Chelle L. Gentemann

Toward Improved Validation of Satellite Sea Surface Skin Temperature Measurements for Climate Research

A poor validation strategy will compromise the quality of satellite-derived sea surface temperature (SST) products because confidence limits cannot be quantified. This paper addresses the question of how to provide the best operational strategy to validate satellite-derived skin sea surface temperature (SST skin) measurements. High quality in situ observations obtained using different state-of-the-art infrared radiometer systems are used to characterize the relationship between the SST skin, the subsurface SST at depth (SSTdepth), and the surface wind speed. Data are presented for different oceans and seasons. These data indicate that above a wind speed of approximatel y6ms 21 the relationship between the SSTskin and SSTdepth, is well characterized for both day- and nighttime conditions by a cool bias of 20.17 6 0.07 K rms. At lower wind speeds, stratification of the upperocean layers during the day may complicate the relationship, while at night a cooler skin is normally observed. Based on these observations, a long-term global satellite SST skin validation strategy is proposed. Emphasis is placed on the use of autonomous, ship-of-opportunity radiometer systems for areas characterized by prevailing low‐wind speed conditions. For areas characterized by higher wind speed regimes, well-calibrated, qualitycontrolled, ship and buoy SSTdepth observations, corrected for a cool skin bias, should also be used. It is foreseen that SSTdepth data will provide the majority of in situ validation data required for operational satellite SST validation. We test the strategy using SSTskin observations from the Along Track Scanning Radiometer, which are shown to be accurate to approximately 0.2 K in the tropical Pacific Ocean, and using measurements from the Advanced Very High Resolution Radiometer. We note that this strategy provides for robust retrospective calibration and validation of satellite SST data and a means to compare and compile in a meaningful and consistent fashion similar datasets. A better understanding of the spatial and temporal variability of thermal stratification of the upper-ocean layers during low‐wind speed conditions is fundamental to improvements in SST validation and development of multisensor satellite SST products.

The Global Ocean Data Assimilation Experiment High-resolution Sea Surface Temperature Pilot Project

A new generation of integrated sea surface temperature (SST) data products are being provided by the Global Ocean Data Assimilation Experiment (GODAE) High-Resolution SST Pilot Project (GHRSST-PP). These combine in near-real time various SST data products from several different satellite sensors and in situ observations and maintain the fine spatial and temporal resolution needed by SST inputs to operational models. The practical realization of such an approach is complicated by the characteristic differences that exist between measurements of SST obtained from subsurface in-water sensors, and satellite microwave and satellite infrared radiometer systems. Furthermore, diurnal variability of SST within a 24-h period, manifested as both warm-layer and cool-skin deviations, introduces additional uncertainty for direct intercomparison between data sources and the implementation of data-merging strategies. The GHRSST-PP has developed and now operates an internationally distributed system that provides operatio...

Multi-satellite measurements of large diurnal warming events

[1] Diurnal warming events between 5 and 7 K, spatially coherent over large areas (1000 km), are observed in independent satellite measurements of ocean surface temperature. The majority of the large events occurred in the extra-tropics. Given sufficient heating (from solar radiation), the location and magnitude of these events appears to be primarily determined by large-scale wind patterns. The amplitude of the measured diurnal heating scales inversely with the spatial resolution of the different sensors used in this study. These results indicate that predictions of peak diurnal warming using wind speeds with a 25 km spatial resolution available from satellite sensors and those with 50–100 km resolution from Numerical Weather Prediction models may have underestimated warming. Thus, the use of these winds in modeling diurnal effects will be limited in accuracy by both the temporal and spatial resolution of the wind fields. Citation: Gentemann, C. L., P. J. Minnett, P. Le Borgne, and C. J. Merchant (2008), Multi-satellite measurements of large diurnal warming events, Geophys. Res. Lett., 35, L22602,

