The Cloud Aerosol Interaction and Precipitation Enhancement Experiment (CAIPEEX): overview and preliminary results

While the demand for enhancing rainfall through cloud seeding is strong and persistent in the country, considerable uncertainty exists on the success of such an endeavour at a given location. To understand the pathways of aerosol-cloud interaction through which this might be achieved, a national experiment named Cloud Aerosol Interaction and Precipitation Enhancement EXperiment (CAIPEEX) in two phases, was carried out. The rationale of CAIPEEX, the strategy for conducting the experiment, data quality and potential for path-breaking science are described in this article. Pending completion of quality control and calibration of the CAIPEEX phase-II data, here we present some initial results of CAIPEEX phase-I aimed at documenting the prevailing microphysical characteristics of aerosols and clouds and associated environmental conditions over different regions of the country and under different monsoon conditions with the help of an instrumented research aircraft. First-time simultaneous observations of aerosol, cloud condensation nuclei (CCN) and cloud droplet number concentration (CDNC) over the Ganges Valley during monsoon season show very high concentrations (> 1000 cm-3) of CCN at elevated layers. Observations of elevated layers with high aerosol concentration over the Gangetic valley extending up to 6 km and relatively less aerosol concentration in the boundary layer are also documented. We also present evidence of strong cloud- aerosol interaction in the moist environments with an increase in the cloud droplet effective radius. Our observations also show that pollution increases CDNC and the warm rain depth, and delays its initiation. The critical effective radius for warm rain initiation is found to be between 10 and 12 µm in the polluted clouds and it is between 12 and 14 µm in cleaner monsoon clouds

The cloud aerosol interaction and precipitation enhancement experiment (CAIPEEX): Overview and preliminary results

While the demand for enhancing rainfall through cloud seeding is strong and persistent in the country, considerable uncertainty exists on the success of such an endeavour at a given location. To understand the pathways of aerosol-cloud interaction through which this might be achieved, a national experiment named Cloud Aerosol Interaction and Precipitation Enhancement EXperiment (CAIPEEX) in two phases, was carried out. The rationale of CAIPEEX, the strategy for conducting the experiment, data quality and potential for path-breaking science are described in this article. Pending completion of quality control and calibration of the CAIPEEX phase-II data, here we present some initial results of CAIPEEX phase-I aimed at documenting the prevailing microphysical characteristics of aerosols and clouds and associated environmental conditions over different regions of the country and under different monsoon conditions with the help of an instrumented research aircraft. First-time simultaneous observations of aerosol, cloud condensation nuclei (CCN) and cloud droplet number concentration (CDNC) over the Ganges Valley during monsoon season show very high concentrations (> 1000 cm-3) of CCN at elevated layers. Observations of elevated layers with high aerosol concentration over the Gangetic valley extending up to 6 km and relatively less aerosol concentration in the boundary layer are also documented. We also present evidence of strong cloud- aerosol interaction in the moist environments with an increase in the cloud droplet effective radius. Our observations also show that pollution increases CDNC and the warm rain depth, and delays its initiation. The critical effective radius for warm rain initiation is found to be between 10 and 12 μm in the polluted clouds and it is between 12 and 14 μm in cleaner monsoon clouds

Radiatively driven subsidence over the Eastern Arabian Sea from MONSOON-77 data

235-238Aerological observations by Russian ships during MONSOON-77 have been used to explore the subsidence over the Eastern Arabian Sea which is marked by low level inversions. The technique of conserved variable analysis has been used to estimate the rate of subsidence of air at the top of convective boundarv layer (CBL). It has been observed that the CBL top air subsides at the rate of 30 hPa per day

A Deep Neural Network-Based Model for Named Entity Recognition for Hindi Language

The aim of this work is to develop efficient named entity recognition from the given text that in turn improves the performance of the systems that use natural language processing (NLP). The performance of IoT-based devices such as Alexa and Cortana significantly depends upon an efficient NLP model. To increase the capability of the smart IoT devices in comprehending the natural language, named entity recognition (NER) tools play an important role in these devices. In general, the NER is a two-step process that initially the proper nouns are identified from text and then classify them into predefined categories of entities such as person, location, measure, organization and time. NER is often performed as a subtask while processing natural languages which increases the accuracy level of a NLP task. In this paper, we propose deep neural network architecture for named entity recognition for the resource-scarce language Hindi, based on convolutional neural network (CNN), bidirectional long short-term memory (Bi-LSTM) neural network and conditional random field (CRF). In the proposed approach, initially, we use skip-gram word2vec model and GloVe model to represent words in semantic vectors which are further used in different deep neural network-based architectures. In the proposed approach, we use character- and word-level embedding to represent the text that includes information at fine-grained level. Due to the use of character-level embeddings, the proposed model is robust for the out-of-vocabulary words. Experimental results show that the combination of Bi-LSTM, CNN and CRF algorithms performs better as compared to the other baseline methods such as recurrent neural network, long short-term memory and Bi-LSTM individually