Summary and critical analysis Essay Example | Topics and Well Written Essays - 250 words - 1

Summary and critical analysis - Essay Example Experience significantly shapes expert progress. It adds to professional and educational qualifications to improve the abilities of the designer with time. Experience is attained through practical performance such as during internships. Internships offer chances for designers to carry out project work. If a student shows excellent ability during internships, employment can be secured from it. Education qualifications are tested through actual work and familiarity. Once education and experience required are obtained, a designer becomes eligible to sit examinations to certify that the minimum competencies to be a member of the profession have been met. Such exams are administered by selected bodies such as the NCDI. Passing the examination entitles one to be licensed to practice and issued with a practicing certificate. From then, an individual can continue professional advancement by joining relevant professional bodies such as the American Association of Interior Designers and the International Interior Design Association. Members enjoy benefits such as sharing of knowledge, prestige, and advocating for rights of designers. Finally, a designer can engage in social responsibility to give back to the profession and the society in contribution to professional development. It can be done in various ways such as training interns and participating in design competitions among other voluntary

French Essay Example | Topics and Well Written Essays - 250 words

French - Essay Example ce moment-l, on permet aux les gens qui travaillent pour Le British Red Cross d'aider sans souci de leurs propres vies au besoin. Ceci devient particulirement ncessaire quand les sauveteurs travaillent pour sauver les vies pendant des catastrophes naturelles, journal. Il est important que les sauveteurs comprennent journalier qu'ils sont interdits de quitter la zone de secours sans permission s'ils travaillent, qui n'est pas tonnante. Ce qui est fascinant est comment les sauveteurs agissent quand il y a galement les types d'urgences personnels se produisant annuellement. Quand les gens avaient des difficults, Le British Red Cross pouvait les aider. L'horaire pour les personnes qui aident ces dsastres peut tre une heure, un jour, une semaine, un mois, une anne, ou plus longtemps. Le British Red Crossatteint beaucoup d'objectifs. Si elle soit pour un dsastre important qui se produit, une catastrophe naturelle qui se produit dans un secteur particulier, ou une urgence personnelle qui se produit, Le British Red Cross peut tre l.

Analysis the Song The Way We Were Essay Example for Free

Analysis the Song The Way We Were Essay There is a song—a love song that has touched several hundred thousand audiences’ hearts; it expresses the overflowing feelings of a woman who used to live in her intense true love. â€Å"The Way We Were,† by Alan Bergman, Marilyn Bergman, and Marvin Hamlisch illustrates the innermost emotions and regret of a middle-aged woman when she reflects on the youth which she had gone through. This song has a light beat and slow tempo forming delightful sensations for the listeners, but it also leaves audiences a profound message to think about. Sophisticated audiences may find themselves in the song. It brings out fervor, especially for people who have been living in love. Marilyn Bergman uses simple words, but it is very touching and emotive. Basically, it is a classical song which was played by symphony orchestras and performed by Barbra Streisand. By just looking at the lyrics, one would feel the sentimental soul and an introspective mind of a sensitive woman. In the first line of the song, she writes â€Å"Memories, light the corners of my mind. Misty watercolor memories of the way we were. These memories have been engraved in her heart; they are neither gloomy nor euphoric, but they are vivid images which she wants to repress in the most innermost sphere of her mind. It is not just something very hurtful; it is not blissful to think of either. Her feeling is a mixture of nostalgia, melancholy, and a little bit of regret that is rushing through her body. Former familiar scenes were depicted in her mind, but, somehow, t hey are both painted with watercolor and blurred, symbolizing a mystical ethereal world that solely exists in her imagination and belongs to her only. Near the middle of the song she wonders, â€Å"If we had the chance to do it all again. Tell me would we? Could we? † She already knows her former love would never come back again; the melody sounds like she is missing and regretting her loss of love. The term â€Å"Would we? Could we? † is deliberately sung slowly and softly as mumbling is also a way to convey her thoughts. Despite her haunting regret, she prefers keeping that regret to making up her love again because she knows wistful moments and romantic longings that she has right now wouldnt exist if her love remained today. It is such an uncanny feeling and an agonizing struggle between the logic and the sentiment of a human being. The last couple lines expose changes in her mood, â€Å"Memories may be beautiful and yet whats too painful to remember. We simply choose to forget, so its the laughter we will remember whenever we remember the way we were. † She used to live in love with both happiness and bitterness, but she had to learn how to forget unpleasant experiences to keep the most exquisite feelings inside herself. Does she â€Å"simply† choose to forget? It is not something easy to do. The way Barbra Streisand sings shows internal struggling when she decides to get rid of mental pains. The word â€Å"laughter† here is not simply something funny and delightful to laugh at; it is also a pang of remorse and regret about the love and the youth that are gone from her life. Marilyn Bergman used simple words to describe emotion of the woman in this song, so it is quite easy for listeners to apprehend the meaning; however, its melody and the way Barbra Streisand performs it, the most essential factor, helps the song convey the message to audiences . The intended stress in every single word of her lyrics is absolutely touching. For example, the word â€Å"smiles† in the second line was purposely lengthened to give a hint to the audiences so that they can comprehend the whole significance of the song. It is a beautiful song evoking diverse memories for listeners. Each listener will feel and interpret the song in different ways depending on their character and their personal experiences. However, one thing for sure is that these audiences will feel more appreciative of people around them so that they will not be regretful later on.

Analyzing Groundhog Day

Analyzing Groundhog Day Bill Murray plays Phil, an arrogant, self obsessed weather forecaster who goes to Punxsutawney, Pennsylvania, in preparation of a broadcast for the next day about the annual occurrence of the coming out of the groundhog Punxsutawney Phil. As he wakes up in the morning and does records his coverage of the event, he is annoyed to find out that he will have to spend another night in Punxsutawney because of an incoming snowstorm. This is where things start to go wrong for Phil as in his earlier forecast; ironically, he predicted that the following days are going to be sunny. When he wakes up the next morning in his room he is very surprised to see that it almost looks like a repetition of the previous day! Everything that happened to him a day earlier at the top of the stairs the same person trying to talk to him; the old high school friend, Ned, recognizing him on the street, the ceremony of the groundhog day everything starts to repeat. Phil spends the day in shock and thinking it is a bad dream but at the end of the day he is stuck in Punxsutawney because of the snowstorm again. When he wakes up the next morning, everything is the same as the previous days, and he again gets stuck in the town for the snowstorm. By this time Phil realizes that this is not just a bad dream, so he starts to look for ways to get out of the situation and remove him from this time loop. Soon Phil realizes that if he doesnt change anything, the events repeat themselves similarly to the first day. But if he only changes his behavior towards the townsfolk, they respond to his actions, which eventually help him reach his goal of winning the heart of Rita. With each repeating day, Phil is the only one who remembers what happened in previous iteration of the same day. At first Phil is awed by this surreal event. As the days keep repeating Phil begins to treat life as a game since nothing he does seem to matter, and the next day is a fresh start of the same scenario: no matter what he does, time resets and he wakes up as if nothing happened. But as the days pass endlessly repeating itself, Phil eventually finds a purpose: get to know as much as he can about Rita, so he can seduce her. When all his tries to win Ritas heart fails day after day, his despair deepens, and he loses his will to live and begins to spend his days killing himself. In desperation, he reveals his plight to Rita and hesitantly she spends the night with him. But again, Phil wakes up to the same music of Sonny and Cher. But, spending a night with Rita makes him realize that someone actually liked him for who he is, he comes to a revelation he begins to live the life he never lived before. Phil starts to take control of circumstances, aided by the fact that he has plenty of time and the safety of starting from scratch if he messes up. He begins to take piano lessons, he learns how to be an ice sculptor, and he becomes more generous. Phil, knowing how the events in the day will happen, begins to use this knowledge to start helping towns people. As he suffered through the repeating days to come to this point, Phil starts to transform and empathizes with other peoples suffering. He becomes a local hero in Punxsutawney. Finally, Rita falls in love with the new Phil, attracted by his generous personality and maturity. He falls asleep by the side of her and when he wakes up she is still there and the curse is broken. In her expert analytical article The Spiritual Power of Repetitive Form: Steps Toward Transcendence in Groundhog Day, Suzanne M. Daughton explains how a modern romantic comedy