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Dust storm approaching Stratford, Texas Dust bowl surveying in Texas Image ID: theb1365, Historic C&GS Collection
Location: Stratford, Texas Photo Date: April 18, 1935
Credit: NOAA George E. Marsh Album
Drought
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Soil moisture percentile (-)
SW Monitor: Regional to global
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VIC Soil Moisture Percentiles (wrt 1960-2008)20131201
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Challenge: Meteorological data
http://www.ncdc.noaa.gov/; Global Summary of the Day
Three requirements:- long term data record (climatology)- near real-time reporting- consistency
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http://trmm.gsfc.nasa.gov/
TRMM instruments:
• Precipitation Radar (PR)
• TRMM Microwave Imager (TMI)
• Visible and InfraRed Scanner (VIRS)
• Cloud and Earth Radiant Energy Sensor (CERES)
• Lightning Imaging Sensor (LIS)
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http://trmm.gsfc.nasa.gov/
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http://www.emc.ncep.noaa.gov/
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Forcing data
For the purpose of this presentation, current is 12/01/2012. However, the RT and GFS4 datasets can be updated daily.
Precipitation data sets used:Sheffield et al: 1948-2008 (T and P) (SH)TRMM V7:
Research product RP: 1/1/1998-6/30/2012Real-time product RT: 3/1/2000-current
available in near real-time
Temperature data sets used:Sheffield et al: 1948-2008NCEP/NCAR Reanalysis: 1979-2011GFS 4: 2009-current
available in near real-time
Station-based: climatology
Station-based: climatology
Satellite + stations: spinup
Satellite only: real-time
Model + stations: spinup
Model + stations: real-time
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The challenge• The climatological period, used to determine the soil moisture
percentiles for each grid cell for a given date, is based on the period 1960-2008 (Sheffield data set)
• Because soil moisture conditions in the realtime simulations are compared with those from the climatological period, systematic differences between the forcing datasets must be avoided
• Correct the precipitation and temperature data sets used for the post-2008 period so that they match the Sheffield dataset (in a statistical sense) for any overlapping period
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Forcing data• Wind speed values are climatological values based on a
reanalysis• All other meteorological forcings needed to drive the models
are estimated from the daily temperature range and precipitation– downward shortwave radiation– downwelling longwave radiation– humidity
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Precipitation data available: • Sheffield (1/1/1948-12/31/2008) (SH)• TMPAV7 3B42 research product (1/1/1998-6/30/2012) (RP)• TMPAV7 3B42RT (3/1/2000-current) (RT)
Goal: Bias correct RP or RT to SH
Precipitation Adjustment
Corrections:1. Match number of rainy days from RT and RP to Sheffield2. Match monthly mean (ratio of long-term mean)
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Annual RP precipitation
Annual corrected RP precipitation
TRMM V7 RP Correction
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Annual RT precipitation
Annual corrected RT precipitation
TRMM V7 RT Correction
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SH/RP ratio (annual)
SH/RT ratio (annual)
Ratios between RT and SH are much larger (than RP and SH), because the RT data does not include station observations
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Temperature Data Adjustment• Data set used:
- Sheffield et al: 1948 - 2008- NCEP/NCAR Reanalysis: 1979 - 2011- GFS4: 2009 - Current
• Individual Bias Correction on daily Tmax and Tmin to ensure temperature range is preserved
• For period 2009/01 – 2012/06:- Interpolate Reanalysis data to 0.5 degrees- Calculate mean monthly Tmax and Tmin offsets from Reanalysis to Sheffield
data set- Impose calculated offsets for each month on interpolated Reanalysis for this
period • For RT (post 2012/06):
- Interpolate GFS4 analysis data to 0.5 degrees- Calculate mean monthly Tmax and Tmin offsets from GFS4 to Reanalysis
data set- Impose calculated offsets for each month on interpolated GFS4 for this
period
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Temperature Adjustment Results• GFS4 to Reanalysis
- Before Adjustment- January Tmax and Tmin
GFS4 Reanalysis
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Adjusted GFS4 Data
Raw Reanalysis Data
Adjusted GFS4 Data
Raw Reanalysis Data
Tmax
Tmin
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Historical Drought Events
1. 1952 drought in the U.S.
2. 1965 drought in Australia
3. 1983 drought in Africa
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1952 drought in the U.S. (GSWM upper vs SWM lower)
VIC Noah 2.8
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1965 drought in Australia
VIC Noah 2.8
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1983 drought in Africa
VIC Noah 2.8
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Recent Drought: multimodel 2012/07/01
Note that the percentile method is very sensitive to small changes in moisture in dry regions such as the Sahara. The high percentiles (“wetness”) in that region is not realistic.
In the realtime version of the global surface water monitor the desert areas of the world will be grayed out and set to “no data”
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VIC
Noah
SAC
Recent Drought: individual models 2012/07/01
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2012 Monthly Drought Information
2012/01/01 - Multimodel
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2012/02/01 - Multimodel
2012 Monthly Drought Information
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2012/03/01 - Multimodel
2012 Monthly Drought Information
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2012/04/01 - Multimodel
2012 Monthly Drought Information
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2012/05/01 - Multimodel
2012 Monthly Drought Information
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2012/06/01 - Multimodel
2012 Monthly Drought Information
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2012/07/01 - Multimodel
2012 Monthly Drought Information
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2012/08/01 - Multimodel
2012 Monthly Drought Information
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2012/09/01 - Multimodel
2012 Monthly Drought Information
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2012/10/01 - Multimodel
2012 Monthly Drought Information
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2012/11/01 - Multimodel
2012 Monthly Drought Information
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2012/12/01 - Multimodel
2012 Monthly Drought Information
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Comparison with other Drought Monitors2012/07/01
http://ncc.cma.gov.cn/influ/en_hljc.phpUW Surface Water Monitor Chinese Drought and Flood Monitor
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Comparison with other Drought Monitors2012/10/01
UW Surface Water Monitor http://ncc.cma.gov.cn/influ/en_hljc.phpChinese Drought and Flood Monitor
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Comparison with other Drought Monitors2012/12/01
UW Surface Water Monitor http://ncc.cma.gov.cn/influ/en_hljc.phpChinese Drought and Flood Monitor
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Conclusions
1) Global multimodel drought nowcasting is entirely feasible now
2) Consistent forcings are essential
3) Droughts only make sense when expressed relative to a
climatology – hence climatically consistent near real-time data
are essential
4) Skill in drought forecasting (not the focus of this talk) is critically
linked to getting the initial conditions (nowcast) right ... and still
a major challenge