已經跟呼吸一樣重要的網路和手機論文書籍站
Meteorological data的問題，透過圖書和論文來找解法和答案更準確安心。 我們找到下列免費下載的地點或者是各式教學
Meteorological data的問題，我們搜遍了碩博士論文和台灣出版的書籍，推薦Shibuya, Ryosuke寫的 Dynamical Characteristics of Inertia-Gravity Waves in the Antarctic Mesosphere: Analyses Combining High-Resolution Observations 和Ahmed, Marmar Badr Mohamed的 Climate Change and Development Impacts on Groundwater Resources in the Nile Delta Aquifer, Egypt都 可以從中找到所需的評價。
另外網站Meteorological Data and Processors | US EPA也說明：Observational Meteorological Data - Data consisting of physical parameters that are measured directly by instrumentation, and include ...
這兩本書分別來自 和所出版 。
明志科技大學 環境與安全衛生工程系環境工程碩士班 程裕祥所指導 杜育誠的 大台北地區冬季期間細懸浮微粒中水溶性離子組成特徵探討 （2021），提出Meteorological data關鍵因素是什麼，來自於PM2.5、水溶性離子組成、氣體與氣膠同步採樣連續監測儀、硫氧化率、氮氧化率、中和率。
而第二篇論文國立金門大學 理工學院工程科技碩士在職專班 馮玄明所指導 黃靖涵的 神經網路模型於金門空氣品質PM2.5 預測 （2021），提出因為有 空氣品質、神經網路、細懸浮微粒、污染物的重點而找出了 Meteorological data的解答。
最後網站Instrumental Meteorological Data - JCDP則補充：Instrumental Meteorological Data · West Japan Temperature Series · Nagasaki · Kobe, Osaka, Yokohama, Tokyo · Yokohama (Precipitation).
Dynamical Characteristics of Inertia-Gravity Waves in the Antarctic Mesosphere: Analyses Combining High-Resolution Observations
為了解決Meteorological data 的問題，作者Shibuya, Ryosuke 這樣論述：
Ryosuke Shibuya is a Postdoctoral Researcher in the Project Team for HPC Advanced Predictions utilizing Big Data Research Unit for Predictability of Global Weather Extremes, Japan Agency for Marine-Earth Science and Technology (JAMSTEC). He received his B.Sc., M.Sc. and Ph.D. from the University of
Tokyo in March 2012, March 2014, and October 2017, respectively. He was awarded the Japan Society for Promotion of Science (JSPS) research fellowship for young scientists, and his research throughout his doctoral program was supported by the JSPS. He has also received a number of prizes and awards:
the School of Science Research Award (Master) from the University of Tokyo in 2014, the best student oral presentation award at the American Meteorological Society’s 21th Symposium in 2014, and the School of Science Research Award (Ph.D.) from the University of Tokyo in 2017.
大台北地區冬季期間細懸浮微粒中水溶性離子組成特徵探討
為了解決Meteorological data 的問題，作者杜育誠 這樣論述：
本研究探討冬季期間大台北地區大氣中細懸浮微粒的水溶性離子組成特性及逐時變動趨勢。於2021年1月1日至2021年3月31日在新北市泰山區明志科技大學校園內利用氣體與氣膠同步採樣連續監測儀(2060 MARGA R)分析每小時HNO3、SO2及NH3氣體與NH4+、Na+、K+、Ca2+、Mg2+、Cl-、NO3-及SO42-離子濃度。結果顯示HNO3、SO2及NH3在採樣期間的平均濃度分別為0.39 μg/m3、0.26 μg/m3及3.24 μg/m3。陽離子NH4+、Na+、K+、Ca2+及Mg2+的平均濃度分別為1.73、0.30、0.17、0.08及0.06 μg/m3。陰離子SO4
2-、NO3-及Cl-的平均濃度分別為3.12、2.32及0.40 μg/m3。其中Na+、Ca2+、K+及SO42-分別約有49.06%、10.65%、5.83%及2.30%是來自於海鹽飛沫。採樣期間水溶性離子占PM2.5質量濃度約42.44%，其中以SO42-、NO3-及NH4+為主要組成，占總水溶性離子約84.06%。採樣期間的硫氧化率(SOR)與氮氧化率(NOR)平均值分別為0.91與0.78，顯示採樣期間微粒中所含的SO42-及NO3-主要來自衍生性硫酸鹽及硝酸鹽。而本研究採樣期間的中和率(NR)平均值為1.02，顯示微粒接近於中性。採樣期間共有180小時PM2.5質量濃度超過35
μg/m3，事件小時(PM2.5≥ 35 μg/m3)的水溶性離子由高到低依序為NO3-、SO42-、NH4+、Cl-、K+、Na+、Ca2+、Mg2+。在事件小時中，除了Na+以外，其餘水溶性離子平均質量濃度皆高於非事件小時(PM2.5< 35 μg/m3)。但若依照水溶性離子占PM2.5比例來看，除了NO3-及NH4+占比有所提升外，其餘水溶性離子占PM2.5皆為下降趨勢。同時NOR在事件小時期間顯著增加，可見在事件小時期間所增加的衍生氣膠主要以NH4NO3微粒為主。另外藉由NR中和率來看，在事件小時的NR較非事件小時略高，相較而言較偏鹼性，表示有較多量的NH3可以中和大氣中HNO3及H2
SO4。
Climate Change and Development Impacts on Groundwater Resources in the Nile Delta Aquifer, Egypt