such as Groundhog Day has a deeper meaning than just entertainment for the viewers. She explains that the movie portrays the lead actor as a stereotypical male figure with ego boundary that, at the start of the film, keeps every other character away from him. When he is trapped in the time loop of repeating days it takes Phil nearly 34 days to break all the barriers and to finally embrace the situation to use it to his advantage. Daughton describes Phils character before transformation as: Phil Connors, miserable cynic: self-centered, abrasive, and deservedly friendless. When Phil progresses to different transformations in the movie Daughton lists these stages that Phil goes through as: cynicism, alarm, hedonism, depression and anger, denial and avoidance, resignation, acceptance and growth. The list describes all the emotions Phil goes though to reach his final stage where is becomes the ideal male and is able to win Ritas heart and break the curse. Groundhog Day shows that the turning point in Phils life is when he starts to build relations with the common people and start to communicate. According to Daughton, Groundhog Day has more to do with its protagonist coming to appreciate the stereotypically feminine focus on connection with others, than with reinscribing the stereotypically masculine individuation and domination glorified in films. Unlike other change-of-heart films, Connors does not renounce independent thought and retreat to little-boy status; instead he grows out of certain boyhood beliefs and practices. At the end of the film Phil starts his acceptance and growth phase where he breaks the traditional stereotypical masculinity and accepts feminism to break the loop. Favorite film and its communication relevance My favorite film is When Harry Met Sally.. It is a simple romantic comedy on the surface but digging deeper into the characters reveals a lot about how males and females communicate with each other within their gender and how this gender based communication is very different than that of their counterparts. Harry approaches Sally as if she was his male buddy and very soon he steps out of the boundary of their newly formed relationship by asking her questions such as has she ever had great sex. This is not an appropriate question for that stage of the relationship, and it makes Sally defensive and uncomfortable. Harry portrays himself as an accomplished, cocky person and Sally starts to reveal more about her personal life to prove to Harry that she also has led a successful life like Harry. In the movie both character experiences interpersonal communication. Our text Reflect and relate defines it as a dynamic form of communication between two or more people in which the messages excha nged significantly influence their thoughts, emotions, behaviors and relationships. An example of this is the scene when Harry and Sally happen to run into each other at the bookstore; they talk and communicate as matured person, unlike the first time they met. As their friendship deepens and they both deal with their breakups, they start to depend on each other for support and their friendship deepens. Eventually we see that they begin to fall for each other. This is interpersonal communication by definition as their communication is changing the way they feel about each other. At the party, seeing one another with other dates become a concern even though, they did not reveal to each other that they want to move to the next step. They both share things with the other that they wouldnt share with anyone else. These are all examples of interpersonal communication developed by the characters in the film where their actions are being influenced by their relationship. Film I didnt care for The film I didnt care for is Children of a Lesser God. The film revolves around the characters Sarah and James, and their relationship. Sarah is deaf and does not use her voice; James is a new teacher at the school who is not deaf and falls in love with Sarah. There are several communication issues presented throughout the film such as: the dialectical tension of autonomy between Sarah and James, social exchange theory which explains why people are drawn to those people that can offer them substantial benefits, social penetration theory, uncertainty reduction in a relationship etc. The film shows very well how to progress in a relationship when it is new and when it has matured. We see Sarah and James eventually realize that even though they want to be independent and live their own way, if they want to stay in a relationship the best way to move forward is to compromise and come to a middle ground that both can live with. The reason why I dont care for this film is because of the pl ot and background of the movie which is not a good fit for the majority of the people who has never experienced being a deaf or mute. While the film opens our eyes to the deaf culture and brings many communication issues to light, it is hard to relate to in comparison with our regular life. To me it feels like the film has almost a documentary like feeling to it than a movie. On the other hand I can relate to characters like Harry from When Harry Met Sally, or Phil Connors from Groundhog Day because we have seen people like them in real world. We have all met with someone at some point who was cocky and annoying or someone who asked inappropriate questions. That is the only fundamental flaw of the Children of a Lesser God in my opinion. Useful concepts in RR, and the articles There are several useful concepts from RR which helped to analyze the films such as interpersonal communication, self-fulfilling prophecy, communication competence, supportiveness and empathy, uncertainty reduction, pseudo-listening and active listening etc. The articles were also helpful in finding even deeper meaning behind the character and plot of the film, but I feel like some of them went too deep for the scope of the class such as SM Daughtons Groundhog Day article that explained a lot about how the character breaks out of stereotypical male model and embraces feminism to grow and mature. My favorite article is Crossing the barriers to friendship between men and women by Lee west et al. The film When Harry Met Sally, and the article helps a lot in understanding which mistakes males typically make when communicating with other gender. How can we make the relationship smoother and what are the things to avoid. This is a good learning opportunity for all of us presented in a come dic way by the film. Overall all the films and articles touched on different topic within communication that will be helpful in our daily lives as we strive to create better, fulfilling relationship with other around us. Works Cited Groundhog Day: The Movie. Groundhog Day: The Movie. Transparency, n.d. Web. 10 Mar. 2017. . The spiritual power of repetitive form: Steps toward transcendence in Groundhog Day, Daughton SM. Critical Studies in Mass Communication, 1996 McCornack, Steven. Interpersonal Communication. Reflect Relate: An Introduction to Interpersonal Communication. Boston: Bedford/St. Martins, 2016. N. pag. Print.

Going the Speedlimit Essay -- Speeding Driving Tickets Essays

Going the Speedlimit   Ã‚  Ã‚  Ã‚  Ã‚  There are many approaches that we humans use to weasel out of a speeding ticket. This is not an easy task, but it has been mastered by a select few. The very reasonable way of just not speeding is used quite often. The method of lying is frequently used. This is sometimes effective and usually safe. The flirtatious approach (which usually is used only by females) is tried numerous times on male officers. Finally, there is the method of "outrunning the cops". This method quite often ends in arrest.   Ã‚  Ã‚  Ã‚  Ã‚  Not speeding is probably the best bet of not getting a ticket. If you aren't speeding then you have removed yourself from the situation of ever getting a ticket. Although this is the most intelligent and safe way to avoid tickets, it is also the least enjoya...

Pony Express :: essays research papers

Pony Express   Ã‚  Ã‚  Ã‚  Ã‚  Before 1860 it was virtually impossible to get a letter or other form of correspondence from St. Joseph Missouri to Sacramento California in less than 20 days. 20 days seemed entirely too long for the west coast merchants and bankers to wait for documents from the east. So three men, William Russell, Alexander Majors and William Waddell created the Pony Express. The Pony Express ensured fast and safe delivery of the mail.   Ã‚  Ã‚  Ã‚  Ã‚  To ensure the fast pace delivery of the mail, the Pony Express purchased 600 broncos, mostly thorough breads, mustangs, and morgans. Still the horses weren’t all needed. They also had to find men who would be able to handle the adverse weather conditions, Indians and be able to ride for 75 miles with out stopping. The riders were generally under 20 years of age and weighed no more than 120 pounds. They had to be excellent horsemen and have experience with guns. The Pony Express was a relay of mail running day and night.   Ã‚  Ã‚  Ã‚  Ã‚     Ã‚  Ã‚  Ã‚  Ã‚  On April 3rd, 1860, the first rider Johnny Fug headed west bound for Sacramento. Every rider road for 75 miles and they would switch horses every 10-15 miles. The very first letter traveled the 2,000-mile journey from St Joseph Missouri to Sacramento California in only 10 days to the hour of departure. This journey used 27 men and 133 horses. The Pony Express cut the normal 20 days of travel into 10 days, nearly cutting it in half.   Ã‚  Ã‚  Ã‚  Ã‚  With the amazing delivery of mail in 10 days across 2,000-miles of prairie land, mountains and deserts led many people to use them. With the growth communication between the east and the west the Pony Express grew in popularity. The Pony Express proved that the central route through the U.S could be traveled all winter. It supported the cattle route for the Transcontinental Railroad to meet with the Union Pacific Railroad. Communications was kept open with California during the Civil War. The Pony Express was the fastest communication between the east and west until the telegraph. It captured the hearts and imagination of people all over the world.   Ã‚  Ã‚  Ã‚  Ã‚  With all its success the Pony Express like all things suffered failures. Such as the owners spent $17,000, on the Pony Express and has a 2,000 deficit.

