The Probability Distribution of Daily Rainfall in the United States

Hanson, Lars S.; Vogel, Richard M.

doi:10.1061/40976(316)585

Cited by 40 publications

(28 citation statements)

References 27 publications

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“…This is in line with Fig. 2 of Hanson and Vogel (2008), which shows that when the skewness of daily rainfall across the US is well estimated by the lognormal distribution, then the kurtosis is much overestimated. Note that Papalexiou et al (2013) did not find such ill-fitted tails with the lognormal distribution but in their case fitting is made on the tail (i.e.…”

supporting

confidence: 75%

An objective cross-validation framework for mapping rainfall hazard based on rain gauge data

Blanchet

Paquet²,

Ayar

et al. 2018

Preprint

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Abstract. We propose an objective framework for estimating rainfall cumulative distribution function within a region when data are only available at rain gauges. Our methodology is based on the evaluation of several goodness-of-fit scores in a crossvalidation framework, allowing to assess goodness-of-fit of the full distribution but with a particular focus on its tail. Crossvalidation is applied both to select the most appropriate statistical distribution at station locations and to validate the mapping of these distributions. Our methodology is applied to daily rainfall in the Ardèche catchment in South of France, a 2260 km 2 5 catchment with strong disparities in rainfall distribution. Results show preference for a mixture of Gamma distribution over seasons and weather patterns, with parameters interpolated with thin plate spline across this region. However the framework presented in this paper is general and could be likewise applied in any region, with possibly different conclusion depending on the subsequent rainfall processes.

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supporting

confidence: 75%

An objective cross-validation framework for mapping rainfall hazard based on rain gauge data

Blanchet

Paquet²,

Ayar

et al. 2018

Preprint

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“…Climatologically, temperature PDFs generally obey normal distributions, while daily precipitation PDFs generally obey two-parameter gamma distributions (Harmel et al, 2002;Hanson and Vogel, 2008). The formulas for precipitation are represented by:…”

Section: Bias Correctionmentioning

confidence: 99%

The Polar WRF Downscaled Historical and Projected Twenty-First Century Climate for the Coast and Foothills of Arctic Alaska

et al. 2018

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Climate change is most pronounced in the northern high latitude region. Yet, climate observations are unable to fully capture regional-scale dynamics due to the sparse weather station coverage, which limits our ability to make reliable climate-based assessments. A set of simulated data products was therefore developed for the North Slope of Alaska through a dynamical downscaling approach. The polar-optimized Weather Research and Forecast (Polar WRF) model was forced by three sources: The ERA-interim reanalysis data (for 1979-2014), the Community Earth System Model 1.0 (CESM1.0) historical simulation (for 1950-2005), and the CESM1.0 projected (for 2006-2100) simulations in two Representative Concentration Pathways (RCP4.5 and RCP8.5) scenarios. Climatic variables were produced in a 10-km grid spacing and a 3-h interval. The ERA-interim forced WRF (ERA-WRF) proves the value of dynamical downscaling, which yields more realistic topographical-induced precipitation and air temperature, as well as corrects underestimations in observed precipitation. In summary, dry and cold biases to the north of the Brooks Range are presented in ERA-WRF, while CESM forced WRF (CESM-WRF) holds wet and warm biases in its historical period. A linear scaling method allowed for an adjustment of the biases, while keeping the majority of the variability and extreme values of modeled precipitation and air temperature. CESM-WRF under RCP 4.5 scenario projects smaller increase in precipitation and air temperature than observed in the historical CESM-WRF product, while the CESM-WRF under RCP 8.5 scenario shows larger changes. The fine spatial and temporal resolution, long temporal coverage, and multi-scenario projections jointly make the dataset appropriate to address a myriad of physical and biological changes occurring on the North Slope of Alaska.

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“…For estimating the statistics of return periods, the threeparameter generalized extreme value distribution is fitted using the method of Lmoments, and the spatial patterns of heavy and very heavy rainfall events across the islands are mapped separately based on the above three methods for annual maximum of daily rainfall data. Hanson and Vogel (2008) have studied the probability distribution of daily rainfall in the United States to represent the precipitation depth at various durations which has long been a focus of interest in hydrology. Sharma and Singh (2010) analyzed the daily maximum rainfall data of Pantnagar, India for a period of 37 years for annually, seasonally, monthly and weekly, and the best fitted probability distribution is identified using the least square method among the 16 compared distributions.…”

Section: Introductionmentioning

confidence: 99%