NOAA National Environmental Satellite Data and Information Service

A weekly 18 spatial resolution optimum interpolation (OI) sea surface temperature (SST) analysis has been produced at the National Oceanic and Atmospheric Administration (NOAA) using both in situ and satellite data from November 1981 to the present. The weekly product has been available since 1993 and is widely used for weather and climate monitoring and forecasting. Errors in the satellite bias correction and the sea ice to SST conversion algorithm are discussed, and then an improved version of the OI analysis is developed. The changes result in a modest reduction in the satellite bias that leaves small global residual biases of roughly 20.038C. The major improvement in the analysis occurs at high latitudes due to the new sea ice algorithm where local differences between the old and new analysis can exceed 18C. Comparisons with other SST products are needed to determine the consistency of the OI. These comparisons show that the differences among products occur on large time- and space scales with monthly rms differences exceeding 0.58C in some regions. These regions are primarily the mid- and high-latitude Southern Oceans and the Arctic where data are sparse, as well as high-gradient areas such as the Gulf Stream and Kuroshio where the gradients cannot be properly resolved on a 18 grid. In addition, globally averaged differences of roughly 0.058C occur among the products on decadal scales. These differences primarily arise from the same regions where the rms differences are large. However, smaller unexplained differences also occur in other regions of the midlatitude Northern Hemisphere where in situ data should be adequate. 1.

Abstract Two new high-resolution sea surface temperature (SST) analysis products have been developed using optimum interpolation (OI). The analyses have a spatial grid resolution of 0.25° and a temporal resolution of 1 day. One product uses the Advanced Very High Resolution Radiometer (AVHRR) infrared satellite SST data. The other uses AVHRR and Advanced Microwave Scanning Radiometer (AMSR) on the NASA Earth Observing System satellite SST data. Both products also use in situ data from ships and buoys and include a large-scale adjustment of satellite biases with respect to the in situ data. Because of AMSR’s near-all-weather coverage, there is an increase in OI signal variance when AMSR is added to AVHRR. Thus, two products are needed to avoid an analysis variance jump when AMSR became available in June 2002. For both products, the results show improved spatial and temporal resolution compared to previous weekly 1° OI analyses. The AVHRR-only product uses Pathfinder AVHRR data (currently available from January 1985 to December 2005) and operational AVHRR data for 2006 onward. Pathfinder AVHRR was chosen over operational AVHRR, when available, because Pathfinder agrees better with the in situ data. The AMSR–AVHRR product begins with the start of AMSR data in June 2002. In this product, the primary AVHRR contribution is in regions near land where AMSR is not available. However, in cloud-free regions, use of both infrared and microwave instruments can reduce systematic biases because their error characteristics are independent.

We present the impact tests that preceded the most recent operational upgrades to the land surface model used in the National Centers for Environmental Prediction (NCEP) mesoscale Eta model, whose operational domain includes North America. These improvements consist of changes to the “Noah” land surface model (LSM) physics, most notable in the area of cold season processes. Results indicate improved performance in forecasting low‐level temperature and humidity, with improvements to (or without affecting) the overall performance of the Eta model quantitative precipitation scores and upper air verification statistics. Remaining issues that directly affect the Noah LSM performance in the Eta model include physical parameterizations of radiation and clouds, which affect the amount of available energy at the surface, and stable boundary layer and surface layer processes, which affect surface turbulent heat fluxes and ultimately the surface energy budget.

Abstract Observations of sea surface and land–near-surface merged temperature anomalies are used to monitor climate variations and to evaluate climate simulations; therefore, it is important to make analyses of these data as accurate as possible. Analysis uncertainty occurs because of data errors and incomplete sampling over the historical period. This manuscript documents recent improvements in NOAA’s merged global surface temperature anomaly analysis, monthly, in spatial 5° grid boxes. These improvements allow better analysis of temperatures throughout the record, with the greatest improvements in the late nineteenth century and since 1985. Improvements in the late nineteenth century are due to improved tuning of the analysis methods. Beginning in 1985, improvements are due to the inclusion of bias-adjusted satellite data. The old analysis (version 2) was documented in 2005, and this improved analysis is called version 3.