Trends in Indian Rainfall

National Climate Centre Research Report No: 2/2006 Trends in the rainfall pattern over India P. Guhathakurta and M. Rajeevan National Climate Centre India Meteorological Department PUNE. INDIA 411005 [email  protected] gov. in Abstract Monthly, seasonal and annual rainfall time series of 36 meteorological sub-divisions of India were constructed using a fixed but a large network of about 1476 rain-gauge stations. These rainfall series are thus temporally as well as spatially homogenous. Trend analysis was carried out to examine the long-term trends in rainfall over different sub divisions.Also monthly contributions of each of the monsoon months to annual rainfall in each year were computed and the trend analysis was performed. It has been found that the contribution of June, July and September rainfall to annual rainfall is decreasing for few sub-divisions while contribution of August rainfall is increasing in few other subdivisions. 1 1. Introduction In the context of climate chang e, it is pertinent to ascertain whether the characteristics of Indian summer monsoon also is changing.The Indian summer monsoon (June to September) rainfall is very crucial for the economic development, disaster management, hydrological planning for the country. Earlier, Mooley and Parthasarathy (1984), Parthasarathy et al. (1993), Parthasarathy et al. (1994), constructed all India rainfall series based on 306 uniformly distributed stations. They have also used area weighted method to calculate all India rainfall using rainfall data of the 306 districts outside the hilly regions like Jammu and Kashmir, Himachal Pradesh, Hills of west Uttar Pradesh, Sikkim and Arunachal Pradesh, Bay Islands and Arabian Sea Island.Presently this time series is updated by the Indian Institute of Tropical Meteorology, Pune (www. tropmet. res. in) and this rainfall time series was extensively used by many researchers. At present there are more than 500 districts in the country. Using only 306 raingauge s tations, it may not be possible to represent all the districts and prepare district-wise rainfall climatology. All the districts are having geographical area more than 100 square km (except Andaman and Nicobar Islands). Only one station in the district may not produce reliable district rainfall climatology as rainfall is highly variable (WMO, 1983).Spatial homogeneity cannot be achieved with one or two stations in a district. Long term trends of Indian monsoon rainfall for the country as a whole as well as for smaller regions have been studied by several researchers. Most of the studies are based on the rainfall series constructed by Parthasarathy et al. (1994). They have found that the monsoon rainfall is without any trend and mainly random in nature over a long period of time, particularly on the all India time scale (Mooley and Parthasarathy, 1984). But on the spatial scale, existence of trends was noticed by Parthasarathy (1984) and Rupa Kumar et al. (1992).Parthasarathy (1984) found that the monsoon rainfall for the two subdivisions viz. sub-Himalayan West Bengal & Sikkim and the Bihar Plains are having decreasing trends while for the four sub- 2 divisions viz. Punjab, Konkan & Goa, West Madhya Pradesh and Telangana are having increasing trends. Using the network of 306 stations and for the period 18711984, Rupa Kumar et al. (1992) identified the areas having decreasing and increasing trends of monsoon rainfall. The past performances of the monsoon rainfall may give an indication of the future scenario. But in order to do so we should also understand the climatology in a better way.The construction of a homogeneous rainfall data series (spatially as well as temporally) was the first step in this study. India Meteorological Department (IMD) has a good network of rain gauge stations. From the vast data set archived at the National Data Centre, IMD, Pune, a network of 1476 rain-gauge stations was selected which have only 10% or less missing years of data. To construct a homogeneous data series for the complete data period of 1901-2003 missing period of the observatory data are replaced by the neighboring state rain-gauge data and vice versa. Fig. (a) shows the plot of the 1476 stations used for the study while Fig. 1 (b) show the plot of 306 stations used by the Indian Institute of Tropical Meteorology for construction of homogeneous rainfall data (Parthasarathy 1984). Clearly, the network considered in this study is more uniformly spaced and temporally homogeneous, which can be used for analyzing the spatial and temporal variability in a better way. 2. Construction of homogeneous data series To prepare a homogenous rainfall time series, we have selected 1476 rain- gauge stations having maximum data availability during the period 1901-2003.Data for these stations are available for at least 90% of the years considered for this study (1901-2003). However, if some data was found missing for a particular station, the data gap was filled up by the rainfall data of a neighboring rain-gauge station. We have considered 458 districts for the present analysis. Each of these 458 districts of the country has two or more representing stations. First of all, the district rainfall is calculated as the arithmetic average of rainfall data of stations in the district. Thus, the rainfall data series was constructed as spatially and temporally homogeneous. Rainfall for the meteorological subdivisions (36) was calculated as the area weighted rainfall of the districts within the meteorological sub divisions. 3. Rainfall Over the country as a whole All India monthly, seasonal and annual rainfall series were constructed based on the area weighted rainfall of all the 36 meteorological subdivisions of the country. The results are given in Table 1. The mean, standard deviation and coefficient of variation are also given in the same Table. Mean (1901-2003) rainfall of July is 286. 5 mm, which is the highest and contributes 24. % of annual r ainfall (1182. 8 mm). The August rainfall is slightly lower and it contributes 21. 2% of annual rainfall. June and September rainfall are almost similar and they contributes 13. 8 % and 14. 2 % of annual rainfall respectively. The mean south-west monsoon rainfall (877. 2 mm) contributes 74. 