- Table of Contents
[ToC]

# :memo: Meeting summary

## 03.02.2022: Lennart and Emily

**what we covered**
- log in to ela.cscs
- created subdirectory lkoenige
- where is what, how can I find help
- relevant modules
- short reintroduction to ncview

**to-do-list**
- [x] setup HackMd
- [x] familiarize with NCO, CDO, ncview
- [x] play around with available data on Cluster

## 23.03.2022: Lennart and Emily

**notes going into the meeting**
- [x] present possible research questions and methods
- [ ] decide wich analysis method(s) to focus on

**what we covered**
- discussed first proposal draft
- overall reasonable ideas for proposed anlaysis and methods
- TC TITLI it is for the case study!
- ideas for research questions should be 'broader'
e.g. How did TC 'xyz'/ 'Titli' alter the mean flow pattern towards HMA? --->>> How did the flow pattern change during the case study period?

**to-do-list**
- Emily:
- see if CPTP observational data is somehow accessible
- see if proper COSMO output is available/ can be made available for back-trajectory calculation
- ask if Lennart is allowed to access the (case study) paper draft (and preliminary analysis associated)
- Lennart:
- nail down research question(s)
- decide on analysis method tier-list
- look for availability of observational datasets other than TRMM and APHRODITE (maybe something more local?)
- ask Niklas for his first results and have a chat about data he produces (mutual benefit possible?)
- think about possibilities to adress the 'attribution problem' other than back-trajectory analysis
- make familiar with ERA5 and COSMO analysis tools (CDO, NCO and obviously Python...)

## 12.04.2022: Lennart and Emily

**what we covered**
- show plots
- moisture convergence / moisture flux
- precipitation / moisture flux
-> I need to work on that to make it more useful!
- cluster analysis?
- cluster the climatology into groups of influence areas so that the climatology to compare to is more legible? Which parameter makes sense to use?
-> clustering is not the best idea to have. Instead try to use a threshold for the presence of a TC in the vicinity (1000-3000 km?) of the study area. Build a climatology of all those occasions using [best-track-archive](https://www.ncei.noaa.gov/products/international-best-track-archive?name=ib-v4-access)
- ERA5 topography? Derive it from Geopotential divided by g? Or is it somewhere available?
-> yes
- discuss vertical integral of model output
- how to get the layer means correct
- difference layers/levels
-> use values on model levels instead of pressure levels.. the dz error is minimal compared to the dp error
- translate that to COSMO?
-> I will have lots of work with only ERA5 so far

**to-do-list**
- [ ] make plots more usefull (include topography, more useful color coding (maybe diverging?), maybe include storm-track?)
- [ ] use best-track-archive to come up with basin wide frequency map + plot case study TCs (best-track) into that
- [ ] climatology for cyclone events with threshold (1000-3000km) to compare case study event to (how extreme was it?)
- [ ] compute moisture flux perpendicular to slope for the case study event (precip amount)

## 25.04.2022: Lennart and Emily

**what we covered**
- concept presentation
- was ok, but more background would've been good...
- the workload is ambitious, as we agreed on earlier I should focus on the first points on my methods tier-list
- Emily recommended some more papers I should have a look
- Maussion 2010 - TC evaluation
- Moritz Trichtl -> paper moisture budget HMA?
- Lilian Schuster -> paper using LAGRANTO
- according to Fabi MODIS is another option for snow cover analysis instead of LANDSAT
- for the back-trajectory analysis I should talk to Dabih Isidori - he is also working with LAGRANTO

**to-do-list**
- [x] share troPYcal with Emily (clean up script...)
- [x] ERA5 analysis comparison to climatology

## 12.05.2022: Lennart and Emily

**what we covered**
- plots so far

**to-do-list**
- [x] investigate why standardized anomalies are off
- [ ] investigate interaction TC and westerlies
- [ ] plots for other pressure levels (especially 850)
- [x] use vortex tracker software on ERA5 data and compare to the ibtracs data (best track)
- [x] install LAGRANTO (apparently the hardest part about using it)

