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Several feedback mechanisms between ocean and atmosphere are discussed, which seem to have a decisive influence on the interannual variability of the atmosphere, and on climatic fluctuations of a time scale of 10 to 50 years. Satellite requirements to monitor these feedback processes are outlined briefly.
The Space Shuttle System consisting of four major elements, the orbiter, three main engines, an expendable external tank, and solid rocket boosters is examined. Dimensions, thrust and payload capacity are observed, noting cost reduction through recovery and subsequent reuse of some components. Return of the solid rocket boosters (SRB's) via parachute and recovery from the ocean for refurbishment is covered. Objectives for the first mission scheduled for November 1979 include achieving a 150 nautical mile orbit and carrying a payload consisting of development flight instrumentation (DFI), the induced environment contamination monitor (IECM), and the aerodynamic coefficients identification package (ACIP). A section devoted to performance enhancement, discusses weight reduction on the orbiter, external tanks and thrust augmentation through the use of strap on solid motors (SOSM).
Two sets of TM data taken over the ocean off the coast of the Southeastern U.S. Bight were studied for the applicability of TM data to marine environments. First, the results of applying TM and TMS data to determine chlorophyll concentration in the ocean are presented. Chlorophyll quantification in the range of 0.5 to 2.0 mg/cu m was achieved by taking the ratio of TM band-1/band-2. Second, the results of applying TM band-6 data to monitor sea surface temperature are described. A comparison of TM data with AVHRR data shows TM readings coincide with AVHRR data within a scatter of 0.5 deg C in most of the areas studied. Lastly, the results of a technique to map the water depths of coral reefs in the Great Bahama Bank are demonstrated. Depths from 0 to 20 meters were delineated using TM band-1. The classification accuracy and origins of anomalous depth points are discussed.
The planned Soil Moisture Active Passive (SMAP) mission is one of the first Earth observation satellites being developed by NASA in response to the National Research Council's Decadal Survey, Earth Science and Applications from Space: National Imperatives for the Next Decade and Beyond [1]. Scheduled to launch late in 2014, the proposed SMAP mission would provide high resolution and frequent revisit global mapping of soil moisture and freeze/thaw state, utilizing enhanced Radio Frequency Interference (RFI) mitigation approaches to collect new measurements of the hydrological condition of the Earth's surface. The SMAP instrument design incorporates an L-band radar (3 km) and an L band radiometer (40 km) sharing a single 6-meter rotating mesh antenna to provide measurements of soil moisture and landscape freeze/thaw state [2]. These observations would (1) improve our understanding of linkages between the Earth's water, energy, and carbon cycles, (2) benefit many application areas including numerical weather and climate prediction, flood and drought monitoring, agricultural productivity, human health, and national security, (3) help to address priority questions on climate change, and (4) potentially provide continuity with brightness temperature and soil moisture measurements from ESA's SMOS (Soil Moisture Ocean Salinity) and NASA's Aquarius missions. In the planned SMAP mission prelaunch time frame, baseline algorithms are being developed for generating (1) soil moisture products both from radiometer measurements on a 36 km grid and from combined radar/radiometer measurements on a 9 km grid, and (2) freeze/thaw products from radar measurements on a 3 km grid. These retrieval algorithms need a variety of global ancillary data, both static and dynamic, to run the retrieval models, constrain the retrievals, and provide flags for indicating retrieval quality. The choice of which ancillary dataset to use for a particular SMAP product would be based on a number of factors, including its availability and ease of use, its inherent error and resulting impact on the overall soil moisture or freeze/thaw retrieval accuracy, and its compatibility with similar choices made by the SMOS mission. All decisions regarding SMAP ancillary data sources would be fully documented by the SMAP Project and made available to the user community.
