Numerical Weather Forecast Model Research Articles

Seasonal Variation in Vertical Structure for Stratiform Rain at Mêdog Site in Southeastern Tibetan Plateau

Mêdog is located at the entrance of the water vapor channel of the Yarlung Tsangpo Great Canyon on the southeastern Tibetan Plateau (TP). In this study, the seasonal variation in the microphysical vertical structure of stratiform precipitation at the Mêdog site in 2022 was investigated using micro rain radar (MRR) observations, as there is a lack of similar studies in this region. The average melting layer height is the lowest in February, after which it gradually increases, reaches its peak in August, and then gradually decreases. For lower rain categories, the vertical distribution of small drops remains uniform in winter below the melting layer. The medium-sized drops show slight increases, leading to negative gradients in the microphysical profiles. Slight or evident decreases in concentrations of small drops are observed with decreasing height in the premonsoon, monsoon, and postmonsoon seasons, likely due to significant evaporation. The radar reflectivity, rain rate, and liquid water content profiles decrease with decreasing height according to the decrease in concentrations of small drops. With increasing rain rate, the drop size distribution (DSD) displays significant variations in winter, and the fall velocity decreases rapidly with decreasing height. In the premonsoon, monsoon, and postmonsoon seasons, the concentrations of large drops significantly decrease below the melting layer because of the breakup mechanism, leading to the decreases in the fall velocity profiles with decreasing height during these seasons. Raindrops with sizes ranging from 0.3–0.5 mm are predominant in terms of the total drop number concentration in all seasons. Precipitation in winter and postmonsoon seasons is mainly characterized by small raindrops, while that in premonsoon and monsoon seasons mainly comprises medium-sized raindrops. Understanding the seasonal variation in the vertical structure of precipitation in Mêdog will improve the radar quantitative estimation and the use of microphysical parameterization schemes in numerical weather forecast models over the TP.

Investigation of Hydrostatic Imbalance with Field Observations

Abstract The hydrostatic equilibrium addresses the approximate balance between the positive force of the vertical pressure gradient and the negative gravity force and has been widely assumed for atmospheric applications. The hydrostatic imbalance of the mean atmospheric state for the acceleration of vertical motions in the vertical momentum balance is investigated using tower, the global positioning system radiosonde, and Doppler lidar and radar observations throughout the diurnally varying atmospheric boundary layer (ABL) under clear-sky conditions. Because of the negligibly small mean vertical velocity, the acceleration of vertical motions is dominated by vertical variations of vertical turbulent velocity variances. The imbalance is found to be mainly due to the vertical turbulent transport of changing air density as a result of thermal expansion/contraction in response to air temperature changes following surface temperature changes. In contrast, any pressure change associated with air temperature changes is small, and the positive vertical pressure-gradient force is strongly influenced by its background value. The vertical variation of the turbulent velocity variance from its vertical increase in the lower convective boundary layer (CBL) to its vertical decrease in the upper CBL is observed to be associated with the sign change of the imbalance from positive to negative due to the vertical decrease of the positive vertical pressure-gradient force and the relative increase of the negative gravity force as a result of the decreasing upward transport of the low-density air. The imbalance is reduced significantly at night but does not steadily approach zero. Understanding the development of hydrostatic imbalance has important implications for understanding large-scale atmosphere, especially for cloud development. Significance Statement It is well known that the hydrostatic imbalance between the positive pressure-gradient force due to the vertical decrease of atmospheric pressure and the negative gravity forces in the vertical momentum balance equation has important impacts on the vertical acceleration of atmospheric vertical motions. Vertical motions for mass, momentum, and energy transfers contribute significantly to changing atmospheric dynamics and thermodynamics. This study investigates the often-assumed hydrostatic equilibrium and investigate how the hydrostatic imbalance is developed using field observations in the atmospheric boundary layer under clear-sky conditions. The results reveal that hydrostatic imbalance can develop from the large-eddy turbulent transfer of changing air density in response to the surface diabatic heating/cooling. The overwhelming turbulence in response to large-scale thermal forcing and mechanical work of the vast Earth surface contributes to the hydrostatic imbalance on large spatial and temporal scales in numerical weather forecast and climate models.

