Peter Cornillon

The Past, Present, and Future of the AVHRR Pathfinder SST Program

With origins dating back to 1990, the Advanced Very High Resolution Radiometer (AVHRR) Pathfinder Sea Surface Temperature (SST) Program has experienced a 20-year history of reprocessing space-based observations to create accurate, consistent, climate data records. Both scientific and programmatic aspects of this history are reviewed and summarized in this chapter, along with a review of the currently available Pathfinder SST data. In addition, a look forward to the next generation of Pathfinder currently under development is presented.

Edge Detection Algorithm for SST Images

Abstract An algorithm to detect fronts in satellite-derived sea surface temperature fields is presented. Although edge detection is the main focus, the problem of cloud detection is also addressed since unidentified clouds can lead to erroneous edge detection. The algorithm relies on a combination of methods and it operates at the picture, the window, and the local level. The resulting edge detection is not based on the absolute strength of the front, but on the relative strength depending on the context thus, making the edge detection temperature-scale invariant. The performance of this algorithm is shown to be superior to that of simpler algorithms commonly used to locate edges in satellite-derived SST images. This evaluation was performed through a careful comparison between the location of the fronts obtained by applying the various methods to the SST images and the in situ measures of the Gulf Stream position.

A Comparison of Satellite and In Situ-Based Sea Surface Temperature Climatologies

The purpose of this study is to present a satellite-derived sea surface temperature (SST) climatology based on Pathfinder Advanced Very High Resolution Radiometer (AVHRR) data and to evaluate it and several other climatologies for their usefulness in the determination of SST trends. The method of evaluation uses two longterm observational collections of in situ SST measurements: the 1994 World Ocean Atlas (WOA94) and the Comprehensive Ocean‐Atmosphere Data Set (COADS). Each of the SST climatologies being evaluated is subtracted from each raw SST observation in WOA94and COADS to produce several separate long-term anomaly datasets. The anomaly dataset with the smallest standard deviation is assumed to identify the climatology best able to represent the spatial and seasonal SST variability and therefore be most capable of reducing the uncertainty in SST trend determinations. The satellite SST climatology was created at a resolution of 9.28 km using both day and night satellite fields generated with the version 4 AVHRR Pathfinder algorithm and cloud-masking procedures, plus an erosion filter that provides additional cloud masking in the vicinity of cloud edges. Using the statistical comparison method, the performance of this ‘‘Pathfinder 1 erosion’’ climatology is compared with the performances of the WOA94 18 in situ climatology, the Reynolds satellite and in situ blended 1 8 analysis, version 2.2 of the blended 18 Global Sea-Ice and Sea Surface Temperature (GISST) climatology, and the in situ 58 Global Ocean Surface Temperature Atlas (GOSTA). The standard deviation of the anomalies produced using the raw WOA94 in situ observations and the reference SST climatologies indicate that the 9.28-km Pathfinder 1 erosion climatology is more representative of spatial and seasonal SST variability than the traditional in situ and blended SST climatologies. For the anomalies created from the raw COADS observations, the Pathfinder1 erosion climatology is also found to minimize variance more than the other climatologies. In both cases, the 58 GOSTAclimatology exhibits the largest anomaly standard deviations. Regional characteristics of the climatologies are also examined by binning the anomalies by climatological temperature classes and latitudinal bands. Generally, the Pathfinder 1 erosion climatology yields lower anomaly variances in the mid- and high latitudes and the Southern Hemisphere, but larger variances than the 18 climatologies in the warm, Northern Hemisphere low-latitude regions.

Large Diurnal Sea Surface Temperature Variability: Satellite and In Situ Measurements

Data from a surface mooring located in the Sargasso Sea at 34°N, 70°W between May 1982 and May 1984 were compared with satellite data to investigate large diurnal sea surface temperature changes. Mooring and satellite measurements are in excellent agreement for those days on which no clouds covered the site at the time of the satellite pass. During the summer half-year at this site, there is a 20% charm of diurnal warming of more than 0.5°C, with values of up to 3.5°C observed in the two-year period. Diurnal warming observed at the mooring has been simulated well by a one-dimensional model driven by local beat and momentum fluxes. Under the conditions of very light wind and strong insolation that produce the Largest surface warming, the surface mixed-layer depth reduces to the convection depth, and wind-mixing becomes unimportant. The thermal response is then limited to depths between 1 and 2 m, making it likely that such events have been underreported in routine ship observations. In all cases observed, the spatial extent of warming events as determined by satellite data are well correlated with the corresponding atmospheric pressure patterns. Conditions giving rise to the largest diurnal warming events are often associated with a westward-extending ridge of the Bermuda high. In the region studied, 57°–75°W and 29°–43°N, diurnal warming of more than 1°C was found on occasion to cover areas in excess of 300 000 km2, with warming of more than 2°C coveting areas in excess of 130 000 km2.

Climatology and seasonal variability of ocean fronts in the East China, Yellow and Bohai seas from satellite SST data

Front detection and declouding algorithms developed at the URI have been applied to the 12-year (1985–1996) Pathfinder SST data set to produce the first comprehensive, objectively derived, year-round climatology of ocean thermal fronts in the eastern China Seas, from 24°N to 41°N. Ten fronts have been distinguished, namely Kuroshio, Zhejiang-Fujian, Jiangsu, Shandong Peninsula, Bohai Sea, Seohan Bay, Kyunggi Bay, Western and Eastern Chejudo (South Korean Coastal Front), and Yangtze Bank Ring Front. The fronts are seasonally persistent: they emerge and disappear in the same seasons of different years. The frontal pattern remains fairly stable from one year to another, whereas individual fronts display different modes of seasonal variability.

