NOAA Center for Satellite Applications and Research

The NCEP Climate Forecast System Reanalysis (CFSR) was completed for the 31-yr period from 1979 to 2009, in January 2010. The CFSR was designed and executed as a global, high-resolution coupled atmosphere–ocean–land surface–sea ice system to provide the best estimate of the state of these coupled domains over this period. The current CFSR will be extended as an operational, real-time product into the future. New features of the CFSR include 1) coupling of the atmosphere and ocean during the generation of the 6-h guess field, 2) an interactive sea ice model, and 3) assimilation of satellite radiances by the Gridpoint Statistical Interpolation (GSI) scheme over the entire period. The CFSR global atmosphere resolution is ~38 km (T382) with 64 levels extending from the surface to 0.26 hPa. The global ocean's latitudinal spacing is 0.25° at the equator, extending to a global 0.5° beyond the tropics, with 40 levels to a depth of 4737 m. The global land surface model has four soil levels and the global sea ice model has three layers. The CFSR atmospheric model has observed variations in carbon dioxide (CO2) over the 1979–2009 period, together with changes in aerosols and other trace gases and solar variations. Most available in situ and satellite observations were included in the CFSR. Satellite observations were used in radiance form, rather than retrieved values, and were bias corrected with “spin up” runs at full resolution, taking into account variable CO2 concentrations. This procedure enabled the smooth transitions of the climate record resulting from evolutionary changes in the satellite observing system. CFSR atmospheric, oceanic, and land surface output products are available at an hourly time resolution and a horizontal resolution of 0.5° latitude × 0.5° longitude. The CFSR data will be distributed by the National Climatic Data Center (NCDC) and NCAR. This reanalysis will serve many purposes, including providing the basis for most of the NCEP Climate Prediction Center's operational climate products by defining the mean states of the atmosphere, ocean, land surface, and sea ice over the next 30-yr climate normal (1981–2010); providing initial conditions for historical forecasts that are required to calibrate operational NCEP climate forecasts (from week 2 to 9 months); and providing estimates and diagnoses of the Earth's climate state over the satellite data period for community climate research. Preliminary analysis of the CFSR output indicates a product that is far superior in most respects to the reanalysis of the mid-1990s. The previous NCEP–NCAR reanalyses have been among the most used NCEP products in history; there is every reason to believe the CFSR will supersede these older products both in scope and quality, because it is higher in time and space resolution, covers the atmosphere, ocean, sea ice, and land, and was executed in a coupled mode with a more modern data assimilation system and forecast model.

Generic Mapping Tools (GMT) is an open‐source software package for the analysis and display of geoscience data, helping scientists to analyze, interpolate, filter, manipulate, project, and plot time series and gridded data sets. The GMT toolbox includes about 80 core and 40 supplemental program modules sharing a common set of command options, file structures, and documentation. Its power to process data and produce publication‐quality graphic presentations has made it vital to a large scientific community that now includes more than 25,000 individual users. GMT's website ( http://gmt.soest.hawaii.edu/ ) exceeds 20,000 visits per month, and server logs show roughly 2000 monthly downloads.

Abstract The monthly global 2° × 2° Extended Reconstructed Sea Surface Temperature (ERSST) has been revised and updated from version 4 to version 5. This update incorporates a new release of ICOADS release 3.0 (R3.0), a decade of near-surface data from Argo floats, and a new estimate of centennial sea ice from HadISST2. A number of choices in aspects of quality control, bias adjustment, and interpolation have been substantively revised. The resulting ERSST estimates have more realistic spatiotemporal variations, better representation of high-latitude SSTs, and ship SST biases are now calculated relative to more accurate buoy measurements, while the global long-term trend remains about the same. Progressive experiments have been undertaken to highlight the effects of each change in data source and analysis technique upon the final product. The reconstructed SST is systematically decreased by 0.077°C, as the reference data source is switched from ship SST in ERSSTv4 to modern buoy SST in ERSSTv5. Furthermore, high-latitude SSTs are decreased by 0.1°–0.2°C by using sea ice concentration from HadISST2 over HadISST1. Changes arising from remaining innovations are mostly important at small space and time scales, primarily having an impact where and when input observations are sparse. Cross validations and verifications with independent modern observations show that the updates incorporated in ERSSTv5 have improved the representation of spatial variability over the global oceans, the magnitude of El Niño and La Niña events, and the decadal nature of SST changes over 1930s–40s when observation instruments changed rapidly. Both long- (1900–2015) and short-term (2000–15) SST trends in ERSSTv5 remain significant as in ERSSTv4.

