Zobrazeno 1 - 10
of 16
pro vyhledávání: '"Emy Alerskans"'
Autor:
Owen Embury, Christopher J. Merchant, Simon A. Good, Nick A. Rayner, Jacob L. Høyer, Chris Atkinson, Thomas Block, Emy Alerskans, Kevin J. Pearson, Mark Worsfold, Niall McCarroll, Craig Donlon
Publikováno v:
Scientific Data, Vol 11, Iss 1, Pp 1-22 (2024)
Abstract A 42-year climate data record of global sea surface temperature (SST) covering 1980 to 2021 has been produced from satellite observations, with a high degree of independence from in situ measurements. Observations from twenty infrared and tw
Externí odkaz:
https://doaj.org/article/a3a3923d07c940de90bdf024bc3e2713
Publikováno v:
Meteorological Applications, Vol 29, Iss 5, Pp n/a-n/a (2022)
Abstract A new method based on the Transformer model is proposed for post‐processing of numerical weather prediction (NWP) forecasts of 2 m air temperature. The Transformer is a machine learning (ML) model based on self‐attention, which extracts
Externí odkaz:
https://doaj.org/article/125ab4142c51470fa08d685844523956
Autor:
Emy Alerskans, Eigil Kaas
Publikováno v:
Meteorological Applications, Vol 28, Iss 4, Pp n/a-n/a (2021)
Abstract Six adaptive, short‐term post‐processing methods for correcting systematic errors in numerical weather prediction (NWP) forecasts of near‐surface air temperatures using local meteorological observations are assessed and compared. The m
Externí odkaz:
https://doaj.org/article/907b7226be994b1592989f6e38543311
Autor:
Pia Nielsen-Englyst, Jacob L. Høyer, Leif Toudal Pedersen, Chelle L. Gentemann, Emy Alerskans, Tom Block, Craig Donlon
Publikováno v:
Remote Sensing, Vol 10, Iss 2, p 229 (2018)
The Optimal Estimation (OE) technique is developed within the European Space Agency Climate Change Initiative (ESA-CCI) to retrieve subskin Sea Surface Temperature (SST) from AQUA’s Advanced Microwave Scanning Radiometer—Earth Observing System (A
Externí odkaz:
https://doaj.org/article/15cdf0894c23417abfc972a92f85fa4a
Publikováno v:
Alerskans, E, Lussana, C, Nipen, T N N & Seierstad, I A A 2022, ' Optimizing Spatial Quality Control for a Dense Network of Meteorological Stations ', Journal of Atmospheric and Oceanic Technology, vol. 39, no. 7, pp. 973-984 . https://doi.org/10.1175/JTECH-D-21-0184.1
Crowdsourced meteorological observations are becoming more prevalent and in some countries their spatial resolution already far exceeds that of traditional networks. However, due to the larger uncertainty associated with these observations, quality c
Autor:
Ingrid R. Abraham, Emy Alerskans, Cristian Lussana, Thomas N. Nipen, Louise Oram, Ivar A. Seierstad
Titanlib is a library of functions for the automatic quality control of meteorological observations and it is publicly available on github:https://github.com/metno/titanlibTitanlib builds upon the experience of running TITAN (Båserud et al., 2020) f
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8c24fc37b8f2dab6e96290d6baccd370
https://doi.org/10.5194/ems2022-178
https://doi.org/10.5194/ems2022-178
Autor:
Eigil Kaas, Emy Alerskans
Publikováno v:
Alerskans, E & Kaas, E 2021, ' Local temperature forecasts based on statistical post-processing of numerical weather prediction data ', Meteorological Applications, vol. 28, no. 4, 2006 . https://doi.org/10.1002/met.2006
Meteorological Applications, Vol 28, Iss 4, Pp n/a-n/a (2021)
Meteorological Applications, Vol 28, Iss 4, Pp n/a-n/a (2021)
Six adaptive, short‐term post‐processing methods for correcting systematic errors in numerical weather prediction (NWP) forecasts of near‐surface air temperatures using local meteorological observations are assessed and compared. The methods te
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6f48218c7d16e6e3272cb8f1552e714f
https://curis.ku.dk/portal/da/publications/local-temperature-forecasts-based-on-statistical-postprocessing-of-numerical-weather-prediction-data(6e19cd24-7e9a-4e67-9d3e-eb3de1484d21).html
https://curis.ku.dk/portal/da/publications/local-temperature-forecasts-based-on-statistical-postprocessing-of-numerical-weather-prediction-data(6e19cd24-7e9a-4e67-9d3e-eb3de1484d21).html
It is a well-known fact that numerical weather prediction (NWP) models exhibit systematic errors, especially for near-surface variables. Reasons for this are, among other, the inability of these models to successfully handle sub-grid phenomena and sh
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::133a9dc28832a4c2f38ea87202b90db7
https://doi.org/10.5194/ems2021-198
https://doi.org/10.5194/ems2021-198
Numerical weather prediction (NWP) models are known to exhibit systematic errors, especially for near-surface variables such as air temperature. This is partly due to deficiencies in the physical formulation of the model dynamics and the inability of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::169f8f80cc9d50d99b723a4b3c5a7b99
https://doi.org/10.5194/egusphere-egu21-11378
https://doi.org/10.5194/egusphere-egu21-11378
Autor:
Emy Alerskans, Eigil Kaas
Six adaptive post-processing methods for correcting systematic biases in forecasts of near-surface air temperatures, using local meteorological observations, are assessed and compared. The methods tested are based on the simple moving average and the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::eeeacf8cce229af1b53a97e93af0196e
https://doi.org/10.5194/egusphere-egu21-11270
https://doi.org/10.5194/egusphere-egu21-11270