Reliable and accurate environmental sensing is a cornerstone of modern meteorology. This paper presents a laboratory environmental simulator capable of reproducing extreme environments and performing tests and calibrations of meteorological sensor systems under controlled conditions. This facility is available to the research community as well as industry and is intended to encourage advancement in the field of sensor metrology applied to meteorology and climatology. Discussion will be made of the temperature, pressure, humidity and wind flow control, and sensing systems with reference to specific sensor test programs and future research activities.

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We follow up on the idea of Lars Arge to rephrase the Reduce and Apply operations of Binary Decision Diagrams (BDDs) as iterative I/O-efficient algorithms. We identify multiple avenues to simplify and improve the performance of his proposed algorithms. Furthermore, we extend the technique to other common BDD operations, many of which are not derivable using Apply operations alone. We provide asymptotic improvements to the few procedures that can be derived using Apply.Our work has culminated in a BDD package named Adiar that is able to efficiently manipulate BDDs that outgrow main memory. This makes Adiar surpass the limits of conventional BDD packages that use recursive depth-first algorithms. It is able to do so while still achieving a satisfactory performance compared to other BDD packages: Adiar, in parts using the disk, is on instances larger than 9.5 GiB only 1.47 to 3.69 times slower compared to CUDD and Sylvan, exclusively using main memory. Yet, Adiar is able to obtain this performance at a fraction of the main memory needed by conventional BDD packages to function.

Decomposing a directed graph to its strongly connected components (SCCs) is a fundamental task in model checking. To deal with the state-space explosion problem, graphs are often represented symbolically using binary decision diagrams (BDDs), which have exponential compression capabilities. The theoretically-best symbolic algorithm for SCC decomposition is Gentilini et al’s $$\textsc {Skeleton}$$ S K E L E T O N algorithm, that uses O(n) symbolic steps on a graph of n nodes. However, $$\textsc {Skeleton}$$ S K E L E T O N uses $$\Theta (n)$$ Θ ( n ) symbolic objects, as opposed to (poly-)logarithmically many, which is the norm for symbolic algorithms, thereby relinquishing its symbolic nature. Here we present $$\textsc {Chain}$$ C H A I N , a new symbolic algorithm for SCC decomposition that also makes O(n) symbolic steps, but further uses logarithmic space, and is thus truly symbolic. We then extend $$\textsc {Chain}$$ C H A I N to $$\textsc {ColoredChain}$$ C O L O R E D C H A I N , an algorithm for SCC decomposition on edge-colored graphs, which arise naturally in model-checking a family of systems. Finally, we perform an experimental evaluation of $$\textsc {Chain}$$ C H A I N among other standard symbolic SCC algorithms in the literature. The results show that $$\textsc {Chain}$$ C H A I N is competitive on almost all benchmarks, and often faster, while it clearly outperforms all other algorithms on challenging inputs.

We follow up on the idea of Lars Arge to rephrase the Reduce and Apply algorithms of Binary Decision Diagrams (BDDs) as iterative I/Oefficient algorithms. We identify multiple avenues to improve the performance of his proposed algorithms and extend the technique to other basic BDD algorithms.These algorithms are implemented in a new BDD library, named Adiar. We see very promising results when comparing the performance of Adiar with conventional BDD libraries that use recursive depth-first algorithms. For instances of about 50 GiB, our algorithms, using external memory, are only up to 3.9 times slower compared to Sylvan, exclusively using internal memory. Yet, our proposed techniques are able to obtain this performance at a fraction of the internal memory needed by Sylvan to function. Furthermore, with Adiar we are able to manipulate BDDs that outgrow main memory and so surpass the limits of the other BDD libraries.