Improving the Forecasts of Surface Latent Heat Fluxes and Surface Air Temperature in the GRAPES Global Forecast System

The GRAPES (Global/Regional Assimilation and Prediction System) global medium-range forecast system (GRAPES_GFS) is a new generation numerical weather forecast model developed by the China Meteorological Administration (CMA). However, the forecasts of surface latent heat fluxes and surface air temperature have systematic biases, which affect the forecasts of atmospheric dynamics by modifying the lower boundary conditions and degrading the application of GRAPES_GFS since the 2 m air temperature is one of the key components of weather forecast products. Here, we add a soil resistance term to reduce soil evaporation, which ultimately reduces the positive forecast bias of the land surface latent heat flux. We also reduce the positive forecast bias of the ocean surface latent heat flux by considering the effect of salinity in the calculation of the ocean surface vapor pressure and by adjusting the parameterizations of roughness length for the exchanges in momentum, heat, and moisture between the ocean surface and atmosphere. Moreover, we modify the parameterization of the roughness length for the exchanges in heat and moisture between the land surface and atmosphere to reduce the cold bias of the nighttime 2 m air temperature forecast over areas with lower vegetation height. We also consider the supercooled soil water to reduce the warm forecast bias of the 2 m air temperature over northern China during winter. These modified parameterizations are incorporated into the GRAPES_GFS and show good performance based on a set of evaluation experiments. This paper highlights the importance of the representations of the land/ocean surface and boundary layer processes in the forecasting of surface heat fluxes and 2 m air temperature.

An assessment of Arctic diurnal water‐vapour cycles in Canada's weather forecast model and ERA5

AbstractThe diurnal water‐vapour cycle is a critical component of the hydrological cycle, yet it is one of the hardest components to accurately reproduce in forecast and climate models. Previous studies have shown that both forecast and climate models underrepresent the diurnal water‐vapour cycle, which leads to errors in precipitation, cloud, and radiative transfer parameters. Most diurnal cycle studies were conducted in the Tropics, and very few model evaluations of this process exist for the Arctic. Additionally, the majority of studies focus on total column water‐vapour cycles; almost none use height‐resolved measurements. In this study, we evaluate the diurnal water‐vapour cycles in Environment and Climate Change Canada's Global Environmental Multiscale–High Resolution Deterministic Prediction System (GEM–HRDPS) numerical weather forecast model and the European Centre for Medium‐Range Weather Forecasts Reanalysis v5 (ERA5) using a Vaisala preproduction differential absorption lidar (DIAL) and a co‐located Global Positioning System (GPS) located in Iqaluit, Nunavut (63.75° N, 68.55° W). Both numerical products reproduce the phase of the diurnal cycle well below 1 km year‐round. However, ERA5's diurnal amplitudes are significantly smaller than the DIAL and GPS amplitudes. GEM–HRDPS amplitudes are consistently larger in the first few hundred metres but smaller above 1 km; it also underrepresents the amplitude of the total column diurnal cycle. Neither ERA5 nor GEM–HRDPS accurately reproduce the 12‐hr component of the diurnal cycle in either the height‐resolved or total column cycles. The inability to accurately reproduce the 12‐hr component in both numerical products suggests that the representation of some underlying process is incomplete, which can impact the accuracy of precipitation and radiative transfer algorithms. In conclusion, we find that the numerical products are able to reproduce the general behaviour and shape of the cycle but still require improvement at certain altitudes.

Regional Variability of Raindrop Size Distribution from a Network of Disdrometers over Complex Terrain in Southern China

Raindrop size distribution (DSD) over the complex terrain of Guangdong Province, southern China, was studied using six disdrometers operated by the Guangdong Meteorology Service during the period 1 March 2018 to 30 August 2022 (~5 years). To analyze the long-term DSD characteristics over complex topography in southern China, three stations on the windward side, Haifeng, Enping and Qingyuan, and three stations on the leeward side, Meixian, Luoding and Xuwen, were utilized. The median mass-weighted diameter (Dm) value was higher on the windward than on the leeward side, and the windward-side stations also showed greater Dm variability. With regard to the median generalized intercept (log10Nw) value, the log10Nw values decreased from coastal to mountainous areas. Although there were some differences in Dm, log10Nw and liquid water content (LWC) frequency between the six stations, there were still some similarities, with the Dm, log10Nw and LWC frequency all showing a single-peak curve. In addition, the diurnal variation of the mean log10Nw had a negative relationship with Dm diurnal variation although the inverse relationship was not particularly evident at the Haifeng site. The diurnal mean rainfall rate also peaked in the afternoon and exceeded the maximum at night which indicated that strong land heating in the daytime significantly influenced the local DSD variation. What is more, the number concentration of drops, N(D), showed an exponential shape which decreased monotonically for all rainfall rate types at the six observation sites, and an increase in diameter caused by increases in the rainfall rate was also noticeable. As the rainfall rate increased, the N(D) for sites on the windward side (i.e., Haifeng, Enping and Qingyuan) were higher than for the sites on the leeward side (i.e., Meixian, Luoding and Xuwen), and the difference between them also became distinct. The abovementioned DSD characteristic differences also showed appreciable variability in convective precipitation between stations on the leeward side (i.e., Meixian, Luoding and Xuwen) and those on the windward side (Haifeng and Enping, but not Qingyuan). This study enhances the precision of numerical weather forecast models in predicting precipitation and verifies the accuracy of measuring precipitation through remote sensing instruments, including weather radars located on the ground.

