Data Information Page from ArcticRIMS (http://RIMS.unh.edu) Title: DAILY PRECIPITATION, FROM MONTHLY STATION RECORDS AND DISAGGREGATION (Serreze) Description: This data set, spanning the period 1980-2008, is assembled from (1) interpolation of observed monthly totals from available station records with bias adjustments (2) disaggregation of the monthly totals to daily totals, making use of daily precipitation forecasts from the NCEP/NCAR reanalysis [Kalnay et al., 1996]. Station records from 1990 onwards are considered to be too sparse to assemble gridded products using this approach. A daily product from 1980 through the present is described separately (DAILY PRECIPITATION FROM STATISTICAL RECONSTRUCTIONS). This latter product, which is continually updated, makes use of a suite of variables from the NCEP/NCAR reanalysis. Classification: Meteorology, Precipitation, Climate Author/PI: Vorosmarty, Charles, Richard Lammers and Mark Serreze Contact Information for original gridded daily time step data: Mark Serreze Senior Research Scientist 449 UCB, RL-2, #223 National Snow and Ice Data Center University of Colorado Boulder, CO 80309-0449 E-mail: serreze@kryos.colorado.edu Tel: 303-492-2963 Web: http://nsidc.org/research/bios/serreze.html Contact Information for all spatially and temporally aggregated data in RIMS: Charles Vorosmarty Department of Civil Engineering The City College of New York Steinman Hall, Rm T-513 140th Street & Convent Ave, NY NY 10031 USA Email: cvorosmarty@ccny.cuny.edu Tel: (212) 650-7042 Web: http://crest.ccny.cuny.edu/ Richard Lammers Water Systems Analysis Group Institute for the Study of Earth, Oceans, and Space Morse Hall, Room 211 8 College Road University of New Hampshire Durham, NH 03824-3525 USA Email: Richard.Lammers@unh.edu Tel: (603) 862-4699 Web: http://www.wsag.unh.edu/ Temporal Coverage Begin Date (year-month-day): 1980-01-01 End Date (year-month-day): 2008-12-31 Spatial Coverage: Corner coordinates in Ease Projection (Units: Meters form N.P.) (Description at http://nsidc.org/data/ease/ease_grid.html) Minimum X: -4875633.612 m Minimum Y: -4875633.612 m Maximum X: 4875633.612 m Maximum Y: 4875633.612 m Corner coordinates in Geographical projection (Units: Degrees) (Description at http://en.wikipedia.org/wiki/Equirectangular_projection) Minimum latitude: 45.0 Minimum longitude: -180.0 Maximum latitude: 90.0 Maximum longitude: 180.0 Units: mm Aggregation Method: sum General Methods: Monthly Station Records Several different station archives are used to construct the gridded monthly fields. All contain bias adjustments, which primarily address gauge undercatch of solid precipitation [Goodison et al., 1998; Yang et al., 2001]. Some archives also include adjustments for the neglect of trace amounts and wetting losses. The wetting loss is the portion of precipitation that sticks to the walls of the gauge after it is emptied. The first archive represents monthly time series for stations in the Former Soviet Union (FSU) assembled by Groisman et al. [1991] and subsequently updated. Records are available through the early 1990s for most stations and through the late 1990s for a subset. During the 1940s and 1950s, the FSU changed from use of Nipher-shielded gauges to Tretiyakov gauges which were determined to provide more accurate measurements. All data from 1960-1989 are all based on the Tretiyakov gauge. The archive contains corrections for wetting losses. The station- specific wind corrections are a function of climatological wind speed, temperature, snowfall and precipitation intensity at the gauge sites. The second archive is National Climatic Data Center (NCDC) data set TD-9816 "Canadian Monthly Precipitation". Details of the bias corrections are discussed by Groisman [1998]. The most recent records extend through 1990. The Canadian practice is to measure rainfall and snowfall separately. Rainfall is measured at gauges. At the majority of stations, a ruler is used to measure the depth of freshly-fallen snow, which is converted into water equivalent using a 10:1 ratio. Starting in the early 1960s, some stations were equipped with Nipher-shielded elevated snow gauges that directly measure the water equivalent of snow. Wind-induced error is estimated at about 15%. The first part of the correction procedure for the Canadian data was to compute climatological ratios between the water equivalent measured at Nipher gauges and from the ruler