Abstract The Generic Mapping Tools (GMT) software is ubiquitous in the Earth and ocean sciences. As a cross‐platform tool producing high‐quality maps and figures, it is used by tens of thousands of scientists around the world. The basic syntax of GMT scripts has evolved very slowly since the 1990s, despite the fact that GMT is generally perceived to have a steep learning curve with many pitfalls for beginners and experienced users alike. Reducing these pitfalls means changing the interface, which would break compatibility with thousands of existing scripts. With the latest GMT version 6, we solve this conundrum by introducing a new “modern mode” to complement the interface used in previous versions, which GMT 6 now calls “classic mode.” GMT 6 defaults to classic mode and thus is a recommended upgrade for all GMT 5 users. Nonetheless, new users should take advantage of modern mode to make shorter scripts, quickly access commonly used global data sets, and take full advantage of the new tools to draw subplots, place insets, and create animations.

The Global Precipitation Climatology Project (GPCP) has released the GPCP Version 1 Combined Precipitation Data Set, a global, monthly precipitation dataset covering the period July 1987 through December 1995. The primary product in the dataset is a merged analysis incorporating precipitation estimates from low-orbit-satellite microwave data, geosynchronous-orbit-satellite infrared data, and rain gauge observations. The dataset also contains the individual input fields, a combination of the microwave and infrared satellite estimates, and error estimates for each field. The data are provided on 2.5 x2.5 latitude-longitude global grids. Preliminary analyses show general agreement with prior studies of global precipitation and extends prior studies of El Nino-Southern Oscillation precipitation patterns. At the regional scale there are systematic differences with standard climatologies.

Gravity models are powerful tools for mapping tectonic structures, especially in the deep ocean basins where the topography remains unmapped by ships or is buried by thick sediment. We combined new radar altimeter measurements from satellites CryoSat-2 and Jason-1 with existing data to construct a global marine gravity model that is two times more accurate than previous models. We found an extinct spreading ridge in the Gulf of Mexico, a major propagating rift in the South Atlantic Ocean, abyssal hill fabric on slow-spreading ridges, and thousands of previously uncharted seamounts. These discoveries allow us to understand regional tectonic processes and highlight the importance of satellite-derived gravity models as one of the primary tools for the investigation of remote ocean basins.

The MODIS cloud mask uses several cloud detection tests to indicate a level of confidence that the MODIS is observing clear skies. It will be produced globally at single‐pixel resolution; the algorithm uses as many as 14 of the MODIS 36 spectral bands to maximize reliable cloud detection and to mitigate past difficulties experienced by sensors with coarser spatial resolution or fewer spectral bands. The MODIS cloud mask is ancillary input to MODIS land, ocean, and atmosphere science algorithms to suggest processing options. The MODIS cloud mask algorithm will operate in near real time in a limited computer processing and storage facility with simple easy‐to‐follow algorithm paths. The MODIS cloud mask algorithm identifies several conceptual domains according to surface type and solar illumination, including land, water, snow/ice, desert, and coast for both day and night. Once a pixel has been assigned to a particular domain (defining an algorithm path), a series of threshold tests attempts to detect the presence of clouds in the instrument field of view. Each cloud detection test returns a confidence level that the pixel is clear ranging in value from 1 (high) to zero (low). There are several types of tests, where detection of different cloud conditions relies on different tests. Tests capable of detecting similar cloud conditions are grouped together. While these groups are arranged so that independence between them is maximized, few, if any, spectral tests are completely independent. The minimum confidence from all tests within a group is taken to be representative of that group. These confidences indicate absence of particular cloud types. The product of all the group confidences is used to determine the confidence of finding clear‐sky conditions. This paper outlines the MODIS cloud masking algorithm. While no present sensor has all of the spectral bands necessary for testing the complete MODIS cloud mask, initial validation of some of the individual cloud tests is presented using existing remote sensing data sets.

