A Statistically-Based Low-Level Cloud Scheme and Its Tentative Application in a General Circulation Model

In this study, a statistical cloud scheme is first introduced and coupledwith a first-order turbulence scheme with second-order turbulence moments parameterized by thetimescale of the turbulence dissipation and the vertical turbulent diffusion coefficient. Then theability of the scheme to simulate cloud fraction at different relative humidity, verticaltemperature profile, and the timescale of the turbulent dissipation is examined by numericalsimulation. It is found that the simulated cloud fraction is sensitive to the parameter used in thestatistical cloud scheme and the timescale of the turbulent dissipation. Based on the analyses, theintroduced statistical cloud scheme is modified. By combining the modified statistical cloud schemewith a boundary layer cumulus scheme, a new statistically-based low-level cloud scheme is proposedand tentatively applied in NCAR (National Center for Atmospheric Research) CCM3 (Community ClimateModel version 3). It is found that the simulation of low-level cloud fraction is markedly improvedand the centers with maximum low-level cloud fractions are well simulated in the cold oceans off thewestern coasts with the statistically-based low-level cloud scheme applied in CCM3. It suggeststhat the new statistically-based low-level cloud scheme has a great potential in the generalcirculation model for improving the low-level cloud parameterization.

TOPMODEL Hydrometeorological Modeling with Rain Gauge Data Integrated by High-Resolution Satellite Estimates. A Case Study in MuriaéRiver Basin, Brazil

This study consists of hydrological simulations of the Muriaé river watershed with the topography-based hydrological model (TOPMODEL) and available stream gauge and rain measurements between 2009 and 2013 for two subbasins, namely Carangola and Patrocínio do Muriaé. The simulations were carried out with the Climate Prediction Center morphing method (CMORPH) precipitation estimates and rain gauge measurements integrated into CM- ORPH by the Statistical Objective Analysis Scheme (SOAS). TOPMODEL calibration was performed with the shuffled complex evolution (SCE-UA) method with Nash-Sutcliffe efficiency (NSE). The best overall results were obtained with CMORPH (NSE ~ 0.6) for both subbasins. The simulations with SOAS resulted in an NSE ~ 0.2. However, in an analysis of days with high- level stages, SOAS simulations resulted in a better hit rate (23%) compared to CMORPH (10%). CMORPH simulations underestimated the flows at the flood periods, which indicates the importance to use multi-sensor precipitation data. The results with TOPMODEL allow an estimate of future discharges, which allows for better planning of a flood warning system and discharge measurement schedule.

OPERATIONAL FORECAST OF RAINFALL INDUCED BY LANDFALLING TROPICAL CYCLONES ALONG GUANGDONG COAST

Following previous studies of the rainfall forecast in Shenzhen owing to landfalling tropical cyclones(TCs),a nonparametric statistical scheme based on the classification of the landfalling TCs is applied to analyze and forecast the rainfall induced by landfalling TCs in the coastal area of Guangdong province,China.All the TCs landfalling with the distance less than 700 kilometers to the 8 coastal stations in Guangdong province during 1950—2013 are categorized according to their landfalling position and intensity.The daily rainfall records of all the 8 meteorological stations are obtained and analyzed.The maximum daily rainfall and the maximum 3 days’accumulated rainfall at the 8 coastal stations induced by each category of TCs during the TC landfall period(a couple of days before and after TC landfalling time)from 1950 to 2013 are computed by the percentile estimation and illustrated by boxplots.These boxplots can be used to estimate the rainfall induced by landfalling TC of the same category in the future.The statistical boxplot scheme is further coupled with the model outputs from the European Centre for Medium-Range Weather Forecasts(ECMWF)to predict the rainfall induced by landfalling TCs along the coastal area.The TCs landfalling in south China from 2014 to 2017 and the corresponding rainfall at the 8 stations area are used to evaluate the performance of these boxplots and coupled boxplots schemes.Results show that the statistical boxplots scheme and coupled boxplots scheme can perform better than ECMWF model in the operational rainfall forecast along the coastal area in south China.

Comparisons of GCM cloud cover parameterizations with cloud-resolving model explicit simulations

Three kinds of the widely-used cloudiness parameterizations are compared with data produced from the cloud-resolving model(CRM) simulations of the tropical cloud system. The investigated schemes include those based on relative humidity(RH), the semi-empirical scheme using cloud condensate as a predictor, and the statistical scheme based on probability distribution functions(PDFs). Results show that all three schemes are successful in reproducing the timing of cloud generation, except for the RH-based scheme, in which low-level clouds are artificially simulated during cloudless days. In contrast, the low-level clouds are well simulated in the semi-empirical and PDF-based statistical schemes, both of which are close to the CRM explicit simulations. In addition to the Gaussian PDF, two alternative PDFs are also explored to investigate the impact of different PDFs on cloud parameterizations. All the PDF-based parameterizations are found to be inaccurate for high cloud simulations, in either the magnitude or the structure. The primary reason is that the investigated PDFs are symmetrically assumed, yet the skewness factors in deep convective cloud regimes are highly significant, indicating the symmetrical assumption is not well satisfied in those regimes. Results imply the need to seek a skewed PDF in statistical schemes so that it can yield better performance in high cloud simulations.