Observed Temperature Anomalies Research Articles

Detection of landscape features with visible and thermal imaging at the Castle of Puerta Arenas

There are some archaeological sites with hard accessibility which remain unexplored and barely documented. The use of unmanned aerial systems (UAS) alleviates this challenge with aerial observations monitored with distant remote control. In addition to acquiring images in the visible wavelengths, other devices can be coupled on aerial platforms to inspect beyond the remaining structure of an archaeological site. For instance, thermography has proven to be of great help in the detection of buried remains due to observed temperature anomalies. This work explores the Castle of Puerta Arenas fortress to build the first aerial 3D reconstruction of this site by using RGB and thermographic images collected from a UAS. Orthomosaics have been applied to hypothesize about the original shape of the fortress, whereas 3D reconstructions have been rather applied to visualization and analysis. In this regard, the explored remains have been processed as dense point clouds in the visible and long-wave infrared spectrum, with the latter leading to the detection of hypothetical and still unknown towers. The detection of anomalies has been automatized by performing statistical analyses, globally and limited to smaller 3D voxel neighbourhoods. As a result, the studied remains have been documented and observed from an unexplored perspective, helping their conservation and dissemination, as well as suggesting future excavations.

Dielectric ordering of water molecules in the dipole lattice of potassium hexacyanoferrate(II) trihydrate

The structure of potassium hexacyanoferrate(II) trihydrate (yellow prussiate) contains layers of water molecules between the layers of potassium and ferrocyanide octahedra. When the temperature drops down to 120 K, the water molecules have a tendency to rearrange. The movement of protons in molecular and individual rings occurs in a coordinated manner. The asymmetry of the ring environment leads to a significant decrease of the double-well potential barrier. The theory of coherent correlated motions of protons allows to explain the discreteness of the observed temperature anomalies of dielectric permittivity ε, thermally stimulated depolarization current j, and pyroelectric coefficient γ.

Temperature and moisture transport during atmospheric blocking patterns around the Antarctic Peninsula

We assess temperature and moisture transport in and around the Antarctic Peninsula (AP) associated with atmospheric blocking over two domains, one located to the west (150–90°W, 50–70°S, Western AP (WAP)) and the other over and to the east (90–30°W, 50–70°S, Eastern AP (EAP)) of the AP. We make use of surface meteorological observations, ERA5 reanalysis data, and a state-of-the-art atmospheric river (AR) database. Observed temperature anomalies indicate that the WAP and EAP blocking patterns are characterized by significant cold and warm anomalies over the AP, respectively, particularly in austral autumn, winter and spring. Consistent with these changes, cold anomalies depicted by ERA5 are associated with the transport of cold and dry air from the Antarctic continent by southerly and southeasterly flow over the eastern flank of the WAP blocking. ERA5 results highlight the importance of blocking days over the EAP domain (largely centered over the Drake Passage) to the occurrence of warm events associated with northerly and northwesterly warm air transport. Significant increases in integrated vapor transport (IVT) and AR frequency are also evident during the EAP blocking, particularly on the windward side of the AP. During the most extreme blocking days in this domain, there exist significant increases in latent and sensible heat fluxes on the windward side of the AP and the Larsen C Ice Shelf, respectively, indicating the contribution of foehn events to warm anomalies, especially in austral autumn and winter. The co-occurrences between landfalling ARs and blocking are found to amplify foehn effect due to higher IVT and associated latent heat condensation compared to blocking days without ARs. We conclude that blocking patterns are important to understand the occurrence of extremely warm events and landfalling ARs in the AP and their potential impacts on the surface cryospheric processes.

The roles of global warming and Arctic Oscillation in the winter 2020 extremes in East Asia

The 2019/20 winter was extremely warm globally and in the Northern Hemisphere extratropics. The main cause of climate extremes particularly in East Asia, was the extreme positive Arctic Oscillation (AO) event superimposed on steady global warming. The negligible trend in the AO over the preceding 41 years makes it possible to distinguish the roles of AO and global warming in the observed extremes. We estimate and compare contributions to January–March 2020 climate extremes by the AO and global warming represented by local temperature trends using the ERA5 reanalysis data. Based on results from a preliminary study, we estimate the contribution by global warming using linear regression while that by the AO using cubic regression, which is more restrained for the high AO index values than linear. The results show that the temperature extremes were mainly caused by the extreme positive AO event which accounts for approximately 3/4 of the observed temperature anomalies in northern East Asia and 2/3 in eastern East Asia. In southern East Asia, the AO contributes negligibly and positive temperature anomalies are related to global warming and local and regional impacts, particularly extreme sea surface temperature, enhance south-westerlies and local radiative forcing. General conclusion is that the observed strong positive temperature anomalies including extreme anomalies over East Asia could have been achieved only as a combined effect of the extreme positive AO event and global warming. Quantification of the roles of the AO and global warming in climate extremes helps to estimate future anomalies caused by extreme AO events as well as assess uncertainties in climate model projections.

