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NOAA Seasonal Drought Outlooks: NOAA Seasonal Drought Outlooks:
Status and ProgressStatus and Progress
NOAA Seasonal Drought Outlooks: NOAA Seasonal Drought Outlooks:
Status and ProgressStatus and Progress
Douglas Le ComteDouglas Le Comte
SRH/ERH Climate WorkshopPeachtree City, GA
June 2007
OutlineOutline
• Current Techniques for Drought Current Techniques for Drought ForecastingForecasting
• A closer look at the tools used to forecast A closer look at the tools used to forecast the Southeast Droughtthe Southeast Drought
• Ideas for the future of drought forecastingIdeas for the future of drought forecasting
New Forecast ToolNew Forecast Tool
• Cambodian royal cows predict drought, Cambodian royal cows predict drought, poor harvest this year (poor harvest this year (2007/5/62007/5/6PHNOM PENHPHNOM PENH, AFP), AFP)
• Cambodia'sCambodia's royal cows signaled a drought royal cows signaled a drought and poor harvests in an ancient ceremony and poor harvests in an ancient ceremony Saturday to mark the beginning of the Saturday to mark the beginning of the kingdom's planting season.kingdom's planting season.
Recent Draft Drought OutlookRecent Draft Drought Outlook
The Three Pillars of the ForecastThe Three Pillars of the Forecast
• Two-week soil moisture forecastsTwo-week soil moisture forecasts
• Climatology: Palmer probabilities, Climatology: Palmer probabilities, analogues, compositesanalogues, composites
• Seasonal temperature, precipitation, and Seasonal temperature, precipitation, and soil moisture forecastssoil moisture forecasts
Features of the Seasonal Drought Features of the Seasonal Drought OutlookOutlook
• A schematic designed to provide a broad indication of A schematic designed to provide a broad indication of drought trends for the following ~3 monthsdrought trends for the following ~3 months
• Four categories: improve, some improvement (e.g., better Four categories: improve, some improvement (e.g., better soil moisture and grassland growth but continuing water soil moisture and grassland growth but continuing water supply shortages), persist/worsen, developmentsupply shortages), persist/worsen, development
• Issued 3Issued 3rdrd and 1 and 1stst Thursday each month (NEW!) Thursday each month (NEW!)
• Incorporates forecasts from all time periodsIncorporates forecasts from all time periods
• Starts with the most recent U.S. Drought monitor D1 areasStarts with the most recent U.S. Drought monitor D1 areas
2-Wk Soil Moisture
Constructed Analogue Soil Model
Medium-Range Fcst
Palmer 4-moProbabilities
CPC Long-LeadPrecip. Outlook
Principal Drought Outlook InputsPrincipal Drought Outlook Inputs
Selected Forecast ToolsSelected Forecast Tools
““These are some of my favorite things.”These are some of my favorite things.”
