Systems Engineering Analysis of a TRMM PR-Like Rainfall Retrieval Algorithm

Systems engineering constitutes a group of processes and methods to design and implement a system for optimal performance given limited time, technology, or resources. As with any system, it is important to understand which subcomponents are most important and which are less important so that appropriate resource allocations may be made. An example of a complex system is the Tropical Rainfall Measuring Mission (TRMM). Its subsystems include the satellite vehicle, the precipitation radar (PR), the ground validation system, and the retrieval algorithms. Each of these subsystems contributes to the overall success of the mission. Sensitivity analysis (SA) is a method whereby the output response from a model can be linked back to the variability in the input parameters. This paper describes a method of performing SA on a TRMM PR-like (TL) rainfall retrieval algorithm (based on the TRMM 2A25 algorithm) to better describe how the uncertainty in the model output can be apportioned to the uncertainty in the input factors and gain greater understanding as to the relative importance of each factor. For example, assuming a model with several input factors, if one factor is found to be the dominating cause of model error, and the others contribute relatively little, then resources can be devoted to improving the accuracy of one factor, thereby improving the overall model accuracy. This paper is based on global SA using a variance decomposition technique. Analyses are done and results are presented for factor importance for cases over both ocean and land. Results for the simple TL algorithm considered in this paper show that at low rain rate, the a and b coefficients in the R=aZ_e ^b relationship contribute the greatest amount to the output variance. At higher rain rates, above about 8 mm/h, the error from Deltasigmadeg is the greatest contributor to error in algorithm output