Interactive Numeric & Spatial Information Data Engine

Geoprocessing services that produce raster data products from downscaled climate data. The current tools operate on ArcGIS 10.0. The tools are currently being updated for ArcGIS Server 10.1.

This data series contains 2868 temporal datasets. These data are climate model outputs that have been downscaled to 4-km spatial resolution using the Bias Corrected Statistical Downscaling (BCSD) method. Moore and Walden have modified the BCSD method described by Wood et al (2002), Long-range experimental hydrologic forecasting for the eastern United States. Journal of Geophysical Research-Atmospheres 107: 4429-4443 and Salathe (2005), Downscaling simulations of future global climate with application to hydrologic modeling. International Journal of Climatology 25: 419-436. The modifications include a different interpolation scheme between GCM grid cells and a different approach to dealing with extreme values (Z-scores versus CDF method). The spatial resolution of these data are determined by the historical dataset used to derive statisitcal relationships between the GCM and past measurements. The 4-km Parameter-elevation Relationships on Independent Slopes Model (PRISM) data are used here from Daly et al, (1994), A statistical-topographic model for mapping climatological precipitation over mountainous terrain. Journal of Applied Meteorology 33: 140-158.

This data series contains 336 temporal datasets. These data are are climate model outputs, primarily precipitation and temperature. They have been bias-corrected and spatially downscaled (BCSD) to 12-km by Maurer et al. (2007) using the CMIP3 climate model data. These monthly products are available from this link (http://gdo-dcp.ucllnl.org/downscaled_cmip3_projections/) which we temporally disaggregated to a daily time step using the Delta method. This temporal disaggregation involved the following steps. This included a random picking of a historical year to compute the mean of the daily precipitation and temperature of the gridded observed record for the same month as the future year. Then, by calculating the difference between the future monthly mean temperature and historical mean of monthly mean temperature, "delta t" and by calculating the ratio between the future monthly mean precipitation and historical mean of monthly mean precipitation, "r", we obtained these two ratios, namely addition (for temperature) and multiplication (for precipitation) factors. Finally, we compute the temperature and precipitation by adding "delta t" to daily temperature of the month of a randomly selected year and multiplying daily precipitation by "r" for the month of a randomly selected year for the given month.

This data series contains 336 temporal datasets. These data are are climate model outputs, primarily precipitation and temperature. They have been bias-corrected and spatially downscaled (BCSD) to 12-km by Maurer et al. (2007) using the CMIP3 climate model data. These monthly products are available from this link (http://gdo-dcp.ucllnl.org/downscaled_cmip3_projections/) which we temporally disaggregated to a daily time step using the Delta method. This temporal disaggregation involved the following steps. This included a random picking of a historical year to compute the mean of the daily precipitation and temperature of the gridded observed record for the same month as the future year. Then, by calculating the difference between the future monthly mean temperature and historical mean of monthly mean temperature, "delta t" and by calculating the ratio between the future monthly mean precipitation and historical mean of monthly mean precipitation, "r", we obtained these two ratios, namely addition (for temperature) and multiplication (for precipitation) factors. Finally, we compute the temperature and precipitation by adding "delta t" to daily temperature of the month of a randomly selected year and multiplying daily precipitation by "r" for the month of a randomly selected year for the given month.

This data series contains 540 temporal datasets. Wildfire adheres to meteorological enablers and drivers across a spectrum of timescales. However, a majority of downscaling methods are ill suited for wildfire application due the lack of daily timescales and variables such as humidity and winds that are important for fuel flammability and fire spread. Two statistical downscaling methods, the daily Bias-Corrected Spatial Downscaling (BCSD) and the Multivariate Adapted Constructed Analogs (MACA), that directly incorporate daily data were validated over the Western United States with reanalysis data. While both methods outperformed the null interpolation only method, MACA exhibited additional skill in temperature, humidity, wind and precipitation due to its ability to jointly downscale temperature and dew point temperature and its use of analog patterns rather than interpolation. Both downscaling methods exhibited value added information in tracking fire danger indices and periods of extreme fire danger; however, due to its ability to more accurately capture relative humidity and winds, MACA outperformed the daily BCSD.

This data series contains 330 datasets. Landscape-scale ecological modeling has been hindered by suitable high-resolution surface meteorological datasets that include temperature, precipitation, downward shortwave radiation, humidity and winds. To overcome these limitations, desirable spatial attributes of gridded climate data from PRISM are combined with desirable temporal attributes of regional-scale reanalysis and daily gauge-based precipitation from NLDAS-2 to derive a spatially and temporally complete, high-resolution (4-km) gridded dataset of surface meteorological variables required in ecological modeling for the contiguous United States from 1979-2010. Validation of the resulting gridded surface meteorological data was conducted against an extensive network of weather stations including RAWS, AgriMet, AgWeatherNet and USHCN-2. The validation showed skill comparable to that derived from interpolation using station observations, however is advantageous in that it provides spatially and temporally complete data across several variables. These qualities suggest the dataset can serve as suitable surrogate for landscape-scale ecological modeling across vast unmonitored areas of the United States.

Processed LiDAR remote sensing data for the Stanley, Idaho study area.