The RAN2 sea surface temperature (SST) dataset has been created under the NOAA AVHRR GAC SST Reanalysis 2 (RAN2) project from 40+ years of 4 km Global Area Coverage (GAC) data of the Advanced Very High-Resolution Radiometers (AVHRR/2s and /3s) flown onboard ten NOAA satellites (N07/09/11/12/14/15/16/17/18/19). The data were reprocessed with the NOAA Advanced Clear Sky Processor for Ocean (ACSPO) enterprise SST system. The RAN2 reports two SSTs in the full ~3,000 km AVHRR swath: ‘subskin’ (highly sensitive to true skin SST, while being anchored to in situ depth SST) and ‘depth’ (a closer proxy for in situ data, but less sensitive to true skin SST). Long-term orbital and sensor changes were minimized by daily recalculation of regression coefficients using matchups with drifters and tropical moored buoys, (D+TM), collected within limited time windows centered at the processed day. For N07/09, (D+TM) matchups were sparse and supplemented by ships. The adverse effects of nighttime Sun impingements on the sensor were mitigated by recalculating the AVHRR L1b calibration coefficients, while similar effects of stray light in Earth view were flagged and excluded. Massive cold SST outliers caused by atmospheric contamination following major volcanic eruptions (El Chichon in 1982, and Mt Pinatubo and Mt Hudson in 1991) were filtered out by more conservative cloud screening with the modified ACSPO clear-sky mask. This paper evaluates the performance of the RAN2 relative to the two others available AVHRR GAC SST datasets, NOAA Pathfinder v5.3 (PF) and ESA Climate Change Initiative v2.1 (CCI).
The 2nd Reanalysis (RAN2) of the 4 km Global Area Coverage (GAC) data of the Advanced Very High-Resolution Radiometers (AVHRR/2s and /3s) flown onboard ten NOAA satellites from Sep’1981 – present was performed, and global sea surface temperature (SST) dataset created with the NOAA Advanced Clear-Sky Processor for Oceans (ACSPO) enterprise system. The RAN2 dataset includes two SST products retrieved in a full ~3,000 km AVHRR swath: “Subskin” (highly sensitive to true skin SST) and “Depth” (agreeing much closer with in situ SST, but with a reduced sensitivity). The performance of both RAN2 SST products were improved compared with RAN1, by mitigation of several AVHRR sensor issues. The long-term AVHRR calibration trends were mitigated by daily recalculation of the regression coefficients using matchups with in situ SSTs collected within limited time windows centered at the processed day. Biases with respect to in situ SST were further minimized on a monthly basis by adjustment of the offsets of regression equations based on 31-day moving windows. Massive cold SST outliers, caused by nighttime Sun impingements on the AVHRR’s black body calibration targets, were corrected by interpolating the L1b calibration coefficients between the unaffected parts of the orbit. The Earth view pixels affected by stray sunlight were identified and screened out using the elevated signal in the AVHRR band 2 (0.86 µm). This paper describes the methodology and demonstrates its effects on the RAN2 SST.
Global long-term SST record is being created under the AVHRR GAC Reanalysis (RANs) project, by historical reprocessing 4 km data of the AVHRR/2 and /3 instruments flown onboard multiple NOAA satellites from 1981 – present. The AVHRR data are reprocessed with the NOAA Advanced Clear-Sky Processor for Oceans (ACSPO) system. During RANs, the ACSPO algorithms are being adjusted to mitigate various issues intrinsic to AVHRR sensors, especially AVHRR/s on the earlier NOAA missions in 1980s and 1990s. The focus of the latest interim release of RAN2, Beta 02, is the contamination of retrieved SSTs with massive cold biases, originating from two sources. First, in all AVHRR missions, periodic cold SST outliers occur at night due to solar impingement on the black body calibration target, when the satellite orbit approaches the terminator. Second, multiple cold outliers appear in the NOAA-7, -11 and -12 SSTs following three major volcanic eruptions of Mt. El Chichon (1982), Mt. Pinatubo (1991) and Mt. Hudson (1991). The current mitigation algorithm exploits the fact that in both cases, the spatial densities of the cold outliers exhibit well-expressed latitudinal dependencies. The algorithm identifies 5° latitudinal bands with abnormally high density of outliers and makes the cloud mask more conservative within those bands. This improves filtering cold SST outliers in the contaminated areas without increasing the false cloud detection rate in the unaffected parts of the ocean. We also discuss the ongoing development to mitigate cold SST biases by correcting AVHRR L1b calibration (rather than eliminating the affected SST data).
