Empirical Mode Decomposition in 2-D space and time: a tool for space-time rainfall analysis and nowcasting

International audienceA data-driven method for extracting information, at temporally predictable scales, from spatial rainfall data (as measured by radar/satellite) is described, which extends the Empirical Mode Decomposition (EMD) algorithm into two dimensions. The EMD technique is used here to separate spatial rainfall data into a sequence of high through to low frequency components. This process is equivalent to a low-pass spatial filter, but based on the observed properties of the data rather than the predefined basis functions used in traditional Fourier or Wavelet decompositions. It has been suggested in the literature that the lower frequency components of spatial rainfall data exhibit greater temporal persistence than the higher frequency ones. This idea is explored here in the context of Empirical Mode Decomposition, to prepare rainfall data for nowcasts based on the temporal evolution of the lower frequency components. The paper focuses on the implementation and development of the two-dimensional extension to the EMD algorithm and it's application to radar rainfall data, as well as examining temporal persistence in the data at different spatial scales

Improved radar rainfall estimation at ground level

International audienceA technique has been developed to provide an estimate of the rainfall reaching the earth's surface by extrapolating radar data contained aloft to ground level, simultaneously estimating unknown data in the radar volume scan. The technique has been developed so as to be computationally fast, to work in real time and comprises the following steps. A rainfall classification algorithm is applied to separate the rainfall into two separate types: convective and stratiform rainfall. Climatological semivariograms based on the rainfall type are then defined and justified by testing, which result in a fast and effective means of determining the semivariogram parameters anywhere in the radar volume scan. Then, extrapolations to ground level are computed by utilising 3-D Universal and Ordinary Cascade Kriging; computational efficiency and stability in Kriging are ensured by using a nearest neighbours approach and a Singular Value Decomposition (SVD) matrix rank reduction technique. To validate the proposed technique, a statistical comparison between the temporally accumulated radar estimates and the Block Kriged raingauge estimates is carried out over matching areas, for selected rainfall events, to determine the quality of the rainfall estimates at ground level

Radar rainfall image repair techniques

International audienceThere are various quality problems associated with radar rainfall data viewed in images that include ground clutter, beam blocking and anomalous propagation, to name a few. To obtain the best rainfall estimate possible, techniques for removing ground clutter (non-meteorological echoes that influence radar data quality) on 2-D radar rainfall image data sets are presented here. These techniques concentrate on repairing the images in both a computationally fast and accurate manner, and are nearest neighbour techniques of two sub-types: Individual Target and Border Tracing. The contaminated data is estimated through Kriging, considered the optimal technique for the spatial interpolation of Gaussian data, where the "screening effect" that occurs with the Kriging weighting distribution around target points is exploited to ensure computational efficiency. Matrix rank reduction techniques in combination with Singular Value Decomposition (SVD) are also suggested for finding an efficient solution to the Kriging Equations which can cope with near singular systems. Rainfall estimation at ground level from radar rainfall volume scan data is of interest and importance in earth bound applications such as hydrology and agriculture. As an extension of the above, Ordinary Kriging is applied to three-dimensional radar rainfall data to estimate rainfall rate at ground level. Keywords: ground clutter, data infilling, Ordinary Kriging, nearest neighbours, Singular Value Decomposition, border tracing, computation time, ground level rainfall estimatio

A comparison of ASCAT and modelled soil moisture over South Africa, using TOPKAPI in land surface mode

In this paper we compare two independent soil moisture estimates over South Africa. The first estimate is a Soil Saturation Index (SSI) provided by automated real-time computations of the TOPKAPI hydrological model, adapted to run as a collection of independent 1 km cells with centres on a grid with a spatial resolution of 0.125°, at 3 h intervals. The second set of estimates is the remotely sensed ASCAT Surface Soil Moisture product, temporally filtered to yield a Soil Wetness Index (SWI). For the TOPKAPI cells, the rainfall forcing used is the TRMM 3B42RT product, while the evapotranspiration forcing is based on a modification of the FAO56 reference crop evapotranspiration (ET<sub>0</sub>). ET<sub>0</sub> is computed using forecast fields of meteorological variables from the Unified Model (UM) runs done by the South African Weather Service (SAWS); the UM forecast fields were used, because reanalysis is not done by SAWS. To validate these ET<sub>0</sub> estimates we compare them with those computed using observed meteorological data at a network of weather stations; they were found to be unbiased with acceptable scatter. Using the rainfall and evapotranspiration forcing data, the percentage saturation of the TOPKAPI soil store is computed as a Soil Saturation Index (SSI), for each of 6984 unconnected uncalibrated TOPKAPI cells at 3 h time-steps. These SSI estimates are then compared with the SWI estimates obtained from ASCAT. The comparisons indicate a good correspondence in the dynamic behaviour of SWI and SSI for a significant proportion of South Africa

