Statistical Modeling of RPCA-FCM in Spatiotemporal Rainfall Patterns Recognition

This study was conducted to identify the spatiotemporal torrential rainfall patterns of the East Coast of Peninsular Malaysia, as it is the region most affected by the torrential rainfall of the Northeast Monsoon season. Dimension reduction, such as the classical Principal Components Analysis (PCA) coupled with the clustering approach, is often applied to reduce the dimension of the data while simultaneously performing cluster partitions. However, the classical PCA is highly insensitive to outliers, as it assigns equal weights to each set of observations. Hence, applying the classical PCA could affect the cluster partitions of the rainfall patterns. Furthermore, traditional clustering algorithms only allow each element to exclusively belong to one cluster, thus observations within overlapping clusters of the torrential rainfall datasets might not be captured effectively. In this study, a statistical model of torrential rainfall pattern recognition was proposed to alleviate these issues. Here, a Robust PCA (RPCA) based on Tukey’s biweight correlation was introduced and the optimum breakdown point to extract the number of components was identified. A breakdown point of 0.4 at 85% cumulative variance percentage efficiently extracted the number of components to avoid low-frequency variations or insignificant clusters on a spatial scale. Based on the extracted components, the rainfall patterns were further characterized based on cluster solutions attained using Fuzzy C-means clustering (FCM) to allow data elements to belong to more than one cluster, as the rainfall data structure permits this. Lastly, data generated using a Monte Carlo simulation were used to evaluate the performance of the proposed statistical modeling. It was found that the proposed RPCA-FCM performed better using RPCA-FCM compared to the classical PCA coupled with FCM in identifying the torrential rainfall patterns of Peninsular Malaysia’s East Coast.

Cloudburst events observed over Uttarakhand during monsoon season 2017 and their analysis

Cloudburst is an extreme weather event characterised by the occurrence of a large amount of rainfall over a small area within a short span of time with a rainfall of 100 mm or more in one hour. It is responsible for flash flood, inundation of low lying areas and landslides in hills causing extensive damages to life and property. During monsoon season 2017 five number of cloudburst events are observed over Uttarakhand and analysed. Self Recording Rain Gauge (SRRG) and 15 minutes interval data from the newly installed General Packet Radio Service (GPRS) based Automatic Weather Station (AWS) are able to capture the cloudburst events over some areas in Uttarakhand. In this paper, an attempt has been made to find out the significant synoptic and thermodynamic conditions associated with the occurrence of the cloudburst events in Uttarakhand. These 5 cases of cloudburst events that are captured during the month of June, July and August 2017 in Uttarakhand are studied in detail. Synoptically, it is observed that the existence of trough at mean sea level from Punjab to head Bay of Bengal running close to Uttarakhand, the movement of Western Disturbance over north Pakistan and adjoining Jammu & Kashmir and existence of cyclonic circulation over north Rajasthan and neighbourhood are favourable conditions. Also, the presence of strong south-westerly wind flow from the Arabian Sea across West Rajasthan and Haryana on upper air charts are found during these events. Thermodynamically, the Convective Available Potential Energy (CAPE) is found to be high (more than 1100 J/Kg) during most of the cases and vertically integrated precipitable water content (PWC) is more than 55mm. The GPRS based AWS system can help in prediction of the cloud burst event over the specified location with a lead time upto half to one hour in association with radar products.

Analysis of rainfall trends over Tripura

Rainfall in India has very high temporal and spatial variability. The rainfall variability affects the livelihood and food habits of people from different regions. In this study, the rainfall trends in two stations in the north-eastern state of Tripura, namely Agartala and Kailashahar have been studied for the period 1955-2017. The state experiences an annual mean of more than 2000 mm of rainfall, out of which, about 60% occurs during the monsoon season and about 30% in pre-monsoon. An attempt has been made to analyze the trends in seasonal and annual rainfall, rainy days and heavy rainfall in the two stations, during the same period.Non-parametric Mann-Kendall test has been used to find out the significance of these trends. Both increasing and decreasing trends are observed over the two stations. Increasing trends in rainfall, rainy days and heavy rainfall are found at Agartala during pre-monsoon season and decreasing trends in all other seasons and at annual scale. At Kailashahar, rainfall amount (rainy days & heavy rainfall) is found to be increasing during pre-monsoon and monsoon seasons (pre-monsoon season). At annual scale also, rainfall and rainy days show increasing trends at Kailashahar. The parameters are showing decreasing trends during all other seasons at the station. Rainy days over Agartala show a significantly decreasing trend in monsoon, whereas no other trend is found to be significant over both the stations.

