maps on the basis of a priori material. An
evaluation of the success of the forecaat of the mean concentration for -
the city in the first and second halves of the day was made separately
for the three groups. For sulfur gas: low (0.00-0.15), increased (0.16- -
0.29) and high 0.30), an3 for N02 the corresponding figures were: low
(0.00-0.040), increased (0.041-0.084) and high (~j 0.085). The results of
this checking are given in Table 2, from which it follows that according
to a priori data the total probable success for S02 is 58.2%, and for N02
60.4X.
Table 2
Probable Succesa of Forecasts Using A Priori Material, in %
KonHVecreo
IlporxosxpyeMag nporeo3oe 3 Onpasaw�
17pKMecb rpynna eaes:ocrb
xcn~rax- onpae-
_ 1 2 xdx q AaHxwx 6
SO' floxxxceitxaR 7 3 2 66,7
TioB~tmeeFtaA g 76 43 36,6
BdcaxaR 9 12 8 66,?
06u~ee lQ 9l 53 58,2
NOz iloxxxceaxaA 3 2 66,7
[los~wexxaA 75 44 58,7
B~coxaa 13 9 69,2
06ntee 91 55 60,4
KEY:
1. Admixture 7. Low
2. Predicted group 8. Increased
3. Number of forecasts 9. High
4. Tested 10. Total
= 5. Successful
6. Success
. 30
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In order to take sanitizing and prophylactic measures the "high" category
is af the greatest interest. The probable success of this group for SOZ
ia 66.7%, and for N02 69.2~.
If one also takes into account the number of forecasts not falling into
the predicted group, but close to it, the probable success for S02 ie in-
creased to 81.8X and for NO2 to 86.5~. The probable success of the fore-
casts was evaluated using the index
n+ + 0,5 nn,s
Q= N .
where ~ is the number of cases in which the actusl and predicted values of
the concentration fell in one group, n~ 5 is the number of cases in which
the predicted and actual concentrations~fell into ad3acent groups, N is
the number of tests.
In conclusion it must be noted that the considered schemes are now being
used in routine work.
The authors express deep appreciation to M. Ye. Berlyand and Ye. L. (;en-
ikhovich for a number of valuable proposals and comments, used in these
investigations.
BIBLIOGRAPHY
1. Bagrov, N. A., "Orthogonalization of Random Values," METEOROLOGIYA I
GIDROLOGIYA (Meteorology and Hydrology), No 4., 1976.
2. Bezuglaya, E. Yu., "Determination of Air Contamination Potential,"
TRUDY GGO (Transactions o~ the Main Geophysical ObsErvatory), No 234,
- 1968.
3. Berlyand, M. Ye., et al., "Numerical Investigation of Atmospheric
Diffusion Under Normal and Anomalous Conditions," TRUDY GGO, No 158,
- 1964.
4. Berlyand, M. Ye., "Dangerous Conditions for Atmospheric Contamination
by Industrial Effluent," TRUDY GGO, No 185, 1966.
5. Vavilov, N. G., Genikhovich, Ye. L., Son'kin, L. R., "Statietical An-
alysis of Data on Air Contamination in Cities Uaing Natural Functions,"
TRUDY GGO, No 238, 1969.
6. Vdovin, B. I., Gorchiyev; A. A., "Typical Temperature Profiles in the
Lower Kilometer Layer of the Atmosphere Over the Apsheron Peninsula,"
TRUDY GGO, No 238, 1969.
7. Son'kin, L. R., Razbegayeva, Ye. A., Terekhova, K. M., "On the Problem
of the Meteorological Causes of Air Contamination Over Cities," TRUDY
GGO, No 185, 1966.
31
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8. Yudin, M. I., Meshcherskaya, A. V., "Some Evaluations of Natural Com-
ponents as Predictora and Predictants," TRUDY GGO, No 273, 1972.
~
; i
; ;
32 !
