drift correction approaches Comparison of parametric and ... · correction of hincast H considering...
1
Comparison of parametric and non-parametric drift correction approaches J. Grieger, I. Kröner, T. Kruschke, H.W. Rust, U. Ulbrich Institute of Meteorology, Freie Universität Berlin, Germany SPECS/PREFACE/WCRP Workshop on Initial Shock, Drift, and Bias Adjustment 2016 [email protected] 1. Introduction • Analysis of decadal prediction skill of tem- perature and Northern Hemisphere (NH) winter windstorms • Using initialized decadal hindcasts • Performing drift correction approaches – non-parametric – parametric (polynomial model) • comparison of methods 2. Data 1. Near surface temperature (tas) 2. NH windstorm frequency [Leckebusch et al., 2008] for extended winter (Oct-Mar) • coherent exceedance of local 98th per- centile of near surface wind speed • observation/reanalysis: – for (1) HadCRUT4 [Jones et al., 2012] – for (2) ERA-Mix – ERA40 (1961/1962-1989/90) and ERA- Interim (1990/91-2009/2010) – ERA40 corrected, in order that mean and variance correspond for overlap- ping years of ERA40 and ERA-Interim • model simulations with MPI-ESM [Kr- uschke et al., 2015], 10 member – decadal hindcasts: full-field initialized with ORA S4 – uninitialized historical simulations 3. Method • correction of hincast H considering lead- time τ dependent bias, i.e. drift D • non-parametric (NP) approach calculates D τ for each τ separately [ICPO, 2011] • parametric method uses a polynomial of different order on lead-time τ to esti- mate drift. Polynomial also depends on initialization-time t [Kruschke et al., 2015] D (τ,t) =(b 0 + b 1 t)+(b 2 + b 3 t)τ + (b 4 + b 5 t)τ 2 +(b 6 + b 7 t)τ 3 • using mean square error skill score (MSESS) comparing forecast (FC) and reference (REF) [Illing et al., 2014] MSE = 1 n X j (H j - O j ) 2 , MSESS =1 - MSE FC MSE REF 4. Results Drift ID Drift correction A NP B τ 0 +NP C τ 3 Table 1: Overview of drift-correction combinations (non-parametric and polynomial of different order) using for the comparison (cf. Tab. 2) MSESS ID FC REF I A climatology II A uninitialized III C A IV C B Table 2: Overview of MSESS combinations for the comparison of drift correction approaches. MSESS IDs refer to the panel plot below. 1. Near surface temperature (tas) 90°S 60°S 30°S 0° 30°N 60°N 90°N 0° 60°E 120°E 180° 120°W 60°W 0° MurCSS − 1.0 − 0.8 − 0.6 − 0.4 − 0.2 0.0 0.2 0.4 0.6 0.8 1.0 I 90°S 60°S 30°S 0° 30°N 60°N 90°N 0° 60°E 120°E 180° 120°W 60°W 0° MurCSS − 1.0 − 0.8 − 0.6 − 0.4 − 0.2 0.0 0.2 0.4 0.6 0.8 1.0 II 90°S 60°S 30°S 0° 30°N 60°N 90°N 0° 60°E 120°E 180° 120°W 60°W 0° MurCSS − 1.0 − 0.8 − 0.6 − 0.4 − 0.2 0.0 0.2 0.4 0.6 0.8 1.0 III 90°S 60°S 30°S 0° 30°N 60°N 90°N 0° 60°E 120°E 180° 120°W 60°W 0° MurCSS − 1.0 − 0.8 − 0.6 − 0.4 − 0.2 0.0 0.2 0.4 0.6 0.8 1.0 IV MSESS of near surface temperature for the different combinations of forecast (FC) and reference (REF) shown in Tab. 2 1960 1970 1980 1990 2000 2010 4.5 5.0 5.5 6.0 6.5 Forecast winter (lead time) BIAS temperature 1 2 3 4 5 6 7 8 9 -4 -3 -2 1 9 6 0 - > I N I T I A L I SAT I ON- > 2 0 0 9 Temperature in the Northern Euro- pean region defined within the IPCC SREX [IPCC, 2012]. (Upper) Time series of (black) observed and (red) modeled (uninitialized simulations) temperature. (Lower) Drift D(τ, t) whereas color denote initialization- time t. 2. Winter wind storms 90°S 60°S 30°S 0° 30°N 60°N 90°N 180° 180° 120°W 60°W 0° 60°E 120°E 180° 180° MurCSS − 1.0 − 0.8 − 0.6 − 0.4 − 0.2 0.0 0.2 0.4 0.6 0.8 1.0 I 90°S 60°S 30°S 0° 30°N 60°N 90°N 180° 180° 120°W 60°W 0° 60°E 120°E 180° 180° MurCSS − 1.0 − 0.8 − 0.6 − 0.4 − 0.2 