Toshio Koike

Development of a distributed biosphere hydrological model and its evaluation with the Southern Great Plains Experiments (SGP97 and SGP99)

[1]xa0A distributed biosphere hydrological model, the so-called water and energy budget-based distributed hydrological model (WEB-DHM), has been developed by fully coupling a biosphere scheme (SiB2) with a geomorphology-based hydrological model (GBHM). SiB2 describes the transfer of turbulent fluxes (energy, water, and carbon fluxes) between the atmosphere and land surface for each model grid. The GBHM redistributes water moisture laterally through simulating both surface and subsurface runoff using grid-hillslope discretization and then flow routing in the river network. The WEB-DHM was calibrated and validated for the Little Washita Basin using field observations from Southern Great Plains Hydrology Experiments (SGP97 and SGP99). For the SGP97 period, the model was calibrated and it shows an ability to reproduce point-scale energy fluxes (RMSE < 50 W m−2) as well as CO2 flux (RMSE = 4.6 μ mol m−2s−1). At basin scale, the WEB-DHM can simulate a reasonable hydrograph (Nash = 0.956) and spatial soil moisture distribution with calibration of only a few soil hydraulic parameters for discharge. The model was then validated using SGP99 data sets and observed discharge. For the validation period, the model shows good performance in reproducing the soil surface temperature at 11 sites and the spatial distribution of surface soil moisture, as well as long-term discharges (Nash = 0.715) in the hydroyear from 1 September 1998 to 31 August 1999 that covers both the annual largest flood peak of 1999 and the SGP99 period. To our knowledge, this work is the first to undertake the development and evaluation of a distributed biosphere hydrological model using such comprehensive field observations.

Inverse analysis of the role of soil vertical heterogeneity in controlling surface soil state and energy partition

[1]xa0Surface soil moisture and temperature have been widely addressed in land surface processes modeling and satellite remote sensing because they play a key role in land surface energy and water budget. However, it is rather difficult for some land surface models to reproduce the surface soil state in areas with high soil vertical heterogeneity because these models use a single parameter set to characterize soil hydraulic and thermal processes. This study develops a single-source land surface model to parameterize this heterogeneity. Its soil parameters are inversely estimated by minimizing a cost function that is objectively determined by the discrepancy between observed and model-predicted values of soil moisture and temperature. The approach is then used to investigate how the soil vertical heterogeneity affects subsurface processes and thus controls soil surface state and surface energy budget. This approach is applied to a synthetic numerical experiment and a Tibet field experiment, where the horizontal heterogeneity can be neglected. We indicate that (1) vertical heterogeneous soils cannot be effectively approximated by vertically homogenous soils in a land surface model no matter how the soil parameters are adjusted; (2) soil vertical heterogeneity obviously affects soil subsurface processes and plays a very important role in controlling surface soil wetness and surface energy partition; and (3) in particular, the existence of dense vegetation roots in topsoils may significantly reduce thermal conductivity, increase soil water potential, and enhance surface evaporation. We therefore conclude that it is indispensable to take the soil vertical heterogeneity into account in land surface models, although some of them still assume vertically uniform soil parameters.

Field-Supported Verification and Improvement of a Passive Microwave Surface Emission Model for Rough, Bare, and Wet Soil Surfaces by Incorporating Shadowing Effects

To investigate the potential of passive microwave techniques for observing the atmosphere over land, it is important to understand the nature of emissions from the land surface. The heterogeneity of large-scale land surface emissions has been cited as a major impediment in conducting observations of the atmosphere over land. Many models, both theoretical and empirical, have been developed to explain the surface emission with varying degrees of success. In the past, most field-supported research in soil observations using microwave techniques has concentrated on lower frequencies (L-band). This paper reports on a study, supported by field data, that seeks to improve our understanding of surface emission at various frequencies using passive microwave radiometers. This provides a crucial link between remote sensing of the land surface and the atmosphere. We show that it is important to consider shadowing associated with rough wet surfaces. By incorporating shadowing effects, the advanced integral equation model (AIEM) shows remarkable agreement with observations at all frequencies and polarizations. Although the roughness parameters obtained during our experiment correspond to very rough conditions, by including shadowing effects the AIEM model is able to transition from the not so rough natural condition as observed from space to the very rough as obtained during field experiments

