validation of operational nwp forecasts for global ... · solar exposure forecasts using the access...
TRANSCRIPT
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Validation of operational NWP forecasts for global, diffuse and direct
solar exposure over Australia
www.bom.gov.au
Lawrie Rikus, Paul Gregory, Zhian Sun,
Tomas Glowacki
Bureau of Meteorology Research Branch,
15 June 2015
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Motivation: why am I here?
The Background: Model evaluation Need to compare model variables with observational data not included as
input to DA.
Surface solar radiation is an essential variable for the model forecast process
NWP solar radiation forecasts are potentially a basis for solar power forecasts
Solar power stations could be a source of additional validation data
The Question: How well do the raw NWP surface solar radiation
fields agree with the observations? Compare raw NWP fields with the Bureau’s surface solar measurements
Hourly accumulations available from all operational models
Limitations: Full radiation calculation is done at most each hour
Cloud fixed over the hour
Solar zenith angle corrected at each time step
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ACCESS-NWP (APS1 - Domains)
APS0: • Operational 2Q2010 • N144 global (~80 km) • ACCESS-R 40 km • ACCESS-A 11 km • ACCESS-C 5 km • L60
The ACCESS NWP Systems
Australian Community Climate Earth-System Simulator
Based on MetOffice Unified Model and 4DVar data assimilation system
APS1: • Implemented 3Q2013 • N320 global (~40 km) • ACCESS-R 11 km • ACCESS-C 4 km • L70
APS2: • Implemented around now • N512 global (~40 km) • ACCESS-R 11 km • ACCESS-C 1.5 km • L70
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http://www.bom.gov.au/climate/data/oneminsolar/stations.shtml
Station name Start End Years
Adelaide 1994 open 15
Alice Springs 1993 open 21
Broome 1996 open 18
Cairns 1997 2004 6
Cape Grim 1998 open 16
Cobar 2012 2014 1
Cocos Island 2004 open 9
Darwin 1993 open 20
Geraldton Airport 2012 2014 1
Geraldton Airport
Comparison 1996 2006 9
Kalgoorlie-Boulder 1998 2013 9
Learmonth 1996 2013 10
Longreach Aero 2012 open 1
Melbourne Airport 1999 open 15
Mildura 1996 2013 10
Mount Gambier 1993 2006 11
Rockhampton Aero 1996 open 19
Tennant Creek Airport 1996 2006 10
Townsville Aero 2012 2014 1
Wagga Wagga 1997 open 18
Woomera 2012 2013 1
The Validation sites
1 minute high quality data available
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The relationship between the observations and
the model domains
4 domains have one long-term site
• DN
• BN
• AD
• SY
VT has 3 long-term sites
PH has no long-term sites
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Documentation
o Legacy (pre-ACCESS) 12 km model o Gregory, P. A., L. J. Rikus, and J. D. Kepert, 2012: Testing and Diagnosing the Ability of the
Bureau of Meteorology’s Numerical Weather Prediction Systems to Support Prediction of Solar
Energy Production. J. Appl. Meteor. Climatol, 51, 1577–1601.
oAPS0 12km model (ACCESS-A) o Gregory, P. A. and L. J. Rikus: Validation of Bureau of Meteorology’s Global, Diffuse and Direct
Solar Exposure Forecasts using the ACCESS Numerical Weather Prediction Systems, submitted
to J. Appl. Meteor. Climatol
The 1-minute site data were aggregated into the relevant hour spanned by the
model’s forecasts
Hourly accumulated global, direct and diffuse solar irradiance at the surface
processed
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Forecast metrics
•Solar variability is predominantly due to cloud cover and solar
position •Variation in solar position is completely deterministic
•Variation in cloud cover is mostly stochastic
•Need to de-couple these two factors, otherwise you inflate the skill of
the NWP model. •A clear sky model (Ineichen and Perez (2002)) was used to normalise forecast
and observed data.
•Standard statistical metrics used for validation •RMSE, correlation, multiplicative bias
•Metrics developed by Espinar et al. (2009) •Integrate the absolute difference between the observed and forecast empirical
cumulative distribution functions (CDFs)
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Standard forecast metrics
Paul Gregory developed the scripts to implement the validation process for ACCESS We can now apply them easily to the model archive for any period (since late 2013).
