Inclusion of mineral dust radiative effects could lead to a significant improvement in the radiation balance of numerical weather prediction models with subsequent improvements in the weather ...forecast itself. In this study the radiative effects of mineral dust have been fully incorporated into a regional atmospheric dust model. Dust affects the radiative fluxes at the surface and the top of the atmosphere and the temperature profiles at every model time step when the radiation module is processed. These changes influence the atmospheric dynamics, moisture physics, and near‐surface conditions. Furthermore, dust emission is modified by changes in friction velocity and turbulent exchange coefficients; dust turbulent mixing, transport, and deposition are altered by changes in atmospheric stability, precipitation conditions, and free‐atmosphere winds. A major dust outbreak with dust optical depths reaching 3.5 at 550 nm over the Mediterranean region on April 2002 is selected to assess the radiative dust effects on the atmosphere at a regional level. A strong dust negative feedback upon dust emission (35–45% reduction of the AOD) resulted from the smaller outgoing sensible turbulent heat flux decreasing the turbulent momentum transfer from the atmosphere and consequently dust emission. Significant improvements of the atmospheric temperature and mean sea‐level pressure forecasts are obtained over dust‐affected areas by considerably reducing both warm and cold temperature biases existing in the model without dust‐radiation interactions. This study demonstrates that the use of the proposed model with integrated dust and atmospheric radiation represents a promising approach for further improvements in numerical weather prediction practice and radiative impact assessment over dust‐affected areas.
The mineralogical composition of airborne dust particles is an important but often neglected parameter for several physiochemical processes, such as atmospheric radiative transfer and ocean ...biochemistry. We present the development of the METAL-WRF module for the simulation of the composition of desert dust minerals in atmospheric aerosols. The new development is based on the GOCART-AFWA dust module of WRF-Chem. A new wet deposition scheme has been implemented in the dust module alongside the existing dry deposition scheme. The new model includes separate prognostic fields for nine (9) minerals: illite, kaolinite, smectite, calcite, quartz, feldspar, hematite, gypsum, and phosphorus, derived from the GMINER30 database and also iron derived from the FERRUM30 database. Two regional model sensitivity studies are presented for dust events that occurred in August and December 2017, which include a comparison of the model versus elemental dust composition measurements performed in the North Atlantic (at Izaña Observatory, Tenerife Island) and in the eastern Mediterranean (at Agia Marina Xyliatos station, Cyprus Island). The results indicate the important role of dust minerals, as dominant aerosols, for the greater region of North Africa, South Europe, the North Atlantic, and the Middle East, including the dry and wet depositions away from desert sources. Overall, METAL-WRF was found to be capable of reproducing the relative abundances of the different dust minerals in the atmosphere. In particular, the concentration of iron (Fe), which is an important element for ocean biochemistry and solar absorption, was modeled in good agreement with the corresponding measurements at Izaña Observatory (22% overestimation) and at Agia Marina Xyliatos site (4% overestimation). Further model developments, including the implementation of newer surface mineralogical datasets, e.g., from the NASA-EMIT satellite mission, can be implemented in the model to improve its accuracy.
Icelandic topsoil sediments, as confirmed by numerous scientific studies, represent the largest and the most important European source of mineral dust. Strong winds, connected with the intensive ...cyclonic circulation in the North Atlantic, induce intense emissions of mineral dust from local sources all year and carry away these fine aerosol particles for thousands of kilometers. Various impacts of airborne mineral dust particles on local air quality, human health, transportation, climate and marine ecosystems motivated us to design a fully dynamic coupled atmosphere–dust numerical modelling system in order to simulate, predict and quantify the Icelandic mineral dust process including: local measurements and source specification over Iceland. In this study, we used the Dust Regional Atmospheric Model (DREAM) with improved Icelandic high resolution dust source specification and implemented spatially variable particle size distribution, variable snow cover and soil wetness. Three case studies of intense short- and long-range transport were selected to evaluate the model performance. Results demonstrated the model’s capability to forecast major transport features, such as timing, and horizontal and vertical distribution of the processes. This modelling system can be used as an operational forecasting system, but also as a reliable tool for assessing climate and environmental Icelandic dust impacts.