Modeling the Spatial Distribution of Snow Cover in the Dudhkoshi Region of the Nepal Himalayas

AbstractIn this study, a distributed biosphere hydrological model with three-layer energy-balance snow physics [an improved version of the Water and Energy Budget–based Distributed Hydrological Model (WEB-DHM-S)] is applied to the Dudhkoshi region of the eastern Nepal Himalayas to estimate the spatial distribution of snow cover. Simulations are performed at hourly time steps and 1-km spatial resolution for the 2002/03 snow season during the Coordinated Enhanced Observing Period (CEOP) third Enhanced Observing Period (EOP-3). Point evaluations (snow depth and upward short- and longwave radiation) at Pyramid (a station of the CEOP Himalayan reference site) confirm the vertical-process representations of WEB-DHM-S in this region. The simulated spatial distribution of snow cover is evaluated with the Moderate Resolution Imaging Spectroradiometer (MODIS) 8-day maximum snow-cover extent (MOD10A2), demonstrating the model’s capability to accurately capture the spatiotemporal variations in snow cover across the s...

Seasonal variation of cloud activity and atmospheric profiles over the eastern part of the Tibetan Plateau

[1]xa0Cumulus activity over the Tibetan Plateau has a clear pattern of seasonal progression. Activity occurs with significant frequency in the eastern part of the plateau from April to mid-May. From mid-May to mid-June the frequency of cumulus activity drastically decreases and then increases after mid-June. Causes of such definite seasonal progression are revealed by in situ radiosonde observation data. In early spring the vertical profile of potential temperature is almost uniform from the surface to 7000 m above sea level around noon, and atmospheric stratification is vertically neutral for dry convection. Under such a condition, dry convection easily occurs and develops cumulus cloud that is frequently observed. From mid-May to mid-June the atmosphere has conditionally unstable stratification, but the atmosphere is relatively dry and hardly saturated and cumulus activity occurs less frequently. Though the atmosphere is also conditionally unstable after mid-June, the increase in total precipitable water content allows easy atmospheric saturation and cumulus activity recommences. At the same time, satellite data and reanalysis data show, in the eastern part of the Tibetan Plateau, a consistent correspondence of the seasonal variation of cumulus activity, total precipitable water content, and vertical instability of the atmosphere. Results of synoptic-scale analysis indicate the enhancement of cumulus convection in the northern part of India and the Tibetan High cause a moisture increase over the Tibetan Plateau and leads to the recommencement of frequent cumulus activity over the plateau.

A radiative transfer model and an algorithm for soil moisture including very dry conditions

Many of microwave radiometer algorithms for soil moisture tend to provide overestimation in very dry cases, partly due to the effects of the volume scattering effects. This study investigates the feasibility of an AMSR-E soil moisture algorithm, which includes the volume scattering effect in soil, by applying it to the dry/wet transition conditions. The algorithm is based on the radiative transfer in the soil medium by the discrete ordinate method (4 streams) and the Henyey-Greenstein phase function. The scattering effects of soil particles are calculated using the Mie theory. The effects of surface roughness are incorporated by using the polarization mixing parameter and the roughness parameter. The vegetation water content and albedo are used to estimate the effects of vegetation. The algorithm was applied to the observed data by the Soil Moisture Temperature Measurement System (SMTMS) and Automatic Weather Stations (AWS) in Mongolia from July to August, 2003. Results show that the proposed algorithm can improve accuracy of soil moisture estimation even in very dry cases. Keywords-soil moisture; AMSR-E; radiative transfe model; volume scattering; CEOP; SMTMS

Improving the AMSR-E Soil Moisture Algorithm of the University of Tokyo through Field Experiments and Parameters Optimization

This paper reports the progresses of AMSR-E soil moisture algorithm development at the University of Tokyo. The first progress is made through improving the forward model, i.e. radiative transfer model (RTM), based on field experiment and numerical simulation. The second progress is the development of a new parameterization method, through which the parameters necessary for the algorithm are optimized by a land data assimilation system developed at the University of Tokyo (LDAS-UT). The capability of LDAS-UT was validated successfully with winter wheat experiment data. Finally, the new RTM and parameterization method was validated on AMSR-E match up data set. The results demonstrate that the simulated brightness temperature is in good agreements with the one observed by AMSR-E.

