Extended Abstract

determine the origin of these differences, we compared both to a set of global gauge analyses.Table 1 summarizes our findings.In general, both GFS forecasts and CMORPH satellite estimates capture the large-scaleprecipitation systems better than they do convective-scale storms (see Table 1). However, thesatellite estimates are slightly better at handling (with marginally better correlation scores) theconvective-scale precipitations than the NWP model. This may reflect the fact that the model’sconvective parameterization generates storms with significant spatial displacement and temporalphase errors. The model has a tendency to over-forecast precipitation for both large-scale andconvective-scale storms, while satellite retrievals tend to underestimate intense rainfall amountbut to overestimate stratiform-rain areas. CMORPH precipitation analysis also appears to presentproblems along coasts, where it tends to underestimate precipitation amount and fails to distributethe rain area deep enough over land. This may indicate an effect of surface emissivitydifferences over the ocean and land, to which satellite retrievals are typically subject.The above results are based on the verification of the NWP model and satellite precipitationfields against the GPCC gauge analyses, which are treated as observational “truth”. From Table1, we see that in regions of dense gauge data available, global model QPF tends to outperformsatellite-retrieved QPE. This is especially true for large-scale precipitation systems in NorthAmerica and European-Mediterranean regions. However, in several cases studied, such as in theSouth America and Africa domains, the correlation scores for GFS or CMORPH precipitationfields with GPCC analyses were extremely low. We conjecture that these problems are related tothe uncertainties in the verification data itself. It is well known that gauge measurements are verysparse in the Amazon River basin in South America and in the savanna plain in Africa (Schneideret al. 2008). Therefore, it is not surprising that in these situations the model and satellite are moresimilar than either to gauge analyses. The possible problems involved in the gridded gaugeanalyses are summarized in Table 1. Further investigations for these cases or areas with betterverification datasets are recommended.ReferencesBech, C., J. Grieser, and B. Rundolf, 2004: A new monthly precipitation climatology for theglobal land areas for the period 1951 to 2000. Available at:http://www.dwd.de/en/FundE/Klima/KLIS/int/GPCC/GPCC.htmEbert, E. E., J. E. Janowiak, and C. Kidd, 2007: Comparison of near-real-time precipitationestimates from satellite observations and numerical models. Bull. Amer. Meteor. Soc., 88,47-64.Joyce, R. J., J. E. Janowiak, P. A. Arkin, and P. Xie, 2004: CMORPH: A method that pro ducesglobal precipitation estimates from passive microwave and infrared data at high spatial andtemporal resolution. J. Hydrometeor., 5, 487-503.Lu, C., H. Y uan, E. Tollerud, and N. Wang, 2010: Scale-dependent uncertainties in global QPFsand QPEs from NWP model and satellite fields. J. Hydrometeor., 11, 139-155.Kanamitsu, M., 1989: Description of the NMC Global Data Assimilation and Forecast System.Wea. Forecasting, 4, 335-342.NOAA NCEP, 2003: Global Climate and Weather Modeling Branch, NOAA/NWS/NCEP OfficeNote, 442, [http://www.emc.ncep.noaa.gov/officenotes/newernotes/on442.pdf.], 1-15.Schneider, U., T. Fuchs, A. Meyer-Christoffer and B. Rudolf, 2008: Global PrecipitationAnalysis Products of the GPCC. Available at: http://gpcc.dwd.de-335-