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\"Modeling atmospheric processes in order to forecast the weather or future climate change is an extremely complex and computationally intensive undertaking. One of the main difficulties is that there are a huge number of factors that need to be taken into account, some of which are still poorly understood. The Factor Separation (FS) method is a computational procedure that helps deal with these nonlinear factors. In recent years many scientists have applied FS methodology to a range of modeling problems, including paleoclimatology, limnology, regional climate change, rainfall analysis, cloud modeling, pollution, crop growth, and other forecasting applications. This book is the first to describe the fundamentals of the method, and to bring together its many applications in the atmospheric sciences. The main audience is researchers and graduate students using the FS method, but it is also of interest to advanced students, researchers, and professionals across the atmospheric sciences\"-- Provided by publisher.

Modeling atmospheric processes in order to forecast the weather or future climate change is an extremely complex and computationally intensive undertaking. One of the main difficulties is that there are a huge number of factors that need to be taken into account, some of which are still poorly understood. The Factor Separation (FS) method is a computational procedure that helps deal with these nonlinear factors. In recent years many scientists have applied FS methodology to a range of modeling problems, including paleoclimatology, limnology, regional climate change, rainfall analysis, cloud modeling, pollution, crop growth, and other forecasting applications. This book is the first to describe the fundamentals of the method, and to bring together its many applications in the atmospheric sciences. The main audience is researchers and graduate students using the FS method, but it is also of interest to advanced students, researchers, and professionals across the atmospheric sciences.

The critical role of trace gases in global atmospheric change makes an improved understanding of these gases imperative. Measurements of the distributions of these gases in space and time provide important information, but the interpretation of this information often involves ill-conditioned model inversions. A variety of techniques have therefore been developed to analyze these problems. Inverse Problems in Atmospheric Constituent Transport is the first book to give comprehensive coverage of work on this topic. The trace gas inversion problem is presented in general terms and the various different approaches are unified by treating the inversion problem as one of statistical estimation. Later chapters demonstrate the application of these methods to studies of carbon dioxide, methane, halocarbons and other gases implicated in global climate change. This book is aimed at graduate students and researchers embarking upon studies of global atmospheric change, biogeochemical cycles and Earth systems science.

Helping tackle the challenges of managing international assignment programs in today's business environment-in which companies have more nationalities, locations, and assignment types to deal with-this guide highlights critical issues such as dealing with policy dilemmas, addressing expatriate concerns, and establishing effective administration systems. The comprehensive resource also offers solutions for companies disadvantaged by having a limited human resources staff to manage such programs

It is well known that the urban canopy (UC) layer, i.e., the layer of air corresponding to the assemblage of the buildings, roads, park, trees and other objects typical to cities, is characterized by specific meteorological conditions at city scales generally differing from those over rural surroundings. We refer to the forcing that acts on the meteorological variables over urbanized areas as the urban canopy meteorological forcing (UCMF). UCMF has multiple aspects, while one of the most studied is the generation of the urban heat island (UHI) as an excess of heat due to increased absorption and trapping of radiation in street canyons. However, enhanced drag plays important role too, reducing mean wind speeds and increasing vertical eddy mixing of pollutants. As air quality is strongly tied to meteorological conditions, the UCMF leads to modifications of air chemistry and transport of pollutants. Although it has been recognized in the last decade that the enhanced vertical mixing has a dominant role in the impact of the UCMF on air quality, very little is known about the uncertainty of vertical eddy diffusion arising from different representation in numerical models and how this uncertainty propagates to the final species concentrations as well as to the changes due to the UCMF.To bridge this knowledge gap, we set up the Regional Climate Model version 4 (RegCM4) coupled to the Comprehensive Air Quality Model with Extensions (CAMx) chemistry transport model over central Europe and designed a series of simulations to study how UC affects the vertical turbulent transport of selected pollutants through modifications of the vertical eddy diffusion coefficient (Kv) using six different methods for Kv calculation. The mean concentrations of ozone and PM2.5 in selected city canopies are analyzed. These are secondary pollutants or having secondary components, upon which turbulence acts in a much more complicated way than in the case of primary pollutants by influencing their concentrations not only directly but indirectly via precursors too. Calculations are performed over cascading domains (of 27, 9, and 3 km horizontal resolutions), which further enables to analyze the sensitivity of the numerical model to grid resolution. A number of model simulations are carried out where either urban canopies are considered or replaced by rural ones in order to isolate the UC meteorological forcing. Apart from the well-pronounced and expected impact on temperature (increases up to 2 ∘C) and wind (decreases by up to 2 ms-1), there is a strong impact on vertical eddy diffusion in all of the six Kv methods. The Kv enhancement ranges from less than 1 up to 30 m2s-1 at the surface and from 1 to 100 m2s-1 at higher levels depending on the methods. The largest impact is obtained for the turbulent kinetic energy (TKE)-based methods.The range of impact on the vertical eddy diffusion coefficient propagates to a range of ozone (O3) increase of 0.4 to 4 ppbv in both summer and winter (5 %–10 % relative change). In the case of PM2.5, we obtained decreases of up to 1 µgm-3 in summer and up to 2 µgm-3 in winter (up to 30 %–40 % relative change). Comparing these results to the “total-impact”, i.e., to the impact of all meteorological modifications due to UCMF, we can conclude that much of UCMF is explained by the enhanced vertical eddy diffusion, which counterbalances the opposing effects of other components of this forcing (temperature, humidity and wind). The results further show that this conclusion holds regardless of the resolution chosen and in both the warm and cold parts of the year.

