3. Data analysis and computing procedure
Monsoon rainfall over India during the months of
June to September (122 days) exhibits interesting
oscillations over the country. In the present analysis,
the rainfall over the country has been
expressed as a linear combination of orthogonal
functions. This technique was suggested by
Lorenz (1956) and later used by Kutzbach
(1967), and Weare (1977) to evaluate the principal
component of sea-level pressure over the
Northern Hemisphere and sea-surface temperature
over the Atlantic Ocean. Also, the daily
OLR data sets (1979–1988) for the monsoon period
(June to September) have been used at 2.5
longitude=latitude intervals with 68 stations in
India. The location of 68 stations has been chosen
on the basis that they have represented the
true network of rainfall distributions over India.
Interestingly, the set of 68 stations selected for
the rainfall exhibited equally promising results
alongwith a higher correlation with all India seasonal
rainfall (Singh, 1994).
Let P be a (n m) matrix of monsoon rainfall
over m stations and a series of n years. Here,
n ¼ 10 years and m ¼ 68 stations.
The element (Prs) of P represents departure of
rainfall from their mean value for the s-th station
and r-th year.
Let P represents the time and space variability
of rainfall. It can be defined that
P ¼ Q F; ð1Þ
where the matrix Q represents the time variation
and F represents the space variation of monsoon
rainfall. The element of the P matrix is
given by
Prsðx; y; tÞ ¼ Xm
k¼1
qrkðtÞ fksðx; yÞ; ð2Þ
where the element qrk (t) represents the time and
fks (x, y) represents the space respectively. The
matrix F is an orthonormal matrix, hence the
transpose and product of this matrix should be
represented as a unique identity matrix. It is
defined that
F F0 ¼ I; ð3Þ
where F0 is the transpose of F and I is the identity
matrix. It is further stated that F and Q matrix
derive from the matrix P after defining the matrix
S, where
P0
P ¼ S:
3. Data analysis and computing procedureMonsoon rainfall over India during the months ofJune to September (122 days) exhibits interesting oscillations over the country. In the present analysis,the rainfall over the country has beenexpressed as a linear combination of orthogonalfunctions. This technique was suggested byLorenz (1956) and later used by Kutzbach(1967), and Weare (1977) to evaluate the principalcomponent of sea-level pressure over theNorthern Hemisphere and sea-surface temperatureover the Atlantic Ocean. Also, the dailyOLR data sets (1979–1988) for the monsoon period(June to September) have been used at 2.5longitude=latitude intervals with 68 stations inIndia. The location of 68 stations has been chosenon the basis that they have represented thetrue network of rainfall distributions over India.Interestingly, the set of 68 stations selected forthe rainfall exhibited equally promising resultsalongwith a higher correlation with all India seasonalrainfall (Singh, 1994).Let P be a (n m) matrix of monsoon rainfallover m stations and a series of n years. Here,n ¼ 10 years and m ¼ 68 stations.The element (Prs) of P represents departure ofrainfall from their mean value for the s-th stationand r-th year.Let P represents the time and space variabilityof rainfall. It can be defined thatP ¼ Q F; ð1Þwhere the matrix Q represents the time variationand F represents the space variation of monsoonปริมาณน้ำฝน องค์ประกอบของเมทริกซ์ P คือกำหนดโดยPrsðx y tÞ ¼ Xmk¼1qrkðtÞ fksðx yÞ ð2Þที่ qrk องค์ประกอบ (t) หมายถึงเวลา และfks (x, y) แทนช่องว่างตามลำดับ ที่เมตริกซ์ F เป็น orthonormal เมทริกซ์การ ดังนั้นการเมทริกซ์สลับเปลี่ยนและผลคูณของเมทริกซ์นี้ควรแสดงเป็นเมทริกซ์เอกลักษณ์เฉพาะ จึงกำหนดไว้ว่าF F0 ¼ฉัน ð3Þที่ F0 คือ เมทริกซ์สลับเปลี่ยนของ F และฉันมีตัวตนเมตริกซ์การ มันจะระบุว่า เมทริกซ์ F และ Qสืบทอดมาจากเมทริกซ์ P หลังจากกำหนดเมตริกซ์S ที่P0P S:¼
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