WaveWatch_timeseries

Timeseries of wave data extracted from .grb2 files. Timeseries contain a 30-year wave climatology that has been generated with the NOAA WAVEWATCH III® using the Ardhuin et al (2010) physics package, 15 regular lat-lon grids, and the NCEP Climate Forecast System Reanalysis and Reforecast (CFSRR) homogeneous dataset of hourly high-resolution winds. Model setup: Propagation scheme: Higher-order schemes with Tolman (2002) averaging technique (PR3) Linear input: Cavaleri and Malanotte-Rizzoli with filter (LN1) Nonlinear interactions: Discrete interaction approximation (NL1) Bottom friction: JONSWAP bottom friction formulation (BT1) Depth induced breaking: Battjes-Janssen (DB1) Use Miche-style shallow water limiter in equation for maximum wave energy (MLIM) The model was run with the Ardhuin et al (2010) source term package (ST4) which includes the flux computation in the sources (FLX0, STAB0). Additionally, the model uses a third order propagation scheme (UQ), with no damping or scattering by sea ice (IC0, IS0), and no reflection (REF0). Partition output in NetCDF format. These provide bulk spectral estimates for each wave system. Available hourly for each individual grid (where there is data). Data can be consulted by usage of the next examples: with the next python example: https://polar.ncep.noaa.gov/waves/how_to_read_partition.py (Python), https://polar.ncep.noaa.gov/waves/how_to_read_partition.m (Matlab) See for more information: https://polar.ncep.noaa.gov/waves/hindcasts/nopp-phase2.php Contact person for this dataset is Jaap Nienhuis - [email protected]

A New Snow Density Parameterization for Land Data Initialization

Snow initialization is crucial for weather and seasonal prediction, but the National Centers for Environmental Prediction (NCEP) operational models have been found to produce too little snow water equivalent, partly because they assume a constant and unrealistically low snow density for the snowpack. One possible solution is to use the snow density formulation from the Noah land model used in NCEP operational forecast models. While this solution is better than the constant density assumption, the seasonal evolution of snow density in Noah is still found to be unrealistic, through the evaluation of both the offline Noah model output and the Noah snow density formulation itself. A physically based snow density parameterization is then developed, which performs considerably better than the Noah parameterization based on the measurements from the SNOTEL network over the western United States and Alaska. It also performs better than the snow density schemes used in three other models. This parameterization could be easily implemented in NCEP operational snow initialization. With the consideration of up to 10 snow layers, this parameterization can also be applied to multilayer snowpack initiation or to estimate snow water equivalent from in situ and airborne snow depth measurements.6 month embargo; published online 10 January 2017.This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]