## 07.07.2022: Lennart and Emily

**what we covered**
- precipitation anomalies
- lagranto

**to-do-list**
- [ ] precipitation anomalies for ERA5 and IMERG (comparison)
- [ ] backtrajectory analysis (with different starting heights)
- [ ] revise concept (e.g. write first two thesis chapters)

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# :calendar: Anticipated Schedule
As a reference point:
- 30 ECTS are equal to 750 hours of work
- Finishing end of June: 17 weeks of 40 working hours each makes 680 total hours

-> this schedule is probably to optimistic - more likely: end of July

## KW 10
### to-do-list
- [x] setup schedule until concept presentation (KW17)
- reading list preparation
- [x] prepare preliminary list
- [x] decide on the most relevant and schedule for the upcoming week
- [x] read two most relevant papers
- [x] paper 8: overview about TC exploration (genesis, progression, typical patterns)
(not fully finished because it is very extensive and exhausting)
- [x] paper 10: WRF simulations for unusual accumulation events due to extratropical cyclones (maybe I can use some of the methods and transfer them to TCs)
- login to CSCS and:
- [x] setup jupyter-notebooks and spyder
- [x] familiarize with NCO, CDO, ncview
- [x] play around with available data on Cluster
- [x] CSCS User Lab on friday at 11:00 (https://ethz.zoom.us/j/69667563663)