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Validation of ACCESS-A hourly data
Global exposure Diffuse exposure Direct exposure
Bias MAE RAE (%) Bias MAE RAE (%) Bias MAE RAE (%)
All sky 1.01 0.28 16.38 0.95 0.21 46.05 1.03 0.44 33.54
Clear sky 1.00 0.16 8.11 1.01 0.13 40.11 1.00 0.26 14.96
Low cloud 0.88 0.05 18.89 0.84 0.05 23.17 4.55 0.01 80.21
Hourly results for January 2012 at Adelaide
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Day 1 Day 2 Day 1 Day 2 Day 1 Day 2 Day 1 Day 2 Day 1 Day 2 Day 1 Day 2 Day 1 Day 2 Day 1 Day 2
Global Bias 1.01 1.00 0.99 0.99 0.97 0.97 1.00 0.98 0.93 0.93 1.00 0.99 0.99 0.98 1.01 1.00
RMSE 0.23 0.25 0.28 0.25 0.28 0.28 0.34 0.35 0.39 0.39 0.33 0.29 0.24 0.27 0.27 0.27
Correlation 0.62 0.62 0.56 0.58 0.59 0.59 0.58 0.46 0.61 0.59 0.58 0.63 0.59 0.55 0.64 0.66
KSI 14.31 11.65 31.61 27.71 59.19 67.59 12.58 13.97 93.98 80.40 9.79 13.23 21.57 24.15 15.00 22.67
OVER 0.00 0.00 0.25 0.00 21.29 38.36 0.00 0.00 66.48 51.96 0.00 0.00 0.00 0.00 0.00 0.00
Direct Bias 1.02 1.01 1.02 1.02 0.97 0.96 0.93 0.94 0.87 0.88 0.97 0.91 1.00 0.97 1.05 1.02
RMSE 0.39 0.40 0.42 0.41 0.46 0.48 0.63 0.57 0.58 0.56 0.50 0.63 0.41 0.46 0.43 0.44
Correlation 0.64 0.65 0.59 0.59 0.57 0.56 0.53 0.51 0.64 0.62 0.56 0.50 0.60 0.56 0.65 0.63
KSI 23.58 24.60 34.28 31.97 47.91 62.14 35.86 35.30 150.10 125.10 15.35 49.43 26.51 31.95 40.25 24.84
OVER 0.00 0.00 8.18 9.67 16.54 23.85 0.00 0.00 135.50 106.90 0.00 0.00 0.00 1.92 0.00 0.00
Diffuse Bias 0.91 0.94 0.82 0.84 0.93 0.94 1.26 1.23 1.22 1.18 1.12 1.28 0.94 1.00 0.82 0.89
RMSE 0.21 0.21 0.20 0.21 0.19 0.19 0.34 0.29 0.23 0.22 0.30 0.42 0.20 0.23 0.20 0.21
Correlation 0.45 0.45 0.39 0.37 0.41 0.41 0.27 0.41 0.47 0.47 0.28 0.18 0.41 0.39 0.46 0.39
KSI 102.20 108.10 237.90 384.40 164.90 191.80 78.38 71.33 168.80 129.30 100.10 131.80 134.90 182.90 138.40 177.40
OVER 65.66 78.52 213.30 365.30 164.90 191.80 52.71 40.33 147.90 113.00 67.48 121.90 119.50 162.20 109.20 153.50
Wagga WaggaAdelaide Alice Springs Broome Cape Grim Darwin Melbourne Rockhampton
Annual ACCESS-A Clear Sky Results
Overall day 1 better than day 2 except for Darwin, Cape Grim
Correlation ~ 0.6 for global and direct
< 0.5 for diffuse
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Day 1 Day 2 Day 1 Day 2 Day 1 Day 2 Day 1 Day 2 Day 1 Day 2 Day 1 Day 2 Day 1 Day 2 Day 1 Day 2
Global Bias 1.02 1.01 0.97 0.97 0.96 0.96 1.03 1.01 0.91 0.92 0.97 0.97 1.00 0.98 1.01 1.00
RMSE 0.50 0.50 0.47 0.48 0.52 0.49 0.50 0.55 0.60 0.62 0.48 0.54 0.44 0.49 0.43 0.47
Correlation 0.62 0.60 0.66 0.62 0.61 0.63 0.64 0.56 0.59 0.55 0.67 0.60 0.74 0.68 0.73 0.70
KSI 26.51 19.82 56.44 59.19 84.05 86.11 38.45 18.54 181.60 157.20 39.18 34.81 17.96 34.72 12.80 15.58
OVER 0.00 0.00 4.35 13.81 59.18 62.25 0.76 0.00 176.40 150.90 0.00 0.00 0.00 0.00 0.00 0.00