~
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- ~ ' !
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UDC 551.577.13
INFLUENCE OF DIRECTION OF TRANSPORT OF AIR MASSES ON THE CONTENT OF
ORGANIC MICROADMIXTURES IN PRECIPITATION
~ Moecow METEOROLOGIYA I GIDROLOGIYA in~Russian No 2, Feb 80 pp 27-31
[Article by L. A. Volokitina and V. S. Shuklin, Institute of Experimental
Meteorology, submitted for publication 24 April 1979]
Abstract: The suthors determined the concentra-
tions of organic carbon in a number of precipi-
tation samples. The influence of the direction
of transport of air masses on the content of or-
anic microadmixtures in precipitation in the cen-
tral part of the European USSR is examined.
[Text] In order to understand the physicochemical procesaes transpiring
in the atmoephere it ia necessary to know the chemical composition of
aerosols. One of the principal ways in which serosol reaches the earth's
- surface ia precipitation, with which more than 50X of all aerosol matter
falls out [2] and the chemical composition of whose admixtures, accord-
inglq, characterizes the aerosol in a particular air masa. Inorganic ad-
- mixtures in prec~ipitation have been invest3gated relatfvely extensively
[lOJ. It has been established that their concentration ia in the limits
1-100 mg/liter. Organic admixtures in precipitation have been determined
sporadically [7, 9], which is due to the diversity of the admixture types
and the complexity of the chemical methods for analysis of organic com-
pounds. However, organic admixtnres constitute a considerablP fraction of
the total contamination of precipitation. The concentrations of organic
aubstances in precipitation are close to those in the surface waters of
the land [9]. Therefore, the data characterizing organic admixtures in
precipitation are also of interest in a study of problems in geochemistry
and hydrochemistry (chemical weathering processes, balance of organic
substances for water bodies, formation of chemical compcsition of surface
waters).
It should be noted that for an anthrbpogenic aerosol it is possible to ex-
- pect a relatively great quantity of organie substancea since man uses
_ mostly organic substances as food products, raw material and fuel.
33
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This makes it possible to hope that an inveatigation of the relationship
between the contamination of precipitation by organic admixtures and the
tranaport of air masses will help to evaluate, on a regional scale, the
influence of different sourcea of anthropogenic ~ontaminations. For this
purpose in this study we have determined the concentration of organic ad-
mixtures in precipitation, taking into account the direction of transport
of air masses to the point where precipitation samples are taken.
The water in precipitation contains a number of organic compounda: hydro-
carbons, alcohols, ethers, acids, etc., most of which have a photolumin-
escent capacity.
However, at roon temperature (T 300 K) the luminescence spectra of these
- compounds ovarlap, which make virtually impossible the photoluminescent
determination of individual compounds in such a mixture. In this case
with a constancy of the component makeup of admixtures it is possible
to determine the total quantity of organic admixtures in solution.
Investigation of the photoluminescent characteristics of precipitation
water has shown that the luminescence spectra of precipitation of dif-
ferent aggregate state (water and snow) differ little from one another
and change insignificantly from season to season [6], which confirms
data on the relatively constant (from the point of view of the content -
of the principal groups of organic compounds) composition of the organic
component of atmoapheric aerosol [11J. This, in turn, makes it possible
to use the intensity of luminescence for determining the total concen-
tration of organic admixtures in precipitation. For this purpose in this _
atudy we have found the relationship between the level of luminescence
of grecipitation and the concentration of organic carbon in it, the frac-
tion of which in precipitation water accounts for approximat~ly half of
all the organic matter [7]. ~ ~
c,~ Hr/n ~org ~?S/liter ~
~s R
.
