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The Climate Prediction Center Rainfall Estimation Algorithm Version 2 Tim Love -- RSIS/CPC. Presentation Outline. Overview Input data / methodology Satellite estimate combination process Merging steps Output data System requirements. CPC RFE 2.0. RFE 2.0 Overview. - PowerPoint PPT Presentation
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The Climate Prediction CenterRainfall Estimation Algorithm Version 2
Tim Love -- RSIS/CPC
Presentation Outline
• Overview• Input data / methodology• Satellite estimate combination process• Merging steps• Output data• System requirements
CPC RFE 2.0
RFE 2.0 Overview
• Run daily at CPC for Africa, southern Asia, Afghanistan area domains
• Final output is minimally biased and greatly improves spatial resolution of information
• Inputs include satellite IR temperature data, microwave precip estimates, gauge fields
• Computing resources required are relatively minimal
• Code highly portable
CPC RFE 2.0
Input Data
• Meteosat files– Half hourly 0.05° infrared temperature data thru a
McIDAS server– Files are ftp’d to host machine once daily and gridded
based on current satellite position constants– Code conducts QC via lag and cross-correlation methods– Fractional coverage for 235K and 275K determined
CPC RFE 2.0
Meteosat Data, cont.
– Resultant field = cold count duration (CCD) @ 0.1° resolution
– CCD used for GOES Precipitation Index (GPI) calculation
hrshrmm
TTGPI #3235
CPC RFE 2.0
– GPI tends to overestimate spatial distribution but underestimates convective precipitation
GPI Quality Control
• Each pixel must have > 4 half hour values, or pixel is undefined
• > 70% of all pixels must be defined after incorporating all half hour data sets
CPC RFE 2.0
GPI Estimate
CPC RFE 2.0
GTS Data• 2534 stations available daily• Only 400-800 report daily• Few reports from Nigeria, none from Liberia, Sierra
Leone• Data ingested from GTS line, QC’d, fed to operational
machine, then gridded to 0.1° resolution file • Other station data may be readily used as input to
algorithm via changing 2 tables in base code• Requirements for RFE processing:
– GPI and GTS inputs
CPC RFE 2.0
GTS Quality Control
• Must have > 200 stations available daily• Station undefined if GTS daily rainfall:
> 200 mm> 1 mm and fc275 = 0 in all surrounding pixels< 0.1 mm and all satellite estimates > 2 mm> 50 mm and all satellites < 20 mm< 5 mm, all satellites > 20 mm, and if sat-GTS > 20> 20 mm and all satellites < 1 mm
CPC RFE 2.0
GTS Interpolation Technique
• Shepard technique• Using an initial search radius (rs0), a new radius is
determined depending on number of stations within rs0
• If an adequate # of gauges is within new radius, interpolate rainfall to 0.1° grid using station-station vector
• Otherwise, interpolate using least squares regression• If rainfall is undef or 0 within a 1.0 degree box,
rainfall at center grid is zero
CPC RFE 2.0
Initial Search Radius
landover grids lon ofnumber where 180
cos1800
6371 where
0 reports num)(7
40
40
0
x
latxarea
areaRS
lat
lat
CPC RFE 2.0
GTS Inputs
CPC RFE 2.0
GTS vs GPI
CPC RFE 2.0
SSM/I Inputs
• 2 instruments estimate precip twice daily ~6 hourly data frequency
• Fails to catch other rainfall in temporal gaps
• Data needs only small conversion in preparation for input to algorithm
CPC RFE 2.0
SSM/I Quality Control
• > 70% of pixels must be defined after combining each input data set
• SSM/I daily rainfall is zero if:– fc275 = 0 (no clouds)– SSM/I rain < 0.1 mm– fc275 < 0.1 and SSM/I rain > 5 mm– target grid is over the coast and 1 or less
neighboring grids have SSM/I rain = 0
CPC RFE 2.0
SSM/I Estimate
CPC RFE 2.0
SSM/I vs GTS vs GPI
CPC RFE 2.0
• As with SSM/I, data is available 4 times daily, staggered temporally
• Tends to overestimate most precip, but does well with highly convective systems
• Data sent in HDF format, thus needs to be deciphered before input to RFE algorithm
• Preprocessing straightforward
AMSU-B Data
CPC RFE 2.0
AMSU-B Quality Control
• > 60% of pixels must be defined after incorporating all input data
• AMSU-B daily rainfall is zero if:– fc275 = 0 (no clouds)– AMSU rain < 0.1 mm– fc275 < 0.1 and AMSU rain > 5 mm– target grid is over the coast and 1 or less
neighboring grids have AMSU rain = 0
CPC RFE 2.0
AMSU-B Estimate
CPC RFE 2.0
CPC RFE 2.0
Combining Satellite Estimates
• Combines 3 satellite data sets linearly
3
1iiiSWS
3
1
2
2
ii
iiW
where Wi = weighting coefficients Si = precip estimates σi = random error CPC RFE 2.0
Bias Removal
• Satellite estimates are merged with station data to remove bias
2 2P S
P Gwhere S = first step output G = gauge observations P = final output
CPC RFE 2.0
Output Data
• Operational: GTS+GPI+SSM/I+AMSUB• Other:
– GTS+GPI– GTS+GPI+SSM/I+AMSUB+GDAS– With and without bias removal
• Archival:– All inputs needed for reprocessing– Some mid-processing outputs
CPC RFE 2.0
System Requirements
• Linux or Unix operating system – System has also been ported to Windows
• Minimum 2Gb hard drive space• Minimum 500MHz processor• Fortran 77/90 compiler• C, Korn, or Bourne Shell• GrADS software to display/create graphics
CPC RFE 2.0
System Outreach
• Seek collaboration with external users to:– Develop local capability– Develop independent validation– Improve algorithm
CPC RFE 2.0