skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


Title: A generalized Spatio-Temporal Threshold Clustering method for identification of extreme event patterns
Abstract. Extreme weather and climate events such as floods, droughts, and heat waves can cause extensive societal damages. While various statistical and climate models have been developed for the purpose of simulating extremes, a consistent definition of extreme events is still lacking. Furthermore, to better assess the performance of the climate models, a variety of spatial forecast verification measures have been developed. However, in most cases, the spatial verification measures that are widely used to compare mean states do not have sufficient theoretical justification to benchmark extreme events. In order to alleviate inconsistencies when defining extreme events within different scientific communities, we propose a new generalized Spatio-Temporal Threshold Clustering method for the identification of extreme event episodes, which uses machine learning techniques to couple existing pattern recognition indices with high or low threshold choices. The method consists of five main steps: (1) construction of essential field quantities; (2) dimension reduction; (3) spatial domain mapping; (4) time series clustering; and (5) threshold selection. We develop and apply this method using a gridded daily precipitation dataset derived from rain gauge stations over the contiguous United States. We observe changes in the distribution of conditional frequency of extreme precipitation from large-scale well-connected spatial patterns to smaller-scale more isolated rainfall clusters, possibly leading to more localized droughts and heat waves, especially during the summer months. The proposed method automates the threshold selection process through a clustering algorithm and can be directly applicable in conjunction with modeling and spatial forecast verification of extremes. Additionally, it allows for the identification of synoptic-scale spatial patterns that can be directly traced to the individual extreme episodes, and it offers users the flexibility to select an extreme threshold that is linked to the desired geometrical properties. The approach can be applied to broad scientific disciplines.  more » « less
Award ID(s):
1828910 1639327
PAR ID:
10284514
Author(s) / Creator(s):
;
Date Published:
Journal Name:
Advances in Statistical Climatology, Meteorology and Oceanography
Volume:
7
Issue:
1
ISSN:
2364-3587
Page Range / eLocation ID:
35 to 52
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; (, Journal of Climate)
    Abstract Although the intensity of extreme precipitation is predicted to increase with climate warming, at the weather scale precipitation extremes over most of the globe decrease when temperature exceeds a certain threshold, and the spatial extent of this negative scaling is projected to increase as the climate warms. The nature and cause of the negative scaling at high temperature and its implications remain poorly understood. Based on sub-daily data from observations, reanalysis data, and output from a coarse-resolution (∼200 km) global model and a fine-resolution (4 km) convection-permitting regional model, we show that the negative scaling is primarily a reflection of high temperature suppressing precipitation over land and storm-induced temperature variation over the ocean. We further identify the high temperature-induced increase of saturation deficit as a critical condition for the negative scaling of extreme precipitation over land. Large saturation deficit reduces precipitation intensity by slowing down the convective updraft condensation rate and accelerating condensate evaporation. The heat-induced suppression of precipitation, both for its mean and extremes, provides one mechanism for the co-occurrence of drought and heatwaves. As the saturation deficit over land is expected to increase in a warmer climate, our results imply a growing prevalence of negative scaling, potentially increasing the frequency of compound drought and heat events. Understanding the physical mechanisms underlying the negative scaling of precipitation at high temperature is, therefore, essential for assessing future risks of extreme events, including not only flood due to extreme precipitation but also drought and heatwaves. 
    more » « less
  2. ; (, Earth's Future)
    Abstract Climate change is expected to increase the global occurrence and intensity of heatwaves, extreme precipitation, and flash droughts. However, it is not well understood how the compound heatwave, extreme precipitation, and flash drought events will likely change, and how global population, agriculture, and forest will likely be exposed to these compound events under future climate change scenarios. This research uses eight CMIP6 climate models to assess the current and future global compound climate extreme events, as well as population, agriculture, and forestry exposures to these events, under two climate scenarios, Shared Socioeconomic Pathways (SSP), SSP1‐2.6 and SSP5‐8.5 for three time periods: early‐, mid‐, and late‐ 21st century. Climate extremes are derived for heatwaves, extreme precipitation, and flash droughts using locational‐dependent thresholds. We find that compound heatwaves and flash drought events result in the largest increases in exposure of populations, agriculture, and forest lands, under SSP5‐8.5 late‐century projections of sequential heatwaves and flash droughts. Late‐century projections of sequential heatwaves and flash droughts show hot spots of exposure increases in population exposure greater than 50 million person‐events in China, India, and Europe; increases in agriculture land exposures greater than 90 thousand km2‐events in China, South America, and Oceania; and increase in forest land exposure greater than 120 thousand km2‐events in Oceania and South America regions when compared to the historical period. The findings from this study can be potentially useful for informing global climate adaptations. 
