Operational meteorology is perceived as a fuzzy environment in which information is vaguely defined. The mesoscale processes such as fog, stratus and convection are generally dependent on the topography of the place and has always been difficult to forecast for the meteorologists. The main objective of the present study is to introduce the concept of fuzzy inference system (FIS) in the prediction of fog. This approach uses the concept of a pure fuzzy logic system where the fuzzy rule base consists of a collection of fuzzy IF-THEN rules. The fuzzy inference engine uses these fuzzy IF-THEN rules to determine a mapping from fuzzy sets in the input universe of discourse to fuzzy sets in the output universe of discourse based on fuzzy logic principles. Basic weather elements, which affect weather characteristics of fog, are fuzzified. These are then used in fuzzy weather prediction models based on fuzzy inferences. These models are simulated and the crisp results obtained using developed defuzzification strategies are compared with the actual weather data. The basis of methodology is to construct the fuzzy rule base domain from the available daily current weather observations in winter season over New Delhi. The results reveal that dew point spread and rate of change of dew point spread are the most important parameters for the formation of fog. The results further indicate that fog formation over New Delhi are dominant when (i) dew point is greater then 7°C along with dew point spread between 1 and 3°C. (ii) rate of change of dew point spread must be negative and wind speed should be less than 4 knots. This study presents a technique for predicting the probability of fog over New Delhi for 5-6 hours in advance. The skill score indicates that the Photonirvachak 123 RESEARCH ARTICLE 244

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Se desarrolla un modelo de red neuronal para predecir el número estacional de ciclones tropicales (CT) que se desarrollan en el Océano Índico septentrional después de la estación del monzón (octubre a diciembre). Se analizan la frecuencia de los CT y las variables climáticas de gran escala derivadas de la base de datos de reanálisis del NCEP/NCAR con resolución de 2.5 × 2.5º para el periodo 1971-2013. Se utilizaron datos del periodo 1971-2002 para desarrollar el modelo, y éste se probó con datos de muestreo independientes del periodo 2003-2013. Se eligieron cinco variables climáticas de gran escala (altura geopotencial a 500 hPa, [septiembre]) como predictores para aplicar un análisis de correlación. Con base en algunos parámetros ran con el modelo lineal de regresión múltiple. Los resultados indican que el número de ciclones tropicales calculado por medio de ambos modelos es muy similar al número real de ciclones ocurridos en cada año. Sin embargo, los resultados del modelo de redes neuronales fueron superiores a los del modelo linear de regresión múltiple, de modo que esta técnica de predicción de ciclones tropicales puede ser muy útil para propósitos operativos de predicción.

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The upper ocean heat content up to 700 m depth (OHC 700) is an important climatic parameter required for atmospheric and oceanographic studies like a cyclone. In this study, therefore, an attempt has been made to examine the inter-decadal variations of tropical cyclone (TC) activity and OHC 700 over the Bay of Bengal (BOB) for the post-monsoon season (October-December) during 1955-2013 periods. The sea-surface temperature (SST), geopotential height at 500 hPa, low-level vorticity at 850 hPa, vertical wind shear between 200 and 850 hPa, middle tropospheric humidity at 500 hPa and outgoing long-wave radiation are also being studied using seasonal mean data. The results show a significant inter-decadal variation during 1955-2013, with two distinct decadal periods: active decadal period (ADP) (1955-1988) and inactive decadal period (IDP) (1989-2013). The anomalies of these parameters are opposite in phase for two periods. It is found that the large scale atmospheric features and oceanic parameters have significant inter-decadal variability, but frequency of the tropical cyclone is attributed to the variation in the atmospheric dynamic and thermodynamic conditions rather than the variation of oceanic parameters OHC 700 and SSTs during the post-monsoon season.