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NASA NTRS2024

PACE Technical Report Series, Volume 12: The PACE Level 1C data format

NASA's Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission will make global ocean color and atmospheric measurements to provide extended data records of ocean ecology and global biogeochemistry, along with polarimetric measurements for advanced observations of aerosols, clouds and the ocean. PACE will contain three instruments: the primary Ocean Color Instrument (OCI), and two multi-angle polarimeters (MAPs). The latter instruments are contributed under a ‘Do-No-Harm’ (to the rest of the PACE mission) principle, and the PACE Science Data Processing System (SDPS) is only required to produce Level-1b (geolocated radiances with calibration applied) data without performance requirements. However, there is a strong desire to produce data in a format that merges the disparate spatial resolutions, viewing geometry and sampling nature of the three instruments. Our terminology for this format is Level 1c (L1C). This format will be an input to Level 2 algorithms produced from standalone MAP instrument observations, or from algorithms employing multi-sensor fusion. Creating the L1C format has several components. This includes choice of projection method, the means by which multi-angle views are properly incorporated into that projection (‘aggregation’) the means to represent wavelength and light polarization state, the selection of data to be included within the L1C file, and the handling of ancillary data either required for L1C file generation or needed in that format for L2 processing.

PACE↗
NASA NTRSDate not supplied

Inverse Solution to the Electronic Crosstalk Correction of Bands 27-30 in Terra MODIS

In the Terra MODIS Collection 6.1 (C6.1) Level-1B (L1B) product, an electronic crosstalk correction is implemented for bands 27 30 using a linear algorithm. In this algorithm, the measured (contaminated) signal from the detectors in the bands was used as a reference signal for deriving the correction coefficients from lunar images and for applying the correction to the calibration and L1B data. As the mission progressed, the level of contamination steadily increased in each detector, with an additional large increase associated with the Terra MODIS safe-mode anomaly in February of 2016. In this work, we developed a modified algorithm for deriving the crosstalk coefficients and applying the correction which uses the inverse crosstalk coefficient matrix in order to remove the contamination from the reference signal. We apply the coefficients using this modified algorithm to calibration and Earth-view data in order to assess the difference with the data in C6.1. We also perform calculations using simulated contamination to show the difference in the recovery of the corrected signal between the C6.1 algorithm and the modified algorithm. We will show, that although the current levels of contamination are much higher than early in the mission, the overall impact of having contamination on the reference signal for the correction is small. However, this impact is non-linear as a function of the magnitude of the contamination level, so it should be monitored as the mission continues.

Truman Wilson↗
NASA NTRS2019

Retrieval of Snow Freeboard of Antarctic Sea Ice Using Waveform Fitting of CryoSat-2 Returns

In this paper we develop a CryoSat-2 algorithm to retrieve the surface elevation of the air–snow interface over Antarctic sea ice. This algorithm utilizes a two-layer physical model that accounts for scattering from a snow layer atop sea ice as well as scattering from below the snow surface. The model produces waveforms that are fit to CryoSat-2 level 1B data through a bounded trust region least-squares fitting process. These fit waveforms are then used to track the air–snow interface and retrieve the surface elevation at each point along the CryoSat-2 ground track, from which the snow freeboard is computed. To validate this algorithm, we compare retrieved surface elevation measurements and snow surface radar return power levels with those from Operation IceBridge, which flew along a contemporaneous CryoSat-2 orbit in October 2011 and November 2012. Average elevation differences (standard deviations) along the flight lines (IceBridge Airborne Topographic Mapper, ATM – CryoSat-2) are found to be 0.016 cm (29.24 cm) in 2011 and 2.58 cm (26.65 cm) in 2012. The spatial distribution of monthly average pan-Antarctic snow freeboard found using this method is similar to what was observed from NASA's Ice, Cloud, and land Elevation Satellite (ICESat), where the difference (standard deviation) between October 2011–2017 CryoSat-2 mean snow freeboard and spring 2003–2007 mean freeboard from ICESat is 1.92 cm (9.23 cm). While our results suggest that this physical model and waveform fitting method can be used to retrieve snow freeboard from CryoSat-2, allowing for the potential to join laser and radar altimetry data records in the Antarctic, larger (∼30 cm) regional differences from ICESat and along-track differences from ATM do exist, suggesting the need for future improvements to the method. Snow–ice interface elevation retrieval is also explored as a potential to obtain snow depth measurements. However, it is found that this retrieval method often tracks a strong scattering layer within the snow layer instead of the actual snow–ice interface, leading to an overestimation of ice freeboard and an underestimation of snow depth in much of the Southern Ocean but with promising results in areas such as the East Antarctic sector.

