Workshop on Strategies for Calibration and Validation of Global Change Measurements

The Committee on Environment and Natural Resources (CENR) Task Force on Observations and Data Management hosted a Global Change Calibration/Validation Workshop on May 10-12, 1995, in Arlington, Virginia. This Workshop was convened by Robert Schiffer of NASA Headquarters in Washington, D.C., for the CENR Secretariat with a view toward assessing and documenting lessons learned in the calibration and validation of large-scale, long-term data sets in land, ocean, and atmospheric research programs. The National Aeronautics and Space Administration (NASA)/Goddard Space Flight Center (GSFC) hosted the meeting on behalf of the Committee on Earth Observation Satellites (CEOS)/Working Group on Calibration/walidation, the Global Change Observing System (GCOS), and the U. S. CENR. A meeting of experts from the international scientific community was brought together to develop recommendations for calibration and validation of global change data sets taken from instrument series and across generations of instruments and technologies. Forty-nine scientists from nine countries participated. The U. S., Canada, United Kingdom, France, Germany, Japan, Switzerland, Russia, and Kenya were represented

Relationship between remotely-sensed signatures of the ocean and subsurface structure

This is the first progress report under a Joint Research Council/Ministry of Defence research project on the relationship between remotely-sensed ocean signatures and subsurface structure and dynamics. It comprises an inventory of datasets and progress reports on sub-projects utilising in situ, altimetric and infrared data

Harmonization of space-borne infra-red sensors measuring sea surface temperature

Sea surface temperature (SST) is observed by a constellation of sensors, and SST retrievals are commonly combined into gridded SST analyses and climate data records (CDRs). Differential biases between SSTs from different sensors cause errors in such products, including feature artefacts. We introduce a new method for reducing differential biases across the SST constellation, by reconciling the brightness temperature (BT) calibration and SST retrieval parameters between sensors. We use the Advanced Along-Track Scanning Radiometer (AATSR) and the Sea and Land Surface Temperature Radiometer (SLSTR) as reference sensors, and the Advanced Very High Resolution Radiometer (AVHRR) of the MetOp-A mission to bridge the gap between these references. Observations across a range of AVHRR zenith angles are matched with dual-view three-channel skin SST retrievals from the AATSR and SLSTR. These skin SSTs act as the harmonization reference for AVHRR retrievals by optimal estimation (OE). Parameters for the harmonized AVHRR OE are iteratively determined, including BT bias corrections and observation error covariance matrices as functions of water-vapor path. The OE SSTs obtained from AVHRR are shown to be closely consistent with the reference sensor SSTs. Independent validation against drifting buoy SSTs shows that the AVHRR OE retrieval is stable across the reference-sensor gap. We discuss that this method is suitable to improve consistency across the whole constellation of SST sensors. The approach will help stabilize and reduce errors in future SST CDRs, as well as having application to other domains of remote sensing

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

A climate data record of global sea surface temperature (SST) spanning 1981–2016 has been developed from 4 × 10^12 satellite measurements of thermal infra-red radiance. The spatial area represented by pixel SST estimates is between 1 km^2 and 45 km^2. 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

Reducing the Uncertainties in Direct Aerosol Radiative Forcing

Airborne particles, which include desert and soil dust, wildfire smoke, sea salt, volcanic ash, black carbon, natural and anthropogenic sulfate, nitrate, and organic aerosol, affect Earth's climate, in part by reflecting and absorbing sunlight. This paper reviews current status, and evaluates future prospects for reducing the uncertainty aerosols contribute to the energy budget of Earth, which at present represents a leading factor limiting the quality of climate predictions. Information from satellites is critical for this work, because they provide frequent, global coverage of the diverse and variable atmospheric aerosol load. Both aerosol amount and type must be determined. Satellites are very close to measuring aerosol amount at the level-of-accuracy needed, but aerosol type, especially how bright the airborne particles are, cannot be constrained adequately by current techniques. However, satellite instruments can map out aerosol air mass type, which is a qualitative classification rather than a quantitative measurement, and targeted suborbital measurements can provide the required particle property detail. So combining satellite and suborbital measurements, and then using this combination to constrain climate models, will produce a major advance in climate prediction

