4/9/18 - 4/13/18
pangeo - work on using python in the HPC. Open source project.
psyplot - Interactive plotting
opendatacube - Satellite data analysis
Extreme learning machines e.g. hpelm - High-Performance Extreme Learning Machines
Random forrest e.g. scikit-learn - a machine learning method
Ayarzagüena - ENSO on NAO in early winter via Gulf of Mexico by ppt anomalies in GoM and Central America.
Fromang - QGPV idealized mjo perturbation on NAO
Hong - ET forcing the 2015 MJO-ENSO
My talk - 2008/2009 winter had a majow SSW however it was not well forecast. See this paper (comment by Dr. Blanca Ayarzagüena-Porras) and this paper on the event. Could look into likelihood of one year not being forecast. Therefore, 08/09 may just be explained by noise (Comment by Tim Wollings). paper by Doblas-Reyes on multi-model NAO forecasting: 4 AGCM. Argue the use of model PC.
Bernat Jimenez-Esteve (Zurich) - Enso -> Auleition low -> waves in NAtl. Enso -> walker cell -> tropical atlantic -> waves. + NAO late winter El Nino. Transient eddies (2<T<8 bandpass filter); Q-stationary (T>10 days). Opposite response for El Nino and La Nina. paper
Tyrrell et al (Helsinki) - Oct 2016 weak polar vortex, warm anomaly. Advection, not sea-ice loss cause warming
Richard Greatbatch et al (Kiel, de) - Relaxation experiments to determine remote influence on NAO. Relax tropics in one experiment and another in the troposphere. Strat influence is lower that trop influence. MJO is a large influence in some years when it is suppressed. ECMWF seasonal forecasting is good at SSW.
Wolf et al (University of Reading) - Quasi-stationary waves on temperature extremes in Europe.
Rousi - NAO 'flavors' using clustering.
Mecking - Ocean vs atm on Europe summer temperatures
- Reduced November Barents-Kara sea ice is linked to a more negative winter NAO.
PICO - retrograde earth experiences. Used paraview for the visualizations
Chaucer re-insurance company.
Fathom Bristol/Uni Bristol - global flood hazard layer.
OASIS LMF Open source Loss Modeling Framework
Ardhuin - SKIM: Potential ESA mission for measuring surface currents and waves.
Bourassa - WaCM: Wave and current potential satellite mission.
Amores - limits of ocean eddy sensing
Ciani - surface currents in the Med
Martineau - ocean temps and weather extremes.
Li - Relationship between NAO and AMO.
Aoyanagi - Tsunami evacuation simualtion
Jean-Raymond Bidlot - sea-state dependency of air-sea fluxes in ECMWF Earth System Model. Charnock term is dynamic. Cd is influenced by Ch - heat. Janssen (1997) sea-state on heat flux. Tech Mem 239.
Sasmal - coastal waves in Sagami Bay, Japan. WW3 and SWAN.
Markina - NAtl EKE on Hs
Ardhuin - Hs spectrum looks like current spectrum. Collard paper. Climate change initiative starting off in IFREMER. Need currents for high resolution model. Testing global tides and CMEMS 1/12 hindcast.
Stoll - Climatology of polar lows.
Priestley - ETC clustering.
Bertoncelj - Med sea-level/waves storms.
Kettle - North Sea storm surge
Stefanie Rynders - wave, current, time and sea-ice on offshore loads: update morison eq. and add sea-ice. Look at hazards in different regions. e.g. waves in North Sea, currents in shelf slope, tides in some coastal areas. Funding by SOS-SOS (Safer Operations at Sea - Supported by operational simulations) and here.
North Sea: AMM7, WW3 7km
Arctic: CICE, NEMO, SWARP
You can use git to version control your script locally
$ mkdir test
$ vi my_script.py # add 'test'
$ git init # Turned the current directory into a local git repository
$ git status # show status
$ git add my_script.py
# Similarly git rm my_script.py
$ git commit -m "initial commit" # -m is message
$ vi my_scrit.py # change to test2
$ git commit -a -m "added a line to the script" # Add the file and commit
$ git log # show changes
# Have a look at code state on previous commit
$ git checkout ...
# Back to maskter
$ git checkout master
# Revert latest changes
$ git revert HEAD # esc -> :z
You can use git to version control remotely
# sign up for github and create a new repository
# call it 'EGU_test' and description 'test repo for EGU course'
# Push an existing git repo here
$ cd test
$ git remote add origin https://github.com/USERNAME/EGU_test.git
$ git remote -v
# Push to the github repo
$ git push -u origin master
# Create a new branch
$ git checkout -b awesome_feature
# See branches
$ git branch
$ vi my_script.py # change to testb
$ git commit -a -m "working on a new feature"
# Switch back to master branch
$ git checkout master
# Push new branch to github repo
$ git push origin awesome_feature
# Click pull request button on github
# Update local master branch with github repo
$ git pull origin master
# Delete local branch
$ git branch -d awesome_feature
# Delete the remote branch
$ git push origin --delete awesome_feature
Krzysztofowicz - Bayesian Approach to Statistical Post-Processing.
