Extreme Events
Atmospheric River-Induced Precipitation and Snowpack during the Western United States Cold Season
In this study, we characterized precipitation and snow water equivalent associated with ARs intersecting the US western coast during the cold season (November through March)
of water years 1949-2015. For each AR landfalling date, we retrieved the precipitation and relevant hydrometeorological variables
from dynamically downscaled atmospheric reanalyses (NCEP/NCAR) using the WRF mesoscale numerical weather prediction model.
Landfalling ARs resulted in higher precipitation amounts throughout the domain than did non-AR storms.
The average of upper 10th percentile daily precipitation over the western coastal domain during the winter (November to March) ARs for water years 1949-2015.
Highlights
• ARs contributed the most extreme precipitation events during January and February.
• Daily snow water equivalent (SWE) changes during ARs indicate that high positive net snow accumulation conditions accompany ARs in December, January, and February.
• Analysis of ARs during dry and wet years reveals that ARs during wet years are more frequent, and produce heavier precipitation and snow accumulation as compared with dry years.
Scientific Focus Area (SFA): Extreme Events - Infrastructure Resilience
Reference: Eldardiry, H., Mahmood, A., Chen, X., Hossain, F., Nijssen, B., & Lettenmaier, D. P. (2019). Atmospheric River–Induced Precipitation and Snowpack during the Western United States Cold Season. Journal of Hydrometeorology, 20(4), 613-630 (DOI: https://doi.org/10.1175/JHM-D-18-0228.1).
On the Use of Radar-based Quantitative Precipitation Estimates for Precipitation Frequency Analysis
This study investigated three fundamental issues that arise when radar-based QPE products are used in frequency analysis:
(a) Effect of sample size due to the typically short records of radar products;
(b) Effect of uncertainties present in radar-rainfall estimation algorithms; and
(c) Effect of the frequency estimation approach adopted. We used a 13-year dataset of hourly, 4x4 km2 radar-based over a domain that covers Louisiana, USA.
Spatial Distribution of the Mean Annual Maxima (MAM) over Louisiana during the study period (2002-2014).
Highlights
• The relatively short radar records explained the majority of the uncertainty associated with the radar-based quantiles.
• A systematic underestimation bias is observed between radar- and gauge-bases frequency estimates, which was mostly attributable to the conditional
bias inherent in the radar product.
Scientific Focus Area (SFA): Remote Sensing - Extreme Events
Reference:
Eldardiry, H., Habib, E., & Zhang, Y. (2015). On the use of radar-based quantitative precipitation estimates for precipitation frequency analysis.
Journal of Hydrology, 531, 441-453 (DOI: https://doi.org/10.1016/j.jhydrol.2015.05.016).
Artifacts in Stage IV NWS Real-time Multisensor Precipitation Estimates and Impacts on Identification of Maximum Series
This study focuses on data artifacts in the Stage IV radar-based Quantitative Precipitation Estimates (QPE) product over its full record period (2002-2013)
and assesses their impacts on the analysis and derivation of heavy rainfall statistics.
Examining annual maximum series extracted from the Stage IV 1-h accumulation product revealed
the presence of numerous artifacts that were mostly linked to erroneous rain gauge reports and which can be mistaken as rainfall maxima.
First field of Partial Duration Series (PDS) of hourly precipitation (mm) over the CONUS for the period of study, 2002-2013 (maximum value at each individual pixel).
Highlights
• Artifacts detected in Stage IV QPE had negative impacts on the estimation of parameters and quantiles of commonly-used extreme value probability distributions
• Stage IV 6-h and 24-h accumulations, as well as the longer-latency QPE archive from the regional River Forecasting Centers (RFCs),
are mostly free from the artifacts shown in the Stage IV 1-h product.
Scientific Focus Area (SFA): Remote Sensing - Extreme Events
Reference: Eldardiry, H., Habib, E., Zhang, Y., & Graschel, J. (2015). Artifacts in Stage IV NWS real-time multisensor precipitation estimates and impacts on identification of maximum series. Journal of Hydrologic Engineering, 22(5), E4015003. (DOI: https://doi.org/10.1061/(ASCE)HE.1943-5584.0001291).