0.0 0.2 0.4 0.6 0.8 1.0 II 90°S 60°S 30°S 0° 30°N 60°N 90°N 180° 180° 120°W 60°W 0° 60°E 120°E 180° 180° MurCSS − 1.0 − 0.8 − 0.6 − 0.4 − 0.2 0.0 0.2 0.4 0.6 0.8 1.0 III 90°S 60°S 30°S 0° 30°N 60°N 90°N 180° 180° 120°W 60°W 0° 60°E 120°E 180° 180° MurCSS − 1.0 − 0.8 − 0.6 − 0.4 − 0.2 0.0 0.2 0.4 0.6 0.8 1.0 IV MSESS of NH wind storm track density for the different combinations of forecast (FC) and reference (REF) shown in Tab. 2 1960 1970 1980 1990 2000 2010 15 20 25 30 35 40 45 Storm track density in the North Atlantic (-30 ◦ E, 48.75 ◦ N). (Upper) Time series of (black) observed and (red) modeled (uninitialized sim- ulations) track density. (Lower) Drift D(τ, t) whereas color denote initialization-time t (Fig. taken from Kruschke et al. [2015]). 5. Conclusions • Decadal hindcasts show positive skill for temperature compared to the climatologi- cal forecast • Skill is reduced for uninitialized simula- tions as reference • Skill for winter wind storms is small using the non-parametric approach • Hindcasts show positive skill for winter storms compared to climatological forecast as well as uninitialized simulations in cer- tain regions using the parametric approach (not shown) • Parametric correction approach leads to large increase of skill for both temperature and wind storms • Third order polynomial is beneficial for temperature – due to large drift and small deviation of trend • Trend correction (zero order polynomial) has largest effect on wind storms – due to small drift and large deviation of trend Acknowledgement We acknowledge funding from the Federal Min- istry of Education and Research in Germany (BMBF) through the research programme “MiKlip II” Decadal Climate Prediction References ICPO. Data and bias correction for decadal climate predictions. CLIVAR Publication Series, No. 150, January 2011. URL http://www.wcrp- climate.org/decadal/references/DCPP_Bias_ Correction.pdf. Sebastian Illing, Christopher Kadow, Kunst Oliver, and Ulrich Cubasch. Murcss: A tool for standardized evaluation of decadal hindcast systems. Journal of Open Research Software; Vol 2, No 1 (2014), pages –, September 2014. URL http://openresearchsoftware.metajnl.com/article/view/jors.bf. IPCC. Managing the risks of extreme events and disasters to advance climate change adaptation. In C.B. Field, V. Barros, T.F. Stocker, D. Qin, D.J. Dokken, K.L. Ebi, M.D. Mastrandrea, K.J. Mach, G.-K. Plattner, S.K. Allen, M. Tignor, and P.M. Midgley, editors, A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change, page 582 pp. Cambridge Univ. Press, Cambridge, United Kingdom and New York, NY, USA, 2012. P. D. Jones, D. H. Lister, T. J. Osborn, C. Harpham, M. Salmon, and C. P. Morice. Hemispheric and large-scale land-surface air temperature variations: An extensive revision and an update to 2010. Journal of Geophysical Research: Atmospheres, 117(D5):n/a–n/a, 2012. ISSN 2156-2202. doi: 10.1029/2011JD017139. URL http://dx.doi.org/10.1029/2011JD017139. D05127. Tim Kruschke, Henning W. Rust, Christopher Kadow, Wolfgang A.Müller, Holger Pohlmann, Gregor C. Leckebusch, and Uwe Ulbrich. Probabilistic evaluation of decadalprediction skill regarding northern hemisphere winter storms. Meteorologische Zeitschrift, pages –, 01 2015. URL http://dx.doi.org/10.1127/metz/2015/0641. G. C. Leckebusch, D. Renggli, and U. Ulbrich. Development and application of an objective storm severity measure for the northeast atlantic region. Meteorologische Zeitschrift, 17(5):575–587, 2008. doi: 10.1127/0941- 2948/2008/0323.
Transcript of drift correction approaches Comparison of parametric and ... · correction of hincast H considering...
