Evaluation of the turbulence parametrization in the MOLOCH meteorological model

2019 ◽  
Vol 146 (726) ◽  
pp. 124-140 ◽  
Author(s):  
Silvia Trini Castelli ◽  
Andrea Bisignano ◽  
Antonio Donateo ◽  
Tony C. Landi ◽  
Paolo Martano ◽  
...  
Keyword(s):  
Meteorological Model ◽  
Turbulence Parametrization

Integrated Analysis of Low-Level Turbulence around Airport Using Meteorological Model and LES

2013 ◽  
Vol 61 (6) ◽  
pp. 159-166
Author(s):  
Ryota KIKUCHI ◽  
Takashi MISAKA ◽  
Shigeru OBAYASHI ◽  
Tomoo USHIO ◽  
Shigeharu SHIMAMURA ◽  
...  
Keyword(s):  
Integrated Analysis ◽  
Meteorological Model ◽  
Low Level

A Simple, Efficient Solution of Flux–Profile Relationships in the Atmospheric Surface Layer

2006 ◽  
Vol 45 (2) ◽  
pp. 341-347 ◽  
Author(s):  
Jonathan E. Pleim
Keyword(s):  
Surface Layer ◽  
Numerical Models ◽  
Iterative Solution ◽  
State Variables ◽  
Simple Scheme ◽  
Meteorological Model ◽  
Stable Regime ◽  
Stability Functions ◽  
Stable Conditions ◽  
Aerodynamic Roughness

Abstract This note describes a simple scheme for analytical estimation of the surface-layer similarity functions from state variables. What distinguishes this note from the many previous papers on this topic is that this method is specifically targeted for numerical models in which simplicity and economic execution are critical. In addition, it has been in use in a mesoscale meteorological model for several years. For stable conditions, a very simple scheme is presented that compares well to the iterative solution. The stable scheme includes a very stable regime in which the slope of the stability functions is reduced to permit significant fluxes to occur, which is particularly important for numerical models in which decoupling from the surface can be an important problem. For unstable conditions, simple schemes generalized for varying ratios of aerodynamic roughness to thermal roughness (z0/z0h) are less satisfactory. Therefore, a simple scheme has been empirically derived for a fixed z0/z0h ratio, which represents quasi-laminar sublayer resistance.

Simulation of karst floods with a hydrological model improved by meteorological model coupling

2021 ◽  
Keyword(s):  
Flood Control ◽  
Underground Structure ◽  
Wrf Model ◽  
Precipitation Forecast ◽  
Karst Area ◽  
Peak Time ◽  
Flood Prediction ◽  
Meteorological Model ◽  
Flood Peak ◽  
Flood Warning

Abstract Karst basins are prone to rapid flooding because of their geomorphic complexity and exposed karst landforms with low infiltration rates. Accordingly, simulating and forecasting floods in karst regions can provide important technical support for local flood control. The study area, the Liujiang karst river basin, is the most well-developed karst area in South China, and its many mountainous areas lack rainfall gauges, limiting the availability of precipitation information. Quantitative precipitation forecast (QPF) from the Weather Research and Forecasting model (WRF) and quantitative precipitation estimation (QPE) from remote sensing information by an artificial neural network cloud classification system (PERSIANN-CCS) can offer reliable precipitation estimates. Here, the distributed Karst-Liuxihe (KL) model was successfully developed from the terrestrial Liuxihe model, as reflected in improvements to its underground structure and confluence algorithm. Compared with other karst distributed models, the KL model has a relatively simple structure and small modeling data requirements, which are advantageous for flood prediction in karst areas lacking hydrogeological data. Our flood process simulation results suggested that the KL model agrees well with observations and outperforms the Liuxihe model. The average Nash coefficient, correlation coefficient, and water balance coefficient increased by 0.24, 0.19, and 0.20, respectively, and the average flood process error, flood peak error, and peak time error decreased by 13%, 11%, and 2 hours, respectively. Coupling the WRF model and PERSIANN-CCS with the KL model yielded a good performance in karst flood simulation and prediction. Notably, coupling the WRF and KL models effectively predicted the karst flood processes and provided flood prediction results with a lead time of 96 hours, which is important for flood warning and control.

scholarly journals An advanced scheme for wet scavenging and liquid-phase chemistry in a regional online-coupled chemistry transport model

2013 ◽  
Vol 13 (3) ◽  
pp. 1177-1192 ◽  
Author(s):  
C. Knote ◽  
D. Brunner
Keyword(s):  
Aqueous Phase ◽  
Climate Model ◽  
Transport Model ◽  
Measurement Data ◽  
Coupled System ◽  
Cloud Microphysics ◽  
Microphysics Scheme ◽  
Meteorological Model ◽  
Wet Scavenging ◽  
Phase Chemistry

Abstract. Clouds are reaction chambers for atmospheric trace gases and aerosols, and the associated precipitation is a major sink for atmospheric constituents. The regional chemistry-climate model COSMO-ART has been lacking a description of wet scavenging of gases and aqueous-phase chemistry. In this work we present a coupling of COSMO-ART with a wet scavenging and aqueous-phase chemistry scheme. The coupling is made consistent with the cloud microphysics scheme of the underlying meteorological model COSMO. While the choice of the aqueous-chemistry mechanism is flexible, the effects of a simple sulfur oxidation scheme are shown in the application of the coupled system in this work. We give details explaining the coupling and extensions made, then present results from idealized flow-over-hill experiments in a 2-D model setup and finally results from a full 3-D simulation. Comparison against measurement data shows that the scheme efficiently reduces SO2 trace gas concentrations by 0.3 ppbv (−30%) on average, while leaving O3 and NOx unchanged. PM10 aerosol mass was increased by 10% on average. While total PM2.5 changes only little, chemical composition is improved notably. Overestimations of nitrate aerosols are reduced by typically 0.5–1 μg m−3 (up to −2 μg m−3 in the Po Valley) while sulfate mass is increased by 1–1.5 μg m−3 on average (up to 2.5 μg m−3 in Eastern Europe). The effect of cloud processing of aerosols on its size distribution, i.e. a shift towards larger diameters, is observed. Compared against wet deposition measurements the system tends to underestimate the total wet deposited mass for the simulated case study.

