Detection of water vapor time variations associated with heavy rain in northern Italy by geodetic and low-cost GNSS receivers

Barindelli, Stefano; Realini, Eugenio; Venuti, Giovanna; Fermi, Alessandro; Gatti, Andrea

doi:10.1186/s40623-018-0795-7

Cited by 70 publications

(37 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The development is now carried out mostly by GReD srl, with the contributions of users from several institutions, at international level. goGPS has applications in navigation (Realini et al, 2012), but also in other fields such as building monitoring (Caldera et al, 2016) and troposphere estimation (Barindelli et al, 2018).…”

Section: U-blox Gnss Data Processingmentioning

confidence: 99%

Comparison Between RGB and RGB-D Cameras for Supporting Low-Cost GNSS Urban Navigation

Rossi

Gaetani

Pagliari

et al. 2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

A pure GNSS navigation is often unreliable in urban areas because of the presence of obstructions, thus preventing a correct reception of the satellite signal. The bridging between GNSS outages, as well as the vehicle attitude reconstruction, can be recovered by using complementary information, such as visual data acquired by RGB-D or RGB cameras. In this work, the possibility of integrating low-cost GNSS and visual data by means of an extended Kalman filter has been investigated. The focus is on the comparison between the use of RGB-D or RGB cameras. In particular, a Microsoft Kinect device (second generation) and a mirrorless Canon EOS M RGB camera have been compared. The former is an interesting RGB-D camera because of its low-cost, easiness of use and raw data accessibility. The latter has been selected for the high-quality of the acquired images and for the possibility of mounting fixed focal length lenses with a lower weight and cost with respect to a reflex camera. The designed extended Kalman filter takes as input the GNSS-only trajectory and the relative orientation between subsequent pairs of images. Depending on the visual data acquisition system, the filter is different because RGB-D cameras acquire both RGB and depth data, allowing to solve the scale problem, which is instead typical of image-only solutions. The two systems and filtering approaches were assessed by ad-hoc experimental tests, showing that the use of a Kinect device for supporting a u-blox low-cost receiver led to a trajectory with a decimeter accuracy, that is 15&thinsp;% better than the one obtained when using the Canon EOS M camera.

show abstract

Section: U-blox Gnss Data Processingmentioning

confidence: 99%

Comparison Between RGB and RGB-D Cameras for Supporting Low-Cost GNSS Urban Navigation

Rossi

Gaetani

Pagliari

et al. 2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

show abstract

“…GNSS-derived tropospheric products are nowadays used in operational weather prediction applications and climate studies [5]. Some experiments based on GNSS atmospheric data have proved to be useful to study severe precipitation events [3,6,7]. A few assimilation experiments of GNSS tropospheric products into NWMs have proved to be beneficial to improve rainfall forecasts [1,8].…”

Section: Introductionmentioning

confidence: 99%

Neural Network Approach to Forecast Hourly Intense Rainfall Using GNSS Precipitable Water Vapor and Meteorological Sensors

2019

View full text Add to dashboard Cite

This work presents a methodology for the short-term forecast of intense rainfall based on a neural network and the integration of Global Navigation and Positioning System (GNSS) and meteorological data. Precipitable water vapor (PWV) derived from GNSS is combined with surface pressure, surface temperature and relative humidity obtained continuously from a ground-based meteorological station. Five years of GNSS data from one station in Lisbon, Portugal, are processed. Data for precipitation forecast are also collected from the meteorological station. Spaceborne Spinning Enhanced Visible and Infrared Imager (SEVIRI) data of cloud top measurements are also gathered, providing collocated information on an hourly basis. In previous studies it was found that the time-varying PWV is correlated with rainfall and can be used to detected heavy rain. However, a significant number of false positives were found, meaning that the evolution of PWV does not contain enough information to infer future rain. In this work, a nonlinear autoregressive exogenous neural network model (NARX) is used to process the GNSS and meteorological data to forecast the hourly precipitation. The proposed methodology improves the detection of intense rainfall events and reduces the number of false positives, with a good classification score varying from 63% up to 72% and a false positive rate of 36% down to 21%, for the tested years in the dataset. A score of 64% for intense rain events classification with 22% false positive rate is obtained for the most recent years. The method also achieves an almost 100% hit rate for the rain vs no rain detection, with close to no false alarms.

show abstract

“…Singh et al [11] reported that the weekly and monthly variations of total precipitable water vapor over the Arabian Sea and the Indian Ocean are well correlated with onset day of monsoon over the Indian sub-continent. Barindelli et al [12] reported a peak in the PWV values in response to a heavy precipitation event, followed by a steep decrease (5-10 mm in about 1 h) in the observed PWV values as the rain clouds moved past the station using data from a temperate station (of Italy). Shi et al [13] presented some severe rainfall cases and a series of moderate rainfall cases to indicate the feasibility of GPS-PWV values for rainfall monitoring using data from the sub-tropical stations (of China).…”

Section: Introductionmentioning

confidence: 99%

GPS-PWV Based Improved Long-Term Rainfall Prediction Algorithm for Tropical Regions

2019

View full text Add to dashboard Cite

Global positioning system (GPS) satellite delay is extensively used in deriving the precipitable water vapor (PWV) with high spatio–temporal resolution. One of the recent applications of GPS derived PWV values are to predict rainfall events. In the literature, there are rainfall prediction algorithms based on GPS-PWV values. Most of the algorithms are developed using data from temperate and sub-tropical regions. Mostly these algorithms use maximum PWV rate, maximum PWV variation and monthly PWV values as a criterion to predict the rain events. This paper examines these algorithms using data from the tropical stations and proposes the use of maximum PWV value for better prediction. When maximum PWV value and maximum rate of increment criteria are implemented on the data from the tropical stations, the false alarm ( F A ) rate is reduced by almost 17% as compared to the results from the literature. There is a significant reduction in F A rates while maintaining the true detection ( T D ) rates as high as that of the literature. A study done on the varying historical length of data and lead time values shows that almost 80% of the rainfall can be predicted with a false alarm of 26 . 4 % for a historical data length of 2 hours and a lead time of 45 min to 1 hour.

show abstract

Detection of water vapor time variations associated with heavy rain in northern Italy by geodetic and low-cost GNSS receivers

Cited by 70 publications

References 25 publications

Comparison Between RGB and RGB-D Cameras for Supporting Low-Cost GNSS Urban Navigation

Comparison Between RGB and RGB-D Cameras for Supporting Low-Cost GNSS Urban Navigation

Neural Network Approach to Forecast Hourly Intense Rainfall Using GNSS Precipitable Water Vapor and Meteorological Sensors

GPS-PWV Based Improved Long-Term Rainfall Prediction Algorithm for Tropical Regions

Contact Info

Product

Resources

About