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1. Introduction Extreme weather events such as high wind speeds, heavy precipitation, or high temperatures can have severe impacts on society. Improving predictions of such events therefore has a high priority in national weather services, and an important part of this activity is to determine whether or not prediction quality is improved when prediction systems are updated. Assessing the quality of predictions of extreme weather events, however, is complicated by the fact that measures of
1. Introduction Extreme weather events such as high wind speeds, heavy precipitation, or high temperatures can have severe impacts on society. Improving predictions of such events therefore has a high priority in national weather services, and an important part of this activity is to determine whether or not prediction quality is improved when prediction systems are updated. Assessing the quality of predictions of extreme weather events, however, is complicated by the fact that measures of
mainland United States, extreme rainfall events in Hawaii have received little attention. Because of the socioeconomic repercussions of heavy rainfall and the associated floods on the islands, it is important to understand the frequency, intensity, locations, and patterns of these extreme events across the entire Hawaiian Islands. We thus propose to investigate the nature and spatial distributions of heavy and very heavy rainfall events in Hawaii. Three different methods have commonly been used to
mainland United States, extreme rainfall events in Hawaii have received little attention. Because of the socioeconomic repercussions of heavy rainfall and the associated floods on the islands, it is important to understand the frequency, intensity, locations, and patterns of these extreme events across the entire Hawaiian Islands. We thus propose to investigate the nature and spatial distributions of heavy and very heavy rainfall events in Hawaii. Three different methods have commonly been used to
1. Introduction While extreme events are of interest for many climate impacts studies, global coupled general circulation models (GCMs) often mute extremes with their coarse resolution, particularly for precipitation (e.g., Sillmann et al. 2013 ), and alone are not sufficient for assessments on a regional or local level. Downscaling methodologies aim to solve this scale mismatch issue by extracting high-resolution information from the coarse-resolution simulations. Downscaling methods can be
1. Introduction While extreme events are of interest for many climate impacts studies, global coupled general circulation models (GCMs) often mute extremes with their coarse resolution, particularly for precipitation (e.g., Sillmann et al. 2013 ), and alone are not sufficient for assessments on a regional or local level. Downscaling methodologies aim to solve this scale mismatch issue by extracting high-resolution information from the coarse-resolution simulations. Downscaling methods can be
of South America during the different phases of the El Niño–Southern Oscillation (e.g., Ropelewski and Halpert 1987 , 1989 ; Aceituno 1988 ; Rao and Hada 1990 ; Grimm et al. 1998 , 2000 ; Grimm 2003 , 2004 ). Given the dependence of monthly to seasonal precipitation amounts on the frequency of extreme precipitation events, it is reasonable to expect the frequency of extreme rainfall events to be modulated by ENSO in some preferred locations. However, this does not mean that regions with
of South America during the different phases of the El Niño–Southern Oscillation (e.g., Ropelewski and Halpert 1987 , 1989 ; Aceituno 1988 ; Rao and Hada 1990 ; Grimm et al. 1998 , 2000 ; Grimm 2003 , 2004 ). Given the dependence of monthly to seasonal precipitation amounts on the frequency of extreme precipitation events, it is reasonable to expect the frequency of extreme rainfall events to be modulated by ENSO in some preferred locations. However, this does not mean that regions with
formation mechanisms of the extreme events and is particularly important to the reliable assessment of the long-term changes in the extremes. Karl et al. (1996) constructed the climate extremes index (CEI) with multiple quantities, attempting to indicate the overall extreme condition in the climate. In many previous studies, extremes were extracted from a single climate quantity (e.g., the precipitation) and for each of the observation stations or a specific region examined. With daily precipitation
formation mechanisms of the extreme events and is particularly important to the reliable assessment of the long-term changes in the extremes. Karl et al. (1996) constructed the climate extremes index (CEI) with multiple quantities, attempting to indicate the overall extreme condition in the climate. In many previous studies, extremes were extracted from a single climate quantity (e.g., the precipitation) and for each of the observation stations or a specific region examined. With daily precipitation
