Prasanjit Dash

The SST Quality Monitor (SQUAM)

Abstract The National Environmental Satellite, Data, and Information Service (NESDIS) has been operationally generating sea surface temperature (SST) products (TS) from the Advanced Very High Resolution Radiometers (AVHRR) onboard NOAA and MetOp-A satellites since the early 1980s. Customarily, TS are validated against in situ SSTs. However, in situ data are sparse and are not available globally in near–real time (NRT). This study describes a complementary SST Quality Monitor (SQUAM), which employs global level 4 (L4) SST fields as a reference standard (TR) and performs statistical analyses of the differences ΔTS = TS − TR. The results are posted online in NRT. The TS data that are analyzed are the heritage National Environmental Satellite, Data, and Information Service (NESDIS) SST products from NOAA-16, -17, -18, and -19 and MetOp-A from 2001 to the present. The TR fields include daily Reynolds, real-time global (RTG), Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA), and Ocean Data Analy...

AVHRR GAC SST Reanalysis Version 1 (RAN1)

In response to its users’ needs, the National Oceanic and Atmospheric Administration (NOAA) initiated reanalysis (RAN) of the Advanced Very High Resolution Radiometer (AVHRR) Global Area Coverage (GAC; 4 km) sea surface temperature (SST) data employing its Advanced Clear Sky Processor for Oceans (ACSPO) retrieval system. Initially, AVHRR/3 data from five NOAA and two Metop satellites from 2002 to 2015 have been reprocessed. The derived SSTs have been matched up with two reference SSTs—the quality controlled in situ SSTs from the NOAA in situ Quality Monitor (iQuam) and the Canadian Meteorological Centre (CMC) L4 SST analysis—and analyzed in the NOAA SST Quality Monitor (SQUAM) online system. The corresponding clear-sky ocean brightness temperatures (BT) in AVHRR bands 3b, 4 and 5 (centered at 3.7, 11, and 12 µm, respectively) have been compared with the Community Radiative Transfer Model simulations in another NOAA online system, Monitoring of Infrared Clear-sky Radiances over Ocean for SST (MICROS). For some AVHRRs, the time series of “AVHRR minus reference” SSTs and “observed minus model” BTs are unstable and inconsistent, with artifacts in the SSTs and BTs strongly correlated. In the official “Reanalysis version 1” (RAN1), data from only five platforms—two midmorning (NOAA-17 and Metop-A) and three afternoon (NOAA-16, -18 and -19)—were included during the most stable periods of their operations. The stability of the SST time series was further improved using variable regression SST coefficients, similarly to how it was done in the NOAA/NASA Pathfinder version 5.2 (PFV5.2) dataset. For data assimilation applications, especially those blending satellite and in situ SSTs, we recommend bias-correcting the RAN1 SSTs using the newly developed sensor-specific error statistics (SSES), which are reported in the product files. Relative performance of RAN1 and PFV5.2 SSTs is discussed. Work is underway to improve the calibration of AVHRR/3s and extend RAN time series, initially back to the mid-1990s and later to the early 1980s.

Sensor-Specific Error Statistics for SST in the Advanced Clear-Sky Processor for Oceans

AbstractThe formulation of the sensor-specific error statistics (SSES) has been redesigned in the latest implementation of the NOAA Advanced Clear-Sky Processor for Oceans (ACSPO) to enable efficient use of SSES for assimilation of the ACSPO baseline regression SST (BSST) into level 4 (L4) analyses. The SSES algorithm employs segmentation of the SST domain in the space of regressors and derives the segmentation parameter from the statistics of regressors within the global dataset of matchups. For each segment, local regression coefficients and standard deviations (SDs) of BSST minus in situ SST are calculated from the corresponding subset of matchups. The local regression coefficients are used to generate an auxiliary product—piecewise regression (PWR) SST—and SSES biases are estimated as differences between BSST and PWR SST. Correction of SSES biases, which transforms BSST back into PWR SST, reduces the effects of residual cloud; variations in view zenith angle; and, during the daytime, diurnal surface w...

Maximizing the Information Content of Ill-Posed Space-Based Measurements Using Deterministic Inverse Method

For several decades, operational retrievals from spaceborne hyperspectral infrared sounders have been dominated by stochastic approaches where many ambiguities are pervasive. One major drawback of such methods is their reliance on treating error as definitive information to the retrieval scheme. To overcome this drawback and obtain consistently unambiguous retrievals, we applied another approach from the class of deterministic inverse methods, namely regularized total least squares (RTLS). As a case study, simultaneous simulated retrieval of ozone (O3) profile and surface temperature (ST) for two different instruments, Cross-track Infrared Sounder (CrIS) and Tropospheric Emission Spectrometer (TES), are considered. To gain further confidence in our approach for real-world situations, a set of ozonesonde profile data are also used in this study. The role of simulation-based comparative assessment of algorithms before application on remotely sensed measurements is pivotal. Under identical simulation settings, RTLS results are compared to those of stochastic optimal estimation method (OEM), a very popular method for hyperspectral retrievals despite its aforementioned fundamental drawback. Different tweaking of error covariances for improving the OEM results, used commonly in operations, are also investigated under a simulated environment. Although this work is an extension of our previous work for H2O profile retrievals, several new concepts are introduced in this study: (a) the information content analysis using sub-space analysis to understand ill-posed inversion in depth; (b) comparison of different sensors for same gas profile retrieval under identical conditions; (c) extended capability for simultaneous retrievals using two classes of variables; (d) additional stabilizer of Laplacian second derivative operator; and (e) the representation of results using a new metric called “information gain”. Our findings highlight issues with OEM, such as loss of information as compared to a priori knowledge after using measurements. On the other hand, RTLS can produce “information gain” of ~40–50% deterministically from the same set of measurements.