The power spectrum of climate change

Both global, intermediate and local scales of Climate Change have been studied extensively, but a unified diagnostic framework for examining all spatial scales concurrently has remained elusive. Here we present a new tool-set using spherical harmonics to examine climate change through surface temperature anomalies from 1850 to 2021 on spatial scales ranging from planetary to $50$ km scales. We show that the observed temperature anomalies are accurately decomposed in spherical harmonics typically within 0.05 K. This decomposition displays a remarkably simple dependence on spatial scale with a universal shape across seasons and decades. The decomposition separates two distinct regimes by a characteristic turnover-length of approximately $3000$ km. The largest scales confirm established trends, while local fluctuations are consistent with 2-dimensional turbulence. We observe a downward cascade, from which it follows that climate change feeds increasing volatility on all spatial scales from $2.000$ to $50$ km. This increase is primarily driven by growing volatility along longitudes.

Frictional heating in a thick fault zone with empirical slip-weakening friction: implications for slip parameter estimation from temperature observations in deep fault drilling

The strain energy released during an earthquake is consumed by processes related to seismic radiation or dissipation. Deep fault drilling and subsequent temperature measurements in a thick fault zone immediately after an event have provided important insights into this dissipation process. By employing an analytical solution to the heat conduction problem, which involves the sudden injection of an infinitesimally thin heat source into an infinite medium, previous drilling projects have estimated the strength of the heat source and the level of shear stress from observed temperature anomalies. However, it is unclear under what conditions this analytical source solution can be regarded as a good approximation for the thick fault problem, a situation which has led to uncertainty of the approximation error in these previous studies. In this study, I first derived an analytical solution for the thick fault problem that accounted for experimentally derived slip-weakening friction. I then validated the derived solution both analytically and numerically. Using the derived thick solution, I next demonstrated that the thick, planar, and source solutions can be considered equivalent under the typical conditions of the previous drilling projects. Therefore, the slip parameters estimated by using the source solution obtained by these studies are appropriate. These results suggest that coseismic information with spatio-temporal extent, such as shear stress and friction coefficient, are lost due to heat diffusion when the temperature observations are conducted; thus, they cannot be inferred directly from observed temperature anomalies. These results also suggest that for most drilling projects, including future ones, the observed temperature distribution can be well explained by using the source solution instead of the thick solution as long as coseismic slip is not markedly delocalized and the spatial extent of the temperature measurements is not significantly larger than the diffusion length.

Disentangling dynamical and thermodynamical contributions to the record-breaking heatwave over Central Europe in June 2019

An unprecedented heatwave hit central Europe in June 2019 and set record-breaking high temperatures over approximately one-third areas of Europe. The relative role of dynamics and thermodynamics in anchoring this extreme heatwave is investigated via the flow analogue method. It reveals that dynamical processes contributed to 64.7% of the observed temperature anomalies of the event, and thermodynamical processes contributed to the remaining 35.3%. Nevertheless, thermodynamical changes were favorable towards the occurrence of similar events over central Europe in recent decades, whereas dynamical changes acted oppositely. In addition, their relative contributions strongly fluctuated with time, imposing challenges to project how heatwaves will behave on a regional scale.