Extent of diurnal cycle of rainfall and its intra seasonal variation over coastal Tamil Nadu during north east monsoon season

The diurnal variation of north east monsoon rainfall of coastal Tamil Nadu represented by four coastal stations Chennai Nungambakkam (Nbk), Chennai Meenambakkam (Mbk), Nagapattinam (Npt) and Pamban (Pbn) was studied in detail based on hourly rainfall data of rainy days only, for the period 1 Oct-31 Dec for the 47/48 year period 1969-2016/2017. Mean Octet rainfall and its anomaly were computed for the 8 octets 00-03,…., 21-24 hrs of the day and the anomaly was tested for statistical significance. Various analysis for the individual months of Oct, Nov, Dec and the entire period Oct-Dec were separately conducted. The basic technique of evolutionary histogram analysis supplemented by harmonic analysis of octet mean rainfall anomaly was used to detect the diurnal cycle signal. Two indices named as diurnal variation of rainfall index and coefficient of mean absolute octet rainfall anomaly representing the intensity of diurnal variation in dimensionless numbers were defined, computed and interpreted. The analysis based on the above techniques revealed that the diurnal signal which shows an early morning maximum and late afternoon minimum of octet rainfall is well defined in Oct, decreases in Nov and further decreases in Dec for all the 4 stations. Though the diurnal variation manifests a well defined pattern in Dec the signal is not statistically significant in most cases. For Nbk and Mbk there is a weak secondary peak of octet rainfall anomaly occurring in the forenoon and afternoon respectively in Oct and Dec suggesting the presence of semi-diurnal variation of rainfall. Stationwise, the diurnal signal is most well defined for each month/season in Pbn followed by Npt, Nbk and then Mbk. The physical causes behind the diurnal signal and its decrease as the north east monsoon season advances from Oct to Dec have been deliberated. The well known feature of nocturnal maximum of oceanic convection influencing a coastal station with maritime climate and the higher saturation at the lower levels of the upper atmosphere in the early morning hours have been advanced as some of the causes. For the much more complex feature of decrease of diurnal signal with the advancement of the season, the decrease of minimum surface temperature over coastal Tamil Nadu from Oct to Dec causing an early morning conceptual land breeze has been shown as one of the plausible causes based on analysis of temperature and wind. Scope for further work based on data from automatic weather stations, weather satellites and Doppler Weather Radars has been discussed.

Trend assessment in climate variable by Mann Kendall test of Bastar district of Chhattisgarh

Climate change is a natural phenomenon but in present decades its variability of change mainly due to anthropogenic activities is alarming. Agriculture of Chhattisgarh state is mainly dependant on monsoon rain and its distribution. Considering this fact, the present study has been tried to analyze the most important climatic variables, viz., precipitation and temeperature for analyzing their trend in the area. The trends of maximum atmospheric temperature, rainfall and rainy days are analysed statistically for meteorological data of Jagdalpur station of Bastar district, over last three decades stretching between years 1980 to 2014. The long term change in temperature, rainfall and rainy days has been analysed by correlation and linear trend analysis. The annual MMAX temperature has decreased at a rate of -0.465 °C per year during this period at Jagdalpur station and decreasing trend for rainy days during monsoonal season (June to September) is also found and is confirmed by Mann-Kendall trend test. Very weak increasing trend is observed in total month rainfall (TMRF) during season June to September. There are decreasing trends of mean monthly rainfall and south west (June - September) rainfall observed in Bastar district of Chhattisgarh. The agricultural planning and utilization of water is dependent on monsoon rainfall and more than 75% of rainfall occurring during the monsoon season is uneven both in time and space. Therefore its analysis is important for crop planning.

