A Short Course in Cloud Physics

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Statistical Methods For Environmental Pollution Monitoring

An air-sea interaction theory for tropical cyclones [presentation]

Air pollutants concentrations forecasting using back propagation neural network based on wavelet decomposition with meteorological conditions

Rainfall is a primary component of the water cycle, and its variability is associated with drought and flood events. This study investigates the trends in annual and seasonal rainfall at 14 rainfall stations in Shaanxi Province, China, using an innovative trend analysis (ITA), Mann–Kendall test and linear regression analysis. Moreover, using ITA, annual rainfall is analysed for different rainfall intensities, and seasonal rainfall is analysed for extreme values. The results show non-uniform trends in rainfall intensities on a regional and seasonal scale. Annual rainfall shows a significant decreasing trend in the Wei River Basin and north of the Loess Plateau. Overall, the trend is reinforced with the increase of rainfall intensity. A few stations show significant trends in seasonal rainfall. Spring rainfall is the major contributor to the decline in annual rainfall. Heavy rainfall (more than 90th percentile) in summer exhibits a marked downward trend mainly in the basin, which makes it possible for flooding to abate along the Wei River. Light rainfall (less than 10th percentile) shows a prevailing increasing trend in summer, but a decreasing trend in other seasons. From north to south, the seasonal trends become clearer and stronger. In terms of management, more attention should be paid to autumn droughts in the Wei River Basin. A quantitative measurement of a trend for ITA is proposed. Comparison of the three methods endorses the ITA method. Moreover, the ITA shows many advantages, such as graphical results and for observing sub-trends. It is hoped that this study can provide support for water resources planning, for coping with droughts and floods and for future development of the ITA method.

Innovative trend analysis of annual and seasonal rainfall and extreme values in Shaanxi, China, since the 1950s

The purpose of the present research is to identify the trends in the concentrations of few atmospheric pollutants and meteorological parameters over an urban station Kolkata (22° 32' N; 88° 20' E), India, during the period from 2002 to 2011 and subsequently develop models for precise forecast of the concentration of the pollutants and the meteorological parameters over the station Kolkata. The pollutants considered in this study are sulphur dioxide (SO2), nitrogen dioxide (NO2), particulates of size 10-μm diameters (PM10), carbon monoxide (CO) and tropospheric ozone (O3). The meteorological parameters considered are the surface temperature and relative humidity. The Mann-Kendall, non-parametric statistical analysis is implemented to observe the trends in the data series of the selected parameters. A time series approach with autoregressive integrated moving average (ARIMA) modelling is used to provide daily forecast of the parameters with precision. ARIMA models of different categories; ARIMA (1, 1, 1), ARIMA (0, 2, 2) and ARIMA (2, 1, 2) are considered and the skill of each model is estimated and compared in forecasting the concentration of the atmospheric pollutants and meteorological parameters. The results of the study reveal that the ARIMA (0, 2, 2) is the best statistical model for forecasting the daily concentration of pollutants as well as the meteorological parameters over Kolkata. The result is validated with the observation of 2012.

Mann–Kendall trend of pollutants, temperature and humidity over an urban station of India with forecast verification using different ARIMA models

Preferred type of cloud in the genesis of severe thunderstorms — A soft computing approach

Thunderstorms are perennial features of India. However, the severe thunderstorms of pre — monsoon season (April–May) over Kolkata (22°32′N, 88°20′E) are of great concern for imparting devastating effect on life and property on the ground and aviation aloft. The study is thus, focused on developing one hidden layer neural network model with variable learning rate back propagation algorithm to forecast such thunderstorms. Convective available potential energy (CAPE) and convective inhibition energy (CIN) are selected as the input parameters of the model after the estimation of various skill scores like, Probability of Detection (POD), False Alarm Ratio (FAR), Heidke Skill Score (HSS) and Odds Ratio Skill Score (Yule’s Q) on different stability indices. During training the model, the squared error for forecasting severe thunderstorms is observed to be 0.0022 when the values of CIN within the range of 0 to 140 J kg−1 is taken as the input whereas the error is observed to be 0.0114 while the values of CAPE within the range of 2000 to 7000 J kg−1 is considered as the input. The values of CIN and CAPE at twelve to six hours prior to the occurrence of severe thunderstorms are considered in this study. During validation of the model, the percentage of prediction error with the values of CIN as input is observed to be 0.042% and that with CAPE as input is 0.162%. The values of CIN within the range of 0–140 J kg−1 are observed to be more persistent in forecasting severe thunderstorms over Kolkata than the values of CAPE within the range of 2000–7000 J kg−1.

