scholarly journals Microwave Radiometer Calibration on Decadal Time Scales Using On-Earth Brightness Temperature References: Application to the TOPEX Microwave Radiometer

2009 ◽  
Vol 26 (12) ◽  
pp. 2579-2591 ◽  
Author(s):  
Shannon Brown ◽  
Shailen Desai ◽  
Stephen Keihm ◽  
Wenwen Lu
Keyword(s):  
Brightness Temperature ◽  
Time Scales ◽  
Dynamic Range ◽  
Microwave Radiometer ◽  
Sea Surface ◽  
Climate Data ◽  
Radiometer Calibration ◽  
Brightness Temperatures ◽  
Data Record ◽  
Climate Data Record

Abstract A method is described to calibrate a satellite microwave radiometer operating near 18–37 GHz on decadal time scales for the purposes of climate studies. The method uses stable on-earth brightness temperature references over the full dynamic range of on-earth brightness temperatures to stabilize the radiometer calibration and is applied to the Ocean Topography Experiment (TOPEX) Microwave Radiometer (TMR). These references are a vicarious cold reference, which is a statistical lower bound on ocean surface brightness temperature, and heavily vegetated, pseudoblackbody regions in the Amazon rain forest. The sensitivity of the on-earth references to climate variability is assessed. No significant climate sensitivity is found in the cold reference, as it is not sensitive to a climate minimum (e.g., coldest sea surface temperature or driest atmosphere) but arises because of a minimum in the sea surface radio brightness that occurs in the middle of the climatic distribution of sea surface temperatures (SSTs). The hot reference is observed to have a small climate dependency, which is most evident during the 1997/98 El Niño event. A time-dependent model for the hot reference region is constructed using meteorological fields from the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis product. This model is shown to accurately account for the small climate variations in this reference. In addition to the long-term stabilization of the brightness temperatures, an improvement to the TMR antenna pattern correction is described that removes residual geographically correlated errors, in particular errors correlated with distance to land or sea ice. The recalibrated TMR climate data record is cross-validated with the climate data record produced from the Special Sensor Microwave Imager (SSM/I). It is shown that the intersensor drift is small, providing realistic error bars for the climate trends generated from the instrument pair, as well as validating both the methodology described in this paper and the SSM/I climate data record.

scholarly journals Adjusting for Desert-Dust-Related Biases in a Climate Data Record of Sea Surface Temperature

2020 ◽  
Vol 12 (16) ◽  
pp. 2554
Author(s):  
Christopher J. Merchant ◽  
Owen Embury
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Large Scale ◽  
Dust Aerosol ◽  
Sea Surface ◽  
Climate Data ◽  
Desert Dust ◽  
Data Record ◽  
Climate Data Record

Atmospheric desert-dust aerosol, primarily from north Africa, causes negative biases in remotely sensed climate data records of sea surface temperature (SST). Here, large-scale bias adjustments are deduced and applied to the v2 climate data record of SST from the European Space Agency Climate Change Initiative (CCI). Unlike SST from infrared sensors, SST measured in situ is not prone to desert-dust bias. An in-situ-based SST analysis is combined with column dust mass from the Modern-Era Retrospective analysis for Research and Applications, Version 2 to deduce a monthly, large-scale adjustment to CCI analysis SSTs. Having reduced the dust-related biases, a further correction for some periods of anomalous satellite calibration is also derived. The corrections will increase the usability of the v2 CCI SST record for oceanographic and climate applications, such as understanding the role of Arabian Sea SSTs in the Indian monsoon. The corrections will also pave the way for a v3 climate data record with improved error characteristics with respect to atmospheric dust aerosol.

scholarly journals Satellite-based time-series of sea-surface temperature since 1981 for climate applications

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Christopher J. Merchant ◽  
Owen Embury ◽  
Claire E. Bulgin ◽  
Thomas Block ◽  
Gary K. Corlett ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Ocean Model ◽  
Sea Surface ◽  
Climate Data ◽  
Satellite Measurements ◽  
Data Record ◽  
The Mean ◽  
Climate Data Record

