No abstract

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'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 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 detectability and rainfallrate 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.

OLYMPEX is a comprehensive field campaign to study how precipitation in Pacific storms is modified by passage over coastal mountains.

SummaryConvective processes play a critical role in the Earth's energy balance through the redistribution of heat and moisture in the atmosphere and subsequent impacts on the hydrologic cycle. Global observation and accurate representation of these processes in numerical models is vital to improving our current understanding and future simulations of Earth's climate system. Despite improvements in computing power, current operational weather and global climate models are unable to resolve the natural temporal and spatial scales that are associated with convective and stratiform precipitation processes; therefore, they must turn to parameterization schemes to represent these processes. In turn, the physical basis for these parameterization schemes needs to be evaluated for general application under a variety of atmospheric conditions. Analogously, space-based remote sensing algorithms designed to retrieve related cloud and precipitation information for use in hydrological, climate, and numerical weather prediction applications often rely on physical "parameterizations" that reliably translate indirectly related instrument measurements to the physical quantity of interest (e.g., precipitation rate). Importantly, both spaceborne retrieval algorithms and model convective parameterization schemes traditionally rely on field campaign data sets as a basis for evaluating and improving the physics of their respective approaches.The Midlatitude Continental Convective Clouds Experiment (MC3E) will take place in central Oklahoma during the April-May 2011 period. The experiment is a collaborative effort between the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Climate Research Facility and the National Aeronautics and Space Administration's (NASA) Global Precipitation Measurement (GPM) mission Ground Validation (GV) program. The field campaign leverages the unprecedented observing infrastructure currently available in the central United States, combined with an extensive sounding array, remote sensing and in situ aircraft observations, NASA GPM ground validation remote sensors, and new ARM instrumentation purchased with American Recovery and Reinvestment Act funding. The overarching goal is to provide the most complete characterization of convective cloud systems, precipitation, and the environment that has ever been obtained, providing constraints for model cumulus parameterizations and space-based rainfall retrieval algorithms over land that have never before been available. Several different components of convective cloud and precipitation processes tangible to both the convective parameterization and precipitation retrieval algorithm problem are targeted, such as preconvective environment and convective initiation, updraft/downdraft dynamics, condensate transport and detrainment, precipitation and cloud microphysics, spatial and temporal variability of precipitation, influence on the environment and radiation, and a detailed description of the large-scale forcing.MC3E will use a new multi-scale ...

The Mapping Application for Penguin Populations and Projected Dynamics (MAPPPD) is a web-based, open access, decision-support tool designed to assist scientists, non-governmental organisations and policy-makers working to meet the management objectives as set forth by the Commission for the Conservation of Antarctic Marine Living Resources (CCAMLR) and other components of the Antarctic Treaty System (ATS) (that is, Consultative Meetings and the ATS Committee on Environmental Protection). MAPPPD was designed specifically to complement existing efforts such as the CCAMLR Ecosystem Monitoring Program (CEMP) and the ATS site guidelines for visitors. The database underlying MAPPPD includes all publicly available (published and unpublished) count data on emperor, gentoo, Adélie and chinstrap penguins in Antarctica. Penguin population models are used to assimilate available data into estimates of abundance for each site and year. Results are easily aggregated across multiple sites to obtain abundance estimates over any user-defined area of interest. A front end web interface located at www.penguinmap.com provides free and ready access to the most recent count and modelled data, and can act as a facilitator for data transfer between scientists and Antarctic stakeholders to help inform management decisions for the continent.