scholarly journals Estimating Infrared Radiometric Satellite Sea Surface Temperature Retrieval Cold Biases in the Tropics due to Unscreened Optically Thin Cirrus Clouds

2017 ◽  
Vol 34 (2) ◽  
pp. 355-373 ◽  
Author(s):  
Jared W. Marquis ◽  
Alec S. Bogdanoff ◽  
James R. Campbell ◽  
James A. Cummings ◽  
Douglas L. Westphal ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Surface ◽  
Radiative Transfer Model ◽  
Orthogonal Polarization ◽  
Cold Bias ◽  
Transfer Model ◽  
Single Observation ◽  
Cloud Optical Depth ◽  
Level 2

AbstractPassive longwave infrared radiometric satellite–based retrievals of sea surface temperature (SST) at instrument nadir are investigated for cold bias caused by unscreened optically thin cirrus (OTC) clouds [cloud optical depth (COD) ≤ 0.3]. Level 2 nonlinear SST (NLSST) retrievals over tropical oceans (30°S–30°N) from Moderate Resolution Imaging Spectroradiometer (MODIS) radiances collected aboard the NASA Aqua satellite (Aqua-MODIS) are collocated with cloud profiles from the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. OTC clouds are present in approximately 25% of tropical quality-assured (QA) Aqua-MODIS Level 2 data, representing over 99% of all contaminating cirrus found. Cold-biased NLSST (MODIS, AVHRR, and VIIRS) and triple-window (AVHRR and VIIRS only) SST retrievals are modeled based on operational algorithms using radiative transfer model simulations conducted with a hypothetical 1.5-km-thick OTC cloud placed incrementally from 10.0 to 18.0 km above mean sea level for cloud optical depths between 0.0 and 0.3. Corresponding cold bias estimates for each sensor are estimated using relative Aqua-MODIS cloud contamination frequencies as a function of cloud-top height and COD (assuming they are consistent across each platform) integrated within each corresponding modeled cold bias matrix. NLSST relative OTC cold biases, for any single observation, range from 0.33° to 0.55°C for the three sensors, with an absolute (bulk mean) bias between 0.09° and 0.14°C. Triple-window retrievals are more resilient, ranging from 0.08° to 0.14°C relative and from 0.02° to 0.04°C absolute. Cold biases are constant across the Pacific and Indian Oceans. Absolute bias is lower over the Atlantic but relative bias is higher, indicating that this issue persists globally.

scholarly journals Improving Satellite-Derived Sea Surface Temperature Accuracies Using Water Vapor Profile Data

2011 ◽  
Vol 28 (1) ◽  
pp. 85-93 ◽  
Author(s):  
Ian J. Barton
Keyword(s):  
Water Vapor ◽  
Radiative Transfer ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Satellite Data ◽  
Sea Surface ◽  
Radiative Transfer Model ◽  
Clear Correlation ◽  
Transfer Model ◽  
The Difference

Abstract Analyses based on atmospheric infrared radiative transfer simulations and collocated ship and satellite data are used to investigate whether knowledge of vertical atmospheric water vapor distributions can improve the accuracy of sea surface temperature (SST) estimates from satellite data. Initially, a simulated set of satellite brightness temperatures generated by a radiative transfer model with a large maritime radiosonde database was obtained. Simple linear SST algorithms are derived from this dataset, and these are then reapplied to the data to give simulated SST estimates and errors. The concept of water vapor weights is introduced in which a weight is a measure of the layer contribution to the difference between the surface temperature and that measured by the satellite. The weight of each atmospheric layer is defined as the layer water vapor amount multiplied by the difference between the SST and the midlayer temperature. Satellite-derived SST errors are then plotted against the difference in the sum of weights above an altitude of 2.5 km and that below. For the simple two-channel (with typical wavelengths of 11 and 12 μm) analysis, a clear correlation between the weights differences and the SST errors is found. A second group of analyses using ship-released radiosondes and satellite data also show a correlation between the SST errors and the weights differences. The analyses suggest that, for an SST derived using a simple two-channel algorithm, the accuracy may be improved if account is taken of the vertical distribution of water vapor above the ocean surface. For SST estimates derived using algorithms that include data from a 3.7-μm channel, there is no such correlation found.

scholarly journals Effect of Out-of-Band Response in NOAA-16 AVHRR Channel 3b on Top-of-Atmosphere Radiances Calculated with the Community Radiative Transfer Model

2009 ◽  
Vol 26 (9) ◽  
pp. 1968-1972 ◽  
Author(s):  
Quanhua Liu ◽  
Xingming Liang ◽  
Yong Han ◽  
Paul van Delst ◽  
Yong Chen ◽  
...  
Keyword(s):  
High Resolution ◽  
Radiative Transfer ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Surface Wind ◽  
Sea Surface ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Absorption Bands ◽  
Clear Sky

