Snow: Dataset development, NWS products evaluations, and its impact on CFS subseasonal to seasonal prediction
University of Arizona
2 March, 2:30, in 2890
Snow (water equivalent, depth, and fraction) has a major impact on the
energy and water cycle and land-atmosphere interactions. Despite this
importance, high-quality snow datasets are lacking and NWP and climate
models have difficulty in snow data assimilation and in snow modeling.
Through our recent progress documented in 6 papers, here I will discuss
several snow issues that are highly relevant to the NWS weather, water,
and climate prediction.
First, we found large snow depth errors over U.S. in snow
initializations from NCEP global (GFS and CFS) and regional (NAM)
models (Dawson et al. 2016; doi: 10.1175/JHM-D-15-0227.1). The snow
water equivalent (SWE) errors are even larger due to deficiencies in
snow density. Subsequently we developed a new snow density
parameterization for land data assimilation (Dawson et al. 2017a; doi:
10.1175/JHM-D-16-0166.1) that is significantly better than those used
in the above snow initialization or in the NCEP land model (Noah).
Second, we developed a new and innovative method to obtain daily 4 km
SWE and snow depth data from 1981 to present over continental U.S.
based on USDA SNOTEL point SWE and snow depth measurements, NWS COOP
point snow depth measurements, and PRISM daily gridded precipitation
and temperature datasets (Broxton et al. 2016a; doi:
10.1002/2016EA000174). The robustness of our method and our product has
been demonstrated using three approaches. Using this dataset, we found
large SWE errors in reanalyses (including CFSR) and Global Land Data
Assimilation Systems (including GLDAS-Noah) (Broxton et al. 2016b; doi:
10.1175/JHM-D-16-0056.1) and from satellite remote sensing (Dawson et
al., 2017b, under preparation). Furthermore, the primary reasons for
these underestimates are identified.
Finally, we found major impacts of snow initialization on CFS
subseasonal to seasonal forecasting over Northern Hemisphere mid- and
high-latitudes in the transition season (Apr-Jun), which are even
greater than SST effects (Broxton et al. 2017, submitted). Furthermore,
snow initialization deficiencies are primarily compensated by CFS
atmospheric model deficiencies (most probably those related to
atmospheric radiative transfer).
These results suggest that, to improve short-term to seasonal
forecasting in the spring and early summer, CFS (GFS, and NGGPS) should
improve snow initialization first, followed by atmospheric radiative
transfer improvement (e.g., clouds and aerosols), and then followed by
land model improvement.