The aerosol products retrieved using the Moderate Resolution Imaging Spectroradiometer (MODIS) collection 5.1 Deep Blue algorithm have provided useful information about aerosol properties over bright‐reflecting land surfaces, such as desert, semiarid, and urban regions. However, many components of the C5.1 retrieval algorithm needed to be improved; for example, the use of a static surface database to estimate surface reflectances. This is particularly important over regions of mixed vegetated and nonvegetated surfaces, which may undergo strong seasonal changes in land cover. In order to address this issue, we develop a hybrid approach, which takes advantage of the combination of precalculated surface reflectance database and normalized difference vegetation index in determining the surface reflectance for aerosol retrievals. As a result, the spatial coverage of aerosol data generated by the enhanced Deep Blue algorithm has been extended from the arid and semiarid regions to the entire land areas. In this paper, the changes made in the enhanced Deep Blue algorithm regarding the surface reflectance estimation, aerosol model selection, and cloud screening schemes for producing the MODIS collection 6 aerosol products are discussed. A similar approach has also been applied to the algorithm that generates the Sea‐viewing Wide Field‐of‐view Sensor (SeaWiFS) Deep Blue products. Based upon our preliminary results of comparing the enhanced Deep Blue aerosol products with the Aerosol Robotic Network (AERONET) measurements, the expected error of the Deep Blue aerosol optical thickness (AOT) is estimated to be better than 0.05 + 20%. Using 10 AERONET sites with long‐term time series, 79% of the best quality Deep Blue AOT values are found to fall within this expected error.

Abstract The NOAA/NESDIS/NCEI Daily Optimum Interpolation Sea Surface Temperature (SST), version 2.0, dataset (DOISST v2.0) is a blend of in situ ship and buoy SSTs with satellite SSTs derived from the Advanced Very High Resolution Radiometer (AVHRR). DOISST v2.0 exhibited a cold bias in the Indian, South Pacific, and South Atlantic Oceans that is due to a lack of ingested drifting-buoy SSTs in the system, which resulted from a gradual data format change from the traditional alphanumeric codes (TAC) to the binary universal form for the representation of meteorological data (BUFR). The cold bias against Argo was about −0.14°C on global average and −0.28°C in the Indian Ocean from January 2016 to August 2019. We explored the reasons for these cold biases through six progressive experiments. These experiments showed that the cold biases can be effectively reduced by adjusting ship SSTs with available buoy SSTs, using the latest available ICOADS R3.0.2 derived from merging BUFR and TAC, as well as by including Argo observations above 5-m depth. The impact of using the satellite MetOp-B instead of NOAA-19 was notable for high-latitude oceans but small on global average, since their biases are adjusted using in situ SSTs. In addition, the warm SSTs in the Arctic were improved by applying a freezing point instead of regressed ice-SST proxy. This paper describes an upgraded version, DOISST v2.1, which addresses biases in v2.0. Overall, by updating v2.0 to v2.1, the biases are reduced to −0.07° and −0.14°C in the global ocean and Indian Ocean, respectively, when compared with independent Argo observations and are reduced to −0.04° and −0.08°C in the global ocean and Indian Ocean, respectively, when compared with dependent Argo observations. The difference against the Group for High Resolution SST (GHRSST) Multiproduct Ensemble (GMPE) product is reduced from −0.09° to −0.01°C in the global oceans and from −0.20° to −0.04°C in the Indian Ocean.

The authors describe the status of MODIS-N and its companion instrument MODIS-T (tilt), a tiltable cross-track scanning spectrometer with 32 uniformly spaced channels between 0.410 and 0.875 mu m. They review the various methods being developed for the remote sensing of atmospheric properties using MODIS, placing primary emphasis on the principal atmospheric applications of determining the optical, microphysical, and physical properties of clouds and aerosol particles from spectral reflection and thermal emission measurements. In addition to cloud and aerosol properties, MODIS-N will be used for determining the total precipitable water vapor and atmospheric stability. The physical principles behind the determination of each of these atmospheric products are described, together with an example of their application to aircraft and/or satellite measurements.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