2 % of annual rainfall (1182. 8 mm). Contribution of pre-monsoon rainfall and post-monsoon rainfall in annual rainfall is mostly the same (11%). Coefficient of variation is higher during the months of November, December, January and February. Fig. shows the comparison of the IITM southwest monsoon season (June-September) rainfall series with the rainfall series constructed in this study. The correlation coefficient between these two series is found to be very large, 0. 97. The mean seasonal rainfall of IITM series is 844. 5 mm whereas the men value of this time series is 877. 2 mm. The high mean value of the present series is because of the consideration all the 36 meteorological subdivisions, in cluding hilly regions. The standard deviation and coefficient of variability for the IITM series are 81. mm and 9. 6% and the same for the present time series are 71. 0 mm, 8. 1% respectively. Coefficient of variation of the present time series is smaller compared to IITM time series. 4. Epochal patterns of Indian summer monsoon rainfall It is well known that Indian summer monsoon rainfall displays multi-decadal variations in which there is a clustering of wet or dry anomalies (Pant and Kumar, 1997). To examine the epochs of above and below normal rainfall, 31-year running means of Indian summer monsoon rainfall (ISMR) was calculated to isolate low 4 requency behavior. These epochs of above and below normal rainfall are shown in Figure 4. Rainfall was above normal for nearly forty years from 1930s to 1960s. To understand the epochal behavior of rainfall series for different monsoon months, we have also calculated 31-year running means of each of the monsoon months (Figure 5). It is seen that epochal behavior of July and September rainfall is almost similar to that of monsoon seasonal rainfall. In August, the above normal or positive phases started from the middle of 1950s and continued till to the end.Both June and August rainfall are in positive phase in the recent decades while July and September rainfall are in the negative phase. Fig. 6 shows the decadal means of all India summer monsoon rainfall anomalies. The alternating sequence of multi-decadal periods having frequent droughts and flood years are clearly noticed in Fig. 6. We can delineate (i) 1901-1930 dry period (ii) 1931-60 wet period (iii) 1961-90 Dry period (iv) 1991-2020 (possibly) Wet period. Earlier studies by Pant and Kumar(1997) using the data series of Parthasarathy et al. 1994) also found the similar results of 30 years of alternating sequences of dry and wet period. The Table 2 shows the decadal mean, frequencies of drought and flood years. The deficient or excess monsoon years are defined for those years where monsoon rainfall percentage departures from the mean rainfall are less or more than the standard deviation (8. 1% of mean). In the decade 1911-20, there were four deficient and three excess years. During the dry period of 1901-30, we had eight deficient years and three excess years. During the next three decades of wet period, we had three deficient years and five excess years.In the dry period of 1961-90, there were seven deficient years and four excess years. Also during the period of 1901-2003, number of deficient years (19) is more than the number of excess years (13). Fig. 7 shows a similar picture for each of the four monsoon months. Except for the decade 1921-30, behavior of July rainfall was almost similar to that of monsoon seasonal rainfall. During the decade 1921-30, in spite of high contribution from July, seasonal rainfall became negative because of high negative contribution of June and August rainfall. Decadal variability is more in June where a lternating equence of wet and dry periods are seen on almost every decade. Coefficient of variability of July (12. 3%) and August (12. 0%) rainfall are also less compared to June (18. 1%) and September (19. 1%) rainfall. 5 5. Trends in All India monsoon rainfall Fig. 8 shows the All India monsoon seasonal rainfall series as percentage departure from long period average. The series was subjected to a ‘low- pass filter’ in order to suppress the high frequency oscillations. The weights used were nine point Gaussian probability curve (0. 01, 0. 05, 0. 12, 0. 20, 0. 24, 0. 20, 0. 12, 0. 5, 0. 01). It is clearly seen that no linear trend exists in this series. We have also used linear regression technique and the ‘Students t’ test for testing if there is any significance in the trend. All India summer monsoon rainfall as well the rainfall during the four monsoon months do not show any significant trend. 6. Trends in Sub-divisional rainfall It is interesting to se e that for the country as a whole, the all India monsoon rainfall and monthly rainfall for the monsoon months do not show any significant trend. But there can be large variations in the regional scale.In order to study the secular variations of regional rainfall we have then carried out the trend analysis for the monthly rainfall series of June, July, August, and September and also for the season as a whole for all the 36 subdivisions. The results are shown in Fig 9, which shows significant and remarkable variations on the regional scale. We have analyzed July and August rainfall, which contributes major portion of monsoon seasonal rainfall. We find in July, six subdivisions have shown decreasing trends and eight subdivisions have increasing trends. In August, four (ten) subdivisions have decreasing (increasing) trends for August rainfall.We have considered all the cases of 99%, 95% and 90% levels of statistical significance. June rainfall has shown increasing trend for the western and southwestern parts of the country whereas decreasing trends are observed for the central and eastern parts of the country. But July rainfall has decreased for most parts of the central and peninsular India but increased significantly in the northeastern parts of the country. August rainfall has increased significantly (at 95% significance level) for the subdivisions Konkan & Goa, Marathwada, Madhya Maharashtra, Vidarbha, West M.P. , Telangana and west U. P. September rainfall is increasing significantly (at 95% level of significance) in Gangetic West Bengal and decreasing significantly (at 90% level of significance) for 6 the