Direct Bias 0.99 0.98 0.98 0.97 0.94 0.93 0.90 0.88 0.79 0.83 0.84 0.84 0.94 0.91 1.02 1.01
RMSE 0.65 0.68 0.63 0.66 0.71 0.69 0.62 0.66 0.78 0.78 0.62 0.67 0.60 0.66 0.58 0.63
Correlation 0.63 0.60 0.68 0.63 0.63 0.65 0.61 0.56 0.61 0.58 0.64 0.58 0.72 0.67 0.71 0.67
KSI 87.32 83.29 65.25 71.38 106.90 110.50 82.71 103.60 328.90 262.30 148.90 152.20 79.25 113.70 70.88 57.25
OVER 0.00 46.38 38.87 42.01 82.82 85.45 55.80 79.13 326.20 258.60 136.30 137.10 55.49 93.46 24.04 21.28
Diffuse Bias 1.08 1.08 0.92 0.94 0.98 0.99 1.19 1.19 1.21 1.14 1.18 1.19 1.11 1.12 0.96 0.99
RMSE 0.34 0.33 0.30 0.30 0.30 0.29 0.33 0.33 0.33 0.34 0.33 0.34 0.32 0.33 0.29 0.31
Correlation 0.36 0.34 0.44 0.38 0.38 0.40 0.35 0.30 0.34 0.30 0.34 0.28 0.40 0.37 0.42 0.34
KSI 148.30 154.70 205.70 220.60 152.80 154.10 197.10 195.30 234.60 173.90 167.90 199.00 195.20 209.60 127.40 140.40
OVER 0.00 136.20 182.40 201.70 126.80 126.90 181.90 181.80 217.40 157.50 148.70 182.90 184.90 196.70 85.02 116.40
Rockhampton Wagga WaggaAdelaide Alice Springs Broome Cape Grim Darwin Melbourne
Annual ACCESS-A All Sky Results
Direct generally under-predicted
Diffuse generally over-predicted
But not always! The results are
site dependent.
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Global exposure bias and RMS as function of CSI and SZA
Clear Sky Index created by dividing observed exposure by clear-sky model exposure
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Discussion of direct and diffuse irradiance
Model tends to over-estimate direct and under-estimate diffuse Parameterization is tuned for global irradiance at the surface and TOA and
atmospheric heating rate.
Global and direct are calculated separately and differenced to produce
diffuse.
The two stream approach makes approximations for angular integration. Large number of different approximations in the literature
Optimised for different cloud properties
Can we try a different two stream scheme?
Schemes which give same global radiation should not effect
NWP forecast skill. Easier to implement in operational suite.
(Would possibly effect surface parameterization scheme)
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GHI
DNI
DNI
Scaled
Unscaled
Unscaled direct two-stream approximation
Work by Zhian Sun
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T2m 00Z + 24h Exp1 Exp2 Exp3
AD
89
Bias -0.6819 -0.6762 -0.7619
Err St Dev 1.4285 1.4248 1.8738
RMS Error 1.6964 1.6926 2.1304
BN
95
Bias -0.7933 -0.7956 -1.1621
Err St Dev 1.4054 1.4042 1.7572
RMS Error 1.7144 1.7138 2.1924
DN
35
Bias -0.3850 -0.3823 -0.3091
Err St Dev 1.3537 1.3495 2.4741
RMS Error 1.5807 1.5776 2.6110
PH
174
Bias -0.4630 -0.4575 0.0019
Err St Dev 1.6652 1.6623 2.3227
RMS Error 1.8254 1.8220 2.4602
SY
153
Bias -0.5869 -0.5915 -0.6702