### reading list
1. Adnan, Muhammad, Shichang Kang, Guoshuai Zhang, Muhammad Saifullah, Muhammad Naveed Anjum, and Ayaz Fateh Ali. 2019. “Simulation and Analysis of the Water Balance of the Nam Co Lake Using SWAT Model.” Water 11 (7): 1383. https://doi.org/10.3390/w11071383.
2. **Bhardwaj, P., Singh, O. Climatological characteristics of Bay of Bengal tropical cyclones: 1972–2017. Theor Appl Climatol 139, 615–629 (2020). https://doi.org/10.1007/s00704-019-02989-4**
3. Cannon, F., L. M. V. Carvalho, C. Jones, and J. Norris. 2016. “Winter Westerly Disturbance Dynamics and Precipitation in the Western Himalaya and Karakoram: A Wave-Tracking Approach.” Theoretical and Applied Climatology 125 (1-2): 27–44. https://doi.org/10.1007/s00704-015-1489-8.
4. Cannon, Forest, Leila M. V. Carvalho, Charles Jones, and Bodo Bookhagen. 2015. “Multi-Annual Variations in Winter Westerly Disturbance Activity Affecting the Himalaya.” Climate Dynamics 44 (1): 441–55. https://doi.org/10.1007/s00382-014-2248-8.
5. **Chen, B., Xu, X.-D., Yang, S., and Zhang, W.: On the origin and destination of atmospheric moisture and air mass over the Tibetan Plateau, Theor. Appl. Climatol., 110, 423–435, doi:10.1007/s00704-012-0641-y, 2012**
6. **Collier, E., T. Sauter, T. Mölg, and D. Hardy. 2019. “The Influence of Tropical Cyclones on Circulation, Moisture Transport, and Snow Accumulation at Kilimanjaro During the 2006–2007 Season.” Journal of Geophysical Research: Atmospheres 124 (13): 6919–28. https://doi.org/10.1029/2019JD030682.**
7. **Curio, J., F. Maussion, and D. Scherer. 2015. “A 12-Year High-Resolution Climatology of Atmospheric Water Transport over the Tibetan Plateau.” Earth System Dynamics 6 (1): 109–24. https://doi.org/10.5194/esd-6-109-2015.**
8. **Curio, Julia, and Dieter Scherer. 2016. “Seasonality and Spatial Variability of Dynamic Precipitation Controls on the Tibetan Plateau.” Earth System Dynamics 7 (3): 767–82. https://doi.org/10.5194/esd-7-767-2016.**
9. **CWD, 2018. IMD Very Severe Cyclonic Storm “Titli” over Eastcentral Bay of Bengal (08-13 October 2018): A Report, Cyclone Warning Division. India Meteorological Department. http://www.rsmcnewdelhi.imd.gov.in/images/pdf/publications/preliminary-report/titli.pdf. (Accessed 29 March 2022).**
10. **Emanuel, Kerry. 2003. “Tropical Cyclones.” Annual Review of Earth and Planetary Sciences 31 (1): 75–104. https://doi.org/10.1146/annurev.earth.31.100901.141259.**
11. **Emanuel, Kerry. 2018. “100 Years of Progress in Tropical Cyclone Research.” Meteorological Monographs 59 (1): 15.1–15.68. https://journals.ametsoc.org/view/journals/amsm/59/1/amsmonographs-d-18-0016.1.xml.**
12. Gray, William M. 1968. “GLOBAL VIEW OF THE ORIGIN OF TROPICAL DISTURBANCES AND STORMS.” Monthly Weather Review 96 (10): 669–700. https://doi.org/10.1175/1520-0493(1968)096<0669:GVOTOO>2.0.CO;2.
13. Lang, Timothy, and Ana Barros. 2004. “Winter Storms in the Central Himalayas.” Journal of the Meteorological Society of Japan 82 (June): 829–44. https://doi.org/10.2151/jmsj.2004.829.
14. **Norris, J., L. M. V. Carvalho, C. Jones, and F. Cannon. 2015a. “WRF Simulations of Two Extreme Snowfall Events Associated with Contrasting Extratropical Cyclones over the Western and Central Himalaya.” Journal of Geophysical Research: Atmospheres 120 (8): 3114–38. https://doi.org/10.1002/2014JD022592.**
15. **Neckel, N., Kropáček, J., Schröter, B. and Scherer, D. (2015), Effects of Cyclone Hudhud captured by a high altitude Automatic Weather Station in northwestern Nepal. Weather, 70: 208-210. https://doi.org/10.1002/wea.2494**
16. Roy Chowdhury, Riyanka, S. Prasanna Kumar, and Arun Chakraborty. 2021. “Simultaneous Occurrence of Tropical Cyclones in the Northern Indian Ocean: Differential Response and Triggering Mechanisms.” Frontiers in Marine Science 8. https://www.frontiersin.org/article/10.3389/fmars.2021.729269.
17. Schär, Christoph, Nikolina Ban, Erich M. Fischer, Jan Rajczak, Jürg Schmidli, Christoph Frei, Filippo Giorgi, et al. 2016. “Percentile Indices for Assessing Changes in Heavy Precipitation Events.” Climatic Change 137 (1): 201–16. https://doi.org/10.1007/s10584-016-1669-2.
18. Srivastava, Akhil, V. S. Prasad, Ananda Kumar Das, and Arun Sharma. 2021. “A HWRF-POM-TC Coupled Model Forecast Performance over North Indian Ocean: VSCS TITLI & VSCS LUBAN.” Tropical Cyclone Research and Review 10 (1): 54–70. https://doi.org/10.1016/j.tcrr.2021.04.002.
19. Tanguang, Gao, Kang Shichang, Lan Cuo, Zhang Tingjun, Zhang Guoshuai, Zhang Yulan, and Mika Sillanpää. 2015. “Simulation and Analysis of Glacier Runoff and Mass Balance in the Nam Co Basin, Southern Tibetan Plateau.” Journal of Glaciology 61 (227): 447–60. https://doi.org/10.3189/2015JoG14J170.
20. Thapa, Kritika, Theodore A. Endreny, and Craig R. Ferguson. 2018. “Atmospheric Rivers Carry Nonmonsoon Extreme Precipitation Into Nepal.” Journal of Geophysical Research: Atmospheres 123 (11): 5901–12. https://doi.org/10.1029/2017JD027626.
21. Vergara-Temprado, Jesús, Nikolina Ban, Davide Panosetti, Linda Schlemmer, and Christoph Schär. 2020. “Climate Models Permit Convection at Much Coarser Resolutions Than Previously Considered.” Journal of Climate 33 (5): 1915–33. https://doi.org/10.1175/JCLI-D-19-0286.1.
22. Weisman, Morris L., William C. Skamarock, and Joseph B. Klemp. 1997. “The Resolution Dependence of Explicitly Modeled Convective Systems.” Monthly Weather Review 125 (4): 527–48. https://doi.org/10.1175/1520-0493(1997)125<0527:TRDOEM>2.0.CO;2.
23. Wu, Hongbo, Ninglian Wang, Xi Jiang, and Zhongming Guo. 2014. “Variations in Water Level and Glacier Mass Balance in Nam Co Lake, Nyainqentanglha Range, Tibetan Plateau, Based on ICESat Data for 2003-09.” Annals of Glaciology 55 (66): 239–47. https://doi.org/10.3189/2014AoG66A100.
24. Yang, Yan, Tongtiegang Zhao, Guangheng Ni, and Ting Sun. 2018. “Atmospheric Rivers over the Bay of Bengal Lead to Northern Indian Extreme Rainfall.” International Journal of Climatology 38 (2): 1010–21. https://doi.org/10.1002/joc.5229.

