Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.
Topographic-dependent modelling
of surface climate for earth system
modelling and assessment
Michael Hutchinson, Jennifer ...
e-MAST’s objectives
DEVELOP research infrastructure to integrate
TERN (and external) data streams
ENABLE benchmarking, eva...
What e-MAST will provide
Top-level drivers and targets (from TERN and
elsewhere) for models
Software for benchmarking (bas...
Climate data sets (1 km)
Tmin

Tmax

vp

Precip

daily
✔
1970-2011

✔

✔

✔

monthly
✔
1970-2011

✔

✔

✔

✔

monthly
mean...
High-resolution climate surfaces
Daily Rainfall Data Network
Anomaly-based daily interpolation
Background field can be calibrated on full historical data
Can be extended to sites with...
Censored power of normal distribution
Rainα = μ + σz

α

0.3 – 0.9

z

standard normal variable,

μ/σ

-3.0 to 2.0

z ≥ -μ...
α vs -μ/σ

1976-2005
Power Parameter 1976-2005 Jan, July
Parameterisation

Two 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 1976-2005
6400 stations with at least 20 years of record
Additional 320...
Defining the anomalies
For positive rainfall – the z value of the underlying normal
distribution - z = (Rainα - μ)/σ

For ...
Interpolation of anomalies
Adaptive thin plate smoothing spline interpolation of anomalies
More knots for positive rainfal...
Statistics for 6 Representative Days
Statistic

Cross Validation

Residuals of Fit

RMS of normalised
values

0.223

0.300...
Daily rainfall 5 Jan 1970
Daily rainfall 5 Jan 1970
ANUClimate - Interrogation of Elevation Dependent Climate Surfaces
Monthly Mean Daily Maximum Temperature for 2001-2010
Daily Maximum Temperature over NE Qld on 12/02/1999

Temperature (C)
High : 28.7
Low : 19.0
Daily Rainfall over NE Qld on 12/02/1999

Rainfall (mm)
High : 460
Low : 113
Conclusion
Censored square of normal distribution provides a stable
parameterisation of the background daily rainfall dist...
Conclusion
Censored square of normal distribution provides a
stable parameterisation of the background daily
rainfall dist...
Tools
Connect inputs and targets via a model
“Spread” fluxes across the landscape via a model
Connect observed [CO2] and s...
Tools
Connect inputs and targets via a model
“Spread” fluxes across the landscape via a model
Connect observed [CO2] and s...
Tools
Connect inputs and targets via a model
“Spread” fluxes across the landscape via a model
Connect observed [CO2] and s...
Tools
Connect inputs and targets via a model
“Spread” fluxes across the landscape via a model
Connect observed [CO2] and s...
Tools
Connect inputs and targets via a model
“Spread” fluxes across the landscape via a model
Connect observed [CO2] and s...
EcoTas13 Hutchinson e-MAST ANU
Upcoming SlideShare
Loading in …5
×

EcoTas13 Hutchinson e-MAST ANU

449 views

Published on

ANU's Mike Hutchinson presentation on e_MAST and ANU Climate at EcoTas13 in November 2013.