Volcanic eruptions can cause significant disruption to society, and numerical models are crucial for forecasting the dispersion of erupted material. Here we assess the skill and limitations of the Met Office's Numerical Atmospheric-dispersion Modelling Environment (NAME) in simulating the dispersion of the sulfur dioxide (SO2) cloud from the 21–22 June 2019 eruption of the Raikoke volcano (48.3∘ N, 153.2∘ E). The eruption emitted around 1.5±0.2 Tg of SO2, which represents the largest volcanic emission of SO2 into the stratosphere since the 2011 Nabro eruption. We simulate the temporal evolution of the volcanic SO2 cloud across the Northern Hemisphere (NH) and compare our model simulations to high-resolution SO2 measurements from the TROPOspheric Monitoring Instrument (TROPOMI) and the Infrared Atmospheric Sounding Interferometer (IASI) satellite SO2 products. We show that NAME accurately simulates the observed location and horizontal extent of the SO2 cloud during the first 2–3 weeks after the eruption but is unable, in its standard configuration, to capture the extent and precise location of the highest magnitude vertical column density (VCD) regions within the observed volcanic cloud. Using the structure–amplitude–location (SAL) score and the fractional skill score (FSS) as metrics for model skill, NAME shows skill in simulating the horizontal extent of the cloud for 12–17 d after the eruption where VCDs of SO2 (in Dobson units, DU) are above 1 DU. For SO2 VCDs above 20 DU, which are predominantly observed as small-scale features within the SO2 cloud, the model shows skill on the order of 2–4 d only. The lower skill for these high-SO2-VCD regions is partly explained by the model-simulated SO2 cloud in NAME being too diffuse compared to TROPOMI retrievals. Reducing the standard horizontal diffusion parameters used in NAME by a factor of 4 results in a slightly increased model skill during the first 5 d of the simulation, but on longer timescales the simulated SO2 cloud remains too diffuse when compared to TROPOMI measurements. The skill of NAME to simulate high SO2 VCDs and the temporal evolution of the NH-mean SO2 mass burden is dominated by the fraction of SO2 mass emitted into the lower stratosphere, which is uncertain for the 2019 Raikoke eruption. When emitting 0.9–1.1 Tg of SO2 into the lower stratosphere (11–18 km) and 0.4–0.7 Tg into the upper troposphere (8–11 km), the NAME simulations show a similar peak in SO2 mass burden to that derived from TROPOMI (1.4–1.6 Tg of SO2) with an average SO2 e-folding time of 14–15 d in the NH. Our work illustrates how the synergy between high-resolution satellite retrievals and dispersion models can identify potential limitations of dispersion models like NAME, which will ultimately help to improve dispersion modelling efforts of volcanic SO2 clouds.

A new physics package containing revised convection and planetary boundary layer (PBL) schemes in the National Centers for Environmental Prediction’s Global Forecast System is described. The shallow convection (SC) scheme in the revision employs a mass flux parameterization replacing the old turbulent diffusion-based approach. For deep convection, the scheme is revised to make cumulus convection stronger and deeper to deplete more instability in the atmospheric column and result in the suppression of the excessive grid-scale precipitation. The PBL model was revised to enhance turbulence diffusion in stratocumulus regions. A remarkable difference between the new and old SC schemes is seen in the heating or cooling behavior in lower-atmospheric layers above the PBL. While the old SC scheme using the diffusion approach produces a pair of layers in the lower atmosphere with cooling above and heating below, the new SC scheme using the mass-flux approach produces heating throughout the convection layers. In particular, the new SC scheme does not destroy stratocumulus clouds off the west coasts of South America and Africa as the old scheme does. On the other hand, the revised deep convection scheme, having a larger cloud-base mass flux and higher cloud tops, appears to effectively eliminate the remaining instability in the atmospheric column that is responsible for the excessive grid-scale precipitation in the old scheme. The revised PBL scheme, having an enhanced turbulence mixing in stratocumulus regions, helps prevent too much low cloud from forming. An overall improvement was found in the forecasts of the global 500-hPa height, vector wind, and continental U.S. precipitation with the revised model. Consistent with the improvement in vector wind forecast errors, hurricane track forecasts are also improved with the revised model for both Atlantic and eastern Pacific hurricanes in 2008.