**notes taken during reading:** https://hackmd.io/rJD1IEcBRdCaerzsxd6hzA

## KW 11
### to-do-list
- read more papers to get better overview
- [x] paper 4: (maybe I can use some of the methods)
- [x] paper 7: (Annual Review about Thttps://www.ncei.noaa.gov/products/international-best-track-archive?name=ib-v4-accessCs - hopefully more concise than the 100 years review from last week)
- [x] paper 5: (gives a good overview of water transport towards HMA -> what are the relevant regions)
- work out rough concept
- [x] relevant region
-> central to eastern himalaya
-> especially transects that include mayor water transport paths (Brahamaputra)
- [x] analysis to focus on
-> source regions
-> mean flow changes
-> with comparison of with/without TC influence
-> striking spatial patterns
- [x] **sketch some research questions**
most research questions need investigation of many TCs -> as the first idea was to have a case study that is not feasible -> rephrase it so that it matches for a case study instead
- How do TCs alter the mean flow pattern towards HMA? -> How did TC 'xyz' alter the mean flow pattern towards HMA?
-> establish climatology for non TC conditions (like Curio2015)
-> maybe during the same time of year for the last ?? years where no TC was present?
-> depict case study conditions
-> compare
- Description of evolution of TC 'xyz' and water transport bound to it
-> vortex tracking software
-> moisture source regions
-> primary water transport paths to HMA
- Effects of this (increased) water transport to HMA:
-> accumulation event?
-> short/long term contribution to water cycle?
-> increased precipitation due to water recycling during the rest of the season? (Contribution is probably hard)
-> Are TCs an important contributor to HMA water availability?
-> have a heatmap of TC frequency compared to precipitation events?
-> percentage of water provided by TC precip event compared to full season
- Compare modelling (COSMO and ERA5) results with TRMM data
-> this is basically a full new study ... but a short comparative paragraph is necessary in the methods part to motivate the usage of COSMO/ERA5 ... if its totally of we shouldnt use it (there are plenty papers comparing model results to TRMM data!)
### some notes
- ideas for further research
- [ ] Joswiak, D. R., Yao, T., Wu, G., Tian, L., and Xu, B.: Ice-core evidence of westerly and monsoon moisture contributions in the central Tibetan Plateau, J. Glaciol., 59, 56–66, doi:10.3189/2013JoG12J035, 2013. -> this paper could be interesting to motivate my research (stemming from Curio 2015: A shift in the isotope signals implies that the contribution of westerly moisture to the ice-core accumulation was relatively greater before the 1940s)
- [x] Chen, B., Xu, X.-D., Yang, S., and Zhang, W.: On the origin and destination of atmospheric moisture and air mass over the Tibetan Plateau, Theor. Appl. Climatol., 110, 423–435, doi:10.1007/s00704-012-0641-y, 2012 -> this paper investigates moisture sources during summer season (maybe nice to compare too)
- [ ] Lu, N., Qin, J., Gao, Y., Yang, K., Trenberth, K. E., Gehne, M., and Zhu, Y.: Trends and variability in atmospheric precipitable water over the Tibetan Plateau for 2000–2010, Int. J. Climatol., doi:10.1002/joc.4064, in press, 2014. -> i think i already read that one -> have a look again: also stemming from Curio 2015: Lu et al. (2014) analysed the atmospheric conditions and pathways of moisture to the TP for a wet and dry monsoon season and showed that differences in the atmospheric circulation have a direct impact on the moisture transport and on the PW over the TP.
- [x] find out if the follow up paper on Curio.2015 is already published! -> paper 7
- ideas for data analysis
- [x] heatmap of TC frequency / precipitation (compare to data of accumulation events)
- [ ] vortex tracking with: National Oceanic and Atmospheric Administration Geophysical Fluid Dynamics Laboratory vortex tracking scheme version 3.9a (Biswas et al., 2018), which estimates the position and intensity of a storm based on sea level pressure and lower tropospheric winds, relative vorticity, and geopotential height
- [ ] to assess moisture source regions: back trajectories throughout the lifetime of each storm using the model LAGRANTO (Sprenger & Wernli, 2015)
- [x] finding anomalous precip events with: Tropical Rainfall Measuring Mission (TRMM) data for computing 95th percentiles of daily precipitation for each month, considering only days with nonzero values after area averaging. TRMM data available at: https://trmm.gsfc.nasa.gov/data_dir/data.html description at: https://trmm.gsfc.nasa.gov/3b42.html
- [ ] a budget for AWT like in Curio.2015 could be interesting showcasing differences for TC / non TC conditions - but multiple factors influence this budget that are not only dependent on atmospheric mesoscale systems (moisture recycling)

