An Evaluation of the Sensitivity of Local Climate Statistics Generated from the Output of a 3-D Mesoscale Atmospheric Model to Observed Data Availability

.Extensive research sponsored by the DOD Air and Space Natural Environment Modeling and Simulation Executive Agent (ASNEMSEA) has been conducted to determine the feasibility of using a limited-area high-resolution numerical model to simulate high resolution historical conditions which are then used to generate local climate statistics around the globe. Many planning applications and simulations that are impacted by the environment require values from historical and statistical parameters describing the local weather and climate in great detail. The most obvious and direct way to obtain this information is to use historical observational data and then calculate the local climate statistics from long-term point observations. The numerical model method has been developed because of the limitations imposed on the use of long-term observational datasets. Long-term point observations are not available for large areas of the world and the representativeness and quality of observations change in time as observing sites are relocated, the land use around a site is changed or new instrumentation is used. In this research, the numerical model approach is used to estimate historical conditions and the local climate from a set of long period mesoscale simulations. The goal of the research is to simulate the actual historical conditions and calculate the climate statistics for a particular period of time over a specified region. The objective is addressed by executing the model for a long period of time in a data assimilation mode and allowing the model to fuse the available observed data into 3-D historical datasets that can be used to obtained the corresponding climatological datasets. With its database of the surface characteristics of the earth and the basic principles of physics, the model dynamically generates estimates of the historical conditions from which local climate statistics can be calculated at locations for which no observational data is available. This technique has been given the name CLImate statistics by a dynamical MODel (CLIMOD). The research has shown that the quality of the simulated climate statistics are significantly impacted by several modeling factors including the method of data assimilation, model resolution, subgrid parameterization schemes, and observed data availability. The focus of this presentation is the evaluation of the impact of data availability. Research has now been completed for several different climate regimes around the world at model resolutions of 10 and 40 km. The presentation will be on the issues involving the sensitivity of the quality of simulated climate statistics to observed surface and upper air data availability for both data sparse and data rich regions. Methods developed to utilize the knowledge gained from available observations to remove model bias and establish a confidence index for the generated climate statistics also will be discussed. The Air Force Climatology Center plans to present a related presentation that will focus on developing a production system based upon CLIMOD