Topographic-dependent modellingof surface climate for earth systemmodelling and assessment  Michael Hutchinson, Jennifer K...
e-MAST’s objectivesDEVELOP research infrastructure to integrateTERN (and external) data streamsENABLE benchmarking, evalua...
ANU_Climate - Interrogation of Elevation Dependent Climate Surfaces
Climate data sets (1 km)          Tmin   Tmax   vp   Precip   pan    wet    solar   wind                                  ...
High-resolution climate surfaces
Daily Rainfall Data Network
Anomaly-based daily interpolationBackground field can be calibrated on full historical dataCan be extended to sites with m...
Censored power of normal distributionRainα = μ + σz  α     0.3 – 0.9  z     standard normal variable,   z ≥ -μ/σ  μ/σ    -...
α vs -μ/σ   1976-2005
Change in 99% daily rainfall January, July         1946-75 to 1976-2005
ParameterisationTwo parameters – calibrated on a monthly basis:Mean daily rainfall = f(μ/σ).σ2      (σ ranges from 5 to 6)...
μ/σ   1976-2005 Jan, July
Mean daily rain mm/day 1976-2005 Jan, July
Regression extension of short period records –               for 1976-20056400 stations with at least 20 years of recordAd...
Interpolation of anomaliesAdaptive thin plate smoothing spline interpolation of anomaliesMore knots for positive rainfall,...
Statistics for 6 Representative DaysStatistic              Cross Validation   Residuals of FitRMS of normalised      0.223...
Daily rainfall 5 Jan 1970
Daily Rainfall over NE Qld on 12/02/1999                                           Rainfall (mm)                          ...
Daily Maximum Temperature over NE Qld on 12/02/1999                                                      Temperature (C)  ...
ConclusionCensored square of normal distribution provides a stable parameterisation ofthe background daily rainfall distri...
Michael Hutchinson_Topographic-dependent modelling of surface climate for earth system modelling and assessment
Upcoming SlideShare
Loading in …5
×

Michael Hutchinson_Topographic-dependent modelling of surface climate for earth system modelling and assessment

324 views

Published on

0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
324
On SlideShare
0
From Embeds
0
Number of Embeds
1
Actions
Shares
0
Downloads
0
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

Michael Hutchinson_Topographic-dependent modelling of surface climate for earth system modelling and assessment

  1. 1. Topographic-dependent modellingof surface climate for earth systemmodelling and assessment Michael Hutchinson, Jennifer Kesteven, Tingbao Xu Australian National University
  2. 2. e-MAST’s objectivesDEVELOP research infrastructure to integrateTERN (and external) data streamsENABLE benchmarking, evaluation, optimizationof ecosystem modelsSUPPORT ecosystem science, impact assessmentand management
  3. 3. ANU_Climate - Interrogation of Elevation Dependent Climate Surfaces
  4. 4. Climate data sets (1 km) Tmin Tmax vp Precip pan wet solar wind evap days rad speeddaily ✔ ✔ ✔ ✔1970-2011monthly ✔ ✔ ✔ ✔ ✔ ✔1970-2011monthly ✔ ✔ ✔ ✔ ✔ ✔ ✔mean
  5. 5. High-resolution climate surfaces
  6. 6. Daily Rainfall Data Network
  7. 7. Anomaly-based daily interpolationBackground field can be calibrated on full historical dataCan be extended to sites with modest numbers of records –beyond what is available day by dayTopographic dependence can be (largely) incorporated into thebackground field parametersAnomalies from the background field have broader scale spatialpatterns, with little or no dependence on topography – supportsday by day interpolation from limited numbers of sitesHow to do this for daily rainfall?
  8. 8. Censored power of normal distributionRainα = μ + σz α 0.3 – 0.9 z standard normal variable, z ≥ -μ/σ μ/σ -3.0 to 2.0 P(W) = Φ(μ/σ)
  9. 9. α vs -μ/σ 1976-2005
  10. 10. Change in 99% daily rainfall January, July 1946-75 to 1976-2005
  11. 11. ParameterisationTwo parameters – calibrated on a monthly basis:Mean daily rainfall = f(μ/σ).σ2 (σ ranges from 5 to 6)P(W) = Φ(μ/σ) (μ/σ ranges from -3.0 to 2.0)
  12. 12. μ/σ 1976-2005 Jan, July
  13. 13. Mean daily rain mm/day 1976-2005 Jan, July
  14. 14. Regression extension of short period records – for 1976-20056400 stations with at least 20 years of recordAdditional 3200 stations with at least 10 years of recordWithout regression RMSE = 20%With regression RMSE = 10%Cross validation RMSE of interpolated long period stns = 15%Cross validation MAE of interpolated long period stns = 7% (3172 stations, at least 28 years of record)
  15. 15. Interpolation of anomaliesAdaptive thin plate smoothing spline interpolation of anomaliesMore knots for positive rainfall, fewer for latent negatives: – up to 5000 for positives (amounts) – 1500 for negatives (occurrence)Tune the placement and relative weighting of the latent negativesto minimise the RMS of cross validated normalised rainfall valuesPlacement: 0.25, weighting: 4.0Monitor cross validation of occurrence structureMonitor goodness of fit – amounts and occurrence
  16. 16. Statistics for 6 Representative DaysStatistic Cross Validation Residuals of FitRMS of normalised 0.223valuesMAE (mm) 1.43 0.940RMS (mm) 3.62 2.25MAE of positive rain 2.9(mm)Class average of 82.2% 90.6%occurrenceKappa statistic of 0.668 0.810occurrence
  17. 17. Daily rainfall 5 Jan 1970
  18. 18. Daily Rainfall over NE Qld on 12/02/1999 Rainfall (mm) High : 460 Low : 113
  19. 19. Daily Maximum Temperature over NE Qld on 12/02/1999 Temperature (C) High : 28.7 Low : 19.0
  20. 20. ConclusionCensored square of normal distribution provides a stable parameterisation ofthe background daily rainfall distributionAlso provides stable statistical assessment of rainfall extremes and of variousinterpolation statistics – applicationsNot perfect – smoothed interpolation of actual daily extremes – seasonalaggregations reasonableAnomaly-based interpolation is being applied to the other daily and monthlyvariablesDownscale climate drivers to any pointDownscale climate change scenarios to a grid

×