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reference experiment. Modification of the potential vorticity is a common way to perturb the atmospheric fields. Manders et al. (2007) used this method to modify numerical weather forecasts to assess numerical errors within forecasts. There are several model-based possibilities to assess the variability of the intensity of extreme precipitation events. One possibility is to perform ensemble simulations with nested global–regional models and to analyze the resulting bandwidth of precipitation in the
reference experiment. Modification of the potential vorticity is a common way to perturb the atmospheric fields. Manders et al. (2007) used this method to modify numerical weather forecasts to assess numerical errors within forecasts. There are several model-based possibilities to assess the variability of the intensity of extreme precipitation events. One possibility is to perform ensemble simulations with nested global–regional models and to analyze the resulting bandwidth of precipitation in the
1. Introduction Weather generators have been used successfully for a wide array of applications. They became increasingly used in various research topics, including more recently, climate change studies. They can generate series of climatic data with the same statistical properties as the observed ones. Furthermore, weather generators are able to produce series for any length of time. This allows developing various applications linked to extreme events, such as flood analyses. Weather
1. Introduction Weather generators have been used successfully for a wide array of applications. They became increasingly used in various research topics, including more recently, climate change studies. They can generate series of climatic data with the same statistical properties as the observed ones. Furthermore, weather generators are able to produce series for any length of time. This allows developing various applications linked to extreme events, such as flood analyses. Weather
usefulness ( Klemeš 1993 ). When deciding on operational PMP values, the best possible knowledge should be used. The most recent World Meteorological Organization manual for estimation of PMP ( WMO 2009 ) recommends to apply physically based atmospheric models, especially for areas where orographic precipitation is significant. A number of studies have investigated the use of numerical weather prediction models (NWPs) for PMP estimation ( Ohara et al. 2011 ; Ishida et al. 2015a , b ). Ohara et al
usefulness ( Klemeš 1993 ). When deciding on operational PMP values, the best possible knowledge should be used. The most recent World Meteorological Organization manual for estimation of PMP ( WMO 2009 ) recommends to apply physically based atmospheric models, especially for areas where orographic precipitation is significant. A number of studies have investigated the use of numerical weather prediction models (NWPs) for PMP estimation ( Ohara et al. 2011 ; Ishida et al. 2015a , b ). Ohara et al
-scale wind circulations ( Schlögl et al. 2018 ; Segal et al. 1991 ; Letcher and Minder 2018 ), and these in turn impact the lateral advection of heat, moisture, and momentum. Cohen (1994) provides a review of the mechanisms through which snow influences weather and climate. Other studies have documented seasonal snow cover’s impacts on storm track dynamics ( Sobolowski et al. 2010 ) and monsoonal circulations ( Bamzai and Shukla 1999 ). Despite the well-known mechanisms through which snow influences
-scale wind circulations ( Schlögl et al. 2018 ; Segal et al. 1991 ; Letcher and Minder 2018 ), and these in turn impact the lateral advection of heat, moisture, and momentum. Cohen (1994) provides a review of the mechanisms through which snow influences weather and climate. Other studies have documented seasonal snow cover’s impacts on storm track dynamics ( Sobolowski et al. 2010 ) and monsoonal circulations ( Bamzai and Shukla 1999 ). Despite the well-known mechanisms through which snow influences
simultaneous modification of the soil texture and LULC had compensating effects over some regions, the use of updated surface and soil properties has a nonnegligible impact on surface variables and precipitation predictions. Ács et al. (2014) reported spatially and temporally averaged PBL depth differences of up to 500 m for separate changes in the soil texture and LULC over Hungary. For roughly a month in summer 2013, which comprised both dry and wet weather conditions, Lin and Cheng (2016) analyzed
simultaneous modification of the soil texture and LULC had compensating effects over some regions, the use of updated surface and soil properties has a nonnegligible impact on surface variables and precipitation predictions. Ács et al. (2014) reported spatially and temporally averaged PBL depth differences of up to 500 m for separate changes in the soil texture and LULC over Hungary. For roughly a month in summer 2013, which comprised both dry and wet weather conditions, Lin and Cheng (2016) analyzed
