Satellite-derived Rainfall Estimates over the Western U.S.:

Fact or Fiction?

John Janowiak Bob Joyce

Pingping XiePhil Arkin

Mingyue Chen Yelena Yarosh

OUTLINE

1. Brief review of IR & passive microwave info.

2. Describe “CMORPH”

3. Validation (US & Australia)

4. Simple gauge vs. satellite sampling study

5. A look at western US precip

6. Conclusions & on-going work

Surface

InfraredGeostationary & Polar

Surface

Passive Microwave “Emission”

Detects thermal emission from raindrops

- most physically direct - over ocean only- polar platform only

Surface

Freezing Level

Passive Microwave “Scattering”

Upwelling radiationfrom Earth’s surface

Upwelling radiation is scattered by ice particles in the tops of convective clouds

- land & ocean - polar platform only

IR: great sampling / provides poor estimate of rainfall

MW: poor sampling / provides good estimate of rainfall

>>>>> Combine them to meld strengths of each

Others have done this – IR used to produce precip. estimate when MW data unavailable

- Turk (NRL, Monterey),

- Adler & Huffman (GSFC),

- Gao, Hsu,Sarooshian (U. AZ)

3-hr mosaic: good coveragebut time of obs. varies by 3 hrs

CPC Morphing Technique

“CMORPH”

Spatial Grid: 0.0728o lat/lon (8 km at equator)

Temporal Res’n: 30 minutes

Domain: Global (60o N - 60o S)

Period of record: Dec. 2002 - present

“CMORPH” uses IR only as a transport vehicle.

Underlying assumption is that error in using IR to transport percip. features is < error in using IR to estimate precip.

Bob Joyce!

http://www.cpc.ncep.noaa.gov/products/janowiak/MW-precip_index.html

Paper (Joyce et al.) submitted to J. Hydrometeor.

“CMORPH” is NOT a precipitation estimation technique but rather a technique that creates spatially & temporally complete information using existing precipitation products that are derived from passive microwave observations.

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At present, precipitation estimates are used from3 sensor types on 7 platforms:

AMSUB (NOAA 15, 16, 17)

SSM/I (DMSP 13, 14, 15)

TMI (TRMM)

Soon: AMSR (“ADEOS-II”) & AMSR-E (“Aqua”)

2.5o

2.5o

“Advection vectors” are computed from IR for each 2.5ogridbox andallmicrowave pixels contained in that grid box are propagated in the direction of that vector

http://www.cpc.ncep.noaa.gov/products/janowiak/us_web.html

http://www.bom.gov.au/bmrc/wefor/staff/eee/SatRainVal/dailyval_dev.html

Remote Sensing Errors & Limitations

- Indirect estimates inferred radiometrically - Instrument calibration- Conversion from retrieval to rain rate (algo.)- Temporal sampling

ERRORS:

LIMITATIONS:

- Measurements not temporally continuous

- Depending on instrument only convective (“scattering’) precip. may be sensed

Raingauge Errors & Limitations

- Wind & gauge exposure effects - Human element (time, accuracy) - Automated (calibration, maintenance)- Biological contamination

ERRORS:

LIMITATIONS:

- Representativeness of area (point value) - Spatially incomplete- Available frequency (daily, 6-hr)

0 <0.10 .10-.30 .30-.50 > .50

Box Mean Precip: 0.15”Std. Dev. : 0.22 “Min. precip : 0Max. precip. : 0.95”

10 7 5 3 1

1o x1o box in s-central TN (July14, 2003)

Distribution of Rainfall by Amount

BIAS RMSE CORR

1 gauge -0.08 0.03 0.874 gauge 0.00 0.01 0.97Radar 0.00 0.02 0.96Cmorph 0.08 0.04 0.91

40%

57%

66%

50%

62%

70%

Gauge

40%44%

:

Synthetic Data Sampling Study

Question: Are there situations when an estimate from satellite is ‘better’ for assessing area-mean precipitation than a measurement from gauge(s)?

Design:

- randomly assign precip to 169 locations (13 x 13 array)

- 50% of locations have “0” precip.

- Repeat for 1000 ‘days’

- Daily “truth” is the 169 value mean

:

Assumptions:

- gauge measurement is perfect

- gauge values are totally representative of the area sampled by satellite ie. area avg.

