38,621 research outputs found
Flare Forecasting Using the Evolution of McIntosh Sunspot Classifications
Most solar flares originate in sunspot groups, where magnetic field changes
lead to energy build-up and release. However, few flare-forecasting methods use
information of sunspot-group evolution, instead focusing on static
point-in-time observations. Here, a new forecast method is presented based upon
the 24-hr evolution in McIntosh classification of sunspot groups.
Evolution-dependent C1.0 and M1.0 flaring rates are found
from NOAA-numbered sunspot groups over December 1988 to June 1996 (Solar Cycle
22; SC22) before converting to probabilities assuming Poisson statistics. These
flaring probabilities are used to generate operational forecasts for sunspot
groups over July 1996 to December 2008 (SC23), with performance studied by
verification metrics. Major findings are: i) considering Brier skill score
(BSS) for C1.0 flares, the evolution-dependent McIntosh-Poisson
method () performs better than the static
McIntosh-Poisson method (); ii) low BSS
values arise partly from both methods over-forecasting SC23 flares from the
SC22 rates, symptomatic of C1.0 rates in SC23 being on average
80% of those in SC22 (with M1.0 being 50%); iii)
applying a bias-correction factor to reduce the SC22 rates used in forecasting
SC23 flares yields modest improvement in skill relative to climatology for both
methods ( and
) and improved
forecast reliability diagrams.Comment: 21 pages, 9 figure
Representations of sources and data: working with exceptions to hierarchy in historical documents
No abstract available
Planting date, storage and gibberellic acid affect dormancy of Zantedeschia Spreng. hybrids : a thesis presented in partial fulfilment of the requirements for the degree of Masters in Applied Science, Massey University, Palmerston North, New Zealand
To match the supply of Zantedeschia cut flowers and tubers to the demands of the international market, crops have to be timed to a schedule, which requires control of the growth cycle and, in particular, dormancy. In order to improve the predictability and accuracy of timing of Zantedeschia, the effect of different planting seasons and two dormancy-breaking treatments were tested on cultivars 'Black Magic' and 'Treasure', which were known to have a contrasting level of dormancy. Tissue-cultured plants were ex-flasked in July and November 1999, and grown for 180 days in a heated glasshouse (first cycle). Between 120 and 180 days of growth, plants were harvested at 15 days intervals, and tubers cured. Subsequently, tubers were stored for 0 or 3 weeks (10 ± 1°C; 70-80% RH) and dipped in 100 mg.L
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gibberellic acid plus surfactant or water plus surfactant, prior to planting for dormancy assessment (second cycle). Growing the plants with four months difference in planting date did not cause major alteration in the occurrence of dormancy. Dormancy was brought forward by up to 10 days after the November date of ex-flask, but this was most likely to be due to higher temperatures during that period. In contrast, depth of dormancy varied between cultivars, with 'Black Magic' taking in average 16 days longer to emerge than 'Treasure'. Storage partially released bud dormancy of the tubers. It increased emergence to over 80% regardless of the time of harvest in the first cycle and cultivar, but reduced time to emergence mostly after harvests at 180 days. Furthermore, following storage, time to emergence was reduced to over 50 and 30 days for 'Black Magic' and 'Treasure', respectively, which exceeded the commercially acceptable period to emerge. Gibberellic acid also broke bud dormancy, improving emergence to over 80%, and reduced time to emergence to between 29 and 57 days, irrespective of the time of harvest in the first cycle and cultivar. The effectiveness of gibberellic acid at any time following harvest during the first cycle, may imply that dormancy of Zantedeschia is not as deep as in temperate woody plants. Cessation of leaf emergence in the first cycle was found not to be directly related to the occurrence of dormancy. Degree-days, on the other hand, presented a possible alternative to predict this process. It was estimated that deepest dormancy of 'Black Magic' occurred between 2614 and 2732 °C-days after planting, while deepest dormancy of 'Treasure' occurred between 2681 and 2839 °C-days after planting. The present study presents storage and gibberellic acid as possible options to control dormancy, and the use of degree-days to predict the occurrence of this process. Further research is necessary to develop these options as commercially applicable practices, and to further clarify the process of dormancy in Zantedeschia
Multiple causes of interannual sea surface temperature variability in the equatorial Atlantic Ocean
The eastern equatorial Atlantic Ocean is subject to interannual fluctuations of sea surface temperatures, with climatic impacts on the surrounding continents. The dynamic mechanism underlying Atlantic temperature variability is thought to be similar to that of the El Nino/Southern Oscillation (ENSO) in the equatorial Pacific, where air-sea coupling leads to a positive feedback between surface winds in the western basin, sea surface temperature in the eastern basin, and equatorial oceanic heat content. Here we use a suite of observational data, climate reanalysis products, and general circulation model simulations to reassess the factors driving the interannual variability. We show that some of the warm events can not be explained by previously identified equatorial wind stress forcing and ENSO-like dynamics. Instead, these events are driven by a mechanism in which surface wind forcing just north of the equator induces warm ocean temperature anomalies that are subsequently advected toward the equator. We find the surface wind patterns are associated with long-lived subtropical sea surface temperature anomalies and suggest they therefore reflect a link between equatorial and subtropical Atlantic variability
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
Digital image correlation (DIC) analysis of the 3 December 2013 Montescaglioso landslide (Basilicata, Southern Italy). Results from a multi-dataset investigation
Image correlation remote sensing monitoring techniques are becoming key tools for
providing effective qualitative and quantitative information suitable for natural hazard assessments,
specifically for landslide investigation and monitoring. In recent years, these techniques have
been successfully integrated and shown to be complementary and competitive with more standard
remote sensing techniques, such as satellite or terrestrial Synthetic Aperture Radar interferometry.
The objective of this article is to apply the proposed in-depth calibration and validation analysis,
referred to as the Digital Image Correlation technique, to measure landslide displacement.
The availability of a multi-dataset for the 3 December 2013 Montescaglioso landslide, characterized
by different types of imagery, such as LANDSAT 8 OLI (Operational Land Imager) and TIRS
(Thermal Infrared Sensor), high-resolution airborne optical orthophotos, Digital Terrain Models
and COSMO-SkyMed Synthetic Aperture Radar, allows for the retrieval of the actual landslide
displacement field at values ranging from a few meters (2–3 m in the north-eastern sector of the
landslide) to 20–21 m (local peaks on the central body of the landslide). Furthermore, comprehensive
sensitivity analyses and statistics-based processing approaches are used to identify the role of the
background noise that affects the whole dataset. This noise has a directly proportional relationship to
the different geometric and temporal resolutions of the processed imagery. Moreover, the accuracy
of the environmental-instrumental background noise evaluation allowed the actual displacement
measurements to be correctly calibrated and validated, thereby leading to a better definition of
the threshold values of the maximum Digital Image Correlation sub-pixel accuracy and reliability
(ranging from 1/10 to 8/10 pixel) for each processed dataset
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