Summertime evaluation of REFAME over the Unites States for near real-time high resolution precipitation estimation

Precipitation is the key input for hydrometeorological modeling and applications. In many regions of the world, including populated areas, ground-based measurement of precipitation (whether from radar or rain gauge) is either sparse in time and space or nonexistent. Therefore, high-resolution satellite-based precipitation products are recognized as critical data sources, especially for rapidly-evolving hydrometeorological events such as flash floods which primarily occur during summer/warm seasons. As " proof of concept" , a recently proposed algorithm called Rain Estimation using Forward Adjusted-advection of Microwave Estimates (REFAME) and its variation REFAMEgeo are evaluated over the contiguous United States during summers of 2009 and 2011. Both methods are originally designed for near real-time high resolution precipitation estimation from remotely sensed data. High-resolution Q2 (ground radar) precipitation data, in conjunction with two operational near real-time satellite-based precipitation products (PERSIANN, PERSIANN-CCS) are used as evaluation reference and for comparison. The study is performed at half-hour temporal resolution and at a range of spatial resolutions (0.08-, 0.25-, 0.5-, and 1-degree latitude/longitude). The statistical analyses suggest that REFAMEgeo performs favorably among the studied products in terms of capturing both spatial coverage and intensity of precipitation at near real-time with the temporal resolution offered by geostationary satellites. With respect to volume precipitation, REFAMEgeo together with REFAME demonstrates slight overestimation of intense precipitation and underestimation of light precipitation events. Compared to REFAME, It is observed that REFAMEgeo maintains stable performance, even when the amount of accessible microwave (MW) overpasses is limited. Based on the encouraging outcome of this study which was intended as " proof of concept" , further testing for other seasons and data-rich regions is the next logical step. Upon confirmation of the relative reliability of the algorithm, it is reasonable to recommend the use of its precipitation estimates for data-sparse regions of the world. © 2012 Elsevier B.V

Phenotypes and glia-immune cell interactions in animal models of multiple sclerosis

MS is a complex chronic immune-mediated disease of the central nervous system that is associated with the development of large demyelinated plaques, oligodendrocyte destruction, and axonal degeneration. Underlying mechanisms of demyelination and neurodegeneration in MS are still poorly understood. In many studies, anti-CC1 antibodies, presumably recognizing the protein adenomatous polyposis coli (APC) antigen, are used to label mature, myelinating oligodendrocytes. However, anti-CC1 antibodies could as well recognize other cell populations, particularly astrocytes, under pathological conditions. To examine this hypothesis, we used the cuprizone animal model, which is an appropriate model to study the mechanism of the apoptosis of oligodendrocytes and demyelination. We applied transgenic mice in which astrocytes are labeled by an eGFP under the control of the human GFAP promoter. Furthermore, we investigated the co-localization of oligodendrocyte markers, including anti-OLIG2, anti-CC1, anti-NG2, and the astrocyte marker anti-GFAP in the control and five weeks curprizone intoxicated eGFP-GFAP mice. Results of this study suggest that not all CC1+ cells are mature oligodendrocytes, and a continuum might exist between activated astrocytes and oligodendrocytes in cuprizone intoxicated mice. In the context of elucidating underlying mechanism of MS lesion development, some results suggest that inflammatory lesion development starts with a degenerative process within the brain, most likely oligodendrocyte stress, or even degeneration. Therefore, appropriate animal models to study the interplay of inflammation and metabolic injury are necessary. To this end, we introduced a combinatory Cup-EAE animal model, in which lymphocyte recruitment into the forebrain occurs as a consequence of simultaneous cuprizone intoxication and active EAE induction. This model recapitulates important histopathological characteristics of type III MS lesions. In summary, we provide a protocol that allows to study the direct interplay of immune-mediated and metabolic oligodendrocyte injury, and its consequences for the cerebral white and gray matter.Bei der Multiplen Sklerose handelt es sich um eine immunvermittelte chronische Erkrankung des Zentralnervensystems, die mit der Zerstörung von Oligodendrozyten, großen demyelinisierten Plaques und axonaler Schädigung einhergeht. Der genaue Mechanismus der Demyelinisierung und Neurodegeneration bei MS ist noch unklar. In vielen Studien werden anti-CC1 Antikörper, die das adenomatous polyposis coli (APC) Protein binden, verwendet um reife und myelinisierende Oligodendrozyten darzustellen. Studien weißen allerdings darauf hin, dass Antikörper gegen CC1 unter pathologischen Bedingungen auch andere Zell-Populationen, insbesondere Astrozyten binden können. Um diese Hypothese zu testen, habe ich das Cuprizone-Tiermodell verwendet, welches ein geeignetes Modell zur Untersuchung der Apoptose von Oligodendrozyten und der Demyelinisierung darstellt. Im Rahmen meiner Experimente habe ich transgene Mäuse verwendet, in deren Astrozyten eGFP unter Kontrolle des Promotors für das humane GFAP exprimiert wird. Zusätzlich führte ich Immunofloureszenz-Dopplemarkierung mit anti-CC1, anti-OLIG2 und anti-NG2 in den transgenen Tieren durch. Die Ergebnisse meiner Untersuchungen zeigen eine deutlichen Co-Lokalisation von eGFP und verschiedenen Oligodendrozyten Markerproteinen. In dieser Arbeit konnte ich somit zeigen, dass nicht alle CC1+-Zellen reife Oligodendrozyten sind, sondern dass vermutlich auch aktivierten Astrozyten CC1 exprimieren können. Zur Entwicklung von MS Läsionen gibt es einige Hinweise, dass die Bildung inflammatorischer Läsionen mit degenerativen Prozessen im Gehirn beginnen, insbesondere Stress oder Degeneration von Oligodendrozyten. Zu genaueren Untersuchung dieses Wechselspiels zwischen Entzündung und Stoffwechselschädigung sind geeignete Tiermodelle notwendig. Hierfür haben wir ein kombiniertes Cup-EAE-Tiermodell etabliert, bei dem durch die gleichzeitige Behandlung mit Cuprizone und der Induktion einer aktiven EAE eine Rekrutierung von Lymphozyten ins Vorderhirn induziert wird. Dieses Modell spiegelt wichtige histopathologische Eigenschaften von Typ-III MS Läsionen wider. Zusammenfassend lässt sich feststellen, dass wir ein geeignetes Protokoll erarbeiten konnten, mit dem es möglich ist, das Wechselspiel der immunvermittelten und der metabolischen Oligodendrozytenschädigung und deren Auswirkung auf die die weiße und graue Substanz des Gehirns zu untersuchen