. -
~o
. x
x
s x ; x
.
x x
xx
x
a2 q~ 4s E
Fig. 1. Dependence of concentration of organic carbon in precipitation
water on its luminescence level. _
34
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= Table 1
Concentration of Organic Admixtures Corg in Precipitation and Meteorological
Parameters on Days of Collection of Samples at Obninsk in 1976
Clpeo6na-
g~ C A810LL(HA Hanpa~neuNe ne,pe�
I~lara ocaAKOe �P~ aere~n ttoca Ha eacore pa~ox~c sdHOCa ~opua
1 2 ~z/n 3_3~
~ ~~5 ~ 5 eosAynrH6g ~acca o6na~ e
27 T cHer 8 8,0 IO[~B aana~~ioe 12 Ceee~xoe Hope ?p 5t
29 I ro ;iceg 6.4 10 aana;yti~oc ~,eBCqmoe Mope ' Frr.b, A6
30 I s 8,0 I03 roHC}~oe 13 Cpe,~xaeHxoe Hop~ St
2 II s 8,0 IOI03 ceaepo-aocrowxoe ceeep 3artaaHOti St
2IYI s 14,0 Ip3 _ 14 Cx6~?px 22
- ' 4 Ijj s 28,0 IOI03 a~cxoe 13 ~Iepxoe etope 23 As
5III a 12,8 IOI03 socro~HOe Cb
17 III ,a0?KAL 21,6 }p ~o~cHae 15 24 3arta~Haa CFt6Npb Frnb, Ns
13 ~tepHOe Mope Sc, Cb
5 IV cxer 3,2 3I03 ceeepo-aanaANce � Ceeept?aA Arnat~- Sc
13 v AOxtAb], . 22,4 g 16 rxxa 25
~oxct~oe 13 ~Iepr~oe Mope Cb, Sc
18 V ro ;~ce 4,8 IU3 aana~xoe Ceaepxoe Mope Cu
19 V } 3,? C3 ceeepxoe Cxaxu~sct~ani~A Cb
7 VI , 3,g 3 w~110e 13 ~Iepxoe ntope Cu
IO aartafutroe Cesepuoe Mope Cu, Ac
]6 VI a 4.0 BCB satta~~HOe Ceeept+oe Mope Cu, Ac
~ ~ ~ I ~ 5,2 CB ?aaa ~atoe Ceeepxoe Mope Cu
4 VIII x 8,0 IOIOB - .'1s, Cu
6 VIII ~ 3,6 IOB sana.qa~oe CenopHOe Mope ~ Cb, Cu
5 X ~ 6,8 CC3 ~o;xxoe Cpeli~iaeHHOe Hope Sc
12 X cxer $ 3,6 F03 ;^eBCpt~oe 17 JleAOet?r~ti oxeak As, Frnb
1 XI AoxcAb 12,8 CC3 3ana,~HOe ~i� 6ann~kcxoe ~tope Sc, Frnb
30 XI ro x:c 12,0 CC3],g toro�3ana,tHCe 28 3a,naAxas Ee~pona Sc, Cb .
9 XII c~?er l;,2 CBl ~oru-DOCro~rt~oc 29 MaaaR Aa~~R Cb
13 XII ro :fce 8,0 3103 bxcxc+e Cp0AN3eMNOC S10pe Ae(Cu)
14 XII s 4,0 3I03 ax:.xoe CpHAx3eKxoe Hopc Ac(Cu)
_ i7 XII r 10,0 3f03 aHCa~oe CjJtJ(H30M1t08 MOrC Sc, Ac
18 XII s 17,2 3C3 ioxcaioe 13 CpeAxae~cxoe Mope Cb, Ac
20 ~1i ~ 24,U !03 bro-socro~xoe 13 Mana� AsxA 29 Cb St -
,
KEY:
1. DRte 16. Northwesterly
2. ?ype of precipitation 17. Northerly
3. C,~rg, mg/liter 18. Southwesterly
4. Prevailing wind 19. Southeasterly _
5. Transport direction at 1.5 km 20. North Sea
6. Regions of air mass emergence 21. Mediterranean Sea
7. Form of precipitation 22. Northwestern Siberia
8. Snow 23. Black Sea
9. Same 24. Western Siberia
= 10. Rain 25. North Atlantic
11. Abbreviations (see next page) 26. Arctic Oc:e~in
12. Westerly 27. Baltic SE~a -
13. Southerly 28. Western Lurope
14. Northeasterly 29. Asi~ Minor
15. Easterly
35
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ABBREVIATIONS IN TABLE
SSE ~~0 FO B ENE = gCB
S ~ ~ NE = CB
sw ~ ~3 ssE = Fo roa
- SSW ~ F01~ 3 SE = FO B
- wsw = 3~3 wsw = 3 Eo3
E = B NNW = CC ~ -
NW ~ C3 NE = CB
w = 3 wNW = 3C3 . ~
- According to measuremente of 1976, the amplitude of the fluctuations of
concentratione of organic admixtures in the collected precipitation is
3-30 mg/liter. Proceeding on the basis of the idea that during the move-
ment of air masses over great distances there is an accumulation of aero-
sols in clouds, including organic admixtures, it was natural to assume
that the difference in the concentration of organic admixtures in pre-
cipitation collected at a given point was caused, as in casea of trans-
