<p>The fractional snow cover (FSC) product H35 is a daily operational product based on multi-channel analysis of AVHRR onboard to NOAA and MetOp satellites. H35 is supplied by the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Support to Operational Hydrology and Water Management (HSAF). The &#8220;traditional&#8221; H35 FSC product is generated at pixel resolution by exploiting the brightness intensity, which is the convolution of the snow signal and the fraction of snow within the pixel and the sampling is carried out at 1-km intervals. The product for flat/forested regions is generated by Finnish Meteorological Institute (FMI) and the product for mountainous areas is generated by Turkish State Meteorological Service (TSMS). Both products, thereafter, are merged at FMI. This presentation aims to represent the latest findings of our efforts in developing an &#8220;alternative&#8221; H35 FSC product for the mountainous part by using two data-driven machine learning methodologies, namely, multivariate adaptive regression splines (MARS) and random forests (RFs). In total, 332 Sentinel 2 images over Alps, Tatra Mountains and Turkey acquired between November 2018 and April 2019 are used in order to generate the necessary reference FSC maps for the training of the MARS and RF models. AVHRR bands 1-5, NDSI and NDVI are used as predictor variables. Binary classified Sentinel 2 snow maps, ERA5 snow depth and MODIS MOD10A1 NDSI data are employed in the validation of the models. The results show that both MARS- and RF-based H35 product are i) in good agreement with reference FSC maps (as indicated by low RMSE and relatively high R values) and ii) able to capture the spatial variability of the snow extend. However, MARS-based H35 is preferred for an operational FSC product generation due to the high computational cost required in RF model.</p>
ÖzAra tırmada Söylem Dilbilgisi Yakla ımı çerçevesinde yarı planlanmı Türkçe sözlü söylemde var olan kesin, kesinlikle ve mutlakakiplik belirteçlerinin ba lam içi dilbilgisel sıklık ve kullanımlarının belirlenmesi amaçlanmı tır. Bu amaç do rultusunda ulusal kanallardaki sa lık, politik, güncel sohbet ve kültür-sanat konulu yayınlardan kaydedilen 15 saatlik veriler yazıya aktarılarak bir bütünce olu turulmu tur. Çalı ma verileri bütünce temelli söylem çözümlemesi yöntemiyle, "Ba lam çinde Anahtar Sözcük Arama" (B AS-TR) programı kullanılarak çözümlenmi , çözümleme sonucunda belirteçlerin kullanıldı ı ba lamlar ara tırmanın örneklemi olarak seçilmi tir. Çalı manın bulgular bölümünde ise söz konusu belirteçlerin di er dilbilgisel yapılarla kullanım sıklı ı ve anlamları belirlenerek tablola tırılmı , ayrıca bu anlam ve kullanımlar belirlenirken ana dili konu urlarının söylemlerinde; söylem çerçeveleri (ba lam, yer, zaman, katılımcıların özellikleri ve ileti im konusu) dikkate alınmı ve bu belirlemelerin nasıl yapıldı ına dair örneklere yer verilmi tir. Ara tırmada daha önceki çalı malardan farklı olarak kesin, kesinlikle ve mutlakakiplik belirteçlerinin sık kullanıldı ı biçimler ve bu biçimlerle bildirdikleri anlamlar, tümce tabanlı de il; ba lamları içinde açıklanmı , tablolardaki dilbilgisel veriler bu biçimde elde edilmi tir. Tartı ma ve sonuç bölümlerinde ise ula ılan sonuçlar, alanyazında hedef belirteçlere yönelik yapılmı olan ara tırmalarla kıyaslanmı tır. Ara tırmanın Türkçenin hem ana dili olarak hem de yabancı dil olarak ö retimine katkı sa layaca ı dü ünülmektedir.Anahtar Kelimeler: Türkçe Sözlü Söylem, Söylem Dilbilgisi, Kiplik Belirteçleri, Bütünce, Sıklık. AbstractThe aim of this study is to determine the grammatical frequency and usage of the modal adverbs kesin, kesinlikle and mutlaka that appear in semi-planned Turkish spoken discourse within discourse grammatical approach. In accordance with this purpose, a corpus is created by transcribing data about health, politic, current affairs, cultureart which last for 15 hours. The data of the study is analyzed by using corpus based discourse analysis, by the aid of "Keyword In-Context -KWIC" software. In the findings part of the study, the usage frequencies and the meanings of the mentioned adverbs with other grammatical structures are determined and tabulated by heading away from the examples chosen from the corpus and in addition, while determining these meanings and usages consideration is being given according to framework of discourse (context, place, time, properties of the participants and communication subject) of the native speakers. There are examples in the study about how these determinations are made. The results and the conclusion of the study are compared to the other studies aimed at modal adverbs in discussion and conclusion parts of the paper. It is expected that the research will contribute to teaching Turkish both as mother language and as foreign language.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.