PPT-Diurnal and

Seasonal V ariations of Nitrogen O xides Within S nowpack A ir and the Overlying A tmosphere at Summit Greenland C Toro 1 RE Honrath 1 LJ Kramer 1 DHelmig. of . Salinity. Kyla Drushka. 1. , . Sarah Gille. 2. , Janet Sprintall. 2. . kdrushka@apl.uw.edu. 1. Applied Physics Lab, Univ. of Washington . 2. Scripps Inst. of Oceanography. Aquarius Science Team Meeting. Qian Tan (USRA), . Mian. Chin (GSFC), Jack Summers (EPA), Tom Eck (GSFC), . Hongbin. Yu (UMD), . Caterina. . Tassone. (NOAA. ), Yan Zhang (MSU), . EPA/AQS, NASA/AERONET, NOAA/NCDC . Outline. Diurnal variability of surface PM2.5. EFD Final Project. Terrence Hess. Daniel Alexander. Mohammad Khan. Neil Lareau. 1. 21 Apr. 2011. Project Objectives . Characterize the diurnal evolution of turbulent statistics. for . two dissimilar 24 hour periods. Thermotidal and land-heating forcing of the diurnal cycle of oceanic surface winds in the eastern tropical Pacific*. Reuni. ón LMI DISCOH, 29 de marzo, 2012. IMARPE, Callao. * Geophysical Research Letters, . 7:. . Comparison of gene expression during seed development and diurnal rhythm in rice and Arabidopsis seed development networks. Green-to-red color set represents expression fold changes (A) between 0-2DAP and 21-29DAP stages of rice seed development and (B) in Arabidopsis seed between 5-13DAF. Blue-to-red color set represents diurnal phase of expression in (A) rice seedlings and (B) Arabidopsis seedlings. Nodes that are grey in both A and B denote genes that were not queried. Nodes that are colored in the rice network but are grey in the Arabidopsis network indicate genes that lack a homolog in Arabidopsis. Panels were colored in sea green when expression values were not available. A quick visual scan of the two networks identifies similarities and differences in diurnal gene expression in the two species. For example, contrasting diurnal expression pattern is displayed by rice . Kaustav. . Chakravarty. Richard H. Johnson. Paul E. . Ciesielski. Colorado State University. J. L. Davison. University of Louisville. Third Symposium on Prediction of the Madden-Julian Oscillation: Processes, Prediction and . Richard H. Johnson. Colorado State University. T. . Ushiyama. (Andy . Newman , Brian . McNoldy. , and Paul . Ciesielski. ). MAHASRI/. HyARC. workshop, 5-7 March 2009, . DaNang. , Vietnam . Why do we care about the diurnal cycle?. in Hainan Island. Lei Zhu. Paul . Markowski. , . Zhiyong. . Meng. and . Fuqing. Zhang. Research purpose. T. ry to understand the climatology . of . diurnal. . precipitation cycle in Hainan Island. The paper by Suloand co-workers presentslong termsub-3 nm particle concentration and their precursors conducted in Hyytil in the boreal forest The first part of the study focuses on the identification of the UM in different parts of the world.. Humphrey Lean, Kirsty Hanley, Stuart Webster, Will Keat, Thorwald Stein, Todd Lane, Martin Jucker, Elizabeth Kendon and Giorgia Fosser. Met Office has run 1.5km UK version of Unified Model for about 10 years (UKV). . over Sumatra Island, Indonesia, on. synoptic disturbances and its relation to. the Madden-Julian Oscillation. Ayako SEIKI, Satoru YOKOI, and Masaki KATSUMATA. (JAMSTEC, Japan). Seiki et al. (2021), JMSJ. Claire . Bulgin. University. of Reading, National Centre for . Earth. Observation. Background. Every measurement we make (both in-situ or remotely sensed) deviates from the ‘true’ surface temperature due imperfections in the instrumentation and measurement process. Therefore, in order to use the data correctly, you must consider the uncertainty in the measurement.. Variations of the Mei-Yu Season Coastal Rainfall over South China. Chen, X., F, Zhang, and K. Zhao, 2017: Influence of monsoonal wind speed and moisture content on intensity and diurnal variations of the Mei-Yu season coastal rainfall over South China. . Kaustav. . Chakravarty. Richard H. Johnson. Paul E. . Ciesielski. Colorado State University. The Madden-Julian Oscillation: Observations, Theoretical Understanding, Modeling, and Prediction. ; . AGU Fall Meeting.