The problems of diagnosis and prognosis of the extreme natural phenomena at the Far East of Russia

Transcription

1 The problems of diagnosis and prognosis of the extreme natural phenomena at the Far East of Russia Tsitsiashvili G.Sh., Shatilina T.A., Goryainov A.A., Radchenkova T.V.,Matyushenko L.Yu.

2 Exploration of climate forcing to biological and hydrological objects is very complicated task that is hardly subjected to analytical depiction. As a rule modern statistics considers averaged cases. But if we concentrate on studying of extreme processes, it will be possible to find a method for description of complex biological and climate systems. The method of interval recognition is just that proposed method for the extreme processes study.

3 Anomalies of geopotential H500 in January 1978 and in January 1991.Examples of H 500 extreme fields above the Asian-Pacific region. Difference in the situation of growth and fall pressure foci is visible.

4 Intra-annual dynamics of geopotential during in Okhotsk Geopotential Months January February Marc h April May June July Augus t September October November December

5 The method of interval recognition The algorithm of the decisive rule development for Main feature(catches, ice cover in Tatar Strait) Attendant feature (Po pressure, H 500 ) А В recognition of dramatic events. critical level Time Time Upper diagram demonstrates dynamics of the main feature that is subject to a high intra-annual variability (ice cover of seas, catch of Asian hunchback). When choosing the critical level that is specific for the main feature, we mark with x sign those moments of time, when dynamics trajectory of the main feature exceeds the established level (i.e. the anomaly moments of deviation). The rest of moments of time are marked by 0 sign. Then the magnitudes of the attendant feature during dramatic moments of time (marked by x ) are projected to the axis of ordinates and minimum B and maximum A values of the attendant feature are found. The next step is to develop a rule of recognition of a certain moment of time belonging to the critical level according to the belonging of the attendant feature to a section [A, B]. If during a non-dramatic moment of time the attendant feature is found in the section [A, B], then the given moment will be mistakenly attributed to the critical one. This procedure is generalized by finding a separate critical section for each attendant feature. Thus, belonging of a certain moment of time to the critical one will be identified according to the belonging of each of attendant features to their own critical sections.

6 Suppose, that there are N objects 1 N, characterized by main and attendant features: (a(1,b(1)), a(n),b(n)). Let it be chosen some vertical level a for the main feature and all critical objects are singled out i(1),,i(r), that corresponds to the next terms a(i(1))>a,,a(i(r))>a, r number of critical objects. Let sign B=min(b(i(1)),,b(i(r))), A=max(b(i(1)),,b(i(r))), then according to compound expression for any k, 1 k r, inequality is made up B b(i(k)) A (1) Let determine a number m of non-critical objects j apt to the inequality: B b(j) A These objects when applying of interval recognition rule (1) would be falsely related to the range of critical ones. Therefore the coefficient of interval recognition n=r/(r+m), where r number of critical objects, and m number of non-critical objects, is the analog of coefficient of multiple regression.

7 Second natural synoptical region of Northern hemisphere. Near-earth pressure field (Po) and H500 data are in the five degree network.

8 Let s consider the work of proposed original algorithm for example to recognize critical rate of catches of Asian pink salmon over the Western Kamchatka region according to Po data, H500 geopotential in Catches of Asian pink salmon over the Western Kamchatka

9 Counting of interval coefficients n by the method of interval recognition As the main feature there were chosen of average annual catches of pink salmon in the Western Kamchatka region during Attendant feature magnitude of near-earth pressure P0 in 15 knots of synoptical region during each month of each year, that was prior of years of prognostication of the main feature. We choose critical rate for the main feature and register those moments of the time where this main feature is higher than critical magnitude. r number of critical moments of time. Then we choose those moments of time from all of attendant feature, where magnitudes of main feature are higher then critical rate and then we determine the scattering of the attendant feature magnitudes ([AB] on slide 5). Taking into consideration the rest non-critical moments of time we clear up whether this chosen range embraced the pressure magnitudes or not (line [AB]). If they were embraced that means that given moment of time mistakenly is related to the critical one, i.e., m number of non-critical moments of time. The more number of such moments the less this interval coefficient n =r/(r+m). n=1 shows, that attendant features equally determine extreme magnitudes for the main feature.

10 Interval coefficients for maximal catches according to Р0 ин500 data January February March April May June July August September October 1 0,9 0,8 0,7 0,6 0,5 0,4 0,3 0,2 0,1 Р0 Н500 November December

11 1 0,9 0,8 0,7 0,6 0,5 0,4 0,3 0,2 0,1 Interval coefficients for minimal catches according to Р0 ин500 data Р0 Н500 February March April May June July August September October November December January

12 Other example of use of the algorithm suggested by us is recognition of a "critical" level of ice cover in Tatar strait on data Р0 and geopotential Н500. Ice cover in Tatar Strait during январь февраль март

13 Run of anomalies in geopotential Н500 along 140 E longitude and along N over the years of extreme ice cover in Tatar Strait. Difference in localizations of anomalies Н500 in the years with minimal (1963,1974,1991) and maximal (1960,1979,1985) ice cover is visible. anomalie s la titude anomalie s latitude

14 December March April May June July August September October November February January 1 0,9 0,8 0,7 0,6 0,5 0,4 0,3 Interval coefficients for maximal ice cover in February according to Р0 and Н500 data Р0 Н500

15 December February March April May June July August September October November January 1 0,9 0,8 0,7 0,6 0,5 0,4 0,3 Interval coefficients for minimal ice cover in February according to Р0 and Н500 data Р0 Н500

16 C o n c l u s i o n Detection of intra-annual bundles of some climatic parameters gave the opportunity to construct original algorithm for recognition of extreme natural phenomena. The possibility of prognosis of disastrous phenomena (feature, that possesses the most scatter in intra-annual variability) is exhibited according to the dynamics of indicators of attendant feature, that has small scatter values. The results of recognition of critical levels in the pink salmon catches and in the Tatar Strait ice cover indicated, that the given method can be used for prognosis of extreme situation in hydrometeorological processes and also for prognosis of Asian pink salmon anomalies. Interval recognition method allowed to reveal information regions and months for the extreme phenomena recognition with the coefficient large meanings on the second natural synoptical region. And these spaces are different for salmon catches and ice in the Tatar Strait. Higher recognition coefficients are received for ice cover according to both accompanying features. Near-earth pressure field has more information then H500 for the critic salmon catches recognition.

17 Thank you