sub-divisions Marathwada, Vidarbha and Telangana. Fig. 10 shows the trends in southwest monsoon rainfall (in mm in 100 year) for each of the 36 subdivisions. Different levels of significance are shaded with colours. During the season, three subdivisions viz. Jharkhand (95%), Chattisgarh (99%), Kerala (90%) show significant decreasing trends and eight subdiv isions viz.Gangetic WB (90%), West UP (90%), Jammu & Kashmir (90%), Konkan & Goa (95%), Madhya Maharashtra (90%), Rayalseema (90%), Coastal A P (90%) and North Interior Karnataka (95%) show significant increasing trends. In order to examine further, whether the contribution of each month’s rainfall in the annual rainfall shows any significant trend, we have prepared a time series of contribution of rainfall for each month towards the annual total rainfall for each year in percentages. Trend analyses are carried out for each month and for all the 36 subdivisions.Results suggest that contribution of June and August rainfall exhibited significant increasing trends, while contribution of July rainfall exhibited decreasing trends. Fig. 11 shows some very interesting results. June rainfall is getting importance as its contribution to annual rainfall is increasing in almost 19 subdivisions while decreasing in the remaining 17 subdivisions. Contribution of July rainfall is decreasing in central and west peninsular India (significantly in South interior Karnataka (95%), East M. P. (90%) Vidarbha (90%), Madhya Maharashtra (90%), Marathwada (90%), Konkan & Goa (90%), and North interior Karnataka (90%)).Interestingly, contribution of August rainfall is increasing in all these subdivisions. Therefore, we see a major shift in rainfall pattern spatially and temperally during the recent years. 7. Trends in sub-divisional rainfall during other seasons Though south-west monsoon is the major rain producing season over the country, other seasons have also significant contribution in some specific areas. The rainfall during the winter and pre-monsoon seasons are mostly predominant by western disturbances and convective activities whereas during northeast monsoon is predominant over southern states during the October-December period.Therefore, trends analysis was also carried out on sub-divisional rainfall series for the winter 7 season (January – February), pre-monso on season (March-May), post-monsoon season (October-December) and also for the annual rainfall. Fig. 12 shows the increase/decrease in mm in 100 year in each of 36 subdivisions for the winter, premonsoon, post-monsoon seasons and annual. Different levels of significance are shaded with different colours. Rainfall is decreasing in almost all the sub-divisions except for the sub-divisions Himachal Pradesh, Jharkhand and Nagaland, Manipur, Mizoram & Tripura during the winter season.The rainfall for the subdivisions viz. east Uttar Pradesh, Bihar, east Madhya Pradesh where winter rainfall is mostly due to western disturbances is also decreasing significantly. Rainfall is decreasing significantly for the eighteen sub-divisions of the country during the winter season. During the pre-monsoon season, rainfall is decreasing over most parts of the central India. This may indirectly suggests that the convective activity which is the main cause for the rainfall activities during the pre-monsoon season is decreasing over the central parts of the country.Rainfall is decreasing significantly for the six subdivisions viz. Gujarat Region, west M. P. , east M. P. , Vidarbha, Chattisgarh and Jharkhand. However during the post-monsoon season, rainfall is increasing for almost all the sub-divisions except for the nine sub-divisions. It is increasing significantly for the sub-divisions viz. Saurashtra & Kutch, Marathwada and Rayalseema. For the sub-divisions Chattisgarh, Jharkhand and Kerala significant decrease in rainfall is even observed in annual scale.Significant increasing trend is observed in the annual scale for the sub-divisions Konkan & Goa, Madhya Maharashtra, North Interior Karnataka, Rayalseema, coastal Andhra Pradesh, Gangetic West Bengal, Assam & Meghalaya and Jammu & Kashmir. 8. Conclusions There was a need for development of a homogeneous (spatially and temporally) rainfall series for all the 36 meteorological subdivisions as well as for the country as a whole. The newly constructed rainfall series is uniformly distributed through out the country and it represents all the existing districts.Though Indian monsoon rainfall as a whole does not show any significant trend, significant rainfall trends are observed over some specific areas. Present study brings out some of the interesting and also significant changes in the rainfall pattern of the country. The 8 alternating sequence of multi-decadal periods of thirty years having frequent droughts and flood years are observed in the all India monsoon rainfall data. The decades 1961-70, 1971-80 and 1981-90 were dry periods. The first decade (19912000) in the next 30 years period already experienced wet period (Fig. 6).Therefore, there is a chance of wet period for the subsequent two decades viz. 2001-2010 and 2011-2020. Decadal variability is more for the June and September months while decadal variability of July rainfall is almost similar to that of monsoon rainfall. July rainfall is decreasing for most parts of central India while it is decreasing for the north eastern parts of the country. However June and August rainfall is increasing for the central and south western parts of the country. During the southwest monsoon season, three subdivisions viz. Jharkhand, Chattisgarh, Kerala show significant decreasing trend and eight subdivisions viz.Gangetic WB, West UP, Jammu & Kashmir , Konkan & Goa , Madhya Maharashtra, Rayalseema , Coastal A P and North Interior Karnataka show significant increasing trends. For the first time, we have also studied contribution of each of major rain producing month’s (i. e. June, July, August and September) in annual rainfall and examine whether there is any significant change in their contribution. June rainfall is getting importance as its contribution to annual rainfall is increasing in almost 19 sub-divisions while decreasing in the remaining 17 subdivisions. Contribution of July rainfall is decreasing in central and west