ABSTRACT
Precipitation represents a life-critical energy and hydrologic exchange between the Earth's atmosphere and its surface. As such, knowledge of where, when, and how much rain and snow falls is essential for scientific research and societal applications. Building on the 17-year success of the Tropical Rainfall Measurement Mission (TRMM), the Global Precipitation Measurement (GPM) Core Observatory (GPM-CO) is the first U.S. National Aeronautical and Space Administration (NASA) satellite mission specifically designed with sensors to observe the structure and intensities of both rain and falling snow. The GPM-CO has proved to be a worthy successor to TRMM, extending and improving high-quality active and passive microwave observations across all times of day. The GPM-CO launched in early 2014, is a joint mission between NASA and the Japanese Aerospace Exploration Agency (JAXA), with sensors that include the NASA-provided GPM Microwave Imager and the JAXA-provided Dual-frequency Precipitation Radar. These sensors were devised with high accuracy standards enabling them to be used as a reference for inter-calibrating a constellation of partner satellite data. These intercalibrated partner satellite retrievals are used with infrared data to produce merged precipitation estimates at temporal scales of 30 minutes and spatial scales of 0.1° × 0.1°. Precipitation estimates from the GPM-CO and partner constellation satellites, provided in near real time and later reprocessed with all ancillary data, are an indispensable source of precipitation data for operational and scientific users. Advances have been made using GPM data, primarily in improving sensor calibration, retrieval algorithms, and ground validation measurements, and used to further our understanding of the characteristics of liquid and frozen precipitation and the science of water and hydrological cycles for climate/weather forecasting. These advances have extended to societal benefits related to water resources, operational numerical weather prediction, hurricane monitoring, prediction, and disaster response, extremes, and disease.
ABSTRACT
The Integrated Multi-satellitE Retrievals for GPM (IMERG), a global high-resolution gridded precipitation data set, will enable a wide range of applications, ranging from studies on precipitation characteristics to applications in hydrology to evaluation of weather and climate models. These applications focus on different spatial and temporal scale and thus average the precipitation estimates to coarser resolutions. Such a modification of scale will impact the reliability of IMERG. In this study, the performance of the Final run of MERG is evaluated against ground-based measurements as a function of increasing spatial resolution (from 0.1° to 2.5 ) and accumulation periods (from 0.5 h to 24 h) over a region in the southeastern US. For ground reference, a product derived from the Multi-Radar/Multi-Sensor suite, a radar- and gauge-based operational precipitation dataset, is used. The TRMM Multi satellite Precipitation Analysis (TMPA) is also included as a benchmark. In general, both IMERG and TMPA improve when scaled up to larger areas and longer time periods, with better identification of rain occurrences and consistent improvements in systematic and random errors of rain rates. Between the two satellite estimates, IMERG is slightly better than TMPA most of the time. These results will inform users on the reliability of IMERG over the scales relevant to their studies.
ABSTRACT
The GPM mission collects essential rain and snow data for scientific studies and societal benefit.
ABSTRACT
OLYMPEX is a comprehensive field campaign to study how precipitation in Pacific storms is modified by passage over coastal mountains.
ABSTRACT
The Iowa Flood Studies (IFloodS) campaign was conducted in eastern Iowa as a pre-GPM-launch campaign from 1 May to 15 June 2013. During the campaign period, real time forecasts are conducted utilizing NASA-Unified Weather Research and Forecasting (NU-WRF) model to support the everyday weather briefing. In this study, two sets of the NU-WRF rainfall forecasts are evaluated with Stage IV and Multi-Radar Multi-Sensor (MRMS) Quantitative Precipitation Estimation (QPE), with the objective to understand the impact of Land Surface initialization on the predicted precipitation. NU-WRF is also compared with North American Mesoscale Forecast System (NAM) 12 km forecast. In general, NU-WRF did a good job at capturing individual precipitation events. NU-WRF is also able to replicate a better rainfall spatial distribution compare with NAM. Further sensitivity tests show that the high-resolution makes a positive impact on rainfall forecast. The two sets of NU-WRF simulations produce very close rainfall characteristics. The Land surface initialization do not show significant impact on short-term rainfall forecast, and it is largely due to the soil conditions during the field campaign period.
ABSTRACT
The spatial variability of parameters of raindrop size distribution and its derivatives is investigated through a field study where collocated PARSIVEL2 and two-dimensional video disdrometers are operated at six sites in Wallops Island, Virginia from December 2013 to March 2014. The three-parameter exponential function is employed to determine the spatial variability across the study domain where the maximum separation distance was 2.3 km. The nugget parameter of exponential function is set to 0.99 and the correlation distance (d0) and shape parameter (s0) are retrieved minimizing root-mean-square error, after fitting it to the correlations of physical parameters. Fits were very good for almost all fifteen physical parameters. The retrieved d0 and s0 were about 4.5 km and 1.1, respectively, for rain rate (RR) when all twelve disdrometers were reporting rainfall with a rain rate threshold of 0.1 mm h-1 in one-minute observations. The d0 decreased noticeably when one or more disdrometers were required to report rain. The d0 was considerably different for a number of parameters (e.g. mass weighted diameter) but was about the same for the other parameters (e.g. RR) when rainfall threshold was reset to 12 dB for Ka-band and 18 dB for Ku-band reflectivity following the expected Global Precipitation Measurement mission's space-borne radar minimum detectable signals. The reduction of the database through elimination of a site did not alter do as long as the fit was adequate. The correlations of 5-minute rain accumulations were lower when disdrometer observations were simulated for a rain gauge at different bucket sizes.
