Terrestrial Environments, Planetary Atmospheric Modeling, Radiation Shielding
Titan Global Reference Atmospheric Model (Titan-GRAM), Version 1.0
Titan-GRAM is a Fortran-based program that provides engineering estimates of density, temperature, pressure, and winds for the Titan atmosphere. More information on the Space Environments & Effects (SEE) Program can be found at http://see.msfc.nasa.gov/.
General Public Release
Terrestrial Observation and Prediction System (TOPS) Biogeochemical cycle (BGC) model
The Terrestrial Observation and Prediction System (TOPS) is a modeling software that integrates data from satellites, weather stations, climate models with ecosystem models to produce nowcasts and forecasts of ecological conditions. The key tools used in producing the nowcasts and forecasts are simulation models including biogeochemical and ecosystem models that estimate the states (vegetation leaf area, biomass, soil moisture, snow, etc.) and functions (evapotranspiration, photosynthesis, etc.) of various kinds of plant canopies (forests, crops, grass, shrubs). These ecological nowcasts and forecasts are akin to current and forecast weather conditions. Upon further refinement and testing, the ecological nowcasts and forecasts are useful for making a variety of management decisions such as irrigation scheduling, timing of field operations, preparing for floods/droughts, vector-borne diseases, crop phenology and production.
U.S. and Foreign Release
Python Polarimetric Radar Beam Blockage Calculation (PyBlock)
This Python package will calculate beam blockage in polarimetric weather radar data using the specific differential phase (KDP) and fully self-consistent (FSC) methods of Timothy J. Lang et al. (2009; J. Atmos. Oceanic Technol.). This information can be used to correct the radar data when the radar beams intersect objects like trees, buildings, and mountains.
Phenological Parameters Estimation Tool (PPET)
The Phenological Parameters Estimation Tool (PPET) is a set of algorithms implemented in MATLAB that estimates key vegetative phenological parameters. For a given year, the PPET software package takes in temporally processed vegetation index data (3-D spatio-temporal arrays) generated by the Time Series Product Tool (TSPT) and outputs spatial grids (2-D arrays) of vegetation phenological parameters. As a precursor to PPET, the TSPT uses quality information for each pixel of each date to remove bad or suspect data, and then interpolates and digitally filters the time series to produce a continuous, smoothed vegetation index product in which data voids are eliminated. Both the TSPT and PPET use Moderate Resolution Imaging Spectroradiometer (MODIS) satellite multi-spectral data as a default, but each software package is easily modifiable and could be used with any high-temporal-rate remote sensing system that is capable of producing vegetation indices. Unlike other known plant phenological parameter estimation software, the PPET produces not only common phenological parameters but also real-time and custom parameters without a priori assumptions about the shape of the phenological cycle. Common phenological parameters, like those produced in PPET, are associated with the annual vegetation growth cycle. They quantitatively describe vegetative states related to annual cyclical growing seasons, such as green-up, maturity, senescence, and dormancy, by analyzing the temporal shape of given vegetation index time series. The real-time phenological and custom parameters are formed from a cumulative sum (integral) produced at a fixed temporal interval. In addition, a cumulative vegetation index and time-specific/pest-specific phenological parameters can be designed to optimize the detection of vegetation damage from specific pests and diseases. These problem-specific phenological parameters have the potential to be integrated into near real-time, predictive surveillance systems (i.e. early warning systems) and, with improved vegetative state information, could assist decision makers in making intelligent vegetation and associated land resource management choices. MATLAB, MATLAB Runtime Library and ERDAS IMAGINE are required to run the software.
U.S. Government Purpose Release
Lightning Forecasting Algorithm (LFA)
The Lightning Forecasting Algorithm (LFA) is an algorithm that may be implemented within any cloud-allowing or cloud-resolving numerical forecast model that converts gridded forecasts of updraft speeds and graupel hydrometeor mixing ratios in the mixed phase layer into gridded estimates of total lightning flash rate density.
General Public Release
The Parallel Fortran Logger (pFlogger)
The innovation is a software logging facility that is tailored to the needs of parallel Fortran software. The facility greatly simplifies the process of logging routine messages within a scientific simulation across multiple processes in a manner that is configurable at run time. Messages can be annotated with different severity levels such that low-level diagnostic information can be generally suppressed except when desired.
Advanced Land Image Assessment System (ALIAS)
ALIAS supports radiometric and geometric multispectral image processing for the Advanced Land Imager (ALI) instrument onboard NASA's Earth Observing-1 (EO-1) satellite. The radiometric subsystem characterizes and (where possible) corrects: detector operability; gain; bias; coherent, impulse, and random noise; signal-to-noise ratios; saturation levels; striping and banding; and the stability of detector performance. Geometric processing functions support sensor alignment calibrations; sensor chip assembly alignments; modulation transfer function characterizations; image-to-image characterizations; and geodetic accuracy assessments. Please visit the following URL for more information: http://opensource.gsfc.nasa.gov/projects/Alias/index.php
This Java-language software plug-in to HDFView provides an interface for two versions of hierarchical data formats (HDF 4 and HDF 5). Please visit the following URL for more information: http://opensource.gsfc.nasa.gov/projects/HDF/index.php
NASA Forecast Model Web (NFMW) Map Service
NFMW reads weather forecast models outputs; subsets the data to the region of interest; interpolates the data to the specified size; generates a visualization of the data using colors, contour lines, or arrows; and sends the visualization to the client. More information can be found at: http://opensource.gsfc.nasa.gov/projects/NFMW/
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