peninsular In dia.But contribution of August rainfall is increasing in all these areas. Significant increasing trend is also observed in the annual rainfall for the sub-divisions Konkan & Goa, Madhya Maharashtra, North Interior Karnataka, Rayalseema, coastal Andhra Pradesh, Gangetic West Bengal, Assam & Meghalaya and Jammu & Kashmir. Acknowledgements: The authors are thankful to Dr (Mrs) N. Jayanthi, LACD ADGM(R) and Shri Thakur Prasad, DDGM (C) for providing kind support and encouragements for this research work. We also acknowledge the help provided by the staff of the Hydrology section.Note : Electronic version of the all India monthly rainfall shown in Table – 1 can be obtained from National Climate Centre ([email  protected] pune. gov. in) 9 References Pant, G. B. & Rupa Kumar, K. , 1997, Climates of South Asia. John Wiley & Sons, Chichester, 320 pp. Parthasarathy B, 1984, Inter-annual and long term variability of Indian summer monsoon rainfall’, Proc. Indian Acad. Sci. (Eart h Planet. Sci. ), 93, 371-385. Parthasarathy B, Rupa Kumar K and Munot A 1993 Homogeneous Indian monsoon rainfall: variability and prediction; Proc. Indian Acad. Sci. (Earth Planet Science) 102 121-155.Parthasarathy, B. , Munot A and Kothawale D R 1994: All-India monthly and seasonal rainfall series 1887-1993; Theoretical and Applied Climatology 49 217-224. Rupa Kumar, K. , Pant G. B. , Parthasarathy, B. and Sontakke, N. A. 1992, Spatial and sub-seasonal patterns of the long-term trends of Indian summer monsoon rainfall, Int. J. of Climatol. , 12, 257-268. Mooley, D. A. & Parthasarathy, B. , 1984: Fluctuations in All-India summer monsoon rainfall during 1871-1978. Climatic Change, 6, 287-301. World Meteorological Organization, 1983, Guide to Climatological Practices, WMO No 100. 10Table-1 All India monthly, seasonal and annual rainfall Rainfall in mm YEAR 1901 1902 1903 1904 1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919 1920 1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936 1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 JAN 34. 1 11. 4 18. 7 17. 5 24. 9 23. 0 15. 8 22. 2 25. 7 16. 2 41. 1 23. 4 8. 9 6. 4 22. 1 5. 8 9. 6 14. 1 50. 6 24. 9 39. 9 29. 4 26. 0 21. 3 14. 6 29. 4 14. 3 23. 2 28. 0 23. 6 13. 8 10. 0 17. 8 24. 8 30. 0 11. 8 7. 8 30. 8 14. 1 16. 0 25. 7 25. 0 54. 9 28. 5 34. 8 6. 9 23. 2 25. 6 13. 2 30. 2 15. FEB 40. 2 12. 4 14. 0 15. 6 26. 3 49. 2 48. 6 23. 0 21. 4 15. 2 11. 1 23. 7 41. 7 32. 8 42. 1 22. 3 35. 7 7. 1 24. 4 23. 6 10. 8 12. 5 42. 2 25. 7 13. 4 13. 2 34. 2 42. 5 22. 4 22. 2 33. 2 25. 1 32. 9 11. 4 21. 3 41. 5 53. 6 32. 6 33. 4 27. 9 17. 4 45. 1 12. 2 43. 0 10. 8 21. 5 21. 0 29. 0 29. 7 25. 0 15. 3 MAR 29. 6 28. 6 35. 8 38. 5 45. 1 39. 1 46. 7 21. 5 19. 3 22. 5 52. 9 27. 5 31. 0 33. 2 51. 8 17. 8 29. 7 39. 6 27. 6 49. 4 20. 0 18. 4 29. 0 20. 7 20. 9 59. 0 27. 6 25. 6 18. 4 27. 1 22. 2 26. 7 31. 0 22. 2 22. 7 41. 1 24. 6 28. 2 36. 5 45. 1 21. 2 19. 3 26. 7 59. 7 23. 2 26. 0 29. 1 42. 4 24. 4 37. 4 43. 1 APR 41. 9 48. 28. 8 38. 5 39. 5 25. 5 66. 8 38. 2 69. 4 35. 8 32. 4 43. 9 33. 4 50. 6 42. 8 36. 0 43. 2 41. 1 34. 9 36. 8 41. 1 32. 7 32. 4 35. 1 42. 3 42. 9 33. 5 39. 8 50. 4 47. 2 33. 6 32. 6 48. 7 36. 5 46. 9 32. 1 57. 9 31. 1 38. 3 32. 9 30. 7 45. 5 48. 6 37. 6 48. 0 48. 5 35. 4 40. 2 48. 0 26. 9 46. 2 MAY 59. 1 57. 3 66. 3 77. 2 62. 3 45. 6 41. 8 52. 5 59. 9 50. 6 52. 5 49. 7 77. 1 72. 5 62. 0 57. 6 80. 0 89. 4 60. 0 57. 5 40. 1 47. 4 55. 3 59. 7 86. 2 59. 6 54. 5 48. 6 55. 0 59. 8 56. 4 72. 1 98. 8 41. 5 36. 2 79. 4 53. 8 69. 4 39. 4 76. 0 69. 4 56. 1 86. 0 47. 6 50. 4 63. 5 47. 2 75. 2 78. 1 50. 2 58. 4 JUN 129. 4 123. 8 131. 169. 2 112. 1 185. 3 160. 9 135. 7 208. 1 213. 2 196. 8 115. 3 218. 8 166. 9 161. 8 215. 0 221. 3 181. 2 185. 7 151. 4 172. 5 184. 4 98. 6 121. 3 199. 2 95. 1 164. 5 158. 8 179. 0 172. 2 114. 5 125. 0 206. 9 197. 5 141. 9 241. 9 160. 2 245. 5 153. 7 170. 4 162. 2 172. 3 154. 6 138. 1 155. 7 201. 1 124. 0 153. 9 146. 7 142. 7 150. 3 JUL 252. 7 285. 6 298. 9 271. 9 263. 5 290. 7 236. 3 327. 0 314. 9 251. 3 174. 0 329. 3 278. 5 348. 6 232. 9 269. 8 267. 4 160. 9 294. 3 294. 2 274. 6 304. 1 321. 1 315. 0 297. 7 301. 8 333. 6 291. 3 292. 6 289. 1 294. 7 326. 7 275. 7 273. 5 312. 3 276. 2 329. 3 283. 5 262. 5 296. 0 234. 0 339. 305. 5 343. 4 315. 2 297. 4 294. 7 308. 3 298. 9 335. 5 251. 6 AUG 268. 6 209. 8 269. 3 216. 4 211. 3 252. 2 310. 9 308. 5 229. 0 285. 5 214. 6 262. 2 198. 3 239. 8 225. 8 302. 6 287. 3 231. 0 288. 6 177. 9 259. 9 214. 4 272. 2 249. 4 232. 2 326. 6 251. 9 216. 4 240. 9 196. 7 305. 5 227. 8 301. 9 290. 6 228. 1 228. 4 194. 9 239. 6 235. 6 282. 9 223. 4 286. 0 228. 3 288. 4 232. 9 286. 5 287. 4 275. 2 236. 8 235. 6 223. 9 SEP 137. 3 201. 1 195. 3 141. 6 175. 6 182. 5 104. 0 158. 8 165. 9 191. 6 181. 3 128. 8 117. 9 198. 2 175. 8 197. 4 277. 6 105. 2 152. 6 122. 3 193. 5 200. 6 167. 9 232. 9 123. 4 205. 5 152. 6 139. 1 122. 173. 5 186. 3 173. 8 211. 1 164. 4 178. 1 18 5. 4 174. 0 156. 2 150. 3 115. 1 148. 0 180. 4 203. 0 148. 1 210. 6 141. 6 234. 3 176. 5 217. 2 196. 7 130. 5 OCT 59. 5 69. 9 116. 1 73. 7 60. 2 55. 7 31. 8 46. 8 45. 2 111. 8 71. 0 61. 3 69. 6 52. 6 93. 8 140. 4 157. 1 23. 5 77. 1 47. 0 69. 3 56. 5 63. 0 63. 3 72. 2 54. 9 62. 8 115. 3 95. 8 93. 1 121. 7 68. 9 95. 9 62. 2 57. 1 64. 3 94. 5 75. 0 88. 9 62. 4 62. 9 44. 0 90. 5 90. 4 82. 4 79. 6 66. 9 63. 4 93. 2 56. 6 75. 4 NOV 37. 1 29. 3 39. 3 13. 4 12. 9 19. 2 24. 5 8. 9 12. 6 36. 1 43. 8 50. 3 18. 7 22. 3 47. 6 45. 5 27. 4 44. 7 50. 4 26. 5 16. 8 55. 2 17. 7 54. 6 42. 4 11. 8 56. 23. 4 19. 6 47. 7 41. 2 55. 7 21. 3 29. 5 17. 3 56. 2 21. 9 16. 1 29. 7 41. 4 27. 6 18. 2 18. 1 30. 3 21. 2 76. 9 9. 8 70. 0 12. 6 25. 4 31. 7 DEC 14. 0 27. 3 22. 6 19. 7 14. 2 29. 8 16. 6 12. 6 31. 4 9. 5 14. 8 8. 5 25. 1 23. 2 11. 2 5. 8 13. 4 18. 7 22. 5 6. 2 19. 3 16. 5 18. 3 18. 7 18. 4 11. 7 13. 7 29. 1 39. 7 12. 6 23. 8 17. 2 19. 1 14. 7 12. 9 22. 6 21. 0 7. 9 4. 5 18. 0 21. 5 29. 2 8. 5 17. 9 7. 8 39. 4 26. 4 12. 6 5. 7 10. 6 8. 7 J-F 74. 3 23. 8 32. 7 33. 1 51. 2 72. 2 64. 4 45. 2 47. 1 31. 4 52. 2 47. 1 50. 6 39. 2 64. 2 28. 1 45. 3 21. 2 75. 0 48. 5 50. 7 41. 9 68. 2 47. 0 28. 0 42. 6 48. 5 65. 7 50. 4 45. 8 47. 35. 1 50. 7 36. 2 51. 3 53. 3 61. 4 63. 4 47. 5 43. 9 43. 1 70. 1 67. 1 71. 5 45. 6 28. 4 44. 2 54. 6 42. 9 55. 2 31. 2 MAM 130. 6 134. 0 130. 9 154. 2 146. 