2018
DOI: 10.5194/hess-2018-273
|View full text |Cite
Preprint
|
Sign up to set email alerts
|

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

Abstract: Abstract.Understanding the stationarity properties of rainfall is critical when using stochastic weather generators. Rainfall stationarity means that the statistics being accounted for remain constant over a given period, which is required for both inferring model parameters and simulating synthetic rainfall. Despite its critical importance, the stationarity of precipitation statistics is often regarded as a subjective choice whose examination is left to the judgement of the modeler. It is therefore desirable … Show more

Help me understand this report
View published versions

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
2
1

Citation Types

0
7
0

Year Published

2020
2020
2021
2021

Publication Types

Select...
4
1

Relationship

1
4

Authors

Journals

citations
Cited by 6 publications
(7 citation statements)
references
References 27 publications
0
7
0
Order By: Relevance
“…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%
“…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%
“…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%
“…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%