Observationally based evaluation of NWP reanalyses in modeling cloud properties over the Southern Great Plains

[1] This study evaluates three major Numerical-Weather-Prediction reanalyses (ERA-Interim, NCEP/NCAR Reanalysis I, and NCEP/DOE Reanalysis II) in modeling surface relative shortwave cloud forcing, cloud fraction, and cloud albedo. The observations used for this evaluation are decade-long surface-based continuous measurements of the U.S. Atmospheric Radiation Measurement (ARM) program from 03/25/1997 to 12/31/2008 over the Southern Great Plains site. These cloud properties from the reanalyses are evaluated at multiple temporal scales. Like the observations, all the reanalyses show a strong annual cycle, and relatively weak diurnal or inter-annual variations of the cloud properties. The reanalyses exhibit significant underestimation on the cloud properties, and the model biases in the cloud properties in general reveal a linear link to one another and are somewhat related to cloud fraction magnitude. Further examination shows that the cloud properties are strongly related to 2-m relative humidity, especially for the observations and ERA-Interim. However, the relationship between the cloud properties and 2-m temperature and specific humidity is much weaker. Also, the cloud fraction biases in the two NCEP reanalyses increase (decrease) with the relative humidity (temperature and specific humidity), but the cloud fraction biases in ERA-Interim show no (opposite) relationship with the relative humidity (temperature and specific humidity). The relative humidity biases have a positive (negative) linear relationship with the specific humidity (temperature) biases. A combined statistical analysis using the technique of Taylor diagrams and a newly developed metric “Relative Euclidean Distance” indicates that ERA-Interim and NCEP/NCAR reanalyses have the best and worst overall performance in modeling the cloud and meteorological properties examined, respectively, except that NCEP/DOE Reanalysis II ranks the best in modeling the monthly temperature and specific humidity.