Modern climate change is dominated by human influences, which are now large enough to exceed the bounds of natural variability. The main source of global climate change is human-induced changes in atmospheric composition. These perturbations primarily result from emissions associated with energy use, but on local and regional scales, urbanization and land use changes are also important. Although there has been progress in monitoring and understanding climate change, there remain many scientific, technical, and institutional impediments to precisely planning for, adapting to, and mitigating the effects of climate change. There is still considerable uncertainty about the rates of change that can be expected, but it is clear that these changes will be increasingly manifested in important and tangible ways, such as changes in extremes of temperature and precipitation, decreases in seasonal and perennial snow and ice extent, and sea level rise. Anthropogenic climate change is now likely to continue for many centuries. We are venturing into the unknown with climate, and its associated impacts could be quite disruptive.

The Moderate Resolution Imaging Spectroradiometer (MODIS) is one of five instruments aboard the Terra Earth Observing System (EOS) platform launched in December 1999. After achieving final orbit, MODIS began Earth observations in late February 2000 and has been acquiring data since that time. The instrument is also being flown on the Aqua spacecraft, launched in May 2002. A comprehensive set of remote sensing algorithms for cloud detection and the retrieval of cloud physical and optical properties have been developed by members of the MODIS atmosphere science team. The archived products from these algorithms have applications in climate change studies, climate modeling, numerical weather prediction, as well as fundamental atmospheric research. In addition to an extensive cloud mask, products include cloud-top properties (temperature, pressure, effective emissivity), cloud thermodynamic phase, cloud optical and microphysical parameters (optical thickness, effective particle radius, water path), as well as derived statistics. We will describe the various algorithms being used for the remote sensing of cloud properties from MODIS data with an emphasis on the pixel-level retrievals (referred to as Level-2 products), with 1-km or 5-km spatial resolution at nadir. An example of each Level-2 cloud product from a common data granule (5 min of data) off the coast of South America will be discussed. Future efforts will also be mentioned. Relevant points related to the global gridded statistics products (Level-3) are highlighted though additional details are given in an accompanying paper in this issue.

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 Atmospheric Infrared Sounder (AIRS), the Advanced Microwave Sounding Unit (AMSU), and the Humidity Sounder for Brazil (HSB) form an integrated cross-track scanning temperature and humidity sounding system on the Aqua satellite of the Earth Observing System (EOS). AIRS is an infrared spectrometer/radiometer that covers the 3.7-15.4-/spl mu/m spectral range with 2378 spectral channels. AMSU is a 15-channel microwave radiometer operating between 23 and 89 GHz. HSB is a four-channel microwave radiometer that makes measurements between 150 and 190 GHz. In addition to supporting the National Aeronautics and Space Administration's interest in process study and climate research, AIRS is the first hyperspectral infrared radiometer designed to support the operational requirements for medium-range weather forecasting of the National Ocean and Atmospheric Administration's National Centers for Environmental Prediction (NCEP) and other numerical weather forecasting centers. AIRS, together with the AMSU and HSB microwave radiometers, will achieve global retrieval accuracy of better than 1 K in the lower troposphere under clear and partly cloudy conditions. This paper presents an overview of the science objectives, AIRS/AMSU/HSB data products, retrieval algorithms, and the ground-data processing concepts. The EOS Aqua was launched on May 4, 2002 from Vandenberg AFB, CA, into a 705-km-high, sun-synchronous orbit. Based on the excellent radiometric and spectral performance demonstrated by AIRS during prelaunch testing, which has by now been verified during on-orbit testing, we expect the assimilation of AIRS data into the numerical weather forecast to result in significant forecast range and reliability improvements.

Every year, from December to April, anthropogenic haze spreads over most of the North Indian Ocean, and South and Southeast Asia. The Indian Ocean Experiment (INDOEX) documented this Indo‐Asian haze at scales ranging from individual particles to its contribution to the regional climate forcing. This study integrates the multiplatform observations (satellites, aircraft, ships, surface stations, and balloons) with one‐ and four‐dimensional models to derive the regional aerosol forcing resulting from the direct, the semidirect and the two indirect effects. The haze particles consisted of several inorganic and carbonaceous species, including absorbing black carbon clusters, fly ash, and mineral dust. The most striking result was the large loading of aerosols over most of the South Asian region and the North Indian Ocean. The January to March 1999 visible optical depths were about 0.5 over most of the continent and reached values as large as 0.2 over the equatorial Indian ocean due to long‐range transport. The aerosol layer extended as high as 3 km. Black carbon contributed about 14% to the fine particle mass and 11% to the visible optical depth. The single‐scattering albedo estimated by several independent methods was consistently around 0.9 both inland and over the open ocean. Anthropogenic sources contributed as much as 80% (±10%) to the aerosol loading and the optical depth. The in situ data, which clearly support the existence of the first indirect effect (increased aerosol concentration producing more cloud drops with smaller effective radii), are used to develop a composite indirect effect scheme. The Indo‐Asian aerosols impact the radiative forcing through a complex set of heating (positive forcing) and cooling (negative forcing) processes. Clouds and black carbon emerge as the major players. The dominant factor, however, is the large negative forcing (‐20±4 W m −2 ) at the surface and the comparably large atmospheric heating. Regionally, the absorbing haze decreased the surface solar radiation by an amount comparable to 50% of the total ocean heat flux and nearly doubled the lower tropospheric solar heating. We demonstrate with a general circulation model how this additional heating significantly perturbs the tropical rainfall patterns and the hydrological cycle with implications to global climate.