## KW 12
### to-do-list
- read three more papers to get better idea of analysis options
- [x] paper 13: another case study for TITLI and LUBAN -> more about the triggering mechanism and influence on North Indian Ocean
- [x] paper 15: case study for TITLI and LUBAN
- [x] paper 12: read again
- [x] [work out concept](https://hackmd.io/6n1HNdgBSheydqylecizhw)
- [x] Meet Emily to discuss [concept draft](https://hackmd.io/6n1HNdgBSheydqylecizhw) (Wednesday 16:30)

### some notes
- further research:
- [x] **Titli description paper:** CWD, 2018. IMD Very Severe Cyclonic Storm “Titli” over Eastcentral Bay of Bengal (08-13 October 2018): A Report, Cyclone Warning Division. India Meteorological Department. http://www.rsmcnewdelhi.imd.gov.in/images/pdf/publications/preliminary-report/titli.pdf. (Accessed 29 December 2020).

## KW 13
### to-do-list
- read more papers to get better idea of analysis options
- [x] Chen et al 2012 - from further research
- [x] paper 7: Follow up on Curio.2015 -> dynamic precipitation controls on TP
- [ ] paper 2: Bardway - BOB TC climatology
- [ ] paper 20/24: extreme precipitation cases in Nepal and northern India
- [ ] paper 21/22: maybe this gives an idea what is possible with the available data (is convection resolving very important for TC precip?
- [ ] paper 23: possibility for comparison data for TC influence on glacier MB
- [x] work through the further research notes
- [x] KW11
- [x] KW12
- [x] [nail down research question(s)](https://hackmd.io/ZdKkKuF-TZuTLPRg72kIeg)
- [x] [decide on analysis method tier-list](https://hackmd.io/ZdKkKuF-TZuTLPRg72kIeg)
- [x] look for availability of observational datasets other than TRMM and APHRODITE (maybe something more local?)
- CMORPH and IMERG are possibilities -> but not really observational (like in situ), they are both based on aggregation of remote sensing data
- [HadISD](https://www.metoffice.gov.uk/hadobs/hadisd/) is another option
- maybe I can get the observational data from [CPTP](http://rcg.gvc.gu.se/cordex_fps_cptp/)
- [x] ask Niklas for his first results and have a chat about data he produces (mutual benefit possible?)
- [x] think about possibilities to adress the 'attribution problem' other than back-trajectory analysis
- nothing comes to mind that is not even more complicated and does not need extensive observational studies (like investigation of precipitation chemistry)
- of course a qualitative way would be to examine satelite images
- [x] have a look if there is a paper for tropical cyclone influence in HMA to date
- [x] ERA5
- [x] download for 2018/2019

