In Situ SST for Satellite Cal/Val and Quality Control

1. In Situ SST for Satellite Cal/Val and Quality Control Alexander Ignatov and Feng Xu Requirement: A flexible and scalable near real-time quality control (QC) system for in situ SSTs suited for satellite Cal/Val, which should be consistent with sophisticated QC approaches usually adopted by the meteorological community. Science: Different type of QC checks to addressing different errors in in situ data, including the Bayesian-based QC algorithm. Benefit: Satellite SST calibration and validation. iQuam system http://www.star.nesdis.noaa.gov/sod/sst/iquam/ Such QC and monitoring system, iQuam (in situ QUAlity Monitor), is currently running in NESDIS using source in situ data from NCEP. QCed in situ data in HDF format are accessible to public through iQuam web page. Web interfaces are also provided to facilitate human-monitoring of both global and individual platform statistics of QC results and SST anomalies. iQuam web interface provides: -- monthly global maps of in situ measurements -- monthly global statistics of QC results and SST anomalies -- time-series of QC/SST statistics Some screenshots: It also monitors individual platforms: -- track map of individual platform -- SST anomaly of individual platform -- performance history of individual platform Some screenshots: • Quality Control • Left: Percent of reports identified by different QC checks in the NCEP data. Right: SD of SST Anomaly wrt. Reynolds before and after QC • --Clearly see that QC is necessary before in situ data be used in satellite Cal/Val • QC checks can be categorized into five major groups based on the principles they rely on. • Prescreening – aiming to resolve data specific problems, such as duplicate removal, data cleaning or re-organizing, etc. • Single measurement check – assuring the plausibility of each individual field and relationships between them, such as field range check, geolocation check, ID-vs-location check, etc. • Internal consistency check – checking the internal consistency among different measurements from the same platform, such as platform track check and SST spike check. • Mutual consistency check – based on correlation between nearby measurements from different platforms, such as ‘buddy check’ (referred to as ‘cross-platform check’ in this paper). • External consistency check – based on consistency to reference or first-guess, such as ‘background check’ (referred to as ‘reference check’ in this paper) Time-series Time-series of Statistics of different types of in situ SSTs in different data sets No of Platforms No of Observations Mean ΔSST wrt. Reynolds SD ΔSST wrt. Reynolds Global distributions Global maps of in situ data characteristics: number of observation (nobs), bias and SD wrt. Reynolds daily SST. Statistics are for the five-year time period of ICOADS data from 2000–2005 in 2°×2° boxes. Introduction Since the early 1980s, the NESDIS has routinely generated global sea surface temperature (SST) products from the AVHRR sensors onboard NOAA and MetOp satellites. Multi-Channel SST and Non-Linear SST techniques are employed in retrievals. The “ground truth” in situ SSTs are used to initially calibrate the satellite SST algorithms (i.e., derive coefficients of the regression equations) and then to continuously validate retrievals (i.e., monitor global statistics of “satellite minus in situ” SST differences). This process is referred to as “Cal/Val.” Careful inventory and in-depth understanding of the available in situ SSTs is the key to the improved Cal/Val of the historical data records as well as the new generation SST products from AVHRR, VIIRS, and ABI. Three in situ SST data sets are explored in this study: NCEP GTS data currently employed at NESDIS (since 1991), the International Comprehensive Ocean-Atmosphere Data Set (ICOADS) release 2.4 (since 1980) and the US Global Ocean Data Assimilation Experiment (GODAE) Fleet Numerical Meteorology and Oceanography Center (FNMOC) (since 1998). The three data sets are cross-evaluated with the objective to determine their relative value for different Cal/Val tasks. Systematic comparisons are done stratified into four platform types: ships, drifters, and tropical and coastal moorings. Quality control of in situ SST is also studied and implemented. It consists of duplicate removal, platform track check and SST spike check, as well as the reference check and the cross-platform check inherited from the currently existed Bayesian QC method [Lorenc and Hammon, 1988; Ingleby and Huddleston, 2007] with minor modifications. A end-to-end system, iQuam, including QC and monitoring of in situ SSTs is under development at STAR. It uses NCEP GTS in situ data as input, performs QC and generates statistics for web presentation. Initial results are shown. The QC system is implemented as shown in right side. Major checks include Bayesian Reference Check and Cross-platform Check [Lorenc and Hammon, 1988 ; Ingleby and Huddleston, 2007 ], as well as duplicate removal, track check and SST spike check. Besides, a monitoring system is running in parallel to provide timely feedback to the QC system, such as statistics and platform performance. QC configurations are then adjusted accordingly. Science Challenges: Develop and implement complex QC algorithm in near real-time running environment. Next Steps: Establish match-up database of in situ and satellite measurements Transition Path: Quality control information will appended to in situ data for users from satellite SST Cal/Val