Fons, Steven W.↗
NASA NTRS2021

Nonlinear Detector Response of Aqua MODIS Land Imaging Bands

The detectors in Aqua MODIS reflective solar bands (RSB) are calibrated assuming a linear relationship between the observed top-of-atmosphere radiance and the instrument output response. Any non-linear behavior in the system response can contribute to errors in NASA’s Level 1B MODIS reflectance and radiance products, which are used in a wide variety of Earth science applications. While most RSB detectors continue to have very linear behavior even after more than 18 years of operation, we present evidence that the detectors in Aqua MODIS bands 1 (645 nm) and 2 (858 nm) have deviations from gain linearity that change over the mission by up to 3%. We show a clear divergence between gain measurements made with the on-board solar diffuser (SD) at two different radiance levels, achieved by collecting data both with and without an attenuation screen. Radiance trends from lunar calibrations and typical desert and ocean scenes are also compared. We present a simple method to characterize the response non-linearity using a quadratic function by combining the results of the two sets of SD calibrations. Applying this quadratic calibration algorithm to the desert and lunar data results in improved agreement in their long-term radiance trends. A clear understanding of the magnitude of the gain non-linearity could be useful to help improve the quality of numerous MODIS science products, including the land imaging applications that rely heavily on these bands.

MODIS↗
NASA NTRS2015

Laser Remote Sensing From ISS: CATS Cloud and Aerosol Level 2 Data Products (Heritage Edition)

The Cloud-Aerosol Transport System (CATS) instrument was developed at NASA's Goddard Space Flight Center (GSFC) and deployed to the International Space Station (ISS) on 10 January 2015. CATS is mounted on the Japanese Experiment Module's Exposed Facility (JEM_EF) and will provide near-continuous, altitude-resolved measurements of clouds and aerosols in the Earth's atmosphere. The CATS ISS orbit path provides a unique opportunity to capture the full diurnal cycle of cloud and aerosol development and transport, allowing for studies that are not possible with the lidar aboard the CALIPSO platform, which flies in the sun-synchronous A-Train orbit." " One of the primary science objectives of CATS is to continue the CALIPSO aerosol and cloud profile data record to provide continuity of lidar climate observations during the transition from CALIPSO to EarthCARE. To accomplish this, the CATS project at NASA's Goddard Space Flight Center (GSFC) and the CALIPSO project at NASA's Langley Research Center (LaRC) are closely collaborating to develop and deliver a full suite of CALIPSO-like level 2 data products that will be produced using the newly acquired CATS level 1B data whenever CATS is operating in science modes 1. The CALIPSO mission is now well into its ninth year of on-orbit operations, and has developed a robust set of mature and well-validated science algorithms to retrieve the spatial and optical properties of clouds and aerosols from multi-wavelength lidar backscatter signals. By leveraging both new and existing NASA technical resources, this joint effort by the CATS and CALIPSO teams will deliver validated lidar data sets to the user community at the earliest possible opportunity. The science community will have access to two sets of CATS Level 2 data products. The "Operational" data products will be produced by the GSFC CATS team utilizing the new instrument capabilities (e.g., multiple FOVs and 1064 nm depolarization), while the "Heritage" data products created using the existing CALIPSO algorithms and the CATS 532 nm channels and the total 1064 nm channel. " Below is the development of the CATS "Heritage" level 2 software and data along with some initial results with operational data."