Daytime precipitation estimation using bispectral cloud classification system

Two previously developed Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) algorithms that incorporate cloud classification system (PERSIANN-CCS) and multispectral analysis (PERSIANN-MSA) are integrated and employed to analyze the role of cloud albedo from Geostationary Operational Environmental Satellite-12 (GOES-12) visible (0.65 μm) channel in supplementing infrared (10.7 mm) data. The integrated technique derives finescale (0.04° × 0.04° latitudelongitude every 30 min) rain rate for each grid box through four major steps: 1) segmenting clouds into a number of cloud patches using infrared or albedo images; 2) classification of cloud patches into a number of cloud types using radiative, geometrical, and textural features for each individual cloud patch; 3) classification of each cloud type into a number of subclasses and assigning rain rates to each subclass using a multidimensional histogram matching method; and 4) associating satellite gridbox information to the appropriate corresponding cloud type and subclass to estimate rain rate in grid scale. The technique was applied over a study region that includes the U.S. landmass east of 115°W. One reference infrared-only and three different bis-pectral (visible and infrared) rain estimation scenarios were compared to investigate the technique's ability to address two major drawbacks of infrared-only methods: 1) underestimating warm rainfall and 2) the inability to screen out no-rain thin cirrus clouds. Radar estimates were used to evaluate the scenarios at a range of temporal (3 and 6 hourly) and spatial (0.04°, 0.08°, 0.12°, and 0.24° latitude-longitude) scales. Overall, the results using daytime data during June-August 2006 indicate that significant gain over infrared-only technique is obtained once albedo is used for cloud segmentation followed by bispectral cloud classification and rainfall estimation. At 3-h, 0.04° resolution, the observed improvement using bispectral information was about 66% for equitable threat score and 26% for the correlation coefficient. At coarser 0.24° resolution, the gains were 34% and 32% for the two performance measures, respectively. © 2010 American Meteorological Society

Evaluating the utility of multispectral information in delineating the areal extent of precipitation

Data from geosynchronous Earth-orbiting (GEO) satellites equipped with visible (VIS) and infrared (IR) scanners are commonly used in rain retrieval algorithms. These algorithms benefit from the high spatial and temporal resolution of GEO observations, either in stand-alone mode or in combination with higher-quality but less frequent microwave observations from low Earth-orbiting (LEO) satellites. In this paper, a neural network-based framework is presented to evaluate the utility of multispectral information in improving rain/no-rain (R/NR) detection. The algorithm uses the powerful classification features of the self-organizing feature map (SOFM), along with probability matching techniques to map single- or multispectral input space into R/NR maps. The framework was tested and validated using the 31 possible combinations of the five Geostationary Operational Environmental Satellite 12 (GOES-12) channels. An algorithm training and validation study was conducted over the conterminous United States during June-August 2006. The results indicate that during daytime, the visible channel (0.65 μm) can yield significant improvements in R/NR detection capabilities, especially when combined with any of the other four GOES-12 channels. Similarly, for nighttime detection the combination of two IR channels - particularly channels 3 (6.5 μm) and 4 (10.7 μm)-resulted in significant performance gain over any single IR channel. In both cases, however, using more than two channels resulted only in marginal improvements over two-channel combinations. Detailed examination of event-based images indicate that the proposed algorithm is capable of extracting information useful to screen no-rain pixels associated with cold, thin clouds and identifying rain areas under warm but rainy clouds. Both cases have been problematic areas for IR-only algorithms. © 2009 American Meteorological Society

PERSIANN-MSA: A precipitation estimation method from satellite-based multispectral analysis

