An official website of the United States government
Abstract. Conventional rainfall frequency analysis faces several limitations. These include difficulty incorporating relevant atmospheric variables beyond precipitation and limited ability to depict the frequency of rainfall over large areas that is relevant for flooding. This study proposes a storm-based model of extreme precipitation frequency based on the atmospheric water balance equation. We developed a storm tracking and regional characterization (STARCH) method to identify precipitation systems in space and time from hourly ERA5 precipitation fields over the contiguous United States from 1951 to 2020. Extreme “storm catalogs” were created by selecting annual maximum storms with specific areas and durations over a chosen region. The annual maximum storm precipitation was then modeled via multivariate distributions of atmospheric water balance components using vine copula models. We applied this approach to estimate precipitation average recurrence intervals for storm areas from 5000 to 100 000 km2 and durations from 2 to 72 h in the Mississippi Basin and its five major subbasins. The estimated precipitation distributions show a good fit to the reference data from the original storm catalogs and are close to the estimates from conventional univariate GEV distributions. Our approach explicitly represents the contributions of water balance components in extreme precipitation. Of these, water vapor flux convergence is the main contributor, while precipitable water and a mass residual term can also be important, particularly for short durations and small storm footprints. We also found that ERA5 shows relatively good water balance closure for extreme storms, with a mass residual on average 10 % of precipitation. The approach can incorporate nonstationarities in water balance components and their dependence structures and can benefit from further advancements in reanalysis products and storm tracking techniques.
more »
« less
This content will become publicly available on April 25, 2026
Projected changes in rare extreme precipitation design values in the United States due to global warming
Abstract There is high confidence that extreme precipitation will increase in most areas if the globe continues to warm. In the US, NOAA Atlas 14 (NA14) is the most authoritative source for heavy rainfall frequency values used in infrastructure planning and design. However, NA14 assumes a stationary climate and uses only historical observations to estimate values. Thus, use of such values for design may lead to underperformance of long-lived infrastructure, thereby placing people and property at increased risk from flooding. Analyses of global climate model (GCM) simulations suggest that projected extreme precipitation changes will be positive nearly everywhere in the US and will be larger for shorter durations, lower annual exceedance probabilities (AEPs), and higher emissions. Herein, we provide adjustment factors that can be applied to observations-based precipitation frequency values to estimate potential future changes under selected global warming levels. These are derived from two statistically downscaled daily precipitation datasets (STAR and LOCA2) developed using modern methods that focus in part on modeling the high tail of the precipitation distribution with a high degree of fidelity. These datasets, each consisting of 16 ensemble members downscaled from a common set of 16 CMIP6 GCMs, provide estimates for durations of daily and longer. The set of adjustment factors are extended using seven models from the NA-CORDEX suite of dynamically downscaled simulations by analyzing the change in adjustment factors from daily to hourly durations. There is an average increase in the adjustment factors of about 1.3. This factor is applied to the daily adjustment factors from STAR and LOCA2 to produce estimates for the hourly duration.
more »
« less
- PAR ID:
- 10650089
- Publisher / Repository:
- Frontiers of Earth Science
- Date Published:
- Journal Name:
- Frontiers of Earth Science
- ISSN:
- 2095-0195
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
null (Ed.)Abstract Sampling intervals of precipitation geochemistry measurements are often coarser than those required by fine-scale hydrometeorological models. This study presents a statistical method to temporally downscale geochemical tracer signals in precipitation so that they can be used in high-resolution, tracer-enabled applications. In this method, we separated the deterministic component of the time series and the remaining daily stochastic component, which was approximated by a conditional multivariate Gaussian distribution. Specifically, statistics of the stochastic component could be explained from coarser data using a newly identified power-law decay function, which relates data aggregation intervals to changes in tracer concentration variance and correlations with precipitation amounts. These statistics were used within a copula framework to generate synthetic tracer values from the deterministic and stochastic time series components based on daily precipitation amounts. The method was evaluated at 27 sites located worldwide using daily precipitation isotope ratios, which were aggregated in time to provide low resolution testing datasets with known daily values. At each site, the downscaling method was applied on weekly, biweekly and monthly aggregated series to yield an ensemble of daily tracer realizations. Daily tracer concentrations downscaled from a biweekly series had average (+/- standard deviation) absolute errors of 1.69‰ (1.61‰) for δ 2 H and 0.23‰ (0.24‰) for δ 18 O relative to observations. The results suggest coarsely sampled precipitation tracers can be accurately downscaled to daily values. This method may be extended to other geochemical tracers in order to generate downscaled datasets needed to drive complex, fine-scale models of hydrometeorological processes.more » « less