Monsoon, post monsoon rainy days analysis by using normal, binomial distribution and discrete probability for south Gujarat

The analysis will be conducted for standard weekly (SW) 22 to 47 of monsoon and post monsoon season at south Gujarat. The standard weekly rainy days analysis of binomial distribution for monsoon season of Navsari on chi-square test on binomial distribution was found in standard week (SW) 22 to 31, 33 and standard week (SW) 35 to 39 and post monsoon in standard week (SW) 41 to 44 shows significant. The result also reveals that the monsoon season SW 32 and 34 and post monsoon season SW 40, 45, 46 and 47 revealed non-significant result. Analysis reveals the rainfall is not equally distributed during SW 32, 34, 40, 45, 16 and 47, so that the test of binomial distribution is a good fit. Monsoon season rainfall data of Navsari, Bharuch and Valsad reveals that the normal distribution at 10, 20 and 30% probability levels for the month of June, July, August and September shows the possibility of increasing rainy days occurrence. The Navsari and Bharuch districts during post monsoon season rainfall of months of October and November reveals decreasing tendency except Valsad district. The binomial distribution fit only those standard weeks in which rainfall is not equally distributed. The standard weekly rainy days analysis of binomial distribution on chi-square test in Bharuch was found that standard week (SW) 25 only 10% of monsoon season and in post monsoon standard week (SW) 42 and 47 shows non significant (5 and 10% level of significant) result, but SW 25 found significant at 5% level. In case of Valsad district, standard week 22 to 39 of monsoon season and in post monsoon season 41, 42, 43 and 46 standard weeks shows significant result. The result reveals that the monsoon season of Bharuch standard weeks 22 to 39 except from 25 and post monsoon 40, 41, 43, 44, 45 and 46 shows significant result. Further, in Valsad district standard weeks 40, 44, 45 and 47 shows significant result. The trend analysis of rainy days shows that increasing trend in monsoon season and decreasing trend in post monsoon season of Navsari, Bharuch and Valsad districts. From above results observed that the rainfall distribution is not equally distributed so test of binomial distribution at above given standard week is a good fit. The data also shows that, decreasing tendency in rainfall was observed except Valsad district.

On variability of total ozone derived from TOVS data over peninsular Indian sub-continent and adjoining oceanic area

The total ozone derived from TOVS data from NOAA 12 satellite through one step physical retrieval algorithm of International TOVS Processing Package (ITPP) version 5.0 has been used to identify its diurnal, monthly, latitudinal and longitudinal variability during 1998 over the domain Equator to 26° N / 60-100° E. The linkage of maximum total ozone with warmer tropopause and lower stratosphere has been re-established. The colder upper tropospheric temperature which is normally associated with maximum ozone concentration throughout the year elsewhere in the world has also been identified in this study but the relationship gets reversed during southwest monsoon months(June-September) over the domain considered. The moisture available in abundance in the lower troposphere gets precipitated due to the convective instability prevailing in the atmosphere during monsoon season and very little moisture is only available for vertical transport into the upper troposphere atop 500 hPa. The latent heat released by the precipitation processes warms up the middle and upper atmosphere. The warm and dry upper troposphere could be the reason for less depletion of ozone in the upper troposphere during monsoonal months and this is supported by the positive correlation coefficient prevailing in monsoon season between total ozone and upper tropospheric (aloft 300 hPa) temperature. The warmness in middle and upper troposphere which is associated with less depletion and/or production of more ozone in the upper troposphere may perhaps contribute for the higher total ozone during monsoon months than in other seasons over peninsular Indian region. The minimum concentration is observed during January (226 DU) over 6° N and the maximum (283DU) over 18° N during August. Longitudinal variability is less pronounced than the latitudinal variability.