1. Introduction Forecasting extreme weather events is a vital task and a focus for international research activities such as The Observing-System Research and Predictability Experiment (THORPEX) of the World Meteorological Organization. This article addresses a key component of this research: how should we assess the quality of forecasts of extreme events? We focus on events that are both extreme and rare. Such events pose at least three difficulties for forecast verification. First, only a
1. Introduction Forecasting extreme weather events is a vital task and a focus for international research activities such as The Observing-System Research and Predictability Experiment (THORPEX) of the World Meteorological Organization. This article addresses a key component of this research: how should we assess the quality of forecasts of extreme events? We focus on events that are both extreme and rare. Such events pose at least three difficulties for forecast verification. First, only a
). Season (see, e.g., Berg et al. 2009 ; Mishra et al. 2012 ; Shaw et al. 2011 ): For Europe, Berg et al. (2009) found a monotonic increase structure in winter, a monotonic decrease structure in summer, and a peak-like structure for spring and autumn. Time scale: Many studies have shown that the time scale over which extreme rainfall events are estimated has a huge impact on the P extr – T a relationship ( Lenderink and van Meijgaard 2008 ; Haerter et al. 2010 ; Hardwick Jones et al. 2010
). Season (see, e.g., Berg et al. 2009 ; Mishra et al. 2012 ; Shaw et al. 2011 ): For Europe, Berg et al. (2009) found a monotonic increase structure in winter, a monotonic decrease structure in summer, and a peak-like structure for spring and autumn. Time scale: Many studies have shown that the time scale over which extreme rainfall events are estimated has a huge impact on the P extr – T a relationship ( Lenderink and van Meijgaard 2008 ; Haerter et al. 2010 ; Hardwick Jones et al. 2010
1. Introduction Extreme precipitation events are among the most devastating natural hazards in the contiguous United States (CONUS). These events pose significant risks and far-reaching impacts to life, property, and the economy. From 1980 to 2018, the top 30 U.S. inland flooding events cost a combined $124 billion and resulted in over 500 fatalities ( National Centers for Environmental Information 2019 ). Although flooding can arise from many sources (e.g., rapid snowpack melt, overflowing
1. Introduction Extreme precipitation events are among the most devastating natural hazards in the contiguous United States (CONUS). These events pose significant risks and far-reaching impacts to life, property, and the economy. From 1980 to 2018, the top 30 U.S. inland flooding events cost a combined $124 billion and resulted in over 500 fatalities ( National Centers for Environmental Information 2019 ). Although flooding can arise from many sources (e.g., rapid snowpack melt, overflowing
with cloud ice, T w is the reference temperature for a region with cloud water, T i and T w are both 0°C, T 1 and T 2 are the reference temperatures of mixed cloud ice and water, here T 1 = −38°C and T 2 = 0°C, T is the ambient temperature. Using this forward operator of cloud optical depth and cloud phase, the cloud optical depth products can be assimilated in STMAS analyses. 3. Application to an extreme precipitation event For an extreme precipitation event that lasted 8 days from
with cloud ice, T w is the reference temperature for a region with cloud water, T i and T w are both 0°C, T 1 and T 2 are the reference temperatures of mixed cloud ice and water, here T 1 = −38°C and T 2 = 0°C, T is the ambient temperature. Using this forward operator of cloud optical depth and cloud phase, the cloud optical depth products can be assimilated in STMAS analyses. 3. Application to an extreme precipitation event For an extreme precipitation event that lasted 8 days from
1. Introduction Extreme temperature events for several days in the summer have seriously damaged society, human life, the economy, and ecosystems (e.g., Field et al. 2012 ; Hartmann et al. 2014 ; Perkins et al. 2015 ). In particular, the high mortality rate in South Korea over the past 100 years is associated with the frequent occurrence of extreme high temperatures ( Kysely and Kim 2009 ). The Intergovernmental Panel on Climate Change (IPCC) concluded that global warming has caused an
1. Introduction Extreme temperature events for several days in the summer have seriously damaged society, human life, the economy, and ecosystems (e.g., Field et al. 2012 ; Hartmann et al. 2014 ; Perkins et al. 2015 ). In particular, the high mortality rate in South Korea over the past 100 years is associated with the frequent occurrence of extreme high temperatures ( Kysely and Kim 2009 ). The Intergovernmental Panel on Climate Change (IPCC) concluded that global warming has caused an
