A dense GNSS meteorological network for observing deep convection in the Amazon

Adams, D. K.; Fernandes, Rui; Kursinski, E. R.; Maia, Jair Max Furtunato; Sapucci, Luiz Fernando; Machado, Luiz A. T.; Vitorello, Ícaro; Monico, J. F. Galera; Holub, Karel; Gutman, S. I.; Filizola, Naziano; Bennett, Richard A.

doi:10.1002/asl.312

Cited by 43 publications

(31 citation statements)

References 34 publications

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“…For example, the use of accurate high-resolution tropospheric gradients from multi-GNSS processing could allow us to identify more clearly strong humidity gradients in severe weather situations (Li et al, 2015b). Work on PWV tomography from GPS data (Champollion et al, 2009;Adams et al, 2011;Van Baelen et al, 2011) suggests that one may also look at the time evolution of 3-D water vapour fields, directly addressing the impact of deep convection in those fields, or the relevance of surface fluxes in the moistening of the atmospheric boundary layer in specific storms (Iwasaki and Miki, 2001;Champollion et al, 2004;Brenot et al, 2014). In the case of events which are dominated by horizontal advection as in inflows of moist warm air in coastal regions (e.g.…”

Section: Discussionmentioning

confidence: 99%

“…The rapid increase in the density of such networks in some regions has been motivating the exploration of GNSS data for other meteorological applications. Some examples include 3-D water vapour tomography from short-term field experiments (Champollion et al, 2005), the analysis of countrywide networks and even the deployment of high-density GNSS systems specifically designed for the monitoring of atmospheric convection (Adams et al, 2011). It is also expected that with the improvement of the current GNSS systems and the development of future new ones, this technique should provide more refined information about the atmospheric state, allowing also an interoperability in what concerns the water vapour estimation through multi-GNSS processing techniques (Li et al, 2015a) and multi-sensor approaches such as SAR (Benevides et al, 2015a).…”

Section: Introductionmentioning

confidence: 99%

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On the inclusion of GPS precipitable water vapour in the nowcasting of rainfall

Benevides

Catalão

Miranda

2015

Nat. Hazards Earth Syst. Sci.

137

122

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Abstract. The temporal behaviour of precipitable water vapour (PWV) retrieved from GPS delay data is analysed in a number of case studies of intense precipitation in the Lisbon area, in the period 2010-2012 and in a continuous annual cycle of 2012 observations. Such behaviour is found to correlate positively with the probability of precipitation, especially in cases of severe rainfall. The evolution of the GPS PWV in a few stations is analysed by a least-squares fitting of a broken line tendency, made by a temporal sequence of ascents and descents over the data. It is found that most severe rainfall events occur in descending trends after a long ascending period and that the most intense events occur after steep ascents in PWV. A simple algorithm, forecasting rain in the 6 h after a steep ascent of the GPS PWV in a single station, is found to produce reasonable forecasts of the occurrence of precipitation in the nearby region, without significant misses in what concerns larger rain events, but with a substantial amount of false alarms. It is suggested that this method could be improved by the analysis of 2-D or 3-D time-varying GPS PWV fields or by its joint use with other meteorological data relevant to nowcast precipitation.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

On the inclusion of GPS precipitable water vapour in the nowcasting of rainfall

Benevides

Catalão

Miranda

2015

Nat. Hazards Earth Syst. Sci.

137

122

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show abstract

“…From Equation (14), it can be inferred that the surface pressure error of 2.8 hPa produces an approximately 6.7 mm error in ZHD, and the equivalent error will be transferred to ZWD through the computation of Equation (3). This equates to a 1-mm error in PWV.…”

Section: Comparison Between Interpolated and Observed Psmentioning

confidence: 99%

Retrieving Precipitable Water Vapor Data Using GPS Zenith Delays and Global Reanalysis Data in China