scholarly journals A new moisture tagging capability in the Weather Research and Forecasting Model: formulation, validation and application to the 2014 Great Lake-effect snowstorm

2017 ◽  
Author(s):  
Damián Insua-Costa ◽  
Gonzalo Miguez-Macho
Keyword(s):  
Great Lake ◽  
Snow Water Equivalent ◽  
Precipitable Water ◽  
Weather Research And Forecasting ◽  
Atmospheric Conditions ◽  
Meteorological Model ◽  
Moisture Sources ◽  
Lake Effect ◽  
Snow Water ◽  
Weather Research

Abstract. A new moisture-tagging tool, usually known as water vapor tracer (WVT) method or online Eulerian method, has been implemented into the Weather Research and Forecasting (WRF) regional meteorological model, enabling it for precise studies on atmospheric moisture sources and pathways. We present here the method and its formulation, along with details of the implementation into WRF. We perform an in-depth validation with monthly long simulations over North America at 20 km resolution, tagging all possible moisture sources: lateral boundaries, continental, maritime or lake surfaces and initial atmospheric conditions. We estimate errors as the moisture or precipitation amounts that cannot be traced back to any source. Validation results indicate that the method exhibits high precision, with errors considerably lower than 1 % during the entire simulation period, for both precipitation and total precipitable water. We apply the method to the Great Lake-effect snowstorm of November 2014, aiming at quantifying the contribution of lake evaporation to the large snow accumulations observed in the event. We perform simulations in a nested domain at 5 km resolution with the tagging technique, demonstrating that about 30–50 % of precipitation in the regions immediately downwind, originated from evaporated moisture in the Great Lakes. This contribution increases to between 50–60 % of the snow water equivalent in the most severely affected areas, which suggests that evaporative fluxes from the lakes have a fundamental role in producing the most extreme accumulations in these episodes, resulting in the highest socio-economic impacts.

scholarly journals Weather forecast in north-western Greece: RISKMED warnings and verification of MM5 model

2010 ◽  
Vol 10 (2) ◽  
pp. 383-394 ◽  
Author(s):  
A. Bartzokas ◽  
V. Kotroni ◽  
K. Lagouvardos ◽  
C. J. Lolis ◽  
A. Gkikas ◽  
...  
Keyword(s):  
Air Temperature ◽  
Weather Forecasting ◽  
Precipitation Amount ◽  
Warning System ◽  
Weather Forecast ◽  
Early Morning ◽  
Meteorological Model ◽  
Adverse Weather ◽  
North Western ◽  
Western Greece

Abstract. The meteorological model MM5 is applied operationally for the area of north-western Greece for one-year period (1 June 2007–31 May 2008). The model output is used for daily weather forecasting over the area. An early warning system is developed, by dividing the study area in 16 sub-regions and defining specific thresholds for issuing alerts for adverse weather phenomena. The verification of the model is carried out by comparing the model results with observations from three automatic meteorological stations. For air temperature and wind speed, correlation coefficients and biases are calculated, revealing that there is a significant overestimation of the early morning air temperature. For precipitation amount, yes/no contingency tables are constructed for 4 specific thresholds and some categorical statistics are applied, showing that the prediction of precipitation in the area under study is generally satisfactory. Finally, the thunderstorm warnings issued by the system are verified against the observed lightning activity.

scholarly journals Potential Impact of Climate Change on Schistosomiasis: A Global Assessment Attempt

2018 ◽  
Vol 3 (4) ◽  
pp. 117 ◽  
Author(s):  
Guo-Jing Yang ◽  
Robert Bergquist
Keyword(s):  
Climate Change ◽  
Coupled Model ◽  
Maximum Temperature ◽  
Intergovernmental Panel ◽  
Meteorological Model ◽  
Ambient Temperatures ◽  
Global Circulation ◽  
Global Circulation Models ◽  
Parasite Stage ◽  
Near Future

Based on an ensemble of global circulation models (GCMs), four representative concentration pathways (RCPs) and several ongoing and planned Coupled Model Intercomparison Projects (CMIPs), the Intergovernmental Panel on Climate Change (IPCC) predicts that global, average temperatures will increase by at least 1.5 °C in the near future and more by the end of the century if greenhouse gases (GHGs) emissions are not genuinely tempered. While the RCPs are indicative of various amounts of GHGs in the atmosphere the CMIPs are designed to improve the workings of the GCMs. We chose RCP4.5 which represented a medium GHG emission increase and CMIP5, the most recently completed CMIP phase. Combining this meteorological model with a biological counterpart model accounted for replication and survival of the snail intermediate host as well as maturation of the parasite stage inside the snail at different ambient temperatures. The potential geographical distribution of the three main schistosome species: Schistosoma japonicum, S. mansoni and S. haematobium was investigated with reference to their different transmission capabilities at the monthly mean temperature, the maximum temperature of the warmest month(s) and the minimum temperature of the coldest month(s). The set of six maps representing the predicted situations in 2021–2050 and 2071–2100 for each species mainly showed increased transmission areas for all three species but they also left room for potential shrinkages in certain areas.

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