A major challenge in predicting Earth's future climate state is to understand feedbacks that alter greenhouse-gas forcing. Here we synthesize field data from arctic Alaska, showing that terrestrial changes in summer albedo contribute substantially to recent high-latitude warming trends. Pronounced terrestrial summer warming in arctic Alaska correlates with a lengthening of the snow-free season that has increased atmospheric heating locally by about 3 watts per square meter per decade (similar in magnitude to the regional heating expected over multiple decades from a doubling of atmospheric CO2). The continuation of current trends in shrub and tree expansion could further amplify this atmospheric heating by two to seven times.

Results are presented from the multi‐institution partnership to develop a real‐time and retrospective North American Land Data Assimilation System (NLDAS). NLDAS consists of (1) four land models executing in parallel in uncoupled mode, (2) common hourly surface forcing, and (3) common streamflow routing: all using a 1/8° grid over the continental United States. The initiative is largely sponsored by the Global Energy and Water Cycle Experiment (GEWEX) Continental‐Scale International Project (GCIP). As the overview for nine NLDAS papers, this paper describes and evaluates the 3‐year NLDAS execution of 1 October 1996 to 30 September 1999, a period rich in observations for validation. The validation emphasizes (1) the land states, fluxes, and input forcing of four land models, (2) the application of new GCIP‐sponsored products, and (3) a multiscale approach. The validation includes (1) mesoscale observing networks of land surface forcing, fluxes, and states, (2) regional snowpack measurements, (3) daily streamflow measurements, and (4) satellite‐based retrievals of snow cover, land surface skin temperature (LST), and surface insolation. The results show substantial intermodel differences in surface evaporation and runoff (especially over nonsparse vegetation), soil moisture storage, snowpack, and LST. Owing to surprisingly large intermodel differences in aerodynamic conductance, intermodel differences in midday summer LST were unlike those expected from the intermodel differences in Bowen ratio. Last, anticipating future assimilation of LST, an NLDAS effort unique to this overview paper assesses geostationary‐satellite‐derived LST, determines the latter to be of good quality, and applies the latter to validate modeled LST.

Abstract Twentieth century trends of precipitation are examined by a variety of methods to more fully describe how precipitation has changed or varied. Since 1910, precipitation has increased by about 10% across the contiguous United States. The increase in precipitation is reflected primarily in the heavy and extreme daily precipitation events. For example, over half (53%) of the total increase of precipitation is due to positive trends in the upper 10 percentiles of the precipitation distribution. These trends are highly significant, both practically and statistically. The increase has arisen for two reasons. First, an increase in the frequency of days with precipitation ]6 days (100 yr)−1[ has occurred for all categories of precipitation amount. Second, for the extremely heavy precipitation events, an increase in the intensity of the events is also significantly contributing (about half) to the precipitation increase. As a result, there is a significant trend in much of the United States of the highest...

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.

Development is described of a Comprehensive Ocean-Atmosphere Data Set (COADS)—the result of a cooperative project to collect global weather observations taken near the ocean's surface since 1854, primarily from merchant ships, into a compact and easily used data set. As background, a historical overview is given of how archiving of these marine data has evolved from 1854, when systematic recording of shipboard meteorological and oceanographic observations was first established as an international activity. Input data sets used for COADS are described, as well as the processing steps used to pack input data into compact binary formats and to apply quality controls for identification of suspect weather elements and duplicate marine reports. Seventy-million unique marine reports for 1854–1979 were output from initial processing. Further processing is described, which created statistical summaries for each month of each year of the period, using 2° latitude × 2° longitude boxes. Monthly summary products are available giving 14 statistics (such as the median and the mean) for each of eight observed variables (air and sea-surface temperatures, scalar and vector wind, pressure, humidity, and cloudiness), plus 11 derived variables. Examples of known temporal, spatial, and methodological inhomogeneities in marine data, and plans for periodic updates to COADS, including an update through 1986 scheduled for completion by early 1988, are presented.