Impact of Soil Moisture in the Monsoon Region of South America during Transition Season

The land surface is an important component of numerical weather and climate forecast models due to their effect on energy–water balances and fluxes, and it is essential for forecasts on a seasonal scale. The present study aimed to understand the effects of land surface processes on initialization of seasonal forecasts in the austral spring, in particular soil moisture. We built forecasts with the Brazilian global Atmospheric Model hindcast from 2000 to 2010, with a configuration similar to those used in the operational environment. To improve it, we developed a new initial condition of the land surface using the Land Information System over South America and the Global Land Data Assimilation System for the rest of the globe and used it as the input in the forecast model. The results demonstrated that the model is sensitive to changes in soil moisture and that the new high–resolution soil moisture dataset can be used in model initialization, which resulted in an increase in the correlation of precipitation over part of South America. We also noticed an improvement in the representation of surface fluxes and an increase in soil moisture content and specific humidity at medium and low levels of the atmosphere. The analysis of the coupling between the land surface and the atmosphere showed that, for Central Brazil, the states of the continental surface define the surface fluxes. For the Amazon and La Plata Basins, the model did not correctly represent the coupling because it underestimated the soil moisture content.

Application of Artificial Intelligence in the Assessment and Forecast of Avalanche Danger in the Ile Alatau Ridge

The assessment and forecast of avalanche danger are very important means of preventing avalanche fatalities, especially in recreational areas. The use of artificial intelligence methods for these purposes significantly increases the accuracy of avalanche forecasts. The purpose of this re-search was to improve the methods for assessing and forecasting avalanche danger in the Ile Alatau Ridge. To create a training sample, the data from three meteorological and two avalanche stations for the period from 2002 to 2022 were used. The following predictors were chosen: air temperature, snow cover depth, precipitation, and snowpack stability index. The subject of the assessment and forecasts was the level of avalanche danger, assessed on a five-point scale. The program Statistica StatSoft was used as a neurosimulator. When forecasting avalanche danger, the predictive values of air temperature and precipitation, obtained from numerical weather forecast models, were used. The model correctly assessed the current level of avalanche danger in 90% of cases. The forecast of avalanche danger was justified in 80% of cases. The artificial intelligence program helped the avalanche forecaster to improve the forecast quality. This method is currently being used for compiling an avalanche bulletin for two river basins in the Ile Alatau.

Effects of Tropospheric Refraction on Precise Point Positioning in Brazil

Troposphere is one of the most limiting sources of error in the accuracy of 15 Precise Point Positioning method. This work aims to analyze the effects of tropospheric 16 delay on this positioning technique by applying the Bernese scientific program to GNSS 17 data belonging to the Brazilian Network for Continuous Monitoring of GNSS Systems. 18 GNSS data are related to several climatic zones, including Amazon region, referring to four 19 seasons. The data were processed considering six strategies, each one using different 20 troposphere models and mapping functions for analysis. Results were evaluated according 21 to the Root Mean Square, estimated for 15 processing days for each season, involving 89 22 GNSS stations. Results show that the greatest effects of the tropospheric delay occurred in 23 the equatorial region, related to the altimetric component, in all seasons of the year. This 24 climatic zone is under a strong influence of the Amazon region, which presents high annual 25 humidity values. In addition, it can occur a great humidity variation in this region, which 26 can compromise the process of estimating wet component of tropospheric delay. Finally, 27 results showed that the best processing strategy was the use of the Vienna mapping function 28 in conjunction with corrections based on the Numerical Weather Forecast model.

