Dealing with non-stationarity in sub-daily stochastic rainfall models

Benoît, Lionel; Vrac, Mathieu; Mariethoz, Grégoire

doi:10.5194/hess-2018-273

Cited by 6 publications

(7 citation statements)

References 27 publications

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“…Most downscaling methods also consider a constant bias or transfer function over time (Fu et al, 2018), that may not be representative of the changes in large/small scale atmospheric interactions (Van Uytven et al, 2020). In addition, recent studies suggest that a direct scaling between daily and hourly precipitation intensities may not be valid under climate change (Ganguli & Coulibaly, 2017; Innocenti et al, 2019; Martel et al, 2020), requiring more advanced methods able to represent sub‐daily rainfall fields (Benoit et al, 2018).…”

Section: Cprcm Benefits For Impact Studiesmentioning

confidence: 99%

Convection‐permitting modeling with regional climate models: Latest developments and next steps

Lucas‐Picher

Argüeso

Brisson

et al. 2021

WIREs Climate Change

121

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Approximately 10 years ago, convection-permitting regional climate models (CPRCMs) emerged as a promising computationally affordable tool to produce fine resolution (1-4 km) decadal-long climate simulations with explicitly resolved deep convection. This explicit representation is expected to reduce climate projection uncertainty related to deep convection parameterizations found in most climate models. A recent surge in CPRCM decadal simulations over larger domains, sometimes covering continents, has led to important insights into CPRCM advantages and limitations. Furthermore, new observational gridded datasets with fine spatial and temporal ($1 km; $1 h) resolutions have leveraged additional knowledge through evaluations of the added value of CPRCMs. With an improved coordination in the frame of ongoing international initiatives, the production of ensembles of CPRCM simulations is expected to provide more robust climate projections and a better identification of their associated uncertainties. This review paper presents an overview of the methodology to produce CPRCM simulations and the latest research on the related added value in current and future climates. Impact studies that are already taking advantage of these new CPRCM simulations are highlighted. This review paper ends by proposing next steps that could be accomplished to continue exploiting the full potential of CPRCMs.

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Section: Cprcm Benefits For Impact Studiesmentioning

confidence: 99%

Convection‐permitting modeling with regional climate models: Latest developments and next steps

Lucas‐Picher

Argüeso

Brisson

et al. 2021

WIREs Climate Change

121

View full text Add to dashboard Cite

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“…Here, rain classification is performed following the approach proposed by Benoit et al. (2018b), and the number of classes is set to two to ensure that each rain type encompasses enough data for robust calibration of the rainfall models. Briefly, the rain classification procedure is based on the classification of raw radar images (i.e., not adjusted on rain gauge observations) according to their space‐time statistics.…”

Section: Example Data Setmentioning

confidence: 99%

“…For instance, it is not rare to observe a rather convective inception for an otherwise stratiform event, or to notice some convective echos within an image classified as stratiform due to its overall structure. However, the small extent of the area of interest and the event‐based processing adopted here are deemed to minimize the impact of rain type mixtures on catchment scale rainfall behavior (Benoit et al., 2018b). This can be checked visually in Figure 1b where catchment average rain intensity time series are displayed using a rain type‐related color code.…”

Section: Example Data Setmentioning

confidence: 99%

Radar and Rain Gauge Data Fusion Based on Disaggregation of Radar Imagery

Benoît

2021

Water Resources Research

Self Cite

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Remotely sensed radar data and in situ rain gauge observations provide complementary information about rainfall. Specifically, radar data inform rainfall location and extent, while rain gauges provide accurate observations of the local rain intensity and its temporal variability. Drawing on the respective strengths of these two data sources, radar and rain gauge data fusion is becoming more and more common to derive both accurate and comprehensive rain estimates. However, combined precipitation estimates are often restricted to a resolution of 1 km in space and 1 h in time. In this paper, I propose to use radar data disaggregation to merge radar and rain gauge observations at a high space‐time resolution. To this end, a stochastic rainfall model is first trained on rain gauge observations and then combined with area‐to‐point geostatistical simulations to simultaneously: (i) disaggregate radar data, (ii) honor rain gauge observations, and (iii) reproduce local rain statistics. The proposed approach is applied to summer rain data collected in a small Alpine catchment, and data fusion provides rainfall reanalysis results at a resolution of 200 m in space and 2 min in time, including remote locations where in‐situ observation was not feasible.

