Search Results

You are looking at 1 - 6 of 6 items for :

  • Operational forecasting x
  • Catchment-scale Hydrological Modelling & Data Assimilation (CAHMDA) III x
  • All content x
Clear All
Dingchen Hou, Kenneth Mitchell, Zoltan Toth, Dag Lohmann, and Helin Wei

. However, a seamless environmental forecast suite requires streamflow forecast for all river basins and at any point along each river. Therefore, the most convenient approach is to provide streamflow prognosis for a mesh of grid points, just like what is done for NWP products, such as precipitation and temperature. As the land surface models (LSMs) are greatly improved during the last decade and are coupled with the atmospheric models in operational NWP systems ( Mitchell et al. 2005 ) and various

Full access
J. M. Schuurmans and M. F. P. Bierkens

System (EPS) of the European Centre for Medium-Range Weather Forecasts (ECMWF) as input for a spatially distributed hydrological model focusing on soil moisture instead of discharge. The ECMWF EPS system produces six hourly rainfall outputs in the form of an operational run, a control run, and 50 ensembles. The operational run is the full model run at high resolution. The control run has the same input conditions as the operational run; however, for calculation time reduction the model resolution is

Full access
Dongryeol Ryu, Wade T. Crow, Xiwu Zhan, and Thomas J. Jackson

calculated during the cycling of a data assimilation system. Consequently, it may be possible to operationally correct for its effects through relatively simple modifications to the EnKF. To examine this possibility, this analysis will focus on three specific questions: How significant are biases in ensemble model forecasts arising from nonlinearities in model forecasts? How do these ensemble biases affect subsequent EnKF updating? Can these biases be operationally corrected during the

Full access
Damian J. Barrett and Luigi J. Renzullo

1. Introduction Increasingly, methods of data assimilation are being applied to both hydrological and hydrometeorological problems driven by prospects of better characterization of initial conditions and improved forecasting skill ( Mecikalski et al. 1999 ; Reichle et al. 2001 ; Crosson et al. 2002 ; Reichle et al. 2002 ; Heathman et al. 2003 ; Merlin et al. 2006 ; Pan et al. 2008 ; Wang and Cai 2008 ; Barrett et al. 2008 ). The benefits afforded by the application of data assimilation

Full access
Yongqiang Zhang, Francis H. S. Chiew, Lu Zhang, and Hongxia Li

: Operational AVHRR processing modules: Atmospheric correction, cloud masking and BRDF compensation. CSIRO Atmospheric Research, Earth Observation Centre Project Final Rep., 24 pp . Eberhart, R. C. , and Kennedy J. , 1995 : A new optimizer using particle swarm theory. Proc. Sixth Int. Symp. on Micro Machine and Human Science, Nagoya, Japan, Institute of Electrical and Electronics Engineers, 39–43 . Fang, H. L. , Liang S. L. , Townshend J. R. , and Dickinson R. E. , 2008 : Spatially and

Full access
Adriaan J. Teuling, Remko Uijlenhoet, Bart van den Hurk, and Sonia I. Seneviratne

ELDAS LSMs differ widely in their treatment of the parameters governing the soil water balance, and they are likely to be representative for the whole range of operational LSMs. The soil parameterizations of the ELDAS models are described in section 3 . It should be noted that some LSM parameters are calibrated to optimize a particular model’s performance, and their conceptual rather than physical meaning would prohibit direct intercomparison. This is, however, not the case for soil parameters

Full access