The paper gives an overview of the development of satellite oceanography over the past five years focusing on the most relevant issues for operational oceanography. Satellites provide key essential variables to constrain ocean models and/or serve downstream applications. New and improved satellite data sets have been developed and have directly improved the quality of operational products. The status of the satellite constellation for the last five years was, however, not optimal. Review of future missions shows clear progress and new research and development missions with a potentially large impact for operational oceanography should be demonstrated. Improvement of data assimilation techniques and developing synergetic use of high resolution satellite observations are important future priorities.

This paper describes the techniques used to obtain sea surface temperature (SST) retrievals from the Geostationary Operational Environmental Satellite 12 (GOES-12) at the National Oceanic and Atmospheric Administration’s Office of Satellite Data Processing and Distribution. Previous SST retrieval techniques relying on channels at 11 and 12 mm are not applicable because GOES-12 lacks the latter channel. Cloud detection is performed using a Bayesian method exploiting fast-forward modeling of prior clear-sky radiances using numerical weather predictions. The basic retrieval algorithm used at nighttime is based on a linear combination of brightness temperatures at 3.9 and 11 mm. In comparison with traditional split window SSTs (using 11- and 12-mm channels), simulations show that this combination has maximum scatter when observing drier colder scenes, with a comparable overall performance. For daytime retrieval, the same algorithm is applied after estimating and removing the contribution to brightness temperature in the 3.9-mm channel from solar irradiance. The correction is based on radiative transfer simulations and comprises a parameterization for atmospheric scattering and a calculation of ocean surface reflected radiance. Potential use of the 13-mm channel for SST is shown in a simulation study: in conjunction with the 3.9-mm channel, it can reduce the retrieval error by 30%. Some validation results are shown while a companion paper by Maturi et al. shows a detailed analysis of the validation results for the operational algorithms described in this present article.

A coastal cumulus cloud-line formation along the east coast of the USA was observed on a National Oceanic and Atmospheric Administration (NOAA) Polar Orbiting Environmental Satellite (POES) Advanced Very High Resolution Radiometer (AVHRR) satellite image from 17 August 2001. The cloud line starts to form at about 16: 00 UTC (local 12: 00 noon) and follows the coastline from Florida to North Carolina. The length and width of the cloud line are about 850 km and 8.5 km, respectively. A 15-min interval sequence of NOAA Geostationary Operational Environmental Satellite (GOES) images shows that the cloud line maintains the shape of the coastline and penetrates inland for more than 20km over the next 6-h timespan. Model simulation with actual atmospheric conditions as inputs shows that the cloud line is formed near the land-sea surface temperature (SST) gradient. The synoptic flow at all model levels is in the offshore direction prior to 16: 00 UTC whereas low-level winds (below 980 hPa) reverse direction to blow inland after 16: 00 UTC. This reversal is due to the fact that local diurnal heating over the land takes place on shorter time-scales than over the ocean. The vertical wind at these levels becomes stronger as the land-SST increases during the summer afternoon, and the leading edge of the head of the inland wind ascends from 920 hPa to about 850 hPa in the 3 h after 16: 00 UTC. Model simulation and satellite observations show that the cloud line becomes very weak after 21: 00 UTC when the diurnal heating decreases.

India and the United States of America (U.S.A.) held a joint conference from June 21-25, 2004 in Bangalore, India to strengthen and expand cooperation in the area of space science, applications, and commerce. Following the recommendations in the joint vision statement released at the end of the conference, the National Oceanic and Atmospheric Administration (NOAA) and the Indian Space and Reconnaissance Organization (ISRO) initiated several joint science projects in the area of satellite product development and applications. This is an extraordinary step since it concentrates on improvements in the data and scientific exchange between India and the United States, consistent with a Memorandum of Understanding (MOU) signed by the two nations in 1997. With the relationship between both countries strengthening with President Bush's visit in early 2006 and new program announcements between the two countries, there is a renewed commitment at ISRO and other Indian agencies and at NOAA in the U.S. to fulfill the agreements reached on the joint science projects. The collaboration is underway with several science projects that started in 2005 providing initial results. NOAA and ISRO agreed that the projects must promote scientific understanding of the satellite data and lead to a satellite-based decision support systems for disaster and public health warnings. The projects target the following areas:Supporting a drought monitoring system for IndiaImproving precipitation estimates over India from Kalpana-1Increasing aerosol optical depth measurements and products over IndiaDeveloping early indicators of malaria and other vector borne diseases via satellite monitoring of environmental conditions and linking them to predictive modelsMonitoring sea surface temperature (SST) from INSAT-3D to support improved forecasting of regional storms, monsoon onset and cyclonesThe research collaborations and results from these projects will be presented and discussed in the context of India-US cooperation and the Global Earth Observation System of Systems (GEOSS) concept.