Err St Dev 1.5021 1.4996 1.7590
RMS Error 1.7593 1.7584 2.0149
VT
266
Bias -0.6153 -0.6148 -0.5816
Err St Dev 1.5300 1.5328 1.8318
RMS Error 1.7759 1.7771 2.0474
D2m 00Z + 24h Exp1 Exp2 Exp3
AD
68
Bias -0.4438 -0.4571 -0.1234
Err St Dev 1.8676 1.8735 2.0035
RMS Error 2.2411 2.2509 2.3543
BN
62
Bias -0.0376 -0.0334 0.2782
Err St Dev 1.8704 1.8684 2.0874
RMS Error 2.0611 2.0580 2.3316
DN
32
Bias -0.5316 -0.5290 -0.1444
Err St Dev 2.1853 2.1920 2.7055
RMS Error 2.5135 2.5167 3.1528
PH
49
Bias 0.0634 0.0595 0.5265
Err St Dev 1.7709 1.7609 2.0151
RMS Error 1.9622 1.9488 2.2516
SY
69
Bias -0.2387 -0.2339 0.0161
Err St Dev 1.7616 1.7592 1.9189
RMS Error 1.9366 1.9351 2.0821
VT
153
Bias -0.4536 -0.4569 -0.2859
Err St Dev 1.6792 1.6778 1.8901
RMS Error 1.9321 1.9331 2.1025
ACCESS-C2 model experiments – 0UTC
Courtesy: Tomas Glowacki
Expt 1: Control
Expt 2: unscaled direct
Expt 3: PC2
Results for December 2014
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ACCESS-C2 model experiments – Solar All Sky
Results for December 2014 - 0 and 12UTC runs
Direct increased/diffuse decreased in Exp 2
AD AdelaideBN RockhamptonDN Darwin SY Wagga_WaggaVT CapeGrimVT3 Melb_airportVT4 Wagga_Wagga5
Exp 1 Exp 2 Exp 1 Exp 2 Exp 1 Exp 2 Exp 1 Exp 2 Exp 1 Exp 2 Exp 1 Exp 2 Exp 1 Exp 2
Global Bias 1.03 1.03 0.99 0.99 0.93 0.93 1.02 1.02 1.00 1.00 1.06 1.06 1.03 1.03
RMSE 0.47 0.46 0.46 0.44 0.31 0.31 0.40 0.40 0.47 0.45 0.53 0.54 0.38 0.37
Correlation 0.64 0.64 0.76 0.77 0.80 0.81 0.73 0.73 0.64 0.65 0.60 0.60 0.75 0.76
KSI 21.14 21.81 15.94 16.16 47.16 44.26 19.07 74.79 16.78 15.00 28.22 30.22 21.51 20.09
OVER 0 0 0.00 0.00 16.29 15.54 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
Direct Bias 0.98 1.13 0.98 1.11 0.87 0.95 1.04 1.18 0.88 1.07 1.00 1.18 1.07 1.21
RMSE 0.63 0.62 0.61 0.60 0.48 0.41 0.45 0.47 0.62 0.58 0.59 0.59 0.40 0.44
Correlation 0.64 0.65 0.73 0.74 0.77 0.78 0.79 0.80 0.58 0.58 0.63 0.63 0.83 0.83
KSI 21.41 55.25 29.54 61.14 81.15 32.44 24.71 74.79 39.03 29.20 37.42 68.25 34.65 87.32
OVER 0.00 11.11 0.00 8.02 50.05 0.97 0.00 29.97 0.90 0.00 4.24 8.91 0.00 47.27
Diffuse Bias 1.12 0.85 0.96 0.75 1.21 0.88 0.97 0.74 1.21 0.92 1.12 0.91 0.95 0.72
RMSE 0.32 0.33 0.28 0.34 0.20 0.19 0.25 0.31 0.30 0.32 0.31 0.35 0.24 0.32
Correlation 0.39 0.35 0.54 0.49 0.56 0.54 0.44 0.31 0.31 0.22 0.33 0.19 0.53 0.41
KSI 80.40 79.27 60.92 101.40 91.46 89.92 88.98 99.70 79.37 37.78 70.90 43.62 72.86 94.85
OVER 43.73 60.57 33.62 79.58 68.37 68.49 51.63 86.90 64.72 18.59 33.63 27.26 41.78 80.00
Count Global 283 283 318 318 328 328 313 313 290 290 319 319 313 313
Direct 283 283 318 318 328 328 313 313 290 290 319 319 313 313
Diffuse 283 283 318 318 328 328 313 313 290 290 319 319 313 313
Midl Cld Bias 80.78 74.32 155.10 151.30 0.00 0.00 105.70 105.50 0.00 0.00 109.50 108.10 98.94 100.80
