High Performance Software Framework for the Calculation of Satellite-to-Satellite Data Matchups (MMS version 1.2)

Block, Thomas; Embacher, Sabine; Merchant, Christopher J.; Donlon, Craig

doi:10.5194/gmd-2017-54

Cited by 2 publications

(2 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We validate the cloud mask performance using a match-up database (MD), of comparisons between satellite and in situ observations, covering all instruments in the sensor series [31]. We filter matches on the basis of in situ and satellite observations flagged as high quality, a maximum spatial separation of 10 km and a maximum time difference of four hours.…”

Section: Cloud Mask Validation and Performance Assessmentmentioning

confidence: 99%

“…RTTOV v11.3 radiative transfer is coupled with a Cox and Munk parameterisation of surface reflectance and glint [30], to determine suitability for cloud detection purposes. We compare simulated and observed reflectance in the 0.6 and 0.8 µm channels using an SST match-up database [31] including all AVHRR sensors used in the data record. Clear-sky matches were selected using Bayesian cloud detection with infrared channels only, including checks for bad data, navigation and calibration problems.…”

Section: Simulating Clear-sky Observationsmentioning

confidence: 99%

See 1 more Smart Citation

Bayesian Cloud Detection for 37 Years of Advanced Very High Resolution Radiometer (AVHRR) Global Area Coverage (GAC) Data

et al. 2018

View full text Add to dashboard Cite

Abstract:Cloud detection is a source of significant errors in retrieval of sea surface temperature (SST). We apply a Bayesian cloud detection scheme to 37 years of Advanced Very High Resolution Radiometer (AVHRR) Global Area Coverage (GAC) data, which is an important source of multi-decadal global SST information. The Bayesian scheme calculates a probability of clear-sky for each image pixel, conditional on the satellite observations and prior probability. We compare the cloud detection performance to the operational Clouds from AVHRR Extended algorithm (CLAVR-x), as a measure of improvement from reduced cloud-related errors. To do this we use sea surface temperature differences between satellite retrievals and in situ observations from drifting buoys and the Global Tropical Moored Buoy Array (GTMBA). The Bayesian scheme reduces the absolute difference between the mean and median SST biases and reduces the standard deviation of the SST differences by ∼10% for both daytime and nighttime retrievals. These reductions are indicative of removing cloud contaminated outliers in the distribution, as these fall only on one side of the distribution forming a cold tail. At a probability threshold of 0.9 typically used to determine a binary cloud mask for SST retrieval, the Bayesian mask also reduces the robust standard deviation by ∼5-10% during the day, in comparison with the operational cloud mask. This shows an improvement in the central distribution of SST differences for daytime retrievals.

show abstract

Section: Cloud Mask Validation and Performance Assessmentmentioning

confidence: 99%

Section: Simulating Clear-sky Observationsmentioning

confidence: 99%

Bayesian Cloud Detection for 37 Years of Advanced Very High Resolution Radiometer (AVHRR) Global Area Coverage (GAC) Data

et al. 2018

View full text Add to dashboard Cite

show abstract

Optimal Estimation of Sea Surface Temperature from AMSR-E

et al. 2018

Self Cite

View full text Add to dashboard Cite

The Optimal Estimation (OE) technique is developed within the European Space AgencyClimate Change Initiative (ESA-CCI) to retrieve subskin Sea Surface Temperature (SST) from AQUA's Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E). A comprehensive matchup database with drifting buoy observations is used to develop and test the OE setup. It is shown that it is essential to update the first guess atmospheric and oceanic state variables and to perform several iterations to reach an optimal retrieval. The optimal number of iterations is typically three to four in the current setup. In addition, updating the forward model, using a multivariate regression model is shown to improve the capability of the forward model to reproduce the observations. The average sensitivity of the OE retrieval is 0.5 and shows a latitudinal dependency with smaller sensitivity for cold waters and larger sensitivity for warmer waters. The OE SSTs are evaluated against drifting buoy measurements during 2010. The results show an average difference of 0.02 K with a standard deviation of 0.47 K when considering the 64% matchups, where the simulated and observed brightness temperatures are most consistent. The corresponding mean uncertainty is estimated to 0.48 K including the in situ and sampling uncertainties. An independent validation against Argo observations from 2009 to 2011 shows an average difference of 0.01 K, a standard deviation of 0.50 K and a mean uncertainty of 0.47 K, when considering the best 62% of retrievals. The satellite versus in situ discrepancies are highest in the dynamic oceanic regions due to the large satellite footprint size and the associated sampling effects. Uncertainty estimates are available for all retrievals and have been validated to be accurate. They can thus be used to obtain very good retrieval results. In general, the results from the OE retrieval are very encouraging and demonstrate that passive microwave observations provide a valuable alternative to infrared satellite observations for retrieving SST.

show abstract

High Performance Software Framework for the Calculation of Satellite-to-Satellite Data Matchups (MMS version 1.2)

Cited by 2 publications

References 4 publications

Bayesian Cloud Detection for 37 Years of Advanced Very High Resolution Radiometer (AVHRR) Global Area Coverage (GAC) Data

Bayesian Cloud Detection for 37 Years of Advanced Very High Resolution Radiometer (AVHRR) Global Area Coverage (GAC) Data

Optimal Estimation of Sea Surface Temperature from AMSR-E

Contact Info

Product

Resources

About