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AVHRR data record is well alive and continue to improve and be used by a large land user community.Most of the improvement are due to the overlapping with MODIS Aqua, Terra.We recommend operating missions as long as possible to enable overlap of at least a few years (especially for applications).
Fast-framing cameras record data obtained by a multichannel hodoscope which monitors reactor physics experiments. The cameras provide high rates of data acquisition at low equipment cost.
Climate Data Records (CDR) that blend multiple satellite products are invaluable for climate studies, trend analysis and risk assessments. Knowledge of any inhomogeneities in the CDR is therefore critical for making correct inferences. This work proposes a methodology to identify the spatiotemporal extent of the inhomogeneities in a 36-year, global multisatellite soil moisture CDR as the result of changing observing systems. Inhomogeneities are detected at up to 24 percent of the tested pixels with spatial extent varying with satellite changeover times. Nevertheless, the contiguous periods without inhomogeneities at changeover times are generally longer than 10 years. Although the inhomogeneities have measurable impact on the derived trends, these trends are similar to those observed in ground data and land surface reanalysis, with an average error less than 0.003 cubic meters per cubic meter per year. These results strengthen the basis of using the product for long-term studies and demonstrate the necessity of homogeneity testing of multisatellite CDRs in general.
No abstract available
When doing multivariate data analysis, one commonobstacle is the presence of incomplete observations, i.e., observationsfor which one or more key fields are blank. Missing datais often countered by deleting entire observations that containmissing data. The negative effects of deleting entire observationsare multiple: deleting observations reduces sample size andcan also result in biased inferences even if data is missing atrandom. In addition, knowledge contained within incompleteobservations is knowledge lost when they are deleted– and theeffort spent collecting that knowledge is effort wasted. Data imputationmethods, or methods of statistically “filling-in” missingdata, can help combat small sample sizes by using the existinginformation in partially complete observations with the end goalof producing less biased and higher confidence inferences. Whena sample from a multivariate normal population is only partiallycomplete, and the missing data meets appropriate assumptions(missing at random), robust data imputation of the missing datacan be implemented with monotone data augmentation (MDA)using the multivariate t distribution.Missing data imputation is applied to data from the NASA InstrumentCost Model (NICM) using the MDA algorithm underthe assumption of having a multivariate t distribution with fixeddegrees of freedom. A sensitivity analysis to the degrees offreedom parameter is presented to demonstrate robustness ofthe multivariate t distribution when dealing with small samplesas compared to the multivariate normal distribution.
No abstract provided
Data recorded during flights of the NASA Trailblazer Boeing 737 have been analyzed to ascertain the presence of aircraft structural responses from various excitations such as the engine, aerodynamic effects, wind gusts, and control system operations. The NASA Trailblazer Boeing 737 was chosen as a focus of the study because of a large quantity of its flight data records. The goal of this study was to determine if any aircraft structural characteristics could be identified from flight data collected for measuring non-structural phenomena. A number of such data were examined for spatial and frequency correlation as a means of discovering hidden knowledge of the dynamic behavior of the aircraft. Data recorded from on-board dynamic sensors over a range of flight conditions showed consistently appearing frequencies. Those frequencies were attributed to aircraft structural vibrations.
Bubble domain materials and devices are discussed. One of the materials development goals was a materials system suitable for operation of 16 micrometer period bubble domain devices at 150 kHz over the temperature range -10 C to +60 C. Several material compositions and hard bubble suppression techniques were characterized and the most promising candidates were evaluated in device structures. The technique of pulsed laser stroboscopic microscopy was used to characterize bubble dynamic properties and device performance at 150 kHz. Techniques for large area LPE film growth were developed as a separate task. Device studies included detector optimization, passive replicator design and test and on-chip bridge evaluation. As a technology demonstration an 8 chip memory cell was designed, tested and delivered. The memory elements used in the cell were 10 kilobit serial registers.
NASA's Earth Science Program is dedicated to advancing Earth remote sensing and pioneering the scientific use of satellite measurements to improve human understanding of our home planet. Through the MEaSUREs Program, NASA is continuing its commitment to expand understanding of the Earth system using consistent data records. Emphasis is on linking together multiple data sources to form coherent time-series, and facilitating the use of extensive data in the development of comprehensive Earth system models. A primary focus of the MEaSUREs Program is the creation of Earth System Data Records (ESDRs). An ESDR is defined as a unified and coherent set of observations of a given parameter of the Earth system, which is optimized to meet specific requirements for addressing science questions. These records are critical for understanding Earth System processes; for the assessment of variability, long-term trends, and change in the Earth System; and for providing input and validation means to modeling efforts. Seven MEaSUREs projects will be archived and distributed through services at the Goddard Earth Sciences Data and Information Services Center (GES DISC).
