Quantitative Precipitation Estimation of Extremes in CONUS With Radar Data

Molter, Edward; Collins, William D.; Risser, Mark D.

doi:10.1029/2021gl094697

Cited by 6 publications

(7 citation statements)

References 57 publications

(91 reference statements)

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“…First, compared to a single-station analysis that estimates extreme statistics of daily precipitation at each station independently of all others, the R19 product has small root mean squared error in 20-year return values (ranging from 5.5mm in DJF to 11.2mm in JJA as a CONUS average; much of this "error" is smoothing over observational uncertainty) and significantly smaller bootstrap standard errors (ranging from a reduction of 40% in DJF to more than 50% in JJA). More importantly, Molter et al (2021) determine that the statistics of seasonal extreme rainfall across CONUS vary only minimally on spatial scales smaller than 100 km (especially east of the Rocky Mountains), verifying that the R19 product can effectively describe the spatial variability of extreme precipitation since the average spacing of the underlying GHCN station data is ∼ 27 km. Furthermore, Molter et al (2021) find that R19 accurately represents corresponding statistics of extreme precipitation estimated from the NEXRAD Stage IV radar-based daily precipitation product, which has a horizontal resolution of 4 km.…”

Section: Observational Data Productsmentioning

confidence: 75%

“…More importantly, Molter et al (2021) determine that the statistics of seasonal extreme rainfall across CONUS vary only minimally on spatial scales smaller than 100 km (especially east of the Rocky Mountains), verifying that the R19 product can effectively describe the spatial variability of extreme precipitation since the average spacing of the underlying GHCN station data is ∼ 27 km. Furthermore, Molter et al (2021) find that R19 accurately represents corresponding statistics of extreme precipitation estimated from the NEXRAD Stage IV radar-based daily precipitation product, which has a horizontal resolution of 4 km. As a result, the R19 product can safely be considered the "ground truth" for point estimates of the statistics of extreme precipitation, particularly in the eastern United States.…”

Section: Observational Data Productsmentioning

confidence: 75%

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Identifying and correcting biases in localized downscaling estimates of daily precipitation return values

et al. 2021

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Extreme precipitation events are a major cause of economic damage and disruption, and need to be addressed for increasing resilience to a changing climate, particularly at the local scale. Practitioners typically want to understand local changes at spatial scales much smaller than the native resolution of most Global Climate Models, for which downscaling techniques are used to translate planetary-to-regional scale change information to local scales. However, users of statistically downscaled outputs should be aware that how the observational data used to train the statistical models is constructed determines key properties of the downscaled solutions. Specifically for one such downscaling approach, when considering seasonal return values of extreme daily precipitation, we find that the Localized Constructed Analogs (LOCA) method produces a significant low bias in return values due to choices made in building the observational data set used to train LOCA. The LOCA low biases in daily extremes are consistent across event extremity, but do not degrade the overall performance of LOCA-derived changes in extreme daily precipitation. We show that the low (negative) bias in daily extremes is a function of a time-of-day adjustment applied to the training data and the manner of gridding daily precipitation data. The effects of these choices are likely to affect other downscaling methods trained with observations made in the same way. The results developed here show that efforts to improve resilience at the local level using extreme precipitation projections can benefit from using products specifically created to properly capture the statistics of extreme daily precipitation events.

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Section: Observational Data Productsmentioning

confidence: 75%

Section: Observational Data Productsmentioning

confidence: 75%

Identifying and correcting biases in localized downscaling estimates of daily precipitation return values

et al. 2021

Self Cite

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“…(2019), Molter et al. (2021) and de Valk and Overeem (2022)). We develop extreme value methods (see Coles (2001)) for rainfall frequency analysis using both annual maximum and peaks‐over‐threshold formulations, as detailed in Section 2.…”

Section: Introductionmentioning

confidence: 96%

“…Our approach to rainfall frequency analysis using gridded radar data sets is to treat observations from each grid as though they were point observations from a rain gauge (see, for example, Allen and DeGaetano (2005), , Eldardiry et al (2015), Ghebreyesus and Sharif (2021), Marra et al (2017), McGraw et al (2019, Molter et al (2021) andde Valk andOvereem (2022)). We develop extreme value methods (see Coles (2001)) for rainfall frequency analysis using both annual maximum and peaks-over-threshold formulations, as detailed in Section 2.…”

Section: Introductionmentioning

confidence: 99%

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Rainfall Frequency Analysis Based on Long‐Term High‐Resolution Radar Rainfall Fields: Spatial Heterogeneities and Temporal Nonstationarities

Smith,

Baeck,

Miller

et al. 2024

Water Resources Research

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Rainfall frequency analysis methods are developed and implemented based on high‐resolution radar rainfall data sets, with the Baltimore metropolitan area serving as the principal study region. Analyses focus on spatial heterogeneities and time trends in sub‐daily rainfall extremes. The 22‐year radar rainfall data set for the Baltimore study region combines reflectivity‐based rainfall fields during the period from 2000 to 2011 and polarimetric rainfall fields for the period from 2012 to 2021. Rainfall frequency analyses are based on non‐stationary formulations of peaks‐over‐threshold and annual peak methods. Increasing trends in short‐duration rainfall extremes are inferred from both peaks‐over‐threshold and annual peak analyses for the period from 2000 to 2021. There are pronounced spatial gradients in short‐duration rainfall extremes over the study region, with peak values of rainfall between Baltimore City and Chesapeake Bay. Spatial gradients in 100‐year, 1 hr rainfall over 20 km length scale are comparable to time trends over 20 years. Rainfall analyses address the broad challenge of assessing changing properties of short‐duration rainfall in urban regions. Analyses of high‐resolution rainfall fields show that sub‐daily rainfall extremes are only weakly related to daily extremes, pointing to difficulties in inferring climatological properties of sub‐daily rainfall from daily rainfall analyses. Changing measurement properties are a key challenge for application of radar rainfall data sets to detection of time trends. Mean field bias correction of radar rainfall fields using rain gauge observations is an important tool for improving radar rainfall fields and provides a useful tool for addressing problems associated with changing radar measurement properties.

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Assessing irrigation mitigating drought impacts on crop yields with an integrated modeling framework

Deb

Moradkhani

Han

et al. 2022

Journal of Hydrology

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Quantitative Precipitation Estimation of Extremes in CONUS With Radar Data

Abstract: Accurate measurement of the amount of precipitation that falls within a given region and time period is crucial for environmental modeling (e.g.

Cited by 6 publications

References 57 publications

Identifying and correcting biases in localized downscaling estimates of daily precipitation return values

Identifying and correcting biases in localized downscaling estimates of daily precipitation return values

Rainfall Frequency Analysis Based on Long‐Term High‐Resolution Radar Rainfall Fields: Spatial Heterogeneities and Temporal Nonstationarities

Assessing irrigation mitigating drought impacts on crop yields with an integrated modeling framework

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