Satellite‐derived sea surface temperature fronts on the continental shelf off the northeast U.S. coast

The distribution and variability of sea surface temperature (SST) fronts, over the shelf and slope along the east coast of North America from Cape Hatteras to Nova Scotia, are studied using a 12 year time series (1985-1996) of advanced very high resolution radiometer (AVHRR) images. After the masking of cloud- contaminated pixels, an edge-detection algorithm identifies surface temperature fronts in each image. Maps of the seasonal probability of detecting a front indicate substantial spatial and temporal variability in the occurrence of SST fronts. Over the continental shelf south of New England during spring through autumn, surface fronts are rare and observed only in scattered locations. North of Nantucket Shoals, both tidal mixing fronts and fronts associated with the Eastern Maine Coastal Current occur during the summer. A major finding of this study is the observation of fronts in winter over the inner and middle shelf from Cape Hatteras to the Bay of Fundy. These fronts, peaking during January-March, are characterized by cold SST on their inshore (shallow) side and appear to result from the influence of surface cooling on shallow nearshore waters. The shelfbreak front is found to vary strongly with season, being detected most frequently during spring and autumn. South of Hudson Canyon, it essentially disappears during the summer, while from Hudson Canyon to Northeast Channel, it weakens during summer but nevertheless remains detectable in SST.

Global and Regional Sea Surface Temperature Trends

Abstract Individual sea surface temperature (SST) anomalies are calculated using a satellite-based climatology and observations from the World Ocean Atlas 1994 (WOA94) and the Comprehensive Ocean–Atmosphere Data Set (COADS) to characterize global and regional changes in ocean surface temperature since 1942. For each of these datasets, anomaly trends are computed using a new method that groups individual anomalies into climatological temperature classes. These temperature class anomaly trends are compared with trends estimated using a technique representative of previous studies based on 5° latitude–longitude bins. Global linear trends in the data-rich period between 1960 and 1990 calculated from the WOA94 data are found to be 0.14° ± 0.04°C decade−1 for the temperature class approach and 0.13° ± 0.04°C decade−1 for the 5° bin approach. The corresponding results for the COADS data are 0.10° ± 0.03°C and 0.09° ± 0.03°C decade−1. These trends are not statistically different at the 95% confidence level. Addit...

Gulf Stream warm rings: a statistical study of their behavior

A 10 year series of NOAA polar-orbiter satellite infrared-based determinations of Gulf Stream warm-core ring (WCR) locations, is used to derive mean loci, lifetime and size, and movement statistics in the Slope water region off the northeast United States east coast. This time series, produced by the NOAA/NMFS Atlantic Environmental Group, is validated against precision observations of WCR frontal loci by investigators at the Universities of Miami and Rhode Island: an r.m.s. error of ± 15 km in latitude and longitude between the two sets is determined. We find that the ensemble of WCR lifetime is bi-modal with the split at 140 days. Rings are formed with a semi-major axis of ≈75 km. Longer-lived rings show a reduction in size over their lifetime to ≈35 km, shorter-lived rings do not. Ring movement shows some dependence on location, but mean motions are never <1.6 cm s−1; averaged translation speed is 6.5 cm s−1. Longer-lived rings tend to be located farther away from the climatological Gulf Stream north wall than shorter-lived rings east of 66°W. The mean loci are compared with Warm Core Ring Experiment results for 82B. We find similar changes in track corresponding to topographic variation, similar translation speed, size, decrease in size over time, etc., leading us to conclude that the kinematics observed for 82B are representative of those for a long-lived warm-core ring.

Workflows and extensions to the Kepler scientific workflow system to support environmental sensor data access and analysis

Environmental sensor networks are now commonly being deployed within environmental observatories and as components of smaller-scale ecological and environmental experiments. Effectively using data from these sensor networks presents technical challenges that are difficult for scientists to overcome, severely limiting the adoption of automated sensing technologies in environmental science. The Realtime Environment for Analytical Processing (REAP) is an NSF-funded project to address the technical challenges related to accessing and using heterogeneous sensor data from within the Kepler scientific workflow system. Using distinct use cases in terrestrial ecology and oceanography as motivating examples, we describe workflows and extensions to Kepler to stream and analyze data from observatory networks and archives. We focus on the use of two newly integrated data sources in Kepler: DataTurbine and OPeNDAP. Integrated access to both near real-time data streams and data archives from within Kepler facilitates both simple data exploration and sophisticated analysis and modeling with these data sources.

Multi-Image Edge Detection for SST Images

Abstract This paper presents an approach based on the analysis of an image sequence to detect temperature fronts in a sea surface temperature image. The multi-image edge detection algorithm starts by applying a single-image edge detection algorithm to the sequence of images under study. Next, fronts or portions of fronts, which were detected in neighboring images by the single-image algorithm and which match features in the current image, are identified as persistent. The coordinates of these persistent fronts are then passed to the single-image edge detection algorithm so that additional fronts can be detected. The performance of the multi-image edge detection algorithm, of various single-image algorithms, and of a human expert are evaluated on a set of 98 images. For that purpose, the location of the fronts obtained by applying various methods to the SST images is compared to the in situ measures of the Gulf Stream position. With respect to both quality and the number of detected edges, the multi-image ...