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The air traffic management automation imposes stringent requirements on the weather models, in such a way that they should be able to provide reliable short-time forecasts in digital formats in almost real time. The atmospheric boundary layer is one of the regions where aircraft operation and coordination are critical and therefore atmospheric model performance is also vital. This paper presents conventional and innovative techniques to improve the accuracy in the forecasting of winds in the lower atmospheric layer, proposing mechanisms to develop better models including deterministic and stochastic simulations. Accuracy is improved by optimizing the grid, assimilating observations in cycling simulations and managing a number of ensemble members. An operation-driven post-processing stage helps to incorporate detailed terrain definitions and real-time observations without re-running the model. The improvements are checked against mesoscale weather simulations at different scales and a dedicated flight campaign. The results show good performance of the model without sensitively increasing the required throughput.

The complexity of comprehensive ocean models poses an important question for parameterisations: is there a minimum set of equations that should be parameterised, on the one hand, to reduce the development to a minimum, and, on the other hand, to ensure an accurate representation of large-scale flow patterns? This work seeks to answer the question to assist modern parameterisations be more selective in their targets. For this, we considered a model of the North Atlantic and studied contributions of different model equations to the accuracy of representation of the Gulf Stream at low resolution. Our results suggest that one should focus on parameterising the tracer equations for temperature and salinity, and may leave the other equations in the hydrodynamic part, as well as the atmospheric model unmodified. They also suggest that parameterisations representing only Kinetic Energy Backscatter cannot be fully efficient and the main focus should be shifted towards developing parameterisations of combined Potential/Kinetic Energy Backscatters.

This study analyzed the performance of four (REgional MOdel (REMO2009), High-Resolution Hamburg Climate Model 5 (HIRAM5), Climate Limited-Area Modeling Community (CCLM4-8) and Rossby Centre Regional Atmospheric Model (RCA4)) Regional Climate Models (RCMs) simulations from Coordinated Regional Climate Downscaling Experiment (CORDEX) Africa program. The simulation period of 1985–2005 was evaluated considering how each RCM simulated the observed rainfall and air temperature over southwest Ethiopia. It was found that all the RCMs simulated the seasonal rainfall, but not the peak rainfall, with all models including their ensemble underestimating the peak rainfall. However, the ensemble was better than the individual RCMs in simulating both rainfall and air temperature. All models were slightly biased around a warm climate zone in simulating maximum air temperature when compared to the simulation of air minimum temperature. Of the four RCMs, REMO2009 performed well in simulating the maximum and minimum air temperatures. The interseasonal variation in rainfall was greater than the seasonal variation in air temperature. In terms of cumulative distribution, the HIRAM5 captured more extreme rainfall events and overestimated the return period. Overall, the differences in performance among the RCMs provided strong evidence for the use of regional-scale data at the local scale in climate impact assessments being controversial. In relation to the spatial pattern of the rainfall, most of the models simulated the observed minimum rainfall in the north and northeast, medium rainfall in the central region, and maximum rainfall in the south and southwest of the study area. The overall results indicate that choosing a reliable RCM is fundamentally necessary to delivering a strong basis for any climate-change impact study., Air temperature, CORDEX, Ethiopia, Performance, Rainfall, Seasonal.

Since the 1980s, the industrialization and urbanization of the Beijing area has entered a period of high-speed growth. This paper asks the question: How have such great changes in urban land-use over the past decades impacted urban precipitation? In this study, we investigate and analyze the effects of urbanization on the summer precipitation in Beijing using numerical modeling approaches. Applying the numerical mesoscale atmospheric model METRAS, we determine the impact of surface cover on 13 heavy precipitation events. We implement five idealized land-use scenarios: Reference scenario, No-urban scenario, High-building scenario, Urban-expand scenario, and No-vegetation scenario. There is nearly no difference in the mean precipitation sum across all 13 simulated rain events and between the urban-scenarios and the rural-scenario. We find effects of urbanization on precipitation only in some single cases. We conclude urbanization does effect the local precipitation of Beijing; it reduces rainfall in the urban area and increases rainfall downwind of the city. In some cases, larger percentage of sealed area could give rise to the heavier precipitation or extreme rain events. And we conclude the urban pattern significantly impacts rainfall area and intensity. Increased urban size or density may speed up rain clouds while increased urban height may disrupt or bifurcate the clouds. Our results offer a new viewpoint and further the study of urban impacts on precipitation (UIP). The results are important for sustainable and harmonious development of the economy, society, and environment in Beijing as well as other cities with rapid urbanization.

This study newly applies measurements from two geostationary satellites, the Advanced Himawari Imager (AHI) onboard the geostationary satellite Himawari-8 and the Geostationary Ocean Color imager (GOCI) onboard the geostationary satellite COMS, to evaluate a unique regional aerosol-transport model coupled to a non-hydrostatic icosahedral atmospheric model (NICAM) at a high resolution without any nesting technique and boundary conditions of the aerosols. Taking advantage of the unique capability of these geostationary satellites to measure aerosols with unprecedentedly high temporal resolution, we focus on a target area (115°E-155°E, 20°N-50°N) in East Asia in May 2016, which featured the periodic transport of industrial aerosols and a very heavy aerosol plume from Siberian wildfires. The aerosol optical thickness (AOT) fields are compared among the AHI, GOCI, MODIS, AERONET and NICAM data. The results show that both AHI- and GOCI-retrieved AOTs were generally comparable to the AERONET-retrieved ones, with high correlation coefficients of approximately 0.7 in May 2016. They also show that NICAM successfully captured the detailed horizontal distribution of AOT transported from Siberia to Japan on the most polluted day (18 May 2016). The monthly statistical metrics, including correlation between the model and either AHI or GOCI, are estimated to be >0.4 in 42–49% of the target area. With the aid of sensitivity model experiments with and without Siberian wildfires, it was found that a long-range transport of aerosols from Siberian wildfires (from as far as 3000 km) to Japan influenced the monthly mean aerosol levels, accounting for 7–35% of the AOT, 26–49% of the surface PM2.5 concentrations, and 25–66% of the aerosol extinction above 3 km in height over Japan. Therefore, the air pollutants from Siberian wildfire cannot be ignored for the spring over Japan.

