This proposal centers on supporting NOAA/NCEP’s, NOAA/OHD’s, and NOAA/CPC’s operational hydrological and land surface modeling missions, as well as furthering their support of the NOAA Hydrology Test Bed, the NOAA Climate Test Bed, and the National Integrated Drought Information System (NIDIS). New capabilities resulting from this joint NOAA NCEP/OHD/CPC effort will allow for the execution of enhanced Noah and Sacramento Heat Transfer (SAC-HT) models on the 4km HRAP grid over the Continental United States (CONUS). Enhancements will impact all stages of modeling operations and will include improved downscaled forcing data, spin-up strategies, data assimilator modules, model physics, and model validation procedures, and will enable national runoff routing of both Noah and SAC-HT output. Additionally, for the National Integrated Drought Information System, this research will yield 31-year high-resolution model climatologies in support of the monitoring and prediction of drought and other hydrologic variables.
The proposed work will leverage forcing data from the North American Land Data Assimilation System which features a 1/8th degree spatial resolution, and topography that differs significantly from that which will be used in the proposed 4km modeling effort. As such, we will apply a novel lapse-rate-based elevation adjustment scheme to downscale the NLDAS forcing to the 4km HRAP grid. As the lapse rate has been found to vary significantly in space and time, we further propose to find a proxy to quantitatively predict the variations of the lapse rate.
Accurate initialization of land surface and hydrological models is critical for correct hydrological predictions, because the process of a model adjusting to its forcing can severely bias land surface simulations. We propose to make a thorough investigation and develop a technique to generate optimal initial conditions for the 31-year (1979-2009) 4km Noah and SAC-HT retrospective simulations and to provide guidance for realtime simulations.
Complementing the forcing and spin-up work described above will be improvements to the models’ data assimilation modules. We will design and test several innovative data assimilation techniques for ingesting the MODIS snow cover area observations into the SAC-HT/Snow17 and Noah models. The proposed algorithm is superior to existing methods in that it (i) uses the traditional bisection method to study the inverse of the usual problem by finding the SWE which optimally matches the MODIS-derived SCF observations, (ii) incorporates improved error estimates for snow observations, and (iii) dynamically propagates estimated model error for snow states within a Kalman filtering framework. Results will be compared with existing analysis products and validated using ground based measurements.
The final aspect of this research centers on the CONUS-wide testing in NASA’s Land Information System (LIS) of SAC-HT, Snow17, and Noah model physics and parameter improvements. Two recent improvements in SAC-HT will be extensively evaluated: 1) Incorporation of Noah’s evapotranspiration physics, and 2) Improvement of sub-surface runoff modeling. OHD’s Snow 17 model will benefit through imtegratopm of a new dynamic parameterization scheme which will negate the need to manually derive many of the model’s parameters. Similarly, a suite of Noah LSM improvements will be integrated and assessed including a new snow albedo scheme, a canopy conductance formulation, a surface flux formulation. Both models will benefit from incorporation into LIS of OHD’s streamflow routing module. Together, the proposed improvements will greatly enhance the operational and research modeling capabilities of NOAA/NCEP and NOAA/OHD, as well as the numerous research groups who make use of these publically available models.