Evaluation of a multisite weather generator on precipitation simulation in the Yangtze river basin

This paper presents the evaluation of a multisite statistical weather generator (MulGETS: Multisite weather Generator of École de Technologie Supérieure) based on its simulation effect of precipitation in the Yangtze River Basin. MulGETS effectively generates spatially correlated sequences of precipitation simultaneously, while maintaining their spatial and temporal distribution characteristics. On the spatial scales, the accuracy of the model varies from station to station, and in general, the errors are lower at stations in the middle and lower reaches of the Yangtze River Basin than in the upper reaches. This difference is likely to exist because of the lower amount of rainfall and more complex topography than those of the upper river basins. On the temporal scales, the simulated values are more precise on the annual scale than on the seasonal scale. Large relative errors occur more frequently in winter, ranging from −35% to 25%. MulGETS can consistently produce precipitation by considering the intensity, magnitude, and duration indices with sub-basin varied observations. However, the precipitation maxima were much lower than the observations. This work shows the general reasonability of the model in downscaling precipitation in the Yangtze River Basin.

Vertical Distribution of Aerosols during Deep-Convective Event in the Himalaya Using WRF-Chem Model at Convection Permitting Scale

The Himalayan region is facing frequent cloud bursts and flood events during the summer monsoon season. The Kedarnath flooding of 2013 was one of the most devastating recent events, which claimed thousands of human lives, heavy infrastructure, and economic losses. Previous research reported that the combination of fast-moving monsoon, pre-existing westerlies, and orographic uplifting were the major reasons for the observed cloud burst over Kedarnath. Our study illustrates the vertical distribution of aerosols during this event and its possible role using the Weather Research and Forecasting model coupled with chemistry (WRF-Chem) simulations. Model performance evaluation shows that simulations can capture the spatial and temporal patterns of observed precipitation during this event. Model simulation at 25 km and 4 km horizontal grid resolution, without any changes in physical parameterization, shows a very minimal difference in precipitation. Simulation at convection-permitting scale shows detailed information related to parcel motion compared to coarser resolution. This indicates that the parameterization at different resolutions needs to be further examined for a better outcome. The modeled result shows changes of up to 20–50% in the rainfall over the area near Kedarnath due to the presence of aerosols. Simulation at both resolutions shows the significant vertical transport of natural (increases by 50%+) and anthropogenic aerosols (increases by 200%+) during the convective event, which leads to significant changes in cloud properties, rain concentration, and ice concentration in the presence of these aerosols. Simulations can detect changes in important instability indices such as convective available potential energy (CAPE), convective inhibition energy (CIN), vorticity, etc., near Kedarnath due to aerosol–radiation feedback.

Evaluation of coastal Antarctic precipitation in LMDz6 global atmospheric model using ground-based radar observations

In the current context of climate change in the poles, one of the objectives of the APRES3 (Antarctic Precipitation Remote Sensing from Surface and Space) project was to characterize the vertical structure of precipitation in order to better simulate it. Precipitation simulated by models in Antarctica is currently very widespread and it overestimates the data. Sensitivity studies have been conducted using a global climate model and compared to the observations obtained at the Dumont d’Urville coast station, obtained by a Micro Rain Radar (MRR). The LMDz/IPSL general circulation model, with zoomed configuration over Dumont d’Urville, has been considered for this study. A sensitivity study was conducted on the physical and numerical parameters of the LMDz model with the aim of estimating their contribution to the precipitation simulation. Sensitivity experiments revealed that changes in the sedimentation and sublimation parameters do not significantly impact precipitation rate. However, dissipation of the LMDz model, which is a numerical process that dissipates spatially excessive energy and keeps the model stable, impacts precipitation indirectly but very strongly. A suitable adjustment of the dissipation reduces significantly precipitation over Antarctic peripheral area, thus providing a simulated profile in better agreement with the MRR observations.

The Response of the HydroGeoSphere Model to Alternative Spatial Precipitation Simulation Methods

This paper presents the simulation results obtained from a physically based surface-subsurface hydrological model in a 5730 km2 watershed and the runoff response of the physically based hydrological models for three methods used to generate the spatial precipitation distribution: Thiessen polygons (TP), Co-Kriging (CK) interpolation and simulated annealing (SA). The HydroGeoSphere model is employed to simulate the rainfall-runoff process in two watersheds. For a large precipitation event, the simulated patterns using SA appear to be more realistic than those using the TP and CK method. In a large-scale watershed, the results demonstrate that when HydroGeoSphere is forced by TP precipitation data, it fails to reproduce the timing, intensity, or peak streamflow values. On the other hand, when HydroGeoSphere is forced by CK and SA data, the results are consistent with the measured streamflows. In a medium-scale watershed, the HydroGeoSphere results show a similar response compared to the measured streamflow values when driven by all three methods used to estimate the precipitation, although the SA case is slightly better than the other cases. The analytical results could provide a valuable counterpart to existing climate-based drought indices by comparing multiple interpolation methods in simulating land surface runoff.

Comparative Assessment and Future Prediction Using CMIP6 and CMIP5 for Annual Precipitation and Extreme Precipitation Simulation

This study assesses the improvement of the latest Coupled Model Intercomparison Project Phase 6 (CMIP6) over Coupled Model Intercomparison Project Phase 5 (CMIP5) for precipitation simulation. Precipitation simulations under different future climate scenarios are also compared in this work. The results show that: 1) CMIP6 has no overall advantage over CMIP5 in simulating total precipitation (PRCPTOT) and maximum consecutive dry days (CDD). The performance of CMIP6 increases or decreases regionally in PRCPTOT and consecutive dry days. But it is slightly worse than CMIP5 in simulating very wet days (R95pTOT). 2) Comparing the trend test results of CMIP5 and CMIP6 in the future, there are more areas with significant trend based on Mann–Kendall test in CMIP6 compared with that of CMIP5. The differences in PRCPTOT are mainly found in Amazon Basin and Western Africa. The differences between the R95pTOT trends mainly noticeable in South America and Western Africa, and the differences in CDD are mainly reflected in Central Asia, Sahara Desert and central South America. 3) In Southern South America and Western North America, the PRCPTOT changing rate of CMIP6 in the future under various scenarios is always greater than that of CMIP5; in Alaska, Western Africa, Southern Africa, the PRCPTOT changing rate of CMIP6 in the future under various scenarios is always less than that of CMIP5. In Southern South America, the R95pTOT changing rate of CMIP6 in the future under various scenarios is always greater than that of CMIP5; in Alaska, East Asia, North Asia, the R95pTOT changing rate of CMIP6 in the future under various scenarios is always less than that of CMIP5. In almost half of the regions, the CDD changing rate of CMIP6 is less than that of CMIP5 under all scenarios, namely Australia, Amazon Basin, Southern South America, Central America, Western North America, Central North America, Eastern North America, Central Asia, Tibet.