Entire Atmospheric Column Research Articles

Impact of geomagnetic activity on stratosphere and upper troposphere

During active geomagnetic conditions, a large amount of energy is deposited in the polar atmosphere in the form of particle precipitation that leads to Joule heating creating circulation of intense currents in the auroral region. It can affect the existing background pressure fluctuations in the stratospheric and tropospheric heights, leading to anomalous changes in the vertical temperature (T), zonal (u) and meridional (v) wind. In this study, we demonstrate the effect of active geomagnetic conditions on these atmospheric variables in different longitudinal regions. The investigation involves daily, monthly and seasonal variation of active geomagnetic conditions. Active geomagnetic conditions are selected using geomagnetic activity indices like auroral activity index |AL|>1000 nT, Disturbed Storm time index Dst < -150 and polar cap index PC > 5. Events are identified during November to March for 1990 to 2020 period. Among them 99 active geomagnetic conditions occurred in the month of March which are considered for further investigation. Composite analysis of T, u and v reflects that the temperature shows an increase in the entire atmospheric column; the anomalies in u (u′) and v (v′) show a regional dependence and strengthen in their amplitudes. It is seen from the monthly investigation of March that the Western Pacific, Canadian and East Pacific sectors respond to the active geomagnetic conditions at upper atmospheric pressure levels (approximately 40–70 km altitude) in the polar region. This is indicative of a vertical translation of energy to lower atmosphere during active geomagnetic conditions.

Assessing the Monthly Trends in Precipitable Water Vapor over the Indian Subcontinent

This study estimates the trends in precipitable water vapor (PWV), atmospheric moisture budget (AMB), and the factors influencing them: air temperature, evapotranspiration (ET), convective available potential energy (CAPE), and vertical velocity (Omega) over the Indian subcontinent using ERA5 reanalysis data sets between 1980 and 2020. PWV is examined across three atmospheric layers (1000–850 hPa: lower layer; 850–500 hPa: middle layer; 500–300 hPa: upper layer), and the entire atmospheric column (EAC; 1000–300 hPa). The observed PWV trends exhibit variability within the EAC, ranging from −0.53 to 1.25 mm/decade across the study area, with the middle layer showing the most pronounced variation (–0.44 to 0.83 mm/decade), followed by the lower layer (0.10 to 0.45 mm/decade), and the upper layer (–0.02 to 0.23 mm/decade). These fluctuations in PWV are attributed to changes in air temperature, ET, CAPE, and Omega. This investigation, however, underscores the necessity of delving into the impacts of these influencing factors on PWV using finer resolution data, to enhance our comprehension of its spatial and temporal dynamics in the region. Furthermore, the annual AMB analysis reveals a declining trend in the study region. These findings collectively contribute to the understanding of regional water-energy cycles and the recent shifts in atmospheric dynamics.

Cloud top heights and aerosol layer properties from EarthCARE lidar observations: the A-CTH and A-ALD products

Abstract. The high-spectral-resolution Atmospheric Lidar (ATLID) on the Earth Cloud, Aerosol and Radiation Explorer (EarthCARE) provides vertically resolved information on aerosols and clouds with unprecedented accuracy. Together with the Cloud Profiling Radar (CPR), the Multi-Spectral Imager (MSI), and the Broad-Band Radiometer (BBR) on the same platform, it allows for a new synergistic view on atmospheric processes related to the interaction of aerosols, clouds, precipitation, and radiation at the global scale. This paper describes the algorithms for the determination of cloud top height and aerosol layer information from ATLID Level 1b (L1b) and Level 2a (L2a) input data. The ATLID L2a Cloud Top Height (A-CTH) and Aerosol Layer Descriptor (A-ALD) products are developed to ensure the provision of atmospheric layer products in continuation of the heritage from the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). Moreover, the products serve as input for synergistic algorithms that make use of data from ATLID and MSI. Therefore, the products are provided on the EarthCARE joint standard grid (JSG). A wavelet covariance transform (WCT) method with flexible thresholds is applied to determine layer boundaries from the ATLID Mie co-polar signal. Strong features detected with a horizontal resolution of 1 JSG pixel (approximately 1 km) or 11 JSG pixels are classified as thick or thin clouds, respectively. The top height of the uppermost cloud layer together with information on cloud layering are stored in the A-CTH product for further use in the generation of the ATLID-MSI Cloud Top Height (AM-CTH) synergy product. Aerosol layers are detected as weaker features at a resolution of 11 JSG pixels. Layer-mean optical properties are calculated from the ATLID L2a Extinction, Backscatter and Depolarization (A-EBD) product and stored in the A-ALD product, which also contains the aerosol optical thickness (AOT) of each layer, the stratospheric AOT, and the AOT of the entire atmospheric column. The latter parameter is used to produce the synergistic ATLID-MSI Aerosol Column Descriptor (AM-ACD) later in the processing chain. Several quality criteria are applied in the generation of A-CTH and A-ALD, and respective information is stored in the products. The functionality and performance of the algorithms are demonstrated by applying them to common EarthCARE test scenes. Conclusions are drawn for the application to real-world data and the validation of the products after the launch of EarthCARE.

