Pysteps: an open-source Python library for probabilistic precipitation nowcasting (v1.0)

Abstract. Pysteps is an open-source and community-driven Python library for probabilistic precipitation nowcasting – that is to say, very-short range forecasting (0–6 h). The aim of pysteps is to serve two different needs. The first is to provide a modular and well-documented framework for researchers interested in developing new methods for nowcasting and stochastic space-time simulation of precipitation. The second aim is to offer a highly configurable and easily accessible platform for practitioners ranging from weather forecasters to hydrologists. In this sense, pysteps has the potential to become an important component for integrated early warning systems for severe weather. The pysteps library supports standard input/output file formats and implements several optical flow methods as well as advanced stochastic generators to produce ensemble nowcasts. In addition, it includes tools for visualizing and post-processing the nowcasts and methods for deterministic, probabilistic, and neighbourhood forecast verification. The pysteps library is described and its potential is demonstrated using radar composite images from Finland, Switzerland, United States, and Australia. Finally, scientific experiments are carried out to help the reader to understand the pysteps framework and sensitivity to model parameters.

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Pysteps: an open-source Python library for probabilistic precipitation nowcasting (v1.0)

Geoscientific Model Development ◽

10.5194/gmd-12-4185-2019 ◽

2019 ◽

Vol 12 (10) ◽

pp. 4185-4219 ◽

Cited By ~ 7

Author(s):

Seppo Pulkkinen ◽

Daniele Nerini ◽

Andrés A. Pérez Hortal ◽

Carlos Velasco-Forero ◽

Alan Seed ◽

...

Keyword(s):

Open Source ◽

Early Warning Systems ◽

The United States ◽

Model Parameters ◽

Warning Systems ◽

File Formats ◽

Time Simulation ◽

Weather Forecasters ◽

Scientific Experiments ◽

Stochastic Space

Abstract. Pysteps is an open-source and community-driven Python library for probabilistic precipitation nowcasting, that is, very-short-range forecasting (0–6 h). The aim of pysteps is to serve two different needs. The first is to provide a modular and well-documented framework for researchers interested in developing new methods for nowcasting and stochastic space–time simulation of precipitation. The second aim is to offer a highly configurable and easily accessible platform for practitioners ranging from weather forecasters to hydrologists. In this sense, pysteps has the potential to become an important component for integrated early warning systems for severe weather. The pysteps library supports various input/output file formats and implements several optical flow methods as well as advanced stochastic generators to produce ensemble nowcasts. In addition, it includes tools for visualizing and post-processing the nowcasts and methods for deterministic, probabilistic and neighborhood forecast verification. The pysteps library is described and its potential is demonstrated using radar composite images from Finland, Switzerland, the United States and Australia. Finally, scientific experiments are carried out to help the reader to understand the pysteps framework and sensitivity to model parameters.

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Integrating local pore water pressure monitoring in territorial early warning systems for weather-induced landslides

Landslides ◽

10.1007/s10346-020-01599-w ◽

2021 ◽

Author(s):

Gaetano Pecoraro ◽

Michele Calvello

Keyword(s):

Pore Water ◽

Pore Water Pressure ◽

Water Pressure ◽

Model Performance ◽

Early Warning Systems ◽

Model Parameters ◽

Pressure Measurements ◽

Warning Systems ◽

Warning Model ◽

Statistical Indicators

AbstractA methodology designed to integrate widespread meteorological monitoring and pore water pressure measurements is proposed. The procedure is tested in 30 hydrological basins highly susceptible to weather-induced landslides in Norway. The following data are used: a catalog of 125 weather-induced landslides in soils registered between January 2013 and June 2017, widespread meteorological monitoring data employed in a territorial warning model, and pore water pressure measurements retrieved from boreholes installed for a variety of geotechnical projects. The territorial warning model is initially applied to identify the warning events and the correspondent warning level in the test areas over the analysis period. Afterwards, a method for assessing the territorial warning events by analyzing the trends of the monitored pore water pressures is proposed. Finally, an augmented territorial warning model is calibrated and validated using statistical indicators widely adopted in literature. The analysis of the results reveals a satisfactory correspondence between days with landslides and the warning levels provided by the augmented territorial warning model. A final comparison between the results of the model calibration and the model validation highlighted the consistency of the model performance, once the three model parameters are adequately set.

