Level 2 Aerosol Retrieval Algorithm Theoretical Basis
This Algorithm Basis (ATB) document describes the algorithms used to retrieve the aerosol parameters of the MISR Level 2 Aerosol/Surface Product.
Level 2 retrieval algorithm theoretical basis↗
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Level 2 Surface Retrieval Algorithm Theoertical Basis
This Algorithm Theoretical Basis (ATB) document describes the algorithms used to retrieve the surface parameters of the MISR Level 2 Aerosol/Surface Product.
surface algorithm theoretical retrieval↗
Level 2 Cloud Detection and Classification Algorithm Theoretical Basis
This Algorithm Theoretical Basis (ATB) document describes the algorithms used to retrieve the cloud classification parameters of the MISR Level 2 Top-of-Atmosphere (TOA)/Cloud Product.
detection cloud classification algorithm↗
Level 2 Top-of-Atmosphere Albedo Algorithm Theorectical Basis
This Algorithm Theoretical Basis (ATB) document describes the algorithms used to retrieve the albedo parameters of the Multi-angle Imaging SpectroRadiometer (MISR) Level 2 Top-of-Atmosphere (TOA)/Cloud Product.
Top-of-Atmosphere albedo algorithm↗
Tropospheric Emission Spectrometer (TES) Level 2 Algorithm Theoretical Basis
This Algorithm Theoretical Basis Document (ATBD)describes the algorithms used to retrieve all Tropospheric Emission Sprectometer (TES) Standard Data Products.
TES algorithm ATBD theoretical basis D-16474↗
Newly Available Single Field-of-view Sounder Atmospheric Product (SiFSAP) and Its Derivative Product
The Single Field-of-view Sounder Atmospheric product (SiFSAP) has been developed and delivered to NASA GES DISC. The SiFSAP Algorithm Theoretical Basis Documents (ATBD) and users manuals are ready to be released to public. This novel data product supplements other operational products such as AIRS version 7 and the Community Long-term Infrared Microwave Combined Atmospheric Product System (CLIMCAPS) from two main perspectives: 1) improving the spatial resolution of sounder Level-2 data to extend its usage in weather and dynamics focus area; 2) establishing radiance closure between Level-2 data and directly measured Level-1 radiances to facilitate the climate trend analysis. SiFSAP has 3-times higher spatial resolution and 9-times denser data products comparing to current NASA and NOAA operational IR sounder products. SiFSAP is derived using the optimal estimation method based physical retrieval algorithm. The Principal Component-based Radiative Transfer Model (PCRTM) which includes the cloud scattering simulation is used for the forward model so that the solution can fit the spectral radiances under all-sky conditions for individual single field-of-view (SFOV) measurements. A general introduction of the SiFSAP algorithm and corresponding validation work will be presented. Also introduced here is the Climate Fingerprinting Sounder product (ClimFiSP) that is the derivative product of SiFSAP and will be released in the near future. The ClimFiSP algorithm uses pre-constructed fingerprinting relationship to achieve a low-latency Level-3 data production and facilitate the fusion of data of different sounders.
Wan Wu↗
Land Surface Temperature Measurements from EOS MODIS Data
This report summarizes the accomplishments made by the MODIS LST (Land-Surface Temperature) group at University of California, Santa Barbara, under NASA Contract. Version 1 of the MODIS Land-Surface Temperature Algorithm Theoretical Basis Document (ATBD) was reviewed in June 1994, version 2 reviewed in November 1994, version 3.1 in August 1996, and version 3.3 updated in April 1999. Based on the ATBD, two LST algorithms were developed, one is the generalized split-window algorithm and another is the physics-based day/night LST algorithm. These two LST algorithms were implemented into the production generation executive code (PGE 16) for the daily standard MODIS LST products at level-2 (MODII-L2) and level-3 (MODIIA1 at 1 km resolution and MODIIB1 at 5km resolution). PGE codes for 8-day 1 km LST product (MODIIA2) and the daily, 8-day and monthly LST products at 0.05 degree latitude/longitude climate model grids (CMG) were also delivered. Four to six field campaigns were conducted each year since 2000 to validate the daily LST products generated by PGE16 and the calibration accuracies of the MODIS TIR bands used for the LST/emissivity retrieval from versions 2-4 of Terra MODIS data and versions 3-4 of Aqua MODIS data. Validation results from temperature-based and radiance-based methods indicate that the MODIS LST accuracy is better than 1 C in most clear-sky cases in the range from -10 to 58 C. One of the major lessons learn from multi- year temporal analysis of the consistent V4 daily Terra MODIS LST products in 2000-2003 over some selected target areas including lakes, snow/ice fields, and semi-arid sites is that there are variable numbers of cloud-contaminated LSTs in the MODIS LST products depending on surface elevation, land cover types, and atmospheric conditions. A cloud-screen scheme with constraints on spatial and temporal variations in LSTs was developed to remove cloud-contaminated LSTs. The 5km LST product was indirectly validated through comparisons to the 1 km LST product. Twenty three papers related to the LST research work were published in journals over the last decade.
Wan, Zheng-Ming↗
Soil Moisture Active Passive (SMAP) Project Algorithm Theoretical Basis Document SMAP L1B Radiometer Data Product: L1B_TB
The purpose of the Soil Moisture Active Passive (SMAP) radiometer calibration algorithm is to convert Level 0 (L0) radiometer digital counts data into calibrated estimates of brightness temperatures referenced to the Earth's surface within the main beam. The algorithm theory in most respects is similar to what has been developed and implemented for decades for other satellite radiometers; however, SMAP includes two key features heretofore absent from most satellite borne radiometers: radio frequency interference (RFI) detection and mitigation, and measurement of the third and fourth Stokes parameters using digital correlation. The purpose of this document is to describe the SMAP radiometer and forward model, explain the SMAP calibration algorithm, including approximations, errors, and biases, provide all necessary equations for implementing the calibration algorithm and detail the RFI detection and mitigation process. Section 2 provides a summary of algorithm objectives and driving requirements. Section 3 is a description of the instrument and Section 4 covers the forward models, upon which the algorithm is based. Section 5 gives the retrieval algorithm and theory. Section 6 describes the orbit simulator, which implements the forward model and is the key for deriving antenna pattern correction coefficients and testing the overall algorithm.
microwave radiometers↗