1. MELODIST – An open-source MEteoroLOgical observation time series DISaggregation Tool
Kristian Förster, Florian Hanzer, Benjamin Winter, Thomas Marke, Ulrich Strasser

2. How can we benefit from daily meteorological time series for hourly applications?
”2 minutes of madness”
Daily
data
Hourly
data
GHCN = Global Historical Climatology Network, ISD = Integrated Surface Database

3. How to transform daily meteorological series to hourly data?
”2 minutes of madness”
Example: Temperature disaggregation based on minimum and maximum
temperature:
Daily Maximum
Temperature
Daily Minimum
Temperature

4. How to transform daily meteorological series to hourly data?
”2 minutes of madness”
Example: Temperature disaggregation based on minimum and maximum
temperature:
Daily Maximum
Temperature
Daily Minimum
Temperature

5. How to transform daily meteorological series to hourly data?
”2 minutes of madness”
observed
disaggregated
Example:
Precipitation disaggregation based on a “cascade” model (Olsson, 1998):
Idea: Doubling of resolution is studied.
Statistical evaluations of long-term hourly precipitation series are prepared prior to disaggregation.
Based on these statistics, stochastic disaggregation is performed.

6. Thanks for your attention!
”2 minutes of madness”
 PICO
screen PICO4.2
Further reading:
Discussion paper recently published in GMDD (in review)
Software availability:
MELODIST repository on GitHub

7. Overview
Welcome to MELODIST! This presentation gives an overview about the software and its scientific background. Please feel free to browse around. The column of buttons on the right hand sight provides you with some basic information. The bottom row includes links to each variable. The arrows at the lower right helps you to navigate in the presentation.
MELODIST is free open-source software. You can obtain a copy here:
doi: /zenodo.46759
At present, a scientific paper describing MELODIST and all its methods included is under review in Geoscientific Model Development Discussions:
doi: /gmd

8. Preface
Hourly meteorological data are required for numerous applications in geosciences. In order to benefit from the better spatial and temporal coverage of daily meteorological data, MELODIST bridges the gap between the availability (daily data) and the temporal resolution (hourly data) through disaggregating daily to hourly time series.
Hourly
data
Daily

9. Coding Example (Python)

10. Study Sites

11. List of Methods implemented in MELODIST

12. Example in Hydrology: Flood Frequency Analysis
This example shows how MELODIST might be applied in Hydrology: Temperature and precipitation are disaggregated in order to achieve long-term hourly time series for flood modelling using a semi-distributed hydrological model. 100 cascade model runs have been performed prior to hydrological modelling. In this way, the flood frequency statistics derived using “real” hourly data (‘reference run’) is reliably reconstructed given that all realizations need to be considered.
Return period [years]
Discharge [m3/s]

13. Acknowledgements
This work was carried out within the framework of the projects “W01 MUSICALS II -Multiscale Snow/Ice Melt Discharge Simulation for Alpine Reservoirs” and “W03 InsuRe II - Insurance Risk Evaluation of Flooding and Adaptation”, carried out in the COMET research programme of the alpS - Centre for Climate Change Adaptation in Innsbruck, Austria. The authors want to thank the COMET research programme of the Austrian Research Promotion Agency (FFG) and the company partners TIWAG - Tiroler Wasserkraft AG and VLV - Vorarlberger Landes-Versicherung VaG. We also acknowledge the work of all the institutions that collect meteorological data and share these data with the public.

14. Temperature Disaggregation
This example shows how hourly temperature series might be derived using daily minimum and maximum temperature only.
Disaggregate
Daily Maximum
Temperature
Daily Minimum
Temperature

15. Temperature Disaggregation
This example shows how hourly temperature series might be derived using daily minimum and maximum temperature only.
Daily Maximum
Temperature
Daily Minimum
Temperature
 Show Results

16. Temperature Disaggregation
 Go Back
Temperature Disaggregation
Temperature disaggregation has been evaluated for all sites shown on the map (see, ‘Study Sites’). Solid lines represent the results obtained by MELODIST. Long-term averages of observed data are plotted using dashed lines.
Even though minimum and maximum temperatures are involved in the methodology in order to derive hourly values and no information of the timing of these values was available, diurnal temperature features are reconstructed very well.
Skill measures are listed in the discussion paper (see, ‘Overview’).
This example shows how hourly temperature series might be derived using daily minimum and maximum temperature only.
Daily Maximum
Temperature
Daily Minimum
Temperature
 Show Results

17. Precipitation Disaggregation
Each step includes a doubling of temporal resolution. The “branching” is done based on statistics by drawing random numbers. The methodology is reffered to as ”Cascade” model (Olsson, HESS, 1998).
observed
disaggregated
 Show Results

18. Precipitation Disaggregation
 Go Back
Precipitation Disaggregation
Statistical evaluation of rainfall intensity
Statistical evaluation of autocorrelation
Each step includes a doubling of temporal resolution. The “branching” is done based on statistics by drawing random numbers. The methodology is know as ”Cascade” model (Olsson, HESS, 1998)
Replay animation
Time [h]
 Show Results

19. Humidity Disaggregation
The disaggregation methods for humidity require hourly temperature series (i.e. running temperature disaggregation prior to this step.
 Show Results

20. Humidity Disaggregation
 Go Back
Humidity Disaggregation
Humidity disaggregation has been evaluated for all sites shown on the map (see, ‘Study Sites’). Solid lines represent the results obtained by MELODIST. Long-term averages of observed data are plotted using dashed lines.
Here, a simple model was applied that does only account for one humidity value for each acknowledging the fact that minimum and maximum humidity are rarely observed.
Skill measures are listed in the discussion paper (see, ‘Overview’).
The disaggregation methods for humidity require hourly temperature series (i.e. running temperature disaggregation prior to this step.
 Show Results

21. Radiation Disaggregation
This example shows how daily recordings of sunshine duration can be used to derive hourly radiation time series. Moreover, minimum and maximum temperature can also be used as input data if sunshine duration is not available.
 Show Results

22. Radiation Disaggregation
 Go Back
Radiation Disaggregation
Radiation disaggregation has been evaluated only for those sites for which daily totals of sunshine duration have been recorded since this is a common case.
Solid lines represent the results obtained by MELODIST. Long-term averages of observed data are plotted using dashed lines.
Skill measures are listed in the discussion paper (see, ‘Overview’).
This example shows how daily recordings of sunshine duration can be used to derive hourly radiation time series. Moreover, minimum and maximum temperature can also be used as input data if sunshine duration is not available.
 Show Results

23. Wind Speed Disaggregation
It is assumed that wind speed is highest in the afternoon due to temperature induced increase of momentum flux on fair weather days. As an alternative, a random number generator might be also applied.
 Show Results

24. Wind Speed Disaggregation
 Go Back
Wind Speed Disaggregation
It is assumed that wind speed is highest in the afternoon due to temperature induced increase of momentum flux on fair weather days. As an alternative, a random number generator might be also applied.
 Show Results