Low Cld Bias 437.10 404.20 262.50 254.60 571.30 500.00 340.30 349.40 0.00 0.00 264.50 271.10 288.50 280.20
High Cld Bias 101.00 97.52 99.64 89.69 1829.00 1822.00 34.01 33.69 0.00 0.00 83.81 84.77 31.46 31.52
Little change in global
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ACCESS-R model experiments – Solar Results
R2_CTL R2 R1 R2_CTL R2 R1 R2_CTL R2 R1 R2_CTL R2 R1 R2_CTL R2 R1 R2_CTL R2 R1 R2_CTL R2 R1 R2_CTL R2 R1 R2_CTL R2 R1
Global Bias 1.05 1.09 1.05 1.04 0.97 0.97 0.97 0.96 0.97 1.05 1.05 1.12 1.16 1.20 1.29 0.96 1.10 1.02 1.09 1.07 1.03 1.14 1.10 1.05 1.02 0.99 0.96
RMSE 0.60 0.57 0.65 0.58 0.59 0.67 0.50 0.51 0.50 0.65 0.62 0.73 0.65 0.71 0.87 0.83 0.84 0.78 0.55 0.57 0.65 0.63 0.68 0.69 0.52 0.53 0.58
Correlation 0.77 0.79 0.73 0.61 0.62 0.62 0.54 0.51 0.50 0.68 0.70 0.61 0.61 0.59 0.47 0.25 0.26 0.25 0.79 0.78 0.71 0.65 0.60 0.54 0.72 0.72 0.68
KSI 48.86 49.93 33.89 20.8 27.8 17.5 37.1 31.3 35.7 18.8 30.0 52.7 59.7 88.3 120.2 75.1 78.4 65.7 22.5 33.4 20.9 52.4 55.4 28.9 23.0 21.7 23.0
OVER 0.0 0.0 0.0 0.00 0.00 0.00 0.0 0.0 0.0 0.0 0.0 6.4 30.1 56.5 100.3 30.8 42.4 5.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Direct Bias 1.06 1.28 1.01 0.95 0.90 0.73 0.83 1.07 0.87 0.94 1.16 0.96 1.26 1.96 1.27 0.85 1.25 0.86 1.05 1.25 0.95 1.17 1.25 0.98 1.03 1.14 0.87
RMSE 0.80 0.82 0.82 1.03 0.94 1.06 0.76 0.79 0.76 0.73 0.65 0.76 0.82 0.97 0.95 0.82 0.82 0.89 0.62 0.67 0.73 0.76 0.81 0.87 0.67 0.66 0.77
Correlation 0.74 0.75 0.71 0.46 0.53 0.60 0.57 0.51 0.53 0.66 0.69 0.62 0.48 0.40 0.30 0.30 0.32 0.19 0.78 0.76 0.71 0.63 0.61 0.51 0.72 0.74 0.68
KSI 54.45 102.80 19.47 86.2 62.5 131.3 74.2 56.6 69.2 35.6 45.7 16.7 91.7 173.1 83.9 59.5 91.2 58.7 22.9 78.2 24.7 46.4 97.0 33.3 54.6 64.7 60.2
OVER 9.0 79.3 0.0 56.1 36.4 118.3 52.5 17.2 53.9 15.2 1.6 1.9 62.7 165.0 53.3 29.2 58.4 43.9 0.7 12.0 0.0 0.7 74.2 6.9 1.4 32.8 5.7
Diffuse Bias 1.02 0.75 1.18 0.90 0.84 1.58 1.32 0.77 1.32 1.19 0.94 1.32 1.03 0.83 1.29 1.16 0.96 1.27 1.15 0.86 1.19 1.09 0.89 1.18 1.00 0.75 1.27
RMSE 0.37 0.44 0.40 0.47 0.50 0.51 0.36 0.45 0.38 0.38 0.37 0.42 0.38 0.48 0.50 0.35 0.37 0.43 0.31 0.39 0.40 0.32 0.36 0.40 0.29 0.36 0.34
Correlation 0.47 0.38 0.43 0.20 0.23 0.38 0.46 0.40 0.43 0.51 0.52 0.48 0.20 0.12 0.09 0.12 -0.01 -0.02 0.49 0.35 0.35 0.43 0.30 0.23 0.52 0.51 0.50
KSI 54.28 101.30 46.30 85.7 99.0 137.5 86.4 105.7 118.6 85.3 34.1 125.1 48.1 80.6 106.7 118.2 39.2 115.3 54.3 62.4 73.7 65.7 61.8 74.4 63.4 99.4 81.0
OVER 23.9 83.6 24.1 49.5 71.2 126.4 50.2 91.5 92.3 68.0 19.1 110.7 8.4 64.3 81.6 103.8 0.0 96.1 4.5 35.1 33.1 31.5 42.7 29.3 33.7 75.3 43.6
Count Global 178 290 290 161 293 293 204 336 336 238 382 382 184 306 306 204 336 336 223 372 372 209 338 338 238 392 392
Direct 178 290 290 161 293 293 204 336 336 238 382 382 184 306 306 204 336 336 223 372 372 209 338 338 238 392 392
