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John Janowiak Climate Prediction Center/NCEP/NWS
Jianyin Liang China Meteorological Agency
Pingping Xie Climate Prediction Center/NCEP/NWS
Robert Joyce CPC / RS Information Systems
IPWG-3 -- Melbourne, Australia October 24, 2006
*CPC Morphing Technique
Rain Gauge Data Merged with CMORPH* Yields: RMORPH
(microwave)
? ?
1.5 hours apart
GPM offers 3-hr sampling … to get finer temporal sampling …take advantage of 30-minute sampling afforded by Geo-IR data
Premise: Error in using IR to interpolate precip. features identified by PMW <
Error in deriving precip. directly from IR
So avoid deriving precipitation estimates directly from IR …
(microwave)
? ?
Derive motion vectors from ½ hourly IR
Apply motion to PMW-derived precipitation
“Morph”
(Joyce et al., J. Hydromet, 2004)
1.5 hours apart
Gauge-CMORPH Merging AlgorithmGauge-CMORPH Merging AlgorithmStep 1: Bias CorrectionStep 1: Bias Correction
• AssumptionsAssumptions
- Biases relatively stable over a region and time - Biases relatively stable over a region and time
- Biases can be approximated as ratios between the estimates & - Biases can be approximated as ratios between the estimates & gauges gauges
• ProceduresProcedures
- Performed once a day using data for all 24 hourly slots- Performed once a day using data for all 24 hourly slots
- Each day: - Each day: RATIO = GAUGE / CMORPHRATIO = GAUGE / CMORPH (last 30 days; each gauge) (last 30 days; each gauge)
- Optimal Interpolation (OI) technique (Gandin 1965) applied to ratios- Optimal Interpolation (OI) technique (Gandin 1965) applied to ratios
- “Un”biased CMORPH: - “Un”biased CMORPH: CMORPH x RATIOCMORPH x RATIOanalyzedanalyzed
Gauge-CMORPH Merging AlgorithmGauge-CMORPH Merging AlgorithmStep 2: Combining Gauge & Satellite DataStep 2: Combining Gauge & Satellite Data
Bias-corrected satellite estimates and gauge data combined via Bias-corrected satellite estimates and gauge data combined via Optimum Interpolation Technique (“OI”) Optimum Interpolation Technique (“OI”)
--Bias-corrected satellite estimates used as first-guessBias-corrected satellite estimates used as first-guess
- Gauge data are incorporated- Gauge data are incorporated
- Relative weighting at a grid box is a function of:- Relative weighting at a grid box is a function of:
- quality of satellite estimates at the grid box;- quality of satellite estimates at the grid box;
- density of local gauge - density of local gauge network densitynetwork density
Proof-of-Concept:Proof-of-Concept:Guang-Dong Province over Southern ChinaGuang-Dong Province over Southern China
South China Sea
Tibet plateau
Topography
Guang-Dong
Hourly precipitation reports from 394 stations over ~150,000 km2
(~380km2/gauge)
An Example for 03Z, May 5, 2005An Example for 03Z, May 5, 2005
GAUGE ONLY ORIGINAL CMORPH
BIAS CORRECTED MERGED
An Example for 03Z, May 5, 2005An Example for 03Z, May 5, 2005
GAUGE ONLY ORIGINAL CMORPH
BIAS CORRECTED MERGED
Mean Precipitation Mean Precipitation from April 1 – June 30, 2005from April 1 – June 30, 2005GAUGE ONLY ORIGINAL CMORPH
BIAS CORRECTED MERGED
PDF of Hourly Precipitation PDF of Hourly Precipitation for April - June, 2005for April - June, 2005
• Frequency of No-Rain EventsFrequency of No-Rain Events
• Gauge Station: Gauge Station: 83.9%83.9%
• Gauge Analysis:Gauge Analysis: 81.5%81.5%
• Original CMORPH:Original CMORPH: 77.3%77.3%
• Gauge-CMORPH Merged:Gauge-CMORPH Merged: 83.3%83.3%
• Frequency of Events with RainFrequency of Events with RainHistogram of precipitation frequency Apr-Jun
0
0.01
0.02
0.03
0.04
0.05
0.06
0-1 1-3 3-5 5-9 9-17 17-33 33-65 65-129 >129Precipitation range(0.1mm/h)
Fre
qu
ency
GaugeOI Gauge+CMORPHOI GaugeCMORPH
Dense Gauge Locations: Disaggregate Gauge Data
Use hourly CMORPH to partition daily gauge amounts into hourly amounts (i.e. “disaggregate”)
- United States
- Australia
- China?
-Daily RMORPH constrained to daily gauge amount
- Gauge data partitioned into hourly amounts
-Spurious coverage of light gauge amounts reduced
Valid for 24 hrs ending 12z August 8, 2006
Daily sum of hourly amounts
Where to from Here?
1. Transition regional prototype to global
2. Experiment with OI tuning parameters
3. Cross-validation testing
4. Explore bias-adjustment for oceanic precip
- Normalize estimates to TRMM “2B31”(TMI/PR)
- ATLAS buoys?- Radar?
An Example for 03Z, May 5, 2005An Example for 03Z, May 5, 2005
GAUGE ONLY ORIGINAL CMORPH
BIAS CORRECTED MERGED
An Example for 03Z, May 5, 2005An Example for 03Z, May 5, 2005
GAUGE ONLY ORIGINAL CMORPH
BIAS CORRECTED MERGED