1. comparison of hom, splidhom and interp 2. ideas for the daily benchmark dataset (temperature)

Zentralanstalt für Meteorologie und Geodynamik

1. Comparison of HOM, SPLIDHOM and INTERP2. Ideas for the daily benchmark dataset (temperature)

Christine Gruber, Ingeborg Auer


Intercomparison experiments

Comparison of: Della-Marta and Wanner, 2006 (HOM) Mestre et al., ???? (SPLIDHOM) Vincent et al., 2002; Brunetti et al., 2006 (INTERP)

I. Semi-synthetic data Use of parallel measurements Combination of series:

artificial but realistic breaks

the truth is known for evaluation of the methods

II. (Preliminary) Application of the methods to a test dataset (Lower Austria) Uncertainty estimation using bootstrap

temperature dependent adjustments


Semi-synthetic data

Parallel measurement breaks Realistic inhomogeneities (relocation, screen change,..) Not only temperature dependence included Can be combined at given break point known position

In Austria not enough stations with long parallel measurements available…


Results for 5 Stations, TMIN/TMAX, 4 seasons=40 series

Absolute differences of percentiles• Homogenized-truth• RAW-truth


Benefit of the homogenization

HOM SPLIDHOM INTERP

Q10 Q50 Q90 Q10 Q50 Q90 Q10 Q50 Q90

TMIN

TMAX


Conclusions

For evaluation parallel measurement data is used+ realistic breaks

- only 40 time series homogenized (*20 different samples)

- Many time series too small inhomogeneities, less temperature dependence

HOM and SPLIDHOM similar, main differences for extreme values

Improvement of HOM/SPLIDHOM compared to INTERP, in the case that: Highly correlated reference stations available Inhomogeneity is temperature dependent


Lower Austria- Experiment

Preliminary analysis of the Lower Austria temperatures

Mainly to see how the methods work for real data

Influence of reference stations, magnitude of the breaks,…

Testing a bootstrap approach for estimating uncertainties

Break detection with HOCLIS and PRODIGE (annual means) Homogenization with SPLIDHOM (HOM)


Lower Austria- Experiment

TMAX PRODIGE HOCLIS META

HOH homogen homogen 1971 05 Station relocation

KRM

1997

197101,198210, 199604

197101,198210,199604

Station relocationStation relocationChange to automated station

RET 19511985

1994

1983 111987 011995 06

1983 11

1995 06

Station relocation

Change to automated station

SPO

1978

1955 091971 011979 041994 01

1955 091971 011979 041994 01

Station relocation21 19 UhrStation relocationChange to automated station

WIE 1951/52

19851994

1953 011971 011980 011993 01

1953 011971 01

1993 01

Station relocation21 19 Uhr

Change to automated station

WMA 19561985(1989)

1956 01

1990 03

1956 01

1990 03

Station relocationStation relocation

ZWE 1971 1971 011980 011994 08

1971 011980 011994 08

21--> 19 UhrStation relocationChange to automated station


WIE summer, SPLIDHOM

Ref=KRM

Ref=HOH

Ref=WMA

Influence of undetected breakpoints (higher order moments) in REF?Too short HSPs for KRM, WMA!

1993 1980 1971 1953adju

stm

ent [

°C]

temperature [°C]


Adjustments Vienna

Error growth!!!?

1993 1980 1971

HOM

SPLIDHOM

How many values are required that breaks can be adjusted reliably?Comparison of different methods useful

Uncertainty of the adjustments seems to be reduced for earlier breaksIntroduction of a “model” easier to adjust in the following (earlier) breaks


WIE winter SPLIDHOM

Ref=KRM

Ref=HOH

Ref=WMA


WIE (ref=HOH)

Q10 Q90

Annual mean

All data estimateMean of bootstrap sample0.9 confidence intervalOriginal

Uncertainties in extremes of the adjustments have hardly any influence(in this case)


WIE (ref=KRM)


Q10 Q90

Annual mean


WIE (ref=WMA)


Q10 Q90

Annual mean


Example for usefulness of uncertainty estimates

Q10

No effect of the adjustments on the 0.1 percentileBut information about the (minimum) uncertainty of the time series


Example for usefulness of uncertainty estimates

Annual mean


Open questions

Requirements for reference stations? correlation length of HSPs

Detection of “higher order moment”- breaks? Is it possible to adjust higher order moments?

Problems due to micro-scale climate changes (test-reference station distribution change)

Uncertainty assessment (especially for extreme values) method uncertainty sampling uncertainty representativeness (references)


Benchmark daily data


The nature of the problem

Extreme value studies homogenization of daily data necessary

Adjusting inhomogeneities in dependence of the weather type, physical reasons (primary effect) Adjustments as function of wind, sunshine duration, global

radiation… (difficult due to data availability)

Adjustment of the temperature dependence of the inhomogeneities (secondary effect) Adjusting the temperature distribution (e.g. Della-Marta and

Wanner, 2006) Effect of inhomogeneities on temperature percentiles/extremes is

reduced (that’s what we want in extreme value studies)

In a first step: Shall we take into account only temperature dependent breaks in the daily benchmark?


Significance of temperature dependence

How often significant temperature dependence occurs?

Typical pattern and range of the magnitude pattern for synthetic inhomogeneities


Possible working steps

I. Case study• Real dataset, metadata (availability?)• Classification of inhomogeneities due to their source• Examination of temperature dependence? (e.g. HOM)• Other dependencies (wind, radiation,…)• Typical pattern benchmark

II. Semi-synthetic (parallel measurement) series• Realistic inhomogeneities, but truth is known for evaluation• Dependencies to other elements could be studied (wind, radiation?)• Data availability? (too few stations in Austria)

III. Surrogate• Based on typical inhomogeneity pattern (temperature dependent)• (If other dependencies shall be treated as well benchmark multiple

series???? ( new adjustment-method multi-parameter???)

Typical pattern?

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1. comparison of hom, splidhom and interp 2. ideas for the daily benchmark dataset (temperature)

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