Search Results
time and from the millimeter scale of microphysical processes to regional and global scales in space. It also exhibits a large variability in magnitude and frequency, from low extremes resulting in prolonged droughts to high extremes resulting in devastating floods. Improving precipitation estimation and prediction has great societal impact for decision support in water resources management, infrastructure protection and design under accelerating climate extremes, quantifying water and energy
time and from the millimeter scale of microphysical processes to regional and global scales in space. It also exhibits a large variability in magnitude and frequency, from low extremes resulting in prolonged droughts to high extremes resulting in devastating floods. Improving precipitation estimation and prediction has great societal impact for decision support in water resources management, infrastructure protection and design under accelerating climate extremes, quantifying water and energy
resolutions are critical for near-real-time applications such as rapid monitoring and forecasting of high-impact societal events like flash floods, debris flows, and shallow landslides. Such resolution can be obtained primarily from satellite sensors on board geostationary Earth orbit (GEO) platforms. NOAA’s Advanced Baseline Imager (ABI) sensor on board the latest generation of Geostationary Operational Environmental Satellites (GOES-R Series) provides 3 times more spectral channels, 4 times the
resolutions are critical for near-real-time applications such as rapid monitoring and forecasting of high-impact societal events like flash floods, debris flows, and shallow landslides. Such resolution can be obtained primarily from satellite sensors on board geostationary Earth orbit (GEO) platforms. NOAA’s Advanced Baseline Imager (ABI) sensor on board the latest generation of Geostationary Operational Environmental Satellites (GOES-R Series) provides 3 times more spectral channels, 4 times the
grouped into various categories in terms of their properties to compare the conditional performance of IMERG-L and NU-WRF. Two characteristics, area and P90, were considered to classify the objects and to explore the detection skills of IMERG-L and NU-WRF as a function of object size and intensity. We also examined PMW and IR inputs to IMERG-L and NU-WRF, which can impact both data- and model-based estimates’ performance ( Tan et al. 2016 ; S. Q. Zhang et al. 2013 ). As mentioned above, there are
grouped into various categories in terms of their properties to compare the conditional performance of IMERG-L and NU-WRF. Two characteristics, area and P90, were considered to classify the objects and to explore the detection skills of IMERG-L and NU-WRF as a function of object size and intensity. We also examined PMW and IR inputs to IMERG-L and NU-WRF, which can impact both data- and model-based estimates’ performance ( Tan et al. 2016 ; S. Q. Zhang et al. 2013 ). As mentioned above, there are
