This paper presents an automated technique which ingests orbital synthetic-aperture radar (SAR) imagery and outputs surface water maps in near real time and on a global scale. The service anticipates ...future open data dissemination of water extent information using the European Space Agency's Sentinel-1 data. The classification methods used are innovative and practical and automatically calibrated to local conditions per 1 × 1° tile. For each tile, a probability distribution function in the range between being covered with water or being dry is established based on a long-term SAR training dataset. These probability distributions are conditional on the backscatter and the incidence angle. In classification mode, the probability of water coverage per pixel of 1 km × 1 km is calculated with the input of the current backscatter – incidence angle combination. The overlap between the probability distributions of a pixel being wet or dry is used as a proxy for the quality of our classification. The service has multiple uses, e.g. for water body dynamics in times of drought or for urgent inundation extent determination during floods. The service generates data systematically: it is not an on-demand service activated only for emergency response, but instead is always up-to-date and available. We validate its use in flood situations using Envisat ASAR information during the 2011 Thailand floods and the Pakistan 2010 floods and perform a first merge with a NASA near real time water product based on MODIS optical satellite imagery. This merge shows good agreement between these independent satellite-based water products.
ABSTRACT The arrival directions of TeV-PeV cosmic rays show weak but significant anisotropies with relative intensities at the level of one per mille. Due to the smallness of the anisotropies, ...quantitative studies require careful disentanglement of detector effects from the observation. We discuss an iterative maximum-likelihood reconstruction that simultaneously fits cosmic-ray anisotropies and detector acceptance. The method does not rely on detector simulations and provides an optimal anisotropy reconstruction for ground-based cosmic-ray observatories located in the middle latitudes. It is particularly well suited to the recovery of the dipole anisotropy, which is a crucial observable for the study of cosmic-ray diffusion in our Galaxy. We also provide general analysis methods for recovering large- and small-scale anisotropies that take into account systematic effects of the observation by ground-based detectors.
Satellite data are often used for their ability to fill in temporal and spatial patterns in data-sparse regions. It is also known that global satellite products generally contain more noise than ...ground-based estimates. Data validation of satellite data often treats ground-based estimates as the ‘gold standard’: without error or uncertainty. In the estimation of evapotranspiration (ET) however, ground-based estimates have considerable uncertainty, caused by the input components of the ET equations. This research presents an analysis of uncertainty of reference ET (ET0) caused by these input components. A dataset of correlated random variables is generated for a country with a diverse climate and diverse density of ground observations: New Zealand. The uncertainty analysis shows that: ET0 is most sensitive to temperature, followed by solar radiation, relative humidity, and cloudiness ratio; and that uncertainty varies between 10% and 40% of ET0, and depends on the ET0 value. Using this uncertainty analysis, a set of correlated random variables, and a Monte-Carlo fitting approach, MOD16 satellite PET data becomes a ‘soft interpolator’ between ground-based ET0 estimates. The resulting 1km×1km monthly nation-wide dataset has the advantage of: taking into account land cover and vegetation characteristics through the use of satellite data; still abiding to local climate diversity and locally used standards through the use of ground-based estimates; and containing an uncertainty estimate. Further comparison suggests that original MOD16 satellite PET could estimate real PET better than using ground-based estimates of ET0. Further research recommends combination with other existing gridded ET estimates, and further validation of real PET estimates.
•Satellite PET data are used to interpolate ground-based ET0 estimates.•Sensitivity analyses of ground-based ETo lead to dynamic error characterisation.•Uncertainty of ground-based ETo leads to better estimates in data-sparse areas.•Global satellite products can be used while still abiding to regional standards.•A national dataset of 1km×1km monthly reference crop ET
Previous analyses of cosmic rays above 4
×
10
19 eV observed by the AGASA experiment have suggested that their arrival directions may be clustered. However, estimates of the chance probability of ...this clustering signal vary from 10
−2 to 10
−6 and beyond. It is essential that the strength of this evidence be well understood in order to compare it with anisotropy studies in other cosmic ray experiments. We apply two methods for extracting a meaningful significance from this data set: one can scan for the cuts which optimize the clustering signal, using simulations to determine the appropriate statistical penalty for the scan. This analysis finds a chance probability of about 0.3%. Alternatively, one can optimize the cuts with a first set of data, and then apply them to the remaining data directly without statistical penalty. One can extend the statistical power of this test by considering cross-correlation between the initial data and the remaining data, as long as the initial clustering signal is not included. While the scan is more useful in general, in the present case only splitting the data set offers an unbiased test of the clustering hypothesis. Using this test we find that the AGASA data is consistent at the 8% level with the null hypothesis of isotropically distributed arrival directions.