measurements. The ratios were increased by a factor of 10/9 to account for average snow undercatch at the Nipher gauges. Ratios interpolated to the station locations were then multiplied by the water equivalent at the stations as determined by the 10:1 ruler conversions. According to Metcalfe et al. [1997], prior to 1975, Canadian gauges had wetting losses of about 0.16 mm per measurement. Information on the number of measurements per day which would allow for systematic wetting corrections is not available at all stations. The data set contains a wetting adjustment based on the mean number of days per month with rainfall or (if not available), a value interpolated from nearby stations. The mean number of rainfall days per month at each station is based on data from the early 1980s onward (when total rainfall days began to be included in the archive). Monthly rainfall prior to 1975 was taken as the measured rainfall plus the mean number of days with rainfall multiplied by 0.2. This adjusted total was then multiplied by 1.02 to account to an estimated wind undercatch. After 1975 an improved "Type-B" gauge began to be used and a wetting correction was considered unnecessary. Monthly rainfall after 1975 was adjusted only for wind-induced undercatch. These adjustments increase rainfall by approximately 5% before 1975 and 2% for later years. There are no corrections for trace rainfall events. The two data sets just described provide coverage over all of the Arctic drainage except for Alaska, Greenland and northern Europe. For these areas, use was made of data from the Global Historical Climatological Network (GHCN) [Vose et al., 1992]. Gauge undercatch, evaporation and wetting losses were adjusted by us through local interpolation of the Legates and Willmott [1990] correction factors (provided on a 0.5 x 0.5 degree grid) to the station locations. Coverage for Eurasia has was further improved with an additional 105 stations for the years 1966-1990 within the Ob, Yenisey and Lena basins obtained through collaboration with V. Vuglinsky (State Hydrometeorological Institute, St. Petersburg, Russia). These data were also adjusted using the Legates and Willmott corrections. Gridding Routine The monthly station time series were interpolated to the EASE grid using the Shepard [1968] scheme. The software was developed at the Department of Geography, University of Delaware [Willmott et al., 1985]. The Shepard algorithm is an inverse distance interpolation. Values are defined for the maximum (MAX) and minimum (MIN) number of data point points (i.e., station precipitation values) to be used in the interpolation. An initial search radius around each grid point is defined from the area of the spatial domain to be interpolated to and the number of available stations. If the number of stations within the search radius exceeds MAX, the closest stations up to MAX are used in the interpolation. If there are fewer that MIN stations in the search radius, the radius is expanded until at least MIN stations are found. The interpolator uses spherical geometry calculate distances and accounts for uneven clustering ("clumpiness") of station distributions. Tests were conducted to determine the values of MAX and MIN. Climatological monthly mean precipitation was calculated for each station. For each station, an interpolation error was found by holding that station out of the data set and interpolating a value at that location from surrounding stations. The absolute interpolation error averaged over all stations was then found. This routine was performed for various combinations of MAX and MIN. MAX and MIN values of 25 and 15 were found to provide the lowest absolute mean errors. Disaggregation to Daily Values For use in the UNH Permafrost/Water Balance Model daily fields are desired. Temporal disaggregation makes use of the NCEP/NCAR precipitation forecasts. Briefly, the daily reanalysis totals are interpolated to the EASE grid and expressed as a fraction of the monthly reanalysis total at the grid. Adjusted daily totals are obtained by multiplying the observed monthly totals by each of the daily NCEP/NCAR fractions. To avoid "drizzle" events, the NCEP/NCAR daily fractions are only computed on the basis of daily events exceeding a selected threshold. The approach assumes that the daily reanalysis totals are suspect in terms of absolute magnitude but that the relative magnitudes of daily events are more correct. Comments: Given the sparse station network and the excessive smoothing by the interpolation, the temporal variability at each grid cell is not representative of that at the 25 km scale. This problem is particularly severe where station data are very sparse, such as in the Canadian Arctic [Serreze et al., 2003]. As for other problems, the NCEP/NCAR reanalysis has a formulation of the horizonal moisture diffusion that causes moisture convergence, resulting in "blotches" of high precipitation in association with topographic features. Smoothing of the data through interpolation mitigates, but does not solve this problem [Serreze and Hurst, 2000]. The problem will influence the quality of the daily disaggregations. A revised product is under development that will be provided at a lower spatial resolution (but "nestable" within the 25 km EASE grid) and employing a "tighter" interpolation. This product will provide a more realistic assessment of precipitation variability at the chosen grid size. This product will be further improved by disaggregating on the basis of ERA-40 reanalysis data once the full archive becomes available. Results from a pilot study show that ERA-40 precipitation forecasts are of considerably higher quality than those from NCEP/NCAR [Serreze and Etringer, 2002]. References: Goodison, B.E., P.Y.T. Louie, and D. Yang, 1998: WMO Solid Precipitation Measurement Intercomparison. Final Report, WMO TD- No. 872, World Meteorological Organization, Geneva, 212 pp. Groisman, P.Y., 1998: National Climatic Data Center Data Documentation for TD-9816, Canadian Monthly Precipitation. National Climatic Data Center, 151 Patton Ave., Asheville, NC, 21 pp. Groisman, P.Y., V.V. Koknaeva, T.A. Belokrylova, and T.R. Karl, 1991: Overcoming biases of precipitation: A history of the USSR experience. Bull. Amer. Meteorol. Soc., 72, 1725-1733. Kalnay, E., M. Kanamitsu, R. Kistler, W. Collins, D. Deaven, L. Gandin, M. Iredell, S. Saha, G. White, J. Woolen, Y. Zhu, M. Chelliah, W. Ebisuzaki, W. Higgens, J. Janowiak, K.C. Mo, C. Ropelewski, J. Wang, A. Leetma, R. Reynolds, R. Jenne, and D. Joseph, 1996: The NCEP/NCAR 40-year reanalysis project. Bull. Amer. Meteorol. Soc., 77, 437-471. Legates, D.R., and C.J. Willmott, 1990: Mean seasonal and spatial variability in gauge-corrected, global precipitation. Int. J. Climatol., 10, 111-127 Metcalfe, J.R., B. Routledge, and K. Devine, 1997: Rainfall measurement in Canada: Changing observational methods and archive adjustment procedures. J. Climate, 10, 92-101. Serreze, M.C. and A.J. Etringer, 2002: Representation of Arctic precipitation in ERA-40, Proceedings, ECMWF Workshop on Reanalysis, November 2001, Reading, UK (in press). Serreze, M.C., M.P. Clark and D.H. Bromwich, 2003: Monitoring precipitation over the terrestrial Arctic drainage system: Data requirements, shortcomings and applications of atmospheric reanalysis. J. Hydrometeorology (in press). Serreze, M.C. and, C.M. Hurst, 2000: Representation of mean Arctic precipitation from NCEP-NCAR and ERA reanalyses. J. Climate, 13, 182-201. Shepard, D., 1968: A two-dimensional interpolation function for irregularly-spaced data. In: Proceedings - 1968 ACM National Conference, pp. 517-524. Vose, R.S., R.L. Schmoyer, P.M. Steurer, T.C. Peterson, R. Heim, T.R. Karl, and J. Eischeid, 1992: The global historical climatology network: long-term monthly temperature, precipitation, sea level pressure, and station pressure data. ORNL/CDIAC-53, NDP-041, Technical Report, Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, Oak Ridge, Tennessee. 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. The American Cartographer, 12, 5-16. Yang, D., B. Goodison, J. Metcalfe, P. Louie, E. Elomaa, C. Hanson, V. Golubev, T. Gunther, J. Milkovic, and M. Lapin, 2001: Compatibility evaluation of national precipitation gauge measurements. J. Geophys. Res., 106(D2), 1481-1491. Arctic RIMS Contact: Richard Lammers Water Systems Analysis Group Institute for the Study of Earth, Oceans, and Space Morse Hall University of New Hampshire Durham, NH 03824 Phone: (603) 862-4699 Fax: (603) 862-0587 Email: Richard.Lammers@unh.edu Web: http://wsag.unh.edu Data Archiving: This ArcticRIMS data set has been permanently stored to the ARCSS Data Archive at NCAR/EOL (http://www.eol.ucar.edu/projects/arcss) with the support of National Science Foundation grants (NSF) OPP-0230243 and Humans and Hydrology at High Latitudes (NSF) ARC-0531354