Search Results

You are looking at 1 - 2 of 2 items for :

  • Author or Editor: Ray Wang x
  • Journal of Hydrometeorology x
  • Refine by Access: Content accessible to me x
Clear All Modify Search
Gab Abramowitz
,
Hoshin Gupta
,
Andy Pitman
,
Yingping Wang
,
Ray Leuning
,
Helen Cleugh
, and
Kuo-lin Hsu

Abstract

Data assimilation in the field of predictive land surface modeling is generally limited to using observational data to estimate optimal model states or restrict model parameter ranges. To date, very little work has attempted to systematically define and quantify error resulting from a model's inherent inability to simulate the natural system. This paper introduces a data assimilation technique that moves toward this goal by accounting for those deficiencies in the model itself that lead to systematic errors in model output. This is done using a supervised artificial neural network to “learn” and simulate systematic trends in the model output error. These simulations in turn are used to correct the model's output each time step. The technique is applied in two case studies, using fluxes of latent heat flux at one site and net ecosystem exchange (NEE) of carbon dioxide at another. Root-mean-square error (rmse) in latent heat flux per time step was reduced from 27.5 to 18.6 W m−2 (32%) and monthly from 9.91 to 3.08 W m−2 (68%). For NEE, rmse per time step was reduced from 3.71 to 2.70 μmol m−2 s−1 (27%) and annually from 2.24 to 0.11 μmol m−2 s−1 (95%). In both cases the correction provided significantly greater gains than single criteria parameter estimation on the same flux.

Full access
Yongqiang Zhang
,
Ray Leuning
,
Francis H. S. Chiew
,
Enli Wang
,
Lu Zhang
,
Changming Liu
,
Fubao Sun
,
Murray C. Peel
,
Yanjun Shen
, and
Martin Jung

Abstract

Satellite and gridded meteorological data can be used to estimate evaporation (E) from land surfaces using simple diagnostic models. Two satellite datasets indicate a positive trend (first time derivative) in global available energy from 1983 to 2006, suggesting that positive trends in evaporation may occur in “wet” regions where energy supply limits evaporation. However, decadal trends in evaporation estimated from water balances of 110 wet catchments do not match trends in evaporation estimated using three alternative methods: 1) , a model-tree ensemble approach that uses statistical relationships between E measured across the global network of flux stations, meteorological drivers, and remotely sensed fraction of absorbed photosynthetically active radiation; 2) , a Budyko-style hydrometeorological model; and 3) , the Penman–Monteith energy-balance equation coupled with a simple biophysical model for surface conductance. Key model inputs for the estimation of and are remotely sensed radiation and gridded meteorological fields and it is concluded that these data are, as yet, not sufficiently accurate to explain trends in E for wet regions. This provides a significant challenge for satellite-based energy-balance methods. Trends in for 87 “dry” catchments are strongly correlated to trends in precipitation (R 2 = 0.85). These trends were best captured by , which explicitly includes precipitation and available energy as model inputs.

Full access