| dataFormat | NetCDF |
| authority | edu.ucar.gdex |
| featureType | GRID |
| dataSize | 64461933 |
| id | files/d583154/hawaii_daily_1996_087.nc4 |
Dataset: hawaii_daily_1996_087.nc4
Catalog: /thredds/catalog/files/d583154/catalog.html
Access:
| Service | Type | Description |
|---|---|---|
| OpenDAP | Data Access | Access dataset through OPeNDAP using the DAP2 protocol. |
| DAP4 | Data Access | Access dataset using the DAP4 protocol. |
| NetcdfSubset | Data Access | A web service for subsetting CDM scientific grid datasets. |
| CdmRemote | Data Access | Provides index subsetting on remote CDM datasets, using ncstream. |
| CdmrFeature | Data Access | Provides coordinate subsetting on remote CDM Feature Datasets, using ncstream. |
| WCS | Data Access | Supports access to geospatial data as 'coverages'. |
| WMS | Data Access | Supports access to georegistered map images from geoscience datasets. |
| HTTPServer | Data Access | HTTP file download. |
| ISO | Metadata | Provide ISO 19115 metadata representation of a dataset's structure and metadata. |
| NCML | Metadata | Provide NCML representation of a dataset. |
| UDDC | Metadata | An evaluation of how well the metadata contained in the dataset conforms to the NetCDF Attribute Convention for Data Discovery (NACDD) |
Viewers:
| Viewer | Type | Description |
|---|---|---|
| Godiva3 | Browser | |
| default_viewer.ipynb | Jupyter Notebook | The TDS default viewer attempts to plot any Variable contained in the Dataset. |
Description:
- Rights: Freely Available
- summary: It is a major challenge to develop gridded precipitation and temperature estimates that adequately resolve the extreme spatial gradients present in the Hawaiian Islands. The challenge is particularly pronounced because the available station networks are irregularly spaced and sparse, creating large uncertainties in gridded spatial meteorological estimates. Here we develop a 100- member, daily ensemble of precipitation and temperature estimates over the Hawaiian Islands for the period 1990-2014 at 1 km grid resolution. We first develop an intermediary ensemble estimate of the monthly climatological precipitation and temperature and use the climatological surfaces to inform daily anomaly interpolation. This Climatologically Aided Interpolation (CAI) method extends our initial ensemble system developed for the continental United States (CONUS). For Hawaii, we show that daily interpolation using only daily data is inferior to the CAI methodology, particularly over longer time periods (years to decades). Daily interpolation has more value for short time periods (e.g. 1-month or less) or when the precipitation distribution substantially diverges from climatology. The CAI ensemble is able to reproduce observed precipitation and temperature patterns, including the probability of precipitation. Leave-one-out cross-validation results illustrate that the ensemble has: 1) minimal biases, 2) reasonable mean absolute errors, 3) good representation of daily precipitation intensity variability, and 4) good reliability and discrimination. Additionally, the ensemble is able to reasonably reproduce the tails (e.g. 99.9th percentile) of the daily precipitation and temperature distributions, with increasing uncertainty for higher percentiles.
- NCAR GDEX - Ensemble gridded (1 km) daily rainfall and temperature for the Hawaiian Islands (1990-2014)(d583154)
Dates:
- modified : 2025-08-27T05:32:12.770Z
Publishers:
-
NCAR/GDEX
- email: datahelp@ucar.edu
- http://gdex.ucar.edu/