- multiple gauges in an area are distributed optimally

Approach (overly?) simplistic:

- ‘real-world’ nonzero rainfall distribution characteristics not modeled

- on average, the % of locations with rain over an area is < 50% used here

- rainfall ‘generators’ exist that more nearly duplicate the statistics of actual rainfall over time-space

- much work on aspects of this topic done in hydro. & satellite sampling communities

(Bell et al. :1990, 1996, 2003)

Samples of synthetic precipitation within a 1o x 1o lat/lon box at satellite resolution

Precip amounts of 0 to 1 chosen randomly; impose condition that 50% are = “0”

1o x 1o lat/lon box containing 169 satellite pixels

X

X

X

2 Gauges

X

X X

3 Gauges

X X

X X

4 Gauges

X X

X X

X

5 Gauges

X

X

X XX

X

X X

X

9 Gauges

X

X

X XX

X

X X

X

XX

X X

13 Gauges

X

X

X XX

X

X X

X

XX

X X

X X

XX X

X X

XX X

X X

25 Gauges

1 gauge

2 gauges

9 gauges

Time series of absolute error (1st 100 days)

Light blue: satellite with 200% positive bias

Dark blue: satellite with 100% positive bias

Green: satellite with 50% positive bias

Red: satellite with 10% positive bias

Light blue: 9 gauges

Dark blue: 5 gauges

Green: 3 gauges

Red: 1 gauge

200% error

10%/ error(satellite)

50% error

100% error

(90%)

“Perfect” Gauge

5 3 1Point of 50% error accumulation

9

1 gauge

2 gauges

9 gauges

Light blue: sat.ellite with 0-200% pos. random error

Dark blue: satellite with 0-100% pos. random error

Green: satellite with 0- 50% pos. random error

Red: satellite with 0-10% pos. random error

Time series of absolute error

(90%)

0-10% error

0-50% error

0-100% error

0-200% error

Light blue: 9 gauges

Dark blue: 5 gauges

Green: 3 gauges

Red: 1 gauge

9 5 3 1

Error at 50% point

“Perfect” Gauge

RMSE 0.251 satellite (200% + bias) 0.103 1 gauge ~10% of earth 0.063 satellite (100% + bias) 0.063 satellite (0-200% random) 0.054 2 gauges 0.033 3 gauges 0.023 4 gauges 0.021 5 gauges 0.016 satellite (50% + bias) 0.016 satellite (0-100% random) 0.011 9 gauges 0.009 13 gauges 0.009 satellite (0-50% random) 0.004 25 gauges 0.001 satellite (10% + bias)

Number of HADS/RFC stations per ¼ degree lat/lon grid box

(9/10/2003)

10% of earth (60N-60S)29% of land area ( “ )

“CAMS” - 1 or more gauges per 1o Grid10% of earth (60N-60S); 29% of land

“CAMS” - 2 or more gauges per 1o Grid

http://www.cpc.ncep.noaa.gov/products/janowiak/us_web.html

Crude RH Adjustment to CMORPH

(Aug 2003)

Scofield, 1987 Rosenfeld and Mintz (1988)McCollum et al. (2000)

CMORPH vs. gauge over ‘NAME’ zones

CMORPH with RH adjustment vs. gauge over ‘NAME’ zones

Time series of statistics over 9 NAME Zones

Evap. adjusted

Evap. adjusted

Conclusions

Fact or Fiction?

1. CMORPH estimates compare quite favorably to raadar estimates over the US and to gauge analyses over the US and Australia.

2. Satellite estimates of rainfall can be useful over the western U.S. (and elsewhere) – perhaps better than gauge data in some situations

3. Many satellite techniques overestimate rainfall considerably in semi-arid regions during the warm season, but an RH adjustment is promising.

Work in Progress

1. Refine & implement evaporation adjustment

2. Investigate use of model winds to advect rainfall

- more accurate results? - allow reprocessing to early 1990’s - reduce processing time

substantially3. Incorporate microwave rainfall estimates from new

instruments (AMSR, AMSR-E)

4. Investigate derivation of advection vectors from

microwave data

- temporal resolution to 10 minutes?

‘0’ precip

0 < precip < 1

Long-term Box Mean = 0.25

So, 200% error = 0.500 100% error = 0.250 50% error = 0.125

Correlation with MW availability