Evaluation on the CFSv2 forecasts of three cold waves during 2015–2016 in China

In this paper, by using forecasts of the highest and lowest daily surface air temperatures from the Coupled Forecast System version 2 (CFSv2) over a period of 9 months, NCEP/DOE reanalysis data from the National Centers for Environmental Prediction (NCEP), and data from the U.S. NOAA Climate Prediction Center (CPC), three cold waves are identified during 2015–2016 in China, and the CFSv2 forecasts of these cold waves are evaluated. The results show that the high-resolution CPC data and the low-resolution NCEP/DOE data both very clearly show the temperature drops and temperature anomalies of these cold waves. The earlier the forecast lead time is, the greater the errors in the beginning and ending dates of the cold waves between the CFSv2 forecasts and the observations; there are errors of 1–2 days in the durations of the cold waves between the CFSv2 forecasts and the observations. CFSv2 exhibits certain abilities to forecast the overall temperature drops and temperature anomalies of cold waves at forecast lead times of 0, 5, 10, and 15 days, but the CFSv2 prediction ability is poor at forecast lead times exceeding 20 days. CFSv2 can predict the spatial distribution of temperature drops better than it can predict that of temperature anomalies, but the absolute errors in the observed temperature drops are greater than those in the observed temperature anomalies. The temperature drops from the CFSv2 forecasts are obviously underestimated in North China, South China, and their nearby areas but overestimated in Mongolia and Northeast China. The temperature anomalies from the CFSv2 forecasts are obviously underestimated in the inland areas of China but overestimated in the surrounding oceans and adjacent coastal areas. The difference fields at forecast lead times of 0 and 30 days are basically the same, but the absolute values of the differences increase with increases in the forecast lead time (with a few exceptions).

Stratospheric temperature anomalies as imprints from the dark Universe

The manifestation of the dark Universe begun with unexpected large scale astronomical observations. Here we are investigating the origin of small scale anomalies, like that of the annually observed temperature anomalies in the stratosphere. Unexpectedly, we observe a planetary relationship of the daily stratospheric temperature distribution. Its spectral shape does not match concurrent solar activity, or solar EUV emission, whose impact on the atmosphere is unequivocal. This behavior points at an additional energy source of exosolar origin. A viable concept behind such observations is based on possible gravitational focusing by the solar system towards the Earth of low speed invisible matter. We denote generic constituents from the dark Universe as invisible matter, in order to distinguish them from ordinary dark matter candidates like axions or WIMPs, which cannot have any noticeable impact on the stratosphere. The observed peaking planetary relations exclude on their own any conventional explanation. Only a somehow strongly interacting invisible streaming matter with the little screened upper stratosphere (noverhead about 1 gr/cm 2) can be behind the occasionally observed temperature increases. We also estimate an associated energy deposition O(W/m 2), which is variable over the 11 years solar cycle. For the widely assumed picture of a quasi not interacting dark Universe, this new exosolar energy is enormous. Since the atmosphere is uninterruptedly monitored since decades, it can serve also parasitically as a novel low threshold detector for the dark Universe, with built-in spatiotemporal resolution and the solar system acting temporally as signal amplifier. In future, analyzing more observations, for example, from the anomalous ionosphere, or, the transient sudden stratospheric warmings, the nature of the assumed invisible streams could be deciphered.

Analysis of Monsoon Mission Coupled Forecasting System (MMCFS) model simulations of sub-division scale temperatures over India for the hot weather season (April–June)

This study examines skill of the Monsoon Mission Climate Forecasting System (MMCFS) model simulations of monthly and seasonal maximum, minimum and mean temperatures of hot weather season (April, May and June) for the period 1982–2008 over India. The hindcast skill at the sub-division and all India scales were computed. The hindcasts were prepared using initial conditions (ICs) pertaining to January, February and March. The bias-corrected forecast for the 2016 AMJ season was also verified with the high resolution gridded temperature data of the India Meteorological Department (IMD). Standard verification skill scores, namely correlation coefficient (CC) and root mean square error (RMSE) have been used to assess the hindcast skill at various lead times. The grid point level statistical bias-correction was successful in reducing the bias and RMSE of the MMCFS model hindcast at the sub-division and all India scales. The hindcast analysis showed that for all the considered ICs and during all the months (April, May and June) and AMJ season for maximum, minimum and mean temperature bias of four sub-divisions Jammu & Kashmir (J&K), Himachal Pradesh, Uttarakhand, and Arunachal Pradesh showed bias $${\le }{-}2.0^{\circ }\hbox {C}$$ and four sub-divisions Saurashtra and Kutch, Bihar, Gangetic West Bengal, Sub-Himalayan West Bengal (SHWB) and Sikkim showed bias $${\ge }2^{\circ }\hbox {C}$$ . Hindcast based on the February and March ICs showed the best skill both in sub-division scale as well as in all India scale. Similarly, among the three months of the AMJ season, model skills based on considered ICs were best for the April month. Many of the sub-divisions from northwest India and neighboring central India and along the west coast showed significant hindcast skill for simulations based on February and March ICs. For the all India averaged temperature, March IC based forecast showed the highest skill for all the months and AMJ season followed by February IC. The MMCFS model forecasts for the 2016 monthly and seasonal temperatures were able to indicate correct signs of the observed temperature anomalies in most of the sub-divisions. The pattern anomaly correlations for the May and June forecasts based on March IC were significant at $$\ge $$ 95% level.