Published in: Technology, News & Politics
  • Be the first to comment

  • Be the first to like this

EcoTas13 Hutchinson e-MAST ANU

  1. 1. Topographic-dependent modelling of surface climate for earth system modelling and assessment Michael Hutchinson, Jennifer Kesteven, Tingbao Xu Australian National University
  2. 2. e-MAST’s objectives DEVELOP research infrastructure to integrate TERN (and external) data streams ENABLE benchmarking, evaluation, optimization of ecosystem models SUPPORT ecosystem science, impact assessment and management
  3. 3. What e-MAST will provide Top-level drivers and targets (from TERN and elsewhere) for models Software for benchmarking (based on PALS) Data-assimilation for optimization Tools for interpolation, downscaling, upscaling, hindcasting, forecasting High-resolution products: climate, canopy conductance, water use, primary production
  4. 4. Climate data sets (1 km) Tmin Tmax vp Precip daily ✔ 1970-2011 ✔ ✔ ✔ monthly ✔ 1970-2011 ✔ ✔ ✔ ✔ monthly mean pan evap wet days ✔ ✔ ✔ ✔ ✔ ✔ solar rad wind speed ✔ ✔
  5. 5. High-resolution climate surfaces
  6. 6. Daily Rainfall Data Network
  7. 7. Anomaly-based daily interpolation Background field can be calibrated on full historical data Can be extended to sites with modest numbers of records – beyond what is available day by day Topographic dependence can be (largely) incorporated into the background field parameters Anomalies from the background field have broader scale spatial patterns, with little or no dependence on topography – supports day by day interpolation from limited numbers of sites How to do this for daily rainfall?
  8. 8. Censored power of normal distribution Rainα = μ + σz α 0.3 – 0.9 z standard normal variable, μ/σ -3.0 to 2.0 z ≥ -μ/σ P(W) = Φ(μ/σ)
  9. 9. α vs -μ/σ 1976-2005
  10. 10. Power Parameter 1976-2005 Jan, July
  11. 11. Parameterisation Two 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 1976-2005 6400 stations with at least 20 years of record Additional 3200 stations with at least 10 years of record Without 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. Defining the anomalies For positive rainfall – the z value of the underlying normal distribution - z = (Rainα - μ)/σ For zero rainfall – invent a latent negative anomaly by placing the normalised value “mid-way” in the zero (dry day) probability region
  16. 16. Interpolation of anomalies Adaptive thin plate smoothing spline interpolation of anomalies More 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 negatives to minimise the RMS of cross validated normalised rainfall values Placement: 0.25, weighting: 4.0 Monitor cross validation of occurrence structure Monitor goodness of fit – amounts and occurrence
  17. 17. Statistics for 6 Representative Days Statistic Cross Validation Residuals of Fit RMS of normalised values 0.223 0.300 MAE (mm) 1.43 0.940 RMS (mm) 3.62 2.25 MAE of positive rain (mm) 2.9 1.80 Class average of occurrence 82.2% 90.6% Kappa statistic of occurrence 0.668 0.810
  18. 18. Daily rainfall 5 Jan 1970
  19. 19. Daily rainfall 5 Jan 1970
  20. 20. ANUClimate - Interrogation of Elevation Dependent Climate Surfaces
  21. 21. Monthly Mean Daily Maximum Temperature for 2001-2010
  22. 22. Daily Maximum Temperature over NE Qld on 12/02/1999 Temperature (C) High : 28.7 Low : 19.0
  23. 23. Daily Rainfall over NE Qld on 12/02/1999 Rainfall (mm) High : 460 Low : 113
  24. 24. Conclusion Censored square of normal distribution provides a stable parameterisation of the background daily rainfall distribution Provides stable assessment of residual interpolation statistics The anomalies, for both positive and zero rainfall, can be effectively interpolated by a TPS with adaptive complexity Possible to incorporate additional fine scale predictors – radar, cloud data, etc Cross validation and goodness of fit statistics show modest, but significant, improvements over some existing methods Further assessment of accuracy, and of the tuning of the adaptive interpolation procedure, is in progress
  25. 25. Conclusion Censored square of normal distribution provides a stable parameterisation of the background daily rainfall distribution Censored square of normal distribution a model “Spread” fluxes across the landscape via a model Connect observed [CO2] and streamflow to modelled CO2 flux and runoff Compute data-model comparison statistics Derive re-analysis products Downscale climate drivers to any point Downscale climate change scenarios to a grid
  26. 26. Tools Connect inputs and targets via a model “Spread” fluxes across the landscape via a model Connect observed [CO2] and streamflow to modelled CO2 flux and runoff Compute data-model comparison statistics Derive re-analysis products Downscale climate drivers to any point Downscale climate change scenarios to a grid
  27. 27. Tools Connect inputs and targets via a model “Spread” fluxes across the landscape via a model Connect observed [CO2] and streamflow to modelled CO2 flux and runoff Compute data-model comparison statistics Derive re-analysis products Downscale climate drivers to any point Downscale climate change scenarios to a grid
  28. 28. Tools Connect inputs and targets via a model “Spread” fluxes across the landscape via a model Connect observed [CO2] and streamflow to modelled CO2 flux and runoff Compute data-model comparison statistics Derive re-analysis products Downscale climate drivers to any point Downscale climate change scenarios to a grid
  29. 29. Tools Connect inputs and targets via a model “Spread” fluxes across the landscape via a model Connect observed [CO2] and streamflow to modelled CO2 flux and runoff Compute data-model comparison statistics Derive re-analysis products Downscale climate drivers to any point Downscale climate change scenarios to a grid
  30. 30. Tools Connect inputs and targets via a model “Spread” fluxes across the landscape via a model Connect observed [CO2] and streamflow to modelled CO2 flux and runoff Compute data-model comparison statistics Derive re-analysis products Downscale climate drivers to any point Downscale climate change scenarios to a grid

×