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Ocean surface air temperature derived from multiple data sets and artificial neural networks
Author(s) -
Gautier Catherine,
Peterson Pete,
Jones Charles
Publication year - 1998
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1029/1998gl900086
Subject(s) - environmental science , root mean square , precipitable water , sea surface temperature , mean squared error , artificial neural network , surface air temperature , air temperature , meteorology , mean radiant temperature , remote sensing , climatology , surface (topology) , atmospheric sciences , statistics , mathematics , geology , computer science , geography , physics , climate change , oceanography , geometry , precipitation , quantum mechanics , machine learning
This paper presents a new method to derive monthly averaged surface air temperature, T a , from multiple data sets. Sea Surface Temperature (SST) from the National Centers for Environmental Prediction (NCEP) and total precipitable water (W) from the SSM/I sensor are used as inputs to Artificial Neural Networks (ANN). Surface air temperature (T a ) measurements from the Surface Marine Data (SMD) are used to develop and evaluate the methodology. When globally evaluated with SMD data, the bias of the new method is small (0.050°C ± 0.26°C), and the accuracy expressed as root‐mean square (rms) differences has a small global mean (0.73°C ± 0.37°C). These biases and rms differences are smaller than those obtained using NCEP reanalyses and TIROS Operational Vertical Sounder (TOVS) data products. When evaluated with the TOGA‐TAO array measurements over the tropical Pacific, the ANN mean bias and rms differences have similarly small values, 0.37°C and 0.61°C, respectively.