A sensitivity analysis of the different setups of the RegCM4.5 model for the Carpathian region

Abstract

The current study focuses on the RegCM4.5 model and specifically on a comparison of hydrostatic and non-hydrostatic approaches as well as on different microphysical parameterisations and planetary boundary layer (PBL) schemes. The main goal of the paper is to simulate the historical regional precipitation characteristics of the Carpathian region as reliably as possible. For this purpose, seven different model experiments at a 10 km horizontal resolution were completed for a 10-year period (1981-1990) using ERA-Interim reanalysis data (with 0.75 resolution) as initial and boundary conditions. Our simulation matrix consists of hydrostatic and non-hydrostatic runs together with different treatments of moisture, namely, the SUBEX and the NogTom schemes. In addition, two PBL schemes are tested, the Holtslag and the UW-PBL scheme. In this detailed validation study, RegCM outputs (e.g., temperature, global radiation, cloud cover, precipitation) are compared to the homogenized, gridded CarpatClim data (available with 0.1 resolution) that are based on measurements at regular meteorological station sites. The validation considers seasonal and monthly means, as well as extreme climatic events. On the basis of the results we can conclude that the role of the non-hydrostatic core can be clearly recognized for precipitation, particularly over mountains. Moreover, it was also found that the UW-PBL scheme performs with a negative bias regarding atmospheric boundary layer thickness and temperature and it reduces the wet/dry biases of the Holtslag PBL scheme. Regarding microphysical schemes, the NogTom scheme performs better than the SUBEX scheme, but the modified SUBEX (SUB4.3) can also reduce the precipitation over mountainous areas.

CCHZ‐DISO: A Timely New Assessment System for Data Quality or Model Performance From Da Dao Zhi Jian

“…In fact, CCHZ‐DISO provide a new framework about the evaluation system, which is a reconstruction of the present system. The most important contribution of our CCHZ‐DISO is that it can comprehensively qualify the overall performances of different datasets or models when the number of statistical metrics is larger than 1, which is highly recommend by large literature (Deng et al., 2021; Kalmar et al., 2021).…”

“…For example, Deng et al (2021) demonstrated that the most important advantage of DISO is that after normalizing the observed and simulated data, the value of DISO can express the performance of the same experiment at different sites. A whole section was used by Kalmar et al (2021) to introduce DISO index, and they pointed out that the advantage of using DISO against the Taylor diagram is that the comprehensive performances of different models are still not quantified in the Taylor diagram. The detailed construction process and advantages of DISO can be founded in Hu et al (2019) and Zhou et al (2021).…”

“…To address this need, a new statistical metric: the distance between indices of simulation and observation (DISO) has been constructed to quantify the comprehensive quality of data or performance of a model (Hu et al, 2019;Zhou et al, 2021). DISO index is based on the Euclidean Distance and statistical metrics and has its obvious advantage compared with the widely used approach: the Taylor diagram (Deng et al, 2021;Kalmar et al, 2021). For example, Deng et al (2021) demonstrated that the most important advantage of DISO is that after normalizing the observed and simulated data, the value of DISO can express the performance of the same experiment at different sites.…”

Decompositions of Taylor diagram and DISO performance criteria

“…A recent work (Hu et al ., 2019) developed such a method called DISO, based on the definition of distance between points to evaluate overall model performance. Because of its explicit definition and solid theoretical background, DISO has started to be used in the climate and disease areas (Amato et al ., 2019; Cui et al ., 2020; Hu et al ., 2020; Kalmar et al ., 2021). In particular, Kalmar et al .…”

“…In particular, Kalmar et al . (2021) clearly proposed that ‘The advantage of using DISO against the Taylor diagram is that the comprehensive performances of the different models are still not quantified in the latter (Hu et al ., 2019)’. In Hu et al .…”