Search Results (8)

The WRF model temperature and precipitation forecast skill for the area of northwestern Greece is examined separately for each of the 10 objectively defined Weather Types (WTs). The WTs are defined for the 10-year period: 1 January 2009–31 December 2018. Their definition is achieved with the application of k-means Cluster Analysis on ERA5 meteorological data. The WRF model is applied in three domains (Europe—Greece—NW Greece) using the one-way nesting technique in a spatial resolution of 18, 6 and 2 km. Specifically, the model runs for 64 days (10% of the number of days attributed to the WT with the highest number of days) with the lowest distances from each WT’s cluster center. The WRF forecast data of 2 m air temperature and precipitation are compared with the available meteorological observations operated by the METEO unit at the National Observatory of Athens. The validation of 2 m air temperature is performed for 04UTC and 12UTC for the first and second days of forecast using the Cressman method, separately for each meteorological station and WT. The validation of precipitation is performed for daily accumulated values for the first and second days of forecast, using forecast data from the 3 × 3 = 9 surrounding grid points of each meteorological station and calculating categorical statistics based on contingency tables for each WT and for different thresholds. According to the results, there is a remarkable overestimation of 04UTC air temperature for the anticyclonic WTs, especially for the inland stations, while the precipitation forecast skill generally depends on the threshold and the WT characteristics. Full article

In this study, a triangular polynomial interpolation (TPI) scheme was developed to estimate the vertical eddy viscosity coefficient (VEVC) on the basis of the Ekman model with adjoint assimilation. In the twin experiments, the advantages and disadvantages of estimating the VEVC using the TPI scheme under different factors are discussed. The results indicated that (1) the TPI scheme proves to be better than the cubic spline interpolation (CSI) and Cressman interpolation (CI) schemes; (2) the inversion results are more sensitive to observations from upper ocean layers than those from lower layers, and the TPI scheme is less likely to be influenced by missing data; (3) for various boundary layer depths, the inversion results of the TPI scheme remain consistent with the given distributions; (4) the inversion results can be influenced considerably by observational errors, and the TPI scheme is more resistant to noise than the CSI and CI schemes; and (5) the inversion accuracy of the TPI scheme can be improved by selecting the temporal wind stress drag coefficients. In practical experiments, the adjoint method with the TPI scheme was developed to estimate the Ekman currents by assimilating the observations from a buoy stationed in the Yellow Sea. The results showed the successful estimation of the VEVC and demonstrated that more precise current velocities can be obtained with this estimation scheme. In summary, this study provides a useful approach for the effective estimation of the VEVC. Full article

In this study, the time-varying wind stress drag coefficient in the Ekman model was inverted by the cubic spline interpolation scheme based on the adjoint method. Twin experiments were carried out to investigate the influences of several factors on inversion results, and the conclusions were (1) the inverted distributions with the cubic spline interpolation scheme were in good agreement with the prescribed distributions of the wind stress drag coefficients, and the cubic spline interpolation scheme was superior to direct inversion by the model scheme and Cressman interpolation scheme; (2) the cubic spline interpolation scheme was more advantageous than the Cressman interpolation scheme even if there is moderate noise in the observations. The cubic spline interpolation scheme was further validated in practical experiments where Ekman currents and wind speed derived from mooring data of ocean station Papa were assimilated. The results demonstrated that the variation of the time-varying wind stress drag coefficient with time was similar to that of wind speed with time, and a more accurate inversion result could be obtained by the cubic spline interpolation scheme employing appropriate independent points. Overall, this study provides a potential way for efficient estimation of time-varying wind stress drag coefficient. Full article