Accuracy of Satellite Sea Surface Temperatures at 7 and 11 GHz

Satellite microwave radiometers capable of accurately retrieving sea surface temperature (SST) have provided great advances in oceanographic research. A number of future satellite missions are planned to carry microwave radiometers of various designs and orbits. While it is well known that the 11 GHz SST retrievals are less accurate than the 7 GHz retrievals, particularly in colder waters, it has not been demonstrated using existing microwave data. The Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) provides the means to examine the accuracies of SST retrievals using these channels in a systematic manner. In this paper, the accuracies of SSTs at 7 and 11 GHz are determined using two approaches: modeled and empirical. The modeled accuracies are calculated using an emissivity model and climatology SSTs, while empirical accuracies are estimated through validation of AMSR-E 7 and 11 GHz SST retrievals using over six years of data. It was found that the 7 GHz SST retrievals have less errors due to radiometer noise and geophysical errors than the 11 GHz retrievals at all latitudes. Additionally, while averaging the 11 GHz retrievals will diminish error due to uncorrelated radiometer noise, the geophysical error is still higher than for the 7 GHz retrievals, particularly at the higher latitudes.

Evaluation of AATSR and TMI Satellite SST Data

Abstract The purpose of this paper is to investigate two satellite instruments for SST: the infrared (IR) Advanced Along Track Scanning Radiometer (AATSR) and the microwave (MW) Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI). Because of its dual view, AATSR has a potential for lower biases than other IR products such as the Advanced Very High Resolution Radiometer (AVHRR), while the tropical TMI record was available for a longer period of time than the global MW instrument, the Advanced Microwave Scanning Radiometer (AMSR). The results show that the AATSR IR retrievals are good quality with biases lower than or as low as other satellite retrievals between 50°S and 50°N. Furthermore, the dual-view algorithm reduces the influence of aerosol contamination. However, the AATSR coverage is roughly half that of AVHRR. North of 50°N there appear to be biases and high variability in summer daytime retrievals, with smaller but consistent biases observed below 50°S. TMI data can significantly impr...

Validation of the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) sea surface temperature in the Southern Ocean

Received 24 February 2005; revised 6 September 2005; accepted 30 December 2005; published 5 April 2006. [1] Satellite sea surface temperature (SST) measurements from Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) are compared with in situ temperature observations from high-resolution expendable bathythermograph and hull-mounted thermosalinograph data along two sections (south of Australia and Drake Passage) in the Southern Ocean. To eliminate the effects of diurnal warming and low wind speed, we use only AMSR-E data collected within 5 hours of the in situ observations, with wind speeds exceeding 6 m s � 1 . The AMSR-E measurements are warmer than in situ observations during summer and are colder than in situ observations during winter. Factors that may cause the temperature difference are examined, including wind speed, columnar water vapor, columnar cloud water, geographic location, local temperature, and time of observation. Of these, wind speed and columnar water vapor are found to be the major factors contributing to the temperature difference between AMSR-E SST and in situ SSTobservations. The temperature difference decreases with increasing wind speed and water vapor. AMSR-E and in situ SST observations are also compared with simultaneous Moderate Resolution Imaging Spectroradiometer (MODIS) SST and weekly Reynolds Optimum Interpolated (OI) SST. Results suggest that the OI SSTs have a warm bias for both summer and winter; MODIS SSTs indicate a cold bias. In contrast, AMSR-E SSTs show little bias relative to expendable bathythermographs.

Three way validation of MODIS and AMSR-E sea surface temperatures

The estimation of retrieval uncertainty and stability are essential for the accurate interpretation of data in scientific research, use in analyses, or numerical models. The primary uncertainty sources of satellite SST retrievals are due to errors in spacecraft navigation, sensor calibration, sensor noise, retrieval algorithms, and incomplete identification of corrupted retrievals. In this study, comparisons to in situ data are utilized to investigate retrieval accuracies of microwave (MW) SSTs from the Advanced Microwave Scanning Radiometer—Earth Observing System (AMSR-E) and infrared (IR) SSTs from the Moderate Resolution Imaging Spectroradiometer (MODIS). The highest quality MODIS data were averaged to 25 km for comparison. The in situ SSTs are used to determine dependencies on environmental parameters, evaluate the identification of erroneous retrievals, and examine biases and standard deviations (STD) for each of the satellite SST data sets. Errors were identified in both the MW and IR SST data sets: (1) at low atmospheric water vapor a posthoc correction added to AMSR-E was incorrectly applied and (2) there is significant cloud contamination of nighttime MODIS retrievals at SST <10°C. A correction is suggested for AMSR-E SSTs that will remove the vapor dependency. For MODIS, once the cloud contaminated data were excluded, errors were reduced but not eliminated. Biases were found to be −0.05°C and −0.13°C and standard deviations to be 0.48°C and 0.58°C for AMSR-E and MODIS, respectively. Using a three-way error analysis, individual standard deviations were determined to be 0.20°C (in situ), 0.28°C (AMSR-E), and 0.38°C (MODIS).

