1 The influences of three-dimensional radiative effects and horizontal heterogeneity effects on the retrieval of cloud optical thickness (COT) and effective diameter (De) for cirrus clouds are explored by the SHDOM radiative transfer model. The stochastic cirrus clouds are generated by the Cloudgen model based on the Atmospheric Radiation Measurement program data. Incorporating a new ice cloud spectral model, we evaluate the retrieval errors for two solar zenith angles (SZAs) (30° and 60°), four solar azimuth angles (0°, 45°, 90°, and 180°), and two sensor settings (Moderate Resolution Imaging Spectrometer (MODIS) onboard Aqua and Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard METEOSAT-8). The domain-averaged relative error of COT (μ) ranges from-24.1 % to-1.0 % (SZA = 30°) and from-11.6 % to 3.3 % (SZA = 60°), with the uncertainty within 7.5 % ~ 12.5 % (SZA = 30°) and 20.0 % ~ 27.5 % (SZA = 60°). For the SZA of 60° only, the relative error and uncertainty are parameterized by the retrieved COT by linear functions, providing bases to correct the retrieved COT and estimate their uncertainties. Besides, De is overestimated by 0.7 μm ~ 15.0 μm on the domain average, with the corresponding uncertainty within 6.7 μm ~ 26.5 μm. The retrieval errors show no discernible dependence on solar azimuth angle due to the flat tops and full coverage of the cirrus samples. The results are valid only for the two samples and for the specific spatial resolution of the radiative transfer simulations.
To ensure successful hosting of the 2022 Olympic Winter Games, a comprehensive understanding of the wind field characteristics in the Chongli Mountain region is essential. The purpose of this research was to accurately simulate the microscale wind in the Chongli Mountain region. Coupling the Weather Research and Forecasting (WRF) model with a computational fluid dynamics (CFD) model is a method for simulating the microscale wind field over complex terrain. The performance of the WRF-CFD model in the Chongli Mountain region was enhanced from two aspects. First, as WRF offers multiple physical schemes, a sensitivity analysis was performed to evaluate which scheme provided the best boundary condition for CFD. Second, to solve the problem of terrain differences between the WRF and CFD models, an improved method capable of coupling these two models is proposed. The results show that these improvements can enhance the performance of the WRF-CFD model and produce a more accurate microscale simulation of the wind field in the Chongli Mountain region.Atmosphere 2019, 10, 731 2 of 21 models [9][10][11]. Therefore, great terrain differences exist between reality and the mesoscale models, and nearly no mesoscale model can work over extremely steep terrain. Some scholars applied WRF in the large eddy simulation (LES) mode to increase model resolution [12][13][14], but the low vertical resolution and the need for a smoothing process for terrain data still exist. Moreover, the computational cost of a WRF-LES simulation is relative high. Fortunately, the computational fluid dynamics (CFD) model can partially compensate for the shortcomings of the mesoscale model. First, CFD can simulate the wind field with higher spatial resolutions (a few meters to tens of meters) than those of the mesoscale models [15][16][17][18]. Moreover, most CFD models are based on the finite volume method, which can improve their ability to depict realistic terrain [19]. However, CFD has its shortcoming in coping with the boundary conditions, and usually uses a simple wind profile as the boundary condition in some research. The mesoscale model can be initialized using global-scale data, such as the National Centers for Environmental Prediction (NCEP) reanalysis data. Therefore, coupling the mesoscale models with the CFD models is one way of simulating the microscale wind field over complex terrain. In this coupled system, the mesoscale and CFD models are combined in an off-line way, and the boundary condition that drives the CFD simulation is taken from the outputs of the mesoscale model [20]. First, the wind field with low spatial resolution is simulated by the WRF model. Second, the WRF wind data are imposed on the boundary of the CFD model. Finally, a wind field with higher spatial resolution is simulated by the CFD model. The advantages of this system is that the mesoscale model can provide more realistic boundary conditions and CFD can provide a wind field simulation with much higher spatial resolution.In recent years, a large amount of research on ...
Cloud base height (CBH) is an important cloud macro parameter that plays a key role in global radiation balance and aviation flight. Building on a previous algorithm, CBH is estimated by combining measurements from CloudSat/CALIPSO and MODIS based on the International Satellite Cloud Climatology Project (ISCCP) cloud-type classification and a weighted distance algorithm. Additional constraints on cloud water path (CWP) and cloud top height (CTH) are introduced. The combined algorithm takes advantage of active and passive remote sensing to effectively estimate CBH in a wide-swath imagery where the cloud vertical structure details are known only along the curtain slice of the nonscanning active sensors. Comparisons between the estimated and observed CBHs show high correlation. The coefficient of association ( 2 ) is 0.8602 with separation distance between donor and recipient points in the range of 0 to 100 km and falls off to 0.5856 when the separation distance increases to the range of 401 to 600 km. Also, differences are mainly within 1 km when separation distance ranges from 0 km to 600 km. The CBH estimation method was applied to the 3D cloud structure of Tropical Cyclone Bill, and the method is further assessed by comparing CTH estimated by the algorithm with the MODIS CTH product.
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