Mesoscale meteorological modelling for Hong Kong-application of the MC2 model

This paper describes the set up and application of a non hydrostatic Canadian meteorological numerical model (MC2) for mesoscale simulations of wind field and other meteorological parameters over the complex terrain of Hong Kong. Results of the simulations of one case are presented and compared with the results of radiosonde and aircraft measurements. The model is proved capable of predicting high resolution, three dimensional fields of wind and other meteorological parameters within the Hong Kong territory, using reasonable computer time and memory resources.

The Meteorological Prediction Model of Lemon Production in Anyue County Based on Correlation

Using the meteorological data during 1971- 2013 and lemon growth and yield data during 2003- 2013 in Anyue,the suitability problem of lemon growth and correlation problem between meteorological factors and lemon growth in Anyue area were studied. According to relevance between the selected meteorological factors and yield of lemon,meteorological prediction model of lemon yield was established in Anyue,and the prediction accuracy was higher. The research had certain guiding significance for management work of lemon production in Anyue area.

Dead Sea Starvation: Towards Enhanced Monitoring of Water Resources by Modeling Meteorological Variables and Remote Sensing Data

Meteorological metrics have been used for weather forecasting and climate prediction. Remote sensing images proved to be a valuable resource to represent the terrain of earth’s surface. Recently, there has been extensive research to model changes on the earth’s landscape including water bodies using remote sensing images. Meanwhile, meteorological data have been used mainly to model climate changes. This research tries to leverage both resources to achieve enhanced monitoring of the Dead Sea shrinkage: first, an attempt to model the relation between several meteorological variables and Dead Sea shrinkage using machine learning;second, formulating Dead Sea shrinkage in terms of water level and surface area using data extraction from remote sensing images;finally, confronting the two models to derive a novel approach for predicting Dead Sea shrinkage based on spatiotemporal images and meteorological measures. The main machine learning algorithms for modeling the water shrinkage in this empirical research are Decision Table, Linear Regression, and Multi Layer Perceptron Neural Networks. The Mean Absolute Error measure of the best model is 1.743 and 0.015. It is challenging to model the relation between meteorological variables and the water level. However, the obtained results are promising to formulate a model of the water level decline rate, which in its turn will be an essential tool for estimating the consumption limits and inflow needs to save the Dead Sea.

Numerical Modeling of Air Pollutants Emitted by Waterway Transportation

The world maritime transportation is suffering a large increase in recent years and as a result of this increased on global trade, there is a consequent increase in waterway transportation and demand for fossil fuels, resulting in emissions of air pollutants. Consequently, the impact of transport emissions on climate change was put on the list of priorities. It has a high fuel demand as a result of continuous use of main engines for propulsion, electricity and heat production. The highest exposure levels of air pollution are found in ports and near them because most of the world fleet is positioned in these areas. The port of Rio de Janeiro city, in the Southeast Brazilian coastal, is inserted in the Guanabara Bay (GB), where the breezes recirculate pollutants in Metropolitan Region of Rio de Janeiro (MRRJ). Therefore, the aim of this research was to use the Brazilian Regional Atmospheric Modeling System (BRAMS) to generate the wind fields in the MRRJ and to calculate the trajectories of pollutants emitted on GB related to the waterway transportation, using a 3D kinematic trajectories model. Results demonstrated that for the periods analysed, the Central and west areas in the coastal region of the Rio de Janeiro city were the local most affected in the summer. In winter the trajectories reached the cities of the Rio de Janeiro and Duque de Caxias. Both in summer and winter, the trajectories followed towards the South Atlantic Ocean in the morning. Conclusions about this study show the need of decision-making process for better management of waterway transportation sector, improving the harmful effects on air quality in cities located in coastal regions.

Comparative Skill of Numerical Weather Forecasts in Eastern Amazonia

The present study evaluates the performance of three numerical weather forecasting models: Global Forecast System (GFS), Brazilian Regional Atmospheric Modelling System (BRAMS) and ETA Regional Model (ETA), by means of the Mean Error (ME) and the Root Mean Square Error (RMSE), during the most rainy four months period (January to April 2012) on Eastern Amazonia. The models displayed errors of superestimation and underestimation with respect to the observed precipitation, mainly over center-north of Pará and all of Amapá, where the precipitation is higher. Among the analyzed models, GFS shows the best performance, except during January and March, when the model to underestimated precipitation, possibly due to the anomalously high values recorded.

