scholarly journals A GA-Based BP Artificial Neural Network for Estimating Monthly Surface Air Temperature of the Antarctic during 1960–2019

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Miao Fang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Air Temperature ◽  
Surface Air Temperature ◽  
Warming Trend ◽  
Antarctic Continent ◽  
Cooling Trend ◽  
Artificial Neural ◽  
June July ◽  
The Antarctic

The spatial sparsity and temporal discontinuity of station-based SAT data do not allow to fully understand Antarctic surface air temperature (SAT) variations over the last decades. Generating spatiotemporally continuous SAT fields using spatial interpolation represents an approach to address this problem. This study proposed a backpropagation artificial neural network (BPANN) optimized by a genetic algorithm (GA) to estimate the monthly SAT fields of the Antarctic continent for the period 1960–2019. Cross-validations demonstrate that the interpolation accuracy of GA-BPANN is higher than that of two benchmark methods, i.e., BPANN and multiple linear regression (MLR). The errors of the three interpolation methods feature month-dependent variations and tend to be lower (larger) in warm (cold) months. Moreover, the annual SAT had a significant cooling trend during 1960–1989 (trend = −0.07°C/year; p = 0.04 ) and a significant warming trend during 1990–2019 (trend = 0.06°C/year; p = 0.05 ). The monthly SAT did not show consistent cooling or warming trends in all months, e.g., SAT did not show a significant cooling trend in January and December during 1960–1989 and a significant warming trend in January, June, July, and December during 1990–2019. Furthermore, the Antarctic SAT decreases with latitude and the distance away from the coastline, but the eastern Antarctic is overall colder than the western Antarctic. Spatiotemporal inconsistencies on SAT trends are apparent over the Antarctic continent, e.g., most of the Antarctic continent showed a cooling trend during 1960–1989 (trend = −0.20∼0°C/year; p = 0.01 ∼ 0.27 ) with a peak over the central part of the eastern Antarctic continent, while the entire Antarctic continent showed a warming trend during 1990–2019 (trend = 0∼0.10°C/year; p = 0.04 ∼ 0.42 ) with a peak over the higher latitudes.

scholarly journals Forecasting soil temperature based on surface air temperature using a wavelet artificial neural network

2017 ◽  
Vol 24 (4) ◽  
pp. 603-611 ◽  
Author(s):  
Alireza Araghi ◽  
Mohammad Mousavi-Baygi ◽  
Jan Adamowski ◽  
Christopher Martinez ◽  
Martine van der Ploeg
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Soil Temperature ◽  
Air Temperature ◽  
Surface Air Temperature ◽  
Artificial Neural

scholarly journals Estimation of microclimatic data in remote mountainous areas using an artificial neural network model-based approach

2013 ◽  
Vol 12 (4) ◽  
pp. 384-389
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Relative Humidity ◽  
Air Temperature ◽  
Meteorological Data ◽  
Application Site ◽  
Mountainous Area ◽  
Mountainous Areas ◽  
Model Based ◽  
Artificial Neural

An artificial neural network (ANN) model-based approach was developed and applied to estimate values of air temperature and relative humidity in remote mountainous areas. The application site was the mountainous area of the Samaria National Forest canyon (Greece). Seven meteorological stations were established in the area and ANNs were developed to predict air temperature and relative humidity for the five most remote stations of the area using data only from two stations located in the two more easily accessed sites. Measured and model-estimated data were compared in terms of the determination coefficient (R2), the mean absolute error (MAE) and residuals normality. Results showed that R2 values range from 0.7 to 0.9 for air temperature and from 0.7 to 0.8 for relative humidity whereas MAE values range from 0.9 to 1.8 oC and 5 to 9%, for air temperature and relative humidity, respectively. In conclusion, the study demonstrated that ANNs, when adequately trained, could have a high applicability in estimating meteorological data values in remote mountainous areas with sparse network of meteorological stations, based on a series of relatively limited number of data values from nearby and easily accessed meteorological stations.

scholarly journals Air Temperature Estimation by Using Artificial Neural Network Models in the Greater Athens Area, Greece

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
A. P. Kamoutsis ◽  
A. S. Matsoukis ◽  
K. I. Chronopoulos
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Air Temperature ◽  
Input Parameter ◽  
Network Models ◽  
Reference Station ◽  
Neural Network Models ◽  
Artificial Neural ◽  
Network Approaches ◽  
Athens Area