Rodier, Sharon↗
NASA NTRS2017

GOES-R L1b Readiness Implementation and Management Plan

A complement of Readiness, Implementation and Management Plans (RIMPs) to facilitate management of post-launch product test activities for the official Geostationary Operational Environmental Satellite (GOES-R) Level 1b (L1b) products have been developed and documented. Separate plans have been created for each of the GOES-R sensors including: the Advanced Baseline Imager (ABI), the Extreme ultraviolet and X-ray Irradiance Sensors (EXIS), Geostationary Lightning Mapper (GLM), GOES-R Magnetometer (MAG), the Space Environment In-Situ Suite (SEISS), and the Solar Ultraviolet Imager (SUVI). The GOES-R program has implemented these RIMPs in order to address the full scope of CalVal activities required for a successful demonstration of GOES-R L1b data product quality throughout the three validation stages: Beta, Provisional and Full Validation. For each product maturity level, the RIMPs include specific performance criteria and required artifacts that provide evidence a given validation stage has been reached, the timing when each stage will be complete, a description of every applicable Post-Launch Product Test (PLPT), roles and responsibilities of personnel, upstream dependencies, and analysis methods and tools to be employed during validation. Instrument level Post-Launch Tests (PLTs) are also referenced and apply primarily to functional check-out of the instruments.

Validation Readiness↗
NASA NTRS2008

On-Orbit Noise Characterization for MODIS Reflective Solar Bands

Since launch, the Moderate Resolution Imaging Spectroradiometer (MODIS) has operated successfully on-board the NASA Earth Observing System (EOS) Terra and EOS Aqua spacecraft. MODIS is a passive cross-track scanning radiometer that makes observations in 36 spectral bands with spectral wavelengths from visible (VIS) to long-wave infrared. MODIS bands 1-19 and 26 are the reflective solar bands (RSB) with wavelengths from 0.41 to 2.2 micrometers. They are calibrated on-orbit using an on-board solar diffuser (SD) and a SD stability monitor (SDSM) system. For MODIS RSB, the level 1B calibration algorithm produces top of the atmosphere reflectance factors and radiances for every pixel of the Earth view. The sensor radiometric calibration accuracy, specified at each spectral band's typical scene radiance, is 2% for the RSB reflectance factors and 5% for the RSB radiances. Also specified at the typical scene radiance is the detector signal-to-noise ratio (SNR), a key sensor performance parameter that directly impacts its radiometric calibration accuracy and stability, as well as the image quality. This paper describes an on-orbit SNR characterization approach developed to evaluate and track MODIS RSB detector performance. In order to perform on-orbit SNR characterization, MODIS RSB detector responses to the solar illumination reflected from the SD panel must be corrected for factors due to variations of the solar angles and the SD bi-directional reflectance factor. This approach enables RSB SNR characterization to be performed at different response levels for each detector. On-orbit results show that both Terra and Aqua MODIS RSB detectors have performed well since launch. Except for a few noisy or inoperable detectors which were identified pre-launch, most RSB detectors continue to meet the SNR design requirements and are able to maintain satisfactory short-term stability. A comparison of on-orbit noise characterization results with results derived from pre-launch calibration and characterization are also provided.

Xiong, X.↗
DOE OSTI2026

CHESS 2025: Spectrometer orthorectified at-sensor radiance from NEON AOP imaging spectroscopy surveys