Abstract The Community Radiative Transfer Model (CRTM) developed at the Joint Center for Satellite Data Assimilation (JCSDA) is used in conjunction with a daily sea surface temperature (SST) and the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) atmospheric data and surface wind to calculate clear-sky top-of-atmosphere (TOA) brightness temperatures (BTs) in three Advanced Very High Resolution Radiometer (AVHRR) thermal infrared channels over global oceans. CRTM calculations are routinely performed by the sea surface temperature team for four AVHRR instruments on board the National Oceanic and Atmospheric Administration (NOAA) satellites NOAA-16, NOAA-17, and NOAA-18 and the Meteorological Operation (MetOp) satellite MetOp-A, and they are compared with clear-sky TOA BTs produced by the operational AVHRR Clear-Sky Processor for Oceans (ACSPO). It was observed that the model minus observation (M−O) bias in the NOAA-16 AVHRR channel 3b (Ch3b) centered at 3.7 μm experienced a discontinuity of ∼0.3 K when a new CRTM version 1.1 (v.1.1) was implemented in ACSPO processing in September 2008. No anomalies occurred in any other AVHRR channel or for any other platform. This study shows that this discontinuity is caused by the out-of-band response in NOAA-16 AVHRR Ch3b and by using a single layer to the NCEP GFS temperature profiles above 10 hPa for the alpha version of CRTM. The problem has been solved in CRTM v.1.1, which uses one of the six standard atmospheres to fill in the missing data above the top pressure level in the input NCEP GFS data. It is found that, because of the out-of-band response, the NOAA-16 AVHRR Ch3b has sensitivity to atmospheric temperature at high altitudes. This analysis also helped to resolve another anomaly in the absorption bands of the High Resolution Infrared Radiation Sounder (HIRS) sensor, whose radiances and Jacobians were affected to a much greater extent by this CRTM inconsistency.

scholarly journals Removing Solar Radiative Effect from the VIIRS M12 Band at 3.7 μm for Daytime Sea Surface Temperature Retrievals

2014 ◽  
Vol 31 (11) ◽  
pp. 2522-2529
Author(s):  
Quanhua Liu ◽  
Alexander Ignatov ◽  
Fuzhong Weng ◽  
XingMing Liang
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Atmospheric Aerosols ◽  
Infrared Imaging ◽  
Research Work ◽  
Sea Surface ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Infrared Band ◽  
Cloud Mask

AbstractOperational sea surface temperature (SST) retrieval algorithms are stratified into nighttime and daytime. The nighttime algorithm uses two split-window Visible Infrared Imaging Radiometer Suite (VIIRS) bands—M15 and M16, centered at ~11 and ~12 m, respectively—and a shortwave infrared band—M12, centered at ~3.7 m. The M12 is most transparent and critical for accurate SST retrievals. However, it is not used during the daytime because of contamination by solar radiation, which is reflected by the ocean surface and scattered by atmospheric aerosols. As a result, daytime VIIRS SST and cloud mask products and applications are degraded and inconsistent with their nighttime counterparts. This study proposes a method to remove the solar contamination from the VIIRS M12 based on theoretical radiative transfer model analyses. The method uses either of the two VIIRS shortwave bands, centered at 1.6 m (M10) or 2.25 m (M11), to correct for the effect of solar reflectance in M12. Subsequently, the corrected daytime brightness temperature in M12 can be used as input into nighttime cloud mask and SST algorithms. Preliminary comparisons with the European Centre for Medium-Range Weather Forecasts (ECMWF) SST analysis suggest that the daytime SST products can be improved and potentially reconciled with the nighttime SST product. However, more substantial case studies and assessments using different SST products are required before the transition of this research work into operational products.

scholarly journals Comparison of SLSTR Thermal Emissive Bands Clear-Sky Measurements with Those of Geostationary Imagers

2020 ◽  
Vol 12 (20) ◽  
pp. 3279
Author(s):  
Bingkun Luo ◽  
Peter J. Minnett
Keyword(s):  
Surface Temperature ◽  
Land Surface ◽  
Azimuth Angle ◽  
Sea Surface ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Clear Sky ◽  
Brightness Temperatures ◽  
Infrared Imager ◽  
Earth Observation Satellite

The Sentinel-3 series satellites belong to the European Earth Observation satellite missions for supporting oceanography, land, and atmospheric studies. The Sea and Land Surface Temperature Radiometer (SLSTR) onboard the Sentinel-3 satellites was designed to provide a significant improvement in remote sensing of skin sea surface temperature (SSTskin). The successful application of SLSTR-derived SSTskin fields depends on their accuracies. Based on sensor-dependent radiative transfer model simulations, geostationary Geostationary Operational Environmental Satellite (GOES-16) Advanced Baseline Imagers (ABI) and Meteosat Second Generation (MSG-4) Spinning Enhanced Visible and Infrared Imager (SEVIRI) brightness temperatures (BT) have been transformed to SLSTR equivalents to permit comparisons at the pixel level in three ocean regions. The results show the averaged BT differences are on the order of 0.1 K and the existence of small biases between them are likely due to the uncertainties in cloud masking, satellite view angle, solar azimuth angle, and reflected solar light. This study demonstrates the feasibility of combining SSTskin retrievals from SLSTR with those of ABI and SEVIRI.