Air temperature (T) data were estimated in the regions of Nea Smirni, Penteli, and Peristeri, in the greater Athens area, Greece, using the T data of a reference station in Penteli. Two artificial neural network approaches were developed. The first approach, MLP1, used the T as input parameter and the second, MLP2, used additionally the time of the corresponding T. One site in Nea Smirni, three sites in Penteli, from which two are located in the Pentelikon mountain, and one site in Peristeri were selected based on different land use and altitude. T data were monitored in each site for the period between December 1, 2009, and November 30, 2010. In this work the two extreme seasons (winter and summer) are presented. The results showed that the MLP2 model was better (higher and lower MAE) than MLP1 for the T estimation in both winter and summer, independently of the examined region. In general, MLP1 and MLP2 models provided more accurate T estimations in regions located in greater distance (Nea Smirni and Peristeri) from the reference station in relation to the nearby Pentelikon mountain. The greater distance T estimations, in most cases, were better in winter compared to summer.

scholarly journals Time evolution of temperature profiles retrieved from 13 years of IASI data using an artificial neural network

2021 ◽  
Author(s):  
Marie Bouillon ◽  
Sarah Safieddine ◽  
Simon Whitburn ◽  
Lieven Clarisse ◽  
Filipe Aires ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Weather Forecasting ◽  
Warming Trend ◽  
Atmospheric Temperature ◽  
Temperature Profiles ◽  
Carbon Dioxide Absorption ◽  
Absorption Bands ◽  
Artificial Neural ◽  
Different Altitudes

Abstract. The three IASI instruments, launched in 2006, 2012, and 2018, are key instruments to weather forecasting, and most meteorological centers assimilate IASI nadir radiance data into atmospheric models to feed their forecasts. The EUropean organisation for the exploitation of METeorological SATellites (EUMETSAT) recently released a reprocessed homogeneous radiance record for the whole IASI observation period, from which thirteen years (2008–2020) of temperature profiles can be obtained. In this work, atmospheric temperatures at different altitudes are retrieved from IASI radiances measured in the carbon dioxide absorption bands (654–800 cm−1 and 2250–2400 cm−1) by selecting the channels that are the most sensitive to the temperature at different altitudes. We rely on an Artificial Neural Network (ANN) to retrieve atmospheric temperatures from a selected set of IASI radiances. We trained the ANN with IASI radiances as input and the European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis version 5 (ERA5) as output. The retrieved temperatures were validated with ERA5, with in-situ radiosonde temperatures from the Analysed RadioSoundings Archive (ARSA) network and with EUMETSAT temperatures retrieved from IASI radiances using a different method. Between 750 and 7 hPa, where IASI is most sensitive to temperature, a good agreement is observed between the three datasets: the differences between IASI on one hand, and ERA5, ARSA or EUMETSAT on the other hand are usually less than 0.5 K at these altitudes. At 2 hPa, as the IASI sensitivity decreases, we found differences up to 2 K between IASI and the three validation datasets. We then computed atmospheric temperature linear trends from atmospheric temperatures between 750 and 2 hPa. We found that in the past thirteen years, there is a general warming trend of the troposphere, that is more important at the poles than at the equator (0.7 K/decade at the equator, 1 K/decade at the North Pole). The stratosphere is globally cooling on average, except at the South Pole as a result of the ozone layer recovery. The cooling is most pronounced in the equatorial upper stratosphere (−1 K/decade). This work shows that ANN can be a powerful and simple tool to retrieve IASI temperatures at different altitudes in the upper troposphere and in the stratosphere, allowing us to construct a homogeneous and consistent temperature data record adapted to trend analyis.

scholarly journals Developing machine learning algorithms for meteorological temperature and humidity forecasting at Terengganu state in Malaysia

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Marwah Sattar Hanoon ◽  
Ali Najah Ahmed ◽  
Nur’atiah Zaini ◽  
Arif Razzaq ◽  
Pavitra Kumar ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Air Temperature ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Unseen Data ◽  
Temperature And Humidity ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

AbstractAccurately predicting meteorological parameters such as air temperature and humidity plays a crucial role in air quality management. This study proposes different machine learning algorithms: Gradient Boosting Tree (G.B.T.), Random forest (R.F.), Linear regression (LR) and different artificial neural network (ANN) architectures (multi-layered perceptron, radial basis function) for prediction of such as air temperature (T) and relative humidity (Rh). Daily data over 24 years for Kula Terengganu station were obtained from the Malaysia Meteorological Department. Results showed that MLP-NN performs well among the others in predicting daily T and Rh with R of 0.7132 and 0.633, respectively. However, in monthly prediction T also MLP-NN model provided closer standards deviation to actual value and can be used to predict monthly T with R 0.8462. Whereas in prediction monthly Rh, the RBF-NN model's efficiency was higher than other models with R of 0.7113. To validate the performance of the trained both artificial neural network (ANN) architectures MLP-NN and RBF-NN, both were applied to an unseen data set from observation data in the region. The results indicated that on either architecture of ANN, there is good potential to predict daily and monthly T and Rh values with an acceptable range of accuracy.

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