Comparison of parametric and non-parametricdrift correction approachesJ. Grieger, I. Kröner, T. Kruschke, H.W. Rust, U. UlbrichInstitute of Meteorology, Freie Universität Berlin, Germany
SPECS/PREFACE/WCRP Workshop on Initial Shock, Drift, and Bias Adjustment 2016 [email protected]
1. Introduction• Analysis of decadal prediction skill of tem-
perature and Northern Hemisphere (NH)winter windstorms
• Using initialized decadal hindcasts
• Performing drift correction approaches
– non-parametric
– parametric (polynomial model)
• comparison of methods
2. Data1. Near surface temperature (tas)
2. NH windstorm frequency [Leckebuschet al., 2008] for extended winter (Oct-Mar)
• coherent exceedance of local 98th per-centile of near surface wind speed
• observation/reanalysis:
– for (1) HadCRUT4 [Jones et al., 2012]
– for (2) ERA-Mix
– ERA40 (1961/1962-1989/90) and ERA-Interim (1990/91-2009/2010)
– ERA40 corrected, in order that meanand variance correspond for overlap-ping years of ERA40 and ERA-Interim
• model simulations with MPI-ESM [Kr-uschke et al., 2015], 10 member
– decadal hindcasts: full-field initializedwith ORA S4
– uninitialized historical simulations
3. Method• correction of hincast H considering lead-
time τ dependent bias, i.e. drift D
• non-parametric (NP) approach calculatesDτ for each τ separately [ICPO, 2011]
• parametric method uses a polynomial ofdifferent order on lead-time τ to esti-mate drift. Polynomial also depends oninitialization-time t [Kruschke et al., 2015]D(τ, t) =(b0 + b1t) + (b2 + b3t)τ+
(b4 + b5t)τ2 + (b6 + b7t)τ
3
• using mean square error skill score (MSESS)comparing forecast (FC) and reference(REF) [Illing et al., 2014]
MSE =1
n
∑j
(Hj −Oj)2 ,MSESS = 1− MSEFC
MSEREF
4. Results
Drift ID Drift correctionA NPB τ0+NPC τ3
Table 1: Overview of drift-correction combinations (non-parametric and
polynomial of different order) using for the comparison (cf. Tab. 2)
MSESS ID FC REFI A climatologyII A uninitializedIII C AIV C B
Table 2: Overview of MSESS combinations for the comparison of drift correction approaches. MSESS IDs refer to
the panel plot below.
1. Near surface temperature (tas)
90°S
60°S
30°S
0°
30°N
60°N
90°N
0° 60°E 120°E 180° 120°W 60°W0°
MurCSS− 1.0
− 0.8
− 0.6
− 0.4
− 0.2
0.0
0.2
0.4
0.6
0.8
1.0I
90°S
60°S
30°S
0°
30°N
60°N
90°N
0° 60°E 120°E 180° 120°W 60°W0°
MurCSS− 1.0
− 0.8
− 0.6
− 0.4
− 0.2
0.0
0.2
0.4
0.6
0.8
1.0II
90°S
60°S
30°S
0°
30°N
60°N
90°N
0° 60°E 120°E 180° 120°W 60°W0°
MurCSS− 1.0
− 0.8
− 0.6
− 0.4
− 0.2
0.0
0.2
0.4
0.6
0.8
1.0III
90°S
60°S
30°S
0°
30°N
60°N
90°N
0° 60°E 120°E 180° 120°W 60°W0°
MurCSS− 1.0
− 0.8
− 0.6
− 0.4
− 0.2
0.0
0.2
0.4
0.6
0.8
1.0IV
MSESS of near surface temperature for the different combinations of forecast (FC) and reference (REF) shown in Tab. 2
1960 1970 1980 1990 2000 2010
4.5
5.0
5.5
6.0
6.5
Forecast winter (lead time)
BIA
S
tem
pera
ture
1 2 3 4 5 6 7 8 9
−4
−3
−2
1 9 6 0 − > I N I T I A L I S A T I ON − > 2 0 0 9
Temperature in the Northern Euro-pean region defined within the IPCCSREX [IPCC, 2012]. (Upper) Timeseries of (black) observed and (red)modeled (uninitialized simulations)temperature. (Lower) DriftD(τ, t)whereas color denote initialization-time t.
