Inter-comparing high resolution satellite precipitation estimates at different scales

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 Common resolution is 0.25˚, 3-hourly

4. 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 Validation data

5. US (SGP) 3-hrly Correlations Bias

6. Oceanic (TAO) 3-hrly Correlations Bias

7. Performance of GFS model data SGP • 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 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 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

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!