This dataset provides Level 1 (L1) orthorectified at-sensor radiance derived from measurements collected by the Imaging Spectrometer-1 (NIS-1) onboard the NEON (National Ecological Observatory Network) Airborne Observation Platform (AOP) for the 2025 Colorado Headwaters Ecological Spectroscopy Study (CHESS). NIS-1 captures light reflected from the Earth’s surface in 426 discrete wavelength bands as raw digital numbers (DNs; Level 0). These data are then calibrated to physical units (uW/cm²·sr·nm) following the processing steps described in the NEON Imaging Spectrometer Level 1B Calibrated Radiance Algorithm Theoretical Basis Document (ATBD; Gallery 2022). The data delivered here are the primary inputs for the surface reflectance product in “Custom surface reflectance, shade masks, and equivalent water thickness maps for the Colorado Headwaters Ecological Spectroscopy Study” (Carroll et al. 2026). For intertemporal comparison, the radiance data here are most directly relatable to the v2 radiance data in “NEON AOP Imaging Spectroscopy Survey of Upper East River Colorado Watersheds: Raw-Space Radiance and Observational Variable Dataset” (Goulden et al. 2018), to which the same processing methodology was applied. Together, the radiance and reflectance data enable users to exploit the unique reflection signatures of different surface objects for land cover classification, foliar trait mapping, plant vigor assessment, water content estimation, trace-element identification, and other scientific applications. The data were acquired over three study domains in the Upper Gunnison river basin: the upper East River watershed (CRBU); Almont Triangle and Taylor Canyon (ALMO); and Upper Taylor River watershed (UPTA) between 2025-06-13 and 2025-07-15. Within each domain, data are delivered by flightline as orthorectified and calibrated hyperspectral rasters in Hierarchical Data Format version 5 (HDF5) format, with radiance values provided in uW/cm²·sr·nm on a fixed, uniform Universal Transverse Mercator (UTM) grid at 1 meter spatial resolution. The radiance rasters include all 426 NIS-1 spectral bands, along with associated quality-assurance (QA) and diagnostic and ancillary layers needed for atmospheric correction workflows. Orthorectified radiance is produced from pushbroom spectrometer observations by applying NEON’s radiometric calibration (including bad pixel masking, dark subtract, dark pedestal shift correction, electronic panel ghost correction, grating ghost correction, deblur correction and flat-fielding) and spectral calibration (using spectral response function band centers and full-width at half-maximum intensity), followed by geolocation and regridding to the fixed grid. CHESS Project Description: The Colorado Headwaters Ecological Spectroscopy Study (CHESS) comprised a multi-week airborne remote sensing and field observation campaign in the Upper Gunnison Basin, Colorado, conducted in June and July of 2025. Airborne remote sensing was conducted by the National Ecological Observatory Network Airborne Observation Platform (NEON AOP), concurrent with a field campaign run by the Rocky Mountain Biological Laboratory (RMBL), the Lawrence Berkeley National Laboratory (LBNL) and SLAC National Accelerator Laboratory Watershed Function Science Focus Area (SFA), and NASA-JPL (Jet Propulsion Laboratory) Earth Surface Mineral Dust Source Investigation (EMIT) program. Between June 10 and July 18, 2025, the NEON AOP flight team collected high-resolution aerial imaging spectroscopy and Light Detection and Ranging (LiDAR) data over three domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). In coordination with the flights, a field campaign acquired ground-truth observations, including observations of vegetation composition, foliar traits, forest demography, and subsurface properties in 18 core sampling areas within the domains. Additional surface water observations were taken at over 380 point locations. All CHESS campaign datasets can be found within the CHESS ESS-DIVE data portal: https://data.ess-dive.lbl.gov/portals/chess. Funding Acknowledgement: Field and remote-sensing data acquisition was performed under a grant from the National Aeronautics and Space Administration (80NSSC24K1005). This work was also supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

2018 NEON and 2025 CHESS Campaigns↗
NASA NTRS1994

MODIS. Volume 2: MODIS level 1 geolocation, characterization and calibration algorithm theoretical basis document, version 1

