Statistical downscaling of CLM precipitation data with an analogue method using radar data of radar Essen (DWD) - Alrun Tessendorf, Thomas ...
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Statistical downscaling of CLM precipitation data with an analogue method using radar data of radar Essen (DWD) Alrun Tessendorf, Thomas Einfalt hydro & meteo GmbH & Co. KG Markus Quirmbach dr. papadakis GmbH
Overview Introduction Data and research area Downscaling procedure Evaluation of the results Summary and conclusion
Introduction: dynaklim
dynaklim:
dynamical adaptation of regional planning and development processes
in the Emscher-Lippe-Region (North-Rhine-Westphalia)
effect of climate change on regional precipitation
Focus on extreme events: heavy rain as a potential risk for urban
flash floods
Introduction: dynaklim
dynaklim:
dynamical adaptation of regional planning and development processes
in the Emscher-Lippe-Region (North-Rhine-Westphalia)
effect of climate change on regional precipitation
Focus on extreme events: heavy rain as a potential risk for urban
flash floods
Generation of input data for small-scale hydrological models and
hydrodynamic sewer modelsIntroduction Small-scale hydrological models and hydrodynamic sewer models require input data with high spatial and temporal resolution: adjusted radar QPE
Introduction Small-scale hydrological models and hydrodynamic sewer models require input data with high spatial and temporal resolution: adjusted radar QPE Idea: using adjusted radar data for a statistical downscaling of regional climate model data
Introduction: statistical downscaling
classification method:
• Classification by weather type: basis for assignment of historical data
• Assumption: the model can better reproduce the large-scale weather
situation than parameters on a smaller scale
analogue method:
• Selection of analogue weather events from historical measurements,
based on a comparison of model and measurement data
stochastic weather generators
• combination of the methodsIntroduction: statistical downscaling
classification method:
• Classification by weather type: basis for assignment of historical data
• Assumption: the model can better reproduce the large-scale weather
situation than parameters on a smaller scale
analogue method:
• Selection of analogue weather events from historical measurements,
based on a comparison of model and measurement data
stochastic weather generators
• combination of the methods
Analogue method with predictors daily sum and objective weather
type (DWD)Data and research area Regional climate model CLM provided by the Climate Service Center (CSC) : Scenario A1B, run 1 and run 2 Reference period: 1961-1990 (“C20”) , Near Future: 2021-2050 + Far Future: 2071-2100 • Daily sums • Objective weather types (Krahé et al., 2011)
Data and research area Measurement data: Corrected and adjusted data from radar Essen (DWD, 1km x 1 , 5min, DX-product), 01.11.2001 – 01.11.2009 • Adjustment based on 580 controlled rain gauges (117 for validation) Objective weather types (daily, measurements of the DWD)
Data and research area
Research area: 10 CLM grid points in the Emscher-Lippe Region
(North-Rhine Westphalia)
• Selection of the area: based on similar precipitation characteristics
and orographic conditions.
• Production of data for 3 catchments near Dortmund, Duisburg and
Bönen, catchment size 70-76 km²
GP_088_092 GP_088_093 GP_088_094 GP_088_095
GP_088_087 GP_088_088 GP_088_089 GP_088_090 GP_088_091
Rorup
Dülmen
GP_087_094 GP_087_095
GP_087_091 GP_087_092 GP_087_093
GP_087_087 GP_087_088 GP_087_089 GP_087_090
Lembeck
Werne-Wessels DWD
Herringen
Werne
Welver DWD GP_086_096
GP_086_092 GP_086_094 GP_086_095
GP_086_091 GP_086_093
GP_086_088 GP_086_089 GP_086_090
GP_086_087
Recklinghausen Im Reitwinkel Soest DWD
Gladbeck Hahnenbach Dortmund Nettebach
Dinslaken Emschermündung
GP_085_096
GP_085_094 GP_085_095
GP_085_091 GP_085_092 GP_085_093
GP_085_087 GP_085_088 GP_085_089 GP_085_090
GP_084_092 GP_084_093 GP_084_094 GP_084_095
GP_084_087 GP_084_088 GP_084_089 GP_084_090 GP_084_091Data and research area
Precipitation distribution from radar of a convective event
on 12 August 2005:
24h-rain sums on 12 Aug 2005, rain sums
are 1-22 mm (left) and 7-31 mm (right)Downscaling procedure, overview:
CLM-Data Selection
Bias Adjusted radar
• obj. weather types procedure
• daily sums correction • based on daily
data (2001-2009)
• 2 runs (CLM 1 + 2) • daily sums sums/ obj. • ∆t 5 min
weather types • 1km x 1
Rain gauge data Data set downscaling
(1961-1990) Evaluation • ∆t 5 min, 1km x 1km
• quality controlled • Dortmund, Duisburg, Bönen
• 28 stations • 1961-90
• 2021-50, 2071-100Bias correction and use of the daily sums Bias correction of daily sums: Piani et al. (2010) Used data: • area means on CLM grid points from radar (2001-2009) • CLM data from 2 runs As RCM values on single grid points are less reliable than averages over several grid points: • daily sums statistically averaged over 10 grid points using the cumulated PDF
Downscaling procedure: selection algorithm For each day from CLM: search for similar days from the measurement period. Criteria: daily sum (close interval around the given value) and objective weather type If no similar days are found: stepwise increase of the interval and inclusion of neighboring weather types Several days are found: random selection of one day
Downscaling procedure: selection algorithm
For each day from CLM: search for similar days from the measurement
