Category: Archive

236 – Hypo-G: Improved Hypoxia Modeling for Nutrient Control Decisions in the Gulf of Mexico
Principal Investigator(s): M. Tzortziou

The main objective of this project is to assess and transition the potential benefits of using NASA satellite data products within the EPA’s Gulf of Mexico Modeling Framework. The hypoxic zone in the Northern Gulf of Mexico forms each summer and can extend up to 80 miles offshore and stretch from the discharge of the Mississippi River westward to coastal waters of Texas. The size of the hypoxic zone varies considerably each year. In 2007, the size of the hypoxic zone was 20,500 km2 approximately the size of Massachusetts. The direct effects of hypoxia include fish kills, depletion of fisheries, and loss of habitat for less mobile animals such as crabs and mussels. The purpose of the EPA Gulf of Mexico Modeling and Monitoring project is to provide the scientific basis to guide a reduction in the frequency, duration, size, and degree of oxygen depletion in the northern Gulf of Mexico as outlined in the recently released Hypoxia Action Plan. The Gulf of Mexico Modeling Framework is a suite of coupled EPA models linking the deposition and transport of sediment and nutrients to subsequent bio-geo chemical processes and concentrations of dissolved oxygen in the coastal of waters of Louisiana and Texas. Use of NASA’s Earth Observations can potentially improve the accuracy of these models by providing more accurate inputs, thus enabling determination of best practices and strategies for managing the Mississippi/Achafalaya river basin.

235 – Arctic and Southern Ocean Sea Ice
Principal Investigator(s): S.L. Farrell

The work being conducted under this project is in support of NASA’s Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) scheduled for launch in 2016. ICESat-2 is a follow-on to the ICESat mission, which operated between 2003 and 2009. ICESat-2 will provide sustained monitoring of changes in ice-sheet mass balance, and Arctic and Southern Ocean sea ice volume. The goals under this ICESat-2 Science Definition Team (SDT) project are to derive the Level 1 and 2 sea ice science requirements and measurement accuracies, and to determine the optimum strategy for sampling the complex sea ice environment of the Arctic and Southern Oceans.

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.