Impact of TRMM SSTs on a Climate-Scale SST Analysis

Prior efforts have produced a sea surface temperature (SST) optimum interpolation (OI) analysis that is widely used, especially for climate purposes. The analysis uses in situ (ship and buoy) and infrared (IR) satellite data from the Advanced Very High Resolution Radiometer (AVHRR). Beginning in December 1997, ‘‘microwave’’ SSTs became available from the Tropical Rainfall Measuring Mission (TRMM) satellite Microwave Imager (TMI). Microwave SSTs have a significant coverage advantage over ‘‘IR’’ SSTs because microwave SSTs can be retrieved in cloud-covered regions while IR SSTs cannot. However, microwave SSTs are at a much lower spatial resolution than the IR SSTs. In this study, the impact of SSTs derived from TMI was tested from the perspective of the OI analysis. Six different versions of the OI were produced weekly from 10 December 1997 to 1 January 2003 using different combinations of AVHRR and TMI data and including versions with and without a bias correction of the satellite data. To make the results more objective, 20% of the buoys were randomly selected and the SSTs from these buoys were withheld from the OI for independent verification. The results of the intercomparisons show that both AVHRR and TMI data have biases that must be corrected for climate studies. These biases change with time as physical properties of the atmosphere change and as satellite instruments and the orbits of the satellites, themselves, change. It is critical to monitor differences between satellite and other products to quickly diagnose any of these changes. For the OI analyses with bias correction, it is difficult using the withheld buoys to clearly demonstrate that there is a significant advantage in adding TMI data. The advantage of TMI data is clearly shown in the OI analyses without bias correction. Because IR and microwave satellite algorithms are affected by different sources of error, biases may tend to cancel when both TMI and AVHRR data are used in the OI. Bias corrections cannot be made in regions where there are no in situ data. In these regions, the results of the analyses without bias corrections apply. Because there are areas of the ocean with limited in situ data and restricted AVHRR coverage due to cloud cover, the use of both TMI and AVHRR should improve the accuracy of the analysis in these regions. In addition, the use of more than one satellite product is helpful in diagnosing problems in these products.

Passive Microwave Remote Sensing of the Ocean: An Overview

Passive microwave observations from satellites provide measurements of sea surface temperature (SST), wind speed, water vapor, cloud liquid water, rain rate, and sea ice that have lead to significant advances in meteorological and oceanographic research as well as improvements in monitoring and forecasting both weather and climate. Future instruments are planned to measure sea surface salinity. The calibration of passive microwave radiometers has continued to improve, along with the retrieval algorithms. The production of accurate geophysical retrievals depends on the close development of both calibrated brightness temperatures and retrieval algorithm design in concert. Data must be carefully screened for near-land emissions, radio frequency interference, rain scattering (for SST, wind, and vapor retrievals), and high wind events (SST retrievals only).

Satellite sea surface temperatures along the West Coast of the United States during the 2014–2016 northeast Pacific marine heat wave

From January 2014 to August 2016, sea-surface temperatures (SSTs) along the Washington, Oregon, and California coasts were significantly warmer than usual, reaching a maximum SST anomaly of 6.2 °C off southern California. This marine heat wave occurred alongside the Gulf of Alaska marine heat wave, and resulted in major disturbances in the California Current ecosystem and massive economic impacts. Here, we use satellite and blended reanalysis products to report the magnitude, extent, duration, and evolution of SSTs and wind stress anomalies along the west coast of the continental United States during this event. Nearshore SST anomalies along the entire coast were persistent during the marine heat wave, and only abated seasonally, during spring upwelling-favorable wind stress. The coastal marine heat wave weakened in July 2016 and disappeared by September 2016.