  Deep and shallow convection calculations occupy significant times in
atmosphere models. These calculations also present significant load imbalances
due to varying cloud covers over different regions of the grid. In this work,
we accelerate these calculations on Intel{\textregistered} Xeon
Phi{\texttrademark} Coprocessor Systems. By employing dynamic scheduling in
OpenMP, we demonstrate large reductions in load imbalance and about 10%
increase in speedups. By careful categorization of data as private,
firstprivate and shared, we minimize data copying overheads for the
coprocessors. We identify regions of false sharing among threads and eliminate
them by loop rearrangements. We also employ proportional partitioning of
independent column computations across both the CPU and coprocessor cores based
on the performance ratio of the computations on the heterogeneous resources.
These techniques along with various vectorization strategies resulted in about
30% improvement in convection calculations.
