Generation of Improved Land-surface Data and an Assessment of its Impact on Mesoscale Predictions is part of a group effort by the Models and Assimilation Team at the National Severe Storms Laboratory, the School of Meteorology at the University of Oklahoma, and the School of Natural Resource Sciences at the University of Nebraska–Lincoln.


Research Results

Empirical Flux Algorithm for AmeriFlux and NSTL Sites
Empirical Flux Algorithm with Reduced Soil Heat Capacity
Graphical Comparisons for Model Simulations with Empirical Flux Algorithms
Graphical Comparisons for Tuned Solar Radiation
Graphical Comparisons
Solar Radiation Tuning
Graphical Comparisons (CXSCA=1.5)
Vegetation Fraction and Leaf Area Index Biases
Interactively Map MM5 Errors for Selected Case Studies (inside NSSL only)
Canopy Resistance - Simple Linear Regression Tests
Canopy Resistance - Multiple Linear Regression Tests
Canopy Resistance Regime Comparisons

Mesonet and Model Plots (inside NSSL only)

MM5 Model Output Plotter (inside NSSL)
Preliminary three-domain daily forecasts
MM5 Control Forecasts (inside NSSL)
Full four-domain forecasts for case studies
MM5 Forecasts Improved by Satellite Data (inside NSSL)
Four-domain forecasts including vegetation fraction and leaf area index
MM5 Forecasts Improved by Oklahoma Mesonet Soil Data (inside NSSL)
Four-domain forecasts including soil temperature and moisture observations
Fully Improved MM5 Forecasts (inside NSSL)
Four-domain forecasts including satellite and Mesonet observations
Oklahoma Mesonet Data Plotter (inside NSSL)
Plot all available Oklahoma Mesonet observations

Observations, Data, and Other Information

List of case studies and current model status
Changes to MM5 model code
Radar and Satellite Observations for Selected Case Studies
Initial and verification data for case studies
Great Plains Archived Surface Observations
Great Plains Storm Archives

Project Publications

Bonifaz, R., J. Merchant, D. Stensrud, and L. Leslie, 2004: Land cover product
derivation from AVHRR HRPT data for numerical weather prediction models:
Using ENVI in batch mode. Images to decisions: remote sensing foundations
for GIS applications, Kansas City, MO, American Society for
Photogrammetry and Remote Sensing.

Bonifaz, R., J. Merchant, D. Stensrud, 2005: Assessing short-term AVHRR-NDVI
change using an expert classifier. The William T. Pecora Memorial
Symposium: PECORA 16 - Global Priorities in Land Remote Sensing,
Sioux Falls, SD, National Center for Earth Resources Observation and Science.

Godfrey, C.M., 2006: The influence of improved land surface and soil data on
mesoscale model predictions. Ph.D. dissertation, University of Oklahoma, 128 pp.

Godfrey, C.M., D.J. Stensrud, and L.M. Leslie, 2005: The influence of improved land
surface and soil data on mesoscale model predictions. Preprints, 19th Conf. on
Hydrology, San Diego, CA, Amer. Meteor. Soc., CD-ROM, 4.7.

Godfrey, C.M., D.J. Stensrud, and L.M. Leslie, 2006: Soil temperature and moisture
errors in Eta model analyses Preprints, 20th Conf. on
Hydrology, Atlanta, GA, Amer. Meteor. Soc., CD-ROM, JP1.2.

Godfrey, C.M., D.J. Stensrud, and L.M. Leslie, 2007: A new latent heat flux param-
eterization for land surface models. Preprints, 21st Conf. on Hydrology, San
Antonio, TX, Amer. Meteor. Soc., CD-ROM, 6A.3.

Stensrud, D., L. Leslie, J. Merchant, A. Taylor, C. Godfrey, and R. Bonifaz, 2004:
Generation of improved land-surface data for high-resolution numerical weather
prediction models. Preprints, 16th Conference on Numerical Weather Prediction,
Seattle, WA, Amer. Meteor. Soc., CD-ROM, P1.38.


Research Projects
Revised: 22 January 2007