KOMPSAT-5 GNSS Radio Occultation Data Operations

The Korea Aerospace Research Institute launched KOMPSAT-5 on August 22, 2013, and has been operating for 10 years. KOMPSAT-5 has SAR (Synthetic Aperture Radar) for earth observation missions, and collects data necessary for earth atmosphere analysis through GNSS RO (Radio Occultation) receivers. RO data can be used for numerical weather forecast model based on temperature, pressure, and humidity by calculating the vertical distribution of atmospheric information. As a part of the Korea-US science and technology cooperation, KARI has been providing RO data of KOMPSAT-5 to the United States NOAA (National Oceanic and Atmospheric Administration) in near-real time since 2018. To this end, KARI receives telemetry data from the satellite about 12 times a day using 3 ground stations from Daejeon, Alaska in the U.S., and Sodankyla in Finland. The pre-processed data is being provided to both the UCAR (University Corporation for Atmospheric Research) in the U.S. and the KASI (Korea Astronomy and Space Science Institute). In this paper, radio occlusion data of KOMPSAT-5 is introduced, and system configuration, operation concepts for providing near-real time data and its application are also presented.

Atmospheric model inversion using infrasound signals from the North Korean underground nuclear explosion and the subsequent collapse event in 2017

SUMMARY This study focuses on the infrasound signals from the September 2017 North Korean underground nuclear explosion (UNE17) and subsequent collapse event (SCE17) that occurred close to the autumnal equinox when the atmospheric temperature structure undergoes rapid change. Multiple arrivals, including contributions from local, diffracted and epicentral infrasound, generated by UNE17, were observed at eight infrasound arrays in the Korean Peninsula and one IMS infrasound station (IS45) in Russia while at the closest five arrays for SCE17 only epicentral infrasound was observed. The UNE17 signals provide the opportunity to explore the utility of each distinct arrival in constraining atmospheric conditions during the change associated with the equinox. The observed characteristics of the multiple epicentral infrasonic phases (celerity, backazimuth, phase velocity and spectra) suggest propagation paths through the tropospheric, stratospheric and thermospheric waveguides, although geometric ray paths based on a global atmospheric model at the time of the explosion predict only thermospheric returns. The absence of predicted stratospheric returns may reflect errors in the atmospheric models due to the lack of predicted stratospheric winds which are weak and changing close to the autumnal equinox or the limited resolution of the fine-scale structure not captured by current atmospheric models. The differences between the model predictions and the observations suggest that the numerical weather forecast models need to be modified to fully explain the observations. In order to explore the model space that can explain the UNE17 data set, an inversion scheme is applied to atmospheric wind model parameters constrained by the multi-array observations. Zonal and meridional wind profiles are parametrized using empirical orthogonal functions (EOFs) estimated from 1-yr of Ground-to-Space atmospheric specifications. A best-fitting atmospheric model is estimated using a Bayesian approach that assesses the uncertainty in the inverse solution using a joint likelihood function combining components of azimuth deviation, traveltime and phase velocity. The updated atmospheric models from six different EOFs inversions have up to 20 m s–1 stronger zonal and meridional wind speeds in the stratosphere compared to the original model, and explain the stratospheric observations in the data set. This investigation illustrates that modest changes to atmospheric wind models at the time of autumnal equinox can improve the prediction of stratospheric returns.

The use of the WRF numerical weather forecasting model in mode ling emergency situations at Belarusian NPP

The expediency of using Weather Research and Forecasting (WRF) numerical weather forecast model as a source of meteorological data during modeling of radioactive substances transfer in the atmosphere is considered, WRF technical details are briefly described. As the reference emergency scenario, the scenario of the maximum design accident hypothetically occurring at Belarusian Nuclear Power Plant (BelNPP) 12 UTC 21.03.2021 was chosen. Four numerical experiments with Global Forecast System (GFS) and WRF meteorological data and RIMPUFF and LASAT atmospheric diffusion models were performed in JRODOS Decision Support Systems (DSS). For each experiment, maps of underlying surface potential radioactive contamination of I131 were created. The potential contamination of the underlying surface I131 from 5 to 100 km from the BelNPP was assessed. The dependency graph of the maximum underlying surface contamination with I131 on the distance from the BelNPP was plotted based on the experiments results. The obtained results indicate the prospects of using the WRF meteorological model in the DSS at various stages of the emergency situations development at the BelNPP. 

