An Inter-comparison of 5 HRPPs with 3-Hourly Gauge Estimates Matt Sapiano Cooperative Institute of Climate Studies (CICS), University of Maryland

Aim PEHRPP Suite 2: High time resolution comparisons Inter-compare high-resolution precipitation products with high time resolution gauge data Aiming to keep it very simple – summary statistics commonly used and understood by the wider community Also interested in performance of model forecast data…

HRPP Data used Validate at 0.25˚, 3-hourly 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) CPC Morphing Technique (CMORPH) NOAA CPC (J. Janowiak, B. Joyce) Passive microwave (PMW) rain rates advected and evolved 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) 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 Validate at 0.25˚, 3-hourly

Validation data Long record, sub-daily gauges required: ARM Southern Great Plains (SGP - Oklahoma, Kansas) TAO/TRITON Buoy array (Tropical Pacific) 2 undercatch corrections applied based on wind and threshold Evaluate between Dec 2002 and March 2006 Separate 6-month seasons; require >1 year of data Use weighted average of nearest 4 grid-points from HRPP Match gauge data to HRPPs (offset of TMPA) Exclude buoy stations with prob of precip <0.1 Also include Stage IV radar and NARR in analysis Use these as benchmarks for skill

3-hourly Correlations SGP ONDJFM AMJJAS TAO Buoys TMPA CMOR HE NRL PER NARR ST-IV TMPA CMOR HE NRL PER NARR ST-IV ONDJFM AMJJAS TAO Buoys TMPA CMOR NRL PER TMPA CMOR NRL PER

3-hourly Correlations by month Plots show the mean 3-hourly correlation over all gauges in both areas as a time series Inputs are similar – do HRPPs have same skill? NARR and Stage IV perform best CMORPH and other “MW” datasets perform well PERSIANN has different skill – relies heavily on IR Suggests that IR contains information that MW misses (duh!) – further combination might be useful… SGP TAO Buoys

Bias SGP ONDJFM AMJJAS TAO Buoys TMPA CMOR HE NRL PER NARR ST-IV TMPA

Model Forecast data: NOAA GFS GFS 12 and 15 hour forecast precipitation used as a test of performance of model data Models have well-known problems with diurnal cycle of precip: sub-daily precip a challenge Treat GFS as a HRPP and include CMORPH and Stage IV for comparison At 3-hourly resolution, GFS is inferior to CMORPH GFS performs much better at daily resolution Generally poor in cold season ONDJFM AMJJAS CMOR GFS ST-IV CMOR GFS ST-IV CMOR GF ST-IV CMOR GFS ST-IV 3-Hourly Daily 3-Hourly Daily

Summary HRPPs all do well; CMORPH out-performs others over these particular validation areas TMPA, PERSIANN and Hydro-Estimator are close behind Bias correction of TMPA is a huge advantage over land, but biases appear comparable to other data over ocean HRPPS have different strengths based on use of IR, MW or other data Further combination of techniques might yield skill increases Model data might be superior at daily scales BUT: inferior at sub-daily scales – users should apply caution These results might not be typical of all areas, but do give interesting insight into sub-daily behavior

Relevance to PEHRPP objectives Workshop Objective 1 /Precip Product issues 1: Common strengths and weaknesses Results show different inputs yield slightly different types of skill (combination good) Precip Product issues 2+3: use of NWP Example of GFS shows skill at daily, but difficulty with sub-daily (need for further evaluation of different datasets) Regional Validation 1: how to validate techniques at sub-daily scales Biggest issue is data length and quality (methods from daily are suitable)

Diurnal Cycle HRPPs mostly capture the shape of the diurnal cycle All have max slightly early; PERSIANN especially - shouldn’t this be the other way around? Gauge corrected TMPA has advantage over land and correctly captures amplitude Same is not true over ocean where it has similar pitfalls to CMORPH GFS not as bad as might be expected