The Ozone Monitoring Instrument (OMI) cloud and NO2 algorithms use a monthly gridded surface reflectivity climatology that does not depend upon the observation geometry. In reality, reflection of incoming direct and diffuse solar light from land or ocean surfaces is sensitive to the sun sensor geometry. This dependence is described by the bidirectional reflectance distribution function (BRDF). To account for the BRDF, we propose to use a new concept of geometry-dependent Lambertian equivalent reflectivity (GLER). Implementation within the existing OMI cloud and NO2 retrieval infrastructure requires changes only to the input surface reflectivity database. GLER is calculated using a vector radiative transfer model with high spatial resolution BRDF information from MODIS over land and the Cox Munk slope distribution over ocean with a contribution from water-leaving radiance. We compare GLER and climatological LER at 466 nm, which is used in the OMI O2-O2cloud algorithm to derive effective cloud fractions. A detailed comparison of the cloud fractions and pressures derived with climatological and GLERs is carried out. GLER and corresponding retrieved cloud products are then used as input to the OMI NO2 algorithm. We find that replacing the climatological OMI-based LERs with GLERs can increase NO2 vertical columns by up to 50 % in highly polluted areas; the differences include both BRDF effects and biases between the MODIS and OMI-based surface reflectance data sets. Only minor changes to NO2 columns (within 5 %) are found over unpolluted and overcast areas.
Nitrous oxide (N2O) is the third most important anthropogenic greenhouse gas (GHG) and a major ozone-depleting substance (ODS). Its main sources include anthropogenic activities (mostly agriculture) and natural emissions from ocean and soils. However, emission estimates for individual sources are highly variable due to uncertainties in N2O lifetime estimates and partitioning among sources. We derive annual global N2O emissions for 1990-2019 using NOAA Global Monitoring Laboratory (GML) surface N2O observations and the N2O lifetime calculated in the NASA GEOS-5 chemistry climate model (CCM). The inferred global mean N2O emissions has gradually increased from ~15.8 TgN/yr in the early 1990s to ~17.8 TgN/yr in the 2010s. This implies that anthropogenic N2O emissions have grown rapidly from ~6.7 TgN/yr in the 1990s to about ~8.7 TgN/yr in the 2010s, a ~30% increase. With specially designed N2O isotopic tracers in 3-D GEOSCCM, we estimate that, on global average, stratospheric enrichment contributes about +7.7‰/yr, +7.6‰/yr, +8.0‰/yr to tropospheric N(sup α)-δ(sup 15), N(sup β)-δ(sup 15), and O-δ(sup 18) budget, respectively. To balance the global mean isotopic signature for pre-industrial terrestrial sources of N(sup α)-δ(sup 15) ~ 6.7‰, N(sup β)-δ(sup 15) ~ -12.6‰, O-δ(sup 18) ~ 35.4‰, our 3-D isotopic budget simulation using the GEOSCCM suggests global mean anthropogenic isotopic signatures in the recent decades are N(sup α)-δ(sup 15) ~ -18‰, N(sup β)-δ(sup 15) ~ -20‰, O-δ(sup 18) ~ 19‰. These anthropogenic isotopic estimates are significantly lighter than results from one-box atmospheric model-based estimates with the largest difference seen for N(sup β)-δ(sup 15). More surface isotopic measurements are needed to better quantify the N2O isotopic signatures.
