Quick Response Code for visual Atmospheric science in .NET Deploy barcode data matrix in .NET Atmospheric science

12.3 Atmospheric science using none touse none with web,windows applicationmake qr code c# Nonlinear singular spectru none for none m analysis (NLSSA) has also been applied to study the QBO (Hsieh and Hamilton, 2003). Madden Julian Oscillation (MJO) The Madden Julian Oscillation (MJO) is the dominant component of the intraseasonal (30 90 day time scale) variability in the tropical atmosphere. It consists of large-scale coupled patterns in atmospheric circulation and deep convection, all propagating slowly eastward over the Indian and Paci c Oceans where the sea surface is warm (Zhang, 2005).

Association between this tropical oscillation and the mid-latitude winter atmospheric conditions has been found (Vecchi and Bond, 2004). Using MLP NN, nonlinear projection (NLP) of an MJO index on to the precipitation and 200 hPa wind anomalies in the northeast Paci c during January March by Jamet and Hsieh (2005) shows asymmetric atmospheric patterns associated with different phases of the MJO, with strong nonlinearity found for precipitation anomalies and moderate nonlinearity for wind anomalies. Indian summer monsoon The Great Indian Desert and adjoining areas of the northern and central Indian subcontinent heat up during summer, producing a low pressure area over the northern and central Indian subcontinent.

As a result, moisture-laden winds from the Indian Ocean rush in to the subcontinent. As the air ows towards the Himalayas, it is forced to rise and precipitation occurs. The southwest monsoon is generally expected to begin around the start of June and dies down by the end of September.

Failure of the Indian summer monsoon to deliver the normal rainfall would bring drought and hardship for the Indian economy. Since the late 1800s, several studies have attempted long-range prediction of the Indian summer monsoon rainfall. Cannon and McKendry (1999, 2002) applied MLP NN to forecast the rainfall using regional circulation PCs as predictors.

Climate change There is even longer time scale variability or change in the climate system e.g. the Paci c Decadal Oscillation (PDO) has main time scales around 15 25 years and 50 70 years (Mantua and Hare, 2002).

The increase of anthropogenic greenhouse gases in the atmosphere may also cause long-term climate change. There have not been many applications of machine learning methods to climate change since the observed records are relatively short for nonlinear signal analysis. Nevertheless, NN methods have been applied to anthropogenic climate change problems (Walter et al.

, 1998; Walter and Schonwiese, 2002; Pasini et al., 2006)..

ASP.NET Web Forms Applications in environmental sciences In Walter et al. (1998), N none none N methods were used to simulate the observed global (and hemispheric) annual mean surface air temperature variations during 1874 1993 using anthropogenic and natural forcing mechanisms as predictors. The two anthropogenic forcings were equivalent CO2 concentrations and tropospheric sulfate aerosol concentrations, while the three natural forcings were volcanism, solar activity and ENSO.

The NN explained up to 83% of the observed temperature variance, signi cantly more than by multiple regression analysis. On a global average, the greenhouse gas signal was assessed to be 0.9 1.

3 K (warming), the sulfate signal 0.2 0.4 K (cooling), which were similar to the ndings from GCM experiments.

The related signals of the three natural forcing mechanisms each covered an amplitude of around 0.1 0.3 K.

. 12.3.3 Radiation in atmosp heric models The earth radiates primarily in the infrared frequency band.

This outgoing radiation is called the longwave radiation (LWR), as the wavelengths are long relative to those of the incoming solar radiation. Greenhouse gases, (e.g.

carbon dioxide, water vapour, methane and nitrous oxide) absorb certain wavelengths of the LWR, adding heat to the atmosphere and in turn causing the atmosphere to emit more radiation. Some of this radiation is directed back towards the Earth, hence warming the Earth s surface. The Earth s radiation balance is very closely achieved since the outgoing LWR very nearly equals the absorbed incoming shortwave radiation (SWR) from the sun (primarily as visible, near-ultraviolet and near-infrared radiation).

Atmospheric general circulation models (GCM) typically spend a major part of their computational resources on calculating the LWR and SWR uxes through the atmosphere. A GCM computes the net LWR heat ux F( p), where the pressure p serves as a vertical coordinate. The cooling rate Cr ( p) is simply proportional to F/ p.

Besides being a function of p, F is also a function of S, variables at the Earth s surface, T, the vertical temperature pro le, V, vertical pro les of chemical concentrations (e.g. CO2 concentration), and C, cloud variables.

Chevallier et al. (1998, 2000) developed MLP NN models to replace LWR uxes in GCMs. For the ux F at the discretized pressure level p j , the original GCM computed F=.

ai (C)Fi (S, T, V),. (12.13). where the summation over i none none is from the Earth s surface to the level p j , Fi is the ux at level pi without the cloud correction factor ai . Neural network models Ni (S, T, V) were developed to replace Fi (S, T, V). This NeuroFlux model was.

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