Visible and infrared data obtained from instruments onboard geostationary satellites have been extensively used for monitoring clouds and their evolution. The Advanced Baseline Imager (ABI) that will be launched onboard the Geostationary Operational Environmental Satellite-R (GOES-R) series in the near future will offer a larger range of spectral bands; hence, it will provide observations of cloud and rain systems at even finer spatial, temporal, and spectral resolutions than are possible with the current GOES. In this paper, a new method called Precipitation Estimation from Remotely Sensed information using Artificial Neural Networks-Multispectral Analysis (PERSIANN-MSA) is proposed to evaluate the effect of using multispectral imagery on precipitation estimation. The proposed approach uses a self-organizing feature map (SOFM) to classify multidimensional input information, extracted from each grid box and corresponding textural features of multispectral bands. In addition, principal component analysis (PCA) is used to reduce the dimensionality to a few independent input features while preserving most of the variations of all input information. The above method is applied to estimate rainfall using multiple channels of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the Meteosat Second Generation (MSG) satellite. In comparison to the use of a single thermal infrared channel, the analysis shows that using multispectral data has the potential to improve rain detection and estimation skills with an average of more than 50% gain in equitable threat score for rain/no-rain detection, and more than 20% gain in correlation coefficient associated with rain-rate estimation. © 2009 American Meteorological Society

Bias adjustment of infrared-based rainfall estimation using Passive Microwave satellite rainfall data

This study explores using Passive Microwave (PMW) rainfall estimation for spatial and temporal adjustment of Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System(PERSIANN-CCS). The PERSIANN-CCS algorithm collects information from infrared images to estimate rainfall. PERSIANN-CCS is one of the algorithms used in the IntegratedMultisatellite Retrievals for GPM (Global Precipitation Mission) estimation for the time period PMW rainfall estimations are limited or not available. Continued improvement of PERSIANN-CCS will support Integrated Multisatellite Retrievals for GPM for current as well as retrospective estimations of global precipitation. This study takes advantage of the high spatial and temporal resolution of GEO-based PERSIANN-CCS estimation and the more effective, but lower sample frequency, PMW estimation. The Probability Matching Method (PMM) was used to adjust the rainfall distribution of GEO-based PERSIANN-CCS toward that of PMW rainfall estimation. The results show that a significant improvement of global PERSIANN-CCS rainfall estimation is obtained

Hydrologic evaluation of satellite precipitation products over a mid-size basin

Since the past three decades a great deal of effort is devoted to development of satellite-based precipitation retrieval algorithms. More recently, several satellite-based precipitation products have emerged that provide uninterrupted precipitation time series with quasi-global coverage. These satellite-based precipitation products provide an unprecedented opportunity for hydrometeorological applications and climate studies. Although growing, the application of satellite data for hydrological applications is still very limited. In this study, the effectiveness of using satellite-based precipitation products for streamflow simulation at catchment scale is evaluated. Five satellite-based precipitation products (TMPA-RT, TMPA-V6, CMORPH, PERSIANN, and PERSIANN-adj) are used as forcing data for streamflow simulations at 6-h and monthly time scales during the period of 2003-2008. SACramento Soil Moisture Accounting (SAC-SMA) model is used for streamflow simulation over the mid-size Illinois River basin.The results show that by employing the satellite-based precipitation forcing the general streamflow pattern is well captured at both 6-h and monthly time scales. However, satellites products, with no bias-adjustment being employed, significantly overestimate both precipitation inputs and simulated streamflows over warm months (spring and summer months). For cold season, on the other hand, the unadjusted precipitation products result in under-estimation of streamflow forecast. It was found that bias-adjustment of precipitation is critical and can yield to substantial improvement in capturing both streamflow pattern and magnitude. The results suggest that along with efforts to improve satellite-based precipitation estimation techniques, it is important to develop more effective near real-time precipitation bias adjustment techniques for hydrologic applications. © 2010 Elsevier B.V

Investigating the Effects of Authentic Leadership of Managers on Organizational Commitment of Teachers with Organizational Justice as the Mediator Variable

The present study was conducted by the aim of investigating the fit of the presented model for the relationship between authentic leadership and organizational commitment of staff with a mediating role of organizational justice. The population of the research included all the teachers in high schools (for male students) in Education district 2 in Qom city. From the population, 300 individuals were selected through cluster sampling. For gathering the data, authentic leadership questionnaire (Avolio et al., 2007), organizational justice questionnaire of Niehoff & Moorman (1993), and Allen & Meyer's Organizational Commitment Questionnaire (2002) were used. For analyzing the data, structural equation modeling – fit indices and path coefficients – was used. The results of the analysis showed that authentic leadership has a direct and significant effect on the organizational commitment of teachers. Also, authentic leadership has an indirect effect, through organizational justice, on organizational commitment. The other finding of the research is that organizational justice has a direct and significant effect on organizational commitment of teachers, and the offered conceptual model has a significant statistical fit, that means the explanatory model for organizational commitment based on authentic leadership and organizational justice has fitness with empirical data. Finally, based on the information obtained from structural equations model, it can also be said that all the components existing in the 3 variables of the research have positive and significant relationships with one another