Assimilation of Low-Peaking Satellite Observations Using the Coupled Interface Framework

2020 ◽  
Vol 148 (2) ◽  
pp. 637-654
Author(s):  
Sergey Frolov ◽  
William Campbell ◽  
Benjamin Ruston ◽  
Craig H. Bishop ◽  
David Kuhl ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Circulation Model ◽  
Satellite Observations ◽  
Sea Surface ◽  
Transfer Model ◽  
Western Boundary ◽  
Diurnal Variability ◽  
Near Surface ◽  
The Impact

Abstract Coupled data assimilation (DA) provides a consistent framework for assimilating satellite observations that are sensitive to several components of the Earth system. In this paper, we focus on low-peaking infrared satellite channels that are sensitive to the lower atmosphere and Earth surface temperature (EST) over both ocean and land. Our atmospheric hybrid-4DVAR system [the Navy Global Environmental Model (NAVGEM)] is extended to include the following: 1) variability in the sea surface temperature (both diurnal variability and climatological perturbations to the ensemble members), 2) the coupled Jacobians of the radiative transfer model for the infrared sensors, and 3) the coupled covariances between the EST and the atmosphere. Our coupling approach is found to improve forecast accuracy and to provide corrections to the EST that are in balance with the atmospheric analysis. The largest impact of the coupling is found on near-surface atmospheric temperature and humidity in the tropics, but the impact extends all the way to the stratosphere. The role of each coupling element on the performance of the global atmospheric circulation model is investigated. Inclusion of variability in the sea surface temperature has the strongest positive impact on the forecast quality. Additional inclusion of the coupled Jacobian and ensemble-based coupled covariances led to further improvements in scores and to modification of the corrections to the ocean boundary layer. Coupled DA had significant impact on latent and sensible heat fluxes over land, locations of western boundary currents, and along the ice edge.

scholarly journals A first comparison of TROPOMI aerosol layer height (ALH) to CALIOP data

2020 ◽  
Vol 13 (6) ◽  
pp. 3043-3059 ◽  
Author(s):  
Swadhin Nanda ◽  
Martin de Graaf ◽  
J. Pepijn Veefkind ◽  
Maarten Sneep ◽  
Mark ter Linden ◽  
...  
Keyword(s):  
Optimal Estimation ◽  
Satellite Observation ◽  
Radiative Transfer Model ◽  
Orthogonal Polarization ◽  
Ultraviolet Region ◽  
Transfer Model ◽  
Aerosol Layer ◽  
Layer Height ◽  
Significant Difference ◽  
Level 2

Abstract. The TROPOspheric Monitoring Instrument (TROPOMI) level-2 aerosol layer height (ALH) product has now been released to the general public. This product is retrieved using TROPOMI's measurements of the oxygen A-band, radiative transfer model (RTM) calculations augmented by neural networks and an iterative optimal estimation technique. The TROPOMI ALH product will deliver ALH estimates over cloud-free scenes over the ocean and land that contain aerosols above a certain threshold of the measured UV aerosol index (UVAI) in the ultraviolet region. This paper provides background for the ALH product and explores its quality by comparing ALH estimates to similar quantities derived from spaceborne lidars observing the same scene. The spaceborne lidar chosen for this study is the Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP) on the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) mission, which flies in formation with NASA's A-train constellation since 2006 and is a proven source of data for studying ALHs. The influence of the surface and clouds is discussed, and the aspects of the TROPOMI ALH algorithm that will require future development efforts are highlighted. A case-by-case analysis of the data from the four selected cases (mostly around the Saharan region with approximately 800 co-located TROPOMI pixels and CALIOP profiles in June and December 2018) shows that ALHs retrieved from TROPOMI using the operational Sentinel-5 Precursor Level-2 ALH algorithm is lower than CALIOP aerosol extinction heights by approximately 0.5 km. Looking at data beyond these cases, it is clear that there is a significant difference when it comes to retrievals over land, where these differences can easily go over 1 km on average.

scholarly journals Improvements of the Daily Optimum Interpolation Sea Surface Temperature (DOISST) Version 2.1

