1. Application of the NMME to Forecast Monthly Drought Conditions
Raymond B. Kiess, Ryan D. Smith, Robb M. Randall, and Jeremy P. Anthony
14th Weather Squadron, Asheville, NC
BACKGROUND
FORECAST METHODOLOGY
Using the Table 4 bias correction factors, we reran the drought forecasts and verification.
For monthly leads 1-5, compute the forecasted precipitation (PF) as a combination of the GPCC climatology (PCLIMO ) and the NMME forecasted anomaly (PFA): PF = PCLIMO + PFA
The forecasted precipitation is added to the historical GPCC time series and then we compute the resulting SPI valid at the forecast months.
For this presentation, only SPI9 is used.
14TH Weather Squadron (14 WS) is DoD’s premier organization for classified and unclassified climate services
Mission is to collect, protect, and exploit climate data to optimize ops and planning for DoD and Intelligence agencies
Serves all branches of US military, contractors, and labs
Cat
Analysis
Fcst-1
Fcst-2
Fcst-3
Fcst-4
Fcst-5
None
75.2
75.1
76.6
77.9
79.1
80.1
Dry
7.1
7.5
7.6
7.7
Mod
9.4
9.8
9.3
8.9
8.5
7.9
Svr
2.9
2.8
2.5
2.3
2.0
1.8
Ext
3.2
3.0
2.6
2.2
1.9
1.7
Exc
2.1
1.4
1.2
1.0
0.8
Cat
Fcst-1
Fcst-2
Fcst-3
Fcst-4
Fcst-5
Same
83.6
80.3
77.7
75.4
73.4
Within 1
95.4
93.0
90.7
88.6
86.7
None
92.0
89.2
84.4
82.1
D1-D4
93.3
91.3
89.4
87.5
85.8
Tables 5 and 6. As tables 1 and 2 with bias factor incorporated. Green cells show improvement while red cell show poorer results.
DROUGHT ANALYSIS and FORECASTING
INTERPRETATION
RESULTS
Global drought analysis produced each month with the Standardized Precipitation Index (SPI) using Global Precipitation Climatology Centre (GPCC) data at 9 and 24 month scales.
Cat
Analysis
Fcst-1
Fcst-2
Fcst-3
Fcst-4
Fcst-5
None
75.2
77.2
79.3
81.4
83.6
85.4
Dry
7.1
6.9
6.6
6.2
5.8
Mod
9.4
9.0
8.2
7.4
6.5
5.7
Svr
2.9
2.5
2.2
1.9
1.5
1.3
Ext
3.2
3.3
1.8
1.4
1.2
Exc
2.1
1.6
1.1
0.8
0.7
While the bias correction caused a slight decrease in lead 1 skill scores, it is encouraging that leads 2-5 show varying levels of skill improvement.
Almost all drought categories showed bias improvement.
These results are encouraging that we can tune PF = PCLIMO + PFA to reduce the bias and maintain or improve the skill.
Table 1. Percentage coverage of global land mass by drought category and forecast lead. Note the tendency to decrease drought occurrence.
ADDITIONAL EXPERIMENTS
Cat
Fcst-1
Fcst-2
Fcst-3
Fcst-4
Fcst-5
Same
84.2
79.9
77.3
75.6
74.6
Within 1
95.8
92.3
89.8
87.8
86.2
None
92.5
88.8
85.9
83.6
81.8
D1-D4
93.9
91.0
87.3
Continue with bias adjustments and tuning. Obtain historical NMME forecasts for a longer study period and independent verification.
Replace the GPCC climatology with the NMME climatology in the calculation of PF. Preliminary analysis shows a bias of 10 mm/month so we will need to compute spatial bias corrections for each month and forecast lead.
Table 2. Percent correct of drought forecasts by categories and forecast lead.
2.5 years of verification statistics show good skill, but a tendency to overly decrease drought coverage and intensity. This is also observable in the depiction to the left.
An obvious question is can we tune PF = PCLIMO + PFA to reduce the bias and maintain or improve the skill?
REFERENCES and CONTACT INFORMATION
Kirtman, B. P., and Coauthors, 2014: The North American Multimodel Ensemble: Phase-I seasonal to interannual prediction; phase-2 toward developing intraseasonal prediction. Bull. Amer. Meteo. Soc., 95,585—601,doi: /BAMS-D
McKee, T. B., N. J. Doesken, and J. Kleist, 1993: The relationship of drought frequency and duration of time scales. Eighth Conference on Applied Climatology, American Meteorological Society, Jan17-23, 1993, Anaheim CA, pp
Schneider, Udo; Becker, Andreas; Finger, Peter; Meyer-Christoffer, Anja; Rudolf, Bruno; Ziese, Markus (2011): GPCC Full Data Reanalysis Version 6.0 at 1.0°: Monthly Land-Surface Precipitation from Rain-Gauges built on GTS-based and Historic Data.
BIAS CORRECTION EXPERIMENT
Year
1 Mo Lead
2 Mo Lead
3 Mo Lead
4 Mo Lead
5 Mo Lead
Cumulative
2015 (partial)
1.00
1.26
1.42
1.85
2.09
7.62
2016 (full)
0.53
0.73
0.67
0.78
3.45
2017 (partial)
-1.06
-1.54
-1.38
-1.66
-1.76
-7.4
0.43
0.51
0.64
0.61
2.72
Table 3. PF bias compared to verifying GPCC. Weak evidence of global forecast bias.
Year
1 Mo Lead
2 Mo Lead
3 Mo Lead
4 Mo Lead
5 Mo Lead
Cumulative
2015 (partial)
2.69
2.53
3.68
5.21
5.97
20.08
2016 (full)
1.42
1.51
1.65
1.60
1.94
8.12
2017 (partial)
1.44
0.2
0.56
-0.68
-0.46
1.06
1.92 (+1.49)
1.64 (+1.14)
2.14 (+1.61)
2.30 (+1.66)
2.62 (+2.01)
10.62 (+7.91)
Table 4. PF bias compared to verifying GPCC with drought conditions already present. There is a stronger signal of a bias.
Author
Figure 1. Drought analysis and corresponding three month forecast.
Approved for public release. Distribution unlimited.