1. Introduction Substantial soil and vegetation contrasts exist within the lower Mississippi River alluvial valley (LMRAV) because of extensive deforestation before 1940 ( Fig. 1 ; MacDonald et al. 1979 ), and these regional soil and vegetation boundaries have been shown to influence local rainfall and temperature patterns through modification of the sensible and latent heat fluxes ( Dyer 2011 ; Brown and Wax 2007 ; Raymond et al. 1994 ). This influence has been noted in other areas at
1. Introduction Substantial soil and vegetation contrasts exist within the lower Mississippi River alluvial valley (LMRAV) because of extensive deforestation before 1940 ( Fig. 1 ; MacDonald et al. 1979 ), and these regional soil and vegetation boundaries have been shown to influence local rainfall and temperature patterns through modification of the sensible and latent heat fluxes ( Dyer 2011 ; Brown and Wax 2007 ; Raymond et al. 1994 ). This influence has been noted in other areas at
, for examining urban modification of thunderstorms (see Shepherd 2005 for a review). Radar rainfall fields are derived from volume scan reflectivity observations from the KMKX Weather Surveillance Radar-1988 Doppler (WSR-88D) radar located in Milwaukee, Wisconsin (see Fig. 1 , left, for location). We use the Hydro-NEXRAD processing system to convert three-dimensional volume scan reflectivity fields in a polar coordinate system to two-dimensional surface rainfall fields in a Cartesian coordinate
, for examining urban modification of thunderstorms (see Shepherd 2005 for a review). Radar rainfall fields are derived from volume scan reflectivity observations from the KMKX Weather Surveillance Radar-1988 Doppler (WSR-88D) radar located in Milwaukee, Wisconsin (see Fig. 1 , left, for location). We use the Hydro-NEXRAD processing system to convert three-dimensional volume scan reflectivity fields in a polar coordinate system to two-dimensional surface rainfall fields in a Cartesian coordinate
1. Introduction The Rapid-Refresh Multiscale Analysis and Prediction System–Short Term (RMAPS-ST), the operational short-range numerical weather prediction (NWP) system of Beijing Meteorological Service (BMS), is a WRF-based system developed by Institute of Urban Meteorology (BMS/IUM). However, noticeable systematic bias of 2-m temperature (T2) and 2-m specific humidity (Q2) forecasts are found in the RMAPS-ST operational forecasts in the winter. The near-surface temperature and humidity
1. Introduction The Rapid-Refresh Multiscale Analysis and Prediction System–Short Term (RMAPS-ST), the operational short-range numerical weather prediction (NWP) system of Beijing Meteorological Service (BMS), is a WRF-based system developed by Institute of Urban Meteorology (BMS/IUM). However, noticeable systematic bias of 2-m temperature (T2) and 2-m specific humidity (Q2) forecasts are found in the RMAPS-ST operational forecasts in the winter. The near-surface temperature and humidity
: General circulation experiments with the primitive equations . Mon. Wea. Rev. , 91 , 99 – 164 , https://doi.org/10.1175/1520-0493(1963)091<0099:GCEWTP>2.3.CO;2 . 10.1175/1520-0493(1963)091<0099:GCEWTP>2.3.CO;2 Smirnova , T. G. , J. M. Brown , S. G. Benjamin , and J. S. Kenyon , 2016 : Modifications to the Rapid Update Cycle land surface model (RUC LSM) available in the Weather Research and Forecasting (WRF) Model . Mon. Wea. Rev. , 144 , 1851 – 1865 , https://doi.org/10.1175/MWR
: General circulation experiments with the primitive equations . Mon. Wea. Rev. , 91 , 99 – 164 , https://doi.org/10.1175/1520-0493(1963)091<0099:GCEWTP>2.3.CO;2 . 10.1175/1520-0493(1963)091<0099:GCEWTP>2.3.CO;2 Smirnova , T. G. , J. M. Brown , S. G. Benjamin , and J. S. Kenyon , 2016 : Modifications to the Rapid Update Cycle land surface model (RUC LSM) available in the Weather Research and Forecasting (WRF) Model . Mon. Wea. Rev. , 144 , 1851 – 1865 , https://doi.org/10.1175/MWR
between the HS18 and GV20 findings, some modifications are made to these methods; these will be described in more detail below. 3. Results There are a few possible reasons for the large differences between the findings of HS18 and GV20 that we will investigate: the different period of study; differences in temporal resolution and temporal sampling; the different MRMS dataset, including the use of radar-only versus gauge-corrected QPE, and the enhancements made in version 12 of the MRMS system
between the HS18 and GV20 findings, some modifications are made to these methods; these will be described in more detail below. 3. Results There are a few possible reasons for the large differences between the findings of HS18 and GV20 that we will investigate: the different period of study; differences in temporal resolution and temporal sampling; the different MRMS dataset, including the use of radar-only versus gauge-corrected QPE, and the enhancements made in version 12 of the MRMS system
