A Markov chain model for daily rainfall occurrences at east Thanjavur district

The occurrences and non-occurrences of the rainfall can be described by a two-state Markov chain. A dry date is denoted by state 0 and wet date is denoted by state 1. We have taken the sample which follows a Poisson process with known parameter. Using this Poisson sample we have given a new approach to affect statistical inference for the law of the Markov chain and state estimation concerning un-observed past values or not yet observed future values. The paper aims at comparing the earlier fit of the data with the new approach.

Effect of monsoon depression of east coast at some distant region in west

Whenever a vortex or system of low or depression forms over head bay during Monsoon months, the west coast experiences heavy rainfall. These heavy rainfall occurrences are usually higher than the normal rainfall. An attempt has been made in this study to visualise the easterly wave model during monsoon months with the help of satellite imageries. The rain is expected heavy and wide spread over Madhya Maharashtra and South Gujarat when third sector of the wave covers these areas, as visualised in satellite wave and depression or vortex lies in the 5th or 6th sector of the wave.

Generalized linear models for analyzing count data of rainfall occurrences

Having the adequate knowledge about the behavior of climatic variables on the occurrences of rainfall is needed to the country’s economists and agriculturists for saving the country’s people from the devastating natural hazards like flash flood, drought, heavy rainfall, etc. Therefore, the study has been taken initiative to identify the influence of climatic variables for the occurrences of rainfall. The study has been developed generalized linear models (GLMs) for Poisson distribution for weekly and fortnightly count data of daily rainfall occurrences for the summer and monsoon seasons for five regional rainfall stations of Bangladesh. For these models, minimum and maximum temperatures and relative humidity are considered as explanatory variables. For five regional rainfall stations, the model selection procedures AIC and BIC indicate that the GLMs for the Poisson distribution satisfactorily explain the influence of climatic variables for the fortnightly occurrences of rainfall in the summer and monsoon seasons. The GLMs for the summer season of fortnightly occurrences of rainfall indicate that if one unit of relative humidity increases, then the probability of rainy days will be increased by 12 percent in Feni station, 6 percent in Sylhet, Khulna and Rajshahi stations, and 7 percent in Dhaka station. Besides, the GLMs for the monsoon season of fortnightly occurrences of rainfall indicate that if one unit increases of minimum temperature, then the probability of rainy days will be increased by 22 percent, 19 percent, 24 percent, 17 percent and 19 percent in Feni, Sylhet, Khulna, Rajshahi and Dhaka stations, respectively. Further, maximum temperature indicates negative influence on the occurrences of rainfall for all the stations and seasons of the period. The study indicates that the relative humidity for summer season and minimum temperature for monsoon season play remarkable role for changing fortnightly occurrences of rainfall in all the regions of the country.

Different Climate Changes at Two Locations on a Small Karst Island Korčula (Adriatic Sea, Croatia)

The article analyses the series of annual, monthly and daily air temperatures and annual and monthly rainfall at two meteorological stations in Vela Luka and Korčula on the island of Korčula (Croatia), for which there are long time series of observations. Today’s locations of meteorological stations are only 33.5 km apart. The average annual air temperature at the Vela Luka station is on average 1°C lower than that measured at the Korčula station. A signifi cant upward trend in mean annual and mean monthly air temperatures was observed at both stations, with the increase being much milder at the Vela Luka station. Warming processes are signifi cantly faster at the Korčula station than at the Vela Luka station. Signifi cantly diff erent values of air temperatures, and in particular the fact of diff erent reactions of air temperatures to climate change at two stations, can be explained by their local position in relation to the open sea and orography of the surrounding terrain. While the Vela Luka station is exposed to the open sea and away from the mainland, at Korčula station the impact of the sea is less signifi cant because the sea is located in a narrow channel between the island of Korčula and the Pelješac peninsula. The distance of the Korčula meteorological station from the Pelješac peninsula and the mainland is signifi cantly smaller, which aff ects the faster trend of rising air temperatures at this station than at the Vela Luka station, where the infl uence of the sea mitigates the eff ect of global warming. Orography and proximity to land aff ect signifi cantly higher rainfall occurrences at Korčula station. Average annual rainfall at this station is 231 mm or 27.5% higher than at Vela Luka station. Both stations show a trend of decreasing annual rainfall.