Diffuse 178 290 290 161 293 293 204 336 336 238 382 382 184 306 306 204 336 336 223 372 372 209 338 338 238 392 392
Midl Cld Bias 179.40 179.40 165.40 289.8 271.4 195.8 835.4 685.3 638.7 0.0 0.0 0.0 66.7 58.5 43.3 3078.0 507.2 602.6 90.2 85.4 91.5 163.9 171.3 246.3 115.5 95.5 98.4
Low Cld Bias 482.10 412.80 334.20 199.1 1125.0 362.7 145.3 135.5 169.7 0.0 0.0 0.0 286.8 236.1 278.7 291.9 269.3 305.6 297.2 271.9 263.8 226.8 268.6 255.8 250.0 318.6 257.8
High CldBias 100.10 75.60 66.80 45.6 28.1 30.8 445.3 417.2 397.9 0.0 0.0 0.0 123.7 106.4 139.1 619.4 449.1 609.9 36.6 44.2 50.7 136.5 150.9 161.8 79.9 54.2 61.9
Adelaide Broome Cape_Grim Cocos_Island DarwinAlice Springs Rockhampton Wagga_WaggaMelb_airport
Results for December 2014 - 0 and 12UTC runs
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Wagga-wagga – all models
SY and VT same model
but different domains (1.5
km)
Wagga is close to the
boundary of SY
SY VT4 A R2 R1
Global Bias 1.02 1.03 1.01 1.02 0.96
RMSE 0.40 0.38 0.43 0.52 0.58
Correlation 0.73 0.75 0.73 0.72 0.68
KSI 19.07 21.51 12.80 23.0 23.0
OVER 0.00 0.00 0.00 0.0 0.0
Direct Bias 1.04 1.07 1.02 1.03 0.87
RMSE 0.45 0.40 0.58 0.67 0.77
Correlation 0.79 0.83 0.71 0.72 0.68
KSI 24.71 34.65 70.88 54.6 60.2
OVER 0.00 0.00 24.04 1.4 5.7
Diffuse Bias 0.97 0.95 0.96 1.00 1.27
RMSE 0.25 0.24 0.29 0.29 0.34
Correlation 0.44 0.53 0.42 0.52 0.50
KSI 88.98 72.86 127.40 63.4 81.0
OVER 51.63 41.78 85.02 33.7 43.6
Midl Cld Bias 105.70 98.94 115.5 98.4
Low Cld Bias 340.30 288.50 250.0 257.8
High Cld Bias 34.01 31.46 79.9 61.9
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Melbourne – all models
VT A R2 R1
Global Bias 1.06 0.97 1.09 1.03
RMSE 0.53 0.48 0.55 0.65
Correlation 0.60 0.67 0.79 0.71
KSI 28.22 39.18 22.5 20.9
OVER 0.00 0.00 0.0 0.0
Direct Bias 1.00 0.84 1.05 0.95
RMSE 0.59 0.62 0.62 0.73
Correlation 0.63 0.64 0.78 0.71
KSI 37.42 148.90 22.9 24.7
OVER 4.24 136.30 0.7 0.0
Diffuse Bias 1.12 1.18 1.15 1.19
RMSE 0.31 0.33 0.31 0.40
Correlation 0.33 0.34 0.49 0.35
KSI 70.90 167.90 54.3 73.7
OVER 33.63 148.70 4.5 33.1
Midl Cld Bias 109.50 90.2 91.5
Low Cld Bias 264.50 297.2 263.8
High Cld Bias 83.81 36.6 50.7
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Darwin – all models
DN is 1.5 km resolution and the
only model which is convection
permitting
DN A R2 R1
Global Bias 0.93 0.91 0.96 1.02
RMSE 0.31 0.60 0.83 0.78
Correlation 0.80 0.59 0.25 0.25
KSI 47.16 181.60 75.1 65.7
OVER 16.29 176.40 30.8 5.2
Direct Bias 0.87 0.79 0.85 0.86
RMSE 0.48 0.78 0.82 0.89
Correlation 0.77 0.61 0.30 0.19
KSI 81.15 328.90 59.5 58.7
OVER 50.05 326.20 29.2 43.9
Diffuse Bias 1.21 1.21 1.16 1.27
RMSE 0.20 0.33 0.35 0.43
Correlation 0.56 0.34 0.12 -0.02
KSI 91.46 234.60 118.2 115.3
OVER 68.37 217.40 103.8 96.1
Midl Cld Bias 0.00 3078.0 602.6
