An insight into the severe floods in India during 2005, 2006 & 2007

In this paper severe flood during South West monsoon seasons of 2005, 2006 & 2007 have been identified with the related synoptic features. A total of 22 states reeled under severe floods during 2005, 2006 & 2007. In the south peninsula only Karnataka and Kerala were affected by severe floods. During 2006 & 2007 severe floods continued for a longer period and affected many states where as during 2005 severe floods affected most of the states except peninsular India but they continued for short periods comparatively.Formation of excess No’s of low pressure areas and depressions in the Bay of Bengal during July, Aug and Sept 2006, in succession , and their WNW movements upto M.P. and east Rajasthan caused severe floods in Gujarat, Maharashtra, Rajasthan, Madhya Pradesh, Chattisgarh, Orissa and Andhra Pradesh for a longer period i.e. from 28 July to 12 September 2006. During 2007, Shifting of monsoon trough, presence of secondary monsoon trough and trough in westerly, caused severe floods in most of the North eastern states from 12 July to 10 October 2007.

Spatial and Temporal Assessment of Rainfall Variability using GIS Approach in Trichy District of Tamil Nadu

Precipitation is one of the transportation components in hydrological cycle. The magnitude of precipitation swings with time and space. Majorly India receives precipitation in the form of rainfall. Precipitation plays a key role in the rainfed agriculture. The present study deals with the rainfall characteristics of Tiruchirappalli district, Tamil Nadu. Seasonal rainfall data from eighteen rain gauge stations (1971-2012) have been taken for analysis of seasonal and annual rainfall pattern of Tiruchirappalli district. Mean rainfall of the district is about 696 mm. The highest rainfall of 1247 mm recorded in the year 2005 and the lowest precipitation of 227 mm recorded in the year 1976. About 48 percent and 35 percent of the rainfall received in North East and South West Monsoon, respectively. Spatial rainfall distribution was studied with the help of Kriging interpolation technique and respective maps were prepared with Geographical Information System. The percentage departure of annual rainfall is classified under the category of excess, normal and large excess category. South East and central part of Tiruchirappalli receives moderate to low rainfall. North East parts of Tiruchirappalli district such as pullambadi, Lalgudi and nearby areas received maximum rainfall during North East Monsoon and South West Monsoon. In winter season Manapparai and Vaiyampatti region received more rainfall while in summer season Thottiam and Mayanur area received more rainfall. The two major highlighted crops in Trichy district are Banana and Onion. Tiruchirappalli district is one of the Banana growing belts in Tamil Nadu. Spatial distribution of rainfall maps will be helpful to form a crop plan for different crops to increase the agricultural productivity of Tiruchirappalli district and to ensure the food security.

Climatology of dry and wet spell over Vidarbha region during monsoon months

In this investigation 10-day period-wise simple probability, 10-day period-wise probability of consecutive dry and wet spells of different lengths, and month-wise different parameters, and properties of Markov Chain Model over Vidarbha region during south-west monsoon months have been studied. For this purpose, daily rainfall data (1 June – 30 September) of 11 stations covering all the districts of Vidarbha for the period 1960-90 have been utilized. The study reveals that over Vidarbha during monsoon season (June - September) probability of a day being wet and probability of consecutive wet spell of different lengths are by and large high during the last and first 10-day periods of July and August respectively when the monsoon is at its peak. During the first two 10-day periods in June and last two 10-day periods in September, the probabilities of a dry day and that of consecutive dry spell of different lengths and quite high. During July and August a maximum of 12-14 wet days are expected and wet spell, on an average, lasts for 2 days. Stationary probability of the occurrence of wet day (pi2) is found to be maximum during July making it the most humid month in the monsoon season.

Wind steadiness up to 35 km and its variability before the southwest monsoon onset and the withdrawal

This paper brings out mainly on the quantitative approach to delineate wind direction variability through Wind Steadiness Factor (WSF) - a single parameter which depends on height, wind speed and wind direction. This can be used as a prognostic parameter for the onset and withdrawal of south west monsoon (SW Monsoon) over Kerala. A brief sketch on wind climatology up to 35 km over TERLS (8° 32' N / 76° 52' E) is also discussed to have a background knowledge. From the derived WSF climatology, it is seen that the region between 12.5 km to 18 km is of highest WSF during the SW Monsoon due to the Tropical Easterly Jet (TEJ). Vertical variation of annual WSF has shown well demarcated four layered structure and the wind rose constructed for each layer provides the contribution of WSF attributed by the dominant direction for that particular layer. A WSF value was estimated for the region between 12.5 km to 18 km over Thiruvananthapuram for each available rawin profile [0530 and 1730 hrs (IST)] and inferred that an early incidence and maintenance of WSF well above 80% prior the SW Monsoon supports an early SW Monsoon onset and reverse for a late onset. A late/early WSF decrease from a value of 80% followed by systematic further decrease is associated with late/early withdrawal of the SW Monsoon.