An Evaluation of Statistical Downscaling Techniques for Simulating Daily Rainfall Occurrences in the Upper Ping River Basin

This study presents an exhaustive evaluation of the performance of three statistical downscaling techniques for generating daily rainfall occurrences at 22 rainfall stations in the upper Ping river basin (UPRB), Thailand. The three downscaling techniques considered are the modified Markov model (MMM), a stochastic model, and two variants of regression models, statistical models, one with single relationship for all days of the year (RegressionYrly) and the other with individual relationships for each of the 366 days (Regression366). A stepwise regression is applied to identify the significant atmospheric (ATM) variables to be used as predictors in the downscaling models. Aggregated wetness state indicators (WIs), representing the recent past wetness state for the previous 30, 90 or 365 days, are also considered as additional potential predictors since they have been effectively used to represent the low-frequency variability in the downscaled sequences. Grouping of ATM and all possible combinations of WI is used to form eight predictor sets comprising ATM, ATM-WI30, ATM-WI90, ATM-WI365, ATM-WI30&90, ATM-WI30&365, ATM-WI90&365 and ATM-WI30&90&365. These eight predictor sets were used to run the three downscaling techniques to create 24 combination cases. These cases were first applied at each station individually (single site simulation) and thereafter collectively at all sites (multisite simulations) following multisite downscaling models leading to 48 combination cases in total that were run and evaluated. The downscaling models were calibrated using atmospheric variables from the National Centers for Environmental Prediction (NCEP) reanalysis database and validated using representative General Circulation Models (GCM) data. Identification of meaningful predictors to be used in downscaling, calibration and setting up of downscaling models, running all 48 possible predictor combinations and a thorough evaluation of results required considerable efforts and knowledge of the research area. The validation results show that the use of WIs remarkably improves the accuracy of downscaling models in terms of simulation of standard deviations of annual, monthly and seasonal wet days. By comparing the overall performance of the three downscaling techniques keeping common sets of predictors, MMM provides the best results of the simulated wet and dry spells as well as the standard deviation of monthly, seasonal and annual wet days. These findings are consistent across both single site and multisite simulations. Overall, the MMM multisite model with ATM and wetness indicators provides the best results. Upon evaluating the combinations of ATM and sets of wetness indicators, ATM-WI30&90 and ATM-WI30&365 were found to perform well during calibration in reproducing the overall rainfall occurrence statistics while ATM-WI30&365 was found to significantly improve the accuracy of monthly wet spells over the region. However, these models perform poorly during validation at annual time scale. The use of multi-dimension bias correction approaches is recommended for future research.

Long-term trends of instability and associated parameters over the Indian region obtained using radiosonde network

Long-term trends of the parameters related to convection and instability obtained from 27 radiosonde stations across 6 sub-divisions over Indian region during the period 1980–2016 is presented. A total of 16 parcel and instability parameters along with moisture content, wind shear, and thunderstorm and rainfall frequencies have been utilized for this purpose. Robust fit regression analysis is employed on the regional average time series to calculate the long-term trends on both seasonal and yearly basis. The Level of Free Convection (LFC) and Equilibrium Level (EL) height is found to ascend significantly in all Indian sub-divisions. Consequently, the coastal regions (particularly the western coasts) experience strengthening in Severe Thunderstorm (TSS) and Severe Rainfall Frequencies (SRF) in the pre-monsoon while the inland regions (especially central India) experience an increase in Ordinary Thunderstorm (TSO) and Weak Rain Frequency (WRF) during the monsoon and post-monsoon. The 16–20 year periodicity is found to dominate the long-term trends significantly compared to other periodicities and the increase in TSS, SRF and CAPE is found more severe after the year 1999. The enhancement in moisture transport and associated cooling at 100 hPa along with dispersion of boundary layer pollutants is found to be the main cause for the increase in Convective Available Potential Energy (CAPE) which leads to more convective severity in the coastal regions. However, in inland regions moisture-laden winds are absent and the presence of strong capping effect of pollutants on instability in the lower troposphere has resulted in more Convective Inhibition Energy (CINE). Hence, TSO and weak rainfall occurrences have increased particularly in these regions.

TROPICAL DAILY RAINFALL AMOUNT MODELLING USING MARKOV CHAIN-MIXED EXPONENTIAL (MCME)

This study is concerned with the development of a stochastic rainfall model that can generate many sequences of synthetic daily rainfall series with the similar properties as those of the observed. The proposed model is Markov chain-mixed exponential (MCME). This model is based on a combination of rainfall occurrence (represented by the first-order two-state Markov chain) and the distribution of rainfall amounts on wet days (described by the mixed exponential distribution). The feasibility of the MCME model is assessed using daily rainfall data from four rainfall stations (station S02, S05, S07 and S11) in Johor, Malaysia. For all the rainfall stations, it was found that the proposed MCME model was able to describe adequately rainfall occurrences and amounts. Various statistical and physical properties of the daily rainfall processes also considered. However, the validation results show that the models’ predictive ability was not as accurate as their descriptive ability. The model was found to have fairly well ability in predicting the daily rainfall process at station S02, S05 and S07. Nonetheless, it was able to predict the daily rainfall process at station S11 accurately.