Stingray Habitat Use Is Dynamically Influenced by Temperature and Tides

Abiotic factors often have a large influence on the habitat use of animals in shallow marine environments. Specifically, tides may alter the physical and biological characteristics of an ecosystem while changes in temperature can cause ectothermic species to behaviorally thermoregulate. Understanding the contextual and relative influences of these abiotic factors is important in prioritizing management plans, particularly for vulnerable faunal groups like stingrays. Passive acoustic telemetry was used to track the movements of 60 stingrays at a remote and environmentally heterogeneous atoll in Seychelles. This was to determine if habitat use varied over daily, diel and tidal cycles and to investigate the environmental drivers behind these potential temporal patterns. Individuals were detected in the atoll year-round, but the extent of their movement and use of multiple habitats increased in the warmer NW-monsoon season. Habitat use varied over the diel cycle, but was inconsistent between individuals. Temperature was also found to influence stingray movements, with individuals preferring the deeper and more thermally stable lagoon habitat when extreme (hot or cold) temperature events were observed on the flats. Habitat use also varied over the tidal cycle with stingrays spending a higher proportion of time in the lagoon during the lowest tides, when movement on the flats were constrained due to shallow waters. The interplay of tides and temperature, and how these varied across diel and daily scales, dynamically influenced stingray habitat use consistently between three species in an offshore atoll.

Tropospheric temperature variation over India and links with the Indian summer monsoon : 1971-2000

In the context of the ever increasing interest in the regional aspects of global warming, understanding the spatio-temporal variations of tropospheric temperature over India is of great importance. The present study, based on the data from 19 well distributed radiosonde stations for the period 1971-2000, examines the seasonal and annual mean temperature variations at the surface and five selected upper levels, viz., 850, 700, 500, 200 and 150 hPa. An attempt has also been made to bring out the association between tropospheric temperature variations over India and the summer monsoon variability, including the role of its major teleconnection parameter, the El Niño/Southern Oscillation (ENSO). Seasonal and annual mean all-India temperature series are analyzed for surface and five tropospheric levels. The mean annual cycles of temperature at different tropospheric levels indicate that the pre-monsoon season is slightly warmer than the monsoon season at the surface, 850 hPa and 150 hPa levels, while it is relatively cooler at all intermediate levels. The mean annual temperature shows a warming of 0.18° C and 0.3° C per 10 years at the surface and 850 hPa, respectively. Tropospheric temperature anomaly composites of excess (deficient) monsoon rainfall years show pronounced positive (negative) anomalies during the month of May, at all the levels. The pre-monsoon pressure of Darwin has significant positive correlation with the monsoon temperature at the surface and 850 hPa.

Recent trends in climate of Bangalore

The linear trends in the monthly, seasonal and annual mean maximum temperature, minimum temperature, average temperature, diurnal range of temperature, rainfall, relative humidities at 0830 & 1730 hr IST of Bangalore city and airport have been analysed based on the data for the period from 1960-95. The variation in surface wind over Bangalore during above period has also been studied to find out impact of urbanisation on weather parameters. It is found that Bangalore city is becoming warmer in terms of mean maximum & mean minimum temperatures. Rate of increase is significantly higher over Bangalore city (central observatory) than that over airport during winter months. Similarly the rising trend of average temperature of Bangalore city is higher than of Bangalore airport during October to April being significantly so during winter season. Also the diurnal range of temperature of Bangalore is becoming larger in winter months with the rising trend being higher over Bangalore city than over airport. Even though rainfall does not show any significant trend, the rising trend during monsoon & falling trend during post monsoon season over Bangalore city are higher than that of Bangalore airport. Also though both Bangalore city & airport show maximum rising trend in mean relative humidity at 0830 hr IST during winter, the rate of rise is less over Bangalore city. Similarly though the relative humidity at 1730 hr IST shows decreasing trend during all the seasons, the rate of decrease is less over Bangalore city for all seasons except post monsoon season. The mean maximum, minimum and average temperatures and relative humidities show cyclic variation of their monthly trend coefficients during the year.