Present operational long range forecasting system for southwest monsoon rainfall over India and its performance during 2010

At present, India Meteorological Department (IMD) issues various monthly and seasonal operational forecasts for the south-west monsoon season using models based on latest statistical techniques with useful skill. Operational models are reviewed regularly and improved through in house research activities. For the forecasting of the south-west monsoon season (June – September) rainfall over the country as a whole, a newly introduced statistical ensemble forecasting system is used. In addition, models have been developed for the forecast of the monsoon season rainfall over four geographical regions (NW India, NE India, Central India and South Peninsula) of the country and forecast for the rainfall over the second half of the monsoon season over the country as a whole. Models have also been developed for issuing operational forecast for the monthly rainfall for the months of July, August & September over the country as a whole. Operational forecasts issued by IMD for 2010 south-west monsoon rainfall have been discussed and verified. In addition, the experimental forecasts for the season rainfall over the country as a whole based on bothstatistical and dynamical models received from various climate research institutes within the country other than IMD arealso discussed. The operational monthly and seasonal long range forecasts issued for the 2010 southwest monsoon season for the country as a whole were accurate. However, forecasts for the season rainfall over the 4 geographical regions (Northwest India, Central India, Northeast India and south Peninsular India) were not accurate as the forecast for South Peninsular India overestimated the actual rainfall and that for northeast India underestimated the actual rainfall. The experimental forecasts for the season rainfall over the country as whole from various climate research institutes within the country showed large variance (91 % - 112% of LPA).

The relationship between oceanographic factors and the ENSO period on weather in Maluku

The surrounding waters strongly influence the waters of North Maluku. These waters are the entrance gate for ARLINDO, which is directly related to the Pacific Ocean. To produce scientific knowledge, our study aims to determine the influence of oceanographic factors on the distribution of sea surface temperatures (SST) and wind pattern, which affects the rainfall throughout the west monsoon and transitional seasons in the Indonesian region literacy. We used data from ERDDAP, where SST data was processed using Ocean Data View and wind data using WRPLOT. The SST of North Maluku waters during October 2020-April 2021 ranges from 29.1°C-29.8°C, with the highest sea surface temperature, was seen during November-December 2020 and April 2021. The result demonstrates the highest rainfall data was in February 2021 and the dominant winds come from north and west. These winds bring water vapor particles that become convective clouds that increase rainfall in Indonesia, especially in the eastern region. Meanwhile, SST originating from the Pacific Ocean moved due to the Walker Circulation from the east. This further caused the SST from the Pacific Ocean that brought a warm water move towards Indonesian waters.

Modeling climate phenomenon with software grids analysis and display system in the development of the global warming module

This study aims to model climate change based on rainfall, air temperature, pressure, humidity and wind with grADS software and create a global warming module. This research uses 3D model, define, design, and develop. The results of the modeling of the five climate elements consist of the annual average temperature in Indonesia in 2009-2015 which is between 29oC to 30.1oC, the horizontal distribution of the annual average pressure in Indonesia in 2009-2018 is between 800 mBar to 1000 mBar, the horizontal distribution the average annual humidity in Indonesia in 2009 and 2011 ranged between 27-57, in 2012-2015, 2017 and 2018 it ranged between 30-60, during the East Monsoon, the wind circulation moved from northern Indonesia to the southern region Indonesia. During the west monsoon, the wind circulation moves from the southern part of Indonesia to the northern part of Indonesia. The global warming module for SMA/MA produced is feasible to use, this is in accordance with the value given by the validate of 69 which is in the appropriate category and the response of teachers and students through a 91% questionnaire.

Stream flow trends in up and midstream of Kirindi Oya river basin in Sri Lanka and its linkages to rainfall

Trend analysis of hydro-climatic variables provide useful information for effective planning, designing and management of water resources and agricultural production. Trends in observed stream flow at upstream and midstream gauging stations (GS), Wellawaya, Thanamalwila & rainfall and temperature in the Kirindi Oya river basin were assessed using the Mann-Kendall, Modified Mann-Kendall and Sen’s slope. Average rainfalls for the two catchments and for the entire basin were computed using ‘Thessen polygon’ method. The relationships between trends in stream flow and catchment rainfall were studied by Spearman’s Rho correlation coefficient . Five year Moving averaged Standardize Anomalies (FMSA) of both annual stream flow and rainfall at Wellawaya and Thanamalwila catchments were in a non-significant (p < 0.05) decreasing trend for 1994 to 2010.Though there was a positive correlation between annual catchment rainfall and stream flow of these two catchments, correlation was significant (p < 0.05) only at Thanamalwila (0.69) suggesting that the variation of annual stream flow at Thanamalwila GS was mainly attributed to the variation of catchment rainfall. However, variation of stream flow during North East Monsoon (NEM) season was mainly attributed to the variation of respective catchment rainfall of both the catchments as evident by significant higher ‘p’ at Wellawaya (0.61) and Thanamalwila (0.69). This study also found that trend of FMSA of South West Monsoon (SWM) rain was significantly (p < 0.05) decreasing for the entire basin, Thanamalwila and Wellawaya sub-catchments. Stream flow at Wellawaya GS during SWM was also found to be significantly decreasing while Thanamalwila stream flow was non-significantly decreasing. Both rainfall and stream flow during First Inter Monsoon (FIM) was in a significant increasing trend particularly in the month of April. These observed trends during SWM and FIM suggest an apparent early onset of SWM in the basin, or drastic deviation in receiving rain during SWM in the Kirindi Oya river basin.