The research of coupling WRF （Weather Research and Forecasting Model） with a land surface model is enhanced to explore the interaction of the atmosphere and land surface; however, regional applicability of WRF model is questioned. In order to do the validation of WRF model on simulating forcing data for the Heihe River Basin, daily meteorological observation data from 15 stations of CMA （China Meteorological Administration） and hourly meteorological observation data from seven sites of WATER （Watershed Airborne Telemetry Experimental Research） are used to compare with WRF simulations, with a time range of a whole year for 2008. Results show that the average MBE （Mean Bias Error） of daily 2-m surface temperature, surface pressure, 2-m relative humidity and 10-m wind speed were -0.19 ℃, -4.49 hPa, 4.08% and 0.92 m/s, the average RMSE （Root Mean Square Error） of them were 2.11 ℃, 5.37 hPa, 9.55% and 1.73 m/s, and the average R （correlation coefficient） of them were 0.99, 0.98, 0.80 and 0.55, respectively. The average MBE of hourly 2-m surface temperature, surface pressure, 2-m relative humidity, 10-m wind speed, downward shortwave radiation and downward longwave were-0.16 ℃,-6.62 hPa,-5.14%, 0.26 m/s, 33.0 W/m^2 and-6.44 W/m^2, the average RMSE of them were 2.62 ℃, 17.10 hPa, 20.71%, 2.46 m/s, 152.9 W/m^2 and 53.5 W/m^2, and the average R of them were 0.96, 0.97, 0.70, 0.26, 0.91 and 0.60, respectively. Thus, the following conclusions were obtained： （1） regardless of daily or hourly validation, WRF model simulations of 2-m surface temperature, surface pressure and relative humidity are more reliable, especially for 2-m surface air temperature and surface pressure, the values of MBE were small and R were more than 0.96; （2） the WRF simulating downward shortwave radiation was relatively good, the average R between WRF simulation and hourly observation data was above 0.9, and the average R of downward longwave radiation was 0.6; （3） both wind speed and rainfall simulated fr
An evolutionary strategy-based error parameterization method that searches for the most ideal error adjustment factors was developed to obtain better assimilation results. Numerical experiments were designed using some classical nonlinear models （i.e., the Lorenz-63 model and the Lorenz-96 model）. Crossover and mutation error adjustment factors of evolutionary strategy were investigated in four aspects： the initial conditions of the Lorenz model, ensemble sizes, observation covarianee, and the observation intervals. The search for error adjustment factors is usually performed using trial-and-error methods. To solve this difficult problem, a new data assimilation system coupled with genetic algorithms was developed. The method was tested in some simplified model frameworks, and the results are encouraging. The evolutionary strategy- based error handling methods performed robustly under both perfect and imperfect model scenarios in the Lorenz-96 model. However, the application of the methodology to more complex atmospheric or land surface models remains to be tested.
从框架、技术等方面介绍了水资源管理决策支持系统发展状态,总结了水资源管理决策支持系统的3个发展阶段:模型模拟阶段、模型模拟+DSS(DSS:Decision Support System)阶段、情景分析+集成建模环境+DSS工具阶段,阐述了这3个发展阶段各自的特点,剖析了制约水资源管理决策支持系统的发展因素,讨论了建立一个成功的水资源管理决策支持系统应具备的条件和采用的方式.最后,提出了集成综合观测系统、集成建模环境和联机协商环境的水资源管理决策支持系统框架.
Prof.Li Xin’s laboratory of Remote Sensing and Geospatial Science,Cold and Arid Regions Environmental and Engineering Research Institute,CAS,reported a water resources management decision support system applied in the Heihe River Basin,which was published in Environmental Software&.Modelling(2013,47:182—192).
Uncertainty is one of the greatest challenges in the quantitative understanding of land-surface systems.This paper discusses the sources of uncertainty in land-surface systems and the possible means to reduce and control this uncertainty.From the perspective of model simulation,the primary source of uncertainty is the high heterogeneity of parameters,state variables,and near-surface atmospheric states.From the perspective of observation,we first utilize the concept of representativeness error to unify the errors caused by scale representativeness.The representativeness error also originates mainly from spatial heterogeneity.With the aim of controlling and reducing uncertainties,here we demonstrate the significance of integrating modeling and observations as they are complementary and propose to treat complex land-surface systems with a stochastic perspective.In addition,through the description of two modern methods of data assimilation,we delineate how data assimilation characterizes and controls uncertainties by maximally integrating modeling and observational information,thereby enhancing the predictability and observability of the system.We suggest that the next-generation modeling should depict the statistical distribution of dynamic systems and that the observations should capture spatial heterogeneity and quantify the representativeness error of observations.