The Ensemble Intermediate Coupled Model (EICM) is a model used for studying the El Niño-Southern Oscillation (ENSO) phenomenon in the Pacific Ocean, which is anomalies in the Sea Surface Temperature (SST) are observed. This research aims to implement Cressman to improve SST forecasts. The simulation considers two cases in this work: the control case and the Cressman initialized case. These cases are simulations using different inputs where the two inputs differ in terms of their resolution and data source. The Cressman method is used to initialize the model with an analysis product based on satellite data and in situ data such as ships, buoys, and Argo floats, with a resolution of 0.25 × 0.25 degrees. The results of this inclusion are the Cressman Initialized Ensemble Intermediate Coupled Model (CIEICM). Forecasting of the sea surface temperature anomalies was conducted using both the EICM and the CIEICM. The results show that the calculation of SST field from the CIEICM was more accurate than that from the EICM. The forecast using the CIEICM initialization with the higher-resolution satellite-based analysis at a 6-month lead time improved the root mean square deviation to 0.794 from 0.808 and the correlation coefficient to 0.630 from 0.611, compared the control model that was directly initialized with the low-resolution in-situ-based analysis. Full article

Observations from spaceborne microwave imagers are important sources of land surface information. However, the low-frequency channels of microwave imagers are easily interfered with by active radio signals with similar frequencies. Radio frequency interference (RFI) signals are widely distributed because of the lack of frequency protection, which seriously hinders the application of microwave imager data in data assimilation and retrieval research. In this paper, a new data restoration method is proposed based on principal component analysis (PCA). Both the ideal and real reconstruction experiments show that the new method can effectively repair abnormal observations interfered by RFI compared with the commonly used Cressman interpolation method because observation information over the whole selected domain is used for restoration in the new method, whereas Cressman interpolation uses only a selection of data around the target observation. The observation errors in the data with RFI can be reduced by one order of magnitude by means of the new method and little artificial information is introduced. One-week restoration validation also proves that the new method has a stable accuracy and broad application prospects. Full article

The available observations for the model are usually sparse and uneven. The application of interpolation methods help researchers obtain an approximate form of the original data. A marine nutrient, phytoplankton, zooplankton and detritus (NPZD) type ecosystem model is applied to simulate the distribution of phytoplankton combined with the spline interpolation (SI) and the Cressman interpolation (CI). In the idealized twin experiments, the performance of these two interpolation methods is validated through the analysis of several quantitative metrics, which show the minor error and high efficiency when using the SI. Namely, the given distributions can be better inverted with the SI. The actual distribution of phytoplankton in the Bohai Sea is interpolated in the practical experiment, where a satisfactory simulation result is obtained by the model with the SI. The model experiments and results verify the feasibility and effectiveness of SI. Full article

Understanding the details of micro-scale wind fields is important in the development of wind energy. Research has proven that coupling Numerical Weather Prediction (NWP) and Computational Fluid Dynamics (CFD) models is a better approach for micro-scale wind field simulation. The main purpose of this work is to improve the NWP/CFD model performance in two parts: (i) developing a new coupling method that is more suitable for complex terrain between the NWP and CFD models, and (ii) applying a data assimilation system in the CFD model. Regarding part (i), in order to solve the problem of great topographical difference at the domain boundaries between the two models, Cressman interpolation is utilized to impose the NWP model wind on the CFD model boundaries. In part (ii), an assimilation method, nudging, to apply assimilation of observations into the CFD model is explored. Based on the Cressman interpolation coupling method, a preliminary implementation of data assimilation is performed. The results show that the NWP/CFD model with the improved coupling method may capture the details of micro-scale wind fields more accurately. Using data assimilation, the NWP/CFD model performance may be further improved by cooperating observation data. Full article

Sufficient and accurate air pollutant data are essential to analyze and control air contamination problems. An orthogonal polynomial fitting (OPF) method using Chebyshev basis functions is introduced to produce spatial distributions of fine particle (PM_2.5) concentrations in central and southern regions of China. Idealized twin experiments (IE1 and IE2) are designed to validate the feasibility of the OPF method. IE1 is designed in accordance with the most common distribution of PM_2.5 concentrations in China, whereas IE2 represents a common distribution in spring and autumn. In both idealized experiments, prescribed distributions are successfully estimated by the OPF method with smaller errors than kriging or Cressman interpolations. In practical experiments, cross-validation is employed to assess the interpolation results. Distributions of PM_2.5 concentrations are well improved when OPF is applied. This suggests that errors decrease when the fitting order increases and arrives at the minimum when both orders reach 6. Results calculated by the OPF method are more accurate than kriging and Cressman interpolations if appropriate fitting orders are selected in practical experiments. Full article