Abstract: Surface water and energy balance plays an important role in land surface models, especially in coupled land surface?atmospheric models due to the complicated interactions between land surfaces and the overlying atmosphere. ...

Abstract: A new cloud-patch method for the identification and removal of no-rain cold clouds from infrared (IR) imagery is presented. A cloud patch is defined as a cluster of connected IR imagery pixels that are colder than a given ...

Abstract: A system for Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) is under development at The University of Arizona. The current core of this system is an adaptive Artificial ...

Abstract: ater managers across the United States face the need to make informed policy decisions regarding long-term impacts of climate change on water resources. To provide a scientifically informed basis for this, the evolution ...

Abstract: This study compares mean areal precipitation (MAP) estimates derived from three sources: an operational rain gauge network (MAPG), a radar/gauge multisensor product (MAPX), and the Precipitation Estimation from Remotely ...

Abstract: In coupled land surface?atmosphere modeling, the possibility and benefits of constraining model parameters using observational data bear investigation. Using the locally coupled NCAR Single-column Community Climate Model ...

Abstract: PERSIANN, an automated system for Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks, has been developed for the estimation of rainfall from geosynchronous satellite longwave infared ...

Abstract: Operational flood forecasting requires that accurate estimates of the uncertainty associated with model-generated streamflow forecasts be provided along with the probable flow levels. This paper demonstrates a stochastic ...