Prediction of the future values by analysing meteorological data is one of the important parts which can be helpful to the society as well as to the economy. Estimates of these values at a specific time of day, from daytime and daily profiles, are needed for a number of environmental, ecological, agricultural and technical applications, ranging from natural hazards assessments, crop growth fore...

The main sources of meteorological data at high altitudes are radiosondes and Aircraft Meteorological Data Relay (AMDAR) data [1]. Mode-select (Mode-S) radars are capable of getting similar readings from aircraft as AMDAR; however, this source of meteorological data has not been used systematically so far. In this article, we analyze the meteorological data obtained by means of Mode-S radars an...

We describe the AEMET meteorological dataset, which makes available some data sources from the Agencia Estatal de Meteorología (AEMET, Spanish Meteorological Office) as Linked Data. The data selected for publication are generated every ten minutes by approximately 250 automatic weather stations deployed across Spain and made available as CSV files in the AEMET FTP server. These files are retrie...

[1] This paper presents indirect observational evidence that desert dust can modulate the amplitude of easterly waves in the Atlantic Ocean. Twenty two years of NCEP/ NCAR reanalysis and dust from a global transport model are used to characterize the evolution of enhanced easterly waves. Lag composites of analysis increments (analysis minus first–guess) of geopotential height (700–hPa) anomalie...

Change detection is an important task in signal analysis, data mining, and image processing. In meteorology research, change is frequently associated with severe weather events or climate anomalies. Detecting change is crucial for weather and climate analysis. In this paper, we propose a wavelet based approach to analyze short term changes in the global meteorological data. We design a suite of...

In the atmospheric science, the scale of meteorological data is massive and growing rapidly. K-means is a fast and available cluster algorithm which has been used in many fields. However, for the large-scale meteorological data, the traditional K-means algorithm is not capable enough to satisfy the actual application needs efficiently. This paper proposes an improved MK-means algorithm (MK-mean...