4. Utilization of multi-source observations
Various ground-based and space-borne sensors provide an extraordinary opportunity to monitor the earth, and are promising to track and predict extreme events. Harmonizing data series across different missions and relevant data fusion experiments is critical. This sub-theme presents progress in dealing with problems on scale-matching (upscaling of point ground measurement, downscaling of low resolution microwave retrieval), integration (e.g., SM and TM, GRACE and AMSR-E), and error estimate (individual), especially within the framework of multi-source land data assimilation.
|Xin Li, CAREERI/Chinese Academy of Sciences
|Rolf Reichle, NASA: “Towards Multivariate Land Data Assimilation in the NASA GEOS-5 System”
|Much of the progress in land data assimilation over the past decade has been made using so-called uni-variate systems. In such a system, only one particular type of observation is assimilated. For example, retrievals of surface soil moisture from space-borne passive microwave observations can be assimilated to obtain improved estimates of the soil moisture profile. Slightly more complex assimilation systems may include more than one type observation but are still focused on estimating one particular geophysical variable. For example, soil moisture retrievals from active and passive microwave observations can be used in a soil moisture assimilation system, or snow cover and snow water equivalent retrievals can be used in a snow assimilation system. While the benefit of assimilating more than one type of observation is already apparent in such systems, they can still be considered uni-variate systems because the analysis targets only one kind of geophysical variable. By contrast, multi-variate systems use several types of observations to analyze different kinds of geophysical variables. Progress towards such a multi-variate system is illustrated using examples from the NASA GEOS-5 land data assimilation system. The first example discusses the assimilation of satellite microwave brightness temperatures (Tbs) along with observations of large-scale terrestrial water storage (TWS) to estimate the TWS components at the scale of the model grid. The challenge is to configure the system such that the estimated uncertainties are realistic at the multiple spatial and temporal scales needed for a successful analysis. The second example addresses the assimilation of Tbs and freeze-thaw observations in the soil moisture and temperature analysis of the NASA SMAP L4_SM product, which involves the use of coarse-resolution radiometer Tbs and retrievals based on high-resolution radar backscatter.
|Paul Houser, George Mason University: “Towards a hyper-resolution integrated water observation and prediction system”
|Society’s welfare, progress, and sustainable economic growth—and life itself—depend on the abundance and vigorous cycling and replenishing of water throughout the global environment. The water cycle operates on a continuum of time and space scales and exchanges large amounts of energy as water undergoes phase changes and is moved from one part of the Earth system to another. We must move toward an integrated observation and prediction paradigm that addresses broad local-to-global science and application issues by realizing synergies associated with multiple, coordinated observations and prediction systems. A central challenge of a future water and energy cycle observation strategy is to progress from single variable water-cycle instruments to multivariable integrated instruments in electromagnetic-band families. The microwave range in the electromagnetic spectrum is ideally suited for sensing the state and abundance of water because of water’s dielectric properties. Eventually, a dedicated high-resolution water-cycle microwave-based satellite mission may be possible based on large-aperture antenna technology that can harvest the synergy that would be afforded by simultaneous multichannel active and passive microwave measurements. A partial demonstration of these ideas can even be realized with existing microwave satellite observations to support advanced multivariate retrieval methods that can exploit the totality of the microwave spectral information. The simultaneous multichannel active and passive microwave retrieval would allow improved-accuracy retrievals that are not possible with isolated measurements. Furthermore, the simultaneous monitoring of several of the land, atmospheric, oceanic, and cryospheric states brings synergies that will substantially enhance understanding of the global water and energy cycle as a system. The multichannel approach also affords advantages to some constituent retrievals—for instance, simultaneous retrieval of vegetation biomass would improve soil-moisture retrieval by avoiding the need for auxiliary vegetation information. This multivariable water-cycle observation system must be integrated with high-resolution, application relevant prediction systems to optimize their information content and utility is addressing critical water cycle issues. One such vision is a real-time ultra-high resolution locally-moasiced global land modeling and assimilation system, that overlays regional high-fidelity information over a baseline global land prediction system. Such a system would provide the best possible local information for use in applications, while integrating and sharing information globally for diagnosing larger water cycle variability. In a sense, this would constitute a hydrologic telecommunication system, where the best local in-situ gage, Doppler radar, and weather station can be shared internationally, and integrated in a consistent manner with global observation platforms like the multivariable water cycle mission. To realize such a vision, large issues must be addressed, such as international data sharing policy, model-observation integration approaches that maintain local extremes while achieving global consistency, and methods for establishing error estimates and uncertainty.
|Kinya Toride, The University of Tokyo, Japan: “Development of an Algorithm for Soil Moisture with High Spatial- and Temporal- Resolution”
|J. M. Bergeron, Université de Sherbrooke, Canada: “Using Multivariate Data Assimilation to Improve Streamflow Predictions for a Mountainous Watershed”