CPC 2-Week Soil Moisture CPC 2-Week Soil Moisture ForecastForecast
http://www.cpc.noaa.gov/products/soilmst/mrf.shtmlhttp://www.cpc.noaa.gov/products/soilmst/mrf.shtml
Constructed Analogues from Soil Constructed Analogues from Soil (CAS) Moisture(CAS) Moisture
http://www.cpc.noaa.gov/products/soilmst/forecasts.shtmlhttp://www.cpc.noaa.gov/products/soilmst/forecasts.shtml
CFS Modeled Soil MoistureCFS Modeled Soil Moisture
http://www.cpc.ncep.noaa.gov/products/people/wwang/cfs_fcst/http://www.cpc.ncep.noaa.gov/products/people/wwang/cfs_fcst/
Palmer Probability DataPalmer Probability Data
September PalmerSeptember Palmer
Probabilities Probabilities
http://www.ncdc.noaa.gov/oa/climate/research/drought/current.html
Historical Analogues: Soil MoistureHistorical Analogues: Soil MoistureJune 1June 1 Oct. 1Oct. 1
20002000
19871987
http://www.hydro.washington.edu/forecast/monitor/index.shtmlhttp://www.hydro.washington.edu/forecast/monitor/index.shtml
Historical AnaloguesHistorical AnaloguesJune 1June 1 Oct. 1Oct. 1
19861986
19851985
Drought Composites Jun-Sep: PalmersDrought Composites Jun-Sep: PalmersRainfallRainfall TemperaturesTemperatures
June PDIJune PDISep PDISep PDI
Drought Outlook Verification Score
0
10
20
30
40
50
60
70
80
90
100
Jul '03 Oct'03 Jan'04
Apr '04 Jul '04 Oct'04
Jan'05
Apr '05 Jul '05 Oct'05
Jan'06
Apr '06 Jul '06 Oct'06
Jan'07
Date of Forecast
Per
cen
t o
f A
rea
Co
rrec
t
Percent of Area Correct Persistence Score MEAN SCORE MEAN Persistence
Mean score=49%Mean score=49%
Mean improvementMean improvement
over persistenceover persistence
=13%=13%
Verification: Percent Area CorrectVerification: Percent Area Correct
Two Path Drought Forecast ApproachTwo Path Drought Forecast Approach
• Continue current schematic for the Continue current schematic for the general publicgeneral public
• Produce objective probability maps for Produce objective probability maps for technical userstechnical users
Near-term Changes to Drought Near-term Changes to Drought OutlookOutlook
• Have increased frequency to twice/monthHave increased frequency to twice/month
• Propose adjustment of forecast categories Propose adjustment of forecast categories so all categories can be verified (include so all categories can be verified (include category for intensification?)category for intensification?)
• Propose using more current GFS Propose using more current GFS ensemble runs for soil moisture forecasts ensemble runs for soil moisture forecasts and initializing CAS with 2-week forecastand initializing CAS with 2-week forecast
• Considering drought probability mapConsidering drought probability map
Proposed Category ChangeProposed Category Change
Possible Probability MapPossible Probability Map
Drought Recovery Prediction MethodologyDrought Recovery Prediction Methodology
• Use observed forcings to drive the model and produce “best knowledge” Use observed forcings to drive the model and produce “best knowledge” initial conditions for forecastinitial conditions for forecast
• Drive the model with precipitation and temperature forcings unconditionally Drive the model with precipitation and temperature forcings unconditionally sampled from climatology creating an ensemble of model trajectoriessampled from climatology creating an ensemble of model trajectories
• At different lead times, estimate probability of soil moisture/runoff At different lead times, estimate probability of soil moisture/runoff percentile exceeding threshold from ensemble = probability of recoverypercentile exceeding threshold from ensemble = probability of recovery
Prototype forecast product from UWPrototype forecast product from UW
Probability of Drought RecoveryProbability of Drought RecoveryInitial Conditions (2/2006) 1-month lead forecast (3/2006)
6-month lead forecast (8/2006)3-month lead forecast (5/2006)
Prototype forecast product from UWPrototype forecast product from UW
Current Princeton Probability Current Princeton Probability ForecastsForecasts
Thoughts for the FutureThoughts for the Future
• Statistical and dynamic (GCM) techniques can be Statistical and dynamic (GCM) techniques can be used to forecast the probabilities for reaching used to forecast the probabilities for reaching various levels of the drought variable—need to various levels of the drought variable—need to effectively combine ST and LT forecastseffectively combine ST and LT forecasts
• For a drought early warning system, need to For a drought early warning system, need to consider current conditions, trends, and consider current conditions, trends, and forecastsforecasts
• Effective early warning systems (DEWS) provide Effective early warning systems (DEWS) provide alerts (e.g. watch, warning) and assessment, not alerts (e.g. watch, warning) and assessment, not just data. “Alert” for developing droughts? just data. “Alert” for developing droughts? “Advisories” for ongoing droughts? “Advisories” for ongoing droughts?
Do we ringDo we ring
a bell fora bell for
drought?drought?