CALIOP-Based Evaluation of Dust Emissions and Long-Range Transport of the Dust from the Aral−Caspian Arid Region by 3D-Source Potential Impact (3D-SPI) Method

The average monthly profiles of the dust extinction coefficient (ε) were analyzed according to the CALIOP lidar data from 2006–2021 for 24 cells (size of 2° × 5°) in the Aral-Caspian arid region (ACAR; 38–48°N, 50–70°E). Using the NOAA HYSPLIT_4 trajectory model and the NCEP GDAS1 gridded (resolution of 1° × 1°) archive of meteorological data, the array of &gt;1 million 10-day forward trajectories (FTs) of air particles that started from the centers of the ACAR cells was calculated. On the basis of the FT array, the average seasonal heights of the mixed layer (ML) for the ACAR cells were reconstructed. Estimates of the average seasonal dust optical depth (DOD) were obtained for ACAR’s lower troposphere, for ACAR’s ML (“dust emission layer” (EL)), and for the lower troposphere above the ML (“dust transit layer” (TL)) above each of the ACAR cells. Using the example of ACAR, it is shown that the analysis of DOD for the EL, TL and the surface layer (SL; the first 200 m AGL) makes it possible to identify dusty surfaces that are not detected on DOD diagrams for the entire atmospheric column, as well as regions where the regular transport of aged dust from remote sources can generate false sources. Based on FT array, the fields of the potential contribution of both the ACAR’s dust transit and the ACAR’s dust emission layers as well as of the entire ACAR’s lower troposphere into the DOD of the surrounding and remote regions are retrieved using the original method of potential impact of a three-dimensional source (3D-PSI). It has been found out that ACAR dust spreads over almost the entire Northern Hemisphere; the south and southeast regions of the ACAR are subject to the maximum impact of the ACAR dust. Quantitative estimates of the potential contribution of ACAR dust to the regional DODs are given for a number of control sites in the Northern Hemisphere. The results could be useful for climatological studies.

A Physics‐Incorporated Deep Learning Framework for Parameterization of Atmospheric Radiative Transfer

AbstractThe atmospheric radiative transfer calculations are among the most time‐consuming components of the numerical weather prediction (NWP) models. Deep learning (DL) models have recently been increasingly applied to accelerate radiative transfer modeling. Besides, a physical relationship exists between the output variables, including fluxes and heating rate profiles. Integration of such physical laws in DL models is crucial for the consistency and credibility of the DL‐based parameterizations. Therefore, we propose a physics‐incorporated framework for the radiative transfer DL model, in which the physical relationship between fluxes and heating rates is encoded as a layer of the network so that the energy conservation can be satisfied. It is also found that the prediction accuracy was improved with the physic‐incorporated layer. In addition, we trained and compared various types of DL model architectures, including fully connected (FC) neural networks (NNs), convolutional‐based NNs (CNNs), bidirectional recurrent‐based NNs (RNNs), transformer‐based NNs, and neural operator networks, respectively. The offline evaluation demonstrates that bidirectional RNNs, transformer‐based NNs, and neural operator networks significantly outperform the FC NNs and CNNs due to their capability of global perception. A global perspective of an entire atmospheric column is essential and suitable for radiative transfer modeling as the changes in atmospheric components of one layer/level have both local and global impacts on radiation along the entire vertical column. Furthermore, the bidirectional RNNs achieve the best performance as they can extract information from both upward and downward directions, similar to the radiative transfer processes in the atmosphere.

