Forecast Of The Optical Turbulence With A Mesoscale Atmospherical Model

Atmospheric turbulence is the major reason for serious decline of imaging qual-ity of the ground based optical applications.The intensity of atmospheric turbulence is usually described by refractive index structure constant,C_n~2.However,systematic direct measurements of C_n~2 for many climates and seasons are not available,especially in severe environment,and it varies considerably from location to location.In many cases,it is impractical and expensive to deploy instrumentation to characterize the at-mospheric turbulence,making simulations a less expensive and convenient alternative.Over the years,an indirect approach has been developed in which C_n~2 can be calculated from the Numerical Weather Prediction(NWP)model outputs.This paper summarizes the representative progress in the study of the atmospheric optical turbulence model and the turbulence prediction model at home and abroad.It is used to estimate or forecast the C_n~2 profile in the typical region of China(sea surface,snow surface,land)C_n~2 time series by using the mesoscale numerical model(WRF).This paper focuses on the feasi-bility of forecasting the C_n~2 profile and the feasibility of predicting the evolution of the surface layer C_n~2,and carries out the following research work:1.The author gives a detailed description on the installation procedure of mesoscale weather prediction model WRF on personal computer.2.An efficient and reliable parameterization model of optical turbulence is one of the many difficulties in using the numerical weather prediction model to predict C_n~2.The Tatarski model is a well-known optical turbulence parameterization model,but its great-est difficulty lies in the uncertainty of the outer scale model(L0).We have compared the C_n~2 profile estimated by the high-resolution conventional meteorological data measured by sounding the balloon and the C_n~2 profile measured by the micro-thermometer by us-ing four kinds of outer scale parameterization models(Dewan mode,Coulman mode,Sterenborg mode and HMNSP99 model),and the results show that the difference of the trend and the magnitude is great when using different outer scale models to estimate C_n~2.Therefore,in different regions of the troposphere and stratosphere,the Dewan model has added the measured wind speed parameters,and the estimates of C_n~2 profiles are rel-atively reasonable but will often transition.From the trend and the magnitude of C_n~2 profile,the HMNSP99 model which has added the temperature gradient and the wind speed shear is more in line with the actual situation of the atmosphere,and the estimated C_n~2 is close to the measured C_n~2 greatly.3.Based on the WRF model application platform,according to the Tatarski model and HMNSP99 model,the profile of temperature,wind speed and C_n~2 are forecasted in three typical regions of china(Gaomei Observatory,Maoming Observatory and Kuerle),respectively.The profile of temperature,wind speed and C_n~2 measured by sounding bal-loon are used as a comparison.The statistically analyzed results show that the forecast profiles of temperature and wind speed are very close to the measured ones in three re-gions,and the correlation can reach 90%and 80%,respectively.The forecast C_n~2 profile in accordance with the feature of C_n~2 profile with height,and with a correlation of about 75%,but the fine feature of C_n~2 profile is slightly different from the measured ones.In the three typical regions,the types of climate are different,and the C_n~2 profiles are also different.Overall,the forecast C_n~2 profile at Gaomeigu is better than that at Maoming or Kuerle.4.In the foundation of WRF model forecast C_n~2 profiles,we try to use WRF model coupled with Monin-Obukhov similarity theory and the Bulk aerodynamics method to predict the conventional meteorological parameters(such as temperature,humidity,wind speed,wind speed,wind direction,etc.)and estimate the surface layer C_n~2 over the ocean near-surface,above Taishan Station in Antarctica and at Chengdu district.The corresponding in-situ measurements from the micro-thermometer are used to compare and evaluate the estimated values.We have analysed the statistical operators:bias,root mean square error(RMSE),bias-corrected RMSE(a)and correlation coefficient(Rxy)on a 20-night dataset to assess how this approach performs.In addition,we employ a widely-used rating method,the contingency tables,to investigate estimation quality of this approach,which provides complementary key information with respect to bias,RMSE,? and Rxy.The quantitative results are very encouraging and permit us to con-firm the excellent performances of this approach.The diurnal variation features of C_n~2 have obvious regional differences over the surface of land,sea surface or snow and sea ice.The C_n~2 forecasted by WRF modle over snow and sea ice is consistent with the measured one the best,followed by the sea,the last land.Major issues may remain with respect to the inversion of the forecasts into the land is more complex,influenced by the surroundings greatly,and the limited horizontal resolution makes the WRF model difficult to match with the measured ones.We have used the experiment results to contrast the C_n~2 forecasted by WRF model,and the quantitative results indicate that this approach in reconstructing the routine me-teorological parameters and C_n~2 in the surface layer is feasible and reliable,although the forecast method needs to be further refined.