The radio occultation (RO) technique, which makes use of radio signals transmitted by the global positioning system (GPS) satellites, has emerged as a powerful and relatively inexpensive approach for sounding the global atmosphere with high precision, accuracy, and vertical resolution in all weather and over both land and ocean. On 15 April 2006, the joint Taiwan-U.S. Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC)/Formosa Satellite Mission 3 (COSMIC/FORMOSAT-3, hereafter COSMIC) mission, a constellation of six microsatellites, was launched into a 512-km orbit. After launch the satellites were gradually deployed to their final orbits at 800 km, a process that took about 17 months. During the early weeks of the deployment, the satellites were spaced closely, offering a unique opportunity to verify the high precision of RO measurements. As of September 2007, COSMIC is providing about 2000 RO soundings per day to support the research and operational communities. COSMIC RO data are of better quality than those from the previous missions and penetrate much farther down into the troposphere; 70%–90% of the soundings reach to within 1 km of the surface on a global basis. The data are having a positive impact on operational global weather forecast models. With the ability to penetrate deep into the lower troposphere using an advanced open-loop tracking technique, the COSMIC RO instruments can observe the structure of the tropical atmospheric boundary layer. The value of RO for climate monitoring and research is demonstrated by the precise and consistent observations between different instruments, platforms, and missions. COSMIC observations are capable of intercalibrating microwave measurements from the Advanced Microwave Sounding Unit (AMSU) on different satellites. Finally, unique and useful observations of the ionosphere are being obtained using the RO receiver and two other instruments on the COSMIC satellites, the tiny ionosphere photometer (TIP) and the tri-band beacon.

Abstract General Bathymetric Chart of the Oceans (GEBCO) has released the GEBCO_2014 grid, a new digital bathymetric model of the world ocean floor merged with land topography from publicly available digital elevation models. GEBCO_2014 has a grid spacing of 30 arc sec and updates the 2010 release (GEBCO_08) by incorporating new versions of regional bathymetric compilations from the International Bathymetric Chart of the Arctic Ocean, the International Bathymetric Chart of the Southern Ocean, the Baltic Sea Bathymetry Database, and data from the European Marine Observation and Data network bathymetry portal, among other data sources. Approximately 33% of ocean grid cells (not area) have been updated in GEBCO_2014 from the previous version, including both new interpolated depth values and added soundings. These updates include large amounts of multibeam data collected using modern equipment and navigation techniques, improving portrayed details of the world ocean floor. Of all nonland grid cells in GEBCO_2014, approximately 18% are based on bathymetric control data, i.e., primarily multibeam and single‐beam soundings or preprepared grids which may contain some interpolated values. The GEBCO_2014 grid has a mean and median depth of 3897 m and 3441 m, respectively. Hypsometric analysis reveals that 50% of the Earth's surface is composed of seafloor located 3200 m below mean sea level and that ~900 ship years of surveying would be needed to obtain complete multibeam coverage of the world's oceans.

An updated global bathymetry and topography grid is presented using a spatial sampling interval of 15 arc sec. The bathymetry is produced using a combination of shipboard soundings and depths predicted using satellite altimetry. New data consists of &gt;33.6 million multibeam and singlebeam measurements collated by several institutions, namely, the National Geospatial‐Intelligence Agency, Japan Agency for Marine‐Earth Science and Technology, Geoscience Australia, Center for Coastal and Ocean Mapping, and Scripps Institution of Oceanography. New altimetry data consists of 48, 14, and 12 months of retracked range measurements from Cryosat‐2, SARAL/AltiKa, and Jason‐2, respectively. With respect to SRTM15_PLUS (Olson et al.,), the inclusion of these new data results in a ∼1.4‐km improvement in the minimum wavelength recovered for sea surface free‐air gravity anomalies, a small increase in the accuracy of altimetrically derived predicted depths, and a 1.24% increase, from 9.60% to 10.84%, in the total area of ocean floor that is constrained by shipboard soundings at 15‐arc sec resolution. Bathymetric grid cells constrained by satellite altimetry have estimated uncertainties of ±150 m in the deep oceans and ±180 m between coastlines and the continental rise. Onshore, topography data are sourced from previously published digital elevation models, predominately SRTM‐CGIAR V4.1 between 60°N and 60°S. ArcticDEM is used above 60°N, while Reference Elevation Model of Antarctica is used below 62°S. Auxiliary grids illustrating shipboard data coverage, marine free‐air gravity anomalies, and vertical gradient gradients are also provided in common data formats.