= port of inorganic admixtures [1, 5], by a change in the direction of ar-
rival of air masses (advection) at a particular point.
Before determining the ~ependence of the concentrations of admixtures in
precipitation on the direction of distant transpcrt (advection), we _
checked their dependence on wind direction at the measurement point. All
casea of ineasurements, in dependence on the prevailing wind directiona
were divided into four groups corre~ponding to the principal directions
- of the compass. _
For each group of days with a particular wind direction we computed the
mean concentration of organic admixtures in precipitation. We did not dis-
cover aignificant differences in the concentration of organic admixtures
in the water when there were winda of different directions. It can there-
fore be concluded that the concentration of admixtures has little depen-
_ denc~ on local sources. In order to detect the dependence of the concen-
tration of admixtures on the origin of air masses we carried out a com-
parison of the concentrations of admixtures in dependence on the region
from which the air mass arrived. The region of air mass origin was deter-
mined on the basis of the 2- or 3-day trajectories from the sampling
point, constructed graphically by the known method [3]. AT850 diagnostic
pressure pattern charts for 0300 and 1500 hours were used. The true air
particle tra~ectories are more complex than those constructed from the _
pressure pattern charts since each air particle is also displaced ver- ~
- tically, which is caused by vertical movements of different origin. The
tra~ectories constr~,~cted by the graphic method and taking into account
only horizontal movctments are the averaged traj e~tories of many air par-
ticlea.
, ~
! 36 ~
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H
H
N ~
~ O ~t �
~ ~ ~ u1 I 1 ^ C~l
E~ a~ ^i ~ O~ ~
a ~ ~ `d
u
C! .t"� I-1 y�r
a~i ~ a �
p :J N ya
d
A ~ y ~ +I 1 I ~O
~ p ~ CO I I ~
~ w ~ p ~ ~
.C
~ ~
cd ~ O "i
~ d ~ q
~b
~ ~ ~ ~ w
o~ ~ r~ o o r~ ~
a~ ~ a N ~ ~ ~
~
~ ~ ~ a -
~
_ ~
~
~
~
~
~ ~ ~ ~
~ ~
F~+ O Cl N o0 ~O o0 W
- V ~i f7~ ~N C~~ ~ ~ a
O ~
0~! 0 !~-i 3 ~ ~
~
, ~ N ~ 7
~ ~ d
'b
~q H
6 e~f c~
- ~ _ D+ � p
~ O ~ N tC N M ~~.1
~ H ~ p M ~ ~
W r~l ~ 'hl .C
z M
o~ ~
u ~
~
q �U
a~ ~
c0
~ ~
G! op
- U ~ ~ _
i ~
- ~ ~ 0 k ~ ~
~ P4 ~ U ~ U A Z
,
36a
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The method for collecting samples involved the following: liquid precipi-
tation was collected using a glass funnel into a beaker, whereas solid pre-
cipitation in the form of snow was collected after its falling onto the
underlying surface. The photoluminescence of samplea of water in precip-
itation was excited by the radiation of a nitrogen laser with a wavelength
337.1 nm. The luminescence apectrum was measured using a DMR-4 mono-
_ chromator and a FEU-51 photomultiplier. The spectral width of the slit in
the region of the maximum of luminescence intensity ( ~1 = 380-390 nm) was
Da � 6.3-6.4 nm. The photoluminescent measurements method was described
- in detail in [6]. The concentration of arganic carbon in precipitation
water was d,~termined from the permanganate oxidability of this water [8].