Critical Data Recorder /CDR/, small autonomous instrumentation package to be carried aboard space vehicle and furnish data on series of maneuvers
The satellite ocean color data record spans multiple decades and, like most long-term satellite observations of the Earth, comes from many sensors. Unfortunately, global and regional chlorophyll estimates from the overlapping missions show substantial biases, limiting their use in combination to construct consistent data records. SeaWiFS and MODIS-Aqua differed by 13% globally in overlapping time segments, 2003-2007. For perspective, the maximum change in annual means over the entire Sea WiFS mission era was about 3%, and this included an El NinoLa Nina transition. These discrepancies lead to different estimates of trends depending upon whether one uses SeaWiFS alone for the 1998-2007 (no significant change), or whether MODIS is substituted for the 2003-2007 period (18% decline, P less than 0.05). Understanding the effects of climate change on the global oceans is difficult if different satellite data sets cannot be brought into conformity. The differences arise from two causes: 1) different sensors see chlorophyll differently, and 2) different sensors see different chlorophyll. In the first case, differences in sensor band locations, bandwidths, sensitivity, and time of observation lead to different estimates of chlorophyll even from the same location and day. In the second, differences in orbit and sensitivities to aerosols lead to sampling differences. A new approach to ocean color using in situ data from the public archives forces different satellite data to agree to within interannual variability. The global difference between Sea WiFS and MODIS is 0.6% for 2003-2007 using this approach. It also produces a trend using the combination of SeaWiFS and MODIS that agrees with SeaWiFS alone for 1998-2007. This is a major step to reducing errors produced by the first cause, sensor-related discrepancies. For differences that arise from sampling, data assimilation is applied. The underlying geographically complete fields derived from a free-running model is unaffected by solar zenith angle requirements and obscuration from clouds and aerosols. Combined with in situ dataenhanced satellite data, the model is forced into consistency using data assimilation. This approach eliminates sampling discrepancies from satellites. Combining the reduced differences of satellite data sets using in situ data, and the removal of sampling biases using data assimilation, we generate consistent data records of ocean color. These data records can support investigations of long-term effects of climate change on ocean biology over multiple satellites, and can improve the consistency of future satellite data sets.
A high-speed data recorder and replay equipment has been developed for reliable high-data-rate recording to disk media. It solves problems with slow or faulty disks, multiple disk insertions, high-altitude operation, reliable performance using COTS hardware, and long-term maintenance and upgrade path challenges. The current generation data recor - ders used within the VLBI community are aging, special-purpose machines that are both slow (do not meet today's requirements) and are very expensive to maintain and operate. Furthermore, they are not easily upgraded to take advantage of commercial technology development, and are not scalable to multiple 10s of Gbit/s data rates required by new applications. The innovation provides a softwaredefined, high-speed data recorder that is scalable with technology advances in the commercial space. It maximally utilizes current technologies without being locked to a particular hardware platform. The innovation also provides a cost-effective way of streaming large amounts of data from sensors to disk, enabling many applications to store raw sensor data and perform post and signal processing offline. This recording system will be applicable to many applications needing realworld, high-speed data collection, including electronic warfare, softwaredefined radar, signal history storage of multispectral sensors, development of autonomous vehicles, and more.
The Ejectable Data Recorder (EDR) subsystem was added to the Crew Module (CM) of the Ascent Abort 2 (AA-2) test flight due to a risk that the communications architecture would be insufficient to downlink all the data to the ground during the test flight. Since the EDR subsystem was a secondary system for data collection, AA-2 management enabled the team to take a different approach to hardware development that was more agile-like. This paper discusses key design decisions, technical challenges, and lessons learned that enabled the success of the EDR system during its flight on July 2, 2019.
Clouds are of high importance for the climate system but they still remain one of its principal uncertainties. Remote sensing techniques applied to satellite observations have assisted tremendously in the creation of long-term and homogeneous data records; however, satellite data sets need to be validated and compared with other data records, especially ground measurements. In the present study, the spatiotemporal distribution and variability of Total Cloud Cover (TCC) from the Satellite Application Facility on Climate Monitoring (CM SAF) Cloud, Albedo And Surface Radiation dataset from AVHRR data—edition 2 (CLARA-A2) and the International Satellite Cloud Climatology Project H-series (ISCCP-H) is analyzed over Europe. The CLARA-A2 data record has been created using measurements of the Advanced Very High Resolution Radiometer (AVHRR) instrument onboard the polar orbiting NOAA and the EUMETSAT MetOp satellites, whereas the ISCCP-H data were produced by a combination of measurements from geostationary meteorological satellites and the AVHRR instrument on the polar orbiting satellites. An intercomparison of the two data records is performed over their common period, 1984 to 2012. In addition, a comparison of the two satellite data records is made against TCC observations at 22 meteorological stations in Europe, from the European Climate Assessment & Dataset (ECA&D). The results indicate generally larger ISCCP-H TCC with respect to the corresponding CLARA-A2 data, in particular in the Mediterranean. Compared to ECA&D data, both satellite datasets reveal a reasonable performance, with overall mean TCC biases of 2.1 and 5.2% for CLARA-A2 and ISCCP-H, respectively. This, along with the higher correlation coefficients between CLARA-A2 and ECA&D TCC, indicates the better performance of CLARA-A2 TCC data.