The first full-mission AVHRR FRAC SST dataset with nominal 1.1km resolution at nadir was created from three Metop First Generation (FG) satellites: A (2006-present), B (2012-present) and C (2018-present), using the NOAA Advanced Clear-Sky Processor for Ocean (ACSPO) enterprise system. Historical reprocessing (Reanalysis-1, RAN1) starts at the beginning of each mission and continues into near-real time (NRT) processing. ACSPO generates two SST products: Global Regression (GR) SST, highly sensitive to the skin SST, and Piecewise Regression (PWR) SST, a proxy for the depth SST. The effect of orbital and sensor drift on the stability of the SST time series is mitigated by retraining the regression coefficients daily against matchups with the drifting and tropical moored buoys. Those matchups are collected within moving windows: 91-day for GR and 361-day for PWR, with the offsets adjusted within a 31 day window. In RAN1, all training and offset correction windows are centered at the processed day. In NRT processing, the training and offset delayed windows of the same sizes and ending in 4-10 days prior to the processed day are used. This mitigates longterm calibration trends on scales from 1-2 months in both RAN1 and NRT. Short-term variations in SST biases in NRT are higher than in RAN1 but do not exceed ~0.05 K. Delayed-mode RAN processing follows the NRT with a lag of ~2 months, resulting in higher quality, more consistent Metop-FG SST record. The presentation evaluates the performance of the ACSPO AVHRR FRAC dataset and compares it with the EUMETSAT OSISAF Metop-A and -B FRAC SSTs available in PO.DAAC.
The goal of the NOAA AVHRR GAC Reanalysis (RAN) project is to create long-term time series of uniform sea surface temperature (SST) retrievals (Level 2 and 3 products) from AVHRR data using the Advanced Clear-Sky Processor for Oceans (ACSPO) system. During Phase 1 (‘RAN1’), data of several AVHRR/3s from 2002-2015 were reprocessed. Ongoing Phase 2 (‘RAN2’) aims to cover the full period of AVHRR GAC data from 1981-on. At the time of this writing, we reprocessed five AVHRR/2s onboard NOAA-07, -09, -11, -12 and -14 and two AVHRR/3s onboard NOAA- 15 and -16, and created an initial “beta” RAN2 data set (‘RAN2 B01’) spanning ~22 years from 1981-2003. The ACSPO algorithms for cloud masking and training SST regression coefficients, initially developed for operational SST processing, required modifications to mitigate the issues, specific to the RAN2 period: multiple sensor issues, and insufficient number of in situ SST data and their degraded quality. Another derived complexity, also related to insufficient and poor quality of satellite and in situ data, is the limited availability and suboptimal quality of first guess SSTs, which is used in ACSPO for cloud masking and quality control, and employed in the right part of the Non-Linear SST equations. The paper describes modifications to the ACSPO algorithms made for the RAN2 B01, and demonstrates the resulting improvements in the retrieved SST.
Under the NOAA AVHRR GAC Reanalysis project (RAN), a global dataset of consistent sea surface temperature (SST) retrievals from 1981-on will be created from multiple NOAA AVHRRs using the ACSPO system. Following release of RAN1 dataset in 2016, the initial RAN2 Beta 01 (“RAN2 B01”) dataset was produced from NOAA-07, 09, 11, 12, 14, 15 and 16 from 1981-2003. This paper evaluates the initial RAN2 B01 dataset and compares it with two other SST datasets, the NOAA-NASA Pathfinder v5.3 (“PF”) and ESA CCI v2.1 (“CCI”). The time series of monthly global biases and standard deviations with respect to uniformly quality controlled in situ SSTs, and clearsky fractions (percent of SST pixels to the total ice-free ocean) are compared. ‘Skin’ and ‘depth’ SSTs, only available in RAN and CCI data sets, and sensitivity of ’skin’ SST to true SST, are also compared. The RAN B01 outperforms PF. Compared to CCI, it generally delivers more clear-sky observations, often with a better accuracy and precision for both ‘skin’ and ‘depth’ SSTs. The sensitivity to true SST is lower and more variable in RAN2 B01, than in CCI. The RAN2 B01 performance following large volcanic eruptions needs improvements.
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