The EOS Moderate Resolution Imaging Spectrometer (MODIS) is being developed by NASA for flight on the Earth Observing System (EOS) series of satellites, the first of which (EOS-AM-1) is scheduled for launch in 1998. This document describes the algorithms and their theoretical basis for the MODIS Level 1B characterization, calibration, and geolocation algorithms which must produce radiometrically, spectrally, and spatially calibrated data with sufficient accuracy so that Global change research programs can detect minute changes in biogeophysical parameters. The document first describes the geolocation algorithm which determines geodetic latitude, longitude, and elevation of each MODIS pixel and the determination of geometric parameters for each observation (satellite zenith angle, satellite azimuth, range to the satellite, solar zenith angle, and solar azimuth). Next, the utilization of the MODIS onboard calibration sources, which consist of the Spectroradiometric Calibration Assembly (SRCA), Solar Diffuser (SD), Solar Diffuser Stability Monitor (SDSM), and the Blackbody (BB), is treated. Characterization of these sources and integration of measurements into the calibration process is described. Finally, the use of external sources, including the Moon, instrumented sites on the Earth (called vicarious calibration), and unsupervised normalization sites having invariant reflectance and emissive properties is treated. Finally, algorithms for generating utility masks needed for scene-based calibration are discussed. Eight appendices are provided, covering instrument design and additional algorithm details.

Barker, John L.↗
NASA NTRS1995

SeaWiFS technical report series. Volume 28: SeaWiFS algorithms, part 1

This document provides five brief reports that address several algorithm investigations sponsored by the Calibration and Validation Team (CVT) within the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) Project. This volume, therefore, has been designated as the first in a series of algorithm volumes. Chapter 1 describes the initial suite of masks, used to prevent further processing of contaminated radiometric data, and flags, which are employed to mark data whose quality (due to a variety of factors) may be suspect. In addition to providing the mask and flag algorithms, this chapter also describes the initial strategy for their implementation. Chapter 2 evaluates various strategies for the detection of clouds and ice in high latitude (polar and sub-polar regions) using Coastal Zone Color Scanner (CZCS) data. Chapter 3 presents an algorithm designed for detecting and masking coccolithosphore blooms in the open ocean. Chapter 4 outlines a proposed scheme for correcting the out-of-band response when SeaWiFS is in orbit. Chapter 5 gives a detailed description of the algorithm designed to apply sensor calibration data during the processing of level-1b data.

Hooker, Stanford B.↗
NASA NTRS1999

Remote Sensing of Volcanic Clouds: Sulfur Gases and Plume Top Topography

New absorption line parameters for H2S were published and submitted to the Gestion et Etude des Informations Spectroscopiques Atmospheriques (GEISA) and high resolution transmission molecular absorption (HITRAN) databases. These new absorption line parameters will make it possible to use observations from the future Tropospheric Emission Spectrometer (TES) instrument [Earth Observing System (EOS) Chemistry Mission (CHEM) platform] to make more accurate H2S measurements if it observes an H2S-rich volcanic cloud. H2S is the second most abundant volcanic sulfur gas, and like SO2, it also converts to H2SO4 aerosols and can have a climate impact. A paper on the Moderate-resolution Imaging-Spectroradiometer (MODIS) SO2 alert is being revised. New aspects in the revision include verification of the SO2 alert during the EOS mission; factors affecting SO2 detection at thermal infrared, ultraviolet, and microwave wavelengths; radiative transfer tests; more description of satellite instruments; and thermal surface alert installed for MODIS. Her research involves the use of remote sensing to generate maps of plume top altitude. This parameter is important for models of volcanic eruption, aircraft hazards, and climate impact. The topographic shape of the top surface of a volcanic plume can provide information necessary to understand the physics controlling the injection and dispersal of a volcanic plume in the atmosphere. Glaze et al. describe the application of a photoclinometric technique to volcanic plumes. The software algorithm has been improved to account for more general plume and illumination geometries and for easily extracting position information directly from Advanced Very High-Resolution Radiometer (AVHRR) level 1B data. Testing of the algorithm has focused on acquiring AVHRR data for a variety of volcanic plumes in an effort to identify problems with the software as well as model sensitivities. The plumes chosen were erupted from volcanoes at a variety of latitudes, at different times of day, and in different seasons. Another important issue that is being investigated is the importance of plume opacity. A diagram illustrates the method for deriving phi, the angle a wind-blown plume makes with the horizontal, in the direction perpendicular to the remote sensing scan. A figure shows a sample output of the algorithm, a wire mesh map of plume top topography. Additional information is contained in the original.

