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

Pulkkinen, Seppo; Nerini, Daniele; Hortal, Andrés A. Pérez; Velasco-Forero, Carlos; Seed, Alan; Germann, Urs; Foresti, Loris

doi:10.5194/gmd-2019-94

Cited by 5 publications

(6 citation statements)

References 44 publications

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“…The pysteps library version 1.0 is available at https://doi.org/10.5281/zenodo.2631911 (Pulkkinen et al, 2019). The scripts to run the experiments and produce the figures of this paper are available at https://doi.org/10.5281/zenodo.3458712 (Pulkkinen et al, 2019). The radar data are available upon request.…”

Section: Potential Extensions and Applications Of Pystepsmentioning

confidence: 99%

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

Pulkkinen

Nerini

Hortal³

et al. 2019

Geosci. Model Dev.

Self Cite

159

184

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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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Section: Potential Extensions and Applications Of Pystepsmentioning

confidence: 99%

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

Pulkkinen

Nerini

Hortal³

et al. 2019

Geosci. Model Dev.

Self Cite

159

184

View full text Add to dashboard Cite

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“…We use the pySTEPS implementation of the FSS (Pulkkinen et al, 2019), which performs the averaging using square convolutions with zero-padding.…”

Section: Fractions Skill Score (Fss)mentioning

confidence: 99%

“…A histogram sloping to the left or right is also indicative of conditional under-forecasting or over-forecasting respectively (Hamill, 2001;Wilks, 2019a). In this work we use the pySTEPS implementation of the rank histogram (Pulkkinen et al, 2019).…”

Section: Rank Histogrammentioning

confidence: 99%

“…The code for the GAN and quantile mapping used in this paper is available at https:// github.com/bobbyantonio/downscaling-cgan; this code was forked from the code in L. Harris et al (2022). All experiments in this paper were performed within TensorFlow 2.7.0, and some of the analysis in this work utilised the PySteps package (Pulkkinen et al, 2019). The ECMWF IFS forecasts can be obtained through MARS, for which aca-…”

Section: Data Availability Statementmentioning

confidence: 99%

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Postprocessing East African rainfall forecasts using a generative machine learning model

Antonio,

McRae,

MacLeod

et al. 2024

Preprint

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Existing weather models are known to have poor skill at forecasting rainfall over East Africa, where there are regular threats of drought and floods. Improved forecasts could reduce the effects of these extreme weather events and provide significant socioeconomic benefits to the region. We present a novel machine learning-based method to improve precipitation forecasts in East Africa, using postprocessing based on a conditional generative adversarial network (cGAN). This addresses the challenge of realistically representing tropical rainfall, where convection dominates and is poorly simulated in conventional global forecast models. We postprocess hourly forecasts made by the European Centre for Medium-Range Weather Forecasts Integrated Forecast System at 6-18h lead times, at $0.1^{\circ}$ resolution. We combine the cGAN predictions with a novel neighbourhood version of quantile mapping, to integrate the strengths of machine learning and conventional postprocessing. Our results indicate that the cGAN substantially improves the diurnal cycle of rainfall, and improves predictions up to the $99.9^{\text{th}}$ percentile ($\sim 10 \text{mm}/\text{hr}$). This improvement extends to the 2018 March–May season, which had extremely high rainfall, indicating that the approach has some ability to generalise to more extreme conditions. We explore the potential for the cGAN to produce probabilistic forecasts and find that the spread of this ensemble broadly reflects the predictability of the observations, but is also characterised by a mixture of under- and over-dispersion. Overall our results demonstrate how the strengths of machine learning and conventional postprocessing methods can be combined, and illuminate what benefits machine learning approaches can bring to this region.

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“…Both products are provided by the European Organisation for the Exploitation of Meteorological Satellites (EUMET-SAT, 2022a, 2022b) Nowcasting Satellite Applications Facility (NWCSAF, 2022). Separately, Burton et al (2022) apply a simple optical flow method based on pysteps (Pulkkinen et al, 2019) to advect CRR images forward in time, and show that the extrapolated CRR has skill for 2 hours at 10-km scales and 4 hours at 200-km scales. All described methods use recent observations to deterministically extrapolate the future tracks of identified convection but do not allow for storm initiation, and decay.…”

Section: Introductionmentioning

confidence: 99%

Nowcasting convective activity for the Sahel: A simple probabilistic approach using real‐time and historical satellite data on cloud‐top temperature

Anderson,

Cole,

Klein

et al. 2024

Quart J Royal Meteoro Soc

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Flash flooding from intense rainfall frequently results in major damage and loss of life across Africa. In the Sahel, automatic prediction and warning systems for these events, driven by Mesoscale Convective Systems (MCSs), are limited, and Numerical Weather Prediction (NWP) forecasts continue to have little skill. The ground observation network is also sparse, and very few operational meteorological radars exist to facilitate conventional nowcasting approaches.Focusing on the western Sahel, we present a novel approach for producing probabilistic nowcasts of convective activity out to 6 h ahead, using the current location of observed convection. Convective parts of the MCS, associated with extreme and heavy precipitation, are identified from 16 years of Meteosat Second Generation thermal‐infrared cloud‐top temperature data, and an offline database of location‐conditioned probabilities calculated. From this database, real‐time nowcasts can be quickly produced with minimal calculation. The nowcasts give the probability of convection occurring within a square neighbourhood surrounding each grid point, accounting for the inherent unpredictability of convection at small scales.Compared to a climatological reference, formal verification approaches show the nowcasts to be skilful at predicting convective activity over the study region, for all times of day and out to the 6‐h lead time considered. The nowcasts are also skilful at capturing extreme 24 h rain gauge accumulations over Dakar, Senegal. The nowcast skill peaks in the afternoon, with a minimum in the evening. We find that the optimum neighbourhood size varies with lead time, from 10 km at the nowcast origin to around 100 km at a 6‐h lead time.This simple and skilful nowcasting method could be highly valuable for operational warnings across West Africa and other regions with long‐lived thunderstorms, and help to reduce the impacts from heavy rainfall and flooding.This article is protected by copyright. All rights reserved.

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

Cited by 5 publications

References 44 publications

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

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

Postprocessing East African rainfall forecasts using a generative machine learning model

Nowcasting convective activity for the Sahel: A simple probabilistic approach using real‐time and historical satellite data on cloud‐top temperature

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