Jiang

Chen

et al. 2016

Remote Sensing

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GPS has become a very effective tool to remotely sense precipitable water vapor (PWV) information, which is important for weather forecasting and nowcasting. The number of geodetic GNSS stations set up in China has substantially increased over the last few decades. However, GPS PWV derivation requires surface pressure to calculate the precise zenith hydrostatic delay and weighted mean temperature to map the zenith wet delay to precipitable water vapor. GPS stations without collocated meteorological sensors can retrieve water vapor using standard atmosphere parameters, which lead to a decrease in accuracy. In this paper, a method of interpolating NWP reanalysis data to site locations for generating corresponding meteorological elements is explored over China. The NCEP FNL dataset provided by the NCEP (National Centers for Environmental Prediction) and over 600 observed stations from different sources was selected to assess the quality of the results. A one-year experiment was performed in our study. The types of stations selected include meteorological sites, GPS stations, radio sounding stations, and a sun photometer station. Compared with real surface measurements, the accuracy of the interpolated surface pressure and air temperature both meet the requirements of GPS PWV derivation in most areas; however, the interpolated surface air temperature exhibits lower precision than the interpolated surface pressure. At more than 96% of selected stations, PWV differences caused by the differences between the interpolation results and real measurements were less than 1.0 mm. Our study also indicates that relief amplitude exerts great influence on the accuracy of the interpolation approach. Unsatisfactory interpolation results always occurred in areas of strong relief. GPS PWV data generated from interpolated meteorological parameters are consistent with other PWV products (radio soundings, the NWP reanalysis dataset, and sun photometer PWV data). The differences between them were approximately 1~3 mm at most at our selected stations, and GPS data processing is the main factor influencing the agreement of the GPS PWV results with those of other methods.

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“…This new experimental site was implemented in 2011 and planned to run continuously during the next years applying a synergy of different instruments to help understanding the interactions and feedback mechanisms between humidity, convection, clouds and aerosols. It was initially implemented by the FAPESP , but also received contribution from FAPESP project -Cloud processes of the main precipitation systems in Brazil: a contribution to cloud resolving modeling and to the GPM = Global Precipitation Measurement Mission (Machado et al, 2014), the project Amazonian Dense GNSS = Global Navigation Satellite System Meteorological Network (Adams et al, 2011) and the Max Planck Institute in Hamburg. Instruments available are UV Raman lidar, ceilometer, sunphotometer, multifilter radiometer, nephelometer, aethalometer, weather station, disdrometer, vertical pointing rain radar and water vapor column using GNSS.…”

Section: Instrument Description and Performancementioning

confidence: 99%

A permanent Raman lidar station in the Amazon: description, characterization, and first results

Barbosa¹,

Barja²,

Pauliquevis

et al. 2014

Atmos. Meas. Tech.

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Abstract.A permanent UV Raman lidar station, designed to perform continuous measurements of aerosols and water vapor and aiming to study and monitor the atmosphere from weather to climatic time scales, became operational in the central Amazon in July 2011. The automated data acquisition and internet monitoring enabled extended hours of daily measurements when compared to a manually operated instrument. This paper gives a technical description of the system, presents its experimental characterization and the algorithms used for obtaining the aerosol optical properties and identifying the cloud layers. Data from one week of measurements during the dry season of 2011 were analyzed as a mean to assess the overall system capability and performance. Both Klett and Raman inversions were successfully applied. A comparison of the aerosol optical depth from the lidar and from a co-located Aerosol Robotic Network (AERONET) sun photometer showed a correlation coefficient of 0.86. By combining nighttime measurements of the aerosol lidar ratio (50-65 sr), back-trajectory calculations and fire spots observed from satellites, we showed that observed particles originated from biomass burning. Cirrus clouds were observed in 60 % of our measurements. Most of the time they were distributed into three layers between 11.5 and 13.4 km a.g.l. The systematic and long-term measurements being made by this new scientific facility have the potential to significantly improve our understanding of the climatic implications of the anthropogenic changes in aerosol concentrations over the pristine Amazonia.

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A dense GNSS meteorological network for observing deep convection in the Amazon

Cited by 43 publications

References 34 publications

On the inclusion of GPS precipitable water vapour in the nowcasting of rainfall

On the inclusion of GPS precipitable water vapour in the nowcasting of rainfall

Retrieving Precipitable Water Vapor Data Using GPS Zenith Delays and Global Reanalysis Data in China

A permanent Raman lidar station in the Amazon: description, characterization, and first results

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