Laboratory of EMDO develops scientific understanding of formation of natural disasters, implements new technology for monitoring them, demonstrates uses of model and data for predicting the hazard events.
Artificial intelligence based on machine-learning offers the opportunity to tackle the challenges complex data analysis for societal impact. Research using data-driven approaches for discovery, verification, and application development is changing the way in which we approach every major challenge in our society. For example, by using complex medical database in conjunction with commercial, social, geospatial, and environmental data, we can use a holistic ecosystem-based approach to predict when and where environmental influences such as pollution levels can affect human health and disease on a local or regional level. We bring together experts in data capture, analysis, and visualization, developing new innovative ways to address these global challenges.
Develop comprehensive observations on aerosols, clouds and precipitations and investigate the effects of aerosols on severe weather formation and developments. Understand the impacts of aerosols on precipitation occurrence in time and intensity and the effect of latest heat on the precipitation extend in space. Derive the latent heat estimation from all the observations. Improve the parameterization scheme on interaction of aerosols and clouds.
Utilize multi-sources of observational data to monitor the storms and construct the storm vortex for model initialization and improve the techniques to assimilate clouds-and precipitation affected radiances and develop the NWP experiments for predicting the storm
Develop multi-source data sources to construct the surface vegetation and temperature climatological data sets, derive the surface vegetation health index from the real-time and climatological information, use them to detect the major drought events and understand the physical mechanism of drought formation, and establish a long-term series of drought events.
Develop multi-source data sources to detect flooding events in mountains and cities. The multi-source data including the soil moisture, precipitation and snow are also used to construct the warming and forecasting models for predicting the flooding events.
Study new methodology for simulating the multiple-scale weather systems and formulate the scale-dependent parameterization schemes. Develop parallel computation capability for the coupling models.
Detects convective initiation area from high temporal observations from geostationary imagery and validate against the ground-based measurements. Develop the data assimilation technique for high resolution (3km) NPW models.
Radiative transfer modeling: We developed surface emissivity modeling, scattering data base, and atmospheric spectroscopy data base, a new radiative transfer system that can be applied for all the wavelengths and cover the scattering and emission from atmosphere and surfaces. The system is also used for generating a fast and accurate observation forward operators that can be used in NWP data assimilation.
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