Synergy of stereo cloud top height and ORAC optimal estimation cloud retrieval: evaluation and application to AATSR

In this paper we evaluate the retrievals of cloud top height when stereo derived heights are combined with the radiometric cloud top heights retrieved from the ORAC (Optimal Retrieval of Aerosol and Cloud) algorithm. This is performed in a mathematically rigorous way using the ORAC optimal estimation retrieval framework, which includes the facility to use independent a priori information. Key to the use of a priori information is a characterisation of their associated uncertainty. This paper demonstrates the improvements that are possible using this approach and also considers their impact on the microphysical cloud parameters retrieved. The AATSR instrument has two views and three thermal channels so is well placed to demonstrate the synergy of the two techniques. The stereo retrieval is able to improve the accuracy of the retrieved cloud top height when compared to collocated Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), particularly in the presence of boundary layer inversions and high clouds. The impact on the microphysical properties of the cloud such as optical depth and effective radius was evaluated and found to be very small with the biggest differences occurring over bright land surfaces and for high clouds. Overall the cost of the retrievals increased indicating a poorer radiative fit of radiances to the cloud model, which currently uses a single layer cloud model. Best results and improved fit to the radiances may be obtained in the future if a multi-layer model is used

Sea surface temperature for climate from the along-track scanning radiometers

This thesis describes the construction of a sea surface temperature (SST) dataset from Along-Track Scanning Radiometer (ATSR) observations suitable for climate applications. The algorithms presented here are now used at ESA for reprocessing of historical ATSR data and will be the basis of the retrieval used on the forthcoming SLSTR instrument on ESA’s Sentinel-3 satellite. In order to ensure independence of ATSR SSTs from in situ measurements, the retrieval uses physics-based methods through the use of radiative transfer (RT) simulations. The RT simulations are based on the Reference ForwardModel line-by-line model linked to a new sea surface emissivity model which accounts for surface temperature, wind speed, viewing angle and salinity, and to a discrete ordinates scattering (DISORT) model to account for aerosol. An atmospheric profile dataset, based on full resolution ERA-40 numerical weather prediction (NWP) data, is defined and used as input to the RTmodel. Five atmospheric trace gases (N2O, CH4, HNO3, and CFC-11 and CFC-12) are identified as having temporal and geographical variability which have a significant (∼0.1K) impact on RT simulations. Several additional trace gases neglected in previous studies are included using fixed profiles contributing ∼0.04K to RT simulations. Comparison against ATSR-2 and AATSR observations indicates that RT model biases are reduced from 0.2–0.5K for previous studies to ∼0.1K. A new coefficient-based SST retrieval scheme is developed from the RT simulations. Coefficients are banded by total column water vapour (TCWV) from NWP analyses reducing simulated regional biases to <0.1K compared to ∼0.2K for global coefficients. An improved treatment of the instrument viewing geometry decreases simulated view-angle related biases from ∼0.1K to <0.005K for the day-time dual-view retrieval. To eliminate inter-algorithmbiases due to remaining RT model biases and uncertainty in the characterisation of the ATSR instruments the offset coefficient for each TCWV band is adjusted to match the results from a reference channel combination. As infrared radiometers are sensitive to the skin SST while in situ buoys measure SST at some depth below the surface an adjustment for the skin effect and diurnal stratification is included. The samemodel allows adjustment for the differing time of observation between ATSR-2 and AATSR to prevent the diurnal cycle being aliased into the final record. The RT simulations are harmonised between sensors using a double-difference technique eliminating discontinuities in the final SST record. Comparison against in situ drifting and tropical moored buoys shows the new SST dataset is of high quality. Systematic differences between ATSR retrieved SST and in situ drifters show zonal, regional, TCWV, and wind speed biases are less than 0.1K except for themost extreme cases (TCWV <5 kgm−2). The precision of ATSR retrieved SSTs is ∼0.15 K, lower than the precision ofmeasurement of the global ensemble of in situ drifting buoys. From 1995 onwards the ARC SSTs are stable with instability of less than 5mK year−1 to 95% confidence (demonstrated for tropical regions)

