Editorial Type:
Article
Article Type:
Research Article

Validation of Mesoscale Precipitation in the NCEP Reanalysis Using a New Gridcell Dataset for the Northwestern United States

Martin Widmann Department of Atmospheric Sciences, University of Washington, Seattle, Washington

Search for other papers by Martin Widmann in
Current site
Google Scholar
PubMed Close
and
Christopher S. Bretherton Department of Atmospheric Sciences, University of Washington, Seattle, Washington

Search for other papers by Christopher S. Bretherton in
Current site
Google Scholar
PubMed Close
Print Publication:
01 Jun 2000
DOI:
https://doi.org/10.1175/1520-0442(2000)013<1936:VOMPIT>2.0.CO;2
Page(s):
1936–1950
Restricted access

Abstract

Precipitation fields from the National Centers for Environmental Prediction (NCEP) reanalysis are validated with high-resolution, gridded precipitation observations over Oregon and Washington. The NCEP reanalysis is thought of as a proxy for an ideal GCM that nearly perfectly represents the synoptic-scale pressure, temperature, and humidity but does not resolve the complex topography of this region. The authors’ main goal is to understand how useful precipitation fields from such a model are for estimating temporal variability in local precipitation.

The gridded observations represent area-averaged precipitation on a 50-km grid and have daily temporal resolution. They are calculated with a newly developed scheme, which explicitly takes into account the effect of the topography on precipitation. This gridding method profits from the already existing, high-resolution climatologies for the monthly mean precipitation in the United States, obtained from the Precipitation–Elevation Regressions on Independent Slopes Model (PRISM), by using these climatologies for calibration. The estimation of daily precipitation on scales as small as 4 km is also discussed.

The reanalysis captures well precipitation amounts and month-to-month variability on spatial scales of about 500 km or three grid cells, which indicates a good performance of the precipitation parameterization scheme. On smaller spatial scales the NCEP reanalysis has systematic biases, which can be mainly attributed to the poor representation of the topography but nevertheless can be used to reconstruct the temporal variability of local precipitation on daily to yearly timescales. This suggests that GCM precipitation might be a good predictor for statistical downscaling techniques.

* Current affiliation: Institute of Hydrophysics, GKSS Research Centre, Geesthacht, Germany.

Corresponding author address: Dr. Martin Widmann, Institute of Hydrophysics, GKSS Research Centre, D-21502 Geesthacht, Germany.

Abstract

Precipitation fields from the National Centers for Environmental Prediction (NCEP) reanalysis are validated with high-resolution, gridded precipitation observations over Oregon and Washington. The NCEP reanalysis is thought of as a proxy for an ideal GCM that nearly perfectly represents the synoptic-scale pressure, temperature, and humidity but does not resolve the complex topography of this region. The authors’ main goal is to understand how useful precipitation fields from such a model are for estimating temporal variability in local precipitation.

The gridded observations represent area-averaged precipitation on a 50-km grid and have daily temporal resolution. They are calculated with a newly developed scheme, which explicitly takes into account the effect of the topography on precipitation. This gridding method profits from the already existing, high-resolution climatologies for the monthly mean precipitation in the United States, obtained from the Precipitation–Elevation Regressions on Independent Slopes Model (PRISM), by using these climatologies for calibration. The estimation of daily precipitation on scales as small as 4 km is also discussed.

The reanalysis captures well precipitation amounts and month-to-month variability on spatial scales of about 500 km or three grid cells, which indicates a good performance of the precipitation parameterization scheme. On smaller spatial scales the NCEP reanalysis has systematic biases, which can be mainly attributed to the poor representation of the topography but nevertheless can be used to reconstruct the temporal variability of local precipitation on daily to yearly timescales. This suggests that GCM precipitation might be a good predictor for statistical downscaling techniques.

* Current affiliation: Institute of Hydrophysics, GKSS Research Centre, Geesthacht, Germany.

Corresponding author address: Dr. Martin Widmann, Institute of Hydrophysics, GKSS Research Centre, D-21502 Geesthacht, Germany.

