The martian atmosphere hosts dynamical phenomena ranging from planet-encircling dust storms to mesoscale orographic clouds and nocturnal low-level jets. General circulation model show capability to simulate these phenomena, but is computationally expensive at resolution needed to resolve mesoscale features. While assimilation of satellite remote sensing observation enable forecasting capabilities using such models, observation record is often sparse, short and fragmented across instrument generators. These constraints motivate the development of a data-driven foundation model for the Martian atmosphere. Foundation models live in a complex design landscape. There is an interplay between the available data, the physics of the underlying processes and corresponding developments in AI. Even though the idea of a foundation model is to address multiple use cases in a data- and compute-efficient manner, it is important to have a clear picture what applications can sensibly addressed by a single model. The purpose of this paper is to elucidate this design landscape. We discuss available data ranging from atmospheric retrievals to reanalysis datasets as well as existing physical models. Moreover, we identify a wide range of candidate downstream applications. Finally, we consider relevant recent developments in artificial intelligence (AI) that can be leveraged in this context. Here, we put a particular emphasis on AI models for atmospheric physics, data-driven approaches to data assimilation as well as methods to work in a limited data setting.

NOx emissions produced by lightning strikes (LNOx) play an increasingly important role in atmospheric chemistry due to their abundance and prevalence in the mid-to-upper troposphere, especially in regions with decreasing trends in anthropogenic NOx emissions. Accurately quantifying LNOx emissions in time and space in chemistry transport models is challenging due to various uncertainties associated with lightning data and LNOx production rates. Currently, LNOx yield is mainly based on lightning flashes either detected by lightning detection networks or estimated by lightning parameterization schemes. However, the definition of a lightning flash varies from one network to another, confounding the use of datasets from different networks. In addition, it has long been recognized that LNOx yield is related to lightning energy levels, which varies across lightning types (e.g., cloud-to-ground, cloud-to-cloud) and exhibits geographical variations. Along with flash and stroke counts, most networks also report the associated energy aspects. Even though the registered energy values are only a tiny fraction of the sampled lightning strikes (either electromagnetic or optical) by different detection techniques, they represent the same lightning activities (if the networks are similarly purposed), thus some relationship should exist among them. Exploratory analysis of the lightning datasets from the World Wide Lightning Location Network (WWLLN) and the Geostationary Lightning Mapper (GLM) aboard the GOES-R satellites have indicated that even though both networks detect total lightning, the WWLLN network is better at detecting cloud-to-ground lightning strikes, while the GLM technique is better at detecting total lightning. The energy aspects detected by the two networks display strong relationship when these energy values are aggregated over time and space, suggesting that synergies between the two datasets could be exploited to produce more realistic LNOx emissions estimate across temporal and spatial scales broader than those covered individually.

Lightning is one of the primary natural sources of nitric oxide (NO), and the influence of lightning-induced NO (LNO) emission on air quality has been investigated in the past few decades. In the current study an LNO emissions model, which derives LNO emission estimates from satellite-observed lightning optical energy, is introduced. The estimated LNO emission is employed in an air quality modeling system to investigate the potential influence of LNO on tropospheric ozone. Results show that lightning produced 0.174 Tg N of nitrogen oxides (NOx = NO + NO2) over the contiguous US (CONUS) domain between June and September 2019, which accounts for 11.4 % of the total NOx emission. In August 2019, LNO emission increased ozone concentration within the troposphere by an average of 1 %–2 % (or 0.3–1.5 ppbv), depending on the altitude; the enhancement is maximum at ∼ 4 km above ground level and minimum near the surface. The southeastern US has the most significant ground-level ozone increase, with up to 1 ppbv (or 2 % of the mean observed value) difference for the maximum daily 8 h average (MDA8) ozone. These numbers are near the lower bound of the uncertainty range given in previous studies. The decreasing trend in anthropogenic NOx emissions over the past 2 decades increases the relative contribution of LNO emissions to total NOx emissions, suggesting that the LNO production rate used in this study may need to be increased. Corrections for the sensor flash detection efficiency may also be helpful. Moreover, the episodic impact of LNO on tropospheric ozone can be considerable. Performing backward trajectory analyses revealed two main reasons for significant ozone increases: long-distance chemical transport and lightning activity in the upwind direction shortly before the event.

A lightning nitrogen oxides (LNOx) emissions model using satellite‐observed lightning optical energy is introduced for utilization in Air Quality modeling systems. The effort supports assessments of air‐quality/climate coupling as related to the influence of LNOx on atmospheric chemistry. The Geostationary Lightning Mapper (GLM), International Space Station Lightning Imaging Sensor (ISS‐LIS), and the Tropical Rainfall Measuring Mission (TRMM) LIS data are used to examine the efficacy of the method, extend the previously derived LNOx record, and demonstrate a path for using ISS‐LIS observations to cross‐calibrate regional LNOx estimates from the future global constellation of geostationary lightning observations. A detailed evaluation of the GLM data set is provided to establish the robustness of observations for LNOx estimates and to make preliminary assessments of the LNOx emissions model. Seasonal and geographical variation, land/ocean contrast, and annual fluctuation in the GLM observed lightning activity and flash optical energy are provided. GLM detection substantially degrades with the increase in the field of view, resulting in 44% more flashes and 40% less optical energy observation by GLM‐16 (compared to GLM‐17) to the east of the middle‐longitude between the two mappers (106.2°W). Regular horizontal striations are found in the optical energy