The figure ahu~s the dependence of the luminescence level of individual
precipitation samples on their concentration of organic carbon. In this
case the error in measuring luminescence intensity did not exceed 10-20%
[6]; the uncertainty with respect to the concentration of organic carbon -
was determined by the uncertainty in the correlation between permangan-
ate oxidability and the concentration of organic carbon and was a value
of about 30y [8]. On the assumptior. of a linear correlation between the
lumitiescence level and the concentration ef organic impurities in the
water we used the least squares method in obtaining a direct regression,
described by the equation Corg =-0.2 + 20.8 E[mg/liter]. The linear _
- correlation coefficient was 0.91; the dispersion for the slope of the
regression line was 2.2. The existence of such a correlation made it
possible to :etermine the concentration of organic carbon (by the photo- _
luminescent me:hod) in small samples (volume 3-5 ml) of precipitation
water, whereas such a determination by chemical methods requires water
samples with a volume not less than 50-100 ml. It should be noted that
the concentration of organic carbon in the samples which we investigated -
Corg,~; 3-30 mg/liter does not contradict the data characteristic for the
precipitation in a number of regions in the USSR (Georgia, Arkhangel'-
akaya, Novoeibirskaya, Omskaya, Rostovskaya, Yaroslavskaya Oblasts, Khab-
arovskiy Kray) and Sweden [7-9J.
An investigation of organic impurities in precipitation was carried out
at Obninsk in the neighborhood of the high meteorological mast (Hr~i) in
different time intervals during the light time of day. Both in the city
itself and in the immediate neighborhood there were no large industrial
enterprises and therefore there were no large local sources of contamin-
ation.
The results of ineasurements of the organic component in precipitation
water are presented in Table 1. The table also gives some meteorological -
parameters characterizing the conditions for the falling of precipita-
tion; the wind direction at the 300-m level and the form of the clouds
from which the precipitation fell (the information was selected from
hourly measurements on the Iil~i); the direction of transport in the tropo-
sphere at the 1.5-km level, determined from AT850 pressure pattern charts,
- was determined twice a~ay at the USSR Hydrometeorological Center.
