Bitmap indices are widely used in commercial databases for processing complex queries, due to their efficient use of bit-wise operations. Bitmap indices apply natively to relational and linear datasets, with distinct separation of the columns or attributes, but do not perform well on multidimensional array scientific data. We propose a new method for multidimensional array indexing that considers the spatial component of multidimensional arrays. The hierarchical indexing method is based on sparse n-dimensional trees for dimension partitioning, and bitmap indexing with adaptive binning for attribute partitioning. This indexing performs well on range queries involving both dimension and attribute constraints, as it prunes the search space early. Moreover, the indexing is easily extensible to membership queries. The indexing method was implemented on top of a state of the art bitmap indexing library Fastbit, using tables partitioned along any subset of the data dimensions. We show that the hierarchical bitmap index outperforms conventional bitmap indexing, where an auxiliary attribute is required for each dimension. Furthermore, the adaptive binning significantly reduces the amount of bins and therefore memory requirements.