Convection-permitting climate simulations using SINGV-RCM and WRF over the Maritime Continent - Venkatraman Prasanna; Srivatsan V. Raghavan; Anurag Dipankar; Jianyu Liu; Gerald Lim; Aurel Moise; Nguyen Ngoc Son; Bhenjamin Jordan Lozada Ona; Pavan Harika Raavi; Anupam Kumar; Jianjun Yu; Chen Chen; et al. (Climate Dynamics, 2026)

SINGV-RCM: the convection-permitting regional climate model for Singapore - Venkatraman Prasanna; Anurag Dipankar; Jianyu Liu; Gerald Lim; Aurel Moise; Xin Rong Chua; Claudio Sanchez; Chen Chen; Jianjun Yu; Harika Pavan Raavi; Fei Luo; Muhammad Eeqmal Hassim; et al. (Climate Dynamics, 2024)
The development of the Singapore regional climate model (SINGV-RCM) is documented in this study. The model’s skill in predicting mean and extreme rainfall over the Maritime Continent at 8 km and 2 km grid resolution is assessed using observed rainfall. The model’s sensitivity to vertical grid and convection parametrization is also evaluated. Results demonstrate the added value of downscaling to 8 and 2 km. The spatial and temporal characteristic of the diurnal rainfall is shown to be in good agreement with the observation. The model also demonstrates skill in capturing extreme rainfall. In general, the rainfall in the 8 km and 2 km simulations are quite similar, when regrided to a coarser resolution. Even then, the convection-permitting simulation at 2 km is found to add value over the 8 km simulation, particularly in capturing the higher rainfall thresholds. Mean biases over the ocean is found to be larger than that over the land, which suggests the need to further tune SINGV-RCM for long climate simulations.

Extended seasonal prediction of spring precipitation over the Upper Colorado River Basin - Siyu Zhao; Rong Fu; Michael L. Anderson; Sudip Chakraborty; Jonathan H. Jiang; Hui Su; Yu Gu (Climate Dynamics, 2023)
This study provides extended seasonal predictions for the Upper Colorado River Basin (UCRB) precipitation in boreal spring using an artificial neural network (ANN) model and a stepwise linear regression model, respectively. Sea surface temperature (SST) predictors are developed taking advantage of the correlation between the precipitation and SST over three ocean basins. The extratropical North Pacific has a higher correlation with the UCRB spring precipitation than the tropical Pacific and North Atlantic. For the ANN model, the Pearson correlation coefficient between the observed and predicted precipitation exceeds 0.45 ( p -value < 0.01) for a lead time of 12 months. The mean absolute percentage error (MAPE) is below 20% and the Heidke skill score (HSS) is above 50%. Such long-lead prediction skill is probably due to the UCRB soil moisture bridging the SST and precipitation. The stepwise linear regression model shows similar prediction skills to those of ANN. Both models show prediction skills superior to those of an autoregression model (correlation < 0.10) that represents the baseline prediction skill and those of three of the North American Multi-Model Ensemble (NMME) forecast models. The three NMME models exhibit different skills in predicting the precipitation, with the best skills of the correlation ~ 0.40, MAPE < 25%, and HSS > 40% for lead times less than 8 months. This study highlights the advantage of oceanic climate signals in extended seasonal predictions for the UCRB spring precipitation and supports the improvement of the UCRB streamflow prediction and related water resource decisions.

Impact of atmospheric model resolution on simulation of ENSO feedback processes: a coupled model study - Lijuan Hua; Lin Chen; Xinyao Rong; Jingzhi Su; Lu Wang; Tim Li; Yongqiang Yu (Climate Dynamics, 2018)

Short-term variability in the dates of the Indian monsoon onset and retreat on the southern and northern slopes of the central Himalayas as determined by precipitation stable isotopes - Wusheng Yu; Tandong Yao; Lide Tian; Yaoming Ma; Rong Wen; Lochan P. Devkota; Weicai Wang; Dongmei Qu; Tek B. Chhetri (Climate Dynamics, 2016)