37
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The accuracy in constructing tra~ectories from pressure pattern charts
was evaluated in [4]. In the temperate latitudes of Eurasia, at levels
_ cloae to the standard isobaric surfacea, the error attains considerable
values. The tra~ectory error in cases of construction from preseure pat-
tern charte increasea with an increaee in wind velocity and the duration
of transport. In our case for construction of 2- or 3-day tra~ectories
from the AT850 charta the error at the end of the tra~ectory is about 200
km. All the cases of ineasurements of the concentrationa of organic im-
purities were grouped in dependence on the direction of air mass arrival. ~
- For each of these groupa we computed the mean concentrations of organic
admixturea in the collected precipitation. The reaulta are preaented in
Table 2. It follows from the data in Table 2 that in the samples of pre-
cipitation falling on days when the arrival of air masses occurred from
the south and southwest (Atlantic Ocean, Mediterranean and Black Seas)
the mean level of organic admixtures in the precipitation was: Corg =
13.5 (7.8) mg/liter (the standard deviation is given in parentheses). For
the group of samples of precipitation falling on days with the westerly
transport of air masses the mean level of the concentration of organic ad-
miatures was: Corg = 6.0 (2.9) mg/liter, and for the group of samples of
precipitation falling on days of arrival of air masses from the north,
- Corg = 3.3 (0.2) mg/liter. We should note the considerable value of the
deviation from the mean level of concentrations in the group of days with
southerly transport. In order to find an explanation for such a scatter
of the concentrations of admixtures within this group, an attempt was
m~de to take into account the differences in the vertical thickness of
cloud cover observed at the hours of collection of precipitation. All
the cases of observations in this group were broken down into two sub-
groups: the first subgroup included days with a cloud cover for the most
part of St and Sc forms (verticai extent up to 2-3 km); the second sub-
group included days when there was a predominance of cloud cover situated
at an altitude of 4-5 km Ns-As, and Cb (vertical thickness 9-10 km). The
clc?ud systems from the second subgroup were usually frontal and were caused -
by extensive zones of ascending movements of moist sir along atmospheric
fronts in cyclones and troughs. The mean concentration of organic admix-
tures in precipitation was computed for subgroups I and II (Table 2).
Without discussing the possible mechanism, we point out that the precip-
itation falling on days with southerly advection from well-developed
cl'oud systems Ns-As and Cb contains ma;~imum concentrations of organic matter
_ for the entire period.
It follows from everything set forth above that the arrival of an air mass
from the south, southwest and southeast is associated with an increase in
organic admixtures in precipitation. The mentioned increase is particular-
ly conspicunus on days when precipitation falls from cloud systems of the
Ns-As and Cb types. The data were obtained using a small amount of exper-
- imental data. Nevertheless, they are evidence of the possibility of de-
termining the region from which the air mass arrives in the central part
of the European USSR on the basis of the concentration of organic admix-
tures in precipitation.
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, BIBLIOGRAPHY
1. Aleksandrov, E. L., Kireyeva, N. M., Mashkova, G. B., Yasevich, N. P.,
"Concentration of Natural Atmospheric Aerosols in Air Masses Trans-
ported from Different Dire~tions," TRUDY IEM (Transactions of the In-
atitute of Experimental Meteorology), No 12(31), 1976.
2. Bashmakova, 0. N.; Matveyeva, A. A., Tarasov, M. N., "Chemical Compo-
aition of Atmospheric Fallout According to Observations in the Region
of the Otkaznenskoye Reservoir," GIDROKHIMICHESKIYE MATERIALY (Hydro-
chemical Materials), Vol 49, 1969.
3. Zverev, A. S., SINOPTICHESRAYA METEOROLOGIYA (Synoptic Meteorology),
Leningrad, Gidrometeoizdat, 1977.
4. Knshel'kov, Yu. P., Britvina, R. A., "Analysis of the Accuracy of
Tra~ectories Determined from Synoptic Charts at Different Altitudes,"
TRUDY TsAO (Transactions of the. Central Aerological Observatory), No _
73, 1967.
S. Malakhov, S. G., Trufakin, V. A., "Influence of the Advection of Air -
Masses on Change in the Concentration of Lead-210 and the Fission
Producte in the Atmoapheric Surface Layer in the Kirov Region," -
TRUDY IEM, No 1(32), 1972.
6. Roma:~ov, N. P., Shuklin, V. S., "Photoluminescence Characteristica
' of Water in Precipitation," TRUDY IEM, No 19(72), 1978.
7. Semenov, A. D., Nemtsova, L. N., Kishkinova, T. S., Pashanova, A. P.,
"Organic Substances in Precipitation," DOId,ADY AN SSSR (Reporta of
the USSR Academy of Sciences), Vol 173, No 5, 1967.
