On using principal components to represent stations in
                        empirical–statistical downscaling

Benestad, Rasmus E.; Chen, Deliang; Mezghani, Abdelkader; Fan, Lijun; Parding, Kajsa M.

doi:10.3402/tellusa.v67.28326

Cited by 27 publications

(16 citation statements)

References 25 publications

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“…The regression constant β 0 did not show a clear dependency with timescale, and was of the order 3.9-4.7 mm/day and small compared to the return-values themselves (25-200 mm/day). This systematic bias is consistent with a previous comparison against corresponding return-values provided by general extreme value distribution (GEV) [32] and the proposition that the exponential distribution has a thinner tail than the precipitation [15].…”

Section: Resultssupporting

confidence: 91%

A simple equation to study changes in rainfall statistics

Benestad

Parding

Erlandsen

et al. 2019

Environ. Res. Lett.

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We test an equation for the probability of heavy 24 h precipitation amounts Pr(X>x) as a function of the wet-day frequency and the wet-day mean precipitation. The expression was evaluated against 9817 daily rain gauge records world-wide and was subsequently used to derive mathematical expressions for different rainfall statistics in terms of the wet-day frequency and the wet-day mean precipitation. This framework comprised expressions for probabilities, mean, variance, and return-values. We differentiated these statistics with respect to time and compared them to trends in number of rainy days and the mean rainfall intensity based on 1875 rain gauge records with more than 50 years of valid data over the period 1961-2018. The results indicate that there has been a general increase in the probability of precipitation exceeding 50 mm/day. The main cause for this increase has been a boost in the intensity of the rain, but there were also some cases where it has been due to more rainy days. In some limited regions there has also been an increase in Pr(X>50 mm/day) that coincided with a decrease in the number of rainy days. We also found a general increasing trend in the variance and the 10-year return-value over 1961-2018 due to increasing wet-day frequency and wet-day mean precipitation.

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Section: Resultssupporting

confidence: 91%

A simple equation to study changes in rainfall statistics

Benestad

Parding

Erlandsen

et al. 2019

Environ. Res. Lett.

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“…In this contribution this method has been trained on a monthly basis (using monthly aggregated data) in the traditional way, but this package is more flexible and it is typically calibrated differently when applied to GCM data. In that case, common EOFs (representative of both reanalysis and GCMs, Benestad, Chen, Mezghani, Fan, & Parding, 2015) are used as predictors and normally PCA are used as predictands for groups of stations which are subject to similar weather phenomena (multi-site application), although the method can be also applied to downscale more general information, such as the occurrence of intense local 24-hr precipitation events over seasonal intervals . Implementation: ESD is implemented in the esd R package .…”

Section: A 2 Pp Methodsmentioning

confidence: 99%

An intercomparison of a large ensemble of statistical downscaling methods over Europe: Results from the VALUE perfect predictor cross‐validation experiment

Gutiérrez

Maraun

Widmann

et al. 2018

Intl Journal of Climatology

196

254

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VALUE is an open European collaboration to intercompare downscaling approaches for climate change research, focusing on different validation aspects (marginal, temporal, extremes, spatial, process‐based, etc.). Here we describe the participating methods and first results from the first experiment, using “perfect” reanalysis (and reanalysis‐driven regional climate model (RCM)) predictors to assess the intrinsic performance of the methods for downscaling precipitation and temperatures over a set of 86 stations representative of the main climatic regions in Europe. This study constitutes the largest and most comprehensive to date intercomparison of statistical downscaling methods, covering the three common downscaling approaches (perfect prognosis, model output statistics—including bias correction—and weather generators) with a total of over 50 downscaling methods representative of the most common techniques. Overall, most of the downscaling methods greatly improve (reanalysis or RCM) raw model biases and no approach or technique seems to be superior in general, because there is a large method‐to‐method variability. The main factors most influencing the results are the seasonal calibration of the methods (e.g., using a moving window) and their stochastic nature. The particular predictors used also play an important role in cases where the comparison was possible, both for the validation results and for the strength of the predictor–predictand link, indicating the local variability explained. However, the present study cannot give a conclusive assessment of the skill of the methods to simulate regional future climates, and further experiments will be soon performed in the framework of the EURO‐CORDEX initiative (where VALUE activities have merged and follow on). Finally, research transparency and reproducibility has been a major concern and substantive steps have been taken. In particular, the necessary data to run the experiments are provided at http://www.value-cost.eu/data and data and validation results are available from the VALUE validation portal for further investigation: http://www.value-cost.eu/validationportal.

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“…Trend analysis for different short‐term periods 1902–1935, 1936–1970 and 1971–2005 as well as one long‐term period 1902–2005 was performed using R software (R Core Team, ) specifically through ‘esd’ (Benestad et al ., ) and ‘wq’ (Jassby and Cloern, ) packages. Similarly, ‘evd’ (Stephenson, ) and ‘stats’ (R Core Team, 2016) packages were used for the probability analysis of rainfall.…”

Section: Methodsmentioning

confidence: 71%

Analysis of winter rainfall change statistics over the Western Himalaya: the influence of internal variability and topography

Meher

Das

Benestad

et al. 2017

Intl Journal of Climatology

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This study investigated a detailed picture of some statistical properties of winter rainfall on monthly and seasonal basis over the Western Himalaya (WH) using rain gauge station data during 1902–2005. Results showed a significant shift in the rainfall and rainy‐day pattern during 1960–1970 in most of the stations over the WH. State‐wise analysis indicated that Himachal Pradesh has received more rainfall (50%) and rainy day (∼57%) compared to the state of Uttarakhand. Station data revealed that past winter rainfall has changed in a periodic manner with averaged periodicity (epoch) of 20 years in which 1920–1940 was characterized by excess rainfall period while 1970–1990 was depicted as deficient rainfall period. Most of the rain gauge stations showed declining trends of rainfall and rainy days while the intensity of rainfall indicated increasing trends during the 1902–2005 period. Regional‐averaged winter rainfall and rainy day have shown a significant declining trend during 1902–2005. It was found that the rainfall trends are mainly arising due to internal variability although short‐term trends (signal) override the internal variability (noise) in some cases. Among five goodness‐of‐fit distribution, the generalized extreme value (GEV) has emerged as the best fit for the majority of the station locations. Estimated monthly assured rainfall at 75% probability level revealed a range of 100–150 mm, which will be useful for agricultural activities over WH region. The impact of topography on rainfall and rainy day indicated increasing trends up to a certain elevation (∼1356 m), thereafter they showed decreasing trends that has been described through a second‐order polynomial fit.

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On using principal components to represent stations in empirical–statistical downscaling

Cited by 27 publications

References 25 publications

A simple equation to study changes in rainfall statistics

A simple equation to study changes in rainfall statistics

An intercomparison of a large ensemble of statistical downscaling methods over Europe: Results from the VALUE perfect predictor cross‐validation experiment

Analysis of winter rainfall change statistics over the Western Himalaya: the influence of internal variability and topography

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