Friederichs - modelling of spatial extremes.
Continuous Rank Probability Score (CRPS) here
van Straaten - stat post-proc of high-res ppt EPS.
Simon - prob forecasting of thunderstorms: generalized additive models; ECMWF will soon have a lightening diagnostic.
Thorarinsdottir - proper skill scoring: squared error; absolute error;
ignorance score (probabilistic) e.g. here and here ; CRPS.
statistical postprocessing of ensemble forecasts book
Peirce skill score; odds ratio skill score e.g.
PRIMAVERA - H2020 EU consortium on community wide high resolution modeling.
Haarsma13 TCs in EC-Earth 25 km AGCM. More TCs/ETCs in Western Europe in the future. Warm seclusion storms paper.
TC and ETC tracks will be available for PRIMAVERA.
Vidale - stochastic physics (SP) and resolution on TCs: No stochastic physics kills TCs. Stochastic physics is equivalent to increasing resolution. More TCs.
SP acts as vortex seeder? Not obvious relationship to vws even though they change.
Francisco J. Doblas-Reyes - S2S climate services: prodhomme15 land surface initialization on forecast. prodhomme16 mod res on seasonal forecasting; Equitable Threat Score. improved predictions for agriculture; S2S4E - S2S for energy; Lledo18 paper - wind anomaly on west coast of US.
Alice Grimm - SA monsoon and the influence of the MJO. RW from Central Pacific to SA. Lin08 eval skill to predict MJO. Bivarate correlation.
Yuejian Zhu - 3-4 week forecast GEFS. FV3GEFS. SubX May 1st 2014 - May 26th 2016. int every 7 days. Stochastic schemes. 2-Tiered SST (not coupled). SP improves in tropics. The schemes add skill in later weeks. FV3 dycore.
Seok-Woo Son - QBO on MJO. Vitart17 Son17. Moisture advection over the maritime continent important for MJO. Cloud long-wave radiation. BMSE amplitude and BMSE phase error. MJO better predicted during EQBO winters by about 5 days. What is seasonal cycle like of MJO?
Gilbret Brunet - wave processes across time-scales.
Laura Baker - over/under confidence of NAO in EUROSIP. Eade14 under-confidence - Signal is too weak. Unpredictable noise. Don't standardize there is a huge spread. GA3, GA6, MF Sys3, Sys4, JMA Sys2. Box based NAO e.g. Stephenson06. GA3 is best. Multi-model is slightly higher. RPC ratio of predictable components see Eade16. >1 is under-confident. More ensembles, more under-confident. All models have common drivers i.e. similar inter-annual variability. ECMWF low skill but not under-confident.
Christopher White - Applications of S2S: paper. s2sdata. Sub-seasonal drivers: SAM, blocking. Early warning, disaster risk. response to resilience. Q: are we getting ahead of ourselves with the lack of skill of science? Case studies can be useful but may over egg the skill. ask about shipping.
Carlo Buontempo - ECMWF Copernicus Climate Change Service: C3S seasonal. EU Seasonal hydrological forecast. Shipping with OSM. Carlo.Buontempo@ecmwf.inf
Ole Wulff - Subseasonal prediction of 2003 European summer heat wave. atm blocking -< SST anomalies; trop-extrop RW. Spring dry soil moisture. Split ensembles by choosing members that get the gph500 best. Also split based on soil moisture.
Mike DeFlorio - sub-seasonal skill of atmospheric rivers: ARcatelogue. AR anomaly as a function of MJO phase.
Michael Walz - Predictability of extreme wind speed over Europe. paper Stasitical entropy; predictive information; predictive power. >95th percentile. Integrated over time steps. Mostly correlated with NAO. Not a great study.
Chaim Garfinkel - Predictability of SSW based on MJO. Strat and MJO on NAtl paper.
Ben Green - Sub-seasonal errors in FIM-iHYCOM.
Stevens - Extreme Earth: Advancing global storm resolving models to usher in a new era of climate modeling and climate change science
Neumann - Storm-Resolving Simulations of the Climate System
Bauer - Energy-efficient Scalable Algorithms for Weather Prediction at Exascale
Roberts - TCs in PRIMAVERA
Chantry - Model precision
Mavilia - Resolution and stoch pyhys on Euro-Atl weather regimes
Satoh - NICAM model
Voigt - High-res of an ETC
Manganello - TC landfall in high-res
Vanniere - Hyd cyc in high-res
Budich - Models for next gen comp
Gettelman - variable res CCSM