period. Criteria: daily sum (close interval around the given value) and
objective weather type
If no similar days are found: stepwise increase of the interval and
inclusion of neighboring weather types
Several days are found: random selection of one day
Increase of the data-base by:
Permitting spatial displacement of the radar data within the 10 grid
points of the research area
making use of the similar precipitation characteristics within the research area
disadvantage: neglect of small-scale orographic effectsDownscaling procedure
selection process
• performed for each catchment separately
Selection results checked on:
• Frequency of use of radar events (max. 3 times / 30 years)
• selection effect (Young 1994)
- Restrictive criteria in the selection process affecting the selection of extreme events
• discrepancies of the daily sum > 4mmDownscaling procedure
selection process
• performed for each catchment separately
Selection results checked on:
• Frequency of use of radar events (max. 3 times / 30 years)
• selection effect (Young 1994)
- Restrictive criteria in the selection process effecting the selection of extreme events
• discrepancies of the daily sum > 4mm
discrepancies of the daily sum > 4 mm: 2-4 events per 30-year period
modification of the radar event with a constant factor to catch
the 24h-sum of the model eventprocedure • High resolution radar data used to produce time series for the catchments (on each 1x1km² field) Time series evaluated using extreme value statistics • evaluation by duration and return period (following the DVWK rules for water management 124/1985) • Evaluation for the reference period, near and far future
Validation: Reference period
Extreme precipitation (1h, 5a), comparison of station mean from
28 stations and downscaling results in the reference period
Extreme precipitation (1h,5a) within the catchments, catchment size is
70-76 km²Results: trend analysis
CLM 1 CLM 2
Extreme precipitation (1h, 5a) for reference period (1961-90), near future (2021-50)
and far future. The shaded uncertainty area is 10% (according to KOSTRA).
• CLM 1 shows significant positive trends (+18.8% / + 16.4%)
• CLM 2 trends are weaker and less significant (+16.4% / +8.8%)Summary and conclusion
• Generation of high resolution data with natural characteristics
on a sub-daily scale possible
• Data can be used as input to small-scale hydrological models
run-off model of Rossbach catchment near Dortmund:
reasonable results in comparison to observations
• Requirement: good measurement data base with the needed
spatial resolutionSummary and conclusion
Problems to overcome:
• Reliable Bias correction of the daily values
(with regard to the effect on climate trends)
• Ensemble evaluation
high effort because combination of climate model runs and
runs of the statistical downscaling model is necessary
considerable size of the produced data sets (high resolution)
• Uncertainties have to be communicated to the end-users, e.g.
general model uncertainties (statistical downscaling: do the
used relationships hold in the future)
uncertainty of extreme events
-> high uncertainty intervals in model and observationsReferences
Deutscher Verband für Wasserwirtschaft und Kulturbau e.V.
(1985): Niederschlag- Starkregenauswertung nach
Wiederkehrzeit und Dauer, DVWK Regeln zur Wasserwirtschaft
124/1985
DWD (2005): KOSTRA-DWD-2000, Starkniederschlagshöhen für
Deutschland (1951-2000), Grundlagenbericht. Offenbach am
Main.
hydro & meteo (2009): The SCOUT Documentation version 3.30.
Lübeck, 69 Seiten
Krahé, P., E. Nilson, U. Gelhardt und J. Lang (2010): Thank you for
Auswertungen ausgewählter globaler Klimamodelle hinsichtlich
atmosphärischer Zirkulationsbedingungen im Nordatlantisch-
Mitteleuropäischen Sektor. BfG-Bericht 1682.
your attention!
Piani, C., J.O. Haerter und E. Coppala (2010): Statistical bias
correction for daily precipitation in regional climate models over
Europe. Theor. Appl. Climatol. 99, 187-192.
Young, K. (1994): A multivariate chain model for simulating
climatic parameters from daily data, J. Appl. Meteorol., 33(6),
661-671.
Zorita, E. und H. von Storch,(1999): The analog method as a
simple statistical downscaling technique: Comparison with more
complicated methods, J. Clim., 12, 2474-2489Result
Preliminary result: hydrological run-off model Rossbach catchment
(Dortmund):
Reference period 1960-90:
Entwicklung der statistischen Abflussscheitel (T = 20 a)
an der Mündung Rossbach (PSe)
20
18
16
Abflussscheitel [m³/s]
14
12
10
8
6
4
2
0
1961-1990 2021-2050 2071-2100
Station/1,3 CLM1 CLM2Zusammenfassung und Ausblick
Das radarbasierte Downskaling zeigt, dass
- das Herunterbrechen von CLM-Daten auf eine in der Stadthydrologie nutzbare Auflösung
möglich ist
- die Daten etwa im Bereich > 30 Minuten oder seltener als 1x pro Jahr eingesetzt werden
können
- die Schwankungsbreite der Ergebnisse in der Statistik höher ist als zwischen Messstationen
- zusätzliche Analysen und Empfehlungen für den Einsatz der Daten erforderlich sindVielen Dank für Ihre Aufmerksamkeit ! Kontakt: Projektleiter Arbeitspaket AP 3.1: „Aufbereitung und Bereitstellung der Klimadaten für die Prognosemodelle“ Dr. Markus Quirmbach dr. papadakis GmbH Tel.: 02324/904489-1 Mail: m.quirmbach@drpapadakis.de
Downscaling procedure
Downscaling procedure
Downscaling procedure
Matched results, 2009Downscaling procedure
Selection effect
• caused by close
criteria in the
selection process
• Result:
unrealistic low
extreme eventsYou can also read
