Satellite-based precipitation estimation using watershed segmentation and growing hierarchical self-organizing map

This paper outlines the development of a multi-satellite precipitation estimation methodology that draws on techniques from machine learning and morphology to produce high-resolution, short-duration rainfall estimates in an automated fashion. First, cloud systems are identified from geostationary infrared imagery using morphology based watershed segmentation algorithm. Second, a novel pattern recognition technique, growing hierarchical self-organizing map (GHSOM), is used to classify clouds into a number of clusters with hierarchical architecture. Finally, each cloud cluster is associated with co-registered passive microwave rainfall observations through a cumulative histogram matching approach. The network was initially trained using remotely sensed geostationary infrared satellite imagery and hourly ground-radar data in lieu of a dense constellation of polar-orbiting spacecraft such as the proposed global precipitation measurement (GPM) mission. Ground-radar and gauge rainfall measurements were used to evaluate this technique for both warm (June 2004) and cold seasons (December 2004-February 2005) at various temporal (daily and monthly) and spatial (0.04 and 0.25) scales. Significant improvements of estimation accuracy are found classifying the clouds into hierarchical sub-layers rather than a single layer. Furthermore, 2-year (2003-2004) satellite rainfall estimates generated by the current algorithm were compared with gauge-corrected Stage IV radar rainfall at various time scales over continental United States. This study demonstrates the usefulness of the watershed segmentation and the GHSOM in satellite-based rainfall estimations

Assessing water availability in Mediterranean regions affected by water conflicts through MODIS data time series analysis

Water scarcity is a widespread problem in arid and semi-arid regions such as the western Mediterranean coastal areas. The irregularity of the precipitation generates frequent droughts that exacerbate the conflicts among agriculture, water supply and water demands for ecosystems maintenance. Besides, global climate models predict that climate change will cause Mediterranean arid and semi-arid regions to shift towards lower rainfall scenarios that may exacerbate water conflicts. The purpose of this study is to find a feasible methodology to assess current and monitor future water demands in order to better allocate limited water resources. The interdependency between a vegetation index (NDVI), land surface temperature (LST), precipitation (current and future), and surface water resources availability in two watersheds in southeastern Spain with serious difficulties in meeting water demands was investigated. MODIS (Moderate Resolution Imaging Spectroradiometer) NDVI and LST products (as proxy of drought), precipitation maps (generated from climate station records) and reservoir storage gauging information were used to compute times series anomalies from 2001 to 2014 and generate regression images and spatial regression models. The temporal relationship between reservoir storage and time series of satellite images allowed the detection of different and contrasting water management practices in the two watersheds. In addition, a comparison of current precipitation rates and future precipitation conditions obtained from global climate models suggests high precipitation reductions, especially in areas that have the potential to contribute significantly to groundwater storage and surface runoff, and are thus critical to reservoir storage. Finally, spatial regression models minimized spatial autocorrelation effects, and their results suggested the great potential of our methodology combining NDVI and LST time series to predict future scenarios of water scarcity.Published versio

An acoustic view of ocean mixing

Knowledge of the parameter K (turbulent diffusivity/"mixing intensity") is a key to understand transport processes of matter and energy in the ocean. Especially the almost vertical component of K across the ocean stratification (diapycnal diffusivity) is vital for research on biogeochemical cycles or greenhouse gas budgets. Recent boost in precision of water velocity data that can be obtained from vessel-mounted acoustic instruments (vmADCP) allows identifying ocean regions of elevated diapycnal diffusivity during research cruises - in high horizontal resolution and without extra ship time needed. This contribution relates acoustic data from two cruises in the Tropical North East Atlantic Oxygen Minimum Zone to simultaneous field observations of diapycnal diffusivity: pointwise measurements by a microstructure profiler as well as one integrative value from a large scale Tracer Release Experiment

Profiling human breast epithelial cells using single cell RNA sequencing identifies cell diversity.

Breast cancer arises from breast epithelial cells that acquire genetic alterations leading to subsequent loss of tissue homeostasis. Several distinct epithelial subpopulations have been proposed, but complete understanding of the spectrum of heterogeneity and differentiation hierarchy in the human breast remains elusive. Here, we use single-cell mRNA sequencing (scRNAseq) to profile the transcriptomes of 25,790 primary human breast epithelial cells isolated from reduction mammoplasties of seven individuals. Unbiased clustering analysis reveals the existence of three distinct epithelial cell populations, one basal and two luminal cell types, which we identify as secretory L1- and hormone-responsive L2-type cells. Pseudotemporal reconstruction of differentiation trajectories produces one continuous lineage hierarchy that closely connects the basal lineage to the two differentiated luminal branches. Our comprehensive cell atlas provides insights into the cellular blueprint of the human breast epithelium and will form the foundation to understand how the system goes awry during breast cancer