Abstract A climate data record of global sea surface temperature (SST) spanning 1981–2016 has been developed from 4 × 1012 satellite measurements of thermal infra-red radiance. The spatial area represented by pixel SST estimates is between 1 km2 and 45 km2. The mean density of good-quality observations is 13 km−2 yr−1. SST uncertainty is evaluated per datum, the median uncertainty for pixel SSTs being 0.18 K. Multi-annual observational stability relative to drifting buoy measurements is within 0.003 K yr−1 of zero with high confidence, despite maximal independence from in situ SSTs over the latter two decades of the record. Data are provided at native resolution, gridded at 0.05° latitude-longitude resolution (individual sensors), and aggregated and gap-filled on a daily 0.05° grid. Skin SSTs, depth-adjusted SSTs de-aliased with respect to the diurnal cycle, and SST anomalies are provided. Target applications of the dataset include: climate and ocean model evaluation; quantification of marine change and variability (including marine heatwaves); climate and ocean-atmosphere processes; and specific applications in ocean ecology, oceanography and geophysics.

scholarly journals An Uncertainty Quantified Fundamental Climate Data Record for Microwave Humidity Sounders

2019 ◽  
Vol 11 (5) ◽  
pp. 548 ◽  
Author(s):  
Imke Hans ◽  
Martin Burgdorf ◽  
Stefan Buehler ◽  
Marc Prange ◽  
Theresa Lang ◽  
...  
Keyword(s):  
Brightness Temperature ◽  
Uncertainty Propagation ◽  
Uncertainty Budget ◽  
Intermediate Step ◽  
Climate Data ◽  
Data Record ◽  
Upper Tropospheric Humidity ◽  
Uncertainty Information ◽  
Climate Data Record

To date, there is no long-term, stable, and uncertainty-quantified dataset of upper tropospheric humidity (UTH) that can be used for climate research. As intermediate step towards the overall goal of constructing such a climate data record (CDR) of UTH, we produced a new fundamental climate data record (FCDR) on the level of brightness temperature for microwave humidity sounders that will serve as basis for the CDR of UTH. Based on metrological principles, we constructed and implemented the measurement equation and the uncertainty propagation in the processing chain for the microwave humidity sounders. We reprocessed the level 1b data to obtain newly calibrated uncertainty quantified level 1c data in brightness temperature. Three aspects set apart this FCDR from previous attempts: (1) the data come in a ready-to-use NetCDF format; (2) the dataset provides extensive uncertainty information taking into account the different correlation behaviour of the underlying errors; and (3) inter-satellite biases have been understood and reduced by an improved calibration. Providing a detailed uncertainty budget on these data, this new FCDR provides valuable information for a climate scientist and also for the construction of the CDR.

Construction of a climate data record of sea surface temperature from passive microwave measurements

2020 ◽  
Vol 236 ◽  
pp. 111485 ◽  
Author(s):  
Emy Alerskans ◽  
Jacob L. Høyer ◽  
Chelle L. Gentemann ◽  
Leif Toudal Pedersen ◽  
Pia Nielsen-Englyst ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Passive Microwave ◽  
Sea Surface ◽  
Microwave Measurements ◽  
Climate Data ◽  
Data Record ◽  
Climate Data Record

scholarly journals Best Practices in Crafting the Calibrated, Enhanced-Resolution Passive-Microwave EASE-Grid 2.0 Brightness Temperature Earth System Data Record

2018 ◽  
Vol 10 (11) ◽  
pp. 1793 ◽  
Author(s):  
Mary Brodzik ◽  
David Long ◽  
Molly Hardman
Keyword(s):  
Best Practices ◽  
Brightness Temperature ◽  
Passive Microwave ◽  
Earth System ◽  
Climate Data ◽  
Enhanced Resolution ◽  
Data Record ◽  
System Data ◽  
Land Surfaces ◽  
Climate Data Record