Investigation of the Sensitivity of Microwave Land Surface Emissivity to Soil Texture in MLEM

This study analyzes the spectral characteristics of desert surface emissivity according to soil classification and the influence of mineral materials and soil texture information using simulation results from the microwave land emissivity model (MLEM). It also aims at exploring the feasibility of reducing the simulation error in MLEM by refining the soil classification characteristic parameters (such as soil composition content, distribution of particle size, etc.). The surface emissivity of the Taklimakan Desert is derived, to our knowledge for the first time, from FY-3B/MWRI (FengYun-3B Microwave Radiation Imager), and then the spectral characteristics of the study area for different soil types are further analyzed according to soil classification. In addition, emissivity spectra of the four most widely mineral materials in the desert area are reproduced using an MLEM under different conditions. Results showed that microwave land emissivity is highly correlated with the soil type and changes are markedly affected by the soil water content, soil texture, mineral composition, and soil particle size. For the desert soil, the emissivity of horizontal/vertical polarization is affected by the frequency in those soils dominated by large-size particles. However, for those dominated by smaller particles, the surface emissivity is almost constant or appears to be somehow dependent on the frequency. Moreover, the season effect on emissivity characteristics is clear, especially for soils composed of small-size particles. The emissivity of horizontal polarization shows stronger seasonal variation than that of vertical polarization. The study findings also showed that refining soil texture information (soil component content, distribution of particle size) improves the simulation accuracy in desert areas. For example, for the soil dominated by clay and clay loam, the simulation error is reduced from 6–9% to less than 6%. The latter is evident, especially for soil types containing a large number of small particles, such as clay and clay loam, for which the simulation error is reduced. All in all, our study contributes to a better understanding of the influencing factors of soil texture and stratification of the near-surface soil, helping to improve microwave land surface emissivity prediction by the studied here model. As MLEM consists of an important part of the global meteorological data assimilation and weather forecast system, results can also help towards increasing the use of satellite data in desert areas and in improving the accuracy of numerical weather forecast models.

Аналіз і моделювання метеорологічних умов поширення радіонуклідів у періоди лісових пожеж та пилової бурі в Чорнобильській зоні відчуження

The simulation of meteorological conditions of the atmospheric transport of radioactive aerosols during the periods of wildland fires (2015, 2018, 2020 and 2022) and dust storm (April 16–17, 2020) in the Chornobyl Exclusion Zone was carried out using the archive of reanalysis data of the European Center for Medium-Term Weather Forecasts (ECMWF). Modeling of meteorological conditions and preparation of aerosynoptic information for the LEDI model of atmospheric transport and deposition of pollutants during periods of intense emission of radionuclides into the atmosphere was carried out using two sources of information: the results of the numerical weather forecast model WRF-ARW (USA) using the databases of the Reanalysis Project, and forecast data obtained according to the GFS global model and the ECMWF operational model, which are stored in the database of the CDS climate data repository (ERA5 Copernicus). The used WRF-ARW numerical model is adapted for the territory of Ukraine by selecting parameterization models of the main physical processes in the atmosphere in accordance with synoptic situations and the season of the year. A description of typical and extreme synoptic situations during the analyzed periods of wildland fires is given using the archive of synoptic maps of surface pressure and topography AT500 for the European territory and modern technologies of geoinformation systems. With the help of the conducted synoptic analysis of wildland fire periods, the most typical synoptic situations associated with the passage of cold fronts and the direction of north and northwest winds, which contribute mostly to the secondary radioactive contamination of environmental objects in the zone of influence of emission sources in the Chornobyl Exclusion Zone, were identified. Because of global climate changes and unpredictable human activity in the form of arsons, military actions, etc., the number of fires in forest areas, industrial and residential facilities is increasing. Open sources of numerical weather forecast data make it possible to create modern meteorological support for modeling of atmospheric transport and deposition of pollutants and assessment of secondary environmental pollution using the LEDI-WRF software complex.

