Rabu, 12 Maret 2014


Artificial rain-making
Journal of the Franklin Institute, Volume 132, Issue 4, October 1891, Pages 308-315
Edwin J. Houston

How artificial rain can be produced? A mathematical model Original Research Article
Nonlinear Analysis: Real World Applications, Volume 11, Issue 4, August 2010, Pages 2659-2668
J.B. Shukla, A.K. Misra, Ram Naresh, Peeyush Chandra
A non-linear mathematical model for rain making from water vapor in the atmosphere is proposed and analyzed. The model considers the process of artificial rain by introducing two kinds of aerosol particles conducive to nucleation of cloud droplets and formation of rain drops. The model analysis shows that, for uninterrupted rain, the water vapor in the atmosphere must be formed continuously with the required rate of rainfall. It is shown further that the intensity of rainfall increases as the concentrations of externally introduced aerosols, as well as the density of water vapor in the atmosphere, increase. Numerical simulation is also performed to see the effect of various parameters on the process of artificial rain making leading to rainfall.

Rain intensity forecast using Artificial Neural Networks in Athens, Greece Original Research Article
Atmospheric Research, Volume 119, January 2013, Pages 153-160
P.T. Nastos, K.P. Moustris, I.K. Larissi, A.G. Paliatsos
The forecast of extreme weather events become imperative due to the emerging climate change and possible adverse effects in humans. The objective of this study is to construct predictive models in order to forecast rain intensity (mm/day) in Athens, Greece, using Artificial Neural Networks (ANN) models. The ANNs outcomes concern the projected mean, maximum and minimum monthly rain intensity for the next four consecutive months in Athens. The meteorological data used to estimate the rain intensity, were the monthly rain totals (mm) and the respective rain days, which were acquired from the National Observatory of Athens, for a 111-year period (1899–2009). The results of the developed and applied ANN models showed a fairly reliable forecast of the rain intensity for the next four months. For the evaluation of the results and the ability of the developed prognostic models, appropriate statistical indices were taken into consideration. In general, the predicted rain intensity compared with the corresponding observed one seemed to be in a very good agreement at a statistical significance level of p < 0.01.

An Artificial Neural Network based approach for estimation of rain intensity from spectral moments of a Doppler Weather Radar Original Research Article
Advances in Space Research, Volume 47, Issue 11, 1 June 2011, Pages 1949-1957
Devajyoti Dutta, Sanjay Sharma, G.K. Sen, B.A.M. Kannan, S. Venketswarlu, R.M. Gairola, J. Das, G. Viswanathan
By using a Doppler Weather Radar (DWR) at Shriharikota (13.66°N & 80.23°E), an Artificial Neural Network (ANN) based technique is proposed to improve the accuracy of rain intensity estimation. Three spectral moments of a Doppler spectra are utilized as an input data to an ANN. Rain intensity, as measured by the tipping bucket rain gauges around the DWR station, are considered as a target values for the given inputs. Rain intensity as estimated by the developed ANN model is validated by the rain gauges measurements. With the help of a developed technique, reasonable improvement in the estimation of rain intensity is observed. By using the developed technique, root mean square error and bias are reduced in the range of 34–18% and 17–3% respectively, compared to ZR approach.

Artificial rain and cold wind act as stressors to captive molting and non-molting European starlings (Sturnus vulgaris) Original Research Article
Comparative Biochemistry and Physiology Part A: Molecular & Integrative Physiology, Volume 164, Issue 3, March 2013, Pages 512-519
Robert de Bruijn, L. Michael Romero
Free-roaming animals continually cope with changes in their environment. One of the most unpredictable environmental phenomena is weather. Being able to respond to weather appropriately is crucial as it can be a threat to survival. The stress response, consisting of increases in heart rate and release of glucocorticoids, is an important mechanism by which animals cope with stressors. This study examined behavioral, heart rate, and corticosterone responses of captive European starlings (Sturnus vulgaris) to two aspects of weather mimicked under controlled conditions, a subtle (3 °C) decrease in temperature and a short, mild bout of rain. Both decreased temperature and exposure to rain elicited increases in heart rate and corticosterone in non-molting starlings. Molt is an important life history stage in birds that affects feather cover and may require a different response to weather-related stressors. We repeated the experiment in molting starlings and found increases in heart rate in response to rain and cold wind. However, the hypothalamic–pituitary–adrenal (HPA)-axis was suppressed during molt, as molting starlings did not increase corticosterone release in response to either stimulus. These data suggest these stimuli induce increased allostatic load in starlings, and that animals may adjust their response depending on the life-history stage.

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