The new Version 2.3 of the GPCP Monthly analysis is described in terms of changes made to improve the homogeneity of the product, especially after 2002. These changes include corrections to cross calibration of satellite data inputs and updates to the gauge analysis. Over ocean, changes starting in 2003 result in an overall precipitation increase of 1.8% after 2009. Updating the gauge analysis to its final, high quality version increases the global land total by 1.8% for the post-2002 period. These changes correct a small, incorrect dip in the estimated global precipitation over the last decade in the earlier Version 2.2. The GPCP analysis is also used to describe global precipitation for 2017. The general La Nina pattern for 2017 is noted and the evolution from the early 2016 El Nino pattern is described. The 2017 global value is one of the highest for the 19792017 period, exceeded only by 2016 and 1998 (both El Nino years) and reinforces the small positive trend. Results for 2017 also reinforce significant trends in precipitation intensity (on a monthly scale) in the tropics. These results for 2017 indicate the value of the GPCP analysis for climate monitoring in addition to research.

Drought is the most damaging environmental phenomenon. During 1967–91, droughts affected 50% of the 2.8 billion people who suffered from weather-related disasters. Since droughts cover large areas, it is difficult to monitor them using conventional systems. In recent years the National Oceanic and Atmospheric Administration has designed a new Advanced Very High Resolution Radiometer- (AVHRR) based Vegetation Condition Index (VCI) and Temperature Condition Index (TCI), which have been useful in detecting and monitoring large area, drought-related vegetation stress. The VCI was derived from the Normalized Difference Vegetation Index (NDVI), which is the ratio of the difference between AVHRR-measured near-infrared and visible reflectance to their sum. The TCI was derived from the 10.3–11.3-μm AVHRR-measured radiances, converted to brightness temperature (BT). Algorithms were developed to reduce the noise and to adjust NDVI and BT for land surface nonhomogeneity. The VCI and TCI are used to determine the water- and temperature-related vegetation stress occuring during drought. This paper provides the principles of these indices, describes data processing, and gives examples of VCI–TCI applications in different ecological environments of the world. The results presented here are the first attempt to use both NDVI and thermal channels on a large area with very diversified ecological resources. The application of VCI and TCI are illustrated and validated by in situ measurements. These indices were also used for assessment of drought impact on regional agricultural production in South America, Africa, Asia, North America, and Europe. For this purpose, the average VCI–TCI values for a given region and for each week of the growing season were calculated and compared with yields of agricultural crops. The results showed a very strong correlation between these indices and yield, particularly during the critical periods of crop growth.

The Moderate Resolution Imaging Spectroradiometer (MODIS) is an Earth-viewing sensor that flies on the Earth Observing System Terra and Aqua satellites, launched in 1999 and 2002, respectively. MODIS scans a swath width of 2330 km that is sufficiently wide to provide nearly complete global coverage every two days from a polar-orbiting, Sun-synchronous, platform at an altitude of 705 km. MODIS provides images in 36 spectral bands between 0.415 and 14.235 /spl mu/m with spatial resolutions of 250 m (two bands), 500 m (five bands), and 1000 m (29 bands). These bands have been carefully selected to enable advanced studies of land, ocean, and atmospheric properties. Twenty-six bands are used to derive atmospheric properties such as cloud mask, atmospheric profiles, aerosol properties, total precipitable water, and cloud properties. We describe each of these atmospheric data products, including characteristics of each of these products such as file size, spatial resolution used in producing the product, and data availability.

Abstract The fate of the terrestrial biosphere is highly uncertain given recent and projected changes in climate. This is especially acute for impacts associated with changes in drought frequency and intensity on the distribution and timing of water availability. The development of effective adaptation strategies for these emerging threats to food and water security are compromised by limitations in our understanding of how natural and managed ecosystems are responding to changing hydrological and climatological regimes. This information gap is exacerbated by insufficient monitoring capabilities from local to global scales. Here, we describe how evapotranspiration (ET) represents the key variable in linking ecosystem functioning, carbon and climate feedbacks, agricultural management, and water resources, and highlight both the outstanding science and applications questions and the actions, especially from a space‐based perspective, necessary to advance them.