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M. Talone, C. Gabarró, A. Camps, R. Sabia, J. Gourrion, M. Vall-llossera, and J. Font

Abstract

The interests of the scientific community working on the Soil Moisture and Ocean Salinity (SMOS) ocean salinity level 2 processor definition are currently focused on improving the performance of the retrieval algorithm, which is based on an iterative procedure where a cost function relating models, measurements, and auxiliary data is minimized. For this reason, most of the effort is currently focused on the analysis and the optimization of the cost function.

Within this framework, this study represents a contribution to the assessment of one of the pending issues in the definition of the cost function: the optimal weight to be given to the radiometric measurements with respect to the weight given to the background geophysical terms.

A whole month of brightness temperature acquisitions have been simulated by means of the SMOS-End-to-End Performance Simulator. The level 2 retrieval has been performed using the Universitat Politècnica de Catalunya (UPC) level 2 processor simulator using four different configurations, namely, the direct covariance matrices, the two cost functions currently described in the SMOS literature, and, finally, a new weight (the so-called effective number of measurement).

Results show that not even the proposed weight properly drives the minimization, and that the current cost function has to be modified in order to avoid the introduction of artifacts in the retrieval procedure. The calculation of the brightness temperature misfit covariance matrices reveals the presence of very complex patterns, and the inclusion of those in the cost function strongly modifies the retrieval performance. Worse but more Gaussian results are obtained, pointing out the need for a more accurate modeling of the correlation between brightness temperature misfits, in order to ensure a proper balancing with the relative weights to be given to the geophysical terms.

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Alberto Arribas, M. Glover, A. Maidens, K. Peterson, M. Gordon, C. MacLachlan, R. Graham, D. Fereday, J. Camp, A. A. Scaife, P. Xavier, P. McLean, A. Colman, and S. Cusack

Abstract

Seasonal forecasting systems, and related systems for decadal prediction, are crucial in the development of adaptation strategies to climate change. However, despite important achievements in this area in the last 10 years, significant levels of skill are only generally found over regions strongly connected with the El Niño–Southern Oscillation. With the aim of improving the skill of regional climate predictions in tropical and extratropical regions from intraseasonal to interannual time scales, a new Met Office global seasonal forecasting system (GloSea4) has been developed. This new system has been designed to be flexible and easy to upgrade so it can be fully integrated within the Met Office model development infrastructure. Overall, the analysis here shows an improvement of GloSea4 when compared to its predecessor. However, there are exceptions, such as the increased model biases that contribute to degrade the skill of Niño-3.4 SST forecasts starting in November. Global ENSO teleconnections and Madden–Julian oscillation anomalies are well represented in GloSea4. Remote forcings of the North Atlantic Oscillation by ENSO and the quasi-biennial oscillation are captured albeit the anomalies are weaker than those found in observations. Hindcast length issues and their implications for seasonal forecasting are also discussed.

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Andrew L. Molthan, Lori A. Schultz, Kevin M. McGrath, Jason E. Burks, J. Parks Camp, Kelsey Angle, Jordan R. Bell, and Gary J. Jedlovec

Abstract

Severe weather events including tornadoes, damaging winds, hail, and their combination produce changes in land surface vegetation and urban settings that are frequently observed through remote sensing. Capabilities continue to improve through a growing constellation of governmental and commercial assets, increasing the spatial resolution of visible, near to shortwave infrared, and thermal infrared remote sensing. Here, we highlight cases where visual interpretation of imagery benefitted severe weather damage assessments made within the NOAA/NWS Damage Assessment Toolkit. Examples demonstrate utility of imagery in assessing tracks and changes in remote areas where staffing limitations or access prevent a ground-based assessment.

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Andrew L. Molthan, Lori A. Schultz, Kevin M. McGrath, Jason E. Burks, J. Parks Camp, Kelsey Angle, Jordan R. Bell, and Gary J. Jedlovec
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