### some notes
- further research
- figure 2 from Chen et al should be relatively easy to recreate from ERA5 data! That would be interesting for the case study period
- PCA in paper Curio.2016 ... what exactly is done here, how is PC1 and PC2 defined? looks like they have spatialy changing importance, but what do they consist of? the 12 months of year? what are the loadings of the linear combinations? I dont get it ...
- CMORPH, IMERG as comparison datasets for precipitation

## KW 14
### to-do-list
- [x] ERA5
- [x] download for 2018 (sep,oct,nov)
- [x] download for climatology (1979-present)
- [x] play around to see evolution during case study (and before/after)
- [x] code analysis scripts for ERA5 evolution
- [x] recreate Chen et al Figure 2 from ERA5 
- [x] during CaseStudy
- [x] climatology
- [x] analyse first results
- do they adress the correct area and processes?
- we do se the cyclones!
- we can see the moisture convergence in the southern part
- the effect on HMA seems to be limited
- what is missing?
- a sufficient scale for the divergence must be established so that one can see what goes on in the himalaya, right now the southern cyclone part dominates the scale
- is the model topography for ERA5 available? can i plot the (e.g) 2000m altitude line so that the plots are better structured?
- a cluster analysis to build a more useful climatology would be nice!
- cluster for what?
- spatial distribution of divergence?
- moisture flux?
- what is the exact connection moisture convergence <-> precipitation? I read it somewhere ... find it again!

## KW 15 (partially vacation)
### to-do-list
- [x] Plot same figures for HudHud ... to see if I can see effects on HMA on those plots
- [x] Meeting Emily (Tuesday @noon)
- show plots
- moisture convergence / moisture flux
- precipitation / moisture flux
- cluster analysis?
- cluster the climatology into groups of influence areas so that the climatology to compare to is more legible? Which parameter makes sense to use?
- ERA5 topography? Derive it from Geopotential divided by g? Is it somewhere available?
- discuss vertical integral of model output
- how to get the layer means correct
- difference layers/levels
- translate that to COSMO?

## KW 16 (partially vacation)
### to-do-list
- [x] Finalize concept
- [x] prepare presentation
- [x] practise it with e.g. Marie to get a feeling for plot holes
- [x] make plots more usefull (include topography, more useful color coding (maybe diverging?), maybe include storm-track?)
- [x] use best-track-archive to come up with basin wide frequency map + plot case study TCs (best-track) into that 

NamCo station:
Degrees Minutes Seconds 30°46'44"N, 90°59'31"E
Decimal Degrees 30.77889,90.99194

## KW 17
### to-do-list
- [x] Thesis concept presentation in ICU meeting
- [x] improve concept
- [x] share troPYcal with Emily
- [x] clean up script...
- [x] GitHub page
- [x] start with ERA5 analysis (comparison to climatology)

## KW 18
### to-do-list
- [x] fix CDS API and download full set to cluster
- [x] get ERA5 topography and make comparison for TP
- [x] think about summing convention
- [x] ERA5 analysis
- [x] precipitation
- [x] temperature