Hawke’s Bay and Otago regions, North and South Islands, New Zealand (NZ).
Evidence that climate change affects groundwater is emerging, but a complete understanding of the processes and impacts on ...the resources remains limited in most countries, including NZ. This paper presents a methodology to explore the potential effects of climate change on groundwater for an envelope of future climate scenarios (i.e., low and high greenhouse gas emissions; mid- and end-century). The methodology uses national and sub-national datasets and models and is applied to two case studies (Hawke’s Bay and Otago). Its structure allows for future implementation in other NZ regions.
Results are provided as projected changes in precipitation minus evapotranspiration, rainfall recharge to groundwater (RR) and water table elevation. General water quantity reductions are projected for Hawke's Bay (e.g., RR reduced by up to −1.5 mm/d), and more limited and variable changes for Otago (e.g., RR varying between +/- 0.4 mm/d).
We then present a discussion about furthering the science of climate change effects on groundwater, characterisation of climate change uncertainty and unique effects, and adaptation in the NZ context. We anticipate that our results, despite limitations and uncertainty, can enable adaptation actions without delay. We recommend integrated and adaptive approaches, including action effectiveness monitoring and community engagement, to develop resilience to climate change threats.
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•Projected climate change-induced effects on groundwater for two NZ regions.•Positive development and trial of a methodology to be replicated nationwide.•Rainfall recharge projected to vary with location and season under climate change.•Results can guide initiation of flexible and community-based adaptation actions.•Despite high uncertainty, groundwater resources can help build resilience.
A common problem in ultra-high energy cosmic ray physics is the comparison of energy spectra. The question is whether the spectra from two experiments or two regions of the sky agree within their ...statistical and systematic uncertainties. We develop a method to directly compare energy spectra for ultra-high energy cosmic rays from two different regions of the sky in the same experiment without reliance on agreement with a theoretical model of the energy spectra. The consistency between the two spectra is expressed in terms of a Bayes factor, defined here as the ratio of the likelihood of the two-parent source hypothesis to the likelihood of the one-parent source hypothesis. Unlike other methods, for example Delta *y2 tests, the Bayes factor allows for the calculation of the posterior odds ratio and correctly accounts for non-Gaussian uncertainties. The latter is particularly important at the highest energies, where the number of events is very small.
Air fluorescence detectors measure the energy of ultra-high energy cosmic rays by collecting fluorescence light emitted from nitrogen molecules along the extensive air shower cascade. To ensure a ...reliable energy determination, the light signal needs to be corrected for atmospheric effects, which not only attenuate the signal, but also produce a non-negligible background component due to scattered Cherenkov light and multiple-scattered light. The correction requires regular measurements of the aerosol attenuation length and the aerosol phase function, defined as the probability of light scattered in a given direction. At the Pierre Auger Observatory in Malargüe, Argentina, the phase function is measured on an hourly basis using two aerosol phase function (APF) light sources. These sources direct a UV light beam across the field of view of the fluorescence detectors; the phase function can be extracted from the image of the shots in the fluorescence detector cameras. This paper describes the design, current status, standard operation procedure, and performance of the APF system at the Pierre Auger Observatory.
Searches for statistically significant correlations between arrival directions of ultra-high-energy cosmic rays and classes of astrophysical objects are common in astroparticle physics. We present a ...method to test potential correlation signals of a priori unknown strength and evaluate their statistical significance sequentially, i.e., after each incoming new event in a running experiment. The method can be applied to data taken after the test has concluded, allowing for further monitoring of the signal significance. It adheres to the likelihood principle and rigorously accounts for our ignorance of the signal strength.
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