9 110. 2 155. 3 112. 2 148. 6 108. 9 137. 8 121. 1 141. 5 156. 3 156. 6 111. 4 152. 9 170. 1 122. 5 143. 7 101. 2 98. 5 116. 7 115. 5 149. 4 161. 5 115. 6 114. 0 123. 8 134. 1 112. 2 131. 4 178. 5 100. 2 105. 8 152. 6 136. 3 128. 7 114. 2 154. 0 121. 3 120. 9 161. 3 144. 9 121. 6 138. 0 111. 7 157. 8 150. 5 114. 5 147. 7 J-S 788. 0 820. 3 894. 9 799. 1 762. 5 910. 7 812. 1 930. 0 917. 9 941. 6 766. 7 835. 6 813. 5 953. 5 796. 3 984. 8 1053. 6 678. 3 921. 2 745. 8 900. 903. 5 859. 8 918. 6 852. 5 929. 0 902. 6 805. 6 835. 4 831. 5 901. 0 853. 3 995. 6 926. 0 860. 4 931. 9 858. 4 924. 8 802. 1 864. 4 767. 6 978. 2 891. 4 918. 0 914. 4 926. 6 940. 4 913. 9 899. 6 910. 5 756. 3 O-D 110. 6 126. 5 178. 0 106. 8 87. 3 104. 7 72. 9 68. 3 89. 2 157. 4 129. 6 120. 1 113. 4 98. 1 152. 6 191. 7 197. 9 86. 9 150. 0 79. 7 105. 4 128. 2 99. 0 136. 6 133. 0 78. 4 133. 4 167. 8 155. 1 153. 4 186. 7 141. 8 136. 3 106. 4 87. 3 143. 1 137. 4 99. 0 123. 1 121. 8 112. 0 91. 4 117. 1 138. 6 111. 4 195. 9 103. 1 146. 0 111. 5 92. 6 115. 8 ANNUAL 1103. 5 1104. 6 1236. 5 1093. 2 1047. 9 1197. 8 1104. 7 1155. 1202. 8 1239. 3 1086. 3 1123. 9 1119. 0 1247. 1 1169. 7 1316. 0 1449. 7 956. 5 1268. 7 1017. 7 1157. 8 1172. 1 1143. 7 1217. 7 1162. 9 1211. 5 1200. 1 1153. 1 1164. 7 1164. 8 1246. 9 1161. 6 1361. 1 1168. 8 1104. 8 1280. 9 1193. 5 1215. 9 1086. 9 1184. 1 1044. 0 1260. 6 1236. 9 1273. 0 1193. 0 1288. 9 1199. 4 1272. 3 1204. 5 1172. 8 1051. 0 11 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 198 5 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 Mean (1901-2003) S. D.C. V. 11. 0 26. 2 30. 4 23. 5 17. 5 32. 2 15. 1 28. 7 16. 3 24. 9 14. 9 13. 5 13. 2 13. 3 15. 7 12. 9 24. 3 12. 7 23. 2 17. 4 12. 4 15. 0 11. 7 17. 3 12. 7 17. 9 14. 8 20. 0 15. 0 26. 9 25. 9 18. 2 20. 4 23. 0 17. 8 17. 7 11. 0 15. 8 16. 8 14. 6 19. 2 18. 6 23. 9 27. 5 22. 3 16. 2 16. 9 20. 3 22. 0 11. 3 20. 4 15. 5 20. 3 8. 5 41. 8 22. 6 13. 9 36. 6 11. 1 16. 6 18. 8 20. 5 25. 1 9. 2 33. 9 24. 1 16. 2 19. 0 22. 9 23. 8 14. 1 21. 4 16. 4 27. 4 23. 1 25. 3 19. 4 14. 4 21. 3 22. 2 18. 4 28. 6 34. 7 21. 5 20. 3 25. 1 23. 5 31. 8 14. 4 34. 5 20. 4 24. 9 17. 8 42. 5 25. 7 23. 5 26. 9 25. 9 29. 0 22. 8 13. 0 33. 7 13. 6 26. 8 14. 20. 1 30. 9 24. 6 10. 0 40. 4 35. 1 24. 8 27. 9 29. 9 36. 7 41. 3 29. 8 30. 0 39. 7 27. 8 26. 3 38. 7 27. 9 34. 2 24. 8 55. 0 30. 5 26. 9 33. 3 26. 3 24. 8 27. 5 25. 0 32. 5 32. 3 27. 9 40. 7 34. 1 31. 7 45. 3 41. 1 38. 4 31. 0 30. 8 28. 5 29. 0 43. 8 32. 2 49. 5 29. 8 33. 9 40. 0 27. 3 28. 7 36. 5 33. 8 41. 9 18. 2 22. 9 23. 2 28. 5 32. 8 32. 0 9. 2 28. 8 37. 8 42. 4 30. 2 39. 5 37. 4 35. 3 39. 8 34. 1 28. 1 33. 5 47. 3 45. 6 38. 0 41. 6 34. 9 33. 1 37. 0 42. 5 37. 4 49. 0 37. 4 31. 9 35. 8 31. 5 39. 6 58. 3 32. 9 26. 9 38. 8 36. 1 50. 0 49. 7 42. 6 36. 6 48. 5 39. 4 45. 6 34. 4 43. 4 50. 9 32. 5 30. 7 47. 2 34. 5 35. 5 2. 9 41. 7 24. 2 43. 7 44. 2 42. 3 39. 0 39. 8 7. 9 19. 9 69. 9 48. 7 56. 3 77. 4 85. 8 62. 4 66. 9 68. 5 64. 3 73. 2 63. 6 58. 7 53. 6 50. 9 59. 8 48. 5 45. 6 66. 0 66. 1 73. 7 60. 4 61. 3 66. 2 54. 5 50. 4 81. 3 64. 1 56. 9 50. 6 60. 9 63. 3 63. 8 56. 3 54. 2 53. 9 64. 0 68. 3 56. 1 101. 7 73. 5 53. 9 67. 6 50. 9 77. 7 60. 9 51. 0 58. 9 81. 9 69. 9 61. 4 59. 7 55. 7 61. 9 12. 5 20. 2 168. 4 163. 3 148. 6 180. 3 208. 4 152. 3 123. 9 162. 6 152. 0 185. 8 123. 5 148. 8 152. 3 121. 0 169. 1 146. 3 146. 5 131. 8 195. 5 203. 3 137. 1 148. 5 126. 0 176. 1 157. 7 184. 3 187. 2 151. 1 212. 2 158. 9 139. 9 150. 3 164. 3 153. 0 174. 130. 1 159. 9 183. 3 180. 8 181. 8 141. 9 165. 7 199. 8 137. 4 170. 7 166. 7 162. 4 167. 3 178. 1 185. 6 161. 2 167. 8 163. 4 29. 5 18. 1 281. 8 312. 9 297. 7 241. 2 351. 3 288. 9 314. 5 345. 5 290. 6 329. 0 272. 9 254. 4 320. 6 279. 4 253. 1 296. 4 302. 4 305. 2 248. 8 260. 3 226. 3 284. 5 271. 6 295. 3 294. 1 305. 7 291. 4 244. 4 290. 4 303. 6 242. 7 279. 8 282. 1 270. 9 265. 7 237. 6 353. 4 308. 2 279. 0 281. 4 257. 9 314. 1 336. 3 301. 3 277. 0 284. 8 293. 1 274. 8 274. 5 275. 4 163. 9 305. 9 286. 7 35. 3 12. 3 249. 0 286. 0 237. 5 313. 9 259. 6 264. 9 285. 4 255. 5 244. 6 277. 3 257. 6 294. 9 273. 2 210. 1 229. 5 266. 214. 8 260. 6 300. 8 260. 3 234. 1 293. 4 240. 0 284. 8 292. 1 254. 9 277. 9 235. 1 263. 3 239. 3 274. 5 294. 4 260. 7 238. 7 235. 2 237. 1 285. 2 238. 1 292. 7 256. 0 269. 0 209. 8 278. 9 256. 1 283. 4 270. 5 253. 3 251. 0 240. 2 229. 8 244. 0 250. 1 255. 3 30. 6 12. 0 121. 9 169. 8 244. 5 217. 7 169. 0 130. 3 215. 0 219. 1 168. 6 228. 0 207. 0 163. 2 198. 4 145. 5 151. 7 176. 8 144. 3 179. 6 203. 3 159. 2 138. 9 182. 7 155. 8 224. 1 150. 2 152. 2 160. 4 146. 4 144. 8 193. 5 135. 5 224. 4 149. 7 157. 6 142. 0 152. 0 214. 0 170. 2 195. 2 135. 6 169. 2 200. 6 153. 3 183. 8 147. 0 163. 0 195. 5 191. 5 154. 3 138. 3 173. 0 181. 171. 8 32. 8 19. 1 76. 7 88. 5 81. 6 145. 9 149. 7 66. 4 103. 9 118. 6 75. 3 116. 7 80. 8 93. 0 68. 5 45. 3 62. 1 52. 0 73. 2 63. 5 75. 2 95. 5 70. 8 106. 2 101. 1 109. 8 39. 0 88. 7 62. 7 63. 5 56. 3 53. 8 60. 3 85. 1 69. 1 115. 0 72. 0 88. 4 57. 1 54. 2 99. 9 63. 9 69. 0 87. 8 87. 6 76. 3 98. 8 68. 1 102. 1 106. 5 60. 7 95. 0 69. 8 93. 8 78. 4 24. 8 31. 6 9. 4 14. 1 5. 6 28. 4 42. 8 27. 2 35. 4 25. 4 34. 8 22. 9 19. 9 25. 9 26. 4 18. 7 49. 4 14. 8 23. 4 35. 5 20. 0 16. 1 31. 3 17. 8 14. 4 23. 8 53. 5 61. 8 44. 5 71. 8 26. 0 29. 1 43. 9 14. 0 18. 2 20. 9 43. 9 45. 2 17. 9 20. 6 32. 2 33. 7 42. 3 29. 0 26. 8 35. 7 16. 5 56. 38. 9 23. 2 20. 6 26. 7 27. 0 26. 0 30. 7 15. 1 49. 3 24. 5 9. 8 16. 9 12. 9 15. 8 16 . 3 18. 7 11. 6 12. 6 13. 6 31. 5 19. 1 14. 8 25. 4 18. 4 46. 1 14. 6 17. 5 10. 0 18. 1 23. 8 19. 3 11. 9 9. 8 13. 0 15. 1 24. 6 16. 4 23. 4 16. 8 15. 0 24. 1 16. 9 22. 5 25. 9 21. 3 17. 0 18. 1 25. 2 22. 0 7. 6 18. 6 19. 1 10. 6 18. 6 45. 1 12. 5 8. 5 11. 5 11. 8 12. 0 20. 7 17. 9 7. 9 44. 0 33. 6 40. 1 67. 0 34. 6 34. 1 51. 0 35. 6 53. 8 25. 5 58. 8 39. 0 29. 7 32. 2 36. 2 39. 5 27. 0 45. 7 29. 1 50. 6 40. 5 37. 7 34. 4 26. 1 38. 6 34. 9 36. 3 43. 4 54. 7 36. 5 47. 2 51. 0 41. 7 52. 2 37. 4 52. 3 38. 1 35. 9 33. 6 59. 3 40. 3 42. 7 45. 49. 8 56. 5 45. 1 29. 2 50. 6 33. 9 48. 8 25. 4 40. 5 46. 4 44. 9 12. 3 27. 3 142. 8 115. 9 114. 4 146. 8 159. 9 139. 0 136. 5 132. 6 132. 1 134. 5 137. 2 143. 0 119. 5 126. 7 119. 5 136. 6 113. 1 135. 4 136. 8 149. 0 122. 6 120. 7 127. 0 118. 5 122. 3 167. 5 137. 7 117. 9 121. 1 142. 3 154. 4 151. 9 129. 