Accurate analysis of observational data are an essential for the development of an operational prototype of a new and integrated approach to monitoring and predicting soil moisture and other components of the water balance for Australia. An effective climate imprint method for mapping daily and monthly weather conditions all over this continent which has a lot of heterogeneous landscapes and data sparse areas is described and assessed. Daily and monthly weather data recorded at stations are integrated with the modified 30 year average climate normal to reconstruct spatially explicit estimates of daily and monthly tmax, tmin and precipitation etc. First, create the 30 year monthly climate normal on stations, with some modification to include more observations at data sparse especially mountainous area. These climate normal on stations are then analysed to the 0.25x0.25 and 0.05x0.05 grid using ANUSPLIN, a 3-dimension Laplacian Smoothing Spline interpolation scheme. Instead of analysing those daily or monthly fields themselves, the anomalies of these fields with topographic impact being greatly reduced, are then interpolated to required grids using a 2-dimensional optimal Barnes analysis. Final analysis is obtained by summing up these anomaly grids and the corresponding climatology grids. Comparisons with the current Operational Optical Barnes schemes are carried out in terms of cross validated RMSE estimation using independent data set. It is found that significant improvements are made for all the daily and monthly analysis of those fields. For daily analysis of Tmax and Tmin, RMSE are reduced from 1.93°C and 2.01°C of Operational Optical Barnes schemes to 1.22°C and 1.8 °C of the new scheme. For their monthly analysis RMSE are reduced from 1.64°C and 1.54°C to 0.67°C and 1.11°C. Monthly rainfall analysis is also improved with RMSE reduced from the current 24.9mm of to 21.12mm for the new analysis, although improvement for daily rainfall analysis is relatively small. Using the similar methodology, a data patch scheme is designed to be used to fill missing observations. This scheme is then further explored to be used to check the quality of observed station data.