Development of a soil moisture retrieval algorithm for spaceborne passive microwave radiometers and its application to AMSR-E and SSM/I

This paper reports the development of a soil moisture retrieval algorithm for spaceborne passive microwave radiometers. The algorithm is based on a modified radiative transfer model, so-called DMRT-AIEM model. The implementation of this algorithm consists of three steps: 1) forward model parameters optimization; 2) lookup table generation and 3) lookup table reversion and soil moisture estimation. The algorithm was tested at a CEOP (coordinate enhanced observing period) reference site on the Mongolia Gobi. The retrieved soil moisture data was compared with the in situ observations. The comparison results show that the performance of the new algorithm is good, giving a standard error of the estimate (SEE) of 3.8% and R-square of 0.4. Moreover, a successful TB validation on SSM/I low frequencies was achieved by the RTM used in this algorithm. It means this algorithm provides a possibility to retrieval around 20 years soil moisture data from SSM/I observations.

Estimating land surface energy and water fluxes by using the Land Data Assimilation System developed at the university of Tokyo (LDASUT)

This paper reports an application of an Land Data Assimilation System developed in the University of Tokyo (LDASUT) on the Gaize PBL site at the northwest of Tibet Plateau, for the period from July to August, 2007. The objectives of this study are: (1) to validate LDASUT in bare soil field using in-situ observation, (2) to check the feasibility to estimate areal land surface variables reliably with using LDASUT driven by GCM output data. For the system validation, LDASUT was first driven by in-situ observed micrometeorological data, and simulated energy fluxes were compared to hourly direct measurements; simulated soil moisture content was compared to the in-situ soil moisture observation at the depth of 4 cm. The results show that LDAS can generally simulate those variables well and thus the capability of LDAS is validated. In order to check the possibility of applying LDAS globally and simulating surface energy and water budget worldwide, Japan Meteorology Agency (JMA) Model Output Local Time Series (MOLTS) data were used as the driven data of LDAS. Performance of LDAS was not so good when it was driven by the JMA MOLTS data. This result demonstrated that there were systemic biases lied in JMA MOLTS data in the study region and thus it can not directly apply to LSM or LDAS.

The Development of 1-D Ice Cloud Microphysics Data Assimilation System (IMDAS) for Cloud Parameter Retrievals by Integrating Satellite Data

Reliable prediction of precipitation by Numerical Weather Prediction (NWP) models depends on the appropriate representation of cloud microphysical processes and accurate initial conditions of observations of atmospheric variables. Therefore, 1D Variational (1D-Var) Ice Cloud Microphysics Data Assimilation System (IMDAS) is developed for retrieving reasonable cloud distributions to improve the predictability of NWP models. The general framework of IMDAS includes the Lin ice cloud microphysics scheme as a model operator, a 4-stream fast microwave radiative transfer model (RTM) in the atmosphere as an observation operator, and a global minimization method known as Shuffled Complex Evolution (SCE). The IMDAS assimilates the satellite microwave radiometer data set of Advanced Microwave Scanning Radiometer (AMSR-E) and retrieves integrated water vapor (IWV) and integrated cloud liquid water content (ICLWC). This new method successfully introduces the heterogeneity into the initial state of the atmosphere, and the modeled microwave brightness temperatures agree well with observations of Wakasa Bay Experiment 2003 in Japan. It has improved the performance of cloud microphysics scheme significantly by the intrusion of heterogeneity into the external Global Reanalysis (GANAL) data, which may improve atmospheric initial conditions.