Abstract
Abstract. Ground-based observatories use multisensor observations to characterize cloud and precipitation properties. One of the challenges is how to design strategies to best use these observations to understand these properties and evaluate weather and climate models. This paper introduces the Cloud-resolving model Radar SIMulator (CR-SIM), which uses output from high-resolution cloud-resolving models (CRMs) to emulate multiwavelength, zenith-pointing, and scanning radar observables and multisensor (radar and lidar) products. CR-SIM allows for direct comparison between an atmospheric model simulation and remote-sensing products using a forward-modeling framework consistent with the microphysical assumptions used in the atmospheric model. CR-SIM has the flexibility to easily incorporate additional microphysical modules, such as microphysical schemes and scattering calculations, and expand the applications to simulate multisensor retrieval products. In this paper, we present several applications of CR-SIM for evaluating the representativeness of cloud microphysics and dynamics in a CRM, quantifying uncertainties in radar–lidar integrated cloud products and multi-Doppler wind retrievals, and optimizing radar sampling strategy using observing system simulation experiments. These applications demonstrate CR-SIM as a virtual observatory operator on high-resolution model output for a consistent comparison between model results and observations to aid interpretation of the differences and improve understanding of the representativeness errors due to the sampling limitations of the ground-based measurements. CR-SIM is licensed under the GNU GPL package and both the software and the user guide are publicly available to the scientific community.
Highlights
Ground-based observatories offer an integrated view of cloud and precipitation systems complementary to that available from satellites with excellent vertical resolution, especially in the boundary layer, and an accompanying description of the large-scale forcing
We introduce an application for estimating cloud fraction profiles (CFPs) using scanning cloud radar (SCR) measurements presented in Oue et al (2016)
Oue et al (2019a) investigate the impact of radar data sampling on the multi-Doppler radar wind retrievals for the mesoscale convective system (MCS) by an Observing system simulation experiments (OSSEs) using Cloud-resolving model Radar SIMulator (CR-SIM)
Summary
Ground-based observatories offer an integrated view of cloud and precipitation systems complementary to that available from satellites with excellent vertical resolution, especially in the boundary layer, and an accompanying description of the large-scale forcing. Determining critical parameters for model evaluation such as the cloud fraction profile requires complementary, synergistic observations from both radar and lidar. Accurate estimation of uncertainties in the retrieval products (e.g., ice water content, liquid water content, vertical velocity) is challenging, forward simulators allow us to emulate the observational retrieval products accounting for known error sources to understand the exact impacts of those error sources on the products by comparisons with the truth, which is usually the input model data. This study demonstrates the application of the CR-SIM forward simulator in several OSSEs in which ARM multisensor products, such as cloud locations and vertical velocity, are evaluated by considering limitations inherently imposed by the nature of the observations. This study demonstrates the application of the CR-SIM forward simulator in several OSSEs in which ARM multisensor products, such as cloud locations and vertical velocity, are evaluated by considering limitations inherently imposed by the nature of the observations. (A list of acronyms is provided in Appendix B for easy reference.)
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