1 Inter-comparing high resolution satellite precipitation estimates at different scales Phil Arkin and Matt Sapiano Cooperative Institute for Climate Studies.

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Presentation transcript:

1 Inter-comparing high resolution satellite precipitation estimates at different scales Phil Arkin and Matt Sapiano Cooperative Institute for Climate Studies (CICS), University of Maryland

2 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

3 HRPP Data used in this study ProductProviderDataMethod 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, NWPTb 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

4 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

5 US (SGP) 3-hrly CorrelationsBias

6 Oceanic (TAO) 3-hrly CorrelationsBias

7 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

8 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

9 First EOF

10 Second EOF

11 Short term trends Short term trends calculated over common period (Jan 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

12 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!