Three approaches are used to reduce the error in the satellite‐derived marine gravity anomalies. First, we have retracked the raw waveforms from the ERS‐1 and Geosat/GM missions resulting in improvements in range precision of 40% and 27%, respectively. Second, we have used the recently published EGM2008 global gravity model as a reference field to provide a seamless gravity transition from land to ocean. Third, we have used a biharmonic spline interpolation method to construct residual vertical deflection grids. Comparisons between shipboard gravity and the global gravity grid show errors ranging from 2.0 mGal in the Gulf of Mexico to 4.0 mGal in areas with rugged seafloor topography. The largest errors of up to 20 mGal occur on the crests of narrow large seamounts. The global spreading ridges are well resolved and show variations in ridge axis morphology and segmentation with spreading rate. For rates less than about 60 mm/a the typical ridge segment is 50–80 km long while it increases dramatically at higher rates (100–1000 km). This transition spreading rate of 60 mm/a also marks the transition from axial valley to axial high. We speculate that a single mechanism controls both transitions; candidates include both lithospheric and asthenospheric processes.

Abstract The Advanced Baseline Imager (ABI) on board the Geostationary Operational Environmental Satellite-R (GOES-R) is America’s next-generation geostationary advanced imager. GOES-R launched on 19 November 2016. The ABI is a state-of-the-art 16-band radiometer, with spectral bands covering the visible, near-infrared, and infrared portions of the electromagnetic spectrum. Many attributes of the ABI—such as spectral, spatial, and temporal resolution; radiometrics; and image navigation/registration—are much improved from the current series of GOES imagers. This paper highlights and discusses the expected improvements of each of these attributes. From ABI data many higher-level-derived products can be generated and used in a large number of environmental applications. The ABI’s design allows rapid-scan and contiguous U.S. imaging automatically interleaved with full-disk scanning. In this paper the expected instrument attributes are covered, as they relate to signal-to-noise ratio, image navigation and registration, the various ABI scan modes, and other parameters. There will be several methods for users to acquire GOES-R imagery and products depending on their needs. These include direct reception of the imagery via the satellite downlink and an online-accessible archive. The information from the ABI on the GOES-R series will be used for many applications related to severe weather, tropical cyclones and hurricanes, aviation, natural hazards, the atmosphere, the ocean, and the cryosphere. The ABI on the GOES-R series is America’s next-generation geostationary advanced imager and will dramatically improve the monitoring of many phenomena at finer time and space scales.

[1] An aerosol component of a new multiangle implementation of atmospheric correction (MAIAC) algorithm is presented. MAIAC is a generic algorithm developed for the Moderate Resolution Imaging Spectroradiometer (MODIS), which performs aerosol retrievals and atmospheric correction over both dark vegetated surfaces and bright deserts based on a time series analysis and image-based processing. The MAIAC look-up tables explicitly include surface bidirectional reflectance. The aerosol algorithm derives the spectral regression coefficient (SRC) relating surface bidirectional reflectance in the blue (0.47 μm) and shortwave infrared (2.1 μm) bands; this quantity is prescribed in the MODIS operational Dark Target algorithm based on a parameterized formula. The MAIAC aerosol products include aerosol optical thickness and a fine-mode fraction at resolution of 1 km. This high resolution, required in many applications such as air quality, brings new information about aerosol sources and, potentially, their strength. AERONET validation shows that the MAIAC and MOD04 algorithms have similar accuracy over dark and vegetated surfaces and that MAIAC generally improves accuracy over brighter surfaces due to the SRC retrieval and explicit bidirectional reflectance factor characterization, as demonstrated for several U.S. West Coast AERONET sites. Due to its generic nature and developed angular correction, MAIAC performs aerosol retrievals over bright deserts, as demonstrated for the Solar Village Aerosol Robotic Network (AERONET) site in Saudi Arabia.

