A Reduced-Space Ensemble Kalman Filter Approach for Flow-Dependent Integration of Radar Extrapolation Nowcasts and NWP Precipitation Ensembles

Nerini, Daniele; Foresti, Loris; Leuenberger, Daniel; Robert, Sylvain; Germann, Urs

doi:10.1175/mwr-d-18-0258.1

Cited by 31 publications

(30 citation statements)

References 52 publications

(53 reference statements)

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“…In locations where NWP performs well, the merging takes place faster than in areas where the performance is poor. Moreover, a complex scheme that attempts to optimize merging, taking into account the spread of the NWP ensemble and nowcasting ensemble, is currently being investigated in MeteoSwiss (Nerini et al ., ). Machine‐learning capabilities have already been implemented within NowPrecip, but not tested entirely. The localized architecture of NowPrecip makes it naturally machine‐learning ready, unlatching the potential for using extrapolation schemes more complex than persistence.…”

Section: Future Workmentioning

confidence: 97%

NowPrecip: localized precipitation nowcasting in the complex terrain of Switzerland

Sideris

Foresti

Nerini

et al. 2020

Quart J Royal Meteoro Soc

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The new precipitation nowcasting system “NowPrecip” is introduced and its methodology is described in detail. NowPrecip is an area‐tracking application, able to generate ensembles of precipitation nowcasts. It supports the paradigm of seamless forecasting: nowcasts start from the most recent radar observation and evolve smoothly, through merging, towards the numerical weather prediction (NWP) ensemble members. NowPrecip relies on the following three pillars. (a) A geostatistics‐based optical flow algorithm, named “NowTrack”, able to determine the scales of interest automatically and equipped with an outlier control scheme. Such a scheme is particularly useful in capturing situations like fine‐scale rotation accurately. (b) A localized architecture motivated by the need to address scenarios where distinct rainfall patterns characterize different parts of the domain. This is mostly relevant for complex terrains like Switzerland, but is also useful for areal nowcasting operating on extensive regions. (c) Techniques to compute and incorporate localized growth and decay into the computation of nowcasts. This is especially useful for Alpine terrain, where there is often systematic growth and decay associated with orographic features. The growth and decay factors are based on analysis of the available NWP product, allowing for a more efficient coupling between the nowcasting sequence and the numerical model output. The main scope of this work is to present the methodology of NowPrecip in detail, but a verification of several representative events is also provided, using both deterministic and probabilistic measures.

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Section: Future Workmentioning

confidence: 97%

NowPrecip: localized precipitation nowcasting in the complex terrain of Switzerland

Sideris

Foresti

Nerini

et al. 2020

Quart J Royal Meteoro Soc

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“…Pysteps already provides a quite comprehensive library, but still misses two important modules: 1) a module to generate QPE ensembles characterizing the radar measurement uncertainty (e.g. Jordan et al, 2003;Germann et al, 2009), and 2) a module for seamless blending of precipitation fields from different data sources, such as radar nowcasts and NWP forecasts (Bowler et al, 2006;Nerini et al, 2019), radar, satellite and NWP data (Renzullo et al, 2017).…”

Section: Potential Extensions and Applications Of Pystepsmentioning

confidence: 99%

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

Pulkkinen¹,

Nerini²,

Hortal³

et al. 2019

Preprint

Self Cite

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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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“…For very short-range forecasts (0 to 6 h), numerous studies have been and are being carried out to improve forecasting using radar data. Blending of fields extrapolated from radar observations and forecasts from numerical weather models is one of the most widely used techniques to combine short-term extrapolation of observations with numerical forecasts to produce a 0-6 h nowcasting forecast (Golding 1998;Pierce et al 2001;Wong et al 2009;Atencia et al 2010;Haiden et al 2011;Bowler et al 2006;Nerini et al 2019). These techniques allow for the limiting of the spin-up problems of numerical models, achieving a continuous transition at the critical period around 1-3 h, when the accuracy of forecasting with extrapolation methods is drastically reduced.…”

Section: Introductionmentioning

confidence: 99%

Forecast of precipitation for the 1994 flood in Piedmont: performance of an ensemble system at convection-permitting resolution

Cerenzia

Pincini

Paccagnella

et al. 2020

Bull. of Atmos. Sci.& Technol.

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The major flood that affected the Piedmont region in Italy in November 1994 is re-forecast after 25 years in ensemble mode at the convection-permitting resolution of 2.2 km using the regional model COSMO. The performance of the probabilistic forecast of precipitation is assessed against rain-gauge observations, also in comparison with the driver system, i.e., the probabilistic re-forecast produced by ECMWF based on the operational IFS (Cycle 46r1) at grid spacings of 18 km. The convection-allowing system dynamically downscales the ECMWF ensemble and includes an explicit treatment of deep convection. Results indicate that both systems can predict up to 4 days in advance the timing and the spatial patterns of the precipitation, although with higher confidence for the convection-resolving system. The benefit of high resolution is shown mainly in the prediction of intense precipitation and in terms of correct amounts and locations, and confidence of occurrence (at day 3, the estimated probability of exceedance of 200 mm was higher than 90% over areas actually hit by such rainfall amounts). Additionally, convection-permitting resolution improves the representation of orographic precipitation, reducing the upwind precipitation displacement typical of coarser models and including the possible development of strong convection episodes embedded in the large-scale-forced orographic rise. For the high-resolution ensemble, the spread indicates large uncertainty at the local scale, mainly in defining the flow tendency to flank or flow over each mountain.

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A Reduced-Space Ensemble Kalman Filter Approach for Flow-Dependent Integration of Radar Extrapolation Nowcasts and NWP Precipitation Ensembles

Cited by 31 publications

References 52 publications

NowPrecip: localized precipitation nowcasting in the complex terrain of Switzerland

NowPrecip: localized precipitation nowcasting in the complex terrain of Switzerland

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

Forecast of precipitation for the 1994 flood in Piedmont: performance of an ensemble system at convection-permitting resolution

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