A spatiotemporal analysis of the relationship between near-surface air temperature and satellite land surface temperatures using 17 years of data from the ATSR series

The relationship between satellite land surface temperature (LST) and ground-based observations of 2m air temperature (T2m) is characterised in space and time using >17 years of data. The analysis uses a new monthly LST climate data record (CDR) based on the Along-Track Scanning Radiometer (ATSR) series, which has been produced within the European Space Agency GlobTemperature project (http://www.globtemperature.info/). Global LST-T2m differences are analysed with respect to location, land cover, vegetation fraction and elevation, all of which are found to be important influencing factors. LSTnight (~10 pm local solar time, clear-sky only) is found to be closely coupled with minimum T2m (Tmin, all-sky) and the two temperatures generally consistent to within ±5 °C (global median LSTnight- Tmin= 1.8 °C, interquartile range = 3.8 °C). The LSTday (~10 am local solar time, clear-sky only)-maximum T2m (Tmax, all-sky) variability is higher (global median LSTday- Tmax= -0.1°C, interquartile range = 8.1 °C) because LST is strongly influenced by insolation and surface regime. Correlations for both temperature pairs are typically >0.9 outside of the tropics. The monthly global and regional anomaly time series of LST and T2m – which are completely independent data sets - compare remarkably well. The correlation between the data sets is 0.9 for the globe with 90% of the CDR anomalies falling within the T2m 95% confidence limits. The results presented in this study present a justification for increasing use of satellite LST data in climate and weather science, both as an independent variable, and to augment T2m data acquired at meteorological stations

Application of Stereo-Photogrammetric Methods to the Advanced Along Track Scanning Radiometer for the Atmospheric Sciences

This thesis studies photogrammetric techniques applied to the ATSR instruments for the extraction of atmospheric parameters with the objective of generating new scientific datasets. The atmospheric parameters under observation are cloud top height, smoke plume injection height, and tropospheric wind components. All have important applications in various tasks, including the initialisation and validation of climate models. To generate accurate stereo measurements from the ATSR imagery the forward and nadir views need to be accurately co-registered. Currently this is not the case, with differences of up to 2 pixels in both axes recorded. In this thesis an automated image tie-pointing and image warping algorithm that improves ATSR co-registration to ≤1 pixel is presented. This thesis also identifies the census stereo matching algorithm for application to the ATSR instruments. When compared against a collocated DEM, census outperforms the previous stereo matching algorithm applied to the ATSR instrument, known as M4, significantly: RMSE ~700m vs. ~1200m; bias ~60m vs ~600m; R2 ~0.9 vs ~0.7. Furthermore, this thesis reviews the M6 algorithm developed for application within the ESA ALANIS Smoke Plume project. Using census a climatological cloud fraction by altitude dataset over Greenland is generated and demonstrated to agree well with current observational datasets from MISR, MODIS and AATSR. The 11μm channel stereo output provides insights into high cloud characteristics over Greenland and appears to be, in comparison with CALIOP, practically unbiased. The ALANIS Smoke plume project is introduced and the inter-comparison of the M6 algorithm against MISR and CALIOP is presented. M6 demonstrates some ability for determining smoke plumes injection heights above 1km in elevation. However, the smoke plume masking approach currently employed is demonstrated to be lacking in quality. Finally, this thesis presents the determination of cloud tracked tropospheric winds from the ATSR2-AATSR tandem operation using the Farneback optical flow algorithm. This algorithm offers accuracy on the order of 0.5 ms-1 at full image resolution, which is unprecedented in comparison to similarly derived datasets