    more » « less
  3. ; ; ; ; (, Bulletin of the American Meteorological Society)
    Abstract Extreme precipitation over a two-week period can cause significant impacts to life and property. Trustworthy and easy-to-understand forecasts of these extreme periods on the subseasonal-to-seasonal timeframe may provide additional time for planning. The Prediction of Rainfall Extremes at Subseasonal to Seasonal Periods (PRES2iP) project team conducted three workshops over six years to engage with stakeholders to learn what is needed for decision-making for subseasonal precipitation. In this study experimental subseasonal to seasonal (S2S) forecast products were designed, using knowledge gained from previous stakeholder workshops, and shown to decision-makers to evaluate the products for two 14-day extreme precipitation period scenarios. Our stakeholders preferred a combination of products that covered the spatial extent, regional daily values, with associated uncertainty, and text narratives with anticipated impacts for planning within the S2S timeframe. When targeting longer extremes, having information regarding timing of expected impacts was seen as crucial for planning. We found that there is increased uncertainty tolerance with stakeholders when using products at longer lead times that typical skill metrics, such as critical success index or anomaly correlation coefficient, do not capture. Therefore, the use of object-oriented verification, that allows for more flexibility in spatial uncertainty, might be beneficial for evaluating S2S forecasts. These results help to create a foundation for design, verification, and implementation of future operational forecast products with longer lead times, while also providing an example for future workshops that engage both researchers and decision-makers. 
    more » « less
  4. ; ; ; (, Nonlinear Processes in Geophysics)
    null (Ed.)
    Abstract. Most available verification metrics for ensemble forecasts focus on univariate quantities. That is, they assess whether the ensemble provides anadequate representation of the forecast uncertainty about the quantity of interest at a particular location and time. For spatially indexed ensemble forecasts, however, it is also important that forecast fields reproduce the spatial structure of the observed field and represent the uncertainty about spatial properties such as the size of the area for which heavy precipitation, high winds, critical fire weather conditions, etc., areexpected. In this article we study the properties of the fraction of threshold exceedance (FTE) histogram, a new diagnostic tool designed forspatially indexed ensemble forecast fields. Defined as the fraction of grid points where a prescribed threshold is exceeded, the FTE is calculated for the verification field and separately for each ensemble member. It yields a projection of a – possibly high-dimensional – multivariatequantity onto a univariate quantity that can be studied with standard tools like verification rank histograms. This projection is appealing since itreflects a spatial property that is intuitive and directly relevant in applications, though it is not obvious whether the FTE is sufficientlysensitive to misrepresentation of spatial structure in the ensemble. In a comprehensive simulation study we find that departures from uniformity ofthe FTE histograms can indeed be related to forecast ensembles with biased spatial variability and that these histograms detect shortcomings in the spatial structure of ensemble forecast fields that are not obvious by eye. For demonstration, FTE histograms are applied in the context of spatiallydownscaled ensemble precipitation forecast fields from NOAA's Global Ensemble Forecast System. 
    more » « less
  5. ; (, Geophysical Research Letters)
    Abstract Paleoclimate data play a critical role in contextualizing recent hydroclimate extremes, but asymmetries in tree‐ring responses to extreme climate conditions pose challenges for reconstruction and interpretation of past climate. Here we establish the extent to which existing tree‐ring records capture precipitation extremes in western North America and evaluate climate factors hypothesized to lead to asymmetric extreme capture, including timing of precipitation, seasonal temperatures, snowpack, and atmospheric river events. We find that while there is dry‐biased asymmetry in one third of western North American tree‐ring records, 45% of sites capture wet extremes as well as or better than dry extremes. Summer extremes are rarely captured at any sites. Latitude and elevation affect site‐level extreme responses, as do seasonal climate conditions, particularly in the autumn and spring. Directly addressing asymmetric extreme value capture in tree‐ring records can aid our interpretation of past climate and help identify alternative avenues for future reconstructions. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email