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Inter-comparing high resolution satellite precipitation estimates

at different scales

Phil Arkin and Matt Sapiano Cooperative Institute for Climate Studies

(CICS), University of Maryland

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HRPP Data• Most scientific and societal applications require fine

spatial and temporal resolution– Daily or finer– 10 – 50 km

• In the past decade, new observations and research have made much higher resolution products possible, and extensive development and implementation has taken place

• The products generally rely on innovative methods that combine geostationary IR observations/estimates with estimates from passive microwave observations

• Time scales of about 3-hourly, spatial resolutions of 0.25°, near-global coverage (60°N-60°S)

• Available at 3-hourly, 0.25º Resolution

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HRPP Data used in this study

Product Provider Data Method

TRMM Multi-satellite precipitation analysis (TMPA, a.k.a. 3B42 or 3B42RT for Real Time)

GSFC (G. Huffman)

Geo-IR, microwave from SSM/I, TRMM, AMSU, AMSR

Merged microwave and microwave-calibrated infrared (IR) - here, forced to GPCC product

CPC Morphing Technique (CMORPH)

NOAA CPC(J. Janowiak, B. Joyce)

Geo-IR, microwave from SSM/I, TRMM, AMSU, AMSR

Passive microwave (PMW) rain rates advected and evolved according to IR imagery

Global Satellite Mapping of Precipitation (GSMaP)

CREST/JST, Japan (K. Okamoto)

Geo-IR, microwave from SSM/I, TRMM, AMSU, AMSR

Passive microwave (PMW) rain rates advected according to IR imagery

Hydro-Estimator (HE) NOAA NESDIS ORA (B. Kuligowski)

Geo-IR, NWP Tb in geostationary-IR, modulated by cloud evolution, stability, total precipitable water, etc.

NRL blended algorithm (NRL-Blended)

NRL (J. Turk) Geo-IR, microwave from SSM/I, TRMM, AMSU, AMSR

Histogram-matching calibration of geo-IR to merged microwave

Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN)

UC Irvine (K.-L. Hsu)

Geo-IR, TRMM microwave

Adaptive neural network calibration of geo-IR to TRMM TMI

Common resolution is 0.25˚, 3-hourly

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Validation data• Long record of sub-daily resolved

gauges required:– ARM Southern Great Plains (SGP -

Oklahoma, Kansas - 16 gauges)– TAO/TRITON Buoy array (Tropical

Pacific - 24 gauges)• Split buoys into 2 groups at 150W• 2 undercatch corrections applied

based on wind and threshold rate

• Compare nearest HRPP grid-point to high-resolution gauges– Evaluate between Dec 2002 and March 2006– Require > 1 year of data; split SGP by 6 month season– Estimate HRPP value as weighted average of nearest 4 grid-points to

gauge– Exclude buoy stations with probability of precipitation <0.1

• Also include Stage IV radar in analysis of SGP– Use as a benchmark for skill

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US (SGP)

3-hrly Correlations Bias

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Oceanic (TAO)

3-hrly Correlations Bias

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Performance of GFS model data

• Satellite datasets limited by high noise– Models are improving and can

provide useful data

• GFS 12hr & 15hr forecast precip is obtained 4 times a day from March 2004

• High cors over SGP in warm season, v. low in cold season– V. low over tropical Pacific

• Big diff between daily and 3hrly cors for GFS: indicative problems with diurnal cycle

SGP

TAO

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Large-scale comparison• These high resolution precipitation products are

increasingly being used for climate purposes– Changes in extremes, the diurnal cycle, MJO etc

• However, unclear whether they are suitable• Aggregate estimates to monthly, 2.5° resolution

and compare with GPCP– Interested in finding artifacts: use EOFs and short-

term linear trends– Some extra processing was required: removed data

from 50-60° for GSMaP and PERSIANN to avoid erroneous, dominant signal here

– Removed some data from NRL-Blended in early period and some ice-infected areas of PERSIANN

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First EOF

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Second EOF

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Short term trends• Short term trends

calculated over common period (Jan 2003 - Dec 2006)– Simple linear regression

with time as only factor– Not indicative of long-term

trends

• Signs of some erroneous trends in CMORPH, NRL-Blended and PERSIANN– As with EOFs, data

boundaries seem to cause discontinuities

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Summary and Conclusions• Satellite-based products all exhibit skill in correlation and bias

– CMORPH has slight advantage over others– Bias correction of TMPA works well over land, but biases are

comparable to others over ocean• In SGP, some seasonality in correlation is seen (but not as

much as might have been expected)– Large positive bias in warm season for non-adjusted products

• Satellite-based products uniformly underestimate relative to buoy gauges - implications for oceanic precipitation?

• Large-scale inter-comparison is encouraging– All datasets do well at monthly scale, although some are better

than others (reprocessing is very important)– Some datasets have minor issues around ice and at higher

latitudes and there may be data boundaries associated with addition of AMSU etc.

• Care is required!