Low Cld Bias 571.30 291.9 305.6
High Cld Bias 1829.00 619.4 609.9
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APS Upgrade Plans
APS1 APS2 APS3 (~2017/2018) APS4 (~2020)
G 40km L70, 4dVAR Mar-2012 (Op)
25km L70, 4dVAR (2 x 240FC + 2 x 78FC)
12km, L85?, 4dVAR / Hybrid (2 x 240FC + 2 x 78FC)
12km, L85?, 4dVAR / Hybrid (2 x 240FC + 2 x 78FC)
R 12km L70, 4dVAR Mar-2013 (Op)
12km L70, 4dVAR (4 x 72FC)
8km, L85?, 4dVAR / Hybrid? (4 x 72FC)
5km, L85?, 4dVAR / Hybrid? (4 x 72FC)
C 4km L70, FC-only Mar-2013 (Op)
1.5km L70, FC-only {6 X C1}
1.5km(V) L85? 4dVAR (Rad), LHN (4 x 36FC + 4 x 18FC + 16 x 9FC )
Unchanged
On Demand
1.5km L70, FC-only
1.5km(V) L85? DS + M * (3dVAR (Rad), LHN), 4 domains max (4 x 36FC + 4 x 18FC + 16 x 9FC )
Unchanged
En-G 60km L70, M24 30km L85?, M24 (2 x 240FC)
30km L85?, M32 (2 x 240FC)
En-C
2.2km(V) L85, M6 “En-C-1” (4 X 24FC, 4 X 36FC )
1.5km(V) L85?, M12? “En-C-1” (4 X 24FC, 4 X 36FC)
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Rapid update cycle model FDP
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The RUC and times
D0H23
D1H22
BA
SE
TIM
E
VALID TIME D0H23
D3H11
Daylight Daylight
Possible ensemble applications?
10 output for wind, screen variables, precip, etc
High frequency solar requires fast surface scheme (e.g.SUNFLUX)
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SUNFLUX: A fast surface radiation parameterization
Zhian Sun's work
Radiative transfer is expensive
Hourly is 30% of model run
time
Clouds, SZA change but
assumed constant
SUNFLUX
Fast but accurate
calculation of surface
irradiance
Efficient enough to run
every time step
Accounts for cloud, SZA
changes
Could be implemented in
APS3
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There is a scatter in the metrics with variations from site to site
• Different synoptics
• Cloud frequencies
• Cloud properties
• Accuracy of radiative transfer assumptions to different cloud
regimes
• Aerosol not accounted for in model
The comparisons all show a scatter in the metrics for individual sites
• Is that significant?
• If so which do we prefer?
Conclusions
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Extend evaluation to all operational models for all archive times
Establish statistical significance for the different metrics
Partition hourly results in terms of solar zenith angle and time of year
(suggestion by John Boland)
Implement fast surface radiation scheme to produce 10 minute forecasts in
Model (SUNFLUX)
Find more data for validation
Global model
Satellite derived fields
Further work
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The Centre for Australian Weather and Climate Research A partnership between CSIRO and the Bureau of Meteorology
Lawrie Rikus
Phone: 03 9669 4452
Email: [email protected]
Web: www.bom.gov.au
Thank you