Physics informed deep neural network embedded in a chemical transport model for the Amazon rainforest

Secondary organic aerosols (SOA) are fine particles in the atmosphere, which interact with clouds, radiation and affect the Earth’s energy budget. SOA formation involves chemistry in gas phase, aqueous aerosols, and clouds. Simulating these chemical processes involve solving a stiff set of differential equations, which are computationally expensive steps for three-dimensional chemical transport models. Deep neural networks (DNNs) are universal function approximators that could be used to represent the complex nonlinear changes in aerosol physical and chemical processes; however, key challenges such as generalizability to extended time periods, preservation of mass balance, simulating sparse model outputs, and maintaining physical constraints have limited their use in atmospheric chemistry. Here, we develop an approach of using a physics-informed DNN that overcomes previous such challenges and demonstrates its applicability for the chemical formation processes of isoprene epoxydiol SOA (IEPOX-SOA) over the Amazon rainforest. The DNN is trained with data generated by simulating IEPOX-SOA over the entire atmospheric column, using the Weather Research and Forecasting Model coupled with Chemistry (WRF-Chem). The trained DNN is then embedded within WRF-Chem to replace the computationally expensive default solver of IEPOX-SOA formation. The trained DNN predictions generalizes well with the default model simulation of the IEPOX-SOA mass concentrations and its size distribution (20 size bins) over several days of simulations in both dry and wet seasons. The embedded DNN reduces the computational expense of WRF-Chem by a factor of 2. Our approach shows promise in terms of application to other computationally expensive chemistry solvers in climate models.

Lidar Profiling of Aerosol Vertical Distribution in the Urbanized French Alpine Valley of Annecy and Impact of a Saharan Dust Transport Event

The vertical aerosol layering of the troposphere is poorly documented in mountainous regions, particularly in the Alpine valleys, which are influenced by valley and mountain winds. To improve our knowledge of particulate matter trapped in the Annecy valley, synergetic measurements performed by a ground-based meteorological Raman lidar and a Rayleigh-Mie lidar aboard an ultralight aircraft were implemented as part of the Lacustrine-Water vApor Isotope inVentory Experiment (L-WAIVE) over Lake Annecy. These observations were complemented by satellite observations and Lagrangian modeling. The vertical profiles of aerosol optical properties (e.g., aerosol extinction coefficient (AEC), lidar ratio (LR), particle linear depolarization ratio (PDR)) are derived from lidar measurements at 355 nm during the period between 13 and 22 June 2019. The background aerosol content with an aerosol optical thickness (AOT) of 0.10 ± 0.05, corresponding to local–regional conditions influenced by anthropogenic pollution, has been characterized over the entirety of Lake Annecy thanks to the mobile ultralight payload. The aerosol optical properties are shown to be particularly variable over time in the atmospheric column, with mean LRs (PDRs) varying between 40 ± 8 and 115 ± 15 sr (2 ± 1 and 35 ± 2%). Those conditions can be disturbed by air masses that have recirculated over the valley, as well as by contributions from neighboring valleys. We have observed an important disruption in the atmospheric aerosol profiles by the arrival of an exceptionally dry air mass (RH ~ 30%), containing aerosols identified as coming from the Great Western Erg (AOT ~ 0.5, LR = 65 ± 10 sr, PDR = 20–35%) in the Sahara. These desert dust particles are shown to influence the entire atmospheric column in the Annecy valley. Such an experimental approach, coupling upward and downward lidar and spaceborne observation/Lagrangian modelling, was shown to be of significant interest for the long-term monitoring of the evolution of aerosol loads over deep valleys. It allows a better understanding of the influence of dust storms in the presence of severe convective weather processes.

Synoptic Climatology of Air Temperatures behind Extreme Cyclone Events over the Bay of Bengal of Southern Bangladesh

Cyclones have occurred more frequently in recent decades in a disaster-prone country like Bangladesh. Twenty-two (22) extreme cyclone events that occurred from 1975-2014 were investigated in this study with respect to air temperature climatology. Air temperature, sea level pressure, rainfall, relative humidity (Rhum), and sunshine hours (SShr) have been analyzed to find out the impact of air temperatures behind cyclogenesis. Historical cyclone data were obtained from Bangladesh Meteorological Department; Bangladesh Bureau of Statistics and Disaster Preparedness Centre (AIT). Historical weather data was collected from the Climate Division of BMD. Principal Component Analysis (PCA) and Clustering were used to find out the whole atmospheric air temperature impact on cyclogenesis. The NCEP–NCAR reanalysis data were used to find out the distribution of air temperature anomaly over Bangladesh and Bay of Bengal (BB). Among the 22 cyclones, cluster 2 belongs to 9 cyclones and cluster 3 comprises of 12 cyclones. Clusters 2 and 3 indicate that temperatures of about 34 to 35°C were mostly responsible for the formation of a total of 21 cyclones from 1975 to 2014. The existence of a relatively cooler zone (strong negative anomaly) near to surface level, in association with a relatively warmer zone (strong positive anomaly) at 850, 700, 500, and 300hPa level were firmly responsible for cyclogenesis over BB. The larger, warmer air mass in the upper atmosphere could have a significant impact on the development of huge instability throughout the entire atmospheric column, potentially leading to the formation of extreme weather phenomena such as severe cyclones in southern Bangladesh.

Modeling Volatility-Based Aerosol Phase State Predictions in the Amazon Rainforest

Organic aerosol (OA) is a complex matrix of various constituents—fresh (primary organic aerosols—POA) and aged via oxidation (secondary organic aerosols—SOA), generated from biogenic, anthropogenic, and biomass burning sources. The viscosity of OA can be critical in influencing new particle formation, reactive uptake processes that impact evaporation-growth kinetics, and the lifetime of particles in the atmosphere. This work utilizes a well-defined relationship between volatility and viscosity for pure compounds, which we incorporated within the Weather Research and Forecasting Model coupled to chemistry (WRF-Chem) to simulate the phase state and viscosity of bulk OA during the dry-to-wet transition season (September–October) in the Amazon rainforest during 2014. Our simulations indicate spatial and temporal heterogeneity in aerosol phase state often not captured by global-scale models. We show the strong role of water associated with organic aerosol (ws) as the dominant factor that can be used to quantitatively estimate OA viscosity. Analysis of WRF-Chem simulations across the entire atmospheric column indicates a strong inverse log-linear relationship between ws and OA viscosity with a correlation coefficient approaching 1, in the background and biomass burning-influenced conditions. At high altitudes where relative humidity (RH) and temperatures are low, our simulations indicate that OA exists in a semisolid-/solid-like phase state, consistent with previous studies. OA hygroscopicity is strongly correlated (ca. −0.8) with OA viscosity at RH ca. 30–50%, but this RH range is found mostly at low OA concentrations and the middle troposphere (ca. 6–10 km altitudes) in our simulated domain. OA hygroscopicity is uncorrelated with viscosity at higher-RH (near surface) and lower-RH (upper troposphere) regimes. At the urban site near surface, where day–night differences in RH are significant, RH is found to drive the phase state. At the background forested site near surface, where day–night RH differences are small, biomass burning-influenced OA is semisolid and a significant OA associated with background conditions is liquid-like. Simulations indicate a long tail of OA viscosity frequency distributions extending in the semisolid/solid regimes over background biogenic-influenced conditions due to the role of low-volatility OA components such as monoterpene oxidation products.

Below-Cloud Atmospheric Correction of Airborne Hyperspectral Imagery Using Simultaneous Solar Spectral Irradiance Observations

Retrieving surface properties from airborne hyperspectral imagery requires the use of an atmospheric correction model to compensate for atmospheric scattering and absorption. In this study, a solar spectral irradiance monitor (SSIM) from the University of Colorado Boulder was flown on a Twin Otter aircraft with the National Ecological Observatory Network's (NEON) imaging spectrometer. Upwelling and downwelling irradiance observations from the SSIM were used as boundary conditions for the radiative transfer model used to atmospherically correct NEON imaging spectrometer data. Using simultaneous irradiance observations as boundary conditions removed the need to model the entire atmospheric column so that atmospheric correction required modeling only the atmosphere below the aircraft. For overcast conditions, incorporating SSIM observations into the atmospheric correction process reduced root-mean-square (rms) error in retrieved surface reflectance by up to 57% compared with a standard approach. In addition, upwelling irradiance measurements were used to produce an observation-based estimate of the adjacency effect. Under cloud-free conditions, this correction reduced the rms error of surface reflectance retrievals by up to 27% compared with retrievals that ignored adjacency effects.

Greenhouse Effect: The Relative Contributions of Emission Height and Total Absorption

AbstractSince the 1970s, results from radiative transfer models unambiguously show that an increase in the carbon dioxide (CO2) concentration leads to an increase of the greenhouse effect. However, this robust result is often misunderstood and often questioned. A common argument is that the CO2 greenhouse effect is saturated (i.e., does not increase) as CO2 absorption of an entire atmospheric column, named absorptivity, is saturated. This argument is erroneous first because absorptivity by CO2 is currently not fully saturated and still increases with CO2 concentration and second because a change in emission height explains why the greenhouse effect may increase even if the absorptivity is saturated. However, these explanations are only qualitative. In this article, we first propose a way of quantifying the effects of both the emission height and absorptivity and we illustrate which one of the two dominates for a suite of simple idealized atmospheres. Then, using a line-by-line model and a representative standard atmospheric profile, we show that the increase of the greenhouse effect resulting from an increase of CO2 from its current value is primarily due (about 90%) to the change in emission height. For an increase of water vapor, the change in absorptivity plays a more important role (about 40%) but the change in emission height still has the largest contribution (about 60%).

Ice versus liquid water saturation in simulations of the Indian summer monsoon

At the same temperature, below 0 °C, the saturation vapor pressure (SVP) over ice is slightly less than the SVP over liquid water. Numerical models use the Clausius–Clapeyron relation to calculate the SVP and relative humidity, but there is not a consistent method for the treatment of saturation above the freezing level where ice and mixed-phase clouds may be present. In the context of current challenges presented by cloud microphysics in climate models, we argue that a better understanding of the impact that this treatment has on saturation-related processes like cloud formation and precipitation, is needed. This study explores the importance of the SVP calculation through model simulations of the Indian summer monsoon (ISM) using the regional spectral model (RSM) at 15 km grid spacing. A combination of seasonal and multiyear simulations is conducted with two saturation parameterizations. In one, the SVP over liquid water is prescribed through the entire atmospheric column (woIce), and in another the SVP over ice is used above the freezing level (wIce). When SVP over ice is prescribed, a thermodynamic drying of the middle and upper troposphere above the freezing level occurs due to increased condensation. In the wIce runs, the model responds to the slight decrease in the saturation condition by increasing, relative to the SVP over liquid water only run, grid-scale condensation of water. Increased grid-scale mean seasonal precipitation is noted across the ISM region in the simulation with SVP over ice prescribed. Modification of the middle and upper troposphere moisture results in a decrease in mean seasonal mid-level cloud amount and an increase in high cloud amount when SVP over ice is prescribed. Multiyear simulations strongly corroborate the qualitative results found in the seasonal simulations regarding the impact of ice versus liquid water SVP on the ISM’s mean precipitation and moisture field. The mean seasonal rainfall difference over All India between wIce and woIce is around 10% of the observed interannual variability of seasonal All India rainfall.

Role of the Atlantic Multidecadal Variability in modulating the climate response to a Pinatubo-like volcanic eruption

The modulation by the Atlantic multidecadal variability (AMV) of the dynamical climate response to a Pinatubo-like eruption is investigated for the boreal winter season based on a suite of large ensemble experiments using the CNRM-CM5 Coupled Global Circulation Model. The volcanic eruption induces a strong reduction and retraction of the Hadley cell during 2 years following the eruption and independently of the phase of the AMV. The mean extratropical westerly circulation simultaneously weakens throughout the entire atmospheric column, except at polar Northern latitudes where the zonal circulation is slightly strengthened. Yet, there are no significant changes in the modes of variability of the surface atmospheric circulation, such as the North Atlantic Oscillation (NAO), in the first and the second winters after the eruption. Significant modifications over the North Atlantic sector are only found during the third winter. Using clustering techniques, we decompose the atmospheric circulation into weather regimes and provide evidence for inhibition of the occurrence of negative NAO-type circulation in response to volcanic forcing. This forced signal is amplified in cold AMV conditions and is related to sea ice/atmosphere feedbacks in the Arctic and to tropical-extratropical teleconnections. Finally, we demonstrate that large ensembles of simulations are required to make volcanic fingerprints emerge from climate noise at mid-latitudes. Using small size ensemble could easily lead to misleading conclusions especially those related to the extratropical dynamics, and specifically the NAO.

Impact of North American intense fires on aerosol optical properties measured over the European Arctic in July 2015

AbstractIn this paper impact of intensive biomass burning (BB) in North America in July 2015, on aerosol optical and microphysical properties measured in the European Arctic, is discussed. This study was made within the framework of the Impact of Absorbing aerosols on Radiating forcing in the European Arctic project. During the BB event aerosol optical depth (AOD) at 500 nm exceeded 1.2 in Spitsbergen and 0.7 in Andenes (Norway). Angstrom exponent exceeded 1.4, while the absorbing Angstrom exponent varied between 1 and 1.25. BB aerosols were observed in humid atmosphere with a total water vapor column between 2 and 2.5 cm. In such conditions aerosols are activated and may produce clouds at different altitudes. Vertical structure of aerosol plumes over Svalbard, obtained from ceilometers and lidars, shows variability of range‐corrected signal between surface and middle and upper troposphere. Aerosol backscattering coefficients show values up to 10−5 m−1 sr−1 at 532 nm. Aerosol surface observations indicate chemical composition typical for biomass burning particles and very high single scattering properties. Scattering and absorption coefficients at 530 nm were up to 130 and 15 Mm−1, respectively. Single scattering albedo at the surface varied from 0.9 to 0.94. The averaged values over the entire atmospheric column ranged from 0.93 to 0.99. Preliminary statistics of model and Sun photometer data as well as previous studies indicate that this event, in the Arctic region, must be considered extreme (such AOD was not observed in Svalbard since 2005) with a significant impact on energy budget.

Parallel Developments and Formal Collaboration between European Atmospheric Profiling Observatories and the U.S. ARM Research Program

The climate research community aims to better charac- terize climate forcings such as aerosols, reactive gases, and greenhouse gases, and to better understand the responses of the climate system to these forcings. Such investigations rely in part on monitoring, studying, and understanding essential climate variables such as temperature, water va- por, clouds, radiation, and perturbations of aerosols and reactive gases. According to Dufresne and Bony (2008), the parameters that play a predominant role in radiative feedbacks of the climate system are atmospheric humidity, adiabatic thermal gradients, clouds, and surface albedo. Interactions between humidity, clouds, aerosols, and radi- ation make climate predictions more complex. The climate research community has long recognized the link between climate prediction uncertainty and at- mospheric process complexity. For more than 20 years, it has demonstrated the necessity to perform collocated long-term observations of thermodynamic parameters (temperature, humidity, wind) and atmospheric con- stituents (gases, aerosols, clouds) distributed along the entire atmospheric column (surface to stratosphere) and associated radiative components.

Vertical Profiling of Aerosol Types Observed across Monsoon Seasons with a Raman Lidar in Penang Island, Malaysia

ABSTRACTAlthough technology has advanced in the past few decades, the understanding of the effect of aerosol on Earth’s climate has remained largely unknown. Even if considerable effort has been made by researchers around the world to study this pressing issue, the non-uniform distribution of aerosols is still a huge challenge on global aerosol characterization studies. Without extensive and reliable measurements from most regions in the world, a complete understanding of aerosol characteristics cannot be achieved. In this study, a Raman LIDAR was used to acquire data to calculate the range-dependent extinction-to-backscattering ratio during a very particular period: when the Northeast monsoon season shifts to the Southwest monsoon season. During this time period, monsoonal winds change direction, leading to changes in aerosol type and properties above Penang Island due to the global atmospheric circulation. From the retrieved extinction-to-backscattering ratio, it is possible to differentiate the aerosol types present at various altitudes. It was found that background marine and urban aerosols were present above Penang Island, as marine and urban aerosols were present both below and above the PBL height regardless of the monsoon season. Additionally, biomass burning aerosols and aged forest fire aerosols were occasionally found, which were normally associated with cases of pollution, such as those in early March 2014 and June 2014. Finally, it was found that during a haze episode, the entire atmospheric column was dominated by wood burning aerosols. AERONET sunphotometer data have been used to validate LIDAR findings. Radiative effect of background and transported aerosols are evaluated with the Fu-Liou-Gu radiative transfer model.

Development of efficient GPU parallelization of WRF Yonsei University planetary boundary layer scheme

Abstract. The planetary boundary layer (PBL) is the lowest part of the atmosphere and where its character is directly affected by its contact with the underlying planetary surface. The PBL is responsible for vertical sub-grid-scale fluxes due to eddy transport in the whole atmospheric column. It determines the flux profiles within the well-mixed boundary layer and the more stable layer above. It thus provides an evolutionary model of atmospheric temperature, moisture (including clouds), and horizontal momentum in the entire atmospheric column. For such purposes, several PBL models have been proposed and employed in the weather research and forecasting (WRF) model of which the Yonsei University (YSU) scheme is one. To expedite weather research and prediction, we have put tremendous effort into developing an accelerated implementation of the entire WRF model using graphics processing unit (GPU) massive parallel computing architecture whilst maintaining its accuracy as compared to its central processing unit (CPU)-based implementation. This paper presents our efficient GPU-based design on a WRF YSU PBL scheme. Using one NVIDIA Tesla K40 GPU, the GPU-based YSU PBL scheme achieves a speedup of 193× with respect to its CPU counterpart running on one CPU core, whereas the speedup for one CPU socket (4 cores) with respect to 1 CPU core is only 3.5×. We can even boost the speedup to 360× with respect to 1 CPU core as two K40 GPUs are applied.

Combined data assimilation of ozone tropospheric columns and stratospheric profiles in a high‐resolution CTM

AbstractStratospheric ozone profiles from MLS and tropospheric ozone columns from IASI have been assimilated into the MOCAGE model during the month of July 2009, using a variational (3D‐FGAT) technique. This study compares the separate and combined analysis of IASI tropospheric columns and MLS stratospheric profiles, in order to investigate possible synergistic effects. The contributions on the ozone distribution of each data assimilation experiment are discussed and self‐consistency is evaluated via χ2 test, Observations minus Analyses and Observations minus Forecasts diagnostics. The results show that MLS assimilation has a significant impact on the model troposphere. An evaluation of the stratospheric distribution is made using independent MIPAS stratospheric profiles. IAGOS flights are used to evaluate the impact of the dataset assimilated in the troposphere and in the UTLS region. Comparisons to MIPAS independent observations show an improvement of the ozone vertical profile in the stratosphere and in the UTLS due to the assimilation of MLS observations. The IASI analyses show the strongest improvements on ozone distributions in the free troposphere. The combined assimilation shows the most realistic ozone fields overall in the stratosphere, UTLS and troposphere. Though neither instrument covers the entire atmospheric column alone, the combined MLS and IASI analyses show also the best agreement with the independent total ozone columns data from the OMI dataset. Bias, RMSE and correlation are significantly improved compared to the free running model. This set of validations show that the vertical structure of the ozone fields is strongly improved by assimilation. By combining the pieces of information brought by IASI and MLS in the analyses, combined assimilation provides highly realistic ozone fields.

Shortwave Cloud and Aerosol Radiative Forcings and Their Effects on the Vertical Local Heating/Cooling Rates

An analysis of atmospheric SW-radiative forcing and local heating/cooling rate is made using a one year temporal and vertical profiles of aerosol and cloud over Yaounde (11.51°E, 3.83°N). It appears that the direct influence of aerosols on the surface compared to the TOA can be 3 times larger. Annual mean value obtained at 559 mb altitude is +27.74 W/m2 with range from 0 to +43 W/m2. At 904 mb, we obtained an annual mean of ﹣46.22 W/m2 with range from ﹣65 to ﹣9 W/m2. Frequency distribution indicates that more than 95% of ARF are between +10 and +70 W/m2 at 559 mb (upper limit of UL), and more than 85% of ARF are between ﹣70 and ﹣10 W/m2 at 904 mb (upper limit of PBL). This sign change is explained by the fact that the backscattering peaks at the upper limit of the aerosol PBL layer. The maximum CRF is noted at TOA where it reaches ﹣600 W/m2 based on the time interval and the structure of clouds. The highest values occur between 11.50 and 13.50 LST. Clouds lead to a general heating of the entire atmospheric column with a much greater effect near the surface. Aerosols effect on the heating rate profile show strong cooling during the day for the lower atmosphere, with slight heating at the upper atmosphere. This cooling contribution generally increases from the surface and peacks at the upper boundary of aerosol layer where reflectivity is the most important. Depending on the moment of the day, average heating effect of clouds peacks at surface or within the middle troposphere due to the absorption by clouds particles. Vertical profiles deeply evolve exhibiting differences that exceed ﹣3 K/day according to altitude from one hour to another during a given mean solar day.

EHP Paper of the Year, 2011

In 2008, Environmental Health Perspectives (EHP) established the Paper of the Year Award as a means of recognizing high-impact papers published in the journal (Tilson 2008). Originally, the Paper of the Year was selected on the basis of citations received over the preceding 60 months. Earlier this year EHP announced that it would be recognizing two papers each year (Tilson 2011). The EHP Classic Paper of the year will be the research article, commentary, or review article that is the most highly cited paper over the preceding 60 months. The winner of the 2011 EHP Classic Paper of the Year was “Maternal Genistein Alters Coat Color and Protects Avy Mouse Offspring from Obesity by Modifying the Fetal Epigenome” by Dana C. Dolinoy, Jennifer R. Weidman, Robert A. Waterland, and Randy Jirtle. The second paper to be recognized each year will be the EHP Paper of the Year. This award will recognize a highly cited paper published during the preceding year. Both awards are subject to the approval of the EHP Board of Associate Editors. In this issue, EHP announces that the Paper of the Year for 2011 is “Global Estimates of Ambient Fine Particulate Matter Concentrations from Satellite-Based Aerosol Optical Depth: Development and Application” by Aaron van Donkelaar, Randall V. Martin, Michael Brauer, Ralph Kahn, Robert Levy, Carolyn Verduzco, and Paul J. Villeneuve. This paper (van Donkelaar et al. 2010) was published in the June 2010 issue of the journal. At the time the paper was written, it was clear that chronic exposure to fine particulate matter (< 2.5 µm in diameter; PM2.5) could harm human health by producing morbidity and mortality. van Donkelaar et al. (2010) recognized that the sparseness of monitoring data, especially in developing countries, had hindered epidemiologic and health impact studies of PM2.5. They noted that satellite remote sensing offers unparalleled global coverage of aerosol optical depth (AOD), a measure of aerosol over the entire atmospheric column, and that the relationship between AOD and near-surface PM2.5 varies in both space and time. van Donkelaar et al. (2010) related AOD to near-surface PM2.5 by using a chemical transport model that predicts the state of the atmosphere from meteorologic data sets, emission inventories, and equations that represent the physical and chemical evolution of atmospheric constituents. van Donkelaar et al. (2010) combined AODs from two NASA (National Aeronautics and Space Administration) satellite instruments [Moderate Resolution Imaging Spectroradiometer (MODIS) and Multi-angle Imaging Spectroradiometer (MISR)] with the Goddard Earth Orbiting System chemical transport model to produce global long-term average (2001–2006) PM2.5 estimates at approximately 10 km × 10 km horizontal resolution. They found significant agreement with ground-based PM2.5 monitors, where present, both in North America and around the world. van Donkelaar et al. (2010) provided an observationally based data set to assess the impact of ambient PM2.5 exposure globally. The authors estimated that the World Health Organization (WHO) Air Quality Guideline of 10 μg/m3 is exceeded for 80% of the world’s population. Over eastern Asia, half the population has annual ambient mean PM2.5 exposures in excess of the WHO PM2.5 interim target of 35 μg/m3, and in eastern China annual mean levels exceed 80 μg/m3. EHP congratulates van Donkelaar and colleagues for their contribution to the environmental health science literature. Their findings have broad implications for the global environmental health and risk assessment communities.