Under cloud-free conditions during the daytime, global synergistic retrievals of sea surface temperature (SST) and aerosol optical depths (AOD, or) are made from the AVHRR instruments flown onboard polar-orbiting sun-synchronous NOAA-16 (equator crossing time, EXT similar to 1400) and -17 (EXT similar to 1000) satellites. Validation against buoys and sun-photometers is customarily considered the ultimate check of the quality and accuracy of SST and AOD retrievals. However, ground-truth data are not available globally and their quality is non-uniform. Moreover, the remotely-sensed parameters may not be fully comparable with their counterparts measured from the surface (e.g. skin vs. bulk SST), and the current procedures to merge data in space and time are not fully objective and may themselves introduce additional errors. In this paper, we propose to supplement the traditional validation with another global diagnostic system. The proposed Quality Control/Assurance (QC/QA) system is based on a comprehensive set of statistical self- and cross-consistency checks. Here, it is illustrated with 8 days of global NOAA-16 and -17 data in December 2003. The AODs and SST anomalies have been first aggregated into 1-day, 1-degree boxes, and their global statistics examined. Analyses are best done in anomalies from the expected state (climatology), which is currently available for the SST but not for the ACID. Histograms of NOAA-16 and -17 SST anomalies are highly correlated (R similar to 0.77), both showing an approximately Gaussian shape, with a mean of similar to+0.3K and RMS similar to 1K. AODs also show much similarity but reveal significant cross-platform biases. The magnitudes and even the signs of these biases are band-specific, suggesting that they are due to calibration differences between the two AVHRRs flown on the two platforms. Recall that the AVHRR solar reflectance bands used for aerosol retrievals lack on-board calibration, and therefore may be subject to large calibration errors.

We propose and demonstrate a fully probabilistic (Bayesian) approach to the detection of cloudy pixels in thermal infrared (TIR) imagery observed from satellite over oceans. Using this approach, we show how to exploit the prior information and the fast forward modelling capability that are typically available in the operational context to obtain improved cloud detection. The probability of clear sky for each pixel is estimated by applying Bayes' theorem, and we describe how to apply Bayes' theorem to this problem in general terms. Joint probability density functions (PDFs) of the observations in the TIR channels are needed; the PDFs for clear conditions are calculable from forward modelling and those for cloudy conditions have been obtained empirically. Using analysis fields from numerical weather prediction as prior information, we apply the approach to imagery representative of imagers on polar‐orbiting platforms. In comparison with the established cloud‐screening scheme, the new technique decreases both the rate of failure to detect cloud contamination and the false‐alarm rate by one quarter. The rate of occurrence of cloud‐screening‐related errors of >1 K in area‐averaged SSTs is reduced by 83%. Copyright © 2005 Royal Meteorological Society.

The orbit drift of National Oceanic & Atmospheric Administration (NOAA)-14 towards the terminator has caused the deterioration of the radiometric calibration of the Advanced Very High Resolution Radiometer (AVHRR) 3.7 mum channel at night. This deterioration is a result of solar contamination of the radiometric calibration system when the sun strikes the instrument from the spacecraft horizon. The long-term trend and seasonal variation of the contamination are analysed in this study based on trending data from 1995 to 2000. The calibration bias is evaluated and its effect on the sea surface temperature retrievals is quantified. The solar contamination in late 2000 affected as much as 25% of an orbit of data, compared to an average of 7% in 1995. The NOAA/NESDIS operational calibration algorithm partially corrects for the bias but residual effects can still contribute bias on the order of 0.5 K in scene brightness temperature.

Remotely sensed sea surface temperatures (SST) derived from the NOAA polar (POES) and geostationary (GOES) satellites continue to be an indispensable resource that directly supports NOAA’s missions and strategic goals. Since the inception of satellite oceanography in the 1970s, NOAA/NESDIS has been an international contributor in the advancement of satellite derived global SST methodologies and operational products. These data are required of a number of users (NOAA internal and external), including the NESDIS CoastWatch/OceanWatch and Coral Reef Watch programs, as well as for academic research and numerical forecast model assimilation. Currently at NESDIS, satellite SST research is coordinated within the Oceanic Research and Applications Division (ORAD) SST Science Team at the Office of Research and Applications (ORA). The overarching goal of the science team is oversee and facilitate efficient, end-to-end satellite product development, beginning at the basic research level and continuing through the transition into NOAA operations. To achieve this, ORAD works closely with the NESDIS Office of Satellite Data Processing and Distribution (OSDPD), which assumes responsibility for operational implementation and maintenance. To facilitate cutting-edge research, ORAD also supports tight collaborations within academia, including the Cooperative Institutes at Colorado State University, Oregon State University, University of Maryland and University of Wisconsin-Madison, as well as contracts/grants with the University of Edinburgh and University of Miami. This paper provides an overview

A fully operational capability for the mapping of National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) data was achieved in the fall of 1992. This capability is being used to provide mapped imagery twice daily in near real-time from the AVHRR on the polar-orbiting NOAA-11 satellite. Current products include sea surface temperature (SST), infrared, and visible channel imagery at full and reduced resolution for the U.S. East Coast, Gulf of Mexico, Caribbean and Great Lakes. Using available interactive workstation software, the visible channel images can be used to calculate relative turbidity in estuaries and near-shore regions with high sediment load. Future products being implemented include the addition of (1) cloud mask imagery, and (2) ocean reflectance imagery for more efficient turbidity product production. These images are being produced in support of NOAA CoastWatch, an activity of the NOAA Coastal Ocean Program. Distributed via CoastWatch Regional Sites around the country and the National Oceanographic Data Center, these products are available to government resource managers and university researchers who have signed access agreements with CoastWatch.