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為了解決Meteorological data 的問題，作者Ahmed, Marmar Badr Mohamed 這樣論述：
Ms. Marmar Badr is a water engineer with 22 years with diverse experiences in the fields of hydrogeology, transboundary and regional water issues in the Nile basin countries and groundwater management. Marmar is currently working as the head of the technical office of the Nile Water Sector, Ministry
of Water Resources and Irrigation in Egypt. She started as a hydrologist in 2001 in the Nile Water Sector (Ministry of Water Resources and Irrigation). Her main responsibilities have included, but are not limited to, hydrological data processing, river Nile forecasting, analysing the hydrological a
nd meteorological data of the Nile basin. She has worked on a wide range of water management issues and in multidisciplinary research projects. In 2006, she worked as a project manager in the National NBI Office (NBI - Nile Basin Initiative - is a partnership initiated and led by the riparian states
of the Nile River to develop the river in a cooperative manner). Her main role was to provide overall coordination and support to the integrated water resources planning and management project.She graduated from the Faculty of Engineering, Cairo University, as a civil engineer. She received her mas
ter degree in water resources management from Tanta University, Egypt. Her thesis was titled ＂management of subsurface water in Fayoum governorate and its effect on soil behaviour＂ in which she studied recent theories and approaches in the field of groundwater management. She obtained a professional
diploma in information technology at the American University in Egypt. She has taken a number of training courses which cover several fields, e.g., guidelines for good practice of water policy, assessing the impacts of climate and global change on groundwater resources, groundwater modeling, Nile d
ecision support tool program, preparing demand scenarios for agricultural water in Nile Basin for 2030, negotiation skills and conflict resolution, results oriented monitoring and evaluation for international projects. She participated in different research projects dealing with vulnerable fresh gro
undwater resources due to climate change in Egypt.This book has been written during her Ph.D. studies at IHE Delft, the Netherlands, on the issues of climate change and development impacts on groundwater behaviour in the Nile Delta, Egypt.
神經網路模型於金門空氣品質PM2.5 預測
為了解決Meteorological data 的問題，作者黃靖涵 這樣論述：
鑒於近年來全球工業產業蓬勃發展，各產業在環境保護及永續發展等意識越來越重視，並且政府在針對造成空氣品質污染的管控也相對要求，在這全球化的現今，不論身在這世界的哪一個地方，都希望能夠維持良好空氣品質的生活環境。金門地區造成空氣品質不良的原因，主要為風面強大、氣候乾燥等因素引起的揚塵所致，因為地理位置與氣候的之間的關係，空氣品質的因素也受中國大陸南下空氣影響，其針對空氣流動、氣流穩定度與氣候間的變化，都足以影響到空氣品質的好壞。所以，本研究中蒐集金門地區監測站自2011年1月到2020年12月每天的氣象偵測平均數據，做為本論文的研究資料，其中蒐集的氣象資料內容，包含了相對濕度（%）、溫度（℃）、
風速（m/sec）、降雨強度（mm）與氣壓（hPa）等，並將歷年的觀測數據，彙整的資料做適當整理後，先透過大數據分析，證明上述的氣象資料是會影響空氣污染物擴散的因素，再將相對濕度（%）、溫度（℃）、風速（m/sec）、降雨強度（mm）與氣壓（hPa）等5項影響因素，透過倒傳遞類神經模型（Back-Propagation Neural Network，BPN），來實驗多組的模擬訓練與進行空氣污染物擴散的預測。本研究將空氣污染物細懸浮微粒PM2.5分成50μg/m3以下和51μg/m3以上的二個級距，並依據不同的影響因子組合，進行每天空氣污染物的擴散預測，準確率最低為86.7%，最高可達88.5%
。依據實驗的測試結果，可證明使用倒傳遞神經模型進行金門當地空氣污染物擴散的模擬與預測是可行性的；但是天氣變化多端，金門島嶼型的氣候更是千變萬化、變幻莫測，因此可以再增加更多會影響空氣品質擴散預測的因素，並且結合其他不同預測方法與演算法，以取得更精準的預測結果，以提供金門當地氣象預測之參考，同時也能提供當地民眾外出時的防範作為。