Open AccessImplementation considerations of a conceptual precipitation model
Author(s) -
Dolcine L.,
Andrieu H.,
French M. N.,
Creutin J. D.
Publication year - 2000
Publication title -
journal of geophysical research: atmospheres
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.67
H-Index - 298
eISSN - 2156-2202
pISSN - 0148-0227
DOI - 10.1029/1999jd900968
Subject(s) - rainwater harvesting , meteorology , environmental science , precipitation , radar , quantitative precipitation forecast , cloud computing , liquid water content , ceilometer , flood myth , computer science , geography , ecology , telecommunications , biology , operating system , archaeology
Some aspects of water management, such as flash flood analysis or sewerage management require spatially distributed rainfall estimates and forecasts over surface areas ranging from a few square kilometers to a few hundred square kilometers. Typically, these requirements cannot be satisfied by operational numerical weather prediction models. Faced with these constraints, an alternative solution consists of designing modeling tools consistent with observations routinely available for the survey of catchments, including ground meteorological data, voluminal radar data, satellite data, and operational numerical model output fields. This research headline is inspired by Georgakakos and Bras [1984a] who proposed a simplified dynamical approach considering an atmospheric column as a reservoir of liquid water to describe rainfall evolution. Initially, based on ground meteorological data, this approach was later adapted to voluminal radar data to model the evolution of vertically integrated rainwater content (VIL) in the atmospheric column. In the present work, the forecast lead time is extended through a proposed solution consisting of implementing a simplified precipitation model explicitly accounting for the cloud water content state. This paper demonstrates the potential interest of taking into account the cloud water state through a feasibility study. The first part of the paper presents the model formulation introducing a reservoir representing the cloud water state. The second part of the paper evaluates the potential improvement gained by introducing this component, and the influence of cloud and rainwater uncertainties on model performance. This evaluation utilizes rainwater content, cloud water content, and related meteorological variables produced by a meteorological microphysical monodimensional model. Results of modeling and forecast experiments are included to demonstrate the value of introducing the cloud water state. The experiments show improved forecast performance using the model accounting for cloud water compared with the simple extrapolation method and a related precipitation model dealing only with the rainwater content state.