Does precipitation pattern foretell climatic shift over Punjab state ?

ABSTRACT. Statistical analysis of 82-years (1901-1982) record of precipitation from 27 rain-recording stations in Punjab state of lndia has been carried out to assess the climate shift if any in the state. The central part of the study is the trend and spectrum analysis of annual. monsoon and winter rainfall of different stations in the region. It is seen that frequency distribution of 19 rainfall series out of 81 series is normally distributed. Maikov linear type of persistence is observed in some of the rainfall series. Marin-Kendall test indicates the decreasing trend in winter rainfall of all the stations and is found to be significant in case of Amritsar, Taran Taran, Tanda, Ludhiana and Ranike. Low-pass filter reveals that trend is not linear but oscillatory consisting of periods of 10 years or more. It is seen that winter rainfall of most of the stations exhibits the decreasing trend from 1935-40 to 1965-70. It is also revealed by the low-pass filter curves that winter rainfall of all t1le sla1ions remained below average from 1960 till the end of the study period. The spectral analysis indicates a significant cycle of 4.1 to 27 years in some of the stations and Quasi-Biennial Oscillations (QBO) over many stations.

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Noise Reduction Analysis of Radar Rainfall Using Chaotic Dynamics and Filtering Techniques

Advances in Meteorology ◽

10.1155/2014/517571 ◽

2014 ◽

Vol 2014 ◽

pp. 1-10 ◽

Cited By ~ 4

Author(s):

Soojun Kim ◽

Huiseong Noh ◽

Narae Kang ◽

Keonhaeng Lee ◽

Yonsoo Kim ◽

...

Keyword(s):

Noise Reduction ◽

Correlation Coefficient ◽

Standard Error ◽

Rainfall Series ◽

Radar Rainfall ◽

Pass Filter ◽

Low Pass Filter ◽

Low Pass ◽

Filtering Technique ◽

Filtering Techniques

The aim of this study is to evaluate the filtering techniques which can remove the noise involved in the time series. For this, Logistic series which is chaotic series and radar rainfall series are used for the evaluation of low-pass filter (LF) and Kalman filter (KF). The noise is added to Logistic series by considering noise level and the noise added series is filtered by LF and KF for the noise reduction. The analysis for the evaluation of LF and KF techniques is performed by the correlation coefficient, standard error, the attractor, and the BDS statistic from chaos theory. The analysis result for Logistic series clearly showed that KF is better tool than LF for removing the noise. Also, we used the radar rainfall series for evaluating the noise reduction capabilities of LF and KF. In this case, it was difficult to distinguish which filtering technique is better way for noise reduction when the typical statistics such as correlation coefficient and standard error were used. However, when the attractor and the BDS statistic were used for evaluating LF and KF, we could clearly identify that KF is better than LF.

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TRENDS AND PERIODICITIES OF SURFACE AIR TEMPERATURE OF DELHI

MAUSAM ◽

10.54302/mausam.v44i1.3771 ◽

2021 ◽

Vol 44 (1) ◽

pp. 35-38

Author(s):

BHUKAN LAL

Keyword(s):

Time Series ◽

Air Temperature ◽

Surface Air Temperature ◽

Linear Type ◽

Pass Filter ◽

Low Pass Filter ◽

Four Seasons ◽

Low Pass ◽

Annual Time Series ◽

Temperature Time

Seasonal and annual surface air temperature of Delhi has been analysed for 90-year period (1901-1990) for finding trends and periodicities. The analysis revealed that frequency distribution of all the four seasons as well as of annual time series is normal. Markov linear-type of persistence is observed in pre and post-monsoon seasons. Recent period {1961-1990) averages of temperature are significantly lower than the long period means in respect of annual, monsoon and pre-monsoon seasons. Decreasing trend is noticed in monsoon and annual temperature time series. Low-pass filter analysis suggests that the trend is not linear but oscillatory consisting periods of 10 years or more.

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