The file associated with this record is under a 24-month embargo from publication in accordance with the publisher's self-archiving policy. The full text may be available through the publisher links provided above.The accuracies of six global burned area (BA) products for year 2008 were compared using the same validation methods and reference data to quantify accuracy of each product. The selected products include MCD64, MCD45 and Geoland2, and three products developed within the Fire Disturbance project (fire_cci), which is part of the European Space Agency's (ESA) Climate Change Initiative (CCI) program. The latter three products were derived from MERIS and VEGETATION sensors (one product from each sensor separately, and a third one from the merging of MERIS and VGT products). The reference fire perimeters were mapped from two multi-temporal Landsat TM/ETM + images at 103 non-overlapping Thiessen scene areas (TSA) selected with a stratified random sampling design. The validation results were based on cross tabulated error matrices from which six accuracy measures were computed following the requirements of end-users of burned area products. While overall accuracy (OA) exceeded 99% for all products, overall accuracy was lower for the burned class. Burned area commission error ratio was above 40% for all products and omission error ratio was above 65% for all products. The statistical significance of differences in accuracy between pairs of products was evaluated based on theory of the stratified combined ratio estimator. Statistical tests identified the MCD64 as the most accurate product, followed by MCD45 and the MERIS product.Peer-reviewedPost-prin

The variation of road surface temperature across a road network is influenced regionally by meteorological parameters and locally by geographical parameters. A fast and reliable technique is described which allows the continuous collection of high resolution, geographical data including the sky‐view factor which is suitable for use in road climate modelling studies. Then, by use of regression analysis, the relative importance of five geographical parameters (altitude, topography, sky‐view factor, landuse and road construction) is assessed with respect to road surface temperature and atmospheric stability. Results show that sky‐view factors dominate surface temperatures at high atmospheric stability whereas altitude becomes increasingly important as stability decreases. Finally, a statistical road surface temperature model is discussed with the ability to explain up to 75% of the variation of residual road surface temperatures in the study area entirely by the interaction of geographical parameters. Copyright © 2001 Royal Meteorological Society

A new ice prediction strategy is presented based on the numerical modelling of surveyed geographical parameters. This approach enables the thermal projection of road surface temperatures across the road network entirely by model predictions and without the need for thermal maps. The influence of eight geographical parameters (latitude, altitude, sky‐view factor, screening, roughness length, road construction, traffic density and topography) is investigated by means of sensitivity analyses. The sky‐view factor is highlighted as the dominant control on road surface temperature, particularly at high levels of atmospheric stability. A numerical road weather model incorporating all eight parameters was run over 20 nights using forecast and retrospective meteorological data. The model has the ability to explain up to 72% of the variation in road surface temperature purely by thermally projecting surface temperature using geographical variables. Retrospective results produce an average r.m.s. error of $1\ ^{\circ}\hbox{C}$ which is comparable to existing UK road weather models. Copyright © 2001 Royal Meteorological Society