ENSO Video meeting
July 13, 2020 2:59 pm Comments Off on ENSO Video meetingPicture (above): Beach. Credit: Frank McKenna (Unsplash).
Picture (above): Beach. Credit: Frank McKenna (Unsplash).
Observational studies over Darwin, Australia, show gravity waves provide a plausible explanation for the patterns of noteworthy variability in mesoscale motions. The findings suggest a two‐way coupling of clouds to their environment
A known weakness of the Ensemble Kalman filter approach is that its ability to provide state estimates that closely match densely distributed observations is very limited. This paper describes a computationally inexpensive innovative variation on the technique that greatly ameliorates this difficulty.
Topic: Land – AtmosphereTime: Wednesday, May 22, 2019 2:55pm Canberra, Melbourne, Sydney Join from PC, Mac, Linux, iOS or Android: https://unsw.zoom.us/j/949596576Or iPhone one-tap: 16465588656,949596576# or 16699006833,949596576#Or Telephone: Dial: +1 646 558 8656 (US Toll) or +1 669 900 6833 (US Toll) Meeting ID: 949 596 576 International numbers available: https://zoom.us/u/aptljBZ8m Or a H.323/SIP room system: SIP: 7588@aarnet.edu.au or H323: 949596576@182.255.112.21 (From Cisco) or H323: 182.255.112.21##949596576 (From Huawei, LifeSize, Polycom) or 162.255.37.11 or 162.255.36.11 (U.S.) Meeting ID: 949596576
How wet the soil is before a storm can determine the amount of rain that falls. This research also produced some interesting findings for rainfall in Australia.
In this project, we seek to utilise 40 years of observations of water vapour profiles from the global radiosonde network to improve our process level understanding of water vapour mixing. The applicant is expected to know the basics of atmospheric physics and statistics. Some knowledge of data processing tools will be an advantage.
This project will examine ‘mixed teleconnections’ using coupled climate models. In particular, we will examine how El Nino and La Nina events can affect western boundary currents in the Pacific and Indian basin. It will involve using large model and observational datasets and require a background in either MATLAB or python. Some experience with linux is desirable.