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.

Parameter specification is an important source of uncertainty in large, complex geophysical models. These models generally have multiple model outputs that require multiobjective optimization algorithms. Although such algorithms have long been available, they usually require a large number of model runs and are therefore computationally expensive for large, complex dynamic models. In this paper, a multiobjective adaptive surrogate modeling-based optimization (MO-ASMO) algorithm is introduced that aims to reduce computational cost while maintaining optimization effectiveness. Geophysical dynamic models usually have a prior parameterization scheme derived from the physical processes involved, and our goal is to improve all of the objectives by parameter calibration. In this study, we developed a method for directing the search processes toward the region that can improve all of the objectives simultaneously. We tested the MO-ASMO algorithm against NSGA-II and SUMO with 13 test functions and a land surface model -the Common Land Model (CoLM). The results demonstrated the effectiveness and efficiency of MO-ASMO.

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Seasonal predictions of precipitation and surface air temperature from the Climate Forecast System, version 2 (CFSv2), are evaluated against gridded daily observations from 1982 to 2007 over 17 hydroclimatic regions in China. The seasonal predictive skill is quantified with skill scores including correlation coefficient, RMSE, and mean bias for spatially averaged seasonal precipitation and temperature forecasts for each region. The evaluation focuses on identifying regions and seasons where significant skill exists, thus potentially contributing to skill in hydrological prediction. The authors find that the predictive skill of CFSv2 precipitation and temperature forecasts has a stronger dependence on seasons and regions than on lead times. Both temperature and precipitation forecasts show higher skill from late summer [July-September (JAS)] to late autumn [October-December (OND)] and from winter [December-February (DJF)] to spring [March-May (MAM)].The skill of CFSv2 precipitation forecasts is low during summer [June-August (JJA)] and winter (DJF) over all of China because of low potential predictability of the East Asian summer monsoon and the East Asian winter monsoon for China. As expected, temperature predictive skill is much higher than precipitation predictive skill in all regions. As observed precipitation shows significant correlation with the Oceanic Niño index over western, southwestern, and central China, the authors found that CFSv2 precipitation forecasts generally show similar correlation pattern, suggesting that CFSv2 precipitation forecasts can capture ENSO signals. This evaluation suggests that using CFSv2 forecasts for seasonal hydrological prediction over China is promising and challenging.

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