Operational Drought Information System Kingtse Mo Climate Prediction Center

NCEP/ NWS/NOAA

Operation--- real time, on time and all the time

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MissionCPC issues operational monthly and seasonal drought

outlook and participates in the Drought Monitor These products are used by government, NIDIS, local

state government , regional centers and private sectors To support the CPC mission, we give drought briefing each

month to review the current conditions and forecasts

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Definition of drought– persistent dry conditions

Colorado basin

SE

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A wet region

drought

6 mo running mean black line

3 mo running mean (black line)

SM 1-2 months delay

No smoothing

Red line: monthly mean, no smoothing

75-85W,31-35N

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SM has much lower freq. over the western region

A dry region

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Good indices

• 1. They do not depend on season• 2. They do not depend on location• 3. They are accessible in real time• 4. It measures the spatial and temporal

scales of drought • 5. All indices should be able to pick upstrong drought evens

6Kelly Redmond

Different faces of droughtdefine drought by impact• Meteorological drought– P deficit• Agricultural drought--- soil moisture

deficit• Hydrological drought_ runoff or

streamflow deficit

Using index to define drought7

SPI fcsts 20130831

Recent rain diminishes drought over the Southwest and California

Continuous rainfall events causes floods over the Southeast and the East cast

The SPI24 still shows the strong drought events of 2012

SPI gives the historical development of drought/floods

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SPI SPI Advantages:• Easy to use and only need station data• Cover all time scales• Do not need a hydrologic model. • (Other indices are model derived products)SPI Disadvantages:1.It does not contain snow information2.Areas where soil moisture feedback is important or

large E, SPI may not be representative (e. g. Amazon)

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North American Land Data Assimilation system They are not TRUTH

• Surface land model- Noah, SAC, VIC, Mosaic and Catchment model

• They are driven by forcing which consists of precipitation (P), Max and min Tsurf and wind speed for a water balance model

• Some models like Noah and Mosaic have the energetics –radiation terms. VIC has both versions

• Outputs: Evaporation, Soil moisture, soil temperature, runoff , Snow water equivalent. And many others

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Don’t worry, be happy!!

Even though the total soil moistures differ from one model to another, their anomalies (or percentiles) are very similar!!

(Robock et al 2004;

Dirmeyer et al. (2004)

Koster et al. (2008)

All models were driven by the same forcing 1511

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Multi model SM information

U Washington

NCEP/EMC

Both captures the wetness over the Eastern and East central United States and dryness over the Southwest and the Plains,But intensity differs

•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,110-115W 13

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. (all stations reports within cutoff time

• Other atmospheric forcing: From the NARR•

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University of Washington system• Four models: Noah, VIC, SAC ,CLM Catchment• (models may have the same name, but versions

may not be the same)• Climatology: 1915-2007• P, Tsurf and low level winds from

NOAA/NCDC co-op stations• P from index stations

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Forcing

• Since the differences among the members of the same system are small, the differences do not come from models.

• Differences come from forcing.• There are two major forcing terms: precipitation

and temperature.• Their differences are larger after 2002

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Experiments The VIC model of 0.5 degrees resolution from the UW system

was chosen for experiments. All experiments started from Jan 1 1979 using the same initial

conditions from the UW VIC model in the UW system. Experiments end on 31Dec 2008

Forcing terms have two components1. P forcing :Precipitation 2. F forcing : Tmax, Tmin and wind

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Four experimentsComparison between• Exp (P uw F uw) vs Exp(Pncep,Fuw) and• Exp (Puw, Fncep) vs Exp(Pnecp,Fncep) indicates the differences caused by Precip

Comparison between• Exp (P uw F uw) vs Exp(Puw,Fncep) and• Exp (Pncep, Fuw) vs Exp(Pnecp,Fncep) indicates the differences caused by F forcing (Tsurf and winds)

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Experiments :RMS differences of SM %Same F forcing Same P forcing

19Large differences between experiments with the same F forcing but the same P forcing are large

Number of station reports averaged over a year

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Number of reports /month averaged over the box

Large drop in real time

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Challenges: improving drought monitoring• Improve historical and real time

Precipitation data and analyses• Improve NLDAS model forcing: P,

downward short wave radiation etc• Improve hydrologic model • Improve and integrate satellite

observations with station data.• Link to attribution

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NMME/IMME seasonal fcsts

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We have 6 models:CFSv2 24 members;GFDL, CMC1 and CMC2 : 10 membersNASA: 11 membersNCAR : 6 members

Hindcasts from 1982-2010P, Tsurf monthly means

JAS 2013

ASO 2013ASO 2013

Hydroclimate FCSTs

• SPI forecasts based on the National Multi Model ensemble (NMME)