Crisp, Joy A.↗
NASA NTRS2000

Assimilation of (A) TOVS data at the NASA Goddard Data Assimilation Office

At the NASA Goddard Data Assimilation Office (DAO), a 1D variational radiance assimilation system has been developed. This system, called DAOTOVS (Tiros operational vertical sounder (TOVS)), uses (A)TOVS level 1b radiances. It has been implemented within the DAO's semi-operational system as well as within the next generation data assimilation system that uses a finite-volume dynamical core. We will show results from (A)TOVS assimilation, including stratospheric analyses and validation. We will also describe our systematic error correction scheme which is based on collocated radiosondes.

Joiner, Joanna↗
NASA NTRS2001

DAO's Next Generation Physical-Space/Finite-Volume Data Assimilation System: Formulation and Initial Evaluation

The Physical-space/Finite-volume Data Assimilation System (fvDAS) is the next generation global atmospheric data assimilation system in development at the Data Assimilation Office at NASA's Goddard Space Flight Center. It is based on a new finite-volume general circulation model jointly developed by NASA and NCAR, and on the Physical-Space Statistical Analysis System (PSAS) developed at the DAO. In this talk we will describe the general system formulation, the adaptive quality control and general aspects of the error covariance modeling. The NASA-NCAR GCM is a completely new model which replaces the CEOs GCM used in the previous GEOS-1/2/3 Data Assimilation systems. A particular configuration of adaptive Statistical Quality Control and the Physical-space Statistical Analysis System (PSAS) are currently implemented in DAO's operational Data Assimilation System. However, the unique finite-volume formulation of the NASA-NCAR GCM, combined with the generality of the observation-space formulation of PSAS, provides for a very simple and accurate model-analysis interface. The system assimilates a variety of conventional and satellite observations. In particular, TOVS Level 1B radiances are assimilated using a 1-D variational scheme, both in clear sky and cloudy conditions. Computationally, the fvDAS runs approximately 10 times faster than the operational GEOS-Terra system. We will show that the next-generation fvDAS has much improved observation-minus-6hr forecast (O-F) statistics, as well as 5-day forecast skills. Top of the atmosphere radiation fields are in closer agreement with CERES measurements, with realistic precipitation and moisture fields. We will also show that the finite-volume formulation of the fvDAS produce assimilated fields which are more suitable for driving constituent transport models.

daSilva, A.↗
NASA NTRS2004

Unique Approaches to Analysis of Time-Variable Gravity from GRACE

We have developed an innovative analysis strategy for analysis of GRACE data. We have developed a capability to recover local/regional gravity changes using non-global functional representations (Le. surface anomalies vs. global spherical harmonics) h m the GRACE data. Our approach can take regularly or irregularly shaped regions, populate them with surface anomaly blocks of suitable area and solve for the resulting mass flux with respect to a mean field. The surface mass or gravity anomalies benefit from the application of spatial and temporal constraints to add stability to the solution. In this paper we discuss the analysis of four months of GRACE Level 1B data (accelerometry, intersatellite data, attitude information and precise orbits) from July to October 2003, recently released to the GRACE Science Team. We compare and contrast this local approach to gravity recovery, with the more conventional approach using global spherical harmonics. We review simulations of this technique which allow us to pinpoint optimum strategies for applications of this local gravity recovery approach.