Comparison of soil moisture fields estimated by catchment modelling and remote sensing: a case study in South Africa

International audienceThe paper compares two independent approaches to estimate soil moisture at the regional scale over a 4625 km2 catchment (Liebenbergsvlei, South Africa). The first estimate is derived from a physically-based hydrological model (TOPKAPI). The second estimate is derived from the scatterometer on board on the European Remote Sensing satellite (ERS). Results show a very good correspondence between the modelled and remotely sensed soil moisture, illustrated over two selected seasons of 8 months by regression R2 coefficients lying between 0.78 and 0.92. Such a close similarity between these two different, independent approaches is very promising for (i) remote sensing in general (ii) the use of hydrological models to back-calculate and disaggregate the satellite soil moisture estimate and (iii) for hydrological models to assimilate the remotely sensed soil moisture

Stochastic properties of water storage

August 1980.Includes bibliographical references (pages 46-48)

Assessing the discordance rate between local and central HER2 testing in women with locally determined HER2-negative breast cancer.

BackgroundThe importance of human epidermal growth factor receptor 2 (HER2) as a prognostic and predictive marker in invasive breast cancer is well established. Accurate assessment of HER2 status is essential to determine optimal treatment options.MethodsBreast cancer tumor tissue samples from the VIRGO observational cohort tissue substudy that were locally HER2-negative were retested centrally with both US Food and Drug Administration (FDA)-approved immunohistochemistry (IHC) and fluorescence in situ hybridization (FISH) assays, using FDA-approved assay cutoffs; results were compared.ResultsOf the 552 unique patient samples centrally retested with local HER2-negative results recorded, tumor samples from 22 (4.0%) patients were determined to be HER2-positive (95% confidence interval [CI] = 2.5%-5.7%). Of these, 18 had been tested locally by only one testing methodology; 15 of 18 were HER2-positive after the central retesting, based on the testing methodology not performed locally. Compared with the 530 patients with centrally confirmed HER2-negative tumors, the 22 patients with centrally determined HER2-positive tumors were younger (median age 56.5 versus 60.0 years) and more likely to have ER/PR-negative tumors (27.3% versus 22.3%). These patients also had shorter median progression-free survival (6.4 months [95% CI = 3.8-15.9 months] versus 9.1 months [95% CI = 8.3-10.3 months]) and overall survival (25.9 months [95% CI = 13.8-not estimable] versus 27.9 months [95% CI = 25.0-32.9 months]).ConclusionsThis study highlights the limitations of employing just one HER2 testing methodology in current clinical practice. It identifies a cohort of patients who did not receive potentially efficacious therapy because their tumor HER2-positivity was not determined by the test initially used. Because of inherent limitations in testing methodologies, it is inadvisable to rely on a single test to rule out potential benefit from HER2-targeted therapy

Governing the UN Sustainable Development Goals: Interactions, Infrastructures, and Institutions

Three of the eight Millennium Development Goals (MDGs) concerned health. There is only one health goal in 17 proposed Sustainable Development Goals (SDGs). Critiques of the MDGs included missed opportunities to realise positive interactions between goals. Here we report on an interdisciplinary analytical review of the SDG process, in which experts in different SDG areas identified potential interactions through a series of interdisciplinary workshops. This process generated a framework that reveals potential conflicts and synergies between goals, and how their interactions might be governed

A Perspective on Challenges and Issues in Biomarker Development and Drug and Biomarker Codevelopment

A workshop sponsored by the National Cancer Institute and the US Food and Drug Administration addressed past lessons learned and ongoing challenges faced in biomarker development and drug and biomarker codevelopment. Participants agreed that critical decision points in the product life cycle depend on the level of understanding of the biology of the target and its interaction with the drug, the preanalytical and analytical factors affecting biomarker assay performance, and the clinical disease process. The more known about the biology and the greater the strength of association between an analytical signal and clinical result, the more efficient and less risky the development process will be. Rapid entry into clinical practice will only be achieved by using a rigorous scientific approach, including careful specimen collection and standardized and quality-controlled data collection. Early interaction with appropriate regulatory bodies will ensure studies are appropriately designed and biomarker test performance is well characterized