Establishment and analysis of global gridded Tm-Ts relationship model

In ground-based GPS meteorology, Tm is a key parameter to calculate the conversion factor that can convert the zenith wet delay（ZWD） to precipitable water vapor（PWV）. It is generally acknowledged that Tm is in an approximate linear relationship with surface temperature Ts, and the relationship presents regional variation. This paper employed sliding average method to calculate correlation coefficients and linear regression coefficients between Tm and Ts at every 2°× 2.5° grid point using Ts data from European Centre for Medium-Range Weather Forecasts（ECMWF） and Tm data from ＂GGOS Atmosphere＂, yielding the grid and bilinear interpolation-based Tm Grid model. Tested by Tm and Ts grid data, Constellation Observation System of Meteorology, Ionosphere, and Climate（COSMIC） data and radiosonde data, the Tm Grid model shows a higher accuracy relative to the Bevis Tm-Ts relationship which is widely used nowadays. The Tm Grid model will be of certain practical value in high-precision PWV calculation.

Defining a Standard Methodology to Obtain Optimum WRF Configuration for Operational Forecast: Application over the Port of Huelva (Southern Spain)

In this contribution, we calibrate the meteorological model weather and research forecasting (WRF) for operational forecasting in the Port of Huelva managed by the Authority Port of Huelva. Meteorological forecasting will allow reducing the impact of the meteorological phenomena over weather sensitive activities in the region. Concretely, the meteorological modeling developed will be used to analyze meteorological hazard impacts and to improve the management of the local air quality. To achieve these goals, numerous sensitive analyses corresponding to different model options have been developed. These analyses consider different physical and dynamical options, the coupling of very high resolution physiographic database (topography and land uses), and data assimilation. Comparing experiments, results with observational measures provide us by the Spanish National Meteorology Agency (AEMET). During a representative period, the optimum WRF configuration for the region is obtained. Calibration has been focused on wind due to this is the main risk factor in the region. When the model is satisfactorily calibrated, WRF is evaluated using whole modeling years 2012 and 2013, working with very high horizontal resolution, up to 0.333 km of horizontal grid resolution. Results obtained from the evaluation indicate that the numerical weather prediction system developed has a confidence level of 70% for the temperature, 81% and 66% for the wind speed and wind direction respectively, and 90% for the relative humidity. Methodology designed defines the quality control assurance of high-accuracy forecasting services of Meteosim S.L.

Prediction of the Diffuse Solar Energy on Horizontal at Different Selected Locations

The main objective of this paper is to predict the diffuse solar energy on a horizontal surface by using data of global solar energy (H) and diffuse solar energy (Hd) at different selected geographical locations in Saudi Arabia during the period time from 1980 to 2019. The low values of the root mean square error RMSE for all correlations indicated a good agreement between the measured and calculated values of Hd. The negative values of mean percentage error MPE % for all models show that for all locations, the proposed correlations slightly overestimate Hd, and the absolute values of MPE never reach 1.35%. The first, second and third order correlations between the diffuse solar fraction Hd/H and the clearness index Kt and between the diffuse transmittance Hd/H0 and the sunshine hours have been proposed for the selected locations using the method of regression analysis. The differences between the measured and calculated values of Hd show that a first order correlation between Hd/H and Kt can be used for estimating Hd at the present locations with good accuracy. However, second order correlations between Hd/H or Hd/H0 and S/So are recommended for estimating Hd at these locations. The average annual differences between measured and calculated values of diffuse solar energy Hd on horizontal at selected sites in the present research are discussed.

