Zobrazeno 1 - 10
of 2 984
pro vyhledávání: '"A. Hoell"'
Publikováno v:
Weather and Climate Dynamics, Vol 4, Pp 963-980 (2023)
Understanding the nature of precipitation over central southwest Asia (CSWA), a data-sparse, semi-arid region, is important given its relation to agricultural productivity and the likelihood of hazards such as flooding. The present study considers ho
Externí odkaz:
https://doaj.org/article/bdda63e838614b15b9d42a7e530d7f02
Publikováno v:
Weather and Climate Dynamics, Vol 3, Pp 1183-1197 (2022)
Subseasonal forecasts of opportunity (SFOs) for precipitation over southwest Asia during January–March at lead times of 3–6 weeks are identified using elevated expected forecast skill from a linear inverse model (LIM), an empirical dynamical mode
Externí odkaz:
https://doaj.org/article/441fcb803999439f9cce0e77d87c4f97
A Central Asia hydrologic monitoring dataset for food and water security applications in Afghanistan
Autor:
A. McNally, J. Jacob, K. Arsenault, K. Slinski, D. P. Sarmiento, A. Hoell, S. Pervez, J. Rowland, M. Budde, S. Kumar, C. Peters-Lidard, J. P. Verdin
Publikováno v:
Earth System Science Data, Vol 14, Pp 3115-3135 (2022)
From the Hindu Kush mountains to the Registan Desert, Afghanistan is a diverse landscape where droughts, floods, conflict, and economic market accessibility pose challenges for agricultural livelihoods and food security. The ability to remotely monit
Externí odkaz:
https://doaj.org/article/3b85faaecd524f16942af24b87eb645f
Publikováno v:
Geophysical Research Letters, Vol 50, Iss 10, Pp n/a-n/a (2023)
Abstract Climate models exhibit known systematic errors in their representation of the El Niño‐Southern Oscillation (ENSO). In this study, we show that such simulation errors are largely present in tropical seasonal prediction, even for short lead
Externí odkaz:
https://doaj.org/article/288be3c0524f434ca59cb6006c13963e
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-16 (2021)
Abstract Small changes in Pacific temperature gradients connected with the El Niño Southern Oscillation (ENSO) influence the Walker Circulation and are related to global climate anomalies. Therefore, it is of paramount importance to develop robust i
Externí odkaz:
https://doaj.org/article/0c765ba056964fe089c477e1dc502eff
As organizations face the challenges of processing exponentially growing data volumes, their reliance on analytics to unlock value from this data has intensified. However, the intricacies of big data, such as its extensive feature sets, pose signific
Externí odkaz:
http://arxiv.org/abs/2405.07658
Autor:
Toride, Kinya, Newman, Matthew, Hoell, Andrew, Capotondi, Antonietta, Schlör, Jakob, Amaya, Dillon
We introduce an interpretable-by-design method, optimized model-analog, that integrates deep learning with model-analog forecasting, a straightforward yet effective approach that generates forecasts from similar initial climate states in a repository
Externí odkaz:
http://arxiv.org/abs/2404.15419
Autor:
C. Funk, A. Hoell, S. Shukla, I. Bladé, B. Liebmann, J. B. Roberts, F. R. Robertson, G. Husak
Publikováno v:
Hydrology and Earth System Sciences, Vol 18, Iss 12, Pp 4965-4978 (2014)
In eastern East Africa (the southern Ethiopia, eastern Kenya and southern Somalia region), poor boreal spring (long wet season) rains in 1999, 2000, 2004, 2007, 2008, 2009, and 2011 contributed to severe food insecurity and high levels of malnutritio
Externí odkaz:
https://doaj.org/article/ec7fa5304e94474ea3156c3dda53bd38
Autor:
Hoell, Nicholas
We present an expository overview of technical and cultural challenges to the development and adoption of automation at various stages in the data science prediction lifecycle, restricting focus to supervised learning with structured datasets. In add
Externí odkaz:
http://arxiv.org/abs/2208.11792
First passage time statistics in disordered systems exhibiting scale invariance are studied widely. In particular, long trapping times in energy or entropic traps are fat-tailed distributed, which slow the overall transport process. We study the stat
Externí odkaz:
http://arxiv.org/abs/2208.10262