The Impact of Dropsonde and Extra Radiosonde Observations during NAWDEX in Autumn 2016

Dropsonde observations from three research aircraft in the North Atlantic region, as well as several hundred additionally launched radiosondes over Canada and Europe, were collected during the international North Atlantic Waveguide and Downstream Impact Experiment (NAWDEX) in autumn 2016. In addition, over 1000 dropsondes were deployed during NOAA’s Sensing Hazards with Operational Unmanned Technology (SHOUT) and Reconnaissance missions in the west Atlantic basin, supplementing the conventional observing network for several intensive observation periods. This unique dataset was assimilated within the framework of cycled data denial experiments for a 1-month period performed with the global model of the ECMWF. Results show a slightly reduced mean forecast error (1%–3%) over the northern Atlantic and Europe by assimilating these additional observations, with the most prominent error reductions being linked to Tropical Storm Karl, Cyclones Matthew and Nicole, and their subsequent interaction with the midlatitude waveguide. The evaluation of Forecast Sensitivity to Observation Impact (FSOI) indicates that the largest impact is due to dropsondes near tropical storms and cyclones, followed by dropsondes over the northern Atlantic and additional Canadian radiosondes. Additional radiosondes over Europe showed a comparatively small beneficial impact

Video-Expedition in the Performance-World = Video-Expedicio a Performansz-Vilagban

Nearly 100 international performance artists are represented in this festival catalogue. Hegyi briefly sketches the genesis of performance as an art form. Includes eight brief statements and the schedule of videos and performances

Skin temperature from the Thermal Infrared Sounder IASI

While long-term temperature time series mostly rely on weather stations, only satellite data are able to provide systematic global temperature data, from pole to pole on a regular basis, over both land and sea. Satellites measure the “skin” temperature derived from upwelling radiation at the Earth’s land surface. The evolution of skin temperature is not yet fully exploited as its measurement is fairly recent.One of the IASI-Flux and Temperature ERC project tasks aims at providing new climate benchmarks by using skin temperature observations from the calibrated radiances measured twice a day at any location by the IASI thermal infrared instrument on the suite of Metop satellites (2006-2025). The uniqueness of this project is that the IASI-data record will be completely independent from third party information, with no other data from observations or models used, and can therefore serve as an independent reference to e.g. reanalysis, or other climate data records. In this presentation, we first describe our iterative method based on entropy reduction combined with artificial neural networks to derive an independent record of IASI temperature, we next compare and validate our novel method with different datasets (e.g. EUMETSAT, ECMWF reanalysis, SEVIRI satellite products and ground measurements). We then show our results of global skin temperature over land and sea and in different regions in the world over the period [2008- present]. The observed trends are analyzed at seasonal and regional scales in order to disentangle natural (weather/dynamical) variability and human-induced climate forcing. Finally, we show how expanding cities are hotspots for skin temperature reflecting the usefulness of skin temperature as a tracer for human-induced land use and climate change.info:eu-repo/semantics/nonPublishe

Artificial Neural Networks to Retrieve Land and Sea Skin Temperature from IASI

International audienceSurface skin temperature (Tskin) derived from infrared remote sensors mounted on board satellites provides a continuous observation of Earth’s surface and allows the monitoring of global temperature change relevant to climate trends. In this study, we present a fast retrieval method for retrieving Tskin based on an artificial neural network (ANN) from a set of spectral channels selected from the Infrared Atmospheric Sounding Interferometer (IASI) using the information theory/entropy reduction technique. Our IASI Tskin product (i.e., TANN) is evaluated against Tskin from EUMETSAT Level 2 product, ECMWF Reanalysis (ERA5), SEVIRI observations, and ground in situ measurements. Good correlations between IASI TANN and the Tskin from other datasets are shown by their statistic data, such as a mean bias and standard deviation (i.e., [bias, STDE]) of [0.55, 1.86 °C], [0.19, 2.10 °C], [−1.5, 3.56 °C], from EUMETSAT IASI L-2 product, ERA5, and SEVIRI. When compared to ground station data, we found that all datasets did not achieve the needed accuracy at several months of the year, and better results were achieved at nighttime. Therefore, comparison with ground-based measurements should be done with care to achieve the ±2 °C accuracy needed, by choosing, for example, a validation site near the station location. On average, this accuracy is achieved, in particular at night, leading to the ability to construct a robust Tskin dataset suitable for Tskin long-term spatio-temporal variability and trend analysis

Skin temperature from the Thermal Infrared Sounder IASI

While long-term temperature time series mostly rely on weather stations, only satellite data are able to provide systematic global temperature data, from pole to pole on a regular basis, over both land and sea. Satellites measure the “skin” temperature derived from upwelling radiation at the Earth’s land surface. The evolution of skin temperature is not yet fully exploited as its measurement is fairly recent.One of the IASI-Flux and Temperature ERC project tasks aims at providing new climate benchmarks by using skin temperature observations from the calibrated radiances measured twice a day at any location by the IASI thermal infrared instrument on the suite of Metop satellites (2006-2025). The uniqueness of this project is that the IASI-data record will be completely independent from third party information, with no other data from observations or models used, and can therefore serve as an independent reference to e.g. reanalysis, or other climate data records. In this presentation, we first describe our iterative method based on entropy reduction combined with artificial neural networks to derive an independent record of IASI temperature, we next compare and validate our novel method with different datasets (e.g. EUMETSAT, ECMWF reanalysis, SEVIRI satellite products and ground measurements). We then show our results of global skin temperature over land and sea and in different regions in the world over the period [2008- present]. The observed trends are analyzed at seasonal and regional scales in order to disentangle natural (weather/dynamical) variability and human-induced climate forcing. Finally, we show how expanding cities are hotspots for skin temperature reflecting the usefulness of skin temperature as a tracer for human-induced land use and climate change.info:eu-repo/semantics/nonPublishe