Category: Archive

206 – Vegetation 3D Structural Parameters from Multi-Sensor Data
Principal Investigator(s): G. Sun

In the year 2010, we have worked on the following issues: : 1) collection of validation data in field; 2) investigation of effects of sampling rates on the accuracy of biomass estimation from lidar data and developing biomass estimation model; 3) polarimetric SAR data processing and application in biomass estimation.

233 – Evaluation of the NASA GMAO Modern Era Retrospective-Analysis for Research and Applications (MERRA) in Polar Latitudes
Principal Investigator(s): R. I. Cullather

MERRA is a state-of-the-art global numerical reanalyses that has recently been produced by the NASA GMAO, and covers the period from 1979 until the present. Reanalyses are 4-dimensional numerical depictions of the atmospheric state that are retrospectively produced through the assimilation of available observations using a weather prediction model. The objective of MERRA is to provide a climate context for the NASA satellite observing system and to improve the representation of the water cycle in reanalyses. The purpose of this work is to characterize the performance of MERRA over polar regions including the Arctic Ocean and continental ice sheets and, where appropriate, apply MERRA for the purpose of regional climate study. An assessment of MERRA in these regions is useful for providing guidance to other users in locations that are of particular importance in climate research. The characterization of high latitude regional climate variability and its depiction in reanalysis products is also applicable to ongoing model validation efforts at GMAO.

204 – GMAO Core MERRA Research & Evaluation of Global Water Cycle in Reanalyses
Principal Investigator(s): J. Chen

MERRA is a new NASA reanalysis dataset which is listed as the core project of GMAO. In 2010, our research is to investigate the impact of observing system on the MERRA dataset, especially in the energy and water cycle parameters.

203 – Diurnal Variation of Tropospheric Trace Gas Amounts and Aerosol Optical Characteristics
Principal Investigator(s): M. Tzortziou

The project involves the development of a new ground-based network of highly accurate spectrometer systems (Pandora and Cleo instruments, Fig 1) and the development, optimization and validation of remote-sensing retrieval algorithms for obtaining new measurements of aerosol optical characteristics and tropospheric trace gas amounts and vertical distribution (NO2, O3, SO2, H2O, HCHO). The resulting data provide a unique dataset for bounding tropospheric photochemical models, and studying the evolution of tropospheric ozone, NO2, other trace gases, and aerosols and their impacts on climate and air quality. Measurements are applied to improve interpretation of current satellite observations and assess more effective design and observing strategies for future NASA satellite missions.

202 – Ground-based Supersite/Network Measurements in Support of NASA/EOS Missions
Principal Investigator(s): Q. Ji

According to NASA’s Science Mission Directorate (SMD), "in order to study the Earth as a whole system and understand how it is changing, NASA develops and supports a large number of Earth observing missions. These missions provide Earth science researchers the necessary data to address key questions about global climate change." (http://science.nasa.gov/earth-science/missions). As a major component of the Earth Science Division of NASA/SMD, "the Earth Observing System (EOS) is a coordinated series of polar-orbiting and low inclination satellites for long-term global observations of the land surface, biosphere, solid Earth, atmosphere, and oceans." (http://eospso.gsfc.nasa.gov). As the EOS follow-on, "the Decadal Survey will generate consensus recommendations from the Earth and environmental science and the applications communities regarding a systems approach to space-based Earth Science observations." (http://decadal.gsfc.nasa.gov). However, satellite observations alone is not sufficient to provide a complete understanding of the complex earth-atmosphere system. Extensive ground-based measurements and comprehensive modeling analysis are also indispensable parts in this endeavor.

231 – Microphysical Processes of Atmospheric Convective Systems
Principal Investigator(s): T. Iguchi

Cloud microphysics focuses on the physical processes on the scale from um to cm orders in clouds. Not only it decides features of precipitation from clouds attributed to convective systems but also it has a large impact on the structure of the system through heating or cooling by the water phase conversion and radiation process. Exact representation of cloud microphysics is thus an important subject of numerical studies for the system. We propose the development of the numerical model package to investigate a role of the cloud microphysics for the atmospheric convective systems.

200 – Global Land Data Assimilation System (GLDAS)
Principal Investigator(s): H. K. Beaudoing

Land surface states and fluxes influence the weather and climate through exchanges of energy, water, and momentum between land and atmosphere. The energy and water stored in land present persistence on diurnal, seasonal, and inter-annual time scales. Because these conditions (e.g. soil moisture, temperature, and snow) are integrated states, biases in forcing data (i.e. meteorological and land characteristics) and parameterizations (i.e. models) lead to incorrect estimates. We are working on deriving accurate surface conditions at global, high spatio-temporal resolutions, and near-real time to help improve weather forecast and prediction skills, water and energy budget studies, and water resource management applications.

140 – Earth Model-Human Model
Principal Investigator(s): E. Kalnay

Human population and consumption has grown significantly over the past few decades. The Earth’s natural resources were assumed to be practically infinite for the whole length of the history, but we are now realizing that they may be scarce. This has rung a bell for the policy makers, scientists, economists, and all other conscious individuals. Economic growth has reached an “uneconomic growth” phase. To cope with such issues, new fields of study like “ecological economics” are born. Several research groups around the globe have developed (mathematical) models to predict the future of human population and nature. Such models have helped scholars to understand and investigate possible scenarios for the future of life on our planet more thoroughly.

The most complete versions of such models incorporate population, climate, energy, and agriculture as main variables. However, some of these variables, like population, are taken as exogenous variables and therefore, the coupling between the variables is uni-directional. This means that, for example, increased population can affect climate by creating more pollution, but the climate change does not feed back on the population.

138 – Comparison of Air Quality Models with Satellite Observations for Improved Model Predictive Capabilities
Principal Investigator(s): C. Flynn

Satellite observations provide several important benefits to air quality, including improved forecasting ability for air quality models, assessment of air quality for attribution to specific sources, and improved estimation of source emissions. However, many challenging problems remain for the use of satellite observations in diagnosing near-surface air pollution. The column-integrated quantities retrieved from satellite instruments for key trace gases and aerosols must be interpreted correctly to derive information about near-surface conditions. Despite these challenges, a major scientific goal remains the use of satellite observations to improve and validate current air quality models for more accurate predictive capability. The DISCOVER-AQ field project will provide surface, in-situ aircraft, and remote sensing data which will aid in the interpretation of satellite data for air quality. This project will be conducted in support of this overall goal, by comparing satellite observations, aircraft measurements, and surface air quality datasets with air quality model output. Such a comparison may lead to better understanding of the factors affecting the correlation of satellite observations with current models.