The Pilot-Mission Exploitation Platform (Pi-MEP) for salinity is an ESA initiative originally meant to support and widen the uptake of Soil Moisture and Ocean Salinity (SMOS) mission data over the ocean. Starting in 2017, the project aims at setting up a computational web-based platform focusing on satellite sea surface salinity data, supporting studies on enhanced validation and scientific process over the ocean. It has been designed in close collaboration with a dedicated science advisory group in order to achieve three main objectives: gathering all the data required to exploit satellite sea surface salinity data, systematically producing a wide range of metrics for comparing and monitoring sea surface salinity products’ quality, and providing user-friendly tools to explore, visualize and exploit both the collected products and the results of the automated analyses. The Salinity Pi-MEP is becoming a reference hub for the validation of satellite sea surface salinity missions by providing valuable information on satellite products (SMOS, Aquarius, SMAP), an extensive in situ database (e.g., Argo, thermosalinographs, moorings, drifters) and additional thematic datasets (precipitation, evaporation, currents, sea level anomalies, sea surface temperature, etc.). Co-localized databases between satellite products and in situ datasets are systematically generated together with validation analysis reports for 30 predefined regions. The data and reports are made fully accessible through the web interface of the platform. The datasets, validation metrics and tools (automatic, user-driven) of the platform are described in detail in this paper. Several dedicated scientific case studies involving satellite SSS data are also systematically monitored by the platform, including major river plumes, mesoscale signatures in boundary currents, high latitudes, semi-enclosed seas, and the high-precipitation region of the eastern tropical Pacific. Since 2019, a partnership in the Salinity Pi-MEP project has been agreed between ESA and NASA to enlarge focus to encompass the entire set of satellite salinity sensors. The two agencies are now working together to widen the platform features on several technical aspects, such as triple-collocation software implementation, additional match-up collocation criteria and sustained exploitation of data from the SPURS campaigns
Visual and infrared measurements from the five channel AVHRR on the NOAA-7 satellite are used operationally to derive sea surface temperatures. The multichannel data perform daytime and nighttime cloud detection tests, and the several atmospheric window channels in the thermal infrared correct for atmospheric attenuation. Monitoring of the sea surface temperature product with buoy data indicates stability in mean bias and rms difference with little variation by season or geographic area. Global mapping enables the derivation of monthly mean isotherms, monthly and annual changes, and anomaly patterns relative to climatology. Problems are associated with noise in the 3.7 micro m window channel, and with the injection of substantial volcanic aerosol into the stratosphere by the El Chichon eruption. Multichannel sea surface temperature charts are used to study phenomena such as equatorial long waves and the recent El Nino episode.
Dr. Norden Huang, of Goddard Space Flight Center, invented a set of algorithms (called the Hilbert-Huang Transform, or HHT) for analyzing nonlinear and nonstationary signals that developed into a user-friendly signal processing technology for analyzing time-varying processes. At an auction managed by Ocean Tomo Federal Services LLC, licenses of 10 U.S. patents and 1 domestic patent application related to HHT were sold to DynaDx Corporation, of Mountain View, California. DynaDx is now using the licensed NASA technology for medical diagnosis and prediction of brain blood flow-related problems, such as stroke, dementia, and traumatic brain injury.
The purpose of this study is to correlate in detail auroral activity with the corresponding disturbances in the magnetotail. The auroral data were recorded by optical instruments aboard an airplane flying over the Arctic Ocean along the Alaska meridian and by the Alaska meridian chain of all-sky cameras. The corresponding magnetotail observations were made by various instruments on Vela 6A and Imp 5; the interplanetary magnetic field was monitored concurrently by Explorer 35. Three successive substorms were observed on February 17, 1971. Each substorm was readily identified by the classical auroral and magnetic signatures which accompanied its onset. The observed variations of plasma and magnetic field in the magnetotail were consistent with the idea that a neutral line formed in the range X sub SM between -12 and -18 R sub E at the onset of each substorm expansive phase and then moved tailward past X sub SM = -18 R sub E some tens of minutes afterward. The Z component of the tail magnetic field decreased rather steadily for a period of 1-2 1/2 hours after each substorm and until the onset of the next expansive phase, reaching a minimum value just before each onset.