8. Skopintsev, B. A., Bakulina, A. G., Mel'nikov, N. I., "Total Organic
Carbon in Atmospheric Water," GIDROKHIMICHESKIYE MATERIALY, Vol 56, _
1971.
9. Supatashvili, G. D., Meladze, R. G., Semenov, A. D., "Organic Matter
in Precipitation at Tbilisi," GIDROKHIMICHESKIYE MATERIALY, Vol 65, -
1977.
10. KHIMIYA NIZHNEY ATMOSFERY ~Chemistry of the Lower Atmosphere), edited -
by S. Rasoul, Moscow, Mir, 1978.
11. Bullrich, K., Hanel, G., "Effects of Organic Aerosol Constituent~ on
Extinction and Absorption Coefficients and Li~uid Water Contents of
Fogs and Clouds," PURE AND APPL. GEOPHYS., Vol 116, No 2-3, 1978.
39
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UDC 551(509.313:510.528)
OBJECTIVE ANALYSIS OF THE TROPOPAUSE ,
Moscow METEOROLOGIYA I GIDROLOGIYA in Russian No 2, Feb 80 pp 32-39
[Article by Candidate of Physical and Mathematical Sciences V. A. Gordin
and Ye. A. Loktionova, USSR Hydrometeorological Scientific Research Cen- -
ter, submitted for publication 5 June 1979]
Abstract: Applying the spline approximation -
method and uaing data on geopotential, the _
authors determine the characteristics of
_ the tropopause, agreeing with the actually
observed values. A variational smoothing
method is proposed for using data on the
trop4p~use surface as initial data in prog-
nostic models. This makes it possible to
obtain a single-aheet surface close to is-
entropic. '
_ [Text] 4~1. Introduction. A method for conatructfng the vertical temperature _
profile from geopotential values at ten atandard isobaric eurfaces by means
of a spline approximation was proposed in [3]. The good accuracy of the -
method made it possible, on the basis of this temperature profile, to cr~n-
struct an algorithm for determining the level of the tropopause in ac-
cordance with the WMO criterion. An exposition of this algorithm is the
main content of this paper.
An alternative to the mentioned algorithm would be "direct" ob3ective anal-
yeis of data on the tropopause, in which these data, contained in aerolog-
_ ical telegrams, are interpolated by some method at the grid points of in-
_ tersection. Realization of this method involves the following difficulties:
1) At the present time there is no program for the checking of data on the
tropopoause. However, for such checking it is possible to employ our algo-
rithm, resting on checked geopotential data.
2) The great spatial variability of the tropopause field. The absence of
data on the correlation function of the tropopause field. Difficulties -
associated with the posaibility of existence of a different number of
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tropopauses at ad~acent stationa.
3) The mismatch between the temperature profile obtained as a result of
ob~ective analysis at a given point in a grid and obtained as a result
of independent ob~ective analysis of the tropopauae level. In our algo-
rithm these difficulties are absent.
In formulating the algorithm we took into account two types of possible
data output: 1) the actual tropopause, which is computed at points in
the ob~ective analyais grid or at stationa; 2) a"etylized" tropopause,
intended for use in a numerical prognostic model in a Q"-,system of co-
ordinates for s~tting of a boundary condition [8, 10].
In the firs;. case the only qv~lity criterion is the closeness of the de- -
termined characteristics of the tropopause (p and T) to the characteris-
tics of the tropopause contained in aerological telegrams (group with
the distinguishing digits 88).
In the second case the finai quality criterion is the quality of the fore-
cast according to a particular model, and intermediate criteria of the
type of smoothness of the tropopause field (mandatorily single-sheet) al-
low different interpretations.
4~2. Definition of Tropopause _
According to the WMO definition [6].
a) The first tropopause is defined as the very loweat level at which the
- vertical temperature gradient is decreased to 2�C/km or less, under the
condition that the mean vertical gradient between this level and all
- higher levels in the limits 2 km does not exceed 2� C/km;
b) If above the first tropopause the mean gradient between any level and
_ all higher levels in t:e limits 1 km exceeds 3� C/km, then a"second _
tropopause" is defined using the sa~e criterion as indicated in (a);
this tropopause can be either within or above a layer with a thickness
- of 1 km.