2020 ◽  
pp. 1-47
Author(s):  
Boyin Huang ◽  
Chunying Liu ◽  
Viva Banzon ◽  
Eric Freeman ◽  
Garrett Graham ◽  
...  
Keyword(s):  
High Resolution ◽  
Indian Ocean ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Surface ◽  
Cold Bias ◽  
Global Average ◽  
Optimum Interpolation ◽  
The Indian Ocean

AbstractNOAA/NESDIS/NCEI Daily Optimum Interpolation Sea Surface Temperature (SST) version 2.0 (DOISST v2.0) is a blend of in situ ship and buoy SSTs with satellite SSTs derived from Advanced Very High Resolution Radiometer (AVHRR). DOISST v2.0 exhibited a cold bias in the Indian Ocean, South Pacific, and South Atlantic due to a lack of ingested drifting-buoy SSTs in the system, which resulted from a gradual data format change from the Traditional Alphanumeric Codes (TAC) to the Binary Universal Form for the Representation of meteorological data (BUFR). The cold bias against Argo was about -0.14°C on global average and -0.28°C in the Indian Ocean from January 2016 to August 2019.We explored the reasons for these cold biases through six progressive experiments. These experiments showed that the cold biases can be effectively reduced by adjusting ship SSTs with available buoy SSTs, using the latest available ICOADS R3.0.2 derived from merging BUFR and TAC, as well as by including Argo observations above 5 m depth. The impact of using satellite MetOp-B instead of NOAA-19 was notable on high-latitude oceans but small on global average, since their biases are adjusted using in situ SSTs. In addition, the warm SSTs in the Arctic were improved by applying freezing-point instead of regressed ice-SST proxy.This paper describes an upgraded version, DOISST v2.1, which addresses biases in v2.0. Overall, by updating v2.0 to v2.1, the biases are reduced to -0.07°C (-0.04°C) and -0.14°C (-0.08°C) in the global and Indian Ocean, respectively, when compared against independent (dependent) Argo observations. The difference against the Group for High Resolution SST (GHRSST) multi-product ensemble (GMPE) product is reduced from -0.09°C to -0.01°C in the global oceans and from -0.20°C to -0.04°C in the Indian Ocean.

Representation of the Seasonal Cycle of sea surface temperature in CMIP6 models

2020 ◽  
Author(s):  
Yanxin Wang ◽  
Karen Heywood ◽  
David Stevens ◽  
Gillian Damerell
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Climate Model ◽  
Coastal Upwelling ◽  
World Ocean ◽  
Sea Surface ◽  
Cold Bias ◽  
Climate Projections ◽  
Annual Cycles ◽  
Warm Bias

<p>Sea surface temperature (SST) seasonal extrema are important for water mass formation, intensification of tropical cyclones and coral bleaching, so should be well-represented in models used for future climate projections. Typically, climate model evaluations focus on annual or longer-term mean SST. However, accurate mean SST does not guarantee accurate seasonal extrema or annual cycles. Models that have no biases in mean SST can have biases in seasonal extrema and annual cycles, and vice versa.</p><p>Here we assess seasonal extrema in a selection of CMIP6 model historical runs (including BCC-CSM2-MR, CanESM5, CESM2, GFDL-CM4 and GISS-E2-1-G), averaged over 1981-2010, against the World Ocean Atlas (WOA18) observational climatology.  The magnitude and pattern of SST biases for seasonal extrema vary from model to model. GFDL-CM4 and CESM2 simulate SST extrema reasonably well, while BCC-CSM2-MR and GISS-E2-1-G have obvious deficiencies. The global area-weighted root mean square (RMS) difference from WOA18 is larger than 2<sup>o</sup>C in BCC-CSM2-MR and GISS-E2-1-G, and their common maximum bias (larger than 5<sup>o</sup>C) is the cold bias located in the subpolar North Pacific, Greenland Sea and Norwegian Sea. The model biases of maximum SST (summer SST) and minimum SST (winter SST) are in some cases different, leading to biased SST annual cycles. The SST biases are typically smaller for summer, except for models with significant winter cold bias in the high latitudes of the Northern Hemisphere (BCC-CSM2-MR and GISS-E2-1-G). Generally speaking, the bias of the SST annual cycle is smaller than that of seasonal extrema; models that are too cold in winter are typically also too cold in summer. In eastern boundary regions, the models have too small annual cycles. In these regions, the warm bias of winter SST is less than the warm bias of summer SST. This is because the warm bias in models due to poorly captured stratocumulus can be compensated by coastal upwelling, which cools the sea surface more in summer than in winter.</p><p>We note that extra attention should be paid when evaluating SST extrema in some polar areas as the observational climatology there can be unrealistic, particularly in winter.</p>

Sign in / Sign up

Export Citation Format

Share Document