Save
Cover Journal of Climate
Journal of Climate

  • Busuioc, A., H. von Storch, and R. Schnur, 1999: Verification of GCM-generated regional seasonal precipitation for current climate and of statistical downscaling estimates under changing climate conditions. J. Climate,12, 258–272.

  • Daly, C., R. P. Neilson, and D. L. Phillips, 1994: A statistical-topographic model for mapping climatological precipitation over mountainous terrain. J. Appl. Meteor.,33, 140–158.

  • ——, G. Taylor, and W. Gibson, 1997: The PRISM approach to mapping precipitation and temperature. Preprints, 10th Conf. on Applied Climatology, Reno, NV, Amer. Meteor. Soc., 10–12.

  • EarthInfo, Inc., 1996: NCDC Summary of the Day. EarthInfo, Inc., CD-ROM. [Available from EarthInfo, Inc., 5541 Central Avenue, Boulder, CO 80301.].

  • Frei, C., and C. Schär, 1998: A precipitation climatology of the Alps from high-resolution raingauge observations. Int. J. Climatol.,18, 873–900.

  • Groisman, P. Y., and D. R. Legates, 1994: The accuracy of the United States precipitation data. Bull. Amer. Meteor. Soc.,75, 215–227.

  • Higgins, R. W., K. C. Mo, and S. D. Schubert, 1996: The moisture budget of the central United States as evaluated in the NCEP/NCAR and the NASA/DAO reanalyses. Mon. Wea. Rev.,124, 939–963.

  • Huffman, G. J., and Coauthors, 1997: The Global Precipitation Climatology Project (GPCP) combined precipitation data set. Bull. Amer. Meteor. Soc.,77, 5–20.

  • Hulme, M., D. Conway, P. D. Jones, T. Jiang, E. M. Barrow, and C. Turney, 1995: Construction of a 1961–90 European climatology for climate change modelling and impact applications. Int. J. Climatol.,15, 1333–1363.

  • Janowiak, J. E., A. Gruber, C. R. Kondragunta, R. E. Livezey, and G. J. Huffman, 1998: A comparison of the NCEP–NCAR reanalysis precipitation and the GPCP rain gauge–satellite combined dataset with observational error considerations. J. Climate,11, 2960–2979.

  • Jutowski, W. J., Jr., Y. Chen, and Z. Ötles, 1997: Atmospheric water vapor transport in NCEP–NCAR reanalyses: Comparison with river discharge in the central United States. Bull. Amer. Meteor. Soc.,78, 1957–1969.

  • Kagan, R. L., 1966: An Evaluation of the Representativeness of Precipitation Data (in Russian). Gidrometeoizdat, 191 pp.

  • Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-Year Reanalysis Project. Bull. Amer. Meteor. Soc.,77, 437–471.

  • Lau, K.-M., J. H. Kim, and Y. Sud, 1996: Intercomparison of hydrologic processes in AMIP GCMs. Bull. Amer. Meteor. Soc.,77, 2209–2227.

  • Legates, D. R., and C. J. Willmott, 1990: Mean seasonal and spatial variability in gauge-corrected, global precipitation. Int. J. Climatol.,10, 111–127.

  • ——, and T. L. DeLiberty, 1993a: Estimates of biases in precipitation gage measurements: An example using the United States raingage network. Preprints, Eighth Symp. on Meteorological Observations and Instrumentation, Anaheim, CA, Amer. Meteor. Soc., J48–J51.

  • ——, and ——, 1993b: Measurement biases in the United States raingage network. Proc. Symp. on Geographic Information Systems and Water Resources, Mobile, AL, Association of American Geographers, 547–557.

  • Leung, L. R., and S. J. Ghan, 1999: Pacific Northwest climate sensitivity simulated by a regional climate model driven by a GCM. Part I: Control simulations. J. Climate,12, 2010–2030.

  • Mo, K. C., and R. W. Higgins, 1996: Large-scale atmospheric moisture transport as evaluated in the NCEP/NCAR and the NASA/DAO reanalyses. J. Climate,9, 1531–1545.