A new model is proposed for the so-called scalar footprint and flux footprint in the atmospheric boundary layer. The underlying semi-analytical model allows computing the scalar concentration and flux fields related to turbulent diffusion of heat, water-vapor or to the dispersion of any scalar (e.g. passive pollutant) in the framework of K-theory. It offers improved capabilities regarding the representation of the gradual stratification in the boundary layer. In this model, the boundary layer is split in a series of sublayers in which the aerodynamic inertivity (a compound parameter aggregating wind-speed and eddy-diffusivity) is approximated by a sum of two power-law functions of a new vertical scale corresponding to the height-dependent downwind extension of the plume. This multilayer approach allows fitting with vanishing error any boundary-layer stratification, in particular those described by the Monin–Obukhov similarity theory (MOST) in the surface layer, while keeping the computation time of the footprint to low values. As a complement, a fully analytical surrogate model is presented for practical applications. For MOST profiles, the flux (resp. concentration) footprint is, to a RMS difference less than 1% (resp. 1.2%), equal (resp. equal to a constant multiplicative factor) to the inverse Gamma distribution. The optimal parameters of this distribution were evaluated for a broad range of atmospheric conditions and height. Regression formulas were also provided to compute the crosswind-integrated flux footprint distribution easily and with less than 1.6% RMS residual error. A comparison with the well-known footprint approximate model by Kormann and Meixner and the one by Hsieh, Katul and Chi has allowed quantifying their performances and limitations.

The maximum information entropy production model (MaxEnt), the relative humidity at equilibrium approach (ETRHEQ), and the Surface Flux Equilibrium model (SFE) are three recently developed models to estimate evapotranspiration. Although the connection between ETRHEQ and SFE is evident, no attempts have been made to investigate the congruence, distinctions, or potential complementarity between the two models and MaxEnt. Our mathematical analysis demonstrates that minimizing the vertical variance of RH in ETRHEQ is equivalent to minimizing the dissipation function of energy fluxes in MaxEnt, under the assumption of the same eddy diffusivity of heat and water vapor and with a specific expression for the ratio between the thermal inertia terms for H and LE. The connection between ETRHEQ, SFE, and MaxEnt is independent of Monin‐Obukhov similarity theory (MOST)’s extremum solution, and MOST's extreme solution can be viewed as equivalent to introducing a constant correction factor to account for atmospheric stability. While ETRHEQ and MaxEnt can be united within a single hydrometeorological framework, they diverge in their approaches to modeling evapotranspiration, particularly in how they address the roles of vegetation and land surface heterogeneity. More importantly, the unified framework suggests that turbulence fluxes within the atmospheric boundary layer adhere to the principles of maximum information entropy production. The way in which dissipation, along with its associated entropy production, is established using information entropy theory deviates from traditional thermodynamic entropy formulations. Exploring the connection between thermodynamic and information entropy and developing proper formulations of dissipation for energy fluxes presents an appealing avenue for prospective research. Plain Language Summary This paper seeks to establish a common theory for explaining the effectiveness of the maximum information entropy production model (MaxEnt), the relative humidity at equilibrium approach (ETRHEQ), and the Surface Flux Equilibrium model (SFE) in estimating evapotranspiration over a wide range of conditions. It uncovers that under reasonable assumptions, ETRHEQ’s method in terms of minimizing the vertical variance in relative humidity is equivalent to MaxEnt’s approach to minimizing dissipation. The united theory suggests that the movement of air and energy in the lower atmosphere follows the principles of maximum information entropy production, a concept that is a bit different from traditional ideas of entropy in thermodynamics. Looking into how thermodynamic entropy and information entropy are connected and figuring out the right way to calculate dissipation and entropy production for energy flux could lead to new insights in atmospheric science and land‐atmosphere interactions. Key Points The study presented a pure theorical analysis to unifying MaxEnt, ETRHEQ, and SFE within the same hydrometeorological framework Minimizing the vertical variance of RH in ETRHEQ is equivalent to minimizing the dissipation function of energy fluxes in MaxEnt The connection between MaxEnt, ETRHEQ, and SFE is independent of Monin‐Obukhov similarity theory (MOST)’s extremum solution