Convective Energies in Forecasting Severe Thunderstorms with One Hidden Layer Neural Net and Variable Learning Rate Back Propagation Algorithm

The endeavor of the present research is to nowcast the spatial visibility during fog over the airport of Kolkata (22.6°N; 88.4°E), India, with artificial neural network (ANN) model. The identification of dominant parameters influencing the visibility during wintertime (November–February) fog over the region is made using the decision tree algorithm. The decision tree is constructed by computing the entropy of the parameters collected during the period from 2001 to 2011. The parameters having minimum entropy are selected as the most useful parameters because it has maximum certainty in influencing the visibility. The result reveals that the moderate range of NO2 (67–134 µg/m3) is the most dominant parameter compared with other parameters that influence the visibility during wintertime fog over Kolkata and is selected as the first node of the tree. The decision tree approach led to select five such parameters having minimum entropy for affecting maximum the visibility during fog over Kolkata airport. The selected parameters are NO2, wind speed, relative humidity, CO and temperature. ANN model is developed with the selected parameters as the input in the form of multilayer perceptron with back propagation learning technique for forecasting the 3 hourly visibility during wintertime fog over Kolkata airport. The result reveals that the forecast of visibility of different categories is possible with ANN model. However, the best forecast is obtained for very dense visibility within the 50 m horizontal distance. The result is validated with observation, and the forecast error is estimated.

Nowcasting visibility during wintertime fog over the airport of a metropolis of India: decision tree algorithm and artificial neural network approach

Precise forecasting of the track and intensity of tropical cyclones remains one of the top priorities for the meteorological community. In the present study multilayer feed forward neural nets with different architectures are developed to identify the best neural net for forecasting the track and intensity of tropical cyclones over the North Indian Ocean (NIO) with 6, 12 and 24 h lead time. Forecast errors are estimated with each neural net. The result reveals that the neural net architecture 1 (NNA 1) with 10 input layers, 2 hidden layers, 5 hidden nodes and 2 output layers provides the best forecast for both the track and the intensity of the tropical cyclones over NIO. Two cyclones of the same category in Saffir Simpson Hurricane Scale, namely Nargis and Phet, that occurred over the Bay of Bengal and the Arabian Sea of the NIO basin are considered in the present study for validation. The result reveals that the prediction errors (%) with NNA 1 model in estimating the intensity of the cyclones Nargis and Phet during the validation are 3.37, 8.29 and 9.74 as well as 6.38, 11.26 and 18.72 with 6, 12 and 24 h lead time, respectively. The mean track errors for 6, 12 and 24 h forecasts are observed to be 45, 69 and 89 km for cyclone Nargis and 54, 87 and 98 km for cyclone Phet. NNA 1 model is observed to perform better than NNA 2 and NNA 3 models and the existing numerical models.

https://rmets.onlinelibrary.wiley.com/doi/pdf/10.1002/met.1488

Sutapa Chaudhuri

Papers

Mann–Kendall trend of pollutants, temperature and humidity over an urban station of India with forecast verification using different ARIMA models

Preferred type of cloud in the genesis of severe thunderstorms — A soft computing approach

Convective Energies in Forecasting Severe Thunderstorms with One Hidden Layer Neural Net and Variable Learning Rate Back Propagation Algorithm

Nowcasting visibility during wintertime fog over the airport of a metropolis of India: decision tree algorithm and artificial neural network approach

Track and intensity forecast of tropical cyclones over the North Indian Ocean with multilayer feed forward neural nets