Related papers: Deep-learning-based prediction of Precipitable Wat…
Long-Short-Term-Memory (LSTM) networks have been used extensively for time series forecasting in recent years due to their ability of learning patterns over different periods of time. In this paper, this ability is applied to learning the…
In this work, we present results from a long-term precipitable water vapor (PWV) study in the Chajnantor area, in northern Chile. Data from several instruments located at relevant sites for sub-millimeter and mid-infrared astronomy were…
Precipitable water vapour (PWV) strongly affects the quality of data obtained from millimetre- and submillimetre-wave astronomical observations, such as those for cosmic microwave background measurements. Some of these observatories have…
We validate the Weather Research and Forecasting (WRF) model for precipitable water vapour (PWV) forecasting as a fully operational tool for optimizing astronomical infrared (IR) observations at Roque de los Muchachos Observatory (ORM). For…
Context. Interest in the use of the Chajnantor area for millimeter and submillimeter astronomy is increasing because of its excellent atmospheric conditions. Knowing the general site annual variability in precipitable water vapor (PWV) can…
Atmospheric water vapor is the main limiting factor of atmospheric transparency in the mm and submm wavelength spectral windows. Thus, dry sites are needed for the installation and successful operation of radio astronomy observatories…
In this paper we present the first results ever obtained by applying the autoregressive (AR) technique to the precipitable water vapour (PWV). The study is performed at the Very Large Telescope. The AR technique has been recently proposed…
In recent years, there has been growing interest in using Precipitable Water Vapor (PWV) derived from Global Positioning System (GPS) signal delays to predict rainfall. However, the occurrence of rainfall is dependent on a myriad of…
We here show that dual-band GPS measurements of precipitable water vapor (PWV) at KPNO predict the overall per-image sensitivity of the Mayall z-band Legacy Survey (MzLS). The per-image variation in the brightness of individual stars is…
Global Positioning System (GPS) derived precipitable water vapor (PWV) is extensively being used in atmospheric remote sensing for applications like rainfall prediction. Many applications require PWV values with good resolution and without…
Accurate short range weather forecasting has significant implications for various sectors. Machine learning based approaches, e.g., deep learning, have gained popularity in this domain where the existing numerical weather prediction (NWP)…
With a rapid increase in the number of geostationary satellites around the earth's orbit, there has been a renewed interest in using Global Positioning System (GPS) to understand several phenomenon in earth's atmosphere. Such study using…
In the search for small exoplanets orbiting cool stars whose spectral energy distributions peak in the near infrared, the strong absorption of radiation in this region due to water vapour in the atmosphere is a particularly adverse effect…
In this paper, the Precipitable Water Vapor (PWV) content of the atmosphere is derived using the Global Positioning System (GPS) signal delays. The PWV values from GPS are calculated at different elevation cut-off angles. It was found that…
We discuss properties of the atmospheric water vapor above the high Andean plateau region known as the Llano de Chajnantor, in the Atacama Desert of northern Chile. A combination of radiometric and radiosonde measurements indicate that the…
Accurate rainfall forecasting is critical because it has a great impact on people's social and economic activities. Recent trends on various literatures show that Deep Learning (Neural Network) is a promising methodology to tackle many…
The Thirty Meter Telescope (TMT) and the European Extremely Large Telescope (E-ELT) site testing teams have recently finalized their site testing studies. Since atmospheric water vapor is the dominant source of absorption and increased…
We report precipitable water vapor (PWV) measurements made at Las Campanas Observatory using optical spectra of H2O lines obtained with the Magellan echelle spectrograph, and calculated using a robust technique that is accurate to 5-10%.…
We present the encoder-forecaster convolutional long short-term memory (LSTM) deep-learning model that powers Microsoft Weather's operational precipitation nowcasting product. This model takes as input a sequence of weather radar mosaics…
This work addresses the challenge of short-term precipitation forecasting by applying Convolutional Long Short-Term Memory (ConvLSTM) neural networks to weather radar data from the Royal Netherlands Meteorological Institute (KNMI). The…