NASA’s ICESat-2 mission was launched in September 2018 with the primary goal of monitoring our rapidly changing polar regions. The sole instrument onboard, the Advanced Topographic Laser Altimeter System (ATLAS), is now providing routine, very high-resolution, surface elevation data across the globe, including the Arctic and Southern Oceans. In this study we demonstrate our new processing chain for converting the along-track ICESat-2 sea ice freeboard product (ATL10) into sea ice thickness, focusing our initial efforts on the Arctic Ocean. For this conversion we primarily make use of snow depth and density data from the NASA Eulerian Snow on Sea Ice Model (NESOSIM). The coarse resolution (~100 km) snow data are redistributed onto the high resolution (~30 - 100 m) ATL10 freeboards using relationships obtained from snow depth and freeboard data collected by NASA’s Operation IceBridge mission. We present regional sea ice thickness distributions and highlight their seasonal evolution through our first winter-season of data collection. We include ice thickness uncertainty estimates, while also acknowledging the limitations of these estimates. We generate a gridded monthly thickness product and compare this with various monthly sea ice thickness estimates obtained from ESA’s CryoSat-2 satellite mission, with ICE 33 Sat-2 showing consistently lower thicknesses. Finally, we compare our February/March 2019 thickness estimates to ICESat February/March (19th February – 21st March) 2008 ice thickness estimates using the same input assumptions, which show a ~0.40 m or ~21% thinning across an inner Arctic Ocean domain in this 11-year time period.
Global warming/climate change has been a subject of scientific interest since the early 19th century. In particular, increases in the atmospheric concentration of carbon dioxide (CO2) have long been thought to account for Earth's increased warming, although the lack of a dependable set of observational data was apparent as late as the mid 1950s. However, beginning in the late 1950s, being associated with the International Geophysical Year, the opportunity arose to begin accurate continuous monitoring of the Earth's atmospheric concentration of CO2. Consequently, it is now well established that the atmospheric concentration of CO2, while varying seasonally within any particular year, has steadily increased over time. Associated with this rising trend in the atmospheric concentration of CO2 is a rising trend in the surface-air and sea-surface temperatures (SSTs). This Technical Publication (TP) examines the statistical relationships between 10-year moving averages (10-yma) of the Global Land-Ocean Temperature Index (GLOTI), sunspot number (SSN), the Atlantic Multidecadal Oscillation (AMO) index, and the Mauna Loa CO2 (MLCO2) index for the common interval 1964-2006, where the 10-yma values are used to indicate trends in the data. Scatter plots using the 10-yma values between GLOTI and each of the other parameters are determined, both as single-variate and multivariate fits. Scatter plots are also determined for MLCO2 using single-variate and bivariate (BV) fits, based on the GLOTI alone and the GLOTI in combination with the AMO index. On the basis of the inferred preferential fits for MLCO2, estimates for MLCO2 are determined for the interval 1885-1964, thereby yielding an estimate of the preindustrial level of atmospheric concentration of CO2. Lastly, 10-yma values of MLCO2 are compared against 10-yma estimates of the total carbon emissions (TCE) to determine the likelihood that manmade sources of carbon emissions are indeed responsible for the recent warming now being experienced. (Parametric values used in this TP are those available prior to the end of 2012.)
The economic benefits of an operational SEASAT system are discussed in the areas of marine transportation, offshore oil and natural gas exploration and development, ocean fishing, and Arctic operations. A description of the candidate economic verification experiments which could be performed with SEASAT-A is given. With the exception of the area of Arctic operations, experiments have been identified in each of the areas of ocean based activity that are expected to show an economic impact from the use of operational SEASAT data. Experiments have been identified in the areas of the offshore oil and natural gas industry, as well as ice monitoring and coastal zone applications. Emphasis has been placed on the identification and the development of those experiments which meet criteria for: (1) end user participation; (2) SEASAT-A data utility; (3) measurability of operational parameters to demonstrate economic effect; and (4) non-proprietary nature of results.