Attribution of surface temperature anomalies induced by land use and land cover changes

AbstractLand use/land cover changes (LULCC) directly impact the surface temperature by modifying the radiative, physiological, and aerodynamic properties controlling the surface energy and water balances. In this study, we propose a new method to attribute changes in the surface temperature induced by LULCC to changes in radiative and turbulent heat fluxes, with the partition of turbulent fluxes controlled by aerodynamic and surface resistances. We demonstrate that previous attribution studies have overestimated the contribution of aerodynamic resistance by assuming independence between the aerodynamic resistance and the Bowen ratio. Our results further demonstrate that acceptable agreement between modeled and observed temperature anomalies does not guarantee correct attribution by the model. When performing an attribution analysis, the covariance among attributing variables needs to be taken into consideration in order to accurately interpret the results.

The Influence of Synoptic Circulations and Local Processes on Temperature Anomalies at Three French Observatories

AbstractThe relative contribution of the synoptic-scale circulations to local and mesoscale processes was quantified in terms of the variability of midlatitude temperature anomalies from 2003 to 2013 using meteorological variables collected from three French observatories and reanalyses. Four weather regimes were defined from sea level pressure anomalies using National Centers for Environmental Prediction reanalyses with a K-means algorithm. No correlation was found between daily temperature anomalies and weather regimes, and the variability of temperature anomalies within each regime was large. It was therefore not possible to evaluate the effect of large scales on temperature anomalies by this method. An alternative approach was found with the use of the analogs method: the principle being that for each day of the considered time series, a set of days that had a similar large-scale 500-hPa geopotential height field within a fixed domain was considered. The observed temperature anomalies were then compared with those observed during the analog days: the closer the two types of series are to each other, the greater is the influence of the large scale. This method highlights a widely predominant influence of the large-scale atmospheric circulation on the temperature anomalies. It showed a potentially larger influence of the Mediterranean Sea and orographic flow on the two southern observatories. Low-level cloud radiative effects substantially modulated the variability of the daily temperature anomalies.

Linking teleconnection patterns to European temperature – a multiple linear regression model

The link between the indices of twelve atmospheric teleconnection patterns (mostly Northern Hemispheric) and gridded European temperature data is investigated by means of multiple linear regression models for each grid cell and month. Furthermore index-specific signals are calculated to estimate the contribution to temperature anomalies caused by each individual teleconnection pattern. To this extent, an observational product of monthly mean temperature (E-OBS), as well as monthly time series of teleconnection indices (CPC, NOAA) for the period 1951–2010 are evaluated. The stepwise regression approach is used to build grid cell based models for each month on the basis of the five most important teleconnection indices (NAO, EA, EAWR, SCAND, POLEUR), which are motivated by an exploratory correlation analysis. The temperature links are dominated by NAO and EA in Northern, Western, Central and South Western Europe, by EAWR during summer/autumn in Russia/Fenno-Scandia and by SCAND in Russia/Northern Europe; POLEUR shows minor effects only. In comparison to the climatological forecast, the presented linear regression models improve the temperature modelling by 30–40 % with better results in winter and spring. They can be used to model the spatial distribution and structure of observed temperature anomalies, where two to three patterns are the main contributors. As an example the estimated temperature signals induced by the teleconnection indices is shown for February 2010.

Quantitative decomposition of radiative and non-radiative contributions to temperature anomalies related to siberian high variability

In this study, we carried out an attribution analysis that quantitatively assessed relative contributions to the observed temperature anomalies associated with strong and weak Siberian High (SH). Relative contributions of radiative and non-radiative processes to the variation of surface temperature, in terms of both amplitude and spatial pattern, were analyzed. The strong SH activity leads to the continental-scale cold temperature anomalies covering eastern Siberia, Mongolia, East China, and Korea (i.e., SH domain). The decomposition of the observed temperature anomalies associated with the SH variation was achieved with the Coupled atmosphere–surface climate Feedback-Responses Analysis Method, in which the energy balance in the atmosphere–surface column and linearization of radiative energy perturbation are formulated. For the mean amplitude of −3.13 K of cold temperature anomaly over the SH domain, sensible heat flux is tightly connected with a cooling of −1.26 K. Atmospheric dynamics adds another −1.13 K through the large-scale cold advection originated from the high latitudes. The longwave effects of cloud and water vapor account for the remaining cold anomalies of −1.00 and −0.60 K, respectively, while surface dynamics (0.71 K) and latent heat flux (0.26 K) help to mitigate the cold temperature anomalies. Influences of ozone and albedo processes are found to be relatively weak.