On 2 June 2014, at about 13 UTC, a dust storm arrived in Tehran as a severe hazard that caused injures, deaths, failures in power supply, and traffic disruption. Such an extreme event is not ...considered as common for the Tehran area, which has raised the question of the dust storm’s origin and the need for increasing citizens’ preparedness during such events. The analysis of the observational data and numerical simulations using coupled dust-atmospheric models showed that intensive convective activity occurred over the south and southwest of Tehran, which produced cold downdrafts and, consequently, high-velocity surface winds. Different dust source masks were used as an input for model hindcasts of the event (forecasts of the past event) to show the capability of the numerical models to perform high-quality forecasts in such events and to expand the knowledge on the storm’s formation and progression. In addition to the proven capability of the models, if engaged in operational use to contribute to the establishment of an early warning system for dust storms, another conclusion appeared as a highlight of this research: abandoned agricultural areas south of Tehran were responsible for over 50% of the airborne dust concentration within the dust storm that surged through Tehran. Such a dust source in the numerical simulation produced a PM10 surface dust concentration of several thousand μm/m3, which classifies it as a dust source hot-spot. The produced evidence indivisibly links issues of land degradation, extreme weather, environmental protection, and health and safety.
Ice particles in high-altitude cold clouds can obstruct aircraft functioning. Over the last 20 years, there have been more than 150 recorded cases with engine power-loss and damage caused by tiny ...cloud ice crystals, which are difficult to detect with aircraft radars. Herein, we examine two aircraft accidents for which icing linked to convective weather conditions has been officially reported as the most likely reason for catastrophic consequences. We analyze whether desert mineral dust, known to be very efficient ice nuclei and present along both aircraft routes, could further augment the icing process. Using numerical simulations performed by a coupled atmosphere-dust model with an included parameterization for ice nucleation triggered by dust aerosols, we show that the predicted ice particle number sharply increases at approximate locations and times of accidents where desert dust was brought by convective circulation to the upper troposphere. We propose a new icing parameter which, unlike existing icing indices, for the first time includes in its calculation the predicted dust concentration. This study opens up the opportunity to use integrated atmospheric-dust forecasts as warnings for ice formation enhanced by mineral dust presence.
When exposed to convective thunderstorm conditions, pollen grains can rupture and release large numbers of allergenic sub-pollen particles (SPPs). These sub-pollen particles easily enter deep into ...human lungs, causing an asthmatic response named thunderstorm asthma (TA). Up to now, efforts to numerically predict the airborne SPP process and to forecast the occurrence of TAs are unsatisfactory. To overcome this problem, we have developed a physically-based pollen model (DREAM-POLL) with parameterized formation of airborne SPPs caused by convective atmospheric conditions. We ran the model over the Southern Australian grass fields for 2010 and 2016 pollen seasons when four largest decadal TA epidemics happened in Melbourne. One of these TA events (in November 2016) was the worldwide most extreme one which resulted to nine deaths and hundreds of hospital patient presentations. By executing the model on a day-by-day basis in a hindcast real-time mode we predicted SPP peaks exclusively only when the four major TA outbreaks happened, thus achieving a high forecasting success rate. The proposed modelling system can be easily implemented for other geographical domains and for different pollen types.
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•A numerical model was used to predict extreme grass pollen events in Melbourne.•Thunderstorm atmospheric conditions produce allergenic sub-pollen particles.•Successful real-time numerical simulations of sub-pollen particles is demonstrated.•Predicting sub-pollen particles could reduce thunderstorm asthma risks.•The proposed model can be implemented for different regions and pollen types.
In this study, the influence of the large-scale circulation patterns on temperature in Europe and Serbia is examined. Among the large-scale circulation patterns in the Northern Hemisphere, less ...attention has been paid to the impact of the East Atlantic/West Russia pattern (EA/WR). The relationship between the EA/WR pattern and geopotential height at 500 hPa is investigated using reanalysis data from National Centres for Environmental Prediction – National Centre for Atmospheric Research. Temperature anomalies have been explored in relation to strong positive and negative phases of the EA/WR pattern for all months.