2. Winter wind storms
90°S
60°S
30°S
0°
30°N
60°N
90°N
180° 180°120°W 60°W 0° 60°E 120°E180° 180°
MurCSS− 1.0
− 0.8
− 0.6
− 0.4
− 0.2
0.0
0.2
0.4
0.6
0.8
1.0I
90°S
60°S
30°S
0°
30°N
60°N
90°N
180° 180°120°W 60°W 0° 60°E 120°E180° 180°
MurCSS− 1.0
− 0.8
− 0.6
− 0.4
− 0.2
0.0
0.2
0.4
0.6
0.8
1.0II
90°S
60°S
30°S
0°
30°N
60°N
90°N
180° 180°120°W 60°W 0° 60°E 120°E180° 180°
MurCSS− 1.0
− 0.8
− 0.6
− 0.4
− 0.2
0.0
0.2
0.4
0.6
0.8
1.0III
90°S
60°S
30°S
0°
30°N
60°N
90°N
180° 180°120°W 60°W 0° 60°E 120°E180° 180°
MurCSS− 1.0
− 0.8
− 0.6
− 0.4
− 0.2
0.0
0.2
0.4
0.6
0.8
1.0IV
MSESS of NH wind storm track density for the different combinations of forecast (FC) and reference (REF) shown in Tab. 2
1960 1970 1980 1990 2000 2010
1520
2530
3540
45
Storm track density in the NorthAtlantic (-30◦E, 48.75◦N). (Upper)Time series of (black) observed and(red) modeled (uninitialized sim-ulations) track density. (Lower)Drift D(τ, t) whereas color denoteinitialization-time t (Fig. taken fromKruschke et al. [2015]).
5. Conclusions• Decadal hindcasts show positive skill for
temperature compared to the climatologi-cal forecast
• Skill is reduced for uninitialized simula-tions as reference
• Skill for winter wind storms is small usingthe non-parametric approach
• Hindcasts show positive skill for winterstorms compared to climatological forecastas well as uninitialized simulations in cer-tain regions using the parametric approach(not shown)
• Parametric correction approach leads tolarge increase of skill for both temperatureand wind storms
• Third order polynomial is beneficial fortemperature
– due to large drift and small deviationof trend
• Trend correction (zero order polynomial)has largest effect on wind storms
– due to small drift and large deviationof trend
AcknowledgementWe acknowledge funding from the Federal Min-istry of Education and Research in Germany (BMBF)through the research programme “MiKlip II”
DecadalClimate Prediction
ReferencesICPO. Data and bias correction for decadal climate predictions. CLIVAR Publication Series, No. 150, January 2011. URL http://www.wcrp-climate.org/decadal/references/DCPP_Bias_
Correction.pdf.Sebastian Illing, Christopher Kadow, Kunst Oliver, and Ulrich Cubasch. Murcss: A tool for standardized evaluation of decadal hindcast systems. Journal of Open Research Software; Vol 2, No 1 (2014),
pages –, September 2014. URL http://openresearchsoftware.metajnl.com/article/view/jors.bf.IPCC. Managing the risks of extreme events and disasters to advance climate change adaptation. In C.B. Field, V. Barros, T.F. Stocker, D. Qin, D.J. Dokken, K.L. Ebi, M.D. Mastrandrea, K.J. Mach,
G.-K. Plattner, S.K. Allen, M. Tignor, and P.M. Midgley, editors, A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change, page 582 pp. Cambridge Univ. Press,Cambridge, United Kingdom and New York, NY, USA, 2012.
P. D. Jones, D. H. Lister, T. J. Osborn, C. Harpham, M. Salmon, and C. P. Morice. Hemispheric and large-scale land-surface air temperature variations: An extensive revision and an update to 2010.Journal of Geophysical Research: Atmospheres, 117(D5):n/a–n/a, 2012. ISSN 2156-2202. doi: 10.1029/2011JD017139. URL http://dx.doi.org/10.1029/2011JD017139. D05127.
Tim Kruschke, Henning W. Rust, Christopher Kadow, Wolfgang A. Müller, Holger Pohlmann, Gregor C. Leckebusch, and Uwe Ulbrich. Probabilistic evaluation of decadal prediction skill regardingnorthern hemisphere winter storms. Meteorologische Zeitschrift, pages –, 01 2015. URL http://dx.doi.org/10.1127/metz/2015/0641.
G. C. Leckebusch, D. Renggli, and U. Ulbrich. Development and application of an objective storm severity measure for the northeast atlantic region. Meteorologische Zeitschrift, 17(5):575–587, 2008.doi: 10.1127/0941-2948/2008/0323.
![Scale Drift Correction of Camera Geo-Localization using ...openaccess.thecvf.com/content_ECCVW_2018/papers/...lizing geo-tagged images, such as those in Google Street View [1], and](https://static.fdocuments.net/doc/165x107/602b02fae18ddd21da6c4d40/scale-drift-correction-of-camera-geo-localization-using-lizing-geo-tagged.jpg)