Lemoine, Frank G.↗
NASA NTRS2004

MODIS On-orbit Calibration: Key Issues and Approaches

MODIS, one of the key instruments for the NASA's Earth Observing System (EOS), is currently operating on both the Terra and Aqua spacecraft making continuous observations in 36 spectral bands from 0.4 to 14.4 micrometers. A complete suite of on-board calibrators (OBC) have been designed for the instruments' on-orbit calibration and characterization, including a solar diffuser (SD) and solar diffuser stability monitor (SDSM) system for the radiometric calibration of the 20 reflective solar bands (RSB), a blackbody (BB) for the radiometric calibration of the 16 thermal emissive bands (TEZB), and a spectro-radiometric calibration assembly (SRCA) for the sensors' spatial and spectral characterization. The task of continuously performing high quality on-orbit calibration and characterization of all 36 spectral bands with a total of 490 detectors located on four focal plane assemblies is extremely challenging. The use of a large two-sided paddle wheel scan mirror with a +/- 55 deg scan angle range and a retractable pinhole attenuation screen in front of the SD panel for calibrating the high gain bands have resulted in additional unanticipated complexity. In this paper, we describe some of the key issues in the Terra and Aqua MODIS on-orbit calibration and characterization, and discuss the methods developed to solve these problems or to reduce their impact on the Level 1B calibration algorithms. Instrument performance and current issues are also presented.

Xiong, Xian-Xiong↗
NASA NTRS2004

Time Variable Gravity from Local Mascon Analysis of GRACE Data

We have analyzed GRACE Level 1-B data in 2003 and assessed a new approach for extracting time variable gravity that isolates the gravity signal in both time and space. The Level-1B satellite-to-satellite range rate (KBRR) data and accelerometry are processed in daily arcs using the precise orbit products produced by the GRACE team from GPS to calibrate both the accelerometer and KBRR data. We then adjusted select components of the intersatellite baseline vector for each data segment isolated to the region of interest. Herein, we solved for mass anomalies in 45 deg x 45 deg blocks over the Amazon and the nearby Atlantic Ocean and estimate mass flux in units of cm of water over each block. We show with this approach that we can recover mass anomalies on a submonthly basis with 10 to 15 day temporal resolution. We discuss the important issues related to this solution, including the size of the mascon blocks, the weight given to the temporal and spatial constraint used to stabalize the solutions, as well as the optimal correlation in time and distance. We compare the the mascon results with solutions obtained from the more standard approach using spherical harmonics and with independent hydrology models and lake data. This technique demonstrates that sub-monthly medium wavelength mass flux phenomena are well sensed by the hyper-precise line of sight velocity data produced from GRACE.

Lemoine, Frank G.↗
NASA NTRS2005

Lower Thermospheric Composition Studies in the Auroral Zone

This TIMED/CEDAR grant had two major objectives. The GUVI instrument on TIMED should routinely produces maps of the flux of energetic particles and their average energy. However, while those data will be available soon there were no plans to routinely produce the O/N2 ratio in the auroral. Ground-based photometric techniques have been available for years which routinely measure all three quantities in the auroral zone. The first objective of the previous proposal was to use these ground-based techniques to provide some validation for the routine measure of average energy. The second objective was to use the ground-based to data to validate GUVI algorithms for the measure of O/N2 in the auroral zone. We have made some progress to these goals while we await the release of the properly precessed GUVI data. For now these objectives are being addressed by making use of algorithms by one of us (Doug Strickland of CPI) that are being used for specific GUVI level 1B data sets that contain data coincident with the groundbased measurements. Also we have collected ground-based data from two Alaska sites and they are available on our web site and on the CEDAR website. The details of the progress we have made are discussed.

Hecht, James↗
NASA NTRS2005

The Use of Mascons to Resolve Time-Variable Gravity from GRACE

We have analyzed GRACE Level 1B data to resolve time-variable gravity using a local mascon approach. The spherical harmonic solutions released to date resolve the signal from surface hydrology over land areas at spatial scales of 750 to 1000 km over one month intervals [Wahr et al., 2004; Tapley et al., 2004]. In our local approach, we solve explicitly for the mass of water in surface blocks using only the KBRR data collected as GRACE overflies the region of interest. The local representation of gravity minimizes leakage of errors from other areas due to aliasing or mismodelling. In this paper, we report on the analysis of GRACE data from January 2003 through August 2004 over three regions: the Amazon, the Indian subcontinent, and the continental United States. We solve for mass change at 10-day intervals using 4 deg x 4 deg blocks. We give an overview of our latest results, and we present the results of error analyses, and comparisons to both hydrology models and in-situ data.

Lemoine, Frank G.↗