Under a NASA program to produce long-term data records from instruments on multiple satellites, data from a series of nine Solar Backscatter Ultraviolet (SBUV and SBUV2) instruments have been re-processed to create a coherent ozone time series. Data from the BUV instrument on Nimbus 4, SBUV on Nimbus 7, and SBUV2 instruments on NOAA 9, 11, 14, 16, 17, 18, and 19 covering the period 1970-1972 and 1979-2011 were used to create a long-term data set. The goal is an ozone Earth Science Data Record - a consistent, calibrated ozone time series that can be used for trend analyses and other studies. In order to create this ozone data set, the radiances were adjusted and used to re-process the entire data records for each of the nine instruments. Inter-instrument comparisons during periods of overlap as well as comparisons with data from other satellite and ground-based instruments were used to evaluate the consistency of the record and make calibration adjustments as needed. Additional improvements in this version 8.6 processing included the use of the Brion, Daumont, and Malicet ozone cross sections, and a cloud-height climatology derived from Aura OMI measurements. Validation of the re-processed ozone shows that total column ozone is consistent with the Brewer Dobson network to within about 1 for the new time series. Comparisons with MLS, SAGE, sondes, and lidar show that ozone at individual levels in the stratosphere is generally consistent to within 5 percent.
The observation and measurement of precipitation across a range of temporal and spatial scales is of great importance for many user requirements and applications. Precipitation is a key component of the global energy and water cycle moving water, and its’ associated energy, through the Earth system at relatively fine spatial and temporal scales. At the societal level, precipitation provides fresh water and moderates our climate, but can also be destructive through storms, droughts and spreading diseases. Monitoring and measuring the distribution and quantities of precipitation, across the Earth’s surface is therefore crucial to physical and social applications, together with changes in precipitation over time. While conventional surface instruments provide a reasonably long record, these tend to be confined to well populated land surface areas: most ocean regions and sparsely populated regions have few measurements. Satellite-based precipitation products can provide global measurements, although the timespan of such products is limited and the data record is comprised of different instruments that operate over different periods of record. The challenge of any climate record is to provide a consistent product over the full period of record. Many satellite precipitation products incorporate ancillary data, such as surface gauge data or model data, which may limit their utility in the validation or verification of other climate data records. This chapter provides a review of the importance of precipitation to the climate system, the basis of satellite precipitation retrievals, climate data records of satellite data for precipitation retrievals and the development of fundamental precipitation data records for climate applications.
Gross primary production (GPP), the amount of carbon dioxide (CO 2 ) assimilated by plants through photosynthesis, is one of the most variable and uncertain components of the global carbon cycle. Global GPP has been estimated with a number of process-based models, data-driven, and hybrid approaches. Dynamic global vegetation models (DGVMs), driven by observed environmental changes, are used for global carbon budget assessments and long-term (climate) prediction. Benchmarking these and other models globally with data-driven GPP estimates is critical for understanding the land sink and ensuring accurate forecasts of the carbon cycle. In addition, global data-driven GPP estimates are crucial for studies of interannual variability, including trends that are linked to mechanisms with large uncertainties, such as the indirect CO 2 fertilization effect related to greening. In response to a community need for a GPP data set that well captures spatio-temporal variability, we developed FluxSat, a data-driven approach that optimizes the use of satellite reflectance data from the NASA MODerate-resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua satellites, calibrated using ground-based eddy covariance (EC) data. We are enhancing (spatially, higher resolution) and extending FluxSat (in time, with additional sensors) to create a high quality long term GPP Earth System Data Record (ESDR) for use in model benchmarking, carbon cycle modeling, and studies of trends and interannual variability. Our team’s objectives are to: 1. Update and document the current MODIS FluxSat GPP (daily, 0.05o and 0.5o resolutions) products with latest available MODIS and EC data sets; 2. Extend FluxSat GPP record forward in time with the Visible Infrared Imaging Radiometer Suite (VIIRS) on operational weather satellites going forward; 3. Extend FluxSat GPP record backward in time using the Advanced Very High Resolution Radiometer (AVHRR) on weather satellites dating back to 1981; 4. Provide higher spatial resolution MODIS and VIIRS GPP (0.0083o). 5. Thoroughly evaluate all FluxSat products with independent data; and 6. Create a homogenized long-term GPP record spanning 40+ years. We will discuss plans for this long-term data set that is supported through the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program.
Deriving Climate Data Records (CDRs) from multiple IR sounders such AIRS on Aqua, CrIS on S-NPP, NOAA20 and JPSS-2, IASI on Metop A, B, and C requires from current operational products are challenging due to different radiative transfer models and retireavl algorithms used for processing level 2 data. We developed a Climate Fingerprinting Sounder Product (ClimFiSP) algorithm, which uses a single set of radiative kernels a robust spectral fingerprinting method to performs retrievals using spatiotemporally averaged L1 hyperspectral radiances directly. The ClimFiSP algorithm provides accurate data fusion CDR products from multiple satellite sensors. We have applied this method to both AIRS and CrIS (on SNPP and on NOAA 20) data and generated two decades climate data records for atmospheric temperature, water vapor, cloud, trace gases, and surface skin temperature. We plan to add IASI to the CDR data set in the future. The ClimFiSP is being transitioned to NASA GES DISC data center for public access.