Since the late 1970s, satellite passive-microwave brightness temperatures have been a mainstay in remote sensing of the cryosphere. Polar snow and ice-covered ocean and land surfaces are especially sensitive to climate change and are observed to fluctuate on interannual to decadal timescales. In regions of limited sunlight and cloudy conditions, microwave measurements are particularly valuable for monitoring snow- and ice-covered ocean and land surfaces, due to microwave sensitivity to phase changes of water. Historically available at relatively low resolutions (25 km) compared to optical techniques (less than 1 km), passive-microwave sensors have provided short-timescale, large-area spatial coverage, and high temporal repeat observations for monitoring hemispheric-wide changes. However, historically available gridded passive microwave products have fallen short of modern requirements for climate data records, notably by using inconsistently-calibrated input data, including only limited periods of sensor overlaps, employing image-reconstruction methods that tuned for reduced noise rather than enhanced resolution, and using projection and grid definitions that were not easily interpreted by geolocation software. Using a recently completed Fundamental Climate Data Record of the swath format passive-microwave record that incorporated new, cross-sensor calibrations, we have produced an improved, gridded data record. Defined on the EASE-Grid 2.0 map projections and derived with numerically efficient image-reconstruction techniques, the Calibrated, Enhanced-Resolution Brightness Temperature (CETB) Earth System Data Record (ESDR) increases spatial resolution up to 3.125 km for the highest frequency channels, and satisfies modern Climate Data Record (CDR) requirements as defined by the National Research Council. We describe the best practices and development approaches that we used to ensure algorithmic integrity and to define and satisfy metadata, content and structural requirements for this high-quality, reliable, consistently gridded microwave radiometer climate data record.

scholarly journals A Fundamental climate data record of SMMR, SSM/I, and SSMIS brightness temperatures

2019 ◽  
Author(s):  
Karsten Fennig ◽  
Marc Schröder ◽  
Axel Andersson ◽  
Rainer Hollmann
Keyword(s):  
Hydrological Cycle ◽  
Sensor Data ◽  
Calibration Model ◽  
Climate Data ◽  
Raw Data ◽  
Brightness Temperatures ◽  
Data Record ◽  
Data Files ◽  
Microwave Imager ◽  
Climate Data Record

Abstract. The Fundamental Climate Data Record (FCDR) of Microwave Imager Radiances from the Satellite Application Facility on Climate Monitoring (CM SAF) comprises inter-calibrated and homogenised brightness temperatures from the Scanning Multichannel Microwave Radiometer (SMMR), the Special Sensor Microwave/Imager (SSM/I) and the Special Sensor Microwave Imager/Sounder SSMIS radiometers. It covers the time period from October 1978 to December 2015 including all available data from the SMMR radiometer aboard Nimbus-7 and all SSM/I and SSMIS radiometers aboard the Defence Meteorological Satellite Program (DMSP) platforms. SMMR, SSM/I and SSMIS data are used for a variety of applications, such as analyses of the hydrological cycle, remote sensing of sea ice or as input into reanalysis projects. The improved homogenisation and inter-calibration procedure ensures the long term stability of the FCDR for climate related applications. All available raw data records from different sources have been reprocessed to a common standard, starting with the calibration of the raw Earth counts, to ensure a completely homogenised data record. The data processing accounts for several known issues with the instruments and corrects calibration anomalies due to along-scan inhomogeneity, moonlight intrusions, sunlight intrusions, and emissive reflector. Corrections for SMMR are limited because the SMMR raw data records were not available. Furthermore, the inter-calibration model incorporates a scene dependent inter-satellite bias correction and a non-linearity correction to the instrument calibration. The data files contain all available original sensor data (SMMR: Pathfinder Level 1b) and meta-data to provide a completely traceable climate data record. Inter-calibration and Earth incidence angle normalisation offsets are available as additional layers within the data files in order to keep this information transparent to the users. The data record is complemented with noise equivalent temperatures (NeΔT), quality flags, surface types, and Earth incidence angles. The FCDR together with its full documentation, including evaluation results, is freely available at: https://doi.org/10.5676/EUM_SAF_CM/FCDR_MWI/V003 (Fennig et al., 2017).

