Author: Travis Swaim

234 – Aerosol Remote Sensing
Principal Investigator(s): M. Petrenko

The effects of atmospheric aerosols on the air quality, the hydrological cycle, and climate are still poorly understood. During the past decade, there have been increased efforts to employ satellite remote-sensing approaches in measuring aerosols in order to complement measurements from ground-based systems. However, because of the differences in the sensor measurement characteristics and algorithms used for aerosol retrievals, the products are often inconsistent, making it difficult to derive objective measures of aerosol amounts and properties. Therefore, it has become necessary to conduct integrated analysis of aerosol measurements acquired with different types of instrumentation, in order to narrow down the uncertainties that delay improvements in the knowledge of the different aerosol impacts. The purpose of this project is to provide an approach and a unified framework for inter-comparison and validation of aerosol measurements from different sensors and instruments, including ground-based, airborne, and spaceborne, obtained at different locations and time around the globe.

224a – A Land Data Assimilation System for Famine Early Warning
Principal Investigator(s): S. Yatheendradas

This project proposes to enhance the operations of the Famine Early Warning Systems Network (FEWS NET), the US Agency for International Development’s (USAID) decision support system for high priority international food aid programs that safeguard the lives and livelihoods of tens of millions of the world’s poorest and most vulnerable people. This will be done through a unified land modeling and assimilation solution-based integration of a custom instance of NASA’s Land Information System (LIS), specifically for the domains, data streams, and monitoring/forecast requirements associated with food security assessment in data-sparse, developing country settings. This FEWSNET Land Data Assimilation System (FLDAS) will transition into long term, routine use at USGS for FEWS NET decision support, capable of making ensemble runs as the basis for weekly and seasonal forecasts/projections of land surface variables and of direct application of IPCC climate change modeling results to produce 21st century scenarios expressed in terms of land surface variables relevant to FEWS NET food security assessments. Increased specificity and confidence in FEWS NET weekly hazard assessments and seasonal food security outlooks will also be achieved. The science hypothesis interspersed with the human food security dimension is then that the higher quality hydroclimatic modeling made possible by FLDAS will lead directly to more accurate food security outlook (FSO) Integrated Phase Classification (IPC) maps of food security and humanitarian action officials.

224 – Improving NOAA/NWS River Forecast Center Decision Support with NASA Satellite and Land Information System Products
Principal Investigator(s): S. Yatheendradas

The overarching goal is to demonstrate improved accuracy in runoff, stream flow, and flood monitoring and simulation that result from the combination of NASA infrastructure of snow data (MODIS) and model (LIS), with operational NOAA National Weather Service (NWS) River Forecast System (NWSRFS) Decision Support Tools (DST). The objectives are to engineer and integrate NASA satellite- and model-derived land surface products, through the Land Information System (LIS), into NWSRFS DST component models. The specific science question we investigated is whether adjusting modeled snow area with MODIS estimates improves modeled streamflow.

221 – Optical Properties Of Mineral Dust Aerosol
Principal Investigator(s): R. Hansell

To investigate the optical properties of mineral dust aerosol between the near to thermal IR using global aerosol field measurements combined with analysis of model data to help advance ground and satellite-based remote sensing applications and column energetic studies.

213 – Applications, Evaluation, and Improvement of Goddard Multi-scale Modeling System
Principal Investigator(s): B-W. Shen

To examine the impacts of resolved moist processes on the short-term and long-term climate simulations with the MMF.

210 – AURA Validation Using Ground-Based Remote Sensing
Principal Investigator(s): A. Cede

Many atmospheric parameters measured by the instruments on the NASA AURA satellite platform need improved validation from independent measurements, e.g. ground-based and aircraft remote sensing or balloon measurements. Aerosols and some of the trace gases retrieved by AURA are highly variable in time and space (e.g. tropospheric O3, NO2, SO2, HCHO). Therefore regional and global monitoring networks are needed for statistically meaningful validation results.

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.