Category: Archive

247 – GPM Algorithm Development
Principal Investigator(s): J. Munchak

The general objective is to develop a general framework and state-of-the-art algorithms to advance precipitation observations from space using combined information from active and passive microwave sensors. In particular, two areas of investigation have been selected: 1) determining the limits of detection of snowfall from passive-only microwave sensors; and 2) analyzing the sensitivity of the combined radar-radiometer algorithm to non-precipitation parameters such as cloud water and water vapor.

246 – GEOS-5 atmospheric modeling and diagnostics
Principal Investigator(s): A. Molod

An overarching goal of the GMAO atmospheric modeling effort is to develop a single atmospheric model suitable for data assimilation, weather forecasting and climate simulation. Climate simulation includes atmosphere only, coupled ocean atmosphere, and coupled chemistry-climate modes. The model’s collection of physical parameterizations is of central importance to the success of the GMAO’s modeling effort.

Part of this year’s effort was focused on the final model changes that led to improved atmosphere-ocean coupled climate simulations, and resulted in the release of the model to be used for decadal climate prediction. This year’s effort was also focused on the analysis and development of the model in data assimilation mode and at higher resolution.

245 – Develop Satellite Snow Data Assimilation Capabilities into the NASA Land Information System (LIS) to Support NWS Hydrologic Applications
Principal Investigator(s): Y. Liu

Accurate snow estimation during snow accumulation and melting periods is essential for various hydrologic and water resources applications in snow-impacted areas. However, estimates of snow water equivalent (SWE) from current land surface models typically contain errors due to poor model physics in representing the snow processes and other sources of uncertainties. Satellite-derived snow products, albeit subject to errors themselves, hold great potential for improving snow estimates if properly assimilated into the models. This project represents a collaborative effort between NASA GSFC and the National Operational Hydrologic Remote Sensing Center (NOHRSC) of the National Weather Service, which aims to develop snow data assimilation (DA) capabilities in LIS to integrate MODIS fraction snow cover area (fSCA) products into several land surface models to improve snow prediction over the Alaska and Afghanistan domains. The developed snow DA capability will be transferred to NOHRSC to support their hydrologic operations.

244 – Global Precipitation Measurement (GPM) Mission
Principal Investigator(s): D. Kirschbaum

Communicate the scientific objectives and societal applications of satellite-based precipitation information from the Global Precipitation Measurement (GPM) mission.

243 – Development of a holistic statistical framework for precipitation estimation
Principal Investigator(s): C. Kidd

The objective is to develop and test techniques for the amalgamation of multi-sensor (satellite and surface) observations to improve global precipitation estimates.

240 – Aerosol Characterization and Radiative Forcing Assessment Using Satellite Data and Models
Principal Investigator(s): H. Yu

Aerosols affect the Earth’s energy budget directly by scattering and absorbing radiation and indirectly by acting as cloud condensation nuclei and, thereby, affecting cloud properties. Aerosols can be transported thousands of miles downwind, thereby having important implications for climate change and air quality on a wide range of scales. Enhanced new satellite passive sensors introduced in the last decade, the emerging measurements of aerosol vertical distributions from space-borne lidars provided the opportunity to attempt measurement-based characterization of aerosol and assessment of aerosol radiative forcing. Such satellite-based methods can play a role in extending temporal and spatial scale of field campaigns and evaluating and constraining model simulations. On the other hand, model simulations and measurements from field campaigns can provide essential parameters that satellites don’t observe. The overall goal of this research is to characterize aerosol distributions and assess the aerosol radiative forcing through an integration of multiple satellite observations and model simulations.

239 – HIMALA: Climate Impacts on Glaciers in the Himalayan Region
Principal Investigator(s): M. Tzortziou

Glaciers are the largest reservoir of freshwater on Earth supporting one third of the world’s population. Himalayas possess one of the largest resources of snow and ice, which act as a freshwater reservoir for more than 1.3 billion people. Monitoring glaciers is important to assess the overall health of this reservoir. Glaciers and snowfields also form potential hazards in the Himalayas, and in similarly glacierised regions of the world. Water resources will be affected by climate change as well as population growth, changing economic activity, land use change, rapid urbanization and inefficient water use. National governments have limited capacity to determine and accurately predict possible impacts to water resources due to scarcity of hydrometeorological data, limited technical capacity, and the transboundary nature of many major river systems. This has also led to recent controversies surrounding the fate of Himalayan glacier melt, which highlight the need for further glaciological and hydrological research in this region.

The HIMALA project aims at developing a system that will aid populations at risk on early warning of floods, droughts and other water and climate-induced natural hazards in the Himalayan region. Among our main goals are to: (i) introduce the use of NASA Earth Science products and models to the International Center for Integrated Mountain Development (ICIMOD) and its member countries, through collaboration with USAID (the United States Agency for International Development) and USGS (the U.S. Geological Survey), (ii) enhance the decision making capacity of ICIMOD and its member countries for management of water resources (floods, agricultural water) in the short (snow, rainfall) and the long-term (glaciers), and (iii) provide projections of climate change impacts on water resources through 2100 using the IPCC models.

238 – Quantifying Systematic Errors and Total Uncertainties in Satellite-based Precipitation Measurements
Principal Investigator(s): Y. Tian

Determining the uncertainties in precipitation measurements by satellite remote sensing is of fundamental importance to many applications. These uncertainties result mostly from the interplay of systematic errors and random errors. Although many satellite-based global precipitation datasets are routinely produced, a quantitative, global picture of their error characteristics is lacking.

237 – Data Assimilation of AMSR-E Soil Moisture Retrievals and Analysis of Soil Moisture Spatial Variability
Principal Investigator(s): B. Li

Recent studies on the assimilation of Advanced Microwave Scanning Radiometer (AMSR-E) derived soil moisture retrievals focus on extracting anomaly signals embedded in the sensor data to improve the anomaly detection of land surface models. Utilizing only anomaly information does not address the issue of model bias, an issue which poses a challenge for most assimilation methods. Models are often biased due to uncertainties in the input parameters and deficiencies in model physics. In particular, the bias in the simulated soil moisture fields has a substantial impact on the estimation of land surface processes such as evapotranspiration (ET) and runoff since they are calculated based on the absolute value of soil moisture.

In contrast to anomaly information, the actual value of AMSR-E retrievals represents the spatial mean, and can be used to reduce model bias. Recognizing the need and the potential, an ensemble Kalman filter with a mass conservation constraint was developed to assimilate the actual value of AMSR-E retrievals without any scaling or preprocessing. This scheme uses different updating equations for the upper and lower two layers so that the bias in the upper two layers can be reduced using a conventional updating scheme while the lower two layers are updated through an equation conserving the mass within each soil column.