VADER - A Satellite Mission Concept For High Precision Dark Energy Studies

We present a satellite mission concept to measure the dark energy equation of state parameter w with percent-level precision. The Very Ambitious Dark Energy Research satellite (VADER) is a multi-wavelength survey mission joining X-ray, optical, and IR instruments for a simultaneous spectral coverage from 4microns (0.3eV) to 10keV over a field of view (FoV) of 1 square degree. VADER combines several clean methods for dark energy studies, the baryonic acoustic oscillations in the galaxy and galaxy cluster power spectrum and weak lensing, for a joint analysis over an unrivalled survey volume. The payload consists of two XMM-like X-ray telescopes with an effective area of 2,800cm^2 at 1.5keV and state-of-the-art wide field DEPFET pixel detectors (0.1-10keV) in a curved focal plane configuration to extend the FoV. The X-ray telescopes are complemented by a 1.5m optical/IR telescope with 8 instruments for simultaneous coverage of the same FoV from 0.3 to 4 microns. The 8 dichroic-separated bands (u,g,r,z,J,H,K,L) provide accurate photometric galaxy redshifts, whereas the diffraction-limited resolution of the central z-band allows precise shape measurements for cosmic shear analysis. The 5 year VADER survey will cover a contiguous sky area of 3,500 square degrees to a depth of z~2 and will yield accurate photometric redshifts and multi-wavelength object parameters for about 175,000 galaxy clusters, one billion galaxies, and 5 million AGN. VADER will not only provide unprecedented constraints on the nature of dark energy, but will additionally extend and trigger a multitude of cosmic evolution studies to very large (>10 Gyrs) look-back times.Comment: 14 pages, 7 figures, accepted for publication in the SPIE conference proceeding

Self-organizing nonlinear output (SONO): A neural network suitable for cloud patch-based rainfall estimation at small scales

Accurate measurement of rainfall distribution at various spatial and temporal scales is crucial for hydrological modeling and water resources management. In the literature of satellite rainfall estimation, many efforts have been made to calibrate a statistical relationship (including threshold, linear, or nonlinear) between cloud infrared (IR) brightness temperatures and surface rain rates (RR). In this study, an automated neural network for cloud patch-based rainfall estimation, entitled self-organizing nonlinear output (SONO) model, is developed to account for the high variability of cloud-rainfall processes at geostationary scales (i.e., 4 km and every 30 min). Instead of calibrating only one IR-RR function for all clouds the SONO classifies varied cloud patches into different clusters and then searches a nonlinear IR-RR mapping function for each cluster. This designed feature enables SONO to generate various rain rates at a given brightness temperature and variable rain/no-rain IR thresholds for different cloud types, which overcomes the one-to-one mapping limitation of a single statistical IR-RR function for the full spectrum of cloud-rainfall conditions. In addition, the computational and modeling strengths of neural network enable SONO to cope with the nonlinearity of cloud-rainfall relationships by fusing multisource data sets. Evaluated at various temporal and spatial scales, SONO shows improvements of estimation accuracy, both in rain intensity and in detection of rain/no-rain pixels. Further examination of the SONO adaptability demonstrates its potentiality as an operational satellite rainfall estimation system that uses the passive microwave rainfall observations from low-orbiting satellites to adjust the IR-based rainfall estimates at the resolution of geostationary satellites. Copyright 2005 by the American Geophysical Union

Photoheliograph study for the Apollo telescope mount

Photoheliograph study for Apollo telescope moun

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

The XMM Cluster Survey: X-ray analysis methodology

The XMM Cluster Survey (XCS) is a serendipitous search for galaxy clusters using all publicly available data in the XMM-Newton Science Archive. Its main aims are to measure cosmological parameters and trace the evolution of X-ray scaling relations. In this paper we describe the data processing methodology applied to the 5,776 XMM observations used to construct the current XCS source catalogue. A total of 3,675 > 4-sigma cluster candidates with > 50 background-subtracted X-ray counts are extracted from a total non-overlapping area suitable for cluster searching of 410 deg^2. Of these, 993 candidates are detected with > 300 background-subtracted X-ray photon counts, and we demonstrate that robust temperature measurements can be obtained down to this count limit. We describe in detail the automated pipelines used to perform the spectral and surface brightness fitting for these candidates, as well as to estimate redshifts from the X-ray data alone. A total of 587 (122) X-ray temperatures to a typical accuracy of < 40 (< 10) per cent have been measured to date. We also present the methodology adopted for determining the selection function of the survey, and show that the extended source detection algorithm is robust to a range of cluster morphologies by inserting mock clusters derived from hydrodynamical simulations into real XMM images. These tests show that the simple isothermal beta-profiles is sufficient to capture the essential details of the cluster population detected in the archival XMM observations. The redshift follow-up of the XCS cluster sample is presented in a companion paper, together with a first data release of 503 optically-confirmed clusters.Comment: MNRAS accepted, 45 pages, 38 figures. Our companion paper describing our optical analysis methodology and presenting a first set of confirmed clusters has now been submitted to MNRA