• ESP forecasts from the UW• Cfsv2_VIC forecasts from the Princeton,

EMC and MSU• NASA SM from their Coupled model

forecasts

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SPI forecast

If you have precip monthly mean fcsts, you can have the SPI forecasts

Yoon et al. JHM 2012

CGCM

SPI fcsts (201308)

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verification

ESP (Ensemble streamflow prediction) vs NMME_VIC Fcsts

IC s

Run VIC with observed P and Tsurf

Jan 1,1915 from UWJan 1, 1979

ESP- P T inputs taken from randomly selected observations

Both ESP and NMME_VIC have the same initial conditions, but ESP has no climate forecast information of P and Tsurf

Fcst forward

Starting date

Feb 5 Feb 6---

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NMME_VIC :forcing s were taken from error corrected T P from CGCM

ESP FCST UW ICs=20130802

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\August lead=1mo

Sep lead=2mo

Oct lead=3mo

Acc ro lead=1mo

SM fcsts EMC_MSU_Princeton

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AUGUST lead=1mo

September lead=2mo Oct lead=3mo

Same as the ESP, but climate forcing is given by the CFSv2 forecasts

NMME_VIC forecasts

• Initial conditions from the VIC simulation taken from the UW NLDAS_VIC (perfect)

• Climate forcing derived from the members of the NMME for each model

• Drive VIC to get SM and Runoff• For a given model and given lead time, we took the

ensemble mean of all members. The climatology of the forecasts is corrected in the cross validated way.

• SM /Runoff or SRI3 ensemble mean is the equally weighted mean of all 6 models

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Fcst skill for SM

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Lead-1 : correlation >0.8 (WOW!!!)

Lead-3: Over the western interior dry region, the fcsts are still skillful for all seasons and the North Central for January (high skill regions)

Low skill regions are circled

Differences btw NMME-ESP

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1. No significant differences for Lead-1 and Lead-2

2. Only October and January forecasts pass

the Livezey Chen field pass3. Differences are in the areas that the skill is low and dynamically active areas

4. Oct fcsts are helped by skillful P forecasts

Lead-3

Two regimes

Dry: western interior & eastern Texas• Forecast skill of SM and Runoff are high at lead-3, • Contributions are from the initial conditions• ESP_VIC also has high skill• Areas with low P mean and Low P variability

WET: Eastern region and monsoon region• Wet areas with large mean P and P variability• Skill is low even at Lead-1• Dynamically active and P depends on the moisture transport• NMME has higher skill than the ESP 33

Problem with hydroclimate prediction- low P fcst skill

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No skill after Lead-1Except the SoutheastIn Oct

When the CF starts to contribute at Lead-2 or higher, the skill of P forecasts are so low, it does not make a difference

Issues of hydroclimate fcsts• At Lead-1, the initial conditions dominant the forecast skill.

The NMMS precipitation forecasts have some skill, but it competes with the initial conditions

• At Lead-2 and Lead-3, the impact of forcing starts to contribute to skill, but the skill of P fcsts decreases . In the western region, the Ics still contribute but over the dynamically active region such as the Southeast or the monsoon region, the P forecasts need to be good enough to contribute to SM or RO forecasts at higher lead

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Conclusions

• GIVE Me :

Better P forecasts at Lead-2 and Lead-3You will have Better SM and Runoff forecasts over the dynamically active region • Give me

better station data reporting in real timeYou will get : better NLDAS with less uncertainties and better forecasts over the dry areas

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Measure the differences among models

Rm for a group of models Wm :the mean intermodel variance (or spread) Wint (m): interannual variance of the ensemble mean

)(int mR mm

Similar formula was used by Dirmeyer et al (2004). to assess Global wetness products except we use variance instead of standard dev.

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R values for SM %1.The spread among the

members from the same system (UW or NCEP) is small. It is less than 0.4. (Fig. a and b)

2. R values with all UW and NCEP members together is much larger (Fig.c).

This implies that the mean differences between two systems are large

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1.The RMS difference (Fig.d) between the ncep and the UW ensemble SM means are large over the western U. S. (> 20%).

2. Largest differences occur after 2001 as indicated by the mean differences for two periods (Fig. f and g)

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PredictionOceanic conditions• ENSO normal • the positive SSTAs over the North Pacific will continue through

summer.Precipitation above normal rainfall over the East above normal rainfall over the SouthwestDrought• All forecasts indicate that drought over the Central U. S. and

Texas will improve • A normal to slightly above normal monsoon will improve

drought conditions over the Southwest

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RMSE (NMME) R(NMME/ESP)

Lead=1mo

Lead=2mo

Lead=3mo

Spread lead=2 and 3 mo

Over the central and western U.S., the ESP has advantages up to lead=2mo

Over the eastern U.S., the NMME has advantage than the esp

For lead=3, skill overall is very low and the NMME and ESP have comparable skill

The spreads are small and located in the area with low skill

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Comparison with ESP

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