### some notes
- most parameters I want to have a look at are not included in ERA5 on daint (but I can use U,V,T,QV and FIS). Data is only available from 2005 onwards
- comparison of different parameters for mean and accumulation (october_climatology, event_average, anomaly)
-> both not necessarily clever
-> after plotting a timeseries of october-dayli-climatology I realised that a strict decline is visible, so a dayli climatology makes more sense than to average/accumulate-weight over full october
- pre-definition of study-area (lon(80-105),lat(25-40), z>2500)
- comparison event/dayli climatology (both summed and weighted over event-days - I extended the event from 04-15 here to capture the whole cycle)
for the whole domain
only for the study area
- timeseries for standardized precipitation anomalies in the study area
in the CORDEX_FPS: CPTP case study proclamation a heavy snowfall event from 04-08 october 2018 is reported. Timeseries plots make it look like this is associated with a west-east moving system. TC-impact in the TP is apparent in the TP from 12 october onwards.

## KW 19
### to-do-list
- [x] ERA5 analysis
- [x] winds
- [x] temperature
- [x] relative humidity
- [x] WRITE DOWN WHAT I FOUND (NOT) OUT SO FAR
- [x] investigate why standardized anomalies are off
- problematic using std on precip ... I should either standardize with the mean, or have to use a more complicated metric (fit gamma distribution -> transform to gaussian -> compute std -> transform back) maybe computing SPI instead is more useful.
- [x] oneD comparison?
- [x] add one more plot for relative humidity, maybe more is visible here

### meeting notes
- notebook ERA5_analysis_precipitation_final.ipynb
- reasoning for dayli climatology instead of monthly climatology weighted by days
- clear decline of average precip over the course of october + spatially visible
- oneD comparison
- not so meaningfull
- but still, we can see two peaks in precip
- timeseries plots for precip anomaly with dayli accumulated climatology
- especially in the second plot (for the study area) we can see that there are two precip events. Probably one associated to the first trough to the north and the second to the TC from the south
- timeseries plots for standardized anomaly with dayli accumulated climatology
- same but standardized with the average amount of precip in the region
- the precip falling in this timeseries is exceptional!
- accumulated precipitation comparison all domain / study area (with dayli climatology summed and weighted by days of event)
- collapsed on time dimension
- especially on the standardized plots we see the exceptional influence of the TCs but unfortunately especially further south

- notebook ERA5_analysis_temperature.ipynb
- its nice to look at but not so insightful
- but its clearly visible that in the north there is some kind of strong wave structure
- also in the study area plots one can see it passing
- compared to the long time average (climatology over 43 years) it was a rather cold october
- notebook Emily_meeting_12May.ipynb
- event evolution on different plots
- all plots are daily averages of 4 timesteps (00, 06, 12, 18)
-> else it would be even more ... (data and plots)
- unfortunately I do not see much in the region I am interested in
- we do see the same pattern as in the temperature timeseries - consecutive high and low pressure systems to the north
- fronts are not very clear to see
- the TCs are less pronounced than I would have expected
- 700hPa is already terrain intersecting, no?
- concentrating on HMA the evolution is not very helpful

## KW 20
### to-do-list
- [x] ERA5 analysis
- [x] equivalent potential temperature? could be helpful to see fronts
- mpcalc for dewpoint and eq pot temp!
- [x] VORTEX TRACKER
- [x] installation
- [x] write the concept
- [x] fill in master thesis form
## KW 21
### to-do-list
- [ ] ERA5 analysis
- [ ] vapour flux
- [x] VORTEX TRACKER
- [x] use vortex tracker software on test data
- [x] make plots for other pressure levels (especially 850)

## KW 22
- VORTEX TRACKER struggle
## KW 23
- VORTEX TRACKER struggle
## KW 24
### to-do-list
- [X] VORTEX TRACKER
- [X] use vortex tracker software on ERA5 data
- [X] generate sufficient input data
- [X] modify namelist
- [x] write proper script to run it automatically
- [x] compare to the ibtracs data (best track)
- [x] partly rewrite jupyter-notebooks to simple scripts and annotate them
## KW 25
### to-do-list
- LAGRANTO struggle
## KW 26
### to-do-list
- LAGRANTO struggle
## KW 27
### to-do-list
- [x] LAGRANTO installation finally works
- [x] SPI calculation
- [x] SPI visualization
## KW 28
### to-do-list

