p0305 [Compatibility Mode]

Evaluating the drought monitoring
capabilities of rainfall estimates for
Africa
Chris Funk
Pete Peterson
Amy McNally
U.S. Department of the Interior
U.S. Geological Survey
What makes good rainfall estimates
go bad?
1.
2.
3.
4.
5.
Sparse station data
Inconsistent networks
Complex mean fields
Biased satellite estimates
Inhomogeneous satellite inputs
1
Climate Hazard Group Climatology Method
Dense gauge observations
Elevation, latitude, longitude
Satellite mean fields
Sparse gauge observations
Satellite anomaly fields
1
1
FEWS NET TRMM-IR Precipitation (FTIP)
Quasi-global (±60°N/S), 0.05°, pentads
Built around 0.05°rainfall mean fields
FEWS NET Climatology (FCLIM)
Two components
TRMM V6/RT as percentage (TR%)
Cold cloud duration IR estimates as percentages (IR%)
Global CCD models trained against TRMM 3B42 pentads
CCD threshold, slope, intercept for each month
FTIP = (0.5 TR% + 0.5 IR%) * FCLIM
Unbiased IR precipitation (UIRP) = IR% * FCLIM
Hispaniola Rainfall dekads: TRMM and FTIP
Satellite %
X
FCLIM
--------------Improved
Rainfall
Estimates
1
TRMM
FTIP
1
Building better climatologies
Use regional moving window regressions to
model long term mean monthly rainfall as a
function of
Elevation, Slope, latitude, longitude, …
TRMM, Land Surface Temperatures, Infrared
brightness temperatures, ….
Use standard interpolation approaches
(kriging or IDW to blend in station anomalies)
1
Monthly means of TRMM v6, LST and IR
Local correlations w/ satellite mean fields
much better than elevation or slope
1
FCLIM Variance
Explained
1
1
FCLIM Annual Means
1
Climatology Validations
Region
N-stns Stn Mean Model Mean MBE
MAE
Pct MBE
Pct MAE R2
FCLIM
Combined
Colombia
Afghanistan
SE Asia
Ethiopia
Sahel
Mexico
2216
194
22
6
76
104
1814
97
168
35
122
97
86
77
99
159
34
144
94
82
78
-1
8
1
-22
3
3
-1
20
30
9
37
10
9
23
-1
5
3
-18
3
4
-2
20
18
25
30
10
11
30
0.69
0.84
0.53
0.28
0.91
0.90
0.65
CRU
Combined
Colombia
Afghanistan
SE Asia
Ethiopia
Sahel
Mexico
2216
194
22
6
76
104
1814
97
168
35
122
97
86
77
106
174
45
152
101
90
75
-9
-6
-10
-30
-4
-4
1
32
46
20
56
23
20
24
-9
-4
-27
-25
-4
-4
2
32
28
57
46
23
24
31
0.51
0.59
0.18
0.25
0.68
0.74
0.60
Worldclim
Combined
Colombia
Afghanistan
SE Asia
Ethiopia
Sahel
Mexico
2216
194
22
6
76
104
1814
97
168
35
122
97
86
77
106
178
41
155
97
88
79
-9
-11
-6
-33
0
-2
-2
26
31
18
50
20
19
17
-9
-6
-17
-27
0
-3
-2
27
19
52
41
21
22
23
0.61
0.82
0.18
0.42
0.72
0.75
0.78
1
Drought monitoring validation study for Africa
Modeled after previous analyses by Grimes and Dinku
0.25°block kriged monthly station data (2001-2010)
Interpolated as %, multiplied by FCLIM
Study looks at drought hits, misses, false alarms and correct
negatives
Drought event defined as a two-month period with less than 190
mm of rainfall
Focused on main rainy season rainfall
For each grid cell,
Identify three month period with max average rainfall
Build time-series of two-month rainfall combinations
1
Building time-series of main season rainfall
Identify main rainy season (i.e. MAM)
For each year, construct three 2-month
combinations: e.g. March+April, April+May,
March+May
Do this for each year to obtain ~27
combinations: 2001MA, 2001AM, 2001MM, 2002MA, 2002AM,
2002MM, 2003MA, 2003AM, 2003MM, 2004MA, 2004AM, 2004MM, 2005MA,
2005AM, 2005MM, 2006MA, 2006AM, 2006MM, 2007MA, 2007AM, 2007MM,
2008MA, 2008AM, 2008MM, 2009MA, 2009AM, 2009MM, 2010MA, 2010AM,
2010MM
Focus on rainfall during germination/grain filling
1
Main growing season correlation maps
1
Main growing season mean bias maps
1
Main growing season mean absolute error maps
1
Hits and misses
Kriged Station Data
190 mm
False Alarm
Correct
Negative
190 mm
Hit
Miss
Satellite Estimate
Main growing season hits
1
Main growing season correct negatives
1
Main growing season misses
1
Main growing season False Alarms
1
Conclusions
1
Incorporating climatologies improves skill
FCLIM unbiasing easily reproducable with TARCAT, ECMWF, …
Fitting CCD models using TRMM 3B42 seems worthwhile
RFE2, FTIP, and TARCAT showed best-correlations
FTIP and then the TRMM showed the smallest mean bias
TARCAT, RFE2 and FTIP had lower MAE
TARCAT, RFE2, FTIP, ECMWF do a good job discriminating hits and
correct negatives
Misses
Some in the Sahel (RFE2, TRMM, RFE2)
Sudan (ECMWF)
False Alarms:
Part of east Africa in each
Sahel in ECMWF
Focus on rainfall during germination/grain filling