In response to users’ needs, NOAA SST team is developing a multi-sensor gridded “super-collated” (L3S) SST product that consolidates “uncollated” L3U data from multiple polar sensors into reduced-volume high spatial resolution product, without adding modeled data. The algorithm uses VIIRS data from multiple NPP and N20 overpasses, to create a satellite-based reference, and use it to bias-correct individual overpasses, suppress the noise, and mitigate residual cloud leakages present in individual L3U data. We present the current status of VIIRS L3S product and results of its monitoring in the NOAA global and regional systems, and validation against in-situ data.
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.
GOES-17 (G17), the second satellite in the NOAA GOES-R series with a new Advanced Baseline Imager (ABI) radiometer onboard, was launched in Mar’2018 and declared the NOAA operational GOES-West satellite in Feb’2019. Sea Surface Temperature (SST) is one of the key geophysical products derived from ABI brightness temperatures (BTs) using the NOAA Advanced Clear-Sky Processor for Ocean (ACSPO) enterprise SST system.
Following the launch of G17, an issue was discovered with its ABI loop heat pipe (LHP) that should maintain the ABI electronics (and in particular, Focal Plane Module, FPM) at their intended temperatures. There are two main implications. During normal operations, the G17 FPM temperature is elevated compared to the specifications (and compared to the ABI twin sensor onboard G16, which has been the NOAA operational GOES-East satellite since Dec’2017), leading to overall noisier BTs. During nighttime, especially in some seasons, when more sunlight impinges directly on the G17 ABI, its FPM temperature is elevated even further and becomes very unstable, resulting in increased noise and degraded ABI calibration (due to increased and band-specific emission from the focal plane itself), rendering measured BTs completely unusable for SST retrievals.
The increased noise of the G17 ABI instrument necessitates changes in the ACSPO clear-sky mask (in particular, its spatial uniformity test), and in the collation-in-time algorithm introduced in ACSPO v2.60 for G16. When the ABI temperature is further elevated, its BTs in the longwave IR bands remain biased (although the calibration algorithm is expected to account for changes in the instrument temperature). When the temperature is near its maximum values, they saturate. The collation-in-time algorithm can partially fill in the periods of saturation, while the remaining biases may potentially be addressed empirically. We discuss the challenges imposed by the G17 ABI LHP and resulting BT anomalies, and our progress with mitigating those in the ACSPO SST products at NOAA. Future plans include tweaking ACSPO algorithms, to generate the best possible SST out of G17 BT data, reprocessing of G17 SST data, archival of data with the NASA/JPL Physical Oceanography Distributed Active Archive Center (PO.DAAC) and NOAA NCEI, and work with users to iteratively improve the processing algorithms and derived SST products.
NOAA produces operational sea surface temperature (SST) data products from two VIIRS sensors, flown onboard NPP (launched in Oct 2011) and NOAA-20 (N20, aka. JPSS-1 prior to launch; launched in Nov 2017). These first two satellites in the US new generation Joint Polar Satellite System (JPSS) series will be followed by JPSS-2 to -4, planned for launch in 2022, 2026, and 2031, respectively. The goal of VIIRS SST Reanalyses (RANs) is to generate stable, accurate and consistent data products, which currently include Level 2P (L2P; swath projection; 26 Gb/day) and gridded 0.02° L3U (0.45 Gb/day). The RAN comprises multiples steps, including 1) RDR-to-SDR conversion, i.e., reprocessing of all available VIIRS L0 (“Raw Data Records”, RDRs) into improved L1b (“Sensor Data Records”, SDRs), using latest and most accurate sensor calibration, 2) pre-processing, which includes destriping the radiances, resampling VIIRS imagery (to minimize the effect of bow-tie deletions and distortions), and aggregating original 86-sec granules into 10-min SDR granules; 3) feeding those into the latest NOAA Advanced Clear-Sky Processor for Oceans (ACSPO) enterprise SST code (currently, version 2.61), and producing L2P; 4) gridding L2P data and producing L3U; 5) matching L2P/3U data with several accurate Level 4 (L4) analyses, and with quality controlled in situ SSTs from the NOAA in situ SST Quality Monitor, iQuam, system; 6) calculation of the corresponding performance statistics – global daily mean biases and standard deviations, SDs, of various paired SST differences, ΔTs, stratified by day and night, and displaying them in the NOAA SST Quality Monitor (SQUAM) web-based system; 6) generation of SST imagery over ~30 regional targets and routine monitoring in another NOAA system, ACSPO Regional Monitor for SST (ARMS); 7) calculation of brightness temperature (BT) differences between measured BTs and those simulated using the fast Community Radiative Transfer Model (CRTM), as a part of ACSPO L2P processing, and displaying in another NOAA web-based system, MICROS; 8) product archival at NOAA (CoastWatch and NCEI) and at NASA/JPL PO.DAAC. NPP RAN1, performed in late 2015 jointly with UW/CIMSS using ACSPO v2.40, covered a period from Mar’2012-Dec’2015. The data from Jan’2016 – on have been supplemented from operational ACSPO products with various versions (2.41, 2.60, and 2.61). Some issues, unresolved in RAN1, are now being addressed in RAN2. The most important features of RAN2 include the addition of N20 data; fixing quarterly spikes in SST time series (resulting from the VIIRS black-body warm-ups/cool-downs, WUCDs); and using a consistent ACSPO version 2.61 for the full NPP and N20 records. As of this writing, 64 months of NPP (Jan 2014 – Apr 2019) and 16 months of N20 (Jan 2018 – Apr 2019) RAN2 data have been generated. The remaining two years of NPP data (Feb 2012 – Dec 2013), are being processed. The SST records appear stable, and consistent with in situ data and across NPP/N20. The RAN2 data are currently being archived at the NASA/JPL PO.DAAC, and NOAA CoastWatch and NCEI archives.
New-generation geostationary sensors, including the Advanced Baseline Imager (ABI) onboard GOES-16/17 and the Advanced Himawari Imager (AHI, a twin to ABI) onboard Himawari-8, capture infrared images with 2km nadir resolution every 10 or 15 minutes. This high temporal resolution is a unique feature of geostationary sensors, facilitating studies of SST diurnal variability.
A collated-in-time geostationary SST product developed for the NOAA Advanced Clear-Sky Processor for Oceans (ACSPO) SST system is able to exploit the temporal information in geostationary SST images by using the temporal context to separate the effects of faster moving clouds and other atmospheric formations from the slower evolution of the SST field. This significantly improves spatial coverage by using measurements from the nearest clear-sky looks in time, reduces the overall noise in the SST time series, and allows for a more accurate characterization of SST diurnal variability. Moreover, the data volume is significantly reduced by reporting at a reduced hourly temporal rate, which is sufficient to resolve the main features in the diurnal cycle.
We present an overview of the collation algorithm, sample validation data, and examples of ocean phenomena, such as thermal fronts, diurnal warming, and tidal motion, which have been observed by new-generation sensors. The improved spatial coverage and temporal resolution give us an unprecedented opportunity to investigate the sub-daily time evolution of these phenomena.