BalkanMed real time severe weather service: Progress and prospects in Bulgaria

Guerova, Guergana; Dimitrova, Tsvetelina; Vassileva, K.; Slavchev, Martin; Stoev, Krasimir; Georgiev, Stefan

doi:10.1016/j.asr.2020.07.005

Cited by 6 publications

(3 citation statements)

References 17 publications

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“…Of particular significance for nowcasting application in Poland are the obtained IWV thresholds for the summer storm season June-July-August and the vertical profiles of wet refractivity. The 2-3 h predictive capabilities of GNSS derived products are of particular interest for the operational service developed in Southeast Europe targeting application in severe weather and hail suppression in Bulgaria in particular [52].…”

Section: Discussionmentioning

confidence: 99%

GNSS-Based Machine Learning Storm Nowcasting

et al. 2020

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Nowcasting of severe weather events and summer storms, in particular, are intensively studied as they have great potential for large economic and societal losses. Use of Global Navigation Satellite Systems (GNSS) observations for weather nowcasting has been investigated in various regions. However, combining the vertically integrated water vapour (IWV) with vertical profiles of wet refractivity derived from GNSS tomography has not been exploited for short-range forecasts of storms. In this study, we introduce a methodology to use the synergy of IWV and tomography-based vertical profiles to predict 0–2 h of storms using a machine learning approach for Poland. Moreover, we present an analysis of the importance of features that take part in the prediction process. The accuracy of the model reached over 87%, and the precision of prediction was about 30%. The results show that wet refractivity below 6 km and IWV on the west of the storm are among the significant parameters with potential for predicting storm location. The analysis of IWV demonstrated a correlation between IWV changes and storm occurrence.

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Section: Discussionmentioning

confidence: 99%

GNSS-Based Machine Learning Storm Nowcasting

et al. 2020

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“…En la troposfera inferior, el vapor de agua atmosférico actúa como el principal recurso de precipitación en todos los sistemas meteorológicos, y dominando la estructura de calentamiento diabático en la troposfera [Trenberth et al 2005]. El vapor de agua juega un papel importante en el desarrollo de, por ejemplo: eventos de precipitación extrema [Fujita y Sato, 2017;Barindelli et al, 2018]; lluvia asociada con ciclones que se profundizan rápidamente en latitudes medias [McMurdie y Katsaros, 1996]; desarrollo de granizo [Guerova et al, 2013;Calori et al, 2016]; y, precipitación orográfica asociados con los ríos atmosféricos [Smith et al, 2010].…”

Section: Introductionunclassified

Análisis del modelo de cálculo utilizado para obtener el vapor de agua troposférico desde los retardos en la señal GNSS en la región centro oeste de Argentina

Mackern

Mateo

Camisay

et al. 2021

ICU

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El agua en su estado gaseoso, es el principal regulador de la temperatura superficial del planeta. La medición del mismo está limitada tanto espacial como temporalmente. La técnica utilizada tradicionalmente para medirlo a través de radiosondas es insuficiente. Esta variable genera un retardo en la señal GNSS al atravesar la atmosfera, por lo cual desde hace tres décadas se viene trabajando en recuperar el valor del IWV en forma indirecta. En el modelo de cálculo se aplica un parámetro crítico, la temperatura media de la columna atmosférica. Se han desarrollado varios modelos para calcularla, la gran mayoría basados en observaciones de radiosondas lanzadas principalmente en el hemisferio norte. En este trabajo se ha validado la aplicación de 4 modelos, el de Bevis, el de Mendes y el de Yao, este último en dos sectores de latitud sur, sobre el centro oeste de Argentina. También se ha analizado la influencia de distintos coeficientes de la refracción en el factor que relaciona la componente húmeda del retardo troposférico y el IWV. En este caso se consideraron los propuestos por Thayer y por Rüeger. Se concluyó que tanto el modelo de Mendes como el de Bevis para el cálculo de la temperatura media son los que mejor representan la variabilidad espacial y temporal de la variable, en la región de estudio. Respecto a los coeficientes de la refracción utilizados en el modelo de cálculo, sus diferencias no son significativas pudiendo utilizarse cualquiera de ellos.

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“…IWV allows for reconnaissance and evolution monitoring of atmospheric rivers, which are responsible for heavy precipitation [20], thus they need to be represented in ensemble forecast systems [21], [22]. IWV variability is also important for forecasting heavy rainfalls [23], monitoring and nowcasting severe storm events [17], [24], thunderstorms classification [25], and hailstorm suppression [26]. Last but not least, GNSS troposphere products, i.e.…”

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confidence: 99%

Monitoring the Water Vapor Content at High Spatio-Temporal Resolution Using a Network of Low-Cost Multi-GNSS Receivers

Marut

Hadaś

Kapłon

et al. 2022

IEEE Trans. Geosci. Remote Sensing

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The Global Navigation Satellite System (GNSS) has the capacity for remote sensing of water vapor content in the atmosphere. Post-processing of GNSS data can provide Integrated Water Vapor (IWV) with accuracies comparable to measurements of traditional sensors, i.e. water vapor radiometers. While GNSS meteorology benefits from thousands of permanent GNSS stations operating worldwide the spatial resolution of GNSS-derived IWV is limited to tens of kilometers. Further densification of GNSS networks is achievable with low-cost GNSS receivers. We investigated the feasibility of low-cost multi-GNSS receivers for monitoring IWV. The post-processing and the realtime solution are validated against 1) the results from a geodeticgrade GNSS receiver, 2) co-located water vapor radiometer, and 3) numerical weather model (NWM). Despite the high variability of the IWV during the validation period, the standard deviation of IWV differences with respect to the water vapor radiometer was 1.0 kg/m 2 and 1.5 kg/m 2 in post-processing and real-time, respectively. The city-scale variability of water vapor content in the atmosphere was monitored by a network of 16 low-cost GNSS receivers deployed in the city of Wroclaw, Poland. During rapidly changing weather conditions the disagreement between the low-cost GNSS-derived IWV field and the NWM reached up to 5.4 kg/m 2 and inter-station IWV differences exceeded 5 kg/m 2 . It has been demonstrated that low-cost GNSS receivers are reliable tools for precise determination of IWV, also in realtime. This study is the first to measure the water vapor content with a spatial resolution of single kilometers and to present a significantly diversified city-scale IWV field.

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BalkanMed real time severe weather service: Progress and prospects in Bulgaria

Cited by 6 publications

References 17 publications

GNSS-Based Machine Learning Storm Nowcasting

GNSS-Based Machine Learning Storm Nowcasting

Análisis del modelo de cálculo utilizado para obtener el vapor de agua troposférico desde los retardos en la señal GNSS en la región centro oeste de Argentina

Monitoring the Water Vapor Content at High Spatio-Temporal Resolution Using a Network of Low-Cost Multi-GNSS Receivers

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