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

hrshr

mm

T

TGPI #

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

180cos

1800

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