Drought Monitoring: progress and challenges

1. Drought Monitoring: progress and challenges Kingtse Mo and Partners Climate Prediction Center NCEP/NWS/NOAA

2. Outline • Every month, CPC issues monthly and seasonal drought outlook and participates in the Drought Monitor operation over the United States • To support operational functions, we monitor hydroclimate conditions and give drought briefing each month to review the current drought conditions and drought forecasts • Satellite data were used to improve Precip and NLDAS • Challenges to cover the global drought

3. Current Partners CPC: Kingtse Mo, LiChuan Chen, Muthuvel Chelliah, Wesley Ebisuzaki EMC: NLDAS Team: Youlong Xia, Jesse Meng, Helin Wei, Michael Ek NASA/GSFC: Randy Koster, Greg Walker Princeton Univ.: Eric Wood, Justin Scheffield Univ. of Washington: Dennis Lettenmaier, S. Shukla, Francisco Munoz-Arriola Web Masters: Joe Harrison RFCs: James noel, Kevin Werner, Andy Wood, SERFC Project Funded by NOAA CPPA, TRACS& NASA

4. Drought Indices More than one index to monitor drought Meteorological drought: Precipitation deficit. (SPI index) Hydrological drought: Streamflow or runoff deficit (SRI index) Agricultural drought: Total soil water storage deficit or soil moisture at the root zone deficit (Total soil moisture percentile) Runoff and soil moisture: Limited data available so we need GLDAS

5. SPI • SPI3 shows short term drought dryness over the Great Plains, Southeast • Wetness over the Midwest • For longer term SPIs • Dryness persisted for 6-months or longer • Typical ENSO signal with dryness over the Southern States and Wetness over the North Warning from the NWS: : Midwest has been wet for more than 6 months and possible for Spring floods D3 D2 D1

6. SPI and other indices SPI Advantages: • Easy to use and only need station data • Cover all time scales • Do not need a hydrological model. (Other indices are model derived products) • Can cover global (NCDC uses station data and CPC uses gridded data) SPI Disadvantages: • Not contain snow information • Areas where soil moisture feedback is important or large E, SPI may not be representative (e. g. Amazon)

7. All three indices do pick up the major drought events SRI3 SM percentiles Dry: Southern states Wet : Midwest and Northeast and the west coast Drought Indices should be able to pick up major drought events

8. Uncertainties in the NLDAS impact on regional applications Ensemble SM % U Washington EMC The patterns are similar, but there are differences: Over Southeast, the UW does not show anomalies, but the EMC does Over AZNM, drought depicted by the UW is stronger

9. The EMC NCEP system • Four models: Noah, VIC, Mosaic and SAC • Climatology: 1979-2007 • On 0.125 degrees grid • P forcing : From the CPC P analysis based on rain gauges with the PRISM correction. • Other atmospheric forcing: From the NARR The University of Washington system • Four models: Noah, VIC, SAC and CLM • Climatology: 1915-2007 • On 0.5 degrees grid • P, Tsurf and low level winds from NOAA/NCDC co-op stations • P from index stations

10. Sensitivity to Precip data • The RMS difference (Fig.d) between the ncep and the UW ensemble SM % are large over the western U. S. (> 20%). • Largest differences occur after 2001 as indicated by the mean differences for two periods (Fig. f and g)

11. Number of station reports averaged over a year

12. Xie et al (2011) CPC Gauge-Satellite Merged Precip Analysis Gauge-based analysis OI of reports at ~30K stations CONUS: 0.125olat/lon from 1948 Global : 0.5olat/lon from 1979 Poor quality over gauge sparse regions CMORPH Satellite Estimates Integration of all available satellite IR and microwave observations 8kmx8km over global land (60oS-60oN) 30-min time resolution from Jan. ‘98 Bias and random error Gauge-satellite merged analysis(available around summer) Bias-corrected CMORPH through PDF matching against gauge data Same time / space resolution / coverage as CMORPH Gauge-CMORPH combined analysis Daily analysis / 0.25olat/lon

13. To develop global DEWS, we need the following • Better P insitu data and better real time reporting 2 Satellite derived P or radar data but need better QC and better calibration. (e. g. CMORPH/RMORPH) 3.Downward radiation, 4.soil moisture data for verification 5. Better snow information Satellite derived E and SM can help

14. What do we need from GLDAS? • Better winter time snow properties: SWE and snow melt

15. Short wave radiation used for the NLDAS forcing was corrected Better DSWRF=> better E=> better partition between E and runoff RUNOFF NARR NLDAS

16. Conclusions • Requirement for drought indices: They should be able to select all major events. • For runoff and soil moisture, there are few data sets available, we need to use the Global Land Data Assimilation System (GLDAS) • To have better GLDAS products, we need to have better Precipitation, downward solar radiation, E . • We also need soil moisture measurements to validate the GLDAS products.

17. Drought forecasts SM and runoff from lead 1-3 months fcsts • A) U Washington ESP –VIC nested in the CFS monthly fcsts with ESP • B) EMC/Princeton – Bayesian corrected Precip from the CFS monthly mean fcsts to drive VIC • C) OHD- SAC model driven by CFS monthly mean fcsts to produce streamflow fcsts • D) CPC- BCSD corrected SPI and SM fcsts from the CFS v2 • E) NSIPP model soil moisture outputs

18. What do we need? • Some thing NOT based on the CFS forecasts. SE( 26-37N,77-89W) High resolution forecasts for regional operations 1, Better Global forecasts 2.Better high resolution P analyses so we will have better initial conditions; 3.. Better observations for calibration 4. downscaling 5. Will ensemble forecasts help? If so, what is the best way to make ensembles

19. P anom Dashed line– monthly mean anomaly, Solid line- 6-mo running mean • P has high frequency (HF) and low frequency (LF) component. • LF– 6 mo running means • NCEP P anomalies have large values and variances than UW. • Before 2001, large differences are in HF bands • After 2002, consistent differences in LF band • Next LF P SM changes

20. 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) standardized SM anomalies for area 38-42N,110-115W NCEP(red),UW(green)

21. A dry region SM has much lower freq. over the western region

22. A wet region 6 mo running mean black line drought 3 mo running mean (black line) No smoothing Red line: monthly mean, no smoothing SM 1-2 months delay

23. Conclusions • Reliability: The spread among the NLDAS driven by the same forcing is small. For NLDAS driven by different forcing, differences are larger. Different systems are able to capture overall drought/floods but the severity is uncertain. • Consistency: All different indices derived from the NLDAS are able to select strong drought events. • Availability : All NLDAS systems are operational in near real time. • What do we need: Better real time reporting of precipitation from stations and better precipitation analyses

24. Number of reports /month averaged over the box Large drop in real time