What is it about?

The temperature readings for all the 365 days and the 24 hours may be fitted through a 3 × 3 matrix. The mean square deviation between this fit and the actual meteorological measurements is smaller than three degrees Celsius. Four entries of this (nonsymmetric) matrix may be fixed by other means, leaving only five independent components. However, the same method applied to the humidity measurements produces a larger mean square deviation. A strong stochastical connection is found between the T-temperature matrix and the U-humidity matrix.

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Why is it important?

The physical meaning of these constants is given only in the case mxMx 1. Our results have also been connected to fundamental cosmological properties: Earth’s orbit, the ecliptic angle, and the latitude of place (or whatever geographical location is chosen). A separate program calculates the angular position of the Sun as measured in the sky of place, to determine the length of the day or the mean value of the solar cosine. In this work introduces several new variables which happen to be stochastically connected.


Likewise, the temperature prediction uses the artificial neural network model and it was able to predict the indoor temperature, but the majority of previous studies applying either ambient or ground have tended to emphasize the structural improvement of individual forecasting models without considering the periodicity of data. In this approach, annual average soil temperatures are determined by air temperature, solar radiance, wind speed, and relative humidity.

Dr. Alejandro Castañeda-Miranda
Creativity and Innovation Center 4.0 (CIC 4.0), Technological University of Queretaro (UTEQ)

Read the Original

This page is a summary of: Meteorological Temperature and Humidity Prediction from Fourier-Statistical Analysis of Hourly Data, Advances in Meteorology, August 2019, Hindawi Publishing Corporation,
DOI: 10.1155/2019/4164097.
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