## KW 29
- VACATION
## KW 30
- VACATION
## KW 31
### to-do-list
- [x] rewrite concept
- [x] LAGRANTO produce first output
- [x] LAGRANTO output visualization
## KW 32
### to-do-list
- [x] proper finish vortex tracker visualization
- [x] put concept together as first chapter of the thesis
## KW 33
### to-do-list
- [x] ERA5 AWT plots event vs climatology (04-15 oct)
event
event-time climatology

- my learnings from the plots I did so far:
- ERA5 underestimates fluxes and precip heavily in high mountain areas
- should I split the event in two: 04-08 and 08-15 because we can see precip patterns for both that seem to be resulting from different sources
- I need to include SPI somehow in those plots to stress the importance of the relatively little amount of precipitation on the TP

## KW 34/35
- [x] ERA5 AWT plots dayli -> I did it but I have no clue how to make use of them ... just to many and not illustrative
- [x] SPI plots
- after a lot of struggle I decided to have the SPI plotted along with the precip anomaly for the full event. so 11 days with reference date 15.Oct (04-15)
- I also have it dayli but I dont think that makes to much sense in the end
- the SPI is not really an index to be used for so short a period
- 11 days is already quite a stretch
- IMERG has only 17 years of base data before the event

ERA5
IMERG

## KW 36
- [x] I refined the plots and show normalized anomalies instead of absolute values now
ERA5
IMERG
- normalized anomalies as measure of importance of precipitation amount
- SPI as measure of probability of precipitation amount
- at NamCo station the amount was unusualy high with precip anomaly -> 2 in both datasets and 1<SPI<2, 2<SPI in ERA5 and IMERG respectively
- this reflects the unusually high snow amount witnessed at the station

- [x] update meeting
- TCs are frequent in HMA
- IBTrACS plot (KW16)
- case study Titli with ERA5 and IMERG
- is Titli well represented?
- comparison ERA5/IBTrACS plot KW32
good alignment but very much weaker
- does the atmospheric water transport differ from usual?
- plots KW 33
- precipitation along with vapour transport
similar patterns, but much more precip
- flux divergence did not work out as in Chen et al 2012 (plot KW 14)
-> I do not know how they got this figure, for me its completely different (2.5° resolution opposed to 31km?)
- what about the importance of this precip? (SPI plots)
- plots KW 36
normalized anomalies and SPI show that amount of precip was unusualy high and unlikely

pending:
- Landsat snow cover comparison
- synoptics plots
- plots not uploaded yet (open in jupyterhub)
meaningful plots still in the making - I have dayli plots but they are not great?
- what about interactions TC/westerlies - what is a good way to investigate that?
- LAGRANTO: still believes in being a spaceflight model

- next up: LAGRANTO and snow cover (LANDSAT)

other topics
- its hard to show everything spatially resolved ... should I start drawing boxes after all? How would I go about it?
e.g. split HMA in parts and get linegraphs for precip anomaly
- what about multi anual trends
is it meaningful to correct for them in every gridpoint?
can I correct with a 'bulk' trend?
isnt it most accurate to not correct and say it?
- what about averaging over moving systems?
e.g. AWT ... this does not work great as seen in KW33 plot (AWT-precip)
- the actual problem is still LAGRANTO
it produces output but this output is questionable to say the least

## to do's
- [ ] make this figure for the methods section

- [ ] try to make this figure from https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/qj.4174 

## KW ??
- [ ] investigate interaction TC and westerlies (using synoptics and backtrajectories)
### to-do-list
- polish analysis in this week!
- [ ] climatology for cyclone events with threshold (1000-3000km) to compare case study event to (how extreme was it?)
- [ ] compute moisture flux perpendicular to slope for the case study event (precip amount)
## KW ??
### to-do-list
- [ ] final revision
- [ ] hand in thesis