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r.massmov: an open-source landslide model for dynamic early warning systems

Natural Hazards ◽

10.1007/s11069-013-0867-8 ◽

2013 ◽

Vol 70 (2) ◽

pp. 1153-1179 ◽

Cited By ~ 3

Author(s):

Monia Elisa Molinari ◽

Massimiliano Cannata ◽

Claudia Meisina

Keyword(s):

Open Source ◽

Early Warning ◽

Early Warning Systems ◽

Warning Systems ◽

Landslide Model

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Assessing the performance of regional landslide early warning models: the EDuMaP method

Natural Hazards and Earth System Science ◽

10.5194/nhess-16-103-2016 ◽

2016 ◽

Vol 16 (1) ◽

pp. 103-122 ◽

Cited By ~ 25

Author(s):

M. Calvello ◽

L. Piciullo

Keyword(s):

Early Warning ◽

Early Warning System ◽

Model Performance ◽

Warning System ◽

Early Warning Systems ◽

Communication Strategy ◽

Performance Criteria ◽

Model Parameters ◽

Warning Systems ◽

Warning Model

Abstract. A schematic of the components of regional early warning systems for rainfall-induced landslides is herein proposed, based on a clear distinction between warning models and warning systems. According to this framework an early warning system comprises a warning model as well as a monitoring and warning strategy, a communication strategy and an emergency plan. The paper proposes the evaluation of regional landslide warning models by means of an original approach, called the "event, duration matrix, performance" (EDuMaP) method, comprising three successive steps: identification and analysis of the events, i.e., landslide events and warning events derived from available landslides and warnings databases; definition and computation of a duration matrix, whose elements report the time associated with the occurrence of landslide events in relation to the occurrence of warning events, in their respective classes; evaluation of the early warning model performance by means of performance criteria and indicators applied to the duration matrix. During the first step the analyst identifies and classifies the landslide and warning events, according to their spatial and temporal characteristics, by means of a number of model parameters. In the second step, the analyst computes a time-based duration matrix with a number of rows and columns equal to the number of classes defined for the warning and landslide events, respectively. In the third step, the analyst computes a series of model performance indicators derived from a set of performance criteria, which need to be defined by considering, once again, the features of the warning model. The applicability, potentialities and limitations of the EDuMaP method are tested and discussed using real landslides and warning data from the municipal early warning system operating in Rio de Janeiro (Brazil).

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The CARE Telematics Network for the Surveillance of Influenza in Europe

Methods of Information in Medicine ◽

10.1055/s-0038-1634617 ◽

1995 ◽

Vol 34 (05) ◽

pp. 518-522 ◽

Cited By ~ 13

Author(s):

M. Bensadon ◽

A. Strauss ◽

R. Snacken

Keyword(s):

Public Health ◽

Early Warning ◽

Early Warning Systems ◽

Warning Systems ◽

Surveillance Networks ◽

Spanish Flu ◽

The 1950S ◽

National Networks ◽

First Time

Abstract:Since the 1950s, national networks for the surveillance of influenza have been progressively implemented in several countries. New epidemiological arguments have triggered changes in order to increase the sensitivity of existent early warning systems and to strengthen the communications between European networks. The WHO project CARE Telematics, which collects clinical and virological data of nine national networks and sends useful information to public health administrations, is presented. From the results of the 1993-94 season, the benefits of the system are discussed. Though other telematics networks in this field already exist, it is the first time that virological data, absolutely essential for characterizing the type of an outbreak, are timely available by other countries. This argument will be decisive in case of occurrence of a new strain of virus (shift), such as the Spanish flu in 1918. Priorities are now to include other existing European surveillance networks.

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