Evaluation of Assimilation Effect of Yunhai-2 Occultation Data in Global Numerical Weather Prediction Model

Based on the T799L91 global numerical weather forecast model and its four-dimensional variational assimilation system, the assimilation process of Yunhai-2 occultation data was constructed, and a one-month assimilation-forecast experiment for Yunhai-2 occultation data was carried out in July 2019. The evaluation of the assimilation forecast results for East Asia, Northern Hemisphere and Southern Hemisphere shows that: In the global numerical weather forecast model, assimilating Yunhai-2 occultation data can effectively improve the accuracy of the forecast field. With the extension of the forecast time and the increase of the forecast days, the improvement range gradually increases. With the longer time efforts of the 72-hour, 120-hour and 168-hour forecasting, the assimilation effects of Yunhai-2 occultation data and GPS (Global Positioning System) occultation data are similar in the middle and early days, while the assimilation of Yunhai-2 occultation data is gradually better than GPS occultation data in the middle and later days. With the increase of the forecasting days, the advantage of the joint assimilation of Yunhai-2 occultation data with GPS occultation data gradually appears.

OpenIFS@home version 1: a citizen science project for ensemble weather and climate forecasting

Abstract. Weather forecasts rely heavily on general circulation models of the atmosphere and other components of the Earth system. National meteorological and hydrological services and intergovernmental organizations, such as the European Centre for Medium-Range Weather Forecasts (ECMWF), provide routine operational forecasts on a range of spatio-temporal scales by running these models at high resolution on state-of-the-art high-performance computing systems. Such operational forecasts are very demanding in terms of computing resources. To facilitate the use of a weather forecast model for research and training purposes outside the operational environment, ECMWF provides a portable version of its numerical weather forecast model, OpenIFS, for use by universities and other research institutes on their own computing systems. In this paper, we describe a new project (OpenIFS@home) that combines OpenIFS with a citizen science approach to involve the general public in helping conduct scientific experiments. Volunteers from across the world can run OpenIFS@home on their computers at home, and the results of these simulations can be combined into large forecast ensembles. The infrastructure of such distributed computing experiments is based on our experience and expertise with the climateprediction.net (https://www.climateprediction.net/, last access: 1 June 2021) and weather@home systems. In order to validate this first use of OpenIFS in a volunteer computing framework, we present results from ensembles of forecast simulations of Tropical Cyclone Karl from September 2016 studied during the NAWDEX field campaign. This cyclone underwent extratropical transition and intensified in mid-latitudes to give rise to an intense jet streak near Scotland and heavy rainfall over Norway. For the validation we use a 2000-member ensemble of OpenIFS run on the OpenIFS@home volunteer framework and a smaller ensemble of the size of operational forecasts using ECMWF's forecast model in 2016 run on the ECMWF supercomputer with the same horizontal resolution as OpenIFS@home. We present ensemble statistics that illustrate the reliability and accuracy of the OpenIFS@home forecasts and discuss the use of large ensembles in the context of forecasting extreme events.

Microphysics of fog bursting in polluted urban air

The goal of this work is to study sudden fog visibility (Vis) changes called fog bursting occurred during polluted urban air conditions. Fog bursting refers to a sudden drop of fog Vis below 500 m over 30 min. A comprehensive field observational campaign called Costal City Fog Microphysical Research (CCFMR) was carried out over the city of Tianjin, China during winter of 2016–2017. The six fog bursting cases occurred during the project suggest that droplet number concentration (Nd) and liquid water content (LWC) were higher than these of other conditions. The mass concentration of PM2.5 (NPM2.5) was also higher before the fog bursting events. The fog droplet spectra (FDS) followed a Junge distribution. During fog bursting events, Vis, surface air temperature (Ta) and dew point depression (Ta-Td) exhibited decreasing trends. On the other hand, mean and turbulence kinetic energy (MKE and TKE), as well as friction velocity (u∗) had increasing trends in time. After the fog bursting, environment stability changed from the neutral stage to weakly unstable stage, and fog became deeper and optically thicker. The FDS broadened drastically between 20 and 35 μm diameter, and smaller Nd increased up to 1 order of magnitude. It is concluded that fog bursting is positively affected by combination of decreasing Ta by radiative cooling, increasing RH, turbulence mixing, and high value of NPM2.5. Results from this work suggest that fog bursting conditions need to be studied in detail by numerical weather forecast (NWP) models that currently lack of these conditions.

Uso de parâmetros estatísticos para a classificação de regiões homogêneas de temperatura do ar.

