Intercomparing and evaluating high- resolution precipitation products M. R. P. Sapiano*, P. A. Arkin*, S. Sorooshian +, K. Hsu + * ESSIC, University of Maryland; + UC-Irvine IntroductionDiurnal cycleSimple summaries High Resolution Precipitation Products Validation data Correlation Taiwan SGP Pacific Buoy Taiwan 3 hourlyDaily DJFJJADJFJJA 3B CMORPH NRL-Blended PERSIANN SGP 3 hourlyDaily DJFJJADJFJJA 3B CMORPH NRL-Blended PERSIANN Hydro-Est NEXRADNA0.62NA0.67 Pacific Buoy 3 hourlyDaily DJFJJADJFJJA 3B CMORPH NRL-Blended PERSIANN The PEHRPP project began in 2005 to compare the many experimental and operational high-resolution (<0.25˚, ~3 hourly resolution) satellite-derived precipitation datasets currently available. The main aims of PEHRPP are to characterize errors in various High Resolution Precipitation Products (HRPP) on many spatial and temporal scales, over varying surfaces and climatic regimes with a view to enabling developers of HRPP to improve their products and potential users to understand the relevant characteristics of the products. PERHPP activities are divided into four suites of work focusing on regional comparisons of large areas over long time periods, high time-resolution comparisons, very high quality spatial resolution comparisons using field programs such as NAME and “big picture” comparisons using large scale quantities at climatic scales This aim of this poster is to compare high temporal resolution, satellite derived, gridded precipitation datasets with high quality fine resolution gauge-based measurements. As such, gauge data from the Coordinated Enhanced Observing Period (CEOP) project is used for validation as well as buoy precipitation measurements from the Tropical Atmosphere-Ocean (TAO) array and NEXRAD radar data (over the US only). Here, we present some preliminary results based on comparison of individual gridpoints from the four high resolution precipitation products over three distinct areas: the Southern Great Plains (SGP) area of the US, Taiwan and the tropical Pacific Ocean. The Coordinated Enhanced Observing Period (CEOP) data offers a range of reference site data which is high quality with high sampling frequencies with many locations across the globe (as shown on the global map). Here, we focus on two main areas (in addition to the buoy data): Taiwan (in the blue box) and the Southern Great Plains (SGP; in the red box). The figure to the right shows the temporal coverage of the sites. One drawback of the CEOP data is its relatively short time period, which is as short as a single year for the SGP data. ProductProviderDataMethod TRMM Multi-satellite precipitation analysis (TMPA, a.k.a. 3B42) 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) Geo-IR, microwave from SSM/I, TRMM, AMSU, AMSR Passive microwave (PMW) rain rates advected and evolved according to IR imagery Hydro-EstimatorNOAA NESDIS ORA (B. Kuligowski) Geo-IR, NWPTb in geostationary-IR, modulated by cloud evolution, stability, total precipitable water, etc. NRL blended algorithmNRL (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 The table to the right shows the five High Resolution Precipitation Products (HRPP) used for these analyses along with a brief summary of the constituent datasets and the method used for combination. Generally, the HRPP used are a combination of Passive Microwave (PMW) and Infra-Red (IR) estimates: The PMW data is generally more accurate but has poor spatial sampling, and the IR data less reliable but has full globe scans available every minutes. Therefore, the goal of most of these datasets is to combine these two types of estimates to get reliable, frequent observations. In several of the datasets, the PMW is used to somehow calibrate the IR data. The Tropical Atmosphere-Ocean/Triangle Trans-Ocean Buoy Network (TAO/TRITON) array of buoys have been recording precipitation measurements since 1998 in the tropical Pacific Ocean in the sites identified on the above map in the black box. The data are available at 10 minute resolution and have been averaged to match the three-hourly resolution of the HRPP data. Tropical Pacific Buoy SGP Taiwan Other CEOP sites The tables to the left show the bias and the 3-hourly and daily Root Mean Squared Error (RMSE) averaged over all the gauges over Taiwan, SGP and the Tropical Pacific. All of the HRPP overestimate (Blue) precipitation over Taiwan and overestimate (red) over SGP. However, TMPA (3B42) and CMORPH underestimate over the Pacific whilst PERSIANN overestimates. In general, the lowest (best) mean RMSE values are achieved by CMORPH, 3B42 or PERSIANN over all sites, although the RMSE for JJA is generally higher than that for DJF over Taiwan and SGP. This is likely caused by more convective events during Northern Hemisphere summer which are harder to capture due to their noisy characteristics. The Hydro-estimator is only available over the SGP area before 2006, but it shows a high level of skill comparable with that of PERSIANN The NRL-Blended dataset performs the least well, although this is most likely due to the lack of reprocessing each time algorithm improvements are made The diurnal cycle is a challenging characteristic to capture correctly for precipitation. The figures to the right show the diurnal cycle from the HRPP along with the diurnal cycle of the validation data. Note that time is in GMT. Over SGP, All of the HRPPs correctly estimate the shape of the diurnal cycle with a peak around 06z. TMPA (3B42) is closest to the real values, whilst the other HRPPs tend to overestimate the precipitation at SGP. Similar behavior is evident over Taiwan, where TMPA closely mirrors the CEOP validation data. However, as seen in the bias values in the tables to the left, the other datasets underestimate the precipitation over Taiwan. These two figures (SGP and Taiwan) suggest that the bias problem is in fact caused by a multiplicative error in the diurnal cycle rather than a simple offset. Diurnal cycle plots TRMM 3B42 CMORPH NRL-Blended PERSIANN Hydro-Estimator Validation data Taiwan Bias3Hourly RMSEDaily RMSE DJFJJADJFJJADJFJJA 3B CMORPH NRL-Blended PERSIANN SGP Bias3Hourly RMSEDaily RMSE DJFJJADJFJJADJFJJA 3B CMORPH NRL-Blended PERSIANN Hydro-Est Pacific Buoy Bias3Hourly RMSEDaily RMSE DJFJJADJFJJADJFJJA 3B CMORPH NRL-Blended PERSIANN Over the Tropical Pacific, TMPA and CMORPH perform the best, with the other HRPP over estimating the main peak in the diurnal cycle rather than the whole cycle. It should be remembered however that this is an average over a very large area, so important fluctuations may have been averaged out. All HRPPs show a good agreement with the validation data, although CMORPH appears to be the most successful followed by TMPA and PERSIANN. This suggests that the morphing technique employed by CMORPH contributes substantial skill and might be a useful improvement for the other HRPPs. Included in the table for SGP is the NEXRAD radar data for JJA. It is generally accepted that radar data is of higher quality than satellite data, and it is therefore a sensible goal to try to emulate the skill of radar data with modern satellite datasets. The HRPPs have similar skill to the radar data in JJA at the daily time resolution, but still lag behind at three hourly resolution. The tables above show the correlation between each of the HRPP and the validation data during DJF and JJA for 3- hourly and daily resolution at each of the three study areas. Generally speaking, CMORPH yields the highest correlations in either season and at either resolution. Over SGP and the tropical Pacific, TMPA is the second most correlated, and is closely followed by PERSIANN. Over Taiwan, PERSIANN outperforms TMPA and is second only to CMORPH. The Hydro-Estimator also performs well over SGP and rivals TMPA in its skill and actually has the highest correlation in JJA. Once again, NRL-Blended seems to under-perform, although the algorithm has been dramatically improved since the period of these data ( ).