Claire E. Bulgin

Validation practices for satellite based earth observation data across communities

Assessing the inherent uncertainties in satellite data products is a challenging task. Different technical approaches have been developed in the Earth Observation (EO) communities to address the validation problem which results in a large variety of methods as well as terminology. This paper reviews state-of-the-art methods of satellite validation and documents their similarities and differences. First, the overall validation objectives and terminologies are specified, followed by a generic mathematical formulation of the validation problem. Metrics currently used as well as more advanced EO validation approaches are introduced thereafter. An outlook on the applicability and requirements of current EO validation approaches and targets is given.

Cloud-clearing techniques over land for land-surface temperature retrieval from the Advanced Along-Track Scanning Radiometer

We present five new cloud detection algorithms over land based on dynamic threshold or Bayesian techniques, applicable to the Advanced Along-Track Scanning Radiometer (AATSR) instrument and compare these to the standard threshold-based SADIST cloud detection scheme. We use a manually classified dataset as a reference to assess algorithm performance and quantify the impact of each cloud detection scheme on land-surface temperature (LST) retrieval. The use of probabilistic Bayesian cloud detection methods improves algorithm true skill scores by 8–9% over SADIST (maximum score of 77.93% compared with 69.27%). We present an assessment of the impact of imperfect cloud masking, in relation to the reference cloud mask, on the retrieved AATSR LST imposing a 2 K tolerance over a 3 × 3 pixel domain. We find an increase of 5–7% in the observations falling within this tolerance when using Bayesian methods (maximum of 92.02% compared with 85.69%). We also demonstrate that the use of dynamic thresholds in the tests employed by SADIST can significantly improve performance, applicable to cloud-test data to be provided by the Sea and Land Surface Temperature Radiometer (SLSTR) due to be launched on the Sentinel 3 mission (estimated 2014).

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.

Quantifying Uncertainty in Satellite-Retrieved Land Surface Temperature from Cloud Detection Errors

Clouds remain one of the largest sources of uncertainty in remote sensing of surface temperature in the infrared, but this uncertainty has not generally been quantified. We present a new approach to do so, applied here to the Advanced Along-Track Scanning Radiometer (AATSR). We use an ensemble of cloud masks based on independent methodologies to investigate the magnitude of cloud detection uncertainties in area-average Land Surface Temperature (LST) retrieval. We find that at a grid resolution of 625 km 2 (commensurate with a 0.25 ∘ grid size at the tropics), cloud detection uncertainties are positively correlated with cloud-cover fraction in the cell and are larger during the day than at night. Daytime cloud detection uncertainties range between 2.5 K for clear-sky fractions of 10–20% and 1.03 K for clear-sky fractions of 90–100%. Corresponding night-time uncertainties are 1.6 K and 0.38 K, respectively. Cloud detection uncertainty shows a weaker positive correlation with the number of biomes present within a grid cell, used as a measure of heterogeneity in the background against which the cloud detection must operate (e.g., surface temperature, emissivity and reflectance). Uncertainty due to cloud detection errors is strongly dependent on the dominant land cover classification. We find cloud detection uncertainties of a magnitude of 1.95 K over permanent snow and ice, 1.2 K over open forest, 0.9–1 K over bare soils and 0.09 K over mosaic cropland, for a standardised clear-sky fraction of 74.2%. As the uncertainties arising from cloud detection errors are of a significant magnitude for many surface types and spatially heterogeneous where land classification varies rapidly, LST data producers are encouraged to quantify cloud-related uncertainties in gridded products.

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

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.

Impact of assimilation of sea-ice surface temperatures on a coupled ocean and sea-ice model

We establish a methodology for assimilating satellite observations of ice surface temperature (IST) into a coupled ocean and sea-ice model. The method corrects the 2 meter air temperature based on the difference between the modelled and the observed IST. Thus the correction includes biases in the surface forcing and the ability of the model to convert incoming parameters at the surface to a net heat flux. A multi-sensor, daily, gap-free surface temperature analysis has been constructed over the Arctic region. This study revealed challenges estimating the ground truth based on buoys measuring IST, as the quality of the measurement varied from buoy to buoy. With these precautions we find a cold temperature bias in the remotely sensed data, and a warm bias in the modelled data relative to ice mounted buoy temperatures, prior to assimilation. As a consequence, this study weighted the modelled IST and the observed IST equally in the correction. The impact of IST was determined for experiments with and without the assimilation of IST and sea-ice concentration. We find that assimilation of remotely sensed data results in a cooling of IST, which improves the timing of the snow melt onset. The improved snow cover in spring is only based on observations from one buoy, thus additional good quality observations could strengthen the conclusions. The ice cover and the sea-ice thickness are increased, primarily in the experiment without sea-ice concentration assimilation.

The Impact of Satellite Derived Land Surface Temperatures on Numerical Weather Prediction Analyses and Forecasts

Land surface temperature (LST) observations from a variety of satellite instruments operating in the infrared have been compared to estimates of surface temperature from the Met Office operational numerical weather prediction (NWP) model. The comparisons show that during the day the NWP model can under predict the surface temperature by up to 10 K in certain regions such as the Sahel and Southern Africa. By contrast at night the differences are generally smaller. Matchups have also been performed between satellite LSTs and observations from an in situ radiometer located in Southern England within a region of mixed land use. These matchups demonstrate good agreement at night and suggest that the satellite uncertainties in LST are less than 2 K. The Met Office surface analysis scheme has been adapted to utilize nighttime LST observations. Experiments using these analyses in an NWP model have shown a benefit to the resulting forecasts of near surface air temperature, particularly over Africa.