There are many applications that require values for statistical parameters describing the local climate. The most obvious and direct way to obtain local climate statistics is to calculate them from long-term point observations. However, the use of long-term observational datasets imposes at least two limitations: (1) long-term point observations are not available for large areas of the world that are data sparse or have sites with a limited period of record; and (2) 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. Research has been conducted to determine the feasibility of using a limited-area, high-resolution numerical model as an alternative to generate local climate statistics.
The goal is to simulate the actual 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 data into a 3-D climatological dataset. In this mode the model continuously or periodically ingests the available observed data through one of several possible data assimilation techniques (Newtonian relaxation periodic reanalysis, etc.). The model then dynamically fuses the available observations with its knowledge of the surface characteristics of the earth and the basic principles of physics to generate estimates of local climate statistics at locations for which no observational data is available. This is, in general, a difficult task because the observed data must be inserted into the simulation in a way in which it will beneficially impact the resulting statistics. Additionally, the data has quality and representativeness attributes that can sometimes cause it to conflict with the values generated by the model. This climate modeling technique has been given the name Advanced Climate Modeling and Environmental Simulations (ACMES).
The research is now being conducted for several different climate regimes around the world at a model resolution of 10 and 40 km to include north America, the Far East, the Middle East, and the Tropics. The focus of this presentation will be the quality of the climate statistics produced in this manner. This dynamic model technique has been used to support the Global 98 Exercise conducted by the Naval War College. The results of our efforts show that it is possible to generate simulated local climate statistics that are very close to actual climate statistics even in complex terrain regions. A companion presentation to this presentation will focus on the research issues involving the sensitivity of the simulated climate statistics to data assimilation techniques, model configuration, and subgrid parameterization schemes.