  • Neff, E. L., 1977: How much rain does a rain gage gage? J. Hydrol.,35, 213–220.

  • New, M., M. Hulme, and P. D. Jones, 1999: Representing twentieth-century space–time climate variability. Part I: Development of a 1961–90 mean monthly terrestrial climatology. J. Climate,12, 829–856.

  • Nijssen, B., D. P. Lettenmaier, X. Liang, S. W. Wetzel, and E. F. Wood, 1997: Streamflow simulation for continental-scale river basins. Water Resour. Res.,33, 711–724.

  • Osborn, T. J., and M. Hulme, 1997: Development of a relationship between station and grid-box rainday frequencies for climate model evaluation. J. Climate,10, 1885–1908.

  • ——, and ——, 1998: Evaluation of the European daily precipitation characteristics from the atmospheric model intercomparison project. Int. J. Climatol.,18, 505–522.

  • ——, D. Conway, M. Hulme, J. M. Gregory, and P. D. Jones, 1999:Air flow influences on local climate: Observed and simulated mean relationships for the United Kingdom. Climate Res.,13, 173–191.

  • Raulin, V., 1879: Über die Vertheilung des Regens im Alpengebiet von Wien bis Marseille. Z. Österr. Ges. Meteor.,14, 233–247.

  • Risbey, J. S., and P. H. Stone, 1996: A case study of the adequacy of GCM simulations for input to regional climate change assessments. J. Climate,9, 1441–1467.

  • Rudolf, B., H. Hauschild, W. Rüth, and U. Schneider, 1994: Terrestrial precipitation analysis: Operational method and required density of point measurements. Global Precipitation and Climate Change, M. Desbois and F. Désalmond, Eds., NATO ASI Series I, Vol. 26, Springer-Verlag, 173–186.

  • ——, ——, ——, and ——, 1996: Comparison of raingauge analyses, satellite-based precipitation estimates and forecast model results. Adv. Space. Res.,7, 53–62.

  • Sevruk, B., 1982: Methods of correction for systematic error in point precipitation measurement for operational use. Operational Hydrology Report 21, WMO No. 589, World Meteorological Organization, 91 pp.

  • Shepard, D. S., 1968: A two-dimensional interpolation function for irregularly spaced data. Proc. 23d Association for Computing Machinery Conf., Brandon/System Press, Inc., 517–524.

  • ——, 1984: Computer Mapping: The SYMAP interpolation algorithm. Spatial Statistics and Models, G. L. Gaile and C. J. Willmott, Eds., D. Reidel, 133–145.

  • Skelly, W. C., and A. Henderson-Sellers, 1996: Grid-box or grid-point: What type of precipitation data do GCMs deliver? Int. J. Climatol.,16, 1079–1086.

  • Stendel, M., and K. Arpe, 1997: Evaluation of the Hydrological Cycle in Reanalyses and Observations. ECMWF Re-Analysis Validation Reports—Part 1. ECMWF Re-Analysis Project Report Series, Vol. 6, ECMWF, 53 pp.

  • Vogel, R. M., and J. R. Stedinger, 1985: Minimum variance streamflow augmentation procedures. Water Resour. Res.,21, 715–723.

  • Widmann, M., and C. Schär, 1997: A principal component and long-term trend analysis of daily precipitation in Switzerland. Int. J. Climatol.,17, 1333–1356.

  • Wilby, R. L., 1997: Nonstationarity in daily precipitation series: Implications for GCM downscaling using atmospheric circulation indices. Int. J. Climatol.,17, 439–454.

  • ——, 1998: Statistical downscaling of daily precipitation using airflow and seasonal teleconnection indices. Climate Res.,10, 163–178.

  • ——, and T. M. L. Wigley, 2000: Precipitation predictors for downscaling: Observed and general circulation model relationships. Int. J. Climatol., in press.

  • Willmott, C. J., C. M. Rowe, and W. D. Philpot, 1985: Small-scale climate maps: A sensitivity analysis of some common assumptions associated with grid-point interpolation and contouring. Amer. Cartogr.,12, 5–16.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 1245 697 43
PDF Downloads 368 75 5