Data Information Page from ArcticRIMS (http://RIMS.unh.edu) Title: DAILY PRECIPITATION FROM STATISTICAL RECONSTRUCTIONS (Serreze) Description: Providing rapid updates of gridded precipitation fields is difficult due to the degradation of the station network. Starting about 1990, many stations in the Former Soviet Union (FSU) and Canada closed. Canada is also seeing a trend toward automation. In recognition, we are providing updated fields using statistical techniques. This reconstruction technique has also been used to compile fields back to 1980 to provide a period of overlap with gridded fields based on interpolation of monthly station precipitation and daily disaggregation (see DAILY PRECIPITATION, FROM MONTHLY STATION RECORDS AND DISAGGREGATION). The reconstructions employ a four-step approach. As part of the data processing of ArcticRIMS (http://RIMS.unh.edu) based at the Water Systems Analysis Group, University of New Hampshire all data sets have been aggregated to multiple temporal and spatial resolutions. 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): 1979-01-01 End Date (year-month-day): 2001-12-31 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: General Methods: STEP 1 involves interpolating monthly precipitation totals from the NCEP/NCAR reanalysis [Kalnay et al., 1996] to the EASE grid and then re-scaling these forecasts via a probability transformation. The re-scaling procedure uses ranked (i.e., sorted) values of NCEP/NCAR and observed precipitation at each grid cell for 1960-1989. Observed monthly precipitation is based on interpolation of bias-adjusted station data using the same techniques described for data set "DAILY PRECIPITATION, FROM MONTHLY STATION RECORDS AND DISAGGREGATION. The ranks are ascribed cumulative probabilities. Imagine that an update of NCEP/NCAR precipitation is obtained for June 2004. We determine where the June 2004 NCEP/NCAR value falls in the 30-year (1960-1989) NCEP/NCAR cumulative probability distribution. The June 2004 NCEP/NCAR value is re-scaled by simply replacing it with the observed precipitation value at the same cumulative probability. Generally, interpolation is necessary because the NCEP/NCAR value to be re-scaled lies between two of the ranked NCEP/NCAR values in the sample (1960- 1989) distribution. If the June 2004 NCEP/NCAR value is smaller than the smallest value in the NCEP/NCAR rankings, it is ascribed the smallest value in the observed 30-year distribution. If the June 2004 NCEP/NCAR value is greater than the largest value in the NCEP/NCAR rankings, it is replaced with the largest observed value in the 30-year distribution. The re-scaling assures that any resulting reconstructed time series has same mean and standard deviation as the corresponding observed time series. In STEP 2, we compile a separate set of monthly reconstructions using a suite of predictor variables from the NCEP/NCAR reanalysis in a multiple linear regression scheme. The predictors include 1) forecasts of precipitation; 2) vertical motion at 500 hPa; 3) precipitation minus evaporation (P-ET) (see PRECIPITATION MINUS EVAPORATION); 4) zonal and meridional vapor fluxes; 5) an index of lower-tropospheric stability; 6) sea level pressure; 7) precipitable water. Regression models for each month and EASE grid were developed using data over the period 1960-1989. A forward-screening approach was used for variable selection. Generally, the best and most frequently used predictors are precipitation, P-E and vertical velocity. The model slopes and intercepts are then applied to updates of NCEP/NCAR data. To eliminate systematic biases in the reconstructions, we again use a re-scaling approach. In this case, the re-scaling uses the distributions of observed precipitation (1960-1989) and of regressed precipitation from the same period. Correlations between time series of observed and regressed (STEP 2) precipitation are generally (but not always) higher than that those between observed and re-scaled (STEP 1) precipitation. In STEP 3, a decision-tree approach is adopted to merge results from the re-scaling and regression, based on 1) relative predictive skill; 2) interpolation biases; 3) mean observed precipitation. In STEP 4, the monthly reconstructions are disaggregated into daily values, using the same basic technique outlined for data set "DAILY PRECIPITATION, FROM MONTHLY STATION RECORDS AND DISAGGREGATION" Comments: Linear regression assumes that the observed precipitation time series at a given grid cell represents "truth". Most of the Arctic is characterized by a low station density. As a result, time series for a given grid based on interpolation often poorly reflect the "true" time series structure for the cell. Hence, one may often be regressing against noise. The problem is compounded in areas of very low precipitation, where even small measurement errors can greatly impact on the interpolated time series. By contrast, the statistical distributions (mean and variance) of precipitation tend to be reasonably well preserved [Serreze et al., 2003]. Our reconstructed precipitation product recognizes these problems. In the decision tree algorithm (STEP 4), the re- scaled values are used instead of the regression-based values in areas of low mean precipitation and where the station network is especially sparse (where interpolation errors are large). In these areas, the re-scaling is on better statistical footing as it relies only on the statistical distributions. A new data set is under development. Details are provided by Serreze et al. [2003]. It is based on: 1) re-scaling the NCEP/NCAR precipitation forecasts; 2) re-scaling P-E; 3) performing tests at each grid cell to determine which re-scaled variable provides the higher skill, and basing the reconstruction on the better of the two values, provided that it beats climatology; 4) further improving the reconstructions through assimilating any available updates of observed precipitation. The key differences, other than the station data assimilation, are: a) elimination of the multiple linear regression; b) provision of the reconstructions at a coarser spatial resolution (but "nestable" within the 25 km EASE grid) employing a "tighter" interpolation of the station data. This will provide a more realistic assessment of precipitation variability at the chosen grid size. References: 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. 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). 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