Hydro-climate forecasts for the upcoming spring flood season · For extremes (p10, p90): Fair Brier...
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This project has received funding from the Horizon 2020 programme under grant agreement n°776787. The content of this presentation reflects only the author’s view. The European Commission is not responsible for any use that may be made of the information it contains. Hydro-climate forecasts for the upcoming spring flood season Ilias Pechlivanidis (SMHI) with contributions from Fredrik Almen, Thomas Bosshard, and Louise Crochemore (SMHI) and Andrea Manrique and Francesc Roura Adserias (BSC)
Transcript of Hydro-climate forecasts for the upcoming spring flood season · For extremes (p10, p90): Fair Brier...
This project has received funding from the Horizon 2020 programme under grant agreement n°776787.
The content of this presentation reflects only the author’s view. The European Commission is not responsible for any use that may be made of the information it contains.
Hydro-climate forecasts for the upcoming spring flood season
Ilias Pechlivanidis (SMHI)
with contributions from Fredrik Almen, Thomas Bosshard, and Louise Crochemore (SMHI)
and Andrea Manrique and Francesc Roura Adserias (BSC)
Agenda
Why focusing on the HP sector?
Understanding the needs in HP
Our approach to address the needs in HP
Analysis of past meteorological status
Forecasts for the upcoming spring flood season (May-July)
Q&A
WHY FOCUSING ON THE HP SECTOR?
Why focusing on the HP sector?
Patsialis, T.; Kougias, I.; Kazakis, N.; Theodossiou, N.; Droege, P. Supporting Renewables’ Penetration in Remote Areas through the Transformation of Non-Powered Dams. Energies 2016, 9, 1054.
Why focusing on the HP sector?
https://s2s4e.eu/climate-services/case-studies
Issued date Lead
time
Variables Characteristics
Feb 2015 3 - precipitation
- snow water
equivalent
- inflows
System: ECMWF
SEAS5
Period: May-July 2015 Mar 2015 2
Apr 2015 1
Case study: Combined snowmelt and high precipitation in north Sweden
Skill (fRPSS) 1 month
(issued in
April)
2 months
(issued in
March)
3 months
(issued in
February)
Precipitation 0.13 0.10 -0.06
SWE 0.81 0.43 0.21
Inflows 0.67 0.48 0.27
UNDERSTANDING THE NEEDS IN HP
Understanding the needs in HP
Asymmetric distribution of inflows and demand over the year
Current operational systems
o Climatological P/T ensemble used to run a hydrological model
o Little improvement in forecasts over the last 30 years
Understanding the needs in HP
Information requested:
Inflows to reservoir
Precipitation
Snow (& Temperature)
Onset of spring flood
Forecast skill
…
Initial conditions
Knowledge of weather
during the forecast period
OUR APPROACH TO ADDRESS THE NEEDS IN HP
35 408 catchments with median size 215 km2
http://hypeweb.smhi.se/ Hydrological model: E-HYPE v.3
Addressing the needs in HP
Hundecha et al. (2016) A regional parameter estimation scheme for a pan-European multi-basin model. J. of Hydrol.: Reg. Studies
Topography: HydroSHEDS, GWD-LR
Land use: ESA CCI, FAO
Soil: HWSD + WISE + FAO
Lakes and reservoirs: GLWD, GRanD
Water flow: GRDC etc.
