'Ground effect' Inter-Modal Fast Sea Transport

Energy and emission reduction strategies are extremely important in actual transport situation. “Ground effect” technology recognized from the late sixties has a problem with wide expanding in sea transport. It is connected with stability and control systems, aerodynamics of landing and take off also sea state of civil and military Ground Effect Craft named also WIGs ((Wing in Ground effect) or ekranoplanes Airfoil Development Gmbh (AFD) was certified in late 90-ies interesting design of ekranoplane “ Airfish 8” by Germanischer Llyod ( +100 A0 WIG – A , WH 0,5/1,5 EXP) like the fist civil ekranoplane. The Hoverwing 50 alias WST 500- has successful flown in Korea under AFD licence. Last developments in the aviation field indicates new role in maritime transport of WIG’s. Innovations in the fields of aviation improving the performances of WIGs in new future make possible to introduce this idea like "Ground effect" Inter-Modal Fast Sea Transport, which complement other modes of transport and flow of passengers also

A comparison of aircraft-based surface-layer observations over Denmark Strait and the Irminger sea with meteorological analyses and QuikSCAT winds

A compilation of aircraft observations of the atmospheric surface layer is compared with several meteorological analyses and QuikSCAT wind products. The observations are taken during the Greenland Flow Distortion Experiment, in February and March 2007, during cold-air outbreak conditions and moderate to high wind speeds. About 150 data points spread over six days are used, with each data point derived from a 2-min run (equivalent to a 12 km spatial average). The observations were taken 30–50 m above the sea surface and are adjusted to standard heights. Surface-layer temperature, humidity and wind, as well as sea-surface temperature (SST) and surface turbulent fluxes are compared against co-located data from the ECMWF operational analyses, NCEP Global Reanalyses, NCEP North American Regional Reanalyses (NARR), Met Office North Atlantic European (NAE) operational analyses, two MM5 hindcasts, and two QuikSCAT products. In general, the limited-area models are better at capturing the mesoscale high wind speed features and their associated structure; often the models underestimate the highest wind speeds and gradients. The most significant discrepancies are: a poor simulation of relative humidity by the NCEP global and MM5 models, a cold bias in 2 m air temperature near the sea-ice edge in the NAE model, and an overestimation of wind speed above 20 m s-1 in the QuikSCAT wind products. In addition, the NCEP global, NARR and MM5 models all have significant discrepancies associated with the parametrisation of surface turbulent heat fluxes. A high-resolution prescription of the SST field is crucial in this region, although these were not generally used at this time

Prediction of visibility and aerosol within the operational Met Office unified model. I: Model formulation and variational assimilation

The formulation and performance of the Met Office visibility analysis and prediction system are described. The visibility diagnostic within the limited-area Unified Model is a function of humidity and a prognostic aerosol content. The aerosol model includes advection, industrial and general urban sources, plus boundary-layer mixing and removal by rain. The assimilation is a 3-dimensional variational scheme in which the visibility observation operator is a very nonlinear function of humidity, aerosol and temperature. A quality control scheme for visibility data is included. Visibility observations can give rise to humidity increments of significant magnitude compared with the direct impact of humidity observations. We present the results of sensitivity studies which show the contribution of different components of the system to improved skill in visibility forecasts. Visibility assimilation is most important within the first 6-12 hours of the forecast and for visibilities below 1 km, while modelling of aerosol sources and advection is important for slightly higher visibilities (1-5 km) and is still significant at longer forecast time

The Fires, Asian, and Stratospheric Transport-Las Vegas Ozone Study (FAST-LVOS)

The Fires, Asian, and Stratospheric Transport&ndash;Las Vegas Ozone Study (FAST-LVOS) was conducted in May and June of 2017 to study the transport of ozone (O3) to Clark County, Nevada, a marginal non-attainment area in the southwestern United States (SWUS). This 6-week (20&nbsp;May&ndash;30&nbsp;June 2017) field campaign used lidar, ozonesonde, aircraft, and in situ measurements in conjunction with a variety of models to characterize the distribution of O3 and related species above southern Nevada and neighboring California and to probe the influence of stratospheric intrusions and wildfires as well as local, regional, and Asian pollution on surface O3 concentrations in the Las Vegas Valley (&asymp;&thinsp;900&thinsp;m above sea level, a.s.l.). In this paper, we describe the FAST-LVOS campaign and present case studies illustrating the influence of different transport processes on background O3 in Clark County and southern Nevada. The companion paper by Zhang et al.&nbsp;(2020) describes the use of the AM4 and GEOS-Chem global models to simulate the measurements and estimate the impacts of transported O3 on surface air quality across the greater southwestern US and Intermountain West. The FAST-LVOS measurements found elevated O3 layers above Las Vegas on more than 75&thinsp;% (35 of 45) of the sample days and show that entrainment of these layers contributed to mean 8&thinsp;h average regional background O3 concentrations of 50&ndash;55 parts per billion by volume (ppbv), or about 85&ndash;95&thinsp;&micro;g&thinsp;m&minus;3. These high background concentrations constitute 70&thinsp;%&ndash;80&thinsp;% of the current US National Ambient Air Quality Standard (NAAQS) of 70&thinsp;ppbv (&asymp;&thinsp;120&thinsp;&micro;g&thinsp;m&minus;3 at 900&thinsp;m&thinsp;a.s.l.) for the daily maximum 8&thinsp;h average (MDA8) and will make attainment of the more stringent standards of 60 or 65&thinsp;ppbv currently being considered extremely difficult in the interior SWUS. &nbsp;</p

Data assimilation in atmospheric chemistry models: current status and future prospects for coupled chemistry meteorology models

Abstract. Data assimilation is used in atmospheric chemistry models to improve air quality forecasts, construct re-analyses of three-dimensional chemical (including aerosol) concentrations and perform inverse modeling of input variables or model parameters (e.g., emissions). Coupled chemistry meteorology models (CCMM) are atmospheric chemistry models that simulate meteorological processes and chemical transformations jointly. They offer the possibility to assimilate both meteorological and chemical data; however, because CCMM are fairly recent, data assimilation in CCMM has been limited to date. We review here the current status of data assimilation in atmospheric chemistry models with a particular focus on future prospects for data assimilation in CCMM. We first review the methods available for data assimilation in atmospheric models, including variational methods, ensemble Kalman filters, and hybrid methods. Next, we review past applications that have included chemical data assimilation in chemical transport models (CTM) and in CCMM. Observational data sets available for chemical data assimilation are described, including surface data, surface-based remote sensing, airborne data, and satellite data. Several case studies of chemical data assimilation in CCMM are presented to highlight the benefits obtained by assimilating chemical data in CCMM. A case study of data assimilation to constrain emissions is also presented. There are few examples to date of joint meteorological and chemical data assimilation in CCMM and potential difficulties associated with data assimilation in CCMM are discussed. As the number of variables being assimilated increases, it is essential to characterize correctly the errors; in particular, the specification of error cross-correlations may be problematic. In some cases, offline diagnostics are necessary to ensure that data assimilation can truly improve model performance. However, the main challenge is likely to be the paucity of chemical data available for assimilation in CCMM

A Kalman-filter bias correction of ozone deterministic, ensemble-averaged, and probabilistic forecasts

Kalman filtering (KF) is used to postprocess numerical-model output to estimate systematic errors in surface ozone forecasts. It is implemented with a recursive algorithm that updates its estimate of future ozone-concentration bias by using past forecasts and observations. KF performance is tested for three types of ozone forecasts: deterministic, ensemble-averaged, and probabilistic forecasts. Eight photochemical models were run for 56 days during summer 2004 over northeastern USA and southern Canada as part of the International Consortium for Atmospheric Research on Transport and Transformation New England Air Quality (AQ) Study. The raw and KF-corrected predictions are compared with ozone measurements from the Aerometric Information Retrieval Now data set, which includes roughly 360 surface stations. The completeness of the data set allowed a thorough sensitivity test of key KF parameters. It is found that the KF improves forecasts of ozone-concentration magnitude and the ability to predict rare events, both for deterministic and ensemble-averaged forecasts. It also improves the ability to predict the daily maximum ozone concentration, and reduces the time lag between the forecast and observed maxima. For this case study, KF considerably improves the predictive skill of probabilistic forecasts of ozone concentration greater than thresholds of 10 to 50 ppbv, but it degrades it for thresholds of 70 to 90 ppbv. Moreover, KF considerably reduces probabilistic forecast bias. The significance of KF postprocessing and ensemble-averaging is that they are both effective for real-time AQ forecasting. KF reduces systematic errors, whereas ensemble-averaging reduces random errors. When combined they produce the best overall forecast

Application of dynamic linear regression to improve the skill of ensemble-based deterministic ozone forecasts

Forecasts from seven air quality models and surface ozone data collected over the eastern USA and southern Canada during July and August 2004 provide a unique opportunity to assess benefits of ensemble-based ozone forecasting and devise methods to improve ozone forecasts. In this investigation, past forecasts from the ensemble of models and hourly surface ozone measurements at over 350 sites are used to issue deterministic 24-h forecasts using a method based on dynamic linear regression. Forecasts of hourly ozone concentrations as well as maximum daily 8-h and 1-h averaged concentrations are considered. It is shown that the forecasts issued with the application of this method have reduced bias and root mean square error and better overall performance scores than any of the ensemble members and the ensemble average. Performance of the method is similar to another method based on linear regression described previously by Pagowski et al., but unlike the latter, the current method does not require measurements from multiple monitors since it operates on individual time series. Improvement in the forecasts can be easily implemented and requires minimal computational cost

A novel method to improve temperature simulations of general circulation models based on ensemble empirical mode decomposition and its application to multi-model ensembles

A novel method based on the ensemble empirical mode decomposition (EEMD) method was developed to improve model performance. This method was evaluated by applying it to global surface air temperatures, which were simulated by eight general circulation models from the Coupled Model Intercomparison Project Phase 5 (CMIP5). The temperature simulations of the eight models were separated into their different components by EEMD. The model's performance improved after the first high-frequency component was removed from the original simulations by EEMD for each model, on both the global and continental scale. Moreover, EEMD was more effective in improving the model's performance compared to the wavelet transform method. The multi-model ensembles (MMEs) were calculated based on the EEMD-improved model simulations using the Average Ensemble Mean, Multiple Linear Regression, Singular Value Decomposition and Bayesian Model Averaging methods. The results showed that the MME forecasts performed better when the calculations were based on the EEMD-improved simulations as opposed to the original simulations on both the global and continental scale. Therefore, the results of the MME were further improved by using the EEMD-improved model simulations. This new method provides a simple way to improve model performance and can be easily applied to further improve MME simulations