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NLDAS-3 Telecon @ 2015.07.15!
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Statistical Precipitation Downscaling
Using Random Forests- Synthetic experiments over Southeast United States -Xiaogang He1, Nathaniel W. Chaney1,2, Justin Sheffield1,Ming Pan1, Eric F. Wood1
1 Department of Civil and Environmental Engineering, Princeton University	

2 Program of Atmospheric and Oceanic Sciences, Princeton University	

Terrestrial Hydrology Group, Princeton University

1

Xiaogang HE:: hexg@princeton.edu

Precipitation downscaling

Opportunities
Big dataMachine learning approach+➾

Explore and construct complicated relationships

High performance computingHyperresolution
precipitation	

downscaling??

Precipitation	

spatial-temporal	structures??
Terrestrial Hydrology Group, Princeton University

2Xiaogang HE:: hexg@princeton.edu

Regression modelMethodology

Pd = f (Pc, A, T, S, V, L, D, …) + 𝓔
Day of Year

ErrorsLocation (Latitude, Longitude)

Downscaled	Precipitation 	(Posterior)

Vegetation Type
Soil texture
Topography (Mean & Std of DEM, Slope, Aspect)
Atmosphere (Wind, Temperature, CAPE, Humidity,Surface Pressure)
Coarse Resolution Precipitation & Adjacent 4 Grid Cells

Terrestrial Hydrology Group, Princeton University3

Xiaogang HE:: hexg@princeton.edu

Ensemble learning

Methodology

Single learnerGroup of learners

VS

Ensemble methods ➙ Reduce overfitting
Terrestrial Hydrology Group, Princeton University4

Xiaogang HE:: hexg@princeton.edu

Ensemble learning

Methodologyp(Pd |v)

RANDOM FORESTS
Breiman (2001)	

Criminisi et al., (2011)

p(Pd |v)

Pdp(Pd |v)

Pd

PdRegression forest posterior: p(Pd |v) =T!
1T t=1pt (Pd |v)

v: covariates 	

Pd: predictand (downscaled prec) 	

pt(Pd|v): individual tree posterior
Terrestrial Hydrology Group, Princeton University5Xiaogang HE:: hexg@princeton.edu

Synthetic
Experiment Design

Step 1:

Generate synthetic covariates

EXPNLDAS2

Data:

North American Land Data Assimilation System 2

Period:2011.06.01—2011.08.31 (Hourly)
Region: Southeast United States
Domain: Grid: 80×72 (0.125º)x1x2x3x4x5x6x7x8x 9 x 10 x 11 x 12x 13 x 14 x 15 x 160.125º

Upsampling

AP 0.25A 0.125 0.25º 0.125º
P 0.25A 0.25 0.25º 0.25º
P 0.5A 0.125 0.5º 0.125º
P 0.5A 0.50.5º0.5º
P 1A 0.1251º 0.125º
P 1A 1
1º1º

PupPobsPCPc

y 1=	

y 2=	0.25∑x i

0.25∑x i

y 3=	

y 4=	

0.25∑x i

0.25∑x i

Downsampling

y1

y1y2

y2

y1

y1

y2

y2

y3

y3

y4

y4

y3

y3

y4

y4

0.25º

0.125ºAdjacent grid cells are also considered	Same procedures for atmospheric covariates
Terrestrial Hydrology Group, Princeton University

6Xiaogang HE:: hexg@princeton.edu

Synthetic
Experiment Design

Step 2:Train and test Random Forests

Coarse P + Dynamic/Fixed Covariates ➾ Downscaled PCAPECAPECAPE

Pressure
CAPE

Pressure
CAPE

Pressure
CAPE

Humidity

Humidity

Humidity

Temperature

Temperature

Temperature

U windU windV windV windPP

T=1…

U windV windP

T=1000

T=2

Training grids
Random sample 10% of total grid cells
from 1000 images for 17 covariates
Training samples: 625000 grid cells

Unselected grid cells

Test

Random	Forests

Terrestrial Hydrology Group, Princeton University7

Xiaogang HE:: hexg@princeton.edu

Spatial pattern

Results
Patchy??

Scaling
Experiments

Drizzle??

P0.25A0.125

P0.5A0.125

P1A0.125
Extreme	events??

Obs (0.125º)
Obs

Example:2011.06.06.23:00

P0.25A0.25

P0.5A0.5

P1A1(mm)012

Terrestrial Hydrology Group, Princeton University3485678910

Xiaogang HE:: hexg@princeton.edu

Feature importance

Results

Feature importance measures the prediction strength for each covariate
Featureimportance

0.8

0.7

0.25º

0.6

0.5

0.5º

0.4

0.3

0.21º

Atmospheric Covariates	

(Upscaled)

0.1

0.1

Precipitation (center)
Precipitation (down)
Precipitation (up)Precipitation (right)
Precipitation (left)
Temperature
Humidity
AspectSlope
Meridional wind
Zonal wind
CAPE
Pressure
Elevation (std)Elevation (mean)
Vegetation Type
Soil Texture

0.21º

0.3

0.4

0.5

0.6

0.7

0.8Featureimportance

0.5º 0.25º

Atmospheric Covariates	(0.125º)★ Central coarse precipitation dominates	★ Dynamic fields matter	★ Topography matters (e.g., P1A1)
Terrestrial Hydrology Group, Princeton University

9Xiaogang HE:: hexg@princeton.edu

Downscaling skill

0.8
0.5
0.7
0.4
0.6

0.25º

0.5º

1º 0.25º0.5º1º0.5

Atmospheric Covariates (0.125º)
Atmospheric Covariates (Upscaled)

Domain averaged prec

0.8(mm)True Positive Ratio

0.6(mm)1.0ROC

0.80.7

P 0.25A 0.25
P 0.25A 0.125
P 0.5A 0.5
P 0.5A 0.125
P 1A 1
P 1A 0.125

0.6
0.5
0.4

050
40

0.9

Observed Prec

(mm)

0.9
R20.3

Distribution

Classification

Goodness-of-fit
RMSE

Results

0.05

0.1

0.15

0.2

False Positive Ratio

Uncertainty

Q-Q Plot3020

P 0.25A 0.25
P 0.25A 0.125
P 0.5A 0.5
P 0.5A 0.125
P 1A 1
P 1A 0.125

1000.250102030

Downscaled Prec

4050(mm)

P0.5A0.5

0.6

Downscaled
0.4Obs0.20

Time step (0-200, hourly)

Terrestrial Hydrology Group, Princeton University10Xiaogang HE:: hexg@princeton.edu

Precipitation downscaling

Future work

Synthetic experiments ➙ Try other regions	Western US (California and mountainous regions)	

Northeastern climate division	

Central US	

Real experiments	

Train StageIV, downscale satellite/reanalysis	

CONUS/Global scale ➙ Moving window approach

Terrestrial Hydrology Group, Princeton University11Xiaogang HE:: hexg@princeton.edu

NLDAS-3 Telecon @ 2015.07.15

Thanks!!
Questions??

Terrestrial Hydrology Group, Princeton University12Xiaogang HE:: hexg@princeton.edu