scholarly journals Jason continuity of services: continuing the Jason altimeter data records as Copernicus Sentinel-6

Ocean Science ◽  
2016 ◽  
Vol 12 (2) ◽  
pp. 471-479 ◽  
Author(s):  
Remko Scharroo ◽  
Hans Bonekamp ◽  
Christelle Ponsard ◽  
François Parisot ◽  
Axel von Engeln ◽  
...  
Keyword(s):  
Radio Occultation ◽  
Sea Surface ◽  
Altimeter Data ◽  
Climate Data ◽  
Satellite Altimeter ◽  
Data Services ◽  
Backward Compatibility ◽  
Data Record ◽  
Climate Data Record ◽  
Satellite Altimeter Data

Abstract. The Sentinel-6 mission is proposed as a multi-partner programme to continue the Jason satellite altimeter data services beyond the Jason-2 and Jason-3 missions. The Sentinel-6 mission programme consists of two identical satellites flying in sequence to prolong the climate data record of sea level accumulated by the TOPEX/Poseidon, Jason-1, Jason-2, and Jason-3 missions from 2020 to beyond 2030. The Sentinel-6 mission intends to maintain these services in a fully operational manner. A key feature is the simultaneous pulse-limited and synthetic aperture radar processing allowing direct and continuous comparisons of the sea surface height measurements based on these processing methods and providing backward compatibility. The Sentinel-6 mission will also include radio occultation user services.

scholarly journals Jason continuity of services: continuing the Jason altimeter data records as Copernicus Sentinel-6

2015 ◽  
Vol 12 (6) ◽  
pp. 2931-2953 ◽  
Author(s):  
R. Scharroo ◽  
H. Bonekamp ◽  
C. Ponsard ◽  
F. Parisot ◽  
A. von Engeln ◽  
...  
Keyword(s):  
Radio Occultation ◽  
Sea Surface ◽  
Altimeter Data ◽  
Climate Data ◽  
Satellite Altimeter ◽  
Data Services ◽  
Backward Compatibility ◽  
Data Record ◽  
Climate Data Record ◽  
Satellite Altimeter Data

Abstract. The Sentinel-6 mission is proposed as a multi-partner programme to continue the Jason satellite altimeter data services beyond the Jason-2 and Jason-3 missions. The Sentinel-6 mission programme consists of two identical satellites flying in sequence to prolong the climate data record of sea level accumulated by the TOPEX/Poseidon, Jason-1, Jason-2, and Jason-3 missions from 2020 to beyond 2030. The Sentinel-6 mission intends to maintain these services in a fully operational manner. A key feature is the simultaneous pulse-limited and synthetic aperture radar processing allowing direct and continuous comparisons of the sea surface height measurements based on these processing methods and providing backward compatibility. The Sentinel-6 mission will also include Radio Occultation user services.

scholarly journals The EUMETSAT sea ice concentration climate data record

2016 ◽  
Vol 10 (5) ◽  
pp. 2275-2290 ◽  
Author(s):  
Rasmus T. Tonboe ◽  
Steinar Eastwood ◽  
Thomas Lavergne ◽  
Atle M. Sørensen ◽  
Nicholas Rathmann ◽  
...  
Keyword(s):  
Sea Ice ◽  
Microwave Radiometer ◽  
Open Water ◽  
The Arctic ◽  
Climate Data ◽  
Sea Ice Concentration ◽  
Ice Concentration ◽  
Data Record ◽  
Microwave Imager ◽  
Climate Data Record

