Geostatistical Mapping of Mountain Precipitation Incorporating Auto-searched Effects of Terrain and...
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![Page 1: Geostatistical Mapping of Mountain Precipitation Incorporating Auto-searched Effects of Terrain and Climatic Characteristics Huade Guan, John L. Wilson,](https://reader035.fdocuments.net/reader035/viewer/2022062318/5516b0f6550346f6208b5251/html5/thumbnails/1.jpg)
Geostatistical Mapping of Mountain Precipitation
Incorporating Auto-searched Effects of Terrain and Climatic Characteristics
Huade Guan, John L. Wilson, Oleg Makhnin
New Mexico Institute of Mining and Technology
American Meteorological Society 85th Annual Meeting San Diego, Jan. 11, 2005
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Why use gauge data for precipitation mapping in mountains?
• Problems with NEXRAD– Beam blockage– Snow estimation– 4km pixel size
NEXRAD rainfallNew Mexico, July 1999
From Hongjie Xie, 2004
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Four types of mapping approaches
Information
incorporated
Spatial covariance
No Yes
Physical process
No Theissen polygon, & inverse square distance
Kriging
YesRegression,
e.g., P-ZCokriging (P-Z)
(examples)
Cokriging (P-Z)& De-trended
residual kriging
today
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Physical process (1)Orographic effects on precip.
P (low Z) < P( high Z)
T↓wind
T↑
Elevatio
n
(Z)
P (windward) > P( leeward)
Orographic lifting, & hindrance Reduction in virga effect
P (low Z) < P( high Z)
We use cos (α-ω) toapproximate terrain aspect effects
wind direction:ω
terrain aspect: α
terrain aspect
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Physical process (2)Atmospheric effects on precipitation
How does this heterogeneous atmospheric moisture distribution (or gradient in atmospheric moisture) influence precipitation?
We use geographic coordinates (Longitude or X, and Latitude or Y)to capture the effect of gradient in atmospheric moisture on precipitation
GOES East 4-km, infrared imagery 2001.05.04
Study area
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Auto-search orographic and atmospheric effects
)cos(43210 bZbYbXbbP
gradient in moist., elevation, aspect & moist. flux direct.
Data: Gauge precip: X, Y, P; Elev. DEM: X, Y, Z, ;
Regression:
But what about moisture flux direction, ?
aspe
ctm
oist
. flu
x di
r.
64
54
sin
cos
:
sinsincoscos)cos(
bb
bb
Let
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Auto-search orographic and atmospheric effects
sincos 653210 bbZbYbXbbP where b5=b4 cosω, and b6=b4 sinω, implicitly contain the moisture flux direction. And b1 and b2 include the information of gradient in atmospheric moisture.
Regression turns to:
For example, if b5 >0 and b6 >0, ω= atan (b6/b5)
Similarly, if b1 >0 and b2 >0, gradient in atmospheric moisture, or the wetter direction = atan (b1/b2)
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ASOADeKAuto-Searched Orographic and Atmospheric effects De-trended Kriging
• Auto-determine moisture gradient, elevation, & moisture direction effects via regressions Included in b0, b1, b2, b3, b5, and b6.
• Construct regression map from DEM• Find residual at each gauge• Generate residual (or de-trended) map by
kriging• Construct the final precipitation map
Regression map + residual map
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Study areas
1930
1940
1950
1960
1970
1980
1990
2000
2010
0 10 20 30 40 50 60 70 80
Weather stations
Yea
r
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0
50
100
150
200
250
300
350
1 2 3 4 5 6 7 8 9 10 11 12
month
MS
E
PZ: P=b0+b3*Z
PZA: P=b0+b3*Z+b5*cosa+b6*sina
PZAXY: P=b0+b1*X+b2*Y+b3*Z+b5*cosa+b6*sina
ASOADeK regression improves estimates
aspect + moisture flux directionaspect + moisture flux direction
moisture gradientmoisture gradient
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M
Moisture flux
direction1
1 213
2 198
3 186
4 1805 174
6 136
7 156
8 172
9 172
10 182
11 180
12 191
ASOADeK inferred moisture flux directions
January April July November
Winter: SouthwesterlySummer: Southeasterly
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Two weather patterns in Summer
• Southwesterly moisture flux related North American Monsoon(picture to the right)
• Easterly moisture flux
ASOADeK: Southeasterly
From NOAA
Mixture of the two may give apparent southeasterly moisture flux as inferred from ASOADeK
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Weather pattern related to heaviest winter precipitation
Southwesterly moisture flux at the study area,
ASOADeK:Southwesterly
From Sellers and Hill, 1974
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MMoisture gradient
1 298
2 295
3 279
4 605 86
6 121
7 134
8 145
9 154
10 195
11 281
12 273
ASOADeK inferred gradient in atm. moisture
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ASOADeK regression vs. PRISM
• Model estimates from both models v. measured values
• Scatter plots and fits (R2) – For three months: Feb, May & Aug.
• ASOADek regression only! – No residual kriging
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horizontal axis: observation values
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ASOADeK vs. PRISM
• Precipitation maps for both models, and
• QQ plots,
• for same three months: Feb, May & Aug.
• ASOADek regression plus residual map.
For ASOADeK let’s now include the residual map
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ASOADeK estimates vs. PRISM
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Cross validation results:
ASOADeK gives better
estimatesthan
kriging &cokriging
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Conclusions
• ASOADeK detects regional climate settings using only precip. gauge data in mountainous terrain.
• ASOADeK vs. PRISM– Precipitation maps: ASOADeK ≈ PRISM– ASOADeK product has higher spatial resolution
• ASOADeK vs. other geostat. approaches– Precipitation estimates improved in comparison
with krigng and co-kriging.
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Future work
• Further testing ASOADeK auto-searching capacity– Event cases– Other geographic regions
• Applications & Extensions of ASOADeK – Mapping Precipitation in mountainous regions– Studying ENSO/PDO effects on precipitation
distribution– Recovering NEXRAD beam-blockage shadow– Downscaling precip. products, e.g., NEXRAD
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ain
Thank you!