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ABSTRACT: A frequently encountered difficulty in assessing model-predicted landâatmosphere exchanges of moisture and energy is the absence of comprehensive observations to which model predictions can be compared at the spatial and temporal resolutions at which the models operate. Various methods have been used to evaluate the land surface schemes in coupled models, including comparisons of model-predicted evapotranspiration with values derived from atmospheric water balances, comparison of model-predicted energy and radiative fluxes with tower measurements during periods of intensive observations, comparison of model-predicted runoff with observed streamflow, and comparison of model predictions of soil moisture with spatial averages of point observations. While these approaches have provided useful model diagnostic information, the observation-based products used in the comparisons typically are inconsistent with the model variables with which they are comparedâfor example, observations are for points or areas much smaller than the model spatial resolution, comparisons are restricted to temporal averages, or the spatial scale is large compared to that resolved by the model. Furthermore, none of the datasets available at present allow an evaluation of the interaction of the water balance components over large regions for long periods. In this study, a model-derived dataset of land surface states and fluxes is presented for the conterminous United States and portions of Canada and Mexico. The dataset spans the period 1950â2000, and is at a 3-h time step with a spatial resolution of â
degree. The data are distinct from reanalysis products in that precipitation is a gridded product derived directly from observations, and both the land surface water and energy budgets balance at every time step. The surface forcings include precipitation and air temperature (both gridded from observations), and derived downward solar and longwave radiation, vapor pressure deficit, and wind. Simulated runoff is shown to match observations quite well over large river basins. On this basis, and given the physically based model parameterizations, it is argued that other terms in the surface water balance (e.g., soil moisture and evapotranspiration) are well represented, at least for the purposes of diagnostic studies such as those in which atmospheric model reanalysis products have been widely used. These characteristics make this dataset useful for a variety of studies, especially where ground observations are lacking.
Journal of Climate. 11/2002; 15:3237--3251.
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ABSTRACT: This paper describes a snow parameter retrieval algorithm from
passive microwave remote sensing measurements. The three components of
the retrieval algorithm include a dense media radiative transfer (DMRT)
model, which is based on the quasicrystalline approximation (QCA) with
the sticky particle assumption, a physically-based snow hydrology model
(SHM) that incorporates meteorological and topographical data, and a
neural network (NN) for computational efficient inversions. The DMRT
model relates physical snow parameters to brightness temperatures. The
SHM simulates the mass and heat balance and provides initial guesses for
the neural network. The NN is used to speed up the inversion of
parameters. The retrieval algorithm can provide speedy parameter
retrievals for desired temporal and spatial resolutions, Four channels
of brightness temperature measurements: 19V, 19H, 37V, and 37H are used.
The algorithm was applied to stations in the northern hemisphere. Two
sets of results are shown. For these cases, the authors use ground-truth
precipitation data, and estimates of snow water equivalent (SWE) from
SHM give good results. For the second set, a weather forecast model is
used to provide precipitation inputs for SHM. Additional constraints in
grain size and density are used. They show that inversion results
compare favorably with ground truth observations
IEEE Transactions on Geoscience and Remote Sensing 09/2001; · 2.89 Impact Factor
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ABSTRACT: Predicting the snow parameters, such as snow depth or snow water
equivalent is an important task in the geoscience study. In the past,
parameter retrieval algorithms mainly use the relationship between a
subset of the snow parameters and passive remote sensing measurements.
However, the brightness temperatures depend not only on snow depth but
also on other snow parameters. Thus, it is desirable to develop a
multi-parametric inversion algorithm using multi-frequency and dual
polarization measurements. The authors' current approach is using a
constrained neural network iterative inversion algorithm incorporating a
priori estimates provided by the snow hydrology model to retrieve the
snow parameters. For the real-time application, the weather forecast
model precipitation input instead of station precipitation data is used
to provide the precipitation input for the snow hydrology model. The
results of the constrained inversion will be illustrated for the
stations in the Northern Hemisphere
Geoscience and Remote Sensing Symposium, 1999. IGARSS '99 Proceedings. IEEE 1999 International; 02/1999
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ABSTRACT: The estimation of snow parameters by using passive microwave
remote sensing data such as SSMR and SSM/I has been studied for many
years. In previous parameter retrievals using passive remote sensing,
the techniques have been largely limited to using linear regression
relationships between measurements and one snow parameter, such as
snow-water equivalent. However, the brightness temperatures are not only
influenced by snow-water equivalent, but also by snow-grain size, snow
temperature, and snow density. Thus, it is important to develop a
multi-parametric inversion algorithm using multi-frequency and dual
polarization measurements. In this paper, we use a neural network
iterative approach for parameter inversion in combination with a snow
hydrology model for a priori estimates. The neural network (NN) approach
is used to invert the parameters from the measurements for computational
efficiency. In the scheme of forward neural network iterative inversion,
the initial guesses of snow parameters are given by a physically based
snow hydrology model which uses a priori information of weather data as
input. The results of applying the algorithm to the Northern Hemisphere
are illustrated
Geoscience and Remote Sensing Symposium Proceedings, 1998. IGARSS '98. 1998 IEEE International; 08/1998
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ABSTRACT: Inverse remote sensing problems are generally ill-posed. In this
paper, we propose an approach, which integrates the dense media
radiative transfer (DMRT) model, snow hydrology model, neural networks
and SSM/I microwave measurements, to infer the snow depth. Four
multilayer perceptrons (MLPs) were trained using the data from DMRT
model. With the provision of an initial guess from snow hydrology
prediction, neural networks effectively invert the snow parameters based
on SSM/I measurements. In addition, a prediction neural network is used
to achieve adaptive learning rates and a good initial estimate of snow
depth for inversion. Result shows that our algorithm can effectively and
accurately retrieve snow parameters from these highly nonlinear and
many-to-one mappings
Acoustics, Speech and Signal Processing, 1998. Proceedings of the 1998 IEEE International Conference on; 06/1998 · 4.63 Impact Factor
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Hydrological Sciences Journal/Journal des Sciences Hydrologiques 01/1998; 43(1):131-141. · 1.54 Impact Factor
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Hydrological Sciences Journal/Journal des Sciences Hydrologiques 01/1998; 43(1):143-158. · 1.54 Impact Factor
