A global sensitivity analysis method was used to identify the parameters of the Weather Research and Forecasting (WRF) model that exert the most influence on precipitation forecasting. Twenty-three adjustable parameters were selected from seven physical components of the WRF model. The sensitivity was evaluated based on skill scores calculated over nine 5 day precipitation forecasts during the summer seasons from 2008 to 2010 in the Greater Beijing Area in China. We found that eight parameters are more sensitive than others. Storm type seems to have no impact on the list of sensitive parameters but does influence the degree of sensitivity. We also examined the physical interpretation of parameter sensitivity. This analysis is useful for further optimization of the WRF model parameters to improve precipitation forecasting.

The specification of model parameters in numerical weather prediction (NWP) models has great influence on model performance. However, how to specify model parameters properly is not a trivial task because a typical NWP model like the Weather Research and Forecasting (WRF) model contains many model parameters and many model outputs. This article presents the results of an investigation into the sensitivities of different WRF model outputs to the specification of its model parameters. Using a global sensitivity analysis method, the sensitivities are evaluated for surface meteorological variables such as precipitation, surface air temperature, humidity and wind speed, as well as for atmospheric variables such as total precipitable water, cloud cover, boundary‐layer height and outgoing long‐wave radiation at the top of the atmosphere, all simulated by the WRF model using different model parameters. The goal of this study is to identify the parameters that exert most influence on the skill of short‐range meteorological forecasts. The study was performed over the Greater Beijing Region of China. A total of 23 adjustable parameters from seven different physical parametrization schemes were considered. The results indicate that parameter sensitivities vary with different model outputs. However, some of the 23 model parameters considered are shown to be sensitive to all model outputs evaluated, while other parameters may be sensitive to a particular output. The sensitivity results from this research are a basis for further optimizations of the WRF model parameters.

Weather forecasting skill has been improved over recent years owing to advances in the representation of physical processes by numerical weather prediction (NWP) models, observational systems, data assimilation and postprocessing, new computational capability, and effective communications and training. There is an area that has received less attention so far but can bring significant improvement to weather forecasting—the calibration of NWP models, a process in which model parameters are tuned using certain mathematical methods to minimize the difference between predictions and observations. Model calibration of the NWP models is difficult because 1) there are a formidable number of model parameters and meteorological variables to tune, and 2) a typical NWP model is very expensive to run, and conventional model calibration methods require many model runs (up to tens of thousands) or cannot handle the high dimensionality of NWP models. This study demonstrates that a newly developed automatic model calibration platform can overcome these difficulties and improve weather forecasting through parameter optimization. We illustrate how this is done with a case study involving 5-day weather forecasting during the summer monsoon in the greater Beijing region using the Weather Research and Forecasting Model. The keys to automatic model calibration are to use global sensitivity analysis to screen out the most important parameters influencing model performance and to employ surrogate models to reduce the need for a large number of model runs. Through several optimization and validation studies, we have shown that automatic model calibration can improve precipitation and temperature forecasting significantly according to a number of performance measures.

Surface water, which is changing constantly, is a crucial component in the global water cycle, as it greatly affects the water flux between the land and the atmosphere through evaporation. However, the influences of changing surface water area on the global water budget have largely been neglected. Here we estimate an extra water flux of 30.38 ± 15.51 km 3 /year omitted in global evaporation calculation caused by a net increase of global surface water area between periods 1984–1999 and 2000–2015. Our estimate is at a similar magnitude to the recent average annual change in global evapotranspiration assuming a stationary surface water area. It is also comparable to the estimated trends in various components of the hydrological cycle such as precipitation, discharge, groundwater depletion, and glacier melting. Our findings suggest that the omission of surface water area changes may cause considerable biases in global evaporation estimation, so an improved understanding of water area dynamics and its atmospheric coupling is crucial to reduce the uncertainty in the estimation of future global water budgets.