With the use of data warehouses, the need for faster access and analysis to historical and multidimensional data has arisen. Online analytical processing (OLAP), developed for this purpose, has provided suitable data structures that overcome some of the limitations of relational databases by providing rapid data analysis. OLAP can display and collect large amounts of data while providing searchable access to any data point and handle a wide variety of complex queries that match user interests. While OLAP enables querying and analysis of multi-dimensional numeric and alphanumeric data, there is still a need to support flexible queries and analyses on uncertain and fuzzy data due to the nature of the data existing the complex applications such as multimedia and spatiotemporal applications. This study presents how to handle various types of fuzzy spatiotemporal queries using our fuzzy SOLAP (spatial OLAP) based framework on meteorological databases, which inherently contain spatiotemporal data in addition to uncertainty and fuzziness. In this context, we describe the support for non-spatial and fuzzy spatial queries as well as fuzzy spatiotemporal query types. In addition, while OLAP mainly includes historical data and associated queries and analyzes, we describe how to handle predictive fuzzy spatiotemporal queries, which may require an inference mechanism. We also show that various complex queries, including predictive fuzzy spatiotemporal queries, are effectively and efficiently handled using our fuzzy SOLAP framework.