The response of a satellite sensor varies during its lifetime; internal calibration devices can be used to follow the sensor degradation or in-flight calibrations are conducted from estimates of the radiance at satellite level for well predictable situations. Changes in gain are evaluated assuming that the spectral response of the sensor is stable with time; i.e., that the filter response as well as the optics or the electronics are not modified since the prelaunch determinations. Nevertheless, there is some evidence that the SPOT interferometer filters are affected by outgassing effects during the launch. Tests in vacuum chambers indicated a narrowing of the filters with a shift of the upper side towards the blue of about 10 nm which is more over consistant with the loss of gain observed during the launch. Also, during the lifetime of SPOT, the relationship between the loss of sensitivity and the filter bandwidth may correspond to this effect. On the other hand, the inconsistancy of the NOAA7 calibration between two methods (desert and ocean) having a different spectral sensitivity may indicate a spectral problem with a shift of the central wavelength of -20 nm. The basic idea here is to take advantage of the good spectral definition of AVIRIS to monitor these potential spectral degradations with an experimental opportunity provided by a field campaign held in La Crau (S.E. of France) in June 1991 which associated ground-based measurements and AVIRIS, SPOT2, NOAA-11 overpasses over both the calibration site of La Crau and an agricultural area.
The Moderate Resolution Imaging Spectroradiometer (MODIS) instruments onboard of Terra and Aqua satellites provide, for the first time, concurrent measurements of sea surface temperature (SST) and ocean color, which are suitable for coastal upwelling studies. The accuracy, the 1-km spatial resolution, and the almost complete daily coverage of the MODIS data compared with historical measurements make it advantageous for resolving important coastal fronts of chlorophyll concentration and temperature. The cold SST anomaly during summer 2003 off the coast of the South Atlantic Bight is an event that is comprehensively covered by NASA's MODIS and SeaWinds satellite observations. These data combined with in situ tide gauge, mooring, and ship measurements can be used to identify important dynamics responsible for the anomalous cold water event. The analysis of the data suggests that coastal upwelling occurs in the climatological summer forced by the climatological southerlies over the South Atlantic Bight area in summer. However, the strong buoyancy barrier in summer prevents the cold water below the thermocline from reaching the ocean surface. In summer 2003, the southwesterlies in July through August were extraordinarily strong and persistent, which generated the upwelling currents strong enough to overcome the buoyancy resistance. The results of this analysis demonstrate the possibility of monitoring and forecasting the event using combination of the satellite and in situ observations. The MODIS data are archived and distributed by the NASA's Goddard Earth Science (GES) Distributed Active Archive Center (DAAC). The data can be accessed via the URL http://wwv.daac.gsfc.nasa.gov/MODIS.
Currently, two nearly identical MODIS instruments are operating in space: one on the Terra spacecraft launched in December 1999 and another on the Aqua spacecraft launched in May 2002. MODIS has 36 spectral bands with wavelengths covering from visible (VIS) to long-wave infrared (LWIR). Since launch, MODIS observations and data products have contributed significantly to studies of changes in the Earth system of land, oceans, and atmosphere. To maintain its on-orbit calibration and data product quality, MODIS was built with a comprehensive set of on-board calibrators, consisting of a solar diffuser (SD) and a solar diffuser stability monitor (SDSM) for the reflective solar bands (RSB) and an on-board blackbody (BB) for the thermal emissive bands (TEB). Both instruments have demonstrated good performance. The primary Level 1 B (LIB) data products are top of the atmosphere (TOA) reflectance for RSB and radiance for TEB This paper provides an overview of MODIS calibration methodologies, activities, lifetime on-orbit performance and challenging issues for each MODIS, the impact on LIB product quality, and lessons learned for future sensors such as the NPOESS VIIRS.
NASA's launch of the GOES-C geostationary satellite from Kennedy Space Center, Florida is planned for June 16, 1978. The launch vehicle is a three stage Delta 2914. As its contribution, GOES-C will contribute information from a data sparse area of the world centered in the Indian Ocean. GOES-C will replace GOES-1 and will become GOES-3 once it has successfully orbited at 35,750 kilometers (22,300 miles). NASA's Spaceflight Tracking and Data Network (STDN) will provide support for the mission. Included in the article are: (1) Delta launch vehicle statistics, first, second and third stages; (2) Delta/GOES-C major launch events; (3) Launch operations; (4) Delta/GOES-C personnel.
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