Abstract. The Coordinated Regional Climate Downscaling Experiment
(CORDEX) is a scientific effort of the World Climate Research Program (WRCP)
for the coordination of regional climate initiatives. In order to accept an
experiment, CORDEX provides experiment guidelines, specifications of regional
domains, and data access and archiving. CORDEX experiments are important to study
climate at the regional scale, and at the same time, they also have a very
prominent role in providing regional climate data of high quality. Data
requirements are intended to cover all the possible needs of stakeholders
and scientists working on climate change mitigation and adaptation policies
in various scientific communities. The required data and diagnostics are
grouped into different levels of frequency and priority, and some of them even
have to be provided as statistics (minimum, maximum, mean) over different
time periods. Most commonly, scientists need to post-process the raw output
of regional climate models, since the latter was not originally designed to
meet the specific CORDEX data requirements. This post-processing procedure
includes the computation of diagnostics, statistics, and final homogenization
of the data, which is often computationally costly and time-consuming.
Therefore, the development of specialized software and/or code is required.
The current paper presents the development of a specialized module (version 1.3) for the Weather Research
and Forecasting (WRF) model capable of
outputting the required CORDEX variables. Additional diagnostic variables not
required by CORDEX, but of potential interest to the regional climate
modeling community, are also included in the module. “Generic” definitions of
variables are adopted in order to overcome the model and/or physics
parameterization dependence of certain diagnostics and variables, thus
facilitating a robust comparison among simulations. The module is
computationally optimized, and the output is divided into different priority
levels following CORDEX specifications (Core, Tier 1, and additional) by
selecting pre-compilation flags. This implementation of the module does not
add a significant extra cost when running the model; for example, the addition
of the Core variables slows the model time step by less than a 5 %. The use
of the module reduces the requirements of disk storage by about a 50 %. The
module performs neither additional statistics over different periods of time
nor homogenization of the output data.
Astronomical seeing forecast for the Thai National Observatory