Coral reefs across the world's oceans are in the midst of the longest bleaching event on record (from 2014 to at least 2016). As many of the world's reefs are remote, there is limited information on how past thermal conditions have influenced reef composition and current stress responses. Using satellite temperature data for 1985-2012, the analysis we present is the first to quantify, for global reef locations, spatial variations in warming trends, thermal stress events and temperature variability at reef-scale (~4 km). Among over 60,000 reef pixels globally, 97% show positive SST trends during the study period with 60% warming significantly. Annual trends exceeded summertime trends at most locations. This indicates that the period of summer-like temperatures has become longer through the record, with a corresponding shortening of the 'winter' reprieve from warm temperatures. The frequency of bleaching-level thermal stress increased three-fold between 1985-91 and 2006-12 - a trend climate model projections suggest will continue. The thermal history data products developed enable needed studies relating thermal history to bleaching resistance and community composition. Such analyses can help identify reefs more resilient to thermal stress.

The ABI will begin a new era in U.S. environmental remote sensing with more spectral bands, faster imaging, and higher spatial resolution than the current imager.

A method of ocean color data processing using the combined near-infrared (NIR) and shortwave infrared (SWIR) bands for atmospheric correction for the Moderate Resolution Imaging Spectroradiometer (MODIS) on Aqua is proposed. MODIS-Aqua has been producing the high quality ocean color products in the open oceans, but there are still some significant errors in the derived products in the coastal regions. With the proposed NIR-SWIR combined algorithm, MODIS ocean color data can be processed using the standard (NIR) atmospheric correction algorithm for the open oceans, whereas for the turbid waters in the coastal region the SWIR atmospheric correction algorithm can be executed. The turbid water index developed by Shi and Wang (2007) (Remote Sens. Environ. 110, 149-161 (2007)) is computed prior to the atmospheric correction for the identification of the productive and/or turbid waters where the SWIR algorithm can be operated. For non-turbid ocean waters (discriminated using the turbid water index criterion), the MODIS data are still processed using the standard (NIR) algorithm. The NIR-SWIR combined algorithm has been tested and evaluated. Two examples from MODIS-Aqua measurements along the U.S. and China east coast regions show improved ocean color products with the new approach. In particular, there are no obvious data discontinuities between using the NIR and SWIR methods. Therefore, with the NIR-SWIR combined approach for the MODIS ocean color data processing, good quality ocean color products can be derived both in clear (open) oceans as well as for turbid coastal waters.

Consistent, cross-mission retrievals of near-surface concentration of chlorophyll-a (Chla) in various aquatic ecosystems with broad ranges of trophic levels have long been a complex undertaking. Here, we introduce a machine-learning model, the Mixture Density Network (MDN), that largely outperforms existing algorithms when applied across different bio-optical regimes in inland and coastal waters. The model is trained and validated using a sizeable database of co-located Chla measurements (n = 2943) and in situ hyperspectral radiometric data resampled to simulate the Multispectral Instrument (MSI) and the Ocean and Land Color Imager (OLCI) onboard Sentinel-2A/B and Sentinel-3A/B, respectively. Our performance evaluations of the model, via two-thirds of the in situ dataset with Chla ranging from 0.2 to 1209 mg/m3 and a mean Chla of 21.7 mg/m3, suggest significant improvements in Chla retrievals. For both MSI and OLCI, the mean absolute logarithmic error (MAE) and logarithmic bias (Bias) across the entire range reduced by 40–60%, whereas the root mean squared logarithmic error (RMSLE) and the median absolute percentage error (MAPE) improved two-to-three times over those from the state-of-the-art algorithms. Using independent Chla matchups (n < 800) for Sentinel-2A/B and -3A, we show that the MDN model provides most accurate products from recorded images processed via three different atmospheric correction processors, namely the SeaWiFS Data Analysis System (SeaDAS), POLYMER, and ACOLITE, though the model is found to be sensitive to uncertainties in remote-sensing reflectance products. This manuscript serves as a preliminary study on a machine-learning algorithm with potential utility in seamless construction of Chla data records in inland and coastal waters, i.e., harmonized, comparable products via a single algorithm for MSI and OLCI data processing. The model performance is anticipated to enhance by improving the global representativeness of the training data as well as simultaneous retrievals of multiple optically active components of the water column.