Time series of hourly temperature from 146 weather stations located in Santa Catarina State – South Brazil were used to show that a compact data representation, using probability density functions (pdf) parameters, could be useful to classify homogeneous air temperature areas. The normal distribution fitted well the 146 weather stations temperature time series, presenting a median value of 0.9721 for the Pearson correlation coefficient. The means and standard deviations obtained by adjusting the Gaussian functions for the 146 stations were used as input parameters for two different classifiers: hierarchical and k-means. Both classifiers separated Santa Catarina's weather stations into four distinct groups. These groups had direct relationship with altitude ranges and with the influence of sea. The classification of weather stations in different homogeneous groups was useful to identify climatic behaviors of hourly temperatures. In addition to the characterization of the climate itself, this classification can be useful as a support for the validation of numerical weather forecast models, and for the identification of abnormal temperature time series in a regional spatial context.

Skill assessment of TIGGE precipitation forecasts in the four basins of Iran

In atmospheric sciences, forecast assessment, the process of examining the quality of forecasts, is very important. For this reason, we carried out a comprehensive evaluation of the most important global numerical weather forecast (NWP) models for the Iranian basins of Karkheh, Karun, Urmia, and Sefidrood. This study aims to evaluate the output of six different NWP models from the TIGGE database in important basins of Iran, to determine the most appropriate model for each basin. The assessment period took place between 2008 and 2018 and evaluated probabilistic and non-probabilistic methods. In the Karun Basin, the global NWP models were shown to perform better than in the other three basins. Overall, the UKMO (United Kingdom Met Office) and ECMWF (European Medium-Range Weather Forecast) performed best, while the Korean Meteorological Administration (KMA) showed the poorest performance among the models in the four basins. In the 25 mm threshold, most models underestimated precipitation events. The ECMWF model showed reliable and sharp forecasts and was better able to discriminate between low, medium, and high precipitation events compared to other models. Compared to those of other models, the UKMO model demonstrated greater brier skill scores. In general, the ECMWF, UKMO, ECCC (Environment and Climate Change Canada), and National Center for Environmental Forecast (NCEP) models performed relatively satisfactory in all four basins, although they forecasted better in basins with higher mean precipitation. These models are recommended for flood warning systems, and water resource decision making in Iran. However, all forecasts will need post-processing for operational applications.

Integrative investigation of dust emissions by dust storms and dust devils in North Africa

Dust aerosols in North Africa account for >50% of the global total; however dust emission areas are still unclear. Based on the analysis of dust storms simulated with the numerical Weather Research and Forecast (WRF) model, satellite aerosol index (AI), and the dust data observed at 300 meteorological stations over 20 years, the spatio-temporal distribution characteristics of dust storm, dust devil and AI are compared and analyzed. The results show that: 1) There are two dust emission mechanisms: the dynamically-dominated dust storm and thermally-dominated dust devil; 2) Dust storms occur most frequently in Spring and are concentrated in the areas of Grand Erg Occidental Desert to the Erg Chech-Adrar Desert, the northern part of Grand Erg Oriental, the Atouila Desert to the Ouarane Desert, the Mediterranean coast, the eastern side of Nubian Desert and Bodélé Depression; 3) Dust devils occur most frequently from April to August and are mainly concentrated in the central part of North Africa, especially in the southwest of Hoggar Mountains to the west of Air Mountains, the border area of Egypt - Sudan - Libya and the vicinity of Tibesti Plateau; 4) The spatio-temporal distribution of AI is correlated more with the dust devils emission whereas the annual average contributions by dust storms and dust devils are 61.3% and 38.7%, respectively. This study discovers a new area of dust emissions by dust devils, and provides a better explanation for the spatio-temporal distribution of AI in North Africa.

Uso de parâmetros estatísticos para a classificação de regiões homogêneas de temperatura do ar em Santa Catarina

Time series of hourly temperature from 146 weather stations located in Santa Catarina State – South Brazil were used to show that a compact data representation, using probability density functions (PDF) parameters, could be useful to classify homogeneous air temperature areas. The normal distribution fitted well the 146 weather stations temperature time series, presenting a median value of 0.9721 for the Pearson correlation coefficient.  The means and standard deviations obtained by adjusting the Gaussian functions for the 146 stations were used as input parameters for two different classifiers: hierarchical and k-means. Both classifiers separated Santa Catarina's weather stations into four distinct groups. These groups had direct relationship with altitude ranges and with the influence of sea. The classification of weather stations in different homogeneous groups was useful to identify climatic behaviors of hourly temperatures. In addition to the characterization of the climate itself, this classification can be useful as a support for the validation of numerical weather forecast models, and for the identification of abnormal temperature time series in a regional spatial context.