Abstract. An Arctic and Antarctic sea ice area and extent dataset has been generated by EUMETSAT's Ocean and Sea Ice Satellite Application Facility (OSISAF) using the record of microwave radiometer data from NASA's Nimbus 7 Scanning Multichannel Microwave radiometer (SMMR) and the Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager and Sounder (SSMIS) satellite sensors. The dataset covers the period from October 1978 to April 2015 and updates and further developments are planned for the next phase of the project. The methodology for computing the sea ice concentration uses (1) numerical weather prediction (NWP) data input to a radiative transfer model for reduction of the impact of weather conditions on the measured brightness temperatures; (2) dynamical algorithm tie points to mitigate trends in residual atmospheric, sea ice, and water emission characteristics and inter-sensor differences/biases; and (3) a hybrid sea ice concentration algorithm using the Bristol algorithm over ice and the Bootstrap algorithm in frequency mode over open water. A new sea ice concentration uncertainty algorithm has been developed to estimate the spatial and temporal variability in sea ice concentration retrieval accuracy. A comparison to US National Ice Center sea ice charts from the Arctic and the Antarctic shows that ice concentrations are higher in the ice charts than estimated from the radiometer data at intermediate sea ice concentrations between open water and 100 % ice. The sea ice concentration climate data record is available for download at www.osi-saf.org, including documentation.

scholarly journals A Fundamental Climate Data Record of SMMR, SSM/I, and SSMIS brightness temperatures

2020 ◽  
Vol 12 (1) ◽  
pp. 647-681 ◽  
Author(s):  
Karsten Fennig ◽  
Marc Schröder ◽  
Axel Andersson ◽  
Rainer Hollmann
Keyword(s):  
Hydrological Cycle ◽  
Sensor Data ◽  
Calibration Model ◽  
Climate Data ◽  
Raw Data ◽  
Brightness Temperatures ◽  
Data Record ◽  
Data Files ◽  
Microwave Imager ◽  
Climate Data Record

Abstract. The Fundamental Climate Data Record (FCDR) of Microwave Imager Radiances from the Satellite Application Facility on Climate Monitoring (CM SAF) comprises inter-calibrated and homogenized brightness temperatures from the Scanning Multichannel Microwave Radiometer (SMMR), the Special Sensor Microwave/Imager (SSM/I), and the Special Sensor Microwave Imager/Sounder SSMIS radiometers. It covers the time period from October 1978 to December 2015 including all available data from the SMMR radiometer aboard Nimbus-7 and all SSM/I and SSMIS radiometers aboard the Defense Meteorological Satellite Program (DMSP) platforms. SMMR, SSM/I, and SSMIS data are used for a variety of applications, such as analyses of the hydrological cycle, remote sensing of sea ice, or as input into reanalysis projects. The improved homogenization and inter-calibration procedure ensures the long-term stability of the FCDR for climate-related applications. All available raw data records from different sources have been reprocessed to a common standard, starting with the calibration of the raw Earth counts, to ensure a completely homogenized data record. The data processing accounts for several known issues with the instruments and corrects calibration anomalies due to along-scan inhomogeneity, moonlight intrusions, sunlight intrusions, and emissive reflector. Corrections for SMMR are limited because the SMMR raw data records were not available. Furthermore, the inter-calibration model incorporates a scene dependent inter-satellite bias correction and a non-linearity correction in the instrument calibration. The data files contain all available original sensor data (SMMR: Pathfinder level 1b) and metadata to provide a completely traceable climate data record. Inter-calibration and Earth incidence angle normalization offsets are available as additional layers within the data files in order to keep this information transparent to the users. The data record is complemented with noise-equivalent temperatures (NeΔT), quality flags, surface types, and Earth incidence angles. The FCDR together with its full documentation, including evaluation results, is freely available at: https://doi.org/10.5676/EUM_SAF_CM/FCDR_MWI/V003 (Fennig et al., 2017).

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