Search Results
UTC from 1 May to 15 June 2013. These forecasts require 7 h to produce with 2048 cores on the NASA Center for Climate Simulation (NCCS) supercomputer. One set of the NU-WRF uses NAM to provide initial and boundary conditions without LIS coupling ( Table 1 , denoted as WRF); the second set uses LIS to provide high-resolution surface initialization and online coupling with the atmospheric component of NU-WRF ( Table 1 , denoted as COUP). Table 1. Key model configurations for NU-WRF simulations. WRF
UTC from 1 May to 15 June 2013. These forecasts require 7 h to produce with 2048 cores on the NASA Center for Climate Simulation (NCCS) supercomputer. One set of the NU-WRF uses NAM to provide initial and boundary conditions without LIS coupling ( Table 1 , denoted as WRF); the second set uses LIS to provide high-resolution surface initialization and online coupling with the atmospheric component of NU-WRF ( Table 1 , denoted as COUP). Table 1. Key model configurations for NU-WRF simulations. WRF
surface processes. Figure 2 shows the domain configuration. The NARR fields are used for initial and boundary conditions. The simulation is initialized at 1800 UTC 24 May and integrated through 1200 UTC 29 May 2013. The results from 0000 UTC 25 May 2013 and beyond are presented in this study. Model physics options used are Noah land surface model, WSM6, Yonsei University (YSU) boundary layer scheme, Dudhia shortwave radiation scheme, and the Rapid Radiative Transfer Model (RRTM) scheme for longwave
surface processes. Figure 2 shows the domain configuration. The NARR fields are used for initial and boundary conditions. The simulation is initialized at 1800 UTC 24 May and integrated through 1200 UTC 29 May 2013. The results from 0000 UTC 25 May 2013 and beyond are presented in this study. Model physics options used are Noah land surface model, WSM6, Yonsei University (YSU) boundary layer scheme, Dudhia shortwave radiation scheme, and the Rapid Radiative Transfer Model (RRTM) scheme for longwave
to 0600 UTC 11 April in Fig. 8d and from 0000 to 1800 UTC 18 April in Fig. 8e ). Here, mixing ratio and relative humidity increase rather abruptly and parcels ascend over a small period of time, suggestive of the lifting of moist air along the frontal boundary present during this AR. Similar conditions are observed in the AR on 30 May, except that parcels are initially lifted around the midpoint of their trajectory. This is perhaps due to the presence of high-elevation mountains over the
to 0600 UTC 11 April in Fig. 8d and from 0000 to 1800 UTC 18 April in Fig. 8e ). Here, mixing ratio and relative humidity increase rather abruptly and parcels ascend over a small period of time, suggestive of the lifting of moist air along the frontal boundary present during this AR. Similar conditions are observed in the AR on 30 May, except that parcels are initially lifted around the midpoint of their trajectory. This is perhaps due to the presence of high-elevation mountains over the
day (center left), instrument status report list (center right), latest forecast summary (bottom center), and links to other reports (right). The IFloodS portal’s addition of a map-based data visualization capability offered a significant improvement over previous GPM GV field campaigns. This map feature supported a display of various types of spatial information, including IFloodS instrument locations and coverage areas, rivers and watershed boundaries, and roads and political boundaries
day (center left), instrument status report list (center right), latest forecast summary (bottom center), and links to other reports (right). The IFloodS portal’s addition of a map-based data visualization capability offered a significant improvement over previous GPM GV field campaigns. This map feature supported a display of various types of spatial information, including IFloodS instrument locations and coverage areas, rivers and watershed boundaries, and roads and political boundaries
. We also attempt to identify strengths and weaknesses of the current DP system to guide future research on the topic. Because the system was only recently implemented and the definitions of QPE algorithms and parameters are based on a limited dataset ( Giangrande and Ryzhkov 2008 ), one of the first steps for further development is to assess how the method performs for different areas, different radars, and under different meteorological conditions. This paper assesses the quality of DP QPE by
. We also attempt to identify strengths and weaknesses of the current DP system to guide future research on the topic. Because the system was only recently implemented and the definitions of QPE algorithms and parameters are based on a limited dataset ( Giangrande and Ryzhkov 2008 ), one of the first steps for further development is to assess how the method performs for different areas, different radars, and under different meteorological conditions. This paper assesses the quality of DP QPE by
water phase within a radar sampling volume. Under all liquid conditions identified by HCS-R, the algorithm attempts to use R – K dp – Z dr or R – Z h – Z dr both of which better constrain variations in rain rate with drop size distribution compared to R – Z h estimators. The selection of each estimator is based on a threshold on the polarimetric data to ensure these are above values severely affected by noise [ Z dr ≥ 0.5 dB, K dp ≥ 0.3° km −1 , and Z h > 38 dB Z at S band; see Fig. 3 in
water phase within a radar sampling volume. Under all liquid conditions identified by HCS-R, the algorithm attempts to use R – K dp – Z dr or R – Z h – Z dr both of which better constrain variations in rain rate with drop size distribution compared to R – Z h estimators. The selection of each estimator is based on a threshold on the polarimetric data to ensure these are above values severely affected by noise [ Z dr ≥ 0.5 dB, K dp ≥ 0.3° km −1 , and Z h > 38 dB Z at S band; see Fig. 3 in
Anagnostou 2012 ). The hydrological models that partition rainfall into runoff components and route runoff to predict streamflow fluctuations represent the second key component of global flood forecasting systems. These models are constructed to obey basin boundaries, which are defined by the selection of points of interest (e.g., major cities) along the river network. In a global system, all of the millions of possible basins should be represented, which poses a computational challenge. Current global
Anagnostou 2012 ). The hydrological models that partition rainfall into runoff components and route runoff to predict streamflow fluctuations represent the second key component of global flood forecasting systems. These models are constructed to obey basin boundaries, which are defined by the selection of points of interest (e.g., major cities) along the river network. In a global system, all of the millions of possible basins should be represented, which poses a computational challenge. Current global
subbasin scales, which is further investigated in this section. A total of 80 simulated monthly streamflow time series (or hydrographs) are selected for bias analyses for the 10 gauge locations in the ICRB, with the DRIVE model driven by the seven precipitation products ( Figs. 1 , 2 ) and the NLDAS-2-Ref. From Fig. 7a , NLDAS-2-Ref, NLDAS-2, and CPC-U generally define the upper boundary of the monthly NSC scores at all places. The resulting NSC scores and bias at the 10 gauge locations can be
subbasin scales, which is further investigated in this section. A total of 80 simulated monthly streamflow time series (or hydrographs) are selected for bias analyses for the 10 gauge locations in the ICRB, with the DRIVE model driven by the seven precipitation products ( Figs. 1 , 2 ) and the NLDAS-2-Ref. From Fig. 7a , NLDAS-2-Ref, NLDAS-2, and CPC-U generally define the upper boundary of the monthly NSC scores at all places. The resulting NSC scores and bias at the 10 gauge locations can be