Evaporation: MODIS
Addressing the needs in HP SUB-SEASONAL
SEASONAL
May
Long term
mean Week 1
Week 2
Week 3
Week 4
SNOW MAX ANOMALY
May
Week 1 Week 2
Week 3
Week 4
INFLOW ANOMALY
Week 1
Week 2
Week 1
Week 2
June
June
Integration for the 1st time of sub-seasonal to seasonal predictions of indicators for the HP sector
Decision Support Tool v1.4.0
https://s2s4e-dst.bsc.es/
ANALYSIS OF PAST METEOROLOGICAL STATUS
March 2020 February 2020 January 2020
Wind speed forecasts Precipitation reanalysis
System used:
JRA-55
March 2020 February 2020 January 2020
System used:
JRA-55
Wind speed forecasts Temperature forecasts
FORECASTS FOR THE UPCOMING SPRING FLOOD SEASON
Above
Below
Predicted tercile Probability range Extremes
50% to 100%
34% to 49%
Max (p90)
Min (p10)
Normal
Wind speed forecasts Precipitation forecasts
SUB-SEASONAL
Prediction system used:
NCEP CFSv2
Maps show areas where
skill (fRPSS) > 0
Probability terms
Enhanced : 34% - 49%
High: 50% - 70%:
Very High: Greater than 70%
4 - 10 May 27 April - 3 May 20 - 26 April
Predicted tercile Probability range Extremes
Wind speed forecasts Temperature forecasts
SUB-SEASONAL
Prediction system used:
NCEP CFSv2
Maps show areas where
skill (fRPSS) > 0
Probability terms
Enhanced : 34% - 49%
High: 50% - 70%:
Very High: Greater than 70%
Above
Below
50% to 100%
34% to 49%
Max (p90)
Min (p10)
Normal
4 - 10 May 27 April - 3 May 20 - 26 April
Above
Below
Predicted tercile Probability range Extremes
50% to 100%
34% to 49%
Max (p90)
Min (p10)
Normal
Wind speed forecasts Precipitation forecasts Probability terms
Enhanced : 34% - 49%
High: 50% - 70%:
Very High: Greater than 70%
SEASONAL
Prediction system used:
ECMWF SEAS5
Maps show areas where
skill (fRPSS) > 0
July 2020 June 2020 May 2020
Probability terms
Enhanced : 34% - 49%
High: 50% - 70%:
Very High: Greater than 70%
SEASONAL
Prediction system used:
ECMWF SEAS5
Maps show areas where
skill (fRPSS) > 0
Predicted tercile Probability range Extremes
Wind speed forecasts Temperature forecasts Above
Below
50% to 100%
34% to 49%
Max (p90)
Min (p10)
Normal
July 2020 June 2020 May 2020
Probability terms
Enhanced : 34% - 49%
High: 50% - 70%:
Very High: Greater than 70%
SEASONAL
Prediction system used:
ECMWF SEAS5
Maps show areas where
skill (fRPSS) > 0
Wind speed forecasts Snow max anomaly forec.
July 2020 June 2020 May 2020
Above
Below
Predicted tercile
Normal
Probability terms
Enhanced : 34% - 49%
High: 50% - 70%:
Very High: Greater than 70%
SEASONAL
Prediction system used:
ECMWF SEAS5
Maps show areas where
skill (fRPSS) > 0
Wind speed forecasts Inflow anomaly forecasts Above
Below
Predicted tercile
Normal
July 2020 June 2020 May 2020
● Webinar series
○ https://s2s4e.eu/climate-services/webinar-series
● Monthly sub-seasonal to seasonal outlooks, published mid-month:
○ s2s4e.eu/climate-services/outlooks
● Case studies demonstrating the potential of the forecasts in the DST
○ s2s4e.eu/climate-services/case-studies
● Reports demonstrating the science and value of the DST
○ s2s4e.eu/climate-services/public-deliverables
Additional services
Q&A
Thank you
Get in touch for more
information!
Public reports of the project will be available for download on the S2S4E website: www.s2s4e.eu
Project coordinator: Albert Soret, Barcelona Supercomputing Center (BSC) Contact us: [email protected]
Follow us on Facebook and Twitter! @s2s4e
This project has received funding from the Horizon 2020 programme under grant agreement n°776787.
The content of this presentation reflects only the author’s view. The European Commission is not responsible
for any use that may be made of the information it contains.
The SMHI Hydrology R&D unit
ADDITIONAL SLIDES
● Tercile probabilities: Probability (number of members) of temperature being
in the lower, middle or upper tercile of the system’s climatology.
p90
p66
p33
p10
Forecast products
● Probability of extremes: Probability (number of
members) of temperatures exceeding the system’s
climatological 10th/90th percentile. The triangles
are shown when the probability is larger than 25%
● Relative measure of the quality a system’s forecasts for the time period
and location
● Typically measured on the system’s hindcast
● For tercile probabilities: Fair Ranked probability skill score (fair RPSS)
● For extremes (p10, p90): Fair Brier Skill Score (fair BSS)
1993
2019 2017
● compared to using a
climatological forecast
● Skill score= (1- (Sfcst /
Sclim))*100
● exceeding the 10th
and 90th percentiles
Skill scores
Coordinated by Barcelona Supercomputing Centre (Spain), and consists of:
The S2S4E Consortium
● Sub-seasonal and seasonal predictions
for the energy sector
● The Decision Support Tool (DST)
launched on 20 June 2019 and has
been operational since then.
● Monthly outlooks and quarterly
webinars to present forecasts to users
S2S4E: Sub-seasonal to seasonal climate predictions for energy