Radiative and Dynamical Forcing of the Surface and Atmospheric Temperature Anomalies Associated with the Northern Annular Mode

Abstract On the basis of the total energy balance within an atmosphere–surface column, an attribution analysis is conducted for the Northern Hemisphere (NH) atmospheric and surface temperature response to the northern annular mode (NAM) in boreal winter. The local temperature anomaly in the European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Re-Analysis (ERA-Interim) is decomposed into partial temperature anomalies because of changes in atmospheric dynamics, water vapor, clouds, ozone, surface albedo, and surface dynamics with the coupled atmosphere–surface climate feedback–response analysis method (CFRAM). Large-scale ascent/descent as part of the NAM-related mean meridional circulation anomaly adiabatically drives the main portion of the observed zonally averaged atmospheric temperature response, particularly the tropospheric cooling/warming over northern extratropics. Contributions from diabatic processes are generally small but could be locally important, especially at lower latitudes where radiatively active substances such as clouds and water vapor are more abundant. For example, in the tropical upper troposphere and stratosphere, both cloud and ozone forcings are critical in leading to the observed NAM-related temperature anomalies. Radiative forcing due to changes in water vapor acts as the main driver of the surface warming of southern North America during a positive phase of NAM, with atmospheric dynamics providing additional warming. In the negative phase of NAM, surface albedo change drives the surface cooling of southern North America, with atmospheric dynamics providing additional cooling. Over the subpolar North Atlantic and northern Eurasia, atmospheric dynamical processes again become the largest contributor to the NAM-related surface temperature anomalies, although changes in water vapor and clouds also contribute positively to the observed surface temperature anomalies while change in surface dynamics contributes negatively to the observed temperature anomalies.

Identifying the causes of the poor decadal climate prediction skill over the North Pacific

While the North Pacific region has a strong influence on North American and Asian climate, it is also the area with the worst performance in several state‐of‐the‐art decadal climate predictions in terms of correlation and root mean square error scores. The failure to represent two major warm sea surface temperature events occurring around 1963 and 1968 largely contributes to this poor skill. The magnitude of these events competes with the largest observed temperature anomalies in the twenty‐first century that might be associated with the long‐term warming. Understanding the causes of these major warm events is thus of primary concern to improve prediction of North Pacific, North American and Asian climate. The 1963 warm event stemmed from the propagation of a warm ocean heat content anomaly along the Kuroshio‐Oyashio extension. The 1968 warm event originated from the upward transfer of a warm water mass centered at 200 m depth. For being associated with long‐lived ocean heat content anomalies, we expect those events to be, at least partially, predictable. Biases in ocean mixing processes present in many climate prediction models seem to explain the inability to predict these two major events. Such currently unpredictable warm events, if occurring again in the next decade, would substantially enhance the effect of long‐term warming in the region.

Process-Based Decomposition of the Global Surface Temperature Response to El Niño in Boreal Winter

Abstract This paper reports an attribution analysis that quantifies addible contributions to the observed temperature anomalies from radiative and nonradiative processes in terms of both amplitude and spatial pattern for the two most prominent surface temperature patterns in an El Niño winter. One is the El Niño SST pattern consisting of warming SST anomalies over the eastern equatorial Pacific basin surrounded by cooling SST anomalies in the western and subtropical Pacific, and the other is a tripole surface temperature anomaly characteristic of a positive Pacific–North American (PNA) teleconnection pattern. The decomposition of the observed temperature anomalies is achieved with the coupled atmosphere–surface climate feedback-responses analysis method (CFRAM), which is formulated utilizing energy balance in the atmosphere–surface columns and linearization of radiative energy perturbation. Out of the mean amplitude of 0.78 K of the El Niño SST pattern, the oceanic dynamics and heat storage term alone contributes to 2.34 K. Water vapor feedback adds another 1.6 K whereas both cloud and atmospheric dynamical feedbacks are negative, reducing the mean amplitude by 2.02 and 1.07 K, respectively. Atmospheric dynamical feedback contributes more than 50% (0.73 K) of the mean amplitude (1.32 K) of the PNA surface temperature pattern. Water vapor and surface albedo feedbacks contribute 0.34 and 0.13 K, respectively. The surface processes, including oceanic dynamics in the North Pacific, heat storage anomalies, and surface sensible/latent heat flux anomalies of ocean and land also contribute positively to the PNA surface temperature pattern (about 0.14 K). Cloud and ozone feedback, although very weak, act to oppose the PNA surface temperature anomaly.