This paper investigates the performance of three different wave model source term packages in narrow fetch geometries. The packages are used to model the sea state in a complex coastal system with narrow fjords on the west coast of Norway. The modelling system is based on the Simulating WAves Nearshore (SWAN) wave model that is forced with winds from a nested atmospheric model and wave spectra from a regional wave model at the boundaries. The performances of the recent ST6, and two older SWAN white-capping and wind input packages, are evaluated by comparing modelled spectra and integrated wave parameters against five wave buoys. The comparison covers long-term statistics and two case studies of narrow fetch geometries (i) without swell and (ii) with swell-wind sea conditions. SWAN’s original saturation-based approach performs best in the fjord system. In narrow fetch geometry without swell, all packages overestimate the wave energy. ST6 shows the highest sensitivity to fetch geometry and local wind changes. The results indicate that the ST6 white-capping is too weak to balance its strong wind input. publishedVersion

With the increasing extraction of methane from Lake Kivu, there is a growing need to evaluate the effect of such operations on the lake’s permanent density stratification. This requires understanding of the spatial structure and variability of flow velocities and constituents in Lake Kivu. In this study, we develop a 3D hydrodynamic model of Lake Kivu, set-up within DELFT3D at a 750 m grid spacing and forced by COSMO-CLM atmosphere model results at a 2.8 km grid spacing. Validation shows that the model correctly reproduces the generation and breakdown of the temperature stratification in the upper mixed layer and predicts flow velocity magnitudes and directions similar to measurements both at the surface and at greater depth. Analysis of currents reveals a surface current pattern with two clockwise circulations, one around the whole lake and a smaller one in the northern part, with velocities around 0.1 m/s. This pattern is consistently present over an (ensemble-)averaged day, both in the wet and in the dry season, while day-by-day variations are large. Time-averaged deep currents are found to be a few mm/s at maximum. However, the variations can be substantial, with standard deviations up to 2 cm/s for the currents at 220 m depth, attributed to internal seiches. The temperature stratification, present during the entire wet season, is found to first break down in the dry season in the southern part of the lake. This is explained by the spatial differences in the wind stress and the evaporation heat fluxes during the dry season.

We estimate the response of snowpack to global warming along with the uncertainty of the snowpack change by using a combination of multiple general-circulation models (GCMs), a single regional atmospheric model, and a one-dimensional multi-layered snowpack model. The target site is Mt. Annupuri in Kutchan, Hokkaido, Japan. The forcing of the snowpack model is taken from dynamically downscaled data from GCMs for the present climate and GCMs in a decade when the global-mean temperature has increased by 2 K from present conditions. The results show that global warming would decrease the monthly-mean snow depth throughout the winter season. Other salient features are the decrease of snow depth by 60 cm with maximum uncertainty of 20 cm at the beginning of the snow ablation period, the occurrence of the snow-depth peak a month earlier, and the dominance of melt forms in an earlier season. The ratio of melt forms for all snowpack layers increases with little uncertainty before the snow ablation period. The ratio of hoar does not change much, even though the air temperature increases. The uncertainty in snowpack evaluation is also discussed. (C) 2017 Elsevier B.V. All rights reserved.

Detection and attribution studies have demonstrated that anthropogenic forcings have been driving significant changes in temperature extremes since the middle of the 20th century. Moreover, new methodologies have been developed for the attribution of extreme events that assess how human influence may have changed their characteristics. Here we combine formal statistical analyses based on optimal fingerprinting to attribute observed long term changes in temperature extremes with an ensemble-based approach for event attribution. Our analyses are applied to 16 indices constructed with daily temperature data that focus on different characteristics of extremes and together build up a more complete representation of historical changes in warm and cold extremes than previous studies. For each index we compute an annual value for all years of the post-1960 period using data from observations and experiments with a coupled Earth System model for the analysis of multi-decadal changes and a high-resolution atmospheric model for event attribution. The models indicate that anthropogenic forcings have influenced almost all indices in recent decades and led to more prominent changes in the frequency of extremes. The optimal fingerprinting analyses show that for most indices the anthropogenic signal is detectable in changes during 1961–2010 both in Europe and on a quasi-global scale. The weaker natural effect, resulting mainly from volcanic eruptions, is in most cases not detectable, with the exception of large scale changes in indices linked to the frequency of cold night-time extremes. Our event analyses estimate how anthropogenic forcings alter the chances of getting new record index values in Europe and find that such extremes would be markedly rare if human influence were not accounted for, whereas in the current climate their return times range from a few years to a few decades.

The WUDAPT (World Urban Database and Access Portal Tools project goal is to capture consistent information on urban form and function for cities worldwide that can support urban weather, climate, hydrology and air quality modeling. These data are provided as urban canopy parameters (UCPs) as used by weather, climate and air quality models to simulate the effects of urban surfaces on the overlying atmosphere. Information is stored with different levels of detail (LOD). With higher LOD greater spatial precision is provided. At the lowest LOD, Local Climate Zones (LCZ) with nominal UCP ranges is provided (order 100 m or more). To describe the spatial heterogeneity present in cities with great specificity at different urban scales we introduce the Digital Synthetic City (DSC) tool to generate UCPs at any desired scale meeting the fit-for-purpose goal of WUDAPT. 3D building and road elements of entire city landscapes are simulated based on readily available data. Comparisons with real-world urban data are very encouraging. It is customized (C-DSC) to incorporate each city's unique building morphologies based on unique types, variations and spatial distribution of building typologies, architecture features, construction materials and distribution of green and pervious surfaces. The C-DSC uses crowdsourcing methods and sampling within city Testbeds from around the world. UCP data can be computed from synthetic images at selected grid sizes and stored such that the coded string provides UCP values for individual grid cells.

Generating scientific evidence on future drought is very crucial for designing adaption strategies ahead of time. In this study, agricultural drought is computed for Ethiopia using bias-corrected Coordinated Regional Climate Downscaling Experiment (CORDEX) Africa climate data considering past and future climate scenarios. Model performance comparison is done among eight CORDEX Africa climate models. Center National de Recherches Meteorologiques-Center Europe’en de Recherche et de Formation Avancee Global Circulation Models (CNRM-CERFACS GCMs) downscaled by RCA4 (Rossby Center Regional Atmospheric Model) regional climate model is selected to compute drought using Palmer Drought Severity Index (PDSI). The findings of the study revealed that the magnitude, frequency as well as areal extent of drought are anticipated to increase in the future. Specifically, western Gambela and Oromia regions, northern SNNP, southern Oromia, the western part of Tigray and Amhara, and the very eastern part of Afar regions will suffer most from drought in the mid of this century. Drought frequency will be every other year at different magnitude which was two to three times in a decade in the past. In the future, more areas will experience severe and moderate drought than it was before, and this situation will have negative implication on agriculture. In response, the agricultural sector should take tactical, strategic, and adaptive decisions to minimize the effect of drought on productivity and livelihoods.

This chapter addresses the basic principles of the radiative transfer theory and nonequilibrium plasma physics, which were developed during the past century and are now widely used to model solar and stellar atmospheres. Many key results have been obtained and new numerical methods developed by solar physicists; they serve as a guide for stellar astrophysics. We start with the basics of radiation transport and demonstrate the classical ideas of the nonequilibrium line formation using a two-level atom example. Then we summarize the equations used to construct standard plane-parallel models of atmospheres in hydrostatic and radiative equilibrium and show examples of models with different levels of complexity. Although such models are well-suited for modeling photospheres, solar and stellar chromospheres require even more advanced approaches. The simplest is represented by so-called semiempirical models; we show some classical examples for the Sun and cool stars. A specific class of problems concerns isolated atmospheric structures illuminated by solar or stellar disk radiation. Based on various atmospheric models, we discuss how model spectra are synthesized and spectral lines are actually formed. Finally, we briefly introduce the topic of radiation hydrodynamics as the most sophisticated tool for solar and stellar atmospheric modeling.

International audience; Recent observations of sulfur containing species (SO2, SO, OCS, and H2SO4) in Venus’ mesosphere have generated controversy and great interest in the scientific community. These observations revealed unexpected spatial patterns and spatial/temporal variability that have not been satisfactorily explained by models. Sulfur oxide chemistry on Venus is closely linked to the global-scale cloud and haze layers, which are composed primarily of concentrated sulfuric acid. Sulfur oxide observations provide therefore important insight into the on-going chemical evolution of Venus’ atmosphere, atmospheric dynamics, and possible volcanism.This paper is the first of a series of two investigating the SO2 and SO variability in the Venus atmosphere. This first part of the study will focus on the vertical distribution of SO2, considering mostly observations performed by instruments and techniques providing accurate vertical information. This comprises instruments in space (SPICAV/SOIR suite on board Venus Express) and Earth-based instruments (JCMT). The most noticeable feature of the vertical profile of the SO2 abundance in the Venus atmosphere is the presence of an inversion layer located at about 70–75 km, with VMRs increasing above. The observations presented in this compilation indicate that at least one other significant sulfur reservoir (in addition to SO2 and SO) must be present throughout the 70–100 km altitude region to explain the inversion in the SO2 vertical profile. No photochemical model has an explanation for this behaviour. GCM modelling indicates that dynamics may play an important role in generating an inflection point at 75 km altitude but does not provide a definitive explanation of the source of the inflection at all local times or latitudesThe current study has been carried out within the frame of the International Space Science Institute (ISSI) International Team entitled ‘SO2 variability in the Venus atmosphere’.

We present a new surface-atmospheric dataset for driving ocean–sea-ice models based on Japanese 55-year atmospheric reanalysis (JRA-55), referred to here as JRA55-do. The JRA55-do dataset aims to replace the CORE interannual forcing version 2 (hereafter called the CORE dataset), which is currently used in the framework of the Coordinated Ocean-ice Reference Experiments (COREs) and the Ocean Model Intercomparison Project (OMIP). A major improvement in JRA55-do is the refined horizontal grid spacing (∼ 55 km) and temporal interval (3 hr). The data production method for JRA55-do essentially follows that of the CORE dataset, whereby the surface fields from an atmospheric reanalysis are adjusted relative to reference datasets. To improve the adjustment method, we use high-quality products derived from satellites and from several other atmospheric reanalysis projects, as well as feedback on the CORE dataset from the ocean modelling community. Notably, the surface air temperature and specific humidity are adjusted using multi-reanalysis ensemble means. In JRA55-do, the downwelling radiative fluxes and precipitation, which are affected by an ambiguous cloud parameterisation employed in the atmospheric model used for the reanalysis, are based on the reanalysis products. This approach represents a notable change from the CORE dataset, which imported independent observational products. Consequently, the JRA55-do dataset is more self-contained than the CORE dataset, and thus can be continually updated in near real-time. The JRA55-do dataset extends from 1958 to the present, with updates expected at least annually. This paper details the adjustments to the original JRA-55 fields, the scientific rationale for these adjustments, and the evaluation of JRA55-do. The adjustments successfully corrected the biases in the original JRA-55 fields. The globally averaged features are similar between the JRA55-do and CORE datasets, implying that JRA55-do can suitably replace the CORE dataset for use in driving global ocean–sea-ice models.

One of the inherent challenges in remote sensing is the effect of atmospheric gases and particles on the light received by the sensor. The effects of atmospheric interference need to be adequately corrected for in order to retrieve quantitative biophysical information about a water body from the remotely sensed signal. Atmospheric correction of remotely sensed data is more challenging for inland waters than for open ocean waters due to a number of factors. The proximity of inland waters to various terrestrial sources of atmospheric pollution results in a more optically heterogeneous atmosphere, which complicates atmospheric modeling; the signal received at the sensor is often contaminated by contribution from the adjacent land, which is particularly problematic in cases of raised topography surrounding the water body and complicates atmospheric correction; non-negligible reflectance of water in the near-infrared region due to high sediment concentrations in inland waters (from terrestrial sources such as soil erosion, surface runoff from rainfall, and agricultural and industrial discharge) makes it difficult to accurately estimate and remove the effect of atmospheric aerosol scattering on the received signal. In spite of these and other challenges, a few algorithms have been developed and used for atmospheric correction of remotely sensed data from inland waters with reasonable success. This chapter contains a discussion of the main challenges in atmospheric correction for inland waters and brief descriptions of a few existing atmospheric correction algorithms that are suitable for inland waters.

The purpose of this chapter is to describe a step-by-step porting approach for weather and climate model Fortran code to run on GPUs using CUDA and OpenACC. At the end of this chapter, the reader will have a basic understanding of: • How to use OpenACC to port components of a production weather and climate model achieving a gain of about a factor of 2.3 × in terms of time to solution and about a factor of 3 × in terms of energy to solution • The step-by-step approach to port large sections weather and climate model code with OpenACC • Optimizations that may be introduced to enhance performance further on GPU when using OpenACC

Since the 1970s, it has been predicted that both CO2 and H2O clouds can form in the Martian atmosphere, and many remote-sounding instruments have directly observed layers of extinction asserted to be clouds composed of either CO2 or H2O ice on Mars. The Mars Climate Sounder, onboard the Mars Reconnaissance Orbiter (MRO/MCS), entered orbit around Mars in 2006, and has been providing near-continuous coverage of the full planet since, at wavelengths from visible through to the mid-infrared, primarily in limb-viewing geometry, making it a suitable candidate to study the parameters of these clouds. In this work, the multiple scattering radiative-transfer tool NemesisMCS has been used to create a large dataset of simulations of CO2 and H2O clouds on Mars as would be measured by MRO/MCS, using a range of atmospheric conditions as well as cloud parameters derived from literature suitable for upper atmospheric clouds, and building specifically on the work of Sefton-Nash et al. (2013). This ensemble of simulations has been used to characterise the spectral signature of plausible CO2 and H2O clouds, as well as to assess the suitability of MRO/MCS to observe, to differentiate between, and to derive properties of such clouds. It has been found, given the noise levels expected for MRO/MCS and the range of atmospheric and cloud parameters sampled in this study, that radiance signals introduced by upper atmospheric clouds having nadir optical depths greater than about 10−5 should be distinguishable, with S/N≥1. This corresponds to specific concentrations greater than about 105 particles/g, particle radii greater than around View the MathML source, and cloud depths greater than about 2 km. MRO/MCS measurements should be able to be used with confidence to differentiate between upper atmospheric cloud and dust in the lower atmosphere, and clear conditions, with high success (≈100%). Lower reliability classification is accomplished for CO2 clouds, with only 60% being correctly identified as CO2, and the remainder classified instead as H2O cloud, in the case of optical depths in the expected range for upper atmospheric cloudswhich are detectable by MRO/MCS, although this result is highly dependent upon the sampled selection of optically thin and thick clouds and the atmospheric model employed. Although almost all the H2O clouds are correctly identified, the fact that such a large proportion of CO2 clouds are misclassified as H2O clouds shows that the spectral information alone from MRO/MCS is insufficient to differentiate between CO2 and H2O clouds when optically thin—but detectable—clouds are included in the analysis. Using a simple look-up table (LUT) scheme and simulated data, retrieval of properties of upper atmospheric clouds of sufficient opacity is possible, with preliminary estimates indicating that H2O cloud and dust parameters can be correctly reproduced between 48% and 100% of the time, and between 18% and 92% of the time for CO2 cloud test cases, although it must be noted that these values must be taken as a qualitative measure which does not capture the full range of atmospheric and cloud conditions on Mars which would be present in real MRO/MCS data. Furthermore, because of the optical properties of H2O and CO2, on a like-with-like selection, the H2O clouds always produce greater perturbations in radiance, thus biasing results to a higher success rate for H2O cloud retrievals. Application of the method to MRO/MCS data with a full-optimal estimation retrieval tool such as NemesisMCS will be the topic of a future study.

In 2011, RPS Group, PLC, Applied Science Associates ASA was contracted to design an operational Meteorological and Oceanographic (MetOcean) forecasting system covering the Persian/Arabian Gulf and coastal waters near Dubai, United Arab Emirates. The system was designed to provide daily forecasts out to 120 h (5 days) for atmospheric and oceanographic parameters using the Weather Research and Forecasting atmospheric model, the Regional Ocean Modeling System hydrodynamic model, and the unstructured Simulating WAves Nearshore wave model. Following the design and calibration phase, the forecast system was installed locally in Dubai. It was realized that infrastructure support and network requirements challenged the system's reliability to routinely create automated forecasts. A decision was made to transfer the operations of the system to the cloud, specifically the Amazon Web Services cloud infrastructure, which offered several high-performance computing options in various regions globally. Transition to the cloud took approximately 4 months of development and testing, with the system being transferred to the cloud in early 2013, in which it continues to run. The cloud implementation process led to the discovery of many benefits, including rapid, cost-effective development and testing of system updates and changes in a parallel environment. The continuous upgrading of the underlying Amazon infrastructure have reduced the initial 2013 cloud runtime from over 5 h to just over 2 h for current runs. The Gulf MetOcean Forecast System provides a valuable example of utilizing regionally diverse cloud computing resources to mitigate infrastructure limits and challenges in certain regions globally where support for high-performance, scientific computing is limited or unavailable. The system has realized its original goal of being a robust, scalable global MetOcean forecasting solution and continues to remain a cost-effective and viable option for providing cutting-edge MetOcean forecasting worldwide.

Atmospheric modeling is an important method to generate physical and numerical measurements of climate parameters, quantify the spatiotemporal changes of atmospheric phenomena over space and time, and predict their occurrences. With simulated data sets from atmospheric models, scientists are able to examine the driving forces of atmospheric phenomena and perform advanced analysis. Due to the inherent complexity and computational intensity of atmospheric models, running such models requires considerable amounts of computing resources. Traditionally, high-performance supercomputers or clusters have been used to perform atmospheric modeling. Recently, cloud computing solutions are emerged as a cost-effective approach to provide on-demand computing resources, remove the technical barriers, and reduce the high costs for computing facility management and maintenance. This chapter presents the design and implementation of a cloud-based framework to facilitate atmospheric modeling. The framework consists of a web portal, cloud instances, and a cloud-based data repository. To evaluate the feasibility of the framework, we have customized and deployed the serial processing version of ModelE onto our framework. Upon the deployment, we conducted two sets of experiments to evaluate the readiness of cloud computing resources to support large-scale atmospheric modeling. Experimental results demonstrate the framework provides scalable and customizable computing resources that meet the computational needs of atmospheric modeling.

A sophisticated radiative transfer model that considers absorption, emission, and multiple scattering by gaseous and particulate constituents over the broad spectral range 0.125–1000 µm is applied to calculate radiative fluxes and temperature change rates in the middle and lower atmosphere of Venus (0–100 km). Responses of these quantities to spectroscopic and atmospheric parameter variations are examined in great detail. Spectroscopic parameter studies include the definition of an optimum spectral grid for monochromatic calculations as well as comparisons for different input data with respect to spectral line databases, continuum absorption, line shape factors, and solar irradiance spectra. Atmospheric parameter studies are based on distinct variations of an initial model data set. Analyses of actual variations of the radiative energy budget using atmospheric features that have been recently retrieved from Venus Express data will be subject of a subsequent paper. The calculated cooling (heating) rates are very reliable at altitudes below 95 (85) km with maximum uncertainties of about 0.25 K/day. Heating uncertainties may reach 3–5 K/day at 100 km. Using equivalent Planck radiation as solar insolation source in place of measured spectra is not recommended. Cooling rates strongly respond to variations of atmospheric thermal structure, while heating rates are less sensitive. The influence of mesospheric minor gas variations is small, but may become more important near the cloud base and in case of episodic SO2 boosts. Responses to cloud mode 1 particle abundance changes are weak, but variations of other mode parameters (abundances, cloud top and base altitudes) may significantly alter radiative temperature change rates up to 50% in Venus' lower mesosphere and upper troposphere. A new model for the unknown UV absorber for two altitude domains is proposed. It is not directly linked to cloud particle modes and permits an investigation of radiative effects regardless of the absorbers’s chemical composition. A globally averaged Bond albedo of Venus of 0.763 is inferred in accordance with previous results. Considering the gaseous UV absorbers SO2 and CO2 shortward of 0.32 µm, the globally averaged deposited solar net flux at the top of atmosphere (TOA) and the outgoing thermal net flux differ by only 1.5 W m−2 around the mean value of 159 W m−2 for the selected initial atmospheric model. Global radiative equilibrium can be achieved by moderate adjustments of cloud mode and UV absorber abundances. Half of the TOA solar net flux is absorbed by atmospheric constituents at altitudes above 63 km. Consideration of the unknown UV absorber provides about 50% more heating at 68 km compared with a neglect of this opacity source. Less than 5% of the incident flux reaches the surface. There is a broad net cooling region between 70 and 80 km with a strong increase of cooling toward the poles. A net radiative temperature change rate gradient is also observed at 65 km where heating occurs at low latitudes. At altitudes above 80 km, net heating dominates the low and mid latitudes, while net cooling prevails at high latitudes leading to a dominant global average net heating that has to be balanced by dynamical processes to maintain the observed thermal structure. The results of energy balance response analyses will serve as reference for ongoing investigations and provide a profound data base to improve the understanding of radiative forcing of atmospheric dynamical processes.

This study suggests that the cause of the stagnation in global warming in the mid 20th century was the atmospheric nuclear explosions detonated between 1945 and 1980. The estimated GST drop due to fine dust from the actual atmospheric nuclear explosions based on the published simulation results by other researchers (a single column model and Atmosphere-Ocean General Circulation Model) has served to explain the stagnation in global warming. Atmospheric nuclear explosions can be regarded as full-scale in situ tests for nuclear winter. The non-negligible amount of GST drop from the actual atmospheric explosions suggests that nuclear winter is not just a theory but has actually occurred, albeit on a small scale. The accuracy of the simulations of GST by IPCC would also be improved significantly by introducing the influence of fine dust from the actual atmospheric nuclear explosions into their climate models; thus, global warming behavior could be more accurately predicted.

Mesoscale systems in the atmosphere are those in which the instantaneous pressure field can be determined accurately by the temperature field, but the winds, even in the absence of surface frictional effects, are out of balance with the horizontal pressure gradient force. The framework of mesoscale models is overviewed and shows that the models include a physics base core comprising the pressure gradient force, advection, and gravity, and all other physical processes that are parameterized using tuned modular representations of turbulence, longwave and shortwave radiation, cumulus, and stable cloud processes. Computational methods, lateral and initial boundary conditions, and model validation are some of the topics discussed.

Earth’s atmosphere is composed of a mixture of gases such as nitrogen, oxygen, carbon dioxide, water vapor, and ozone. A wide variety of fluid flows take place in the atmosphere. In this chapter, we show how the theory of fluid dynamics in Chapter ?? is applied to the atmosphere. For this purpose, we introduce the Navier-Stokes equation, hydrostatic and geostrophic approximations, the Boussinesq approximation, potential temperature and equivalent potential temperature, quasi-geostrophic potential vorticity, buoyancy frequency, and so on.

This article is a revision of the previous edition article by C Jakob, M Miller, volume 4, pp 1692–1698, © 2003, Elsevier Ltd.

Synopsis This article discusses the role of methane in the atmosphere and its historic and recent variations. Techniques are described for monitoring its atmospheric abundance from the ground, and recently also from space. An overview is given of the main sources and sinks of atmospheric methane, and the large-scale constraints on the size of these processes as provided by the atmospheric measurements. The use of models is discussed, including the use of inverse modeling techniques for obtaining more detailed information on the spatiotemporal variation of methane emissions and sinks.

Huge amounts of radioactivity have been released to the environment because of the Fukushima Dai-ichi Nuclear Power Plant (NPP) accident. In order to implement adequate protective actions and to assess the impact of the Fukushima radioactivity on the environment, an environmental monitoring has been conducted by national and local governments, research institutes and universities in Japan and over the world. The environmental monitoring revealed that heavy radioactivity-contaminated areas appeared within about 50 km of the Fukushima Dai-ichi NPP, controlled by land topography as do meteorological factors. The Fukushima-derived radionuclides, in which dominant nuclides were 131 I, 134 Cs and 137 Cs, contaminated food stuffs. The radionuclide levels exceeded the regulation values in a part of food stuffs produced within about 500 km off Fukushima. Based on the comprehensive monitoring data, we describe here levels of the Fukushima-derived radionuclides in terrestrial and marine environments and in food products in Japan and over the globe. Temporal and spatial distributions of Fukushima-derived radionuclides in aerosols revealed the presence of two dominant radionuclide maxima which were observed throughout the Europe with decreasing amplitudes from the North to the South, which were associated with different air masses present in the European air. Modeled forward and backward trajectories indicated a preferential transport of air masses between Fukushima and Europe at 500 hPa (5000 m a. s. l.) air heights. The Lagrangian dispersion modeling showed that the horizontal dispersion in the Europe reached about 4000-km-wide belt, however, the entire world has been labeled with the Fukushima radionuclides, although at very low levels. A typical travel time between Fukushima and Europe has been estimated to be of 10–15 days, with an average speed of the plume of 50–70 km/h. An average 131 I concentration, which was measured over the Europe (∼1 mBq/m 3 ), would result in the total amount of dispersed 131 I of about 1 PBq. Although this represents a high release rate (almost 1% of the total amount of 131 I released from the Fukushima NPP), as it was distributed over a huge area, it has not been of any radiological significance for European citizens.

13 páginas, 8 figuras.-- El Pdf del artículo es la versión pre-print., Grid computing is nowadays an established technology in fields such as High Energy Physics and Biomedicine, offering an alternative to traditional HPC for several problems; however, it is still an emerging discipline for the climate community and only a few climate applications have been adapted to the Grid to solve particular problems. In this paper we present an up-to-date description of the advantages and limitations of the Grid for climate applications (in particular global circulation models), analyzing the requirements and the new challenges posed to the Grid. In particular, we focus on production-like problems such as sensitivity analysis or ensemble prediction, where a single model is run several times with different parameters, forcing and/or initial conditions. As an illustrative example, we consider the Community Atmospheric Model (CAM) and analyze the advantages and shortcomings of the Grid to perform a sensitivity study of precipitation with SST perturbations in El Niño area, reporting the results obtained with traditional (local cluster) and Grid infrastructures. We conclude that new specific middleware (execution workflow managers) is needed to meet the particular requirements of climate applications (long simulations, checkpointing, etc.). This requires the side-by-side collaboration of IT and climate groups to deploy fully ported applications, such as the CAM for Grid (CAM4G) introduced in this paper., This work has benefited from the ESR VO infrastructure of the EU FP7 EGEE-III. This work has been partially supported by the EU FP7 project EELA-2 (Contract number 223797) and the Spanish Ministry of Science and Innovation through project CORWES (CGL2010-22158-C02-01).

Publisher Summary This chapter discusses the recent advances in the application of theoretical methods to the atmospheric oxidation of biogenic hydrocarbons. It introduces the backgrounds on the quantum chemical calculations and kinetic rate theories, recent progress on theoretical studies of isoprene and α -, β -pinenes, and studies on other monoterpenes and sesquiterpenes. The mechanism of isoprene oxidation is better understood than those of other biogenic hydrocarbons. The initial isoprene-oxidant adducts, the peroxy radicals, the alkoxy radicals and the first generation products hae been investigated by means of quantum chemical calculations. The roles of the intermediate species were assessed using kinetic methods and the overall picture of the isoprene oxidation mechanism was obtained. Some of the aspects need to be considered for application of quantum chemical calculations and kinetic methods to the atmospheric biogenic hydrocarbons: (1) the accuracy of the geometries of the species, (2) the accuracy of the reaction energy and activation energy, (3) and other thermochemical data. These thermochemical data are very helpful to atmospheric modeling to understand the complex chemical reactions.

Air pollution modeling (APM) is being developed and used in order to better understand, investigate, assess, and regulate the quality of the atmospheric environment and the distribution of toxic pollutants which are often used in ecotoxicology. Atmospheric models are also used to assess the impact of air pollutants on human health. In this respect, APM covers a very complex and interdisciplinary scientific area, where aspects of environmental policy and decision making mix together with aspects like remote sensing, land-use impact, initial and boundary conditions, data assimilation techniques, chemical schemes, comparison methods between data measured and data modeled, computational efficiency and performance, coupling with the meteorology, long-range transport impact on local air pollution, new satellite data assimilation techniques, real-time and forecasting air quality modeling and sensitivity analysis, and many others. APM has progressed significantly during the last two decades, attracting the interest of various research groups and initiatives worldwide. It covers all aspects related to the life cycle of pollutants, starting from their emission or ‘production’ within the atmosphere, and ending with their impacts on man and the ecosystem. This field of science is interested to describe the generation, transportation, dispersion, physical and chemical transformation, and the impact of various categories of substances that are identified and classified as air pollutants via the environmental legal framework and the scientific community. It should be noted that air pollution is very much related to meteorological fields, since the atmosphere is governed by the general fluid mechanics laws described by the Navier–Stokes equation system, while progress in computer performance during the last decades has substantially impelled the research on air quality modeling in a parallel way. This contribution focuses on providing a general overview of the state of the art on air quality modeling, from the point of view of the ‘user community’, that is, policy makers, urban planners, environmental managers, etc. It also tries to bring to the discussion key questions concerning the air quality modeling success in usage, such as: identification of uncertainties in emission inventories and meteorological fields, capability of simulating pollutants like urban aerosols, the next-generation developments in models to answer new scientific questions, etc.

In this study, forecast errors in dust vertical distributions were analyzed. This was carried out by using quantitative comparisons between dust vertical profiles retrieved from lidar measurements over Rome, Italy, and those predicted by models. Three models were used: the four-particle-size Dust Regional Atmospheric Model (DREAM), the older one-particle-size version of the SKIRON model from the University of Athens (UOA), and the pre-2006 one-particle-size Tel Aviv University (TAU) model. SKIRON and DREAM are initialized on a daily basis using the dust concentration from the previous forecast cycle, while the TAU model initialization is based on the Total Ozone Mapping Spectrometer aerosol index (TOMS AI). The quantitative comparison shows that (1) the use of four-particle-size bins in the dust modeling instead of only one-size bin improves dust forecasts, (2) cloud presence could contribute to additional dust forecast errors in SKIRON and DREAM, (3) as far as the TAU model is concerned, its forecast errors were mainly caused by technical problems with TOMS measurements from the Earth Probe satellite. As a result, dust forecast errors in the TAU model could be significant even under cloudless conditions.

Particulate matter of anthropogenic and/or natural origin in the atmosphere is considered a parameter causing multiple effects on the environment of local and remote locations. The presence of absorbing and/or scattering aerosols in the lower troposphere can affect the atmospheric radiation through the modification of both short- and long-wave components. On a shorter term, aerosols can cause the modification of environmental conditions by influencing atmospheric temperature and dynamics. Atmospheric photochemistry is also affected by the presence of increased particle concentrations due to the alteration of UV and visible radiation fluxes and therefore the modification of the j -values (photolysis rates) of the photochemical reactions. Physiographic characteristics and climatic conditions in the Mediterranean Region are followed by excessive solar radiation leading to high photochemical activity in the Region. In addition long-range transport of fine particles is very common in the area. In this work we focus on the distribution of fine desert particles in the greater Mediterranean Region since Saharan dust is the desert responsible for many severe dust outbreaks that influence the Region. We examine the impact of increased desert dust concentration on the photochemistry of the region. For this purpose, advanced atmospheric and photochemical models are implemented with the aid of air pollutant measurements from stations in the region. The models used are the RAMS atmospheric model, the SKIRON/Eta atmospheric modeling system with the implementation of dust module, and the CAMx photochemical model. Sensitivity tests performed with and without the influence of dust load on the photolysis rates showed some interesting results in the formation and production of gases like ozone, nitrogen oxides, and nitric acid, and aerosols like sulfates and nitrates. The preliminary results of these simulations will be thoroughly discussed in this presentation.

Publisher Summary The future evolution of the community-climate system model into an Earth system model will require a highly scalable and accurate flux-form formulation of atmospheric dynamics .Inherently conservative numerical schemes are of fundamental importance in atmospheric and climate modeling to pertain to conservation properties such as mass and total energy. The baroclinic waves are triggered when overlaying the steady-state initial conditions with the zonal wind perturbation whereas the initial conditions are given as quasi-realistic analytic expressions form. The computational domain is the singularity-free cubed-sphere geometry Time integration follows the third-order SSP-RK scheme. To validate proposed 3D DG model, the baroclinic-instability test suite proposed by Jablonowski and Williamson is investigated. Currently, the 3D DG model performs successfully up to 10-day simulation. The DG discretization uses high-order nodal basis set of Lagrange-Legendrepolynomials and fluxes of interelement boundaries are approximated with Lax-Friedrichs numerical flux. Time step was measured for the main DG time-stepping loop using hardware performance counters for IBM supercomputer.

Publisher Summary The chapter discusses the parallel atmospheric modeling with high-order continuous and discontinuous Galerkin methods. The chapter reviews high-order finite element methods for the atmospheric shallow water equations. The accuracy and efficiency of nodal continuous and discontinuous Galerkin spectral elements are evaluated using standard test problems proposed by Williamson et al. The relative merits of strong-stability preserving (SSP) explicit Runge–Kutta and multistep time discretizations are discussed in the chapter. Distributed memory MPI implementations are compared on the basis of the total computation time required, sustained performance, and parallel scalability. Because a discontinuous Galerkin method permits the overlap of computation and communication, higher sustained execution rates are possible at large processor counts. The discontinuous Oalerkin method converges exponentially for smooth solutions, and standard error metrics compare favorably with the continuous Galerkin spectral element model.

Publisher Summary This chapter describes a moving grid Eulerian-Lagrangian localized adjoint method to solve linear and nonlinear advection-diffusion-reaction equations. Advection-Diffusion-Reaction Equations (ADRE) can be encountered in many domains such as fluid dynamics, atmospheric modeling, petroleum reservoir simulation, transport of solutes in groundwater, transport of chemical contaminants in porous medium, and so on. In this chapter, the moving grid Eulerian-Lagrangian localized adjoint method (ELLAM) is used to solve one-dimensional nonlinear ADRE with variable coefficients. The schema uses a forward characteristic tracking algorithm and exponential-along-characteristic test functions. The moving grid, used to define the solution and test functions of the ELLAM, avoids the need for numerical integration, which is a major problem in conventional ELLAM formulations. The numerical experiments presented show the strength of this schema to solve linear and nonlinear transport equations.

The UK Met Office is preparing a baroclinic model of the NW European continental shelf seas for operational implementation. For the Met Office, Proudman Oceanographic Laboratory have implemented their model on the 1/9 by 1/6 degree grid of the storm surge model. The model predicts the synoptic scale evolution of elevation, temperature, salinity and current profiles, over the continental shelf and shelf break. The model now runs in near-real time, having been spun up from March 1998. It is driven at the surface by hourly winds and pressures, and 6-hourly heat fluxes from the Met Office global weather prediction model, and at open ocean boundaries with tidal harmonics. Verification shows that the model represents well the evolution of SST in the North Sea. Over the Dogger Bank during a short period in May 1998, with neap tides, low winds, and strong surface heating, the model became stratified. As conditions changed the waters over Dogger Bank became well mixed again, but warmer than the week before. Developments include taking boundary data from the Met Office Forecast Ocean Atmosphere Model (FOAM), and surface fluxes from the Met Office mesoscale NWP model.

The circulation of the Atlantic poses a challenge to prediction on all time scales, from changes in the surface mixed layer depth over several days, to SST anomalies influencing seasonal weather prediction, with variations over decades. To analyse and predict the state of the ocean the UK Met Office has developed the Forecast Ocean Atmosphere Model (FOAM). At the heart of FOAM is an ocean circulation model, of the same basic formulation as that used by the Hadley Centre at the UK Met Office to predict climate change. Global FOAM runs on a one-degree grid, with twenty levels, ten of which are in the top 300m. A sea-ice model and surface mixed layer scheme are also included. FOAM runs once a day, driven by surface fluxes from the global prediction model. A 24-hour analysis, assimilating temperature profiles and SST observations, is followed by a 5-day forecast. Verification shows that FOAM improves on climatology, down to 300m depth. Examples will be presented from FOAM, and from nested high resolution. Atlantic models, currently being developed. The data assimilation component of FOAM is being applied for seasonal and decadal prediction studies, examples from which will also be presented.

Taking advantage of the advent of massively parallel computers, sophisticated solution-adaptive techniques, and recent fundamental advances in basic numerical methods we have developed a high performance, adaptive-scale MHD code capable of resolving many of the critical processes in the Sun-Earth system which range over more than 9 orders of magnitude in scale size. The development of such models are of increasing importance as the impact of space weather on vulnerable technological systems increases, and too, as the severity of space weather increases with solar maximum. There is an increasing need to develop physics-based, high performance models of the Sun-Earth system-from the solar surface to the Earth's upper atmosphere-which can operate faster than real time and which can provide reliable predictions of the near Earth space environment based upon solar observations and upstream solar wind measurements. We report the status of the prototype development of a Comprehensive Space Environment Model for this purpose, to be composed of a core based upon the Michigan MHD code coupled to a high performance inner magnetosphere model and ionosphere/upper atmosphere model. The inner magnetosphere model and ionosphere/upper atmosphere models are derived from the Rice Convection Model (RCM) and the Thermosphere Ionosphere Electrodynamic General Circulation Model (TIEGCM) respectively, both reformulated for parallel operation using advanced numerics on adaptive grids with significantly higher spatial resolution.

A 3-dimensional baroclinic model of the North Sea and the Baltic Sea has delivered operational forecasts since 1995. The model is mainly forced by the operational atmospheric model (HIRLAM) at the Swedish Meteorological and Hydrological Institute (SMHI), but also by monthly means of river runoff and wave radiation stress from a wind wave model. The model is running with a nested grid, where a 12 nautical mile grid covers the whole area while Skagerrak, Kattegat, the Belt Sea and the Baltic Sea is covered with a 3 nautical mile grid. A crucial parameter in ocean modelling is how well the model resolves the meso-scale dynamics with a scale of the same order as the internal Rossby radius. For the Baltic Sea, this scale is roughly from 3 to 6 km, i.e. of the same size as the present resolution of 3 nautical miles. When moving to a massive parallel computer with distributed memory, it is now possible to increase the resolution to 1 nautical mile and still run a 48-hours forecast within 1 hour CPU-time. The parallelisation has been performed in two steps. At first the whole computational domain has been partitioned into a large number of rectangular blocks to avoid inactive land-points. The blocks have then been projected into the number of available parallel processors. Secondly, a set of F90 modules have been developed to manage the different grids and blocks while hiding parallelisation details. Only one grid block at a time is presented to the original F77 code. Finally, a new solver has been introduced for the linear equation systems for water level and sea ice dynamics, which now are solved with a distributed multi-frontal solver. We have found that the production of HIROMB, forecasts can successfully be moved from the present vector computer C90 to the massive parallel computer T3E while increasing the resolution from 3 to 1 nautical mile. However, speedup and load balance could still be further improved.

A semi-implicit formulation of the parallel spectral element atmospheric model (SEAM) is presented. Spectral element methods are h-p type finite element methods that combine the geometric flexibility of finite elements with the exponential convergence of pseudospectral methods. To avoid spurious ‘pressure’ modes and obtain a symmetric positive definite Helmholtz operator, a staggered PPp × PPp–2 method is adopted. Parallel preconditioning strategies for an iterative conjugate-gradient solver are described and performance of the model on the NCAR SP supercomputer is summarized.

The chapter explains that geodesic grids, which were experimentally applied to the simulation of the global atmospheric circulation in the 1960s, are now emerging as the computational foundations of full-fledged atmospheric general circulation models. A geodesic grid is constructed by starting with an ordinary icosahedron inscribed inside a unit sphere. The vorticity and divergence equations are well suited for use on geodesic grids. Use of the vorticity and divergence equations without horizontal staggering allows realistic simulation of geostrophic adjustment regardless of the ratio of the grid spacing to the radius of deformation. Geodesic grids hold a great deal of promise for the future of atmospheric general circulation modeling. Multigrid solvers permit efficient inversion of the Laplacian as well as semi-implicit time differencing on geodesic grids. The chapter also discusses the advantages of the isentropic vertical coordinate for numerical modeling of the large-scale circulation of the atmosphere. A full-physics geodesic general circulation model with the generalized σ coordinate is tested. Preliminary results indicate that this approach can give realistic and computationally efficient simulations of the atmospheric general circulation.

Publisher Summary This chapter discusses the use of compensation points in understanding measured atmosphere–biosphere exchange fluxes and describes the fluxes in models. The chapter considers compensation points for terrestrial systems. The fluxes of H 2 , OCS, N 2 O, and CH 4 can be evaluated easily. These gases have long atmospheric lifetimes and constant ambient concentrations. Therefore, it is sufficient to evaluate the magnitude, temporal, and spatial variations of the compensation points and exchange velocities to estimate fluxes to and from the atmosphere. Application of compensation points in models is useful when the compensation concentrations are in the same range as the atmospheric concentrations and the spatial scale of the soil and vegetation properties governing compensation concentrations is similar to the spatial scale of the atmospheric model. The compensation point is described as a function of soil and vegetation parameters and meteorological variables provided by the model.

Publisher Summary This chapter provides a report on how the fluxes of trace gases can be validated among different scales. A variety of techniques determine the trace gas fluxes, including chamber, micrometeorological, and airborne methods. Micrometeorological measurements of oceanic fluxes pose additional problems. For many trace gases, the fluxes between the ocean and the atmosphere are small compared to fluxes between terrestrial ecosystems and the atmosphere. This causes technical problems related to the detection limits of instruments. Two distinct methods exist to derive fluxes using atmospheric models—that is, forward simulations and inverse methods. In forward atmospheric modeling, emissions are used to drive the atmospheric model to predict concentrations and fluxes. Inverse methods use observations of atmospheric concentrations to derive flux estimates. Both methods can be applied at all scale levels. In many cases, fluxes can be determined with different techniques, used simultaneously. Inverse methods use observations of atmospheric concentrations to derive flux estimates.

Publisher Summary This chapter discusses a stretched icosahedral grid for the global cloud resolving model. For modeling of super high-resolution atmospheric/oceanic general circulation, polyhedral grids on tile sphere, such as an icosahedral grid, have several advantages including the homogeneous and isotropic distribution of gridpoints, the treatment of gridpoint data as two-dimensional structured data in the program code, and the easy parallelization on a massively parallel computer with distributed memory. The chapter also discusses the new global atmospheric model developed by the Frontier Research Center for Global Change (FRCGC) toward the climate simulation by cloud resolving. The strategy of model development is to use a modified icosahedral grid over the sphere and a new nohydrostatic dynamical scheme suitable to long time integration. Microphysics is a key issue of this development; however, it is difficult to do trial and error with a few km grids over the globe. For an efficient inspection and/or development of cloud microphysics schemes by less computer resource, the icosahedral grid is modified by the Schmidt transformation and the model is used as a limited area model.

The application problem that is addressed in this contribution is to monitor large agricultural areas for landuse, crop types and crop yield forecasting, using remotely sensed data. Visual image interpretation is too slow and does not produce quantitative results fast enough and bottom up image analysis is unlikely to succeed in terms of the complexity of multitemporal, multisource data. We investigate knowledge based, hypothesis driven image analysis. Hypotheses about surface objects are generated from prior geometric and radiometric model knowledge such as should be available from a 4-dimensional geo information system 4dGIS. Hypotheses are evaluated against a selection of the data and model parameters are estimated. We use a predictor – corrector method of hypothesis and parameter estimation with the expected cost of misclassification of surface elements as the optimization criterion. The predictor model generates (abstracted) RS data from a 3dim+time scene model, a sensor model, an illumination and an atmosphere model. Equivalences between the general modelling and parameter estimation method and existing image processing and pattern recognition algorithms are explored. Feature extraction is shown to be equivalent to the inverse modelling of image data to radiometric and geometric model parameters for some cases of spatial feature extraction. We report on mapping geometric, radiometric and process knowledge onto physical models, how to acquire and use prior knowledge, the role of sensor models and on an experiment on the extraction of agricultural fields from airborne synthetic aperture radar data of Flevoland in The Netherlands.

NASA environmental models are discussed with particular attention given to the Mars Global Reference Atmospheric Model (Mars-GRAM) and the Mars Terrain simulator. The Mars-GRAM model takes into account seasonal, diurnal, and surface topography and dust storm effects upon the atmosphere. It is also capable of simulating appropriate random density perturbations along any trajectory path through the atmosphere. The Mars Terrain Simulator is a software program that builds pseudo-Martian terrains by layering the effects of geological processes upon one another. Output pictures of the constructed surfaces can be viewed from any vantage point under any illumination conditions. Attention is also given to the document 'Environment of Mars, 1988' in which scientific models of the Martian atmosphere and Martian surface are presented.

A low-resolution version of the ECMWF global atmosphere model has been coupled to a global ocean model developed at the Max Planck Institute in Hamburg. The atmosphere model is driven by the sea surface temperature and the ice thickness calculated by the ocean model, which, in return, is driven by the wind stress, the heat flux and the freshwater flux diagnosed by the atmosphere model. Even though each model reaches stationarity when integrated on its own, the coupling of both creates problems, since the fields calculated by each model are not consistent with the ones the other model has to have in order to stay stationary, because some of the fluxes are not balanced. In the coupled experiment the combined ocean-atmosphere system drifts towards a colder state. To counteract this problem, a flux correction has been applied which balances the mean biases of each model. This method almost eliminates the climate drift of the coupled model. Problems still arise over ice covered regions.

A model of the mixed layers of the North Pacific and North Atlantic Oceans has been coupled to a hemispherical, quasigeostrophic, beta-plane model of the atmosphere. The two oceans are represented by the sectors 90°W— 90°E and 0°-45°W, respectively, with the North American and Eurasian land masses by 45°-90°W and 0°-90°E. The mixed-layer model is a simplification of that of Kraus and Turner (1967). The meridional heat flux in each ocean is prescribed during each run of the model and several experiments were carried out between which these transports were varied. This paper considers the role of oceanic thermal inertia in these experiments. Each experiment began with a mixed layer which was everywhere in dynamic equilibrium with the unperturbed heat transport. The results show how the mixed layer affects the dynamics of the model. There are teleconnections between the mixed layers of the two oceans with time scales of months. The model shows an east-west oscillation reminiscent of the seesaw in northern hemisphere temperatures described by Van Loon and Rogers (1978), which involves the northernmost mixed layer of the oceans.