Seasonal evaluation of rainfall estimation by four cumulus parameterization schemes and their sensitivity analysis

Abstract

Abstract:Sustainable water resources management require scientifically sound information on precipitation, as it plays a key role in hydrological responses in a catchment. In recent years, mesoscale weather models in conjunction with hydrological models have gained great attention as they can provide high-resolution downscaled weather variables. Many cumulus parameterization schemes (CPSs) have been developed and incorporated into three-dimensional Pennsylvania State University/National Center for Atmospheric Research (PSU/NCAR) mesoscale model 5 (MM5). This study has performed a comprehensive evaluation of four CPSs (the Anthes-Kuo, Grell, Betts-Miller and Kain-Fritsch93 schemes) to identify how their inclusion influences the mesoscale model's precipitation estimation capabilities. The study has also compared these four CPSs in terms of variability in rainfall estimation at various horizontal and vertical levels. For this purpose, the MM5 was nested down to resolution of 81 km for Domain 1 (domain span 21 ð 81 km) and 3 km for Domain 4 (domain span 16 ð 3 km), respectively, with vertical resolutions at 23, 40 and 53 vertical levels. The study was carried out at the Brue catchment in Southwest England using both the ERA-40 reanalysis data and the land-based observation data. The performances of four CPs were evaluated in terms of their ability to simulate the amount of cumulative rainfall in 4 months in 1995 representing the four seasonal months, namely, January (winter), March (spring), July (summer) and October (autumn). It is observed that the Anthes-Kuo scheme has produced inferior precipitation values during spring and autumn seasons while simulations during winter and summer were consistently good. The Betts-Miller scheme has produced some reasonable results, particularly at the small-scale domain (3 km grid size) during winter and summer. The KF2 scheme was the best scheme for the larger-scale (81 km grid size) domain during winter season at both 23 and 53 vertical levels. This scheme tended to underestimate rainfall for other seasons including the small-scale domain (3 km grid size) in the mesoscale. The Grell scheme was the best scheme in simulating rainfall rates, and was found to be superior to other three schemes with consistently better results in all four seasons and in different domain scales.

Sensitivity and uncertainty analysis of mesoscale model downscaled hydro-meteorological variables for discharge prediction

“…The reason for the poor goodness of fit with the precipitation datasets may be ascribed to the insufficiency of the parameterization scheme. In this study, the best available cumulus parameterization scheme is used as proposed by several past studies (Ishak et al ., ), indicating that there is a need of further development for a new parameterization scheme especially for the maritime climate. The suitability of data for ETo estimations suggests that ECMWF could be used for hydro‐meteorological applications.…”

A Diagnostic Evaluation of Precipitation in CORDEX Models over Southern Africa

“…They can be tuned in terms of domain size, lateral boundary condition (LBC) forcing, horizontal and vertical resolution, and the atmospheric and surface physics (see Cr etat et al 2012). Some model setups have even been found to be seasonally sensitive (Ishak et al 2012). Usually these types of studies focus over a region of interest, or a country or 9496 local area and the models are optimized to get the most accurate results for this location and for a specific season.…”

Sensitivity to Convective Schemes on Precipitation Simulated by the Regional Climate Model MAR over Belgium (1987–2017)

“…Even if certain studies tried to have experimental results reproducible as written by Janjić [8], it is normal that results change when using another model than the experimental one. As introduced in Section 2.3, results clearly show that no convective scheme performs systematically better than the others and that the results are extremely dependent on the internal configuration of the model [52,60]. Despite these individual results, all experiments show that the trends are similar, which suggests that it would be interesting to use all these experiments as part of a model ensemble, as already proposed by Pieri et al [53] and Cortéz-Hernàndez et al [71].…”

“…For example, Evans et al [56] and Ratna et al [57] show that BMJ and KFS produce good precipitation patterns respectively in South-East Australia and in South Africa but they both tend to overestimate precipitation amounts while it is the opposite in Madala et al [58] over South East of India. Although Ratna et al [57] show that BMJ is closest to the observed precipitation amount than KFS, Pohl et al [59] show exactly the opposite over the equatorial east Africa and Ishak et al [60] show better performance for KFS during summer and for BMJ during winter over Southwest England. It appears that none of the convective schemes performs systematically better than the others but instead depend on the region, the season or the model configuration, as concluded by Ishak et al [60].…”

“…Although Ratna et al [57] show that BMJ is closest to the observed precipitation amount than KFS, Pohl et al [59] show exactly the opposite over the equatorial east Africa and Ishak et al [60] show better performance for KFS during summer and for BMJ during winter over Southwest England. It appears that none of the convective schemes performs systematically better than the others but instead depend on the region, the season or the model configuration, as concluded by Ishak et al [60]. This study pointed that it is essential to identify the best convective scheme for each given region and each given period.…”