A Gauge-Satellite Merged Analysis A Gauge-Satellite Merged Analysis of High-Resolution Global of High-Resolution Global Precipitation Precipitation

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A Gauge-Satellite Merged Analysis A Gauge-Satellite Merged Analysis

of High-Resolution Global of High-Resolution Global

Precipitation Precipitation

Pingping Xie Pingping Xie

NOAA NOAA ’ ’ s Climate Prediction Center s Climate Prediction Center

acknowledgements acknowledgements

S.-H. Yoo, R. Joyce, and Y. Yarosh S.-H. Yoo, R. Joyce, and Y. Yarosh

2011.03.30.

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Objective:

• To develop a high-resolution gauge-satellite merged analysis of global precipitation

• Up to 8kmx8km over the globe (60

^o

S-60

^o

N)

• 30-min from Jan.1998, updated real time

• Through combining information from gauge- based analysis and CMORPH satellite

estimates

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Global Daily Gauge Analysis Global Daily Gauge Analysis

• Interpolation of gauge reports from ~30K stations

• Optimal Interpolation (OI) with orographic correction (Xie et al. 2007)

• Interpolated on 0.125ôlat/lon, then averaged on 0.5ô/0.25ôlat/lon grid over global land / CONUS for release

• Global fields from1979 to present updated daily on a real-time basis

• CONUS analysis from 1948

• Example

for July 1, 2003

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CMORPH Satellite Estimates CMORPH Satellite Estimates

 CMORPH : CPC Morphing technique (Joyce et al. 2004)

 Combined use of satellite PMW and IR data

 8kmx8km / 60^oS-60^oN;

 30-min interval / from March1998 / Real-time

 CMORPH back-extended to 1998 to cover the entire TRMM Era

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CMORPH Bias [1]

Global Distribution Global Distribution

• 2000-2009 10-yr annual mean precip

• CMORPH captures the spatial distribution patterns very well

• BIAS exists

– Over-estimates over tropical / sub-tropical areas

– Under-estimates over

mid- and hi-latitudes

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CMORPH Bias [2]

Time Scales of the Bias Time Scales of the Bias

• Bias over CONUS

• Bias presents

substantial variations of

– seasonal (top),

– sub-monthly (middle), and

– year-to-year (bottom)

time scales

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CMORPH Bias [3]

Range Dependence Range Dependence

• Bias as a function of CMORPH Rainfall Intensity over CONUS

• Bias exhibits strong range dependence

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Bias Correction [1]

General Strategy General Strategy

• Seasonal / Year-to-year variations in bias

 correction coefficients change with time

• Sub-monthly variations in bias

 against sub-monthly gauge data

• Range-dependence in bias

 PDF matching

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Bias Correction [2]

PDF Bias Correction against daily gauge data PDF Bias Correction against daily gauge data

• PDF matching

• Collect co-located daily gauge and satellite data over a time / space domain

• Construct cumulated PDF tables for the gauge and satellite data

• Match the PDF of satellite data against that of gauge data to remove the bias

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Bias Correction [3]

Global Implementation Strategy Global Implementation Strategy

• Step 1: Correction using Historical Data

– Establish PDF matching tables

– using historical data – for each 0.25^olat/lon grid – for each calendar date

– using data over nearby regions and

– over a period of +/- 15 days centering at the target date

– At least 500 pairs of non-zero data pairs

– to ensure the PDF tables are created using data over a small space domain

• Step 2: Correction using Real-Time Data

– Perform PDF matching using data over a 30-day period ending at the target date

– To account for year-to-year variations in the bias

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Bias Correction [4]

Results over Global Land Results over Global Land

 2000-2009 annual mean

 Large-scale bias corrected

Daily Gauge

KF-CMORPH

Gauge-Adjusted KF-CMORPH Comparison over Africa

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Bias Correction [5]

Strategy over Ocean

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Bias Correction [6]

Applications : Evaluation of CFSR JJA Precip.

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Bias Correction [7]

Applications : Precip. Diurnal Cycle

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Bias Correction [8]

Applications : Precip. Diurnal Cycle over Oceans

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Bias Correction [9]

Applications : Precip. Diurnal Cycle over Land

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Bias Correction [10]

Applications : Evaluations of MJO Precip in CFSR

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Combining Gauge with Satellite [1]

• Combining bias-corrected satellite estimates with daily gauge over the several regions

– This is only possible for several regions due to different daily ending time in the gauge reports

• Africa (06Z)

• CONUS/MEX (12Z)

• S. America (12Z)

• Australia (00Z)

• China (00Z)

– Combining the bias-corrected CMORPH with gauge

observations through the Optimal Interpolation (OI) over selected regions where gauge observations have the same daily ending time

• in which the CMORPH and gauge data are used as the first guess and observations, respectively

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Combining Gauge with Satellite [2]

Quantifying error in the inputs Quantifying error in the inputs

• Key to the construction of an OI- analysis is the definition of input errors

• Quantified error in the gauge and bias-corrected CMORPH through comparison against real data

• Error variance in gauge analysis

• Proportional to precip intensity

• Inversely proportional to local gauge density

• Error variance in bias-crtd CMORPH

• Proportional to precip intensity

• Correlation between CMORPH error at two different grid boxes

• Decreases exponentially with separation distance

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Combining Gauge with Satellite [3]

Example for Pakistan Flooding Example for Pakistan Flooding

• Gauge analysis depict heavy rain but tend to extend the raining area

• Satellite data tend to under-estimate

• Merged analysis present improved

depiction of the heavy rain

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 Reports from up to 28 stns available in a 0.25^olat/lon grid box over Seoul

 Arithmetic mean of 28-stn reports taken as the ‘truth’

 Gauge analyses using 1000 combinations of sub-set stations are combined with bias-corrected

CMORPH and compared to the ‘truth’ at the grid box over Seoul

 Correlation is calculated for the combined analyses and the input

gauge / CMORPH. Results are plotted in different colors for different gauge network densities

Combining Gauge with Satellite [4]

Independent tests using data over Korea

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Operation System Operation System

Gauge Analysis (0.25^olat/lon)

CMORPH (0.25^olat/lon)

PDF Matching Bias Correction

Bias-Corrected CMORPH

OI Combining

Merged Analysis

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Summary Summary

• We are in final stage of constructing gauge-satellite merged analyses of global precipitation;

• Two sets of gauge-satellite precipitation analyses

• Bias-corrected Satellite Estimates

•Global

•8kmx8km; 30-min

•1998 to the present

• Gauge-satellite combined analyses

•Regional

•0.25^olat/lon; daily

•1998 to the present

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Related R&D Activities [1]

• Developing 2

^nd

generation Kalman filter based CMORPH satellite estimates (Part of PMM project)

• Capable of including information from additional sources (e.g. IR, model)

• Integrating information based on more accurate statistical framework

Simulation tests of the original and KF CMORPH with inputs from 1,2,4,7, and 9 PMW satellites

Comparisons against radar observations over CONUS for different local times

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Related R&D Activities [2]

• Gauge-radar-satellite merged analysis of hourly precipitation over CONUS

• Final goal: a portable module to combine global high-resolution satellite estimates (e.g. CMORPH) with regionally available additional information (e.g. gauge, radar, model) to create precip analysis of higher quality and resolution

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Related R&D Activities [3]

• Extending the CMORPH to cover the entire globe

(pole-to-pole)

• Cloud advection vectors

• Cold season

precipitation rates

• Taking advantage of

model simulations

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Thank You !!

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Backup Slides

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Gauge Error [1]

 Gauge error is defined by comparing the gauge analysis derived from reports of different gauge network

configurations against the ’truth’

 Daily data from an extremely dense station network over Korea is used. Over a 0.25

^o

lat/lon grid box over Seoul,

Korea, reports from 28 stations are available and their arithmetic mean is used as the ‘truth’ for that grid box

 Gauge analyses are constructed using reports from 1,000 combinations of stations to mimic the configuration of those over China; and they are compared to the ‘truth’

for 2005-2007

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Gauge Error [2]



Results are analyzed according to the precipitation

intensity and gauge network density

 Gauge network density is defined using a parameter called Number of Equivalent Gauges (Neg)

Neg = Ng0 + Ng1 + Ng2

Ng0 : # of gauges inside the target grid box Ng1 : # of gauges inside the grid boxes

neighboring to the target grid box Ng2: # of gauges inside the grid boxes one f

urther layer away.

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Gauge Error [3]



Error variance of the gauge

analysis linearly proportional to the precipitation intensity and inversely proportional to the local gauge

network density measured by the Number of Equivalent Gauges (Neg)

 An empirical relation established between the error variance and the precipitation and the Neg:

E² = a + b ∙ R / ( N_eg+ 1 ) a = 0.15 (mm/day)²

b = 4.09 (mm/day)²

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CMORPH Error [1]

 Assuming CMORPH error variance is proportional to the estimated precipitation amount

 Proportional constant is determined through

comparison with gauge analysis over grid boxes with at least one gauge using data over China for

summer 2007

 CMORPH error is defined for each grid box of

0.25^olat/lon using the curve in the bottom panel of the figure

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CMORPH Error Correlation CMORPH Error Correlation

 Correlation between the bias- corrected CMOROH

error at one location and

that at a different location

 Assuming the error

correlation is only a function of separation distance

 Correlation calculated for all combinations of grid boxes using data over China for

summer 2007

 Results fitted to a Gaussian function