9 121. 6 130. 9 132. 4 157. 7 122. 7 194. 6 154. 2 120. 3 138. 3 125. 4 140. 9 132. 9 127. 7 142. 5 124. 3 136. 5 128. 8 130. 5 127. 5 133. 7 17. 7 13. 2 821. 1 932. 0 928. 3 953. 1 988. 3 836. 4 938. 8 982. 7 855. 8 1020. 1 861. 0 861. 3 944. 5 756. 0 803. 4 885. 8 808. 0 877. 2 948. 4 883. 1 736. 4 909. 1 793. 4 980. 894. 1 897. 1 916. 9 777. 0 910. 7 895. 3 792. 6 948. 9 856. 8 820. 2 817. 0 756. 8 1012. 5 899. 8 947. 7 854. 8 838. 0 890. 2 968. 3 878. 6 878. 1 885. 0 904. 3 884. 6 847. 1 829. 1 742. 2 905. 7 877. 2 71. 0 8. 1 110. 6 112. 4 104. 1 187. 2 208. 3 109. 9 158. 0 155. 6 122. 7 153. 2 132. 2 138. 0 109. 7 89. 4 129. 9 112. 9 111. 2 116. 5 105. 2 129. 7 125. 9 143. 3 127. 4 143. 4 105. 5 165. 6 131. 8 151. 7 105. 7 99. 7 119. 2 123. 2 104. 2 158. 4 141. 8 154. 9 92. 0 92. 9 157. 3 119. 6 118. 9 135. 4 133. 5 122. 6 133. 9 169. 9 153. 5 138. 2 92. 8 133. 5 108. 8 140. 5 126. 9 28. 8 22. 7 1108. 1 1200. 4 1213. 8 1321. 7 1390. 1136. 3 1268. 9 1324. 7 1136. 1 1366. 6 1169. 4 1172. 0 1205. 9 1008. 3 1092. 3 1162. 3 1078. 0 1158. 2 1241. 0 1202. 3 1022. 6 1207. 5 1073. 9 1280. 8 1156. 8 1266. 5 1229. 8 1101. 3 1174. 0 1184. 5 1117. 2 1265. 7 1143. 1 1137. 6 1142. 0 1082. 2 1298. 1 1149. 0 1358. 9 1168. 9 1119. 9 1209. 4 1277. 0 1198. 6 1190. 0 1211. 8 1250. 9 1181. 0 1125. 2 1116. 8 1022. 0 1220. 1 1182. 8 87. 0 7. 4 12 Table-2 Decadal mean (% departure from normal), frequency of drought and flood years Decadal mean DECADE Per cent departure from normal 1901-10 1911-20 1921-30 1931-40 1941-50 1951-60 1961-70 1971-80 1981-90 1991-2000 2001-2003 -2. -2. 5 -0. 4 1. 7 3. 3 2. 5 -0. 1 -0. 8 -0. 3 0. 6 -5. 9 3 4 1 1 1 1 2 3 2 0 1 0 3 0 1 1 3 1 1 2 1 0 Freq. of Deficient year Freq. of Excess year 13 Fig. 1 Location of raingauge stations considered for the study 14 Fig. 2. Location of raingauge stations used by IITM (Parthasarathy et al. 1994) 15 R/F IN MM 0. 5 1. 5 2. 5 3. 5 -1 0 1 2 3 -0. 5 -1. 5 1000 1100 -2 500 600 700 800 900 1901 1904 1901 1904 1907 1910 1913 1916 1919 1922 1925 1928 1931 1934 1937 1940 1943 1946 1949 1952 1955 1958 1961 1964 1967 1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003 YEAR 1907 1910 1913 1916 1919 1922 1925 928 1931 1934 1937 1940 1943 1946 1949 IITM(Parthosarothy Series) series and the new IMD series Fig. 3. Comparison between IITM South-West monsoon seasonal rainfall Fig. 4. The 31 year moving averages of all India south-west monsoon seasonal rainfall 16 1952 1955 1958 1961 1964 1967 1970 1973 1976 1979 1982 1985 IMD series 1988 1991 1994 1997 2000 2003 PERCENTAGE DEPARTURE -4 0 1 2 3 4 1901 -6 -4 -2 0 2 4 PERCENTAGE DEPARTURE 6 8 1901 1904 1907 1910 1913 1916 1919 1922 1925 1928 1931 1934 1937 1940 1943 1946 1949 1952 1955 1958 1961 1964 1967 1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003 3 -2 -1 1991-2000 1904 1907 1910 1913 1916 1919 1922 1925 1928 1931 1934 1937 1940 1943 1946 1949 1952 1955 YEAR 2001-03 1901-10 -8. 0 31 YEAR MOVING AVERAGE ALL INDIA AUGUST RAINFALL 1981-90 -6. 0 YEAR 1958 1961 1964 1967 1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003 1971-80 -4. 0 -2. 0 1961-70 1951-60 1941-50 1931-40 192 1-30 1911-20 31 YEAR MOVING AVERAGE ALL INDIA JUNE RAINFAL DECADAL MEANS OF ALL INDIA SUMMER MONSOON RAINFALL (% DEPARTURE FROM MEAN) (June, July, August, September) Fig. 5. 31 year moving average of all India rainfall for the four monsoon months Fig 6.Decadal means of all India summer monsoon rainfall (% departure from mean) 17 -4 1901 1904 1907 1910 1913 1916 1919 1922 1925 1928 1931 1934 1937 1940 1943 1946 1949 YEAR 1952 1955 1958 1961 1964 1967 1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003 -3 -2 -1 0 PERCENTAGE DEPARTURE PERCENTAG E DEPAR TURE 1 2 3 4 5 6 7 -3 1901 1904 1907 1910 1913 1916 1919 1922 1925 1928 1931 1934 1937 1940 1943 1946 1949 YEAR 1952 1955 1958 1961 1964 1967 1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003 -2 -1 0 1 2 3 4 5 6 0. 0 2. 0 4. 0 31 YEAR MOVING AVERAGE ALL INDIA JULY RAINFALL 1 YEAR MOVING AVERAGE ALL INDIA SEPTEMBER RAINFALL June DECADAL MEANS OF ALL INDIA JUNE RAINFALL (% DEPARTURE FROM MEAN) -8. 0 1901-10 1911-20 1911-2 0 1921-30 1921-30 1931-40 1931-40 DECADE -6. 0 -4. 0 -2. 0 0. 0 2. 0 4. 0 6. 0 8. 0 10. 0 12. 0 -15. 0 1901-10 July DECADAL MEANS OF ALL INDIA JULY RAINFALL (%DEPARTURE FROM MEAN) -10. 0 -5. 0 0. 0 5. 0 10. 0 1941-50 1941-50 Decade DECADE 1951-60 1951-60 1961-70 1961-70 1971-80 1971-80 1981-90 1981-90 1991-2000 1991-2000 2001-03 2001-03 August DECADAL MEANS OF ALL INDIA AUGUST RAINFALL (% DEPARTURER FROM MEAN) -8. 1901-10 -6. 0 -4. 0 -2. 0 0. 0 2. 0 4. 0 6. 0 -10. 0 1901-10 -5. 0 0. 0 Septermber DECADAL MEANS OF ALL INDIA SEPTEMBER RAINFALL (%DEPARTURE FROM MEAN) 5. 0 10. 0 15. 0 20. 0 1911-20 1911-20 1921-30 1921-30 1931-40 DECADE 1931-40 1941-50 1941-50 1951-60 1951-60 1961-70 1961-70 1971-80 1971-80 1981-90 1981-90 1991-2000 1991-2000 2001-03 2001-03 Fig. 7. Decadal means of all India rainfall (% departure from mean) for the month of June, July, August and September 25. 0 20. 0 15. 0 10. 0 5. 0 0. 0 -5. 0 -10. 0 -15. 0 ALL INDIA MONSOON R. F -20. 0 -25. 1901 1904 1907 1910 1913 1 916 1919 1922 1925 1928 1931 1934 1937 1940 1943 1946 1949 1952 1955 1958 1961 1964 1967 1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003 Nine point gaussian filter PERCENTAGE DEPARTURE YEAR Fig. 8. All India Monsoon rainfall and its nine point Gaussian filter 18 Fig. 9. Increase/Decrease in rainfall in mm in 100 year for each of 36 subdivisions for the monsoon months. Different levels of significance are shaded with colors 19 Fig. 10. Increase/Decrease in rainfall in mm in 100 year for each of 36 subdivisions for the south-west monsoon season.Different levels of significance are shaded with colors 20 Fig. 11. Increase/Decrease in rainfall in percentage in 100 years in monthly contribution of rainfall to annual rainfall for each of the four monsoon months for 36 subdivisions 21 Fig. 12. Increase/Decrease in rainfall in mm in 100 year in each of 36 subdivisions for the winter, pre-monsoon, post-monsoon seasons and annual. Different levels of significance are shaded with co lors 22 N C C RESEARCH REPORTS New statistical models for long range forecasting of southwest monsoon rainfall over India, M. Rajeevan, D. S. Pai and Anil Kumar Rohilla, Sept. 2005. 23