-
Abstract The magnitude and frequency of heavy precipitation are expected to increase under warming temperatures caused by climate change. These trends have emerged in observational records but with much larger evidence on a daily rather than a subdaily scale. Here, we quantify long‐term changes in heavy precipitation frequency in the United States using hourly observations in 1949–2020 from 332 gauges. We demonstrate that, when analyzed collectively, the frequencies of heavy precipitation at multiple durations from hourly to daily exhibit an increase that cannot be explained by natural climate variability. Upward trends are significant at ∼20%–40% of the gauges throughout the country except for the coastal western and southeastern regions, with higher percentages for longer durations. We also show that the frequency of hourly heavy precipitation has mainly grown after ∼2000, thus explaining the limited evidence of trends at the subdaily scale reported in past studies.more » « less
-
Abstract. Systematic biases and coarse resolutions are major limitations ofcurrent precipitation datasets. Many deep learning (DL)-based studies havebeen conducted for precipitation bias correction and downscaling. However,it is still challenging for the current approaches to handle complexfeatures of hourly precipitation, resulting in the incapability ofreproducing small-scale features, such as extreme events. This studydeveloped a customized DL model by incorporating customized loss functions,multitask learning and physically relevant covariates to bias correct anddownscale hourly precipitation data. We designed six scenarios tosystematically evaluate the added values of weighted loss functions,multitask learning, and atmospheric covariates compared to the regular DLand statistical approaches. The models were trained and tested using theModern-era Retrospective Analysis for Research and Applications version 2(MERRA2) reanalysis and the Stage IV radar observations over the northerncoastal region of the Gulf of Mexico on an hourly time scale. We found thatall the scenarios with weighted loss functions performed notably better thanthe other scenarios with conventional loss functions and a quantilemapping-based approach at hourly, daily, and monthly time scales as well asextremes. Multitask learning showed improved performance on capturing finefeatures of extreme events and accounting for atmospheric covariates highlyimproved model performance at hourly and aggregated time scales, while theimprovement is not as large as from weighted loss functions. We show thatthe customized DL model can better downscale and bias correct hourlyprecipitation datasets and provide improved precipitation estimates at finespatial and temporal resolutions where regular DL and statistical methodsexperience challenges.more » « less
-
Theoretical arguments and modeling experiments suggest that heavy precipitation is expected to intensify in a warmer climate. These projections have been supported by observational evidence at the daily scale, whereas the lack of long-term subdaily records has limited the ability to conduct analyses at shorter durations. In this study, we address this research gap using the Hourly Precipitation Data (HPD) from the National Climatic Data Center (NCDC). Due to the presence of relatively large periods with missing observations, we first implement a procedure to reconstruct probable missing zeros using the Analysis of Record Calibration (AORC) hourly gridded product. After the reconstruction, we identify 1404 gages with more than 75% (median of 94%) of complete records in the period 1979-2019 that cover the continental U.S. with good density. We then perform trend test analyses on the hourly observations where, at each gage, (1) independent events are identified, (2) peak-over- threshold series above the 90th, 95th, and 99th quantiles are extracted, and (3) trend tests are performed on the annual frequency and mean intensity of the POT series. After accounting for field significance, we find that hourly heavy precipitation exhibits statistically significant trends that are increasing for the frequency (+1% - +2% every year) but decreasing for the intensity (- 0.4 mm/h - -1.8 mm/h every 10 years). This is true in most of the country, except for some areas in the Southwest and South regions. Analyses repeated with the signals aggregated at 2, 3, 6, 12, and 24 hours lead to similar patterns, although a lower number of statistically significant trends is found as the duration increases. Overall, the statistical evidence of the trends is higher when focusing on the frequency rather than the intensity of heavy precipitation, and it is reduced when considering higher quantiles likely because of the lower test power. The results of this study are useful for the validation of climate and atmospheric models and the incorporation of nonstationarities due to global warming in intensity-duration- frequency curves of extreme precipitation used for infrastructure design.more » « less