Evaluation of an Infrared Camera Technique for Detecting Mechanically Induced Internal Voids in Syzygium grande

In order to evaluate a proposed tree diagnostic technique employing infrared cameras, research was conducted to evaluate the effect of internal voids on surface temperature using a thermal photographic instrument. Three axial cylindrical voids of increasing size (Void A, 327 cm3; Void B, 745 cm3; Void C, 1159 cm3) were introduced mechanically in 45 cm long stem sections and exposed to direct sunlight. Subsequently, infrared images were collected from two diametrically opposed sides of the stem sections at regular 30-minute intervals over 150 minutes. The collected images were evaluated visually to compare stem features with observed temperature anomalies, and temperature data was extracted from a vertical transect in the infrared images. The data extracted were compared against a control stem section without defects to determine the independent and combined effects of void size and internal position on surface temperature. Mean relative temperature revealed a significant temperature change in the stems containing mechanical voids compared to the control stem. Significant increases in mean relative temperature were recorded on the stems containing Void A and Void B compared to the control. However, there was no significant change in mean relative temperature on the stem section containing Void C.

ENSO Signals in Tropospheric Temperature Simulated by an AGCM GAMIL

AbstractUsing reanalysis data as a benchmark, the authors evaluate the performance of an Atmospheric General Circulation Model (AGCM) named GAMIL (Grid-point Atmospheric Model of LASG/IAP). GAMIL is used to simulate the tropospheric temperature anomalies associated with the El Nino-Southern Oscillation (ENSO) in boreal winters for the period 1980–99. The results show that the symmetrical components of temperature anomalies simulated by GAMIL closely resemble those in the reanalysis data in spatial patterns, especially in the Northern Hemisphere. The limitation of the model is that the simulated cold anomaly over South Asia is located to the east of the reanalysis. The observed temperature anomalies in the South Pacific and the high latitudes of the Southern Hemisphere are not evident in the simulation. The maximum value is 0.8 K smaller and the minimum value is –0.4 K smaller than the reanalysis. The difference between the simulation and the reanalysis is more evident in the regional features of the asymm...

An operational system for the assimilation of the satellite information on wild-land fires for the needs of air quality modelling and forecasting

Abstract. This paper investigates a potential of two remotely sensed wild-land fire characteristics: 4-μm Brightness Temperature Anomaly (TA) and Fire Radiative Power (FRP) for the needs of operational chemical transport modelling and short-term forecasting of atmospheric composition and air quality. The treatments of the TA and FRP data are presented and a methodology for evaluating the emission fluxes of primary aerosols (PM2.5 and total PM) is described. The method does not include the complicated analysis of vegetation state, fuel load, burning efficiency and related factors, which are uncertain but inevitably involved in approaches based on burnt-area scars or similar products. The core of the current methodology is based on the empirical emission factors that are used to convert the observed temperature anomalies and fire radiative powers into emission fluxes. These factors have been derived from the analysis of several fire episodes in Europe (28.4–5.5.2006, 15.8–25.8.2006 and in August 2008). These episodes were characterised by: (i) well-identified FRP and TA values, and (ii) available ground-based observations of aerosol concentrations, and optical thickness for the regions where the contribution of the fire smoke to the concentrations of PM2.5 was dominant, in comparison with those of other pollution sources. The emission factors were determined separately for the forested and grassland areas; in case of mixed-type land use, an intermediate scaling was assumed. Despite significant differences between the TA and FRP methodologies, an accurate non-linear fitting was found between the predictions of these approaches. The agreement was comparatively weak only for small fires, for which the accuracy of both products is expected to be low. The applications of the Fire Assimilation System (FAS) in combination with the dispersion model SILAM showed that both the TA and FRP products are suitable for the evaluation of the emission fluxes from wild-land fires. The fire-originated concentrations of aerosols (PM2.5, PM10, sulphates and nitrates) and AOD, as predicted by the SILAM model were mainly within a factor of 2–3 compared with the observations. The main challenges of the FAS improvement include refining of the emission factors globally, determination of the types of fires (smouldering vs flaming), evaluation of the injection heights of the plumes, and predicting the temporal evolution of fires.