The tropopause level lying below 500 mb is taken only in a case when:
- 1) there is no tropopause above this level,
2) the mean vertical temperature gradient in any layer with a thickness
less than 1 km does not exceed 3�C/km above this level, _
3) the maximum altitude of rising of a radiosonde is not below 200 mb. _
Point (a), defining the tropopause level zl, is formalized in the follow-
- ing way:
~ ~ ~
zl = min z; (for any z such that z+ 2 km~jz> z, -(T(z) - -
P~z)~500 mb .
-r~z~~~2�c;~K.,~ ~~z-z~~. cl)
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If T(z) is a smooth function (and this ia true in our case), then it is
easy to demonstrate that
- ~
as (x~) = 2� C/KM. _ _ (2)
.
Condition (2), it goea without eaying, ie inadequate for the satiafac-
tion of (1), but makes it possible to find so-called "probable tropo-
pauses" at which by definition there is satisfaction of equation (2),
and then check condition (1) for them.
In conditions (1) and (2) it is pos~ble to praceed to the - lln p
coordinate: ;1 = min for any ~ such that H(~ 2 km>H(~ H(f~
(500)
.
-(T(~)-T~~~))~2�C/xet ~N~S)-H~`s))}. ~1')
_ Similarly there ie formalization of point (b) in the definition of tropo- -
pause levels.
413. Description of Algorithm for Finding Probable Tropopauses
We will assume - -
, - -
p(;) _ ~E +LT~
where L= 2�C/km/ This is a spline of the order 2 and defect 2. For find-
- ing its roots it w~ould be adequate to solve (2) for each of the layers
- between the standard levels, within the limite of each of which P(~ ) is
a second-degree polynomial. But it may be found that the roots of this _
polynomial fall outside the mentioned interval, and accordingly, in seek-
ing them we in vain extracted the square roots; the operation is relative-
ly time-consum~ng with reapect to computer time. Therefore, in aeeking the
roota the firat step is to check the preaence of roots in this interval.
Aesume that a, b are the ends of the interval. If P(a)P(b) < 0, there is
one root in the interval, if not, then 0 or 2. In the second case, if
p'~a)P~~b)~ 0, then there are no roots in this interval, and if not, then
we compute the discriminant D= P'(a)Z - 2P(a)P"(a). If D~ 0, there are
no roots (since the case D= 0 is not a general situation, in this program
this condition is replaced by D~ 0), whereas if D~ 0, there can be the '
- four cases illustrated in the figure. Cases (a~ and (c) are distinguished
by the condition P'(a)P(a)~ 0. We note that checking of the condition P'
(a)P`(b)~0 could be excluded without sacrifice for completeness of exam-
ination of variants, but since this condition is frequently realized, and
in this case subsequent checkings are excluded, we deemed it desirable to ~
leave it. If there are roots in the [a, b] interval and P changes sign in
it from negative to positive with a decrease in auch a root is called
, the probable tropopause and is determined using the formula
~1,2 = ~p~~~a)]'1[-P'(a)fD1~2].
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P QI a t'� p 6) b~�+
i
o b ~ v b ~
. , .
p d)~ ~ P 1~
~ v b Q b
~ ~
Fig. 1. Possihla curves of quadratic spline P(~,) in one of smoothness -
segmenta [a, b] under conditions P(a)P(b) ~ 0, P' (a)P` (b)> a~;i~aA 2a-SO 6-~20 10-40 10-,25
Tlpc PapnaTbe ~i ~i -
16 3aha naThe -
17 I7penrop~c Kpd~~a 20-30 6-,10 -~-10
18 lOHCma~ Gc cr KpdMa - ~