Effect of 2-m Temperature Data Assimilation in the CMA-MESO 3DVAR System

Assimilation of surface observations including 2-m temperature(T_(2m))in numerical weather prediction(NWP)models remains a challenging problem owing to differences between the elevation of model terrain and that of actual observation stations.NWP results can be improved only if surface observations are assimilated appropriately.In this study,a T_(2m)data assimilation scheme that carefully considers misrepresentation of model and station terrain was established by using the three-dimensional variational data assimilation(3DVAR)system of the China Meteorological Administration mesoscale model(CMA-MESO).The corresponding forward observation operator,tangent linear operator,and adjoint operator for the T_(2m)observations under three terrain mismatch treatments were developed.The T_(2m)data were assimilated in the same method as that adopted for temperature sounding data with additional representative errors,when station terrain was 100 m higher than model terrain;otherwise,the T_(2m)data were assimilated by using the surface similarity theory assimilation operator.Furthermore,if station terrain was lower than model terrain,additional representative errors were stipulated and corrected.Test of a rainfall case showed that the observation innovation and analysis residuals both exhibited Gaussian distribution and that the analysis increment was reasonable.Moreover,it was found that on completion of the data assimilation cycle,T_(2m)data assimilation obviously influenced the temperature,wind,and relative humidity fields throughout the troposphere,with the greatest impact evident in the lower layers,and that both the area and the intensity of rainfall were better forecasted,especially for the first 12hours.Long-term continuous experiments for 2–28 February and 5–20 July 2020,further verified that T_(2m)data assimilation reduced deviations not only in T_(2m)but also in 10-m wind forecasts.More importantly,the precipitation equitable threat scores were improved over the two experimental periods.In summary,this study confirmed that the T_(2m)data assimilation scheme that we implemented in the kilometer-scale CMA-MESO 3DVAR system is effective.

Chemical characteristics and source apportionment of PM_(10) during a brown haze episode in Harbin, China

This study investigates the correlation between PM10 and meteorological factors such as wind speed, atmospheric visibility, dew point, relative humidity, and ambient temperature during a brown haze episode. In order to identify the potential sources of PMlo during brown haze episode, respirable par- ticulate matter （PM10） was collected during both non-haze days and haze days and further analyzed for metallic elements, ionic species, and carbonaceous contents. Among them, ionic species contributed 45-64% to PM10, while metallic elements contributed 7-21% to PM10 which was smaller than the other chemical constituents. The average OC/EC ratio （42） in haze days was about three times of the average OC/EC ratio （14） in non-haze days. By using chemical mass balance （CMB） receptor model, the major sources were apportioned, including traffics, incinerators, coal combustion, steel industry, petrochemical industry, and secondary aerosols, etc. The contribution to PM10 concentration of each source was calcu- lated for all the samples collected. The results showed that coal combustion was the major source of PM10 in non-haze days and secondary aerosols were the major source in haze days, followed by petrochemical industry, incinerators, and traffics, while other sources had negligible effect.

An Assessment of CAMS-CSM in Simulating Land–Atmosphere Heat and Water Exchanges

The Chinese Academy of Meteorological Sciences(CAMS)has been devoted to developing a climate system model(CSM)to meet demand for climate simulation and prediction for the East Asian region.In this study,we evaluated the performance of CAMS-CSM in regard to sensible heat flux(H),latent heat flux(LE),surface temperature,soil moisture,and snow depth,focusing on the Atmospheric Model Intercomparison Project experiment,with the aim of participating in the Coupled Model Intercomparison Project phase 6.We systematically assessed the simulation results achieved by CAMS-CSM for these variables against various reference products and ground observations,including the FLUXNET model tree ensembles H and LE data,Climate Prediction Center soil moisture data,snow depth climatology data,and Chinese ground observations of snow depth and winter surface temperature.We compared these results with data from the ECMWF Interim reanalysis(ERA-Interim)and Global Land Data Assimilation System(GLDAS).Our results indicated that CAMS-CSM simulations were better than or comparable to ERA-Interim reanalysis for snow depth and winter surface temperature at regional scales,but slightly worse when simulating total column soil moisture.The root-mean-square differences of H in CAMS-CSM were all greater than those from the ERA-Interim reanalysis,but less than or comparable to those from GLDAS.The spatial correlations for H in CAMS-CSM were the lowest in nearly all regions,except for North America.CAMS-CSM LE produced the lowest bias in Siberia,North America,and South America,but with the lowest spatial correlation coefficients.Therefore,there are still scopes for improving H and LE simulations in CAMS-CSM,particularly for LE.