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“…Sub-hourly quantitative precipitation estimates derived from weather radars are central to many meteorological and hydrological applications, because their high spatial and temporal resolution captures detailed precipitation structures. Hourly and sub-hourly radar-based precipitation information is used to study meso-scale atmospheric structures and dynamics such as convective storms (e.g., Goudenhoofdt and Delobbe, 2013;Friedrich et al, 2016), for the (statistical) analysis of short-duration high intensity precipitation events (e.g., Westra et al, 2014), for the characterization of rainfall space-time statistics (e.g., Benoit et al, 2018), for the verification of highresolution climate model simulations (Chan et al, 2014;Prein et al, 2017), as input fields for hydrological modelling of flash flood events (Liechti et al, 2013;Smith et al, 2014;Braud et al, 2018), for the analysis of mud flow and landslide events (Guzzetti et al, 2008;Brunetti et al, 2015) and for nowcasting applications (e.g., Romang et al, 2011;Panziera et al, 2016;Brönnimann et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

A multi‐year assessment of sub‐hourly gridded precipitation for Switzerland based on a blended radar—Rain‐gauge dataset

Barton

Sideris

Raupach

et al. 2020

Intl Journal of Climatology

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Blended high-resolution sub-hourly precipitation fields are increasingly needed for nowcasting and automatic warnings systems. Here we present a 7-year (2012-2018) assessment of (5-min) precipitation characteristics for the topographically complex region of Switzerland. We use CombiPrecip, a blended radar-rain-gauge product that provides high resolution (1 km 2) hourly precipitation fields. Five-minute fields (CPCD), fine enough to disentangle even the shortest convective events from longer-lasting precipitation, are obtained by temporally disaggregating these hourly fields. A rain-gauge verification of the sub-hourly precipitation fields shows that CPCD outperforms the radar-only estimates both in terms of quantitative error and in detecting wet/extreme periods, making CPCD sufficiently accurate to be used with confidence as input fields for hydrological modelling or for regional model validation. The quantitative error of CombiPrecip at the 60 (10)-min timescale , as expressed by the scatter, is 40% (22%) lower than that of the radar-only product. Radar-gauge discrepancies are large and the quantitative error of CPCD increases by 30% when decreasing aggregation times from 60 to 10 min. Although summertime 5-min precipitation extremes are strongest and more frequent on average south of the Alps, where they contribute to approximately 10% of the seasonal total precipitation, the most intense extremes are observed over the Jura Mountains in the afternoon and the highest wet 5-min frequency in the Prealps around the same time. Intense precipitation is more evenly distributed throughout the day south of the Alps. Extreme intensity and frequency peaks 2 hr earlier in the Prealps, the Jura Mountains and the Alps (~1600 UTC) compared to the Swiss Plateau (~1800 UTC). Extremes are less intense and frequent at high elevations (>2,000 MASL) than over the Swiss Plateau (<800 MASL).

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Dealing with non-stationarity in sub-daily stochastic rainfall models

Cited by 6 publications

References 27 publications

Convection‐permitting modeling with regional climate models: Latest developments and next steps

Convection‐permitting modeling with regional climate models: Latest developments and next steps

Radar and Rain Gauge Data Fusion Based on Disaggregation of Radar Imagery

A multi‐year assessment of sub‐hourly gridded precipitation for Switzerland based on a blended radar—Rain‐gauge dataset

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