A new high-resolution sea surface temperature blended analysis

AbstractThe National Oceanic and Atmospheric Administration’s (NOAA) office of National Environmental Satellite, Data, and Information Service (NESDIS) now generates a daily 0.05° (∼5 km) global high-resolution satellite-based sea surface temperature (SST) analyses on an operational basis. The new analysis combines SST data from U.S., Japanese, and European geostationary infrared imagers, and low-Earth-orbiting infrared (United States and Europe) SST data, into a single high-resolution 5-km product. An earlier version produced a 0.1° (∼11 km) resolution, a resolution chosen to approximate the Nyquist sampling criterion for the midlatitude Rossby radius (∼20 km), in order to preserve mesoscale oceanographic features such as eddies and frontal meanders. Comparison between the two analyses illustrates that the higher-resolution grid spacing has more success in this regard. The analysis employs a rigorous multiscale optimum interpolation (OI) methodology that approximates the Kalman filter, together with a da...

Near real time SST retrievals from Himawari-8 at NOAA using ACSPO system

Japanese Himawari-8 (H8) satellite was launched on October 7, 2014 and placed into a geostationary orbit at ~ 140.7°E. The Advanced Himawari Imager (AHI) onboard H8 provides full-disk (FD) observations every 10 minutes, in 16 solar reflectance and thermal infrared (IR) bands, with spatial resolution at nadir of 0.5-1 km and 2 km, respectively. The NOAA Advanced Clear-Sky Processor for Ocean (ACSPO) SST system, previously used with several polar-orbiting sensors, was adapted to process the AHI data. The AHI SST product is routinely validated against quality controlled in situ SSTs available from the NOAA in situ SST Quality monitor (iQuam). The product performance is monitored in the NOAA SST Quality Monitor (SQUAM) system. Typical validation statistics show a bias within ±0.2 K and standard deviation of 0.4-0.6 K. The ACSPO H8 SST is also compared with the NOAA heritage SST produced at OSPO from the Multifunctional Transport Satellite (MTSAT-2; renamed Himawari-7, or H7 after launch) and with another H8 SST produced by JAXA (Japan Aerospace Exploration Agency). This paper describes the ACSPO AHI SST processing and results of validation and comparisons. Work is underway to generate a reduced volume ACSPO AHI SST product L2C (collated in time; e.g., 1-hr instead of current 10-min) and/or L3C (additionally gridded in space). ACSPO AHI processing chain will be applied to the data of the Advanced Baseline Imager (ABI), which will be flown onboard the next generation US geostationary satellite, GOES-R, scheduled for launch in October 2016.

Selecting a first-guess SST as input to ACSPO

Advanced Clear-Sky Processor for Oceans (ACSPO) is a National Environmental Satellite, Data, and Information Service (NESDIS) clear-sky radiance and sea surface temperature (SST) retrieval system. It provides clearsky top of the atmosphere (TOA) observed brightness temperatures (BT) in AVHRR channels 3B(3.7 μm), 4(11 μm), and 5(12 μm) and SST retrieved from these BTs, along with their modeled values calculated with the fast community radiative transfer model (CRTM), using first-guess level 4 (L4) SST (Reynolds daily optimum interpolation SST; OISST) and upper air (NCEP-GFS) fields as inputs. The simulated first-guess BTs are used for accurate ACSPO clearsky mask estimation, physical SST retrievals, monitoring sensor performance, and CRTM validation. Model minus observation (M-O) biases are continuously monitored using the near-real time online-tool, Monitoring of IR Clear-sky radiances over Oceans for SST (MICROS; www.star.nesdis.noaa.gov/sod/sst/micros/). This study tests eleven different gap free L4 SSTs as potential first-guess input fields in ACSPO to improve accuracies of simulated BTs. These L4 SST fields are being cross-compared and validated with quality controlled in situ data in L4-SST Quality Monitor (SQUAM; http://www.star.nesdis.noaa.gov/sod/sst/squam/L4/). In this paper, L4 SSTs are evaluated by comparing them with the ACSPO L2 SST product. Three metrics including the global spatial variance of the L4-L2 biases, and their temporal stability along with the corresponding double-differences, are used to test the performance of these L4 SSTs. It is generally observed that the Group for High-Resolution SST (GHRSST) Multi-Product Ensemble (GMPE), Canadian Meteorological Centre (CMC 0.2°) and UKMO OSTIA provide more consistent first-guess SST fields for use in ACSPO.