title: Applications of Self-Organizing Maps to Statistical Downscaling of Major Regional Climate Variables

authors: Yin, Chonghua

subjects: self-organizing Maps

abstract: This research developed a practical methodological framework, which integrated most of the important aspects related to statistical downscaling. The framework showed high skills when applied to downscale daily precipitation, minimum and maximum temperatures over southeast Australia. Within the framework, self-organizing maps (SOM) algorithm was incorporated as the core technique for interpreting the relationship between the predictor and predictand under consideration following the latest advances in synoptic climatology. The SOM classified large-scale predictors into a small number of synoptic patterns on a physically meaningful basis. By mapping the observed local climate variable (predictand) to these patterns, a downscaling model structure, SOM-SD, was constructed based on the NCAR/NCEP reanalysis data. Moreover, for a new atmospheric state, an ensemble of predictand values was generated by a stochastic re-sampling technique inside the SOM-SD. To improve seasonality of downscaled results, a simple seasonal predictand pool (SPP) scheme was introduced, which can acquire similar skills as the traditional solutions of dividing a year into four seasons. The framework identified and applied a broad suite of statistical indices, including mean, variance, cumulative distribution function (CDF), extreme events to assess the performance of the SOM-SD. In addition, some non-parametric methods were also employed to evaluate the uncertainty of the downscaling approach, which improved its robustness in practice. The quality control of the input data consists of another important component of the framework, which assessed GCM predictors from three aspects: (a) replicate reliably synoptic patterns depicted by the reanalysis data; (b) remain relatively stable in the future; and (c) produce similar downscaling skills as the reanalysis data. Finally, the framework provided an equal-distance CDF mapping method to adjust the discrepancies between the downscaled values and the corresponding observations. This method adjusted the downscaled CDF for the projection period on the difference between the CDFs of the downscaled GCM baseline and observed values. Thus the framework combines the advantages of statistical downscaling model and bias correction method. Moreover, the framework puts a strong emphasis on its flexibility, which underpins its application to other regions, as well as to support impact assessment studies.

text: Applications of Self-Organizing Maps to Statistical Downscaling of Major Regional Climate Variables This research developed a practical methodological framework, which integrated most of the important aspects related to statistical downscaling. The framework showed high skills when applied to downscale daily precipitation, minimum and maximum temperatures over southeast Australia. Within the framework, self-organizing maps (SOM) algorithm was incorporated as the core technique for interpreting the relationship between the predictor and predictand under consideration following the latest advances in synoptic climatology. The SOM classified large-scale predictors into a small number of synoptic patterns on a physically meaningful basis. By mapping the observed local climate variable (predictand) to these patterns, a downscaling model structure, SOM-SD, was constructed based on the NCAR/NCEP reanalysis data. Moreover, for a new atmospheric state, an ensemble of predictand values was generated by a stochastic re-sampling technique inside the SOM-SD. To improve seasonality of downscaled results, a simple seasonal predictand pool (SPP) scheme was introduced, which can acquire similar skills as the traditional solutions of dividing a year into four seasons. The framework identified and applied a broad suite of statistical indices, including mean, variance, cumulative distribution function (CDF), extreme events to assess the performance of the SOM-SD. In addition, some non-parametric methods were also employed to evaluate the uncertainty of the downscaling approach, which improved its robustness in practice. The quality control of the input data consists of another important component of the framework, which assessed GCM predictors from three aspects: (a) replicate reliably synoptic patterns depicted by the reanalysis data; (b) remain relatively stable in the future; and (c) produce similar downscaling skills as the reanalysis data. Finally, the framework provided an equal-distance CDF mapping method to adjust the discrepancies between the downscaled values and the corresponding observations. This method adjusted the downscaled CDF for the projection period on the difference between the CDFs of the downscaled GCM baseline and observed values. Thus the framework combines the advantages of statistical downscaling model and bias correction method. Moreover, the framework puts a strong emphasis on its flexibility, which underpins its application to other regions, as well as to support impact assessment studies.

year: 2011

title: The telegraph approximation for focused cosmic-ray transport in the presence of boundaries

authors: Litvinenko, Yuri E.

subjects: cosmic rays

abstract: Diffusive cosmic-ray transport in nonuniform large-scale magnetic fields in the presence of boundaries is considered. Reflecting and absorbing boundary conditions are derived for a modified telegraph equation with a convective term. Analytical and numerical solutions of illustrative boundary problems are presented. The applicability and accuracy of the telegraph approximation for focused cosmic-ray transport in the presence of boundaries are discussed, and potential applications to modeling cosmic-ray transport are noted.

text: The telegraph approximation for focused cosmic-ray transport in the presence of boundaries Diffusive cosmic-ray transport in nonuniform large-scale magnetic fields in the presence of boundaries is considered. Reflecting and absorbing boundary conditions are derived for a modified telegraph equation with a convective term. Analytical and numerical solutions of illustrative boundary problems are presented. The applicability and accuracy of the telegraph approximation for focused cosmic-ray transport in the presence of boundaries are discussed, and potential applications to modeling cosmic-ray transport are noted.

year: 2015

title: He whakanui i ēnei tau, e rima tekau o Te Whare Wānanga o Waikato

authors: Rewi, Poia

subjects: Tohu mātauranga

abstract: Aku tau ki Waikato: 1986-2003

A Block

Koia tērā, ko te Tari Māori (Māori Department), ko Timoti Kāretu te tumuaki, ko tōna kāhui kawe i ngā mahi ko te momo he wā tōna he pūkenga, he ahonuku, he ahorangi, he tākuta, he tohunga; he wā anō he pāpā, he whaea, he matua kēkē, he whaea kēkē; he wā anō he haututū, he hangarau, he whakatoi, he hoa tonu. Ko te tohu kē ia e whakaatuhia nei, ko te mātau o tēnei hanga ki te hāpai i te momo taera e hua ai te pai ki te ākonga, ki te mahi, ki te hapori.

Koia hoki te momo e whakamātauhia ana e te ao Pākehā; te mōhio kē, te tirohanga kē, te pōhēhē, te ture, te kawa me ngā tini aha kē. Koia tonu te momo e whakamātauhia ana e te ao Māori; ōna manako, ōna wawata, ōna taumahatanga, ōna piki, ōna heke. Ko te wā hui ki ngā kaimahi i Te Tari Māori he whakamoemoe i ngā utanga a te whare wānanga me ā te iwi kia whakapōtaehia he ākonga, me te mea, kia puta tonu hei tamāhine, hei tāhae, hei irāmutu, hei mokopuna e “toa ai rātou hei iti kotahi harakoa mō te iwi”. Kātahi nā te whiu ko tēnā, he taumahatanga kāore e pērā rawa te wahatanga e ngā kaimahi Pākehā i roto i ngā kura wānanga pēnei. Heoi anō, ko ia tērā ngā wheako o te wā, ahakoa nō nākua rawa nei i mārama ai taua here ki aua kaimahi, ka mahara iho au ki taua wā, ka mīharo tonu i te kaha o ērā pūkenga2 ki te whakatutuki i te wāhi ki a rātou. I tēnei punua hokinga mahara, ka aro ki ēnei mātua marau e rua, arā, ko te kawea o ngā mahi e taea ai e te Māori, ākonga mai, pūkenga mai, kaupapa mai, i te ao whare wānanga Pākehā, ka tahi; ka rua, ka aro ki ētahi o ngā āhuatanga e honohono ana ki te iwi.

text: He whakanui i ēnei tau, e rima tekau o Te Whare Wānanga o Waikato Aku tau ki Waikato: 1986-2003

A Block

Koia tērā, ko te Tari Māori (Māori Department), ko Timoti Kāretu te tumuaki, ko tōna kāhui kawe i ngā mahi ko te momo he wā tōna he pūkenga, he ahonuku, he ahorangi, he tākuta, he tohunga; he wā anō he pāpā, he whaea, he matua kēkē, he whaea kēkē; he wā anō he haututū, he hangarau, he whakatoi, he hoa tonu. Ko te tohu kē ia e whakaatuhia nei, ko te mātau o tēnei hanga ki te hāpai i te momo taera e hua ai te pai ki te ākonga, ki te mahi, ki te hapori.

Koia hoki te momo e whakamātauhia ana e te ao Pākehā; te mōhio kē, te tirohanga kē, te pōhēhē, te ture, te kawa me ngā tini aha kē. Koia tonu te momo e whakamātauhia ana e te ao Māori; ōna manako, ōna wawata, ōna taumahatanga, ōna piki, ōna heke. Ko te wā hui ki ngā kaimahi i Te Tari Māori he whakamoemoe i ngā utanga a te whare wānanga me ā te iwi kia whakapōtaehia he ākonga, me te mea, kia puta tonu hei tamāhine, hei tāhae, hei irāmutu, hei mokopuna e “toa ai rātou hei iti kotahi harakoa mō te iwi”. Kātahi nā te whiu ko tēnā, he taumahatanga kāore e pērā rawa te wahatanga e ngā kaimahi Pākehā i roto i ngā kura wānanga pēnei. Heoi anō, ko ia tērā ngā wheako o te wā, ahakoa nō nākua rawa nei i mārama ai taua here ki aua kaimahi, ka mahara iho au ki taua wā, ka mīharo tonu i te kaha o ērā pūkenga2 ki te whakatutuki i te wāhi ki a rātou. I tēnei punua hokinga mahara, ka aro ki ēnei mātua marau e rua, arā, ko te kawea o ngā mahi e taea ai e te Māori, ākonga mai, pūkenga mai, kaupapa mai, i te ao whare wānanga Pākehā, ka tahi; ka rua, ka aro ki ētahi o ngā āhuatanga e honohono ana ki te iwi.

year: 2016