Analyzing the correlation between the EA/WR index (EA/WRI) and geopotential height at 500 hPa, a centre with negative correlation has been found throughout the year over Russia, north of the Caspian Sea. Positive (negative) temperature anomalies prevailed over Eastern (Western) Europe for the strong negative EA/WR phase (EA/WRI < −1). The temperature anomalies associated with the strong positive phase of the EA/WR pattern (EA/WRI > 1) reflect below-average temperatures over Eastern Europe. In addition, we explored the combined effects of positive and negative phases of the North Atlantic Oscillation (NAO) or East Atlantic (EA) pattern with the EA/WR pattern on temperature variations in Europe and Serbia. We find that the effect of the EA/WR pattern on temperature changes is dependent of the EA phase but not of the NAO phase over Serbia. When the EA/WRI is negative and EA pattern or NAO are in positive phase, the positive temperature anomalies prevailed over most of Europe, including Serbia. The highest values of temperature anomalies exist over Serbia for EA/WRI < −1 and EA index > 1. It is found that this case appears more frequently in the last 20 years, contributing to the warming in Europe and Serbia.
•The influence of the large-scale patterns on temperature over Europe is investigated.•Combination of the EA or NAO with EA/WR pattern affects temperature over Serbia.•The EA and EA/WR pattern has stronger influence on temperature than the NAO and EA/WR.•The highest temperature exists for negative EA/WR and positive EA phase over Serbia.
The goal of this article is to apply the regional atmospheric numerical weather prediction Eta model and describe its performance in validation of the wind forecasts for wind power plants. Wind power ...generation depends on wind speed. Wind speed is converted into power through characteristic curve of a wind turbine. The forecasting of wind speed and wind power has the same principle.
Two sets of Eta model forecasts are made: one with a coarse resolution of 22
km, and another with a nested grid of 3.5
km, centered on the Nasudden power plants, (18.22°E, 57.07°N; 3
m) at island Gotland, Sweden. The coarse resolution forecasts were used for the boundary conditions of the nested runs. Verification is made for the nested grid model, for summers of 1996–1999, with a total number of 19 536 pairs of forecast and observed winds. The Eta model is compared against the wind observed at the nearest surface station and against the wind turbine tower 10
m wind. As a separate effort, the Eta model wind is compared against the wind from tower observations at a number of levels (38, 54, 75 and 96
m).
Four common measures of accuracy relative to observations - mean difference (bias), mean absolute difference, root mean square difference and correlation coefficient are evaluated. In addition, scatter plots of the observed and predicted pairs at 10 and 96
m are generated. Average overall results of the Eta model 10
m wind fits to tower observations are: mean difference (bias) of 0.48
m/s, mean absolute difference of 1.14
m/s, root mean square difference of 1.38
m/s, and the correlation coefficient of 0.79. Average values for the upper tower observation levels are the mean difference (bias) of 0.40
m/s; mean absolute difference of 1.46
m/s; root mean square difference of 1.84
m/s and the correlation coefficient of 0.80.
The goal of this article is to apply the regional atmospheric numerical weather prediction – the Eta model, improved by the new proposed MOS (Model Output Statistics) method and to describe its ...performance in validation of the wind forecasts for wind power plants. The Eta model has been compared against the wind from tower observations at a number of levels (10, 38, 54, 75, 96 and 145 m), with a total number of 15984 pairs of forecast and observed winds.
The new MOS method is applied in two different ways: 1) with one predictor – wind from the Eta model forecast; 2) with two predictors – wind from the Eta model forecast and wind from the recent observations, originally proposed in this study. The average overall results are: Mean Error of 0.27 m/s; Mean Absolute Error of 0.93 m/s; Root Mean Square Error of 1.19 m/s and Coefficient of Determination of 0.79. The results indicate that the Eta model with the proposed MOS method is quite usable as a meteorological driver for wind energy modelling and prediction across any geographical region.
•Eta model has improved by the MOS method and applied in forecasts for wind power plants.•The first MOS predictor: wind from the Eta model forecast.•The second MOS predictor: wind from the recent observations.•Average overall results: ME = 0.27 m/s; MAE = 0.93 m/s; RMSE = 1.19 m/s and CD = 0.79.
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