1 Objective Drought Monitoring and Prediction Recent efforts at Climate Prediction Ct. Kingtse Mo & Jinho Yoon Climate Prediction Center

2 Objectives   Develop objective drought monitoring and prediction based on drought indices   Support drought monitor and outlook operation   Develop regional applications with users and the River Forecast Centers

3 Outline of presentation   Current operation Drought briefing each month (8-11 of the month, dial in is available)   If you would like to participate,     Uncertainties of drought indices   Prediction of SPI   Future plan

4 Define drought based on Drought Indices  Meteorological drought: Precipitation deficit. Index: Standardized Precipitation Index Index: Standardized Precipitation Index  Hydrological drought: Runoff deficit Index: Standardized runoff index Index: Standardized runoff index  Agricultural drought: S oil moisture deficit Index: SM anomaly percentile Index: SM anomaly percentile

5 SPI SPI3: SPI3 shows dryness over the Great Lake area Wetness over AZ, New Mexico and western Texas. For longer terms A very wet picture over the Southeast and eastern central United States D3 D2 D1

6 Multi model SM percentiles Multi model SM percentiles Univ of Washington NCEP U. Washington Uncertainties in the NLDAS Feb 2010

7 Differences between two systems are larger than the spread among members of the same system The differences are not caused by one model. They are caused by forcing. In general, extreme values from the UW (Green) are larger than from the NCEP (red)NCEP(red),UW(green) standardized SM anomalies for area 38-42N, W

8 Number of station P reports averaged over a year Reports dropped for real time operation Historical periodReal time

9 Southeast & CBRFC pilot projects Ensemble hydrologic Forecasts in support of NIDIS CPC: Kingtse Mo, Jinho Yoon EMC: Michael Ek, Youlong Xia Princeton University : Eric Wood SERFC: John Schmidt, John Feldt,Jeff Dobur OHD: John Schaake and D. J. Seo CBRFC: Kevin Werner Funded by TRACS program

10 A wet region drought 6 mo running mean black line 3 mo running mean (black line) No smoothing Red line: monthly mean, no smoothing 75-85W,31-35N

11 A dry region 6 mo running mean

12 Hydrologic prediction Develop hydrologic forecasts out to 6 months for drought indices  P, Tmin and Tmax from the CFS forecasts=> downscaling (from 250km to 50km) and error correction=> Vic model=> SM % and runoff Based on the Princeton System developed by Eric Wood’s group Based on the Princeton System developed by Eric Wood’s group  Corrected P => append P time series=> SPI indices

13 Linear Interpolation  Linear Interpolation : correct mean Correct the model climatology and bilinear interpolation to a high resolution grid Correct the model climatology and bilinear interpolation to a high resolution grid For variable A ensemble fcst: assume normal distribution anomaly A’ rwt to model climatology anomaly A’ rwt to model climatology A ’ = A-model climatology A ’ = A-model climatology Corrected A = A’+ observed climatology Corrected A = A’+ observed climatology

14 Bias correction & Downscaling (BCSD) Probability mapping based on distributions Probability mapping based on distributions Get probability distribution PDFs for A (coarse) and A(fine) Get probability distribution PDFs for A (coarse) and A(fine) From A’ (coarse) get percentile based on PDF (coarse) From A’ (coarse) get percentile based on PDF (coarse) => assume the same percentile for the fine grid and work backward based on the PDF fine get A’ fine (anomaly) => assume the same percentile for the fine grid and work backward based on the PDF fine get A’ fine (anomaly) If normally distributed, time ratio of std If normally distributed, time ratio of std Ref Wood et al (U. Washington 2002,2006)

15 Schaake’s linear regression  Schaake’s linear regression – calibrate P ensemble forecasts based on the historical performance Ref: Wood and Schaake (2008) Schaake et al. (2007) Do no harm

16 Bayesian merging & bias correction  Bayesian correction – calibrate P forecast based on the historical performance and spread of members in the forecast ensemble  use all members in the ensemble Ref: Luo et al. (2007); Luo and Wood (2008)

17 Standardized Precipitation Index Forecasts   Append the bias corrected and downscaled P to the observed P time series   Calculate SPI from extended time series   The advantages are (1) no need of hydrologic model and (2) can use any base period. P :time series : Jan1950-oct1981 append fcsts with ICs in Oct lead 1 f1 lead 2 f2 etc Jan1950-oct 1981 (obs) Nov 1981 (fcst)

18 Seasonal dependence of skill 1.For the first 3 months, AC>0.6 and RMS 0.6 and RMS < Overall, Bayesian wins 3. Skill is higher for Nov ICs and low for May ICs LI BCSD Schaake Bayesian

19 Nov Jan Jan Dec Feb LI Bayesian SPI6 T62 RMSE

20 LI Bayesian SPI6 T62 rmse May June July Aug

21 Over the Southeast, SPI6 out to 3 months

22 SPI6 fcst (contours) /ana (colored) 32-40N Hovmoller

23 3 month later

24 High resolution run (T382) and dynamic downscaling (RSM) 1.High resolution T382 run from April ICs run through Nov(5 members) (Thanks Jae Schemm) 2.RSM (regional spectral model) downscaling from the CFS forecasts (April 28-May 3) ICs (50 km resolution) (Thanks, Henry Juang)

25 T62 LI 5 members vs T382 T62 15ensm LI T62 vs RSM 5 mem LI, T62 15 member LI 5 member T382 & T62 Bayesian 15 members 5 member RSM & T62 Bayesian 15 members

26 JJA P anom over the Northern Plains (34-42N, W ) BCSD, RSM or T382, Obs mm/day

27 Future plan &our needs   Develop real time prediction of SPI’s based on the CFSRR hindcasts   The bias corrected P and T will drive VIC model to produce hydrologic forecasts of soil moisture and runoff   Develop regional applications with the RFCs What do we need?   Better station reporting of real time P   Better P analyses   Better global model prediction of P   Better model physics

28 Discussion questions  What current activities (monitoring and forecasts) can we build on?  Regional vs entire United States  How can we network and coordinate drought related information such as drought impact, planning and information exchange?  What gaps do we need to fill?  What issues are important to you, but have not been discussed?

29 Streamflow fcsts the binary event for observed monthly mean Row 2-6 represents the exceedence probability for forecasts initialized from Nov 2006 Luo and Wood