Precipitation Buoyancy Diagnostic Package

The precipitation-buoyancy diagnostics POD is used to assess the thermodynamic sensitivity of model precipitation fields.

Scientific basis

Observations show that over tropical oceans, a lower tropospheric buoyancy metric \(B_L\) has a strong relationship to precipitation ( Ahmed and Neelin 2018, Ahmed et al. 2020). This buoyancy metric can further be decomposed into two components:

  1. A measure of undilute buoyancy termed CAPE L, which measures the difference between boundary layer moist enthalpy and the free-tropospheric temperature. If convection were non-entraining, this would be the dominant thermodynamic measure affecting precipitation.

  2. A measure of lower-free tropospheric sub-saturation SUBSAT L, which is computed as a departure from saturation in the lower free-troposphere. The influence of entrainment on convection is expressed through this measure.

In observations (ERA re-analysis and TRMM precipitation), precipitation appears to about equally sensitive to CAPE L and SUBSAT L. However, climate models can show diverging behavior. To measure this relative sensitivity of precipitation to CAPE L and SUBSAT L, a vector \(\gamma_{CS}\) is introduced. This has a direction that is expressed in degrees and takes values ranging from 0 to 90.

Version & Contact info


The currently package consists of following functionalities:

  1. Precipitation Buoyancy curve and surface

As a module of the MDTF code package, all scripts of this package can be found under the precipitaton_buoyancy_diag

Required programming language and libraries

The is package is written in Python 3.7, and requires the following Python packages: numpy, scipy, matplotlib, cython, numba, & xarray. These Python packages are already included in the standard Anaconda installation.

Required model output variables

The following high-frequency model fields are required:

  1. Precipitation rate

  2. Vertical profile of temperature

  3. Vertical profile of specific humidity

  4. Surface pressure (optional)


  1. Ahmed, F., & Neelin, J. D. (2018). Reverse engineering the tropical precipitation–buoyancy relationship. Journal of the Atmospheric Sciences, 75(5), 1587-1608.`__.

  1. Ahmed, F., Adames, Á. F., & Neelin, J. D. (2020). Deep convective adjustment of temperature and moisture. Journal of the Atmospheric Sciences, 77(6), 2163-2186.`__.

More about this diagnostic