Combining CMORPH Combining CMORPH with Gauge Analysis over with Gauge Analysis over

2010.05.20.2010.05.20.

Objective: Objective:

• To report our recent progress on the development of a new technique

– to remove bias in the CMORPH high-resolution precipitation estimates over land on daily time scale and

– to combine the bias corrected CMORPH with gauge analysis

Data: Data:

• CMORPH:– Currently operational version CMORPH– 2000 – 2009 (in process of backward extension to

Jan.1998)– Integrated to 0.25olat/lon / daily

• Gauge Data– CPC Unified Global Daily Gauge Analysis– Interpolation of QCed gauge reports from ~30K stations– 1979 – present – Integrated to 0.25olat/lon from its original grid of 0.125o

lat/lon resolution– Analysis released on 0.5olat/lon grid over globe and

0.25olat/lon over CONUS

CMORPH Bias [1]CMORPH Bias [1] Global DistributionGlobal 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

CMORPH Bias [2]CMORPH Bias [2] Time Scales of the BiasTime Scales of the Bias

• Bias over CONUS

• Bias presents substantial variations of

– seasonal (top), – sub-monthly (middle),

and – year-to-year (bottom)

time scales

CMORPH Bias [3]CMORPH Bias [3] Range DependenceRange Dependence

• Bias as a function of CMORPH Rainfall Intensity over CONUS

• Bias exhibits strong range dependence

Bias Correction [1]Bias Correction [1] General StrategyGeneral 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

Bias Correction [2]Bias Correction [2] Conceptual Model for Daily Precip. Over ChinaConceptual Model for Daily Precip. Over China

• Principal – Match the PDF of the CMORPH against that of daily gauge

to define and remove the bias, assuming PDF of the gauge analysis represents that of the truth

• Implementation– Collect co-located pairs of gauge and CMORPH over grid

boxes with >=1 reporting stations within a spatial window centering at the target grid box and a time period ending at the target date (30-day);

– A minimum of 300 pairs to ensure stability of PDFs– Define PDF for the CMORPH and gauge analysis

Bias Correction [3]Bias Correction [3] Cross-Validation Results Over ChinaCross-Validation Results Over China

CMORPH Bias (%) Correlation

Original -9.7% 0.706

Adjusted -0.0% 0.785

Bias Correction [4]Bias Correction [4] Spatial Patterns of Remaining BiasesSpatial Patterns of Remaining Biases

bias patterns caused by large spatial domain required to collect data pairs of sufficient number

Bias Correction [5]Bias Correction [5] Global Implementation for Daily Bias CorrectionGlobal Implementation for Daily Bias Correction

• Step 1: Correction using Historical Data– Establish PDF matching tables for each 0.25olat/lon grid for

each calendar date using data over nearby regions and over a period of +/- 15 days centering at the target date

• Step 2: Correction using Real-Time Data – Perform PDF matching using data over a 30-day period

ending at the target date

• Step 3: Combining Results from HIS/RT – Linear combination with weights inversely proportional to the

error variance

Bias Correction [6]Bias Correction [6] Correction Using Real-Time Gauge DataCorrection Using Real-Time Gauge Data

• Data pairs collected from a very large domain over gauge sparse areas (e.g. Africa)

Bias Correction [7]Bias Correction [7] Correction Using Historical Gauge DataCorrection Using Historical Gauge Data

• Spatial patterns of remaining bias much smaller

Bias Correction [8]Bias Correction [8] Defining Error for Bias-Corr. CMORPH Defining Error for Bias-Corr. CMORPH Using HIS/RT DataUsing HIS/RT Data

• Assuming error variance proportional to the rainfall intensity and inversely proportional to the size of data collecting domain

• Determining the coefficients using real data over the 10-yr period

Bias Correction [9]Bias Correction [9] Bias Corrected CMORPG Using HIS/RT DataBias Corrected CMORPG Using HIS/RT Data

• Bias corrected CMORPH with estimated error

Performance [1]Performance [1] 10-yr Mean Annual Mean Bias 10-yr Mean Annual Mean Bias

Performance [2]Performance [2] Remaining Bias & Gauge NetworkRemaining Bias & Gauge Network

• Remaining ‘bias’ appears over gauge sparse regions

• Less desirable

correction due to large data collection domain

• Poor quality in the gauge analysis

Performance [3]Performance [3] Correlation of Daily Precip. Over the 10-yr PeriodCorrelation of Daily Precip. Over the 10-yr Period

Performance [4]Performance [4] Comparison over the Entire Global LandComparison over the Entire Global Land

Performance [5]Performance [5] Comparison over AfricaComparison over Africa

Performance [6]Performance [6] Comparison over CONUSComparison over CONUS

Performance [7]Performance [7] PDF over AfricaPDF over Africa

Combining Gauge and CMORPH [ 1 ]Combining Gauge and CMORPH [ 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

Combining Gauge and CMORPH [ 2 ] Combining Gauge and CMORPH [ 2 ] Example over ChinaExample over China

SummarySummary• Prototype algorithm is developed and test products are constructed for the gauge-satellite merged global precipitation analyses

• Two sets of gauge-satellite precipitation analyses • Bias-corrected Satellite Estimates

•Global •8kmx8km; 30-min •1998 to the present

• Gauge-satellite combined analyses •Regional •0.25olat/lon; daily •1998 to the present

• Unified gauge – satellite precipitation analyses useful for climate monitoring, model verifications, hydrological studies, et al.