Toward a Framework For Systematic Error Modeling Of Spaceborne Precipitation Radar With Noaa/Nssl Ground Radar Based National Mosaic Qpe

Characterization of the error associated with satellite rainfall estimates is a necessary component of deterministic and probabilistic frameworks involving spaceborne passive and active microwave measurements for applications ranging from water budget studies to forecasting natural hazards related to extreme rainfall events. The authors focus here on the error structure of NASA\u27s Tropical Rainfall Measurement Mission (TRMM) Precipitation Radar (PR) quantitative precipitation estimation (QPE) at ground. The problem is addressed by comparison of PR QPEs with reference values derived from ground-based measurements using NOAA/NSSL ground radar based National Mosaic and QPE system (NMQ/Q2). A preliminary investigation of this subject has been carried out at the PR estimation scale (instantaneous and 5 km) using a 3-month data sample in the southern part of the United States. The primary contribution of this study is the presentation of the detailed steps required to derive a trustworthy reference rainfall dataset from Q2 at the PR pixel resolution. It relies on a bias correction and a radar quality index, both of which provide a basis to filter out the less trustworthy Q2 values. Several aspects of PR errors are revealed and quantified including sensitivity to the processing steps with the reference rainfall, comparisons of rainfall delectability and rainfall-rate distributions, spatial representativeness of error, and separation of systematic biases and random errors. The methodology and framework developed herein applies more generally to rainfall-rate estimates from other sensors on board low-earth-orbiting satellites such as microwave imagers and dual-wavelength radars such as with the Global Precipitation Measurement (GPM) mission

Toward a Framework for Systematic Error Modeling of NASA Spaceborne Radar with NOAA/NSSL Ground Radar-Based National Mosaic QPE

Characterization of the error associated to satellite rainfall estimates is a necessary component of deterministic and probabilistic frameworks involving space-born passive and active microwave measurement") for applications ranging from water budget studies to forecasting natural hazards related to extreme rainfall events. We focus here on the error structure of NASA's Tropical Rainfall Measurement Mission (TRMM) Precipitation Radar (PR) quantitative precipitation estimation (QPE) at ground. The problem is addressed by comparison of PR QPEs with reference values derived from ground-based measurements using NOAA/NSSL ground radar-based National Mosaic and QPE system (NMQ/Q2). A preliminary investigation of this subject has been carried out at the PR estimation scale (instantaneous and 5 km) using a three-month data sample in the southern part of US. The primary contribution of this study is the presentation of the detailed steps required to derive trustworthy reference rainfall dataset from Q2 at the PR pixel resolution. It relics on a bias correction and a radar quality index, both of which provide a basis to filter out the less trustworthy Q2 values. Several aspects of PR errors arc revealed and quantified including sensitivity to the processing steps with the reference rainfall, comparisons of rainfall detectability and rainfall rate distributions, spatial representativeness of error, and separation of systematic biases and random errors. The methodology and framework developed herein applies more generally to rainfall rate estimates from other sensors onboard low-earth orbiting satellites such as microwave imagers and dual-wavelength radars such as with the Global Precipitation Measurement (GPM) mission

Collaborating to Compete: Blood Profiling Atlas in Cancer (BloodPAC) Consortium

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/136731/1/cpt666.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/136731/2/cpt666_am.pd

The Global Flood Partnership

Every year riverine flooding affects millions of people in developing countries, due to the large population exposure in the floodplains and the lack of adequate flood protection. Preparedness and monitoring are effective ways to reduce flood risk. State-of-the-art technologies relying on satellite remote sensing as well as numerical hydrological and meteorological predictions can detect and monitor severe flood events at the global scale. This chapter describes the emerging role of the Global Flood Partnership (GFP), a global network of scientists, users, and private and public organizations active in global flood risk management. Currently, a number of GFP partner institutes regularly share results from their experimental products, developed to predict and monitor where and when flooding is taking place in near real time. Products of the GFP have already been used on several occasions by national environmental agencies and humanitarian organizations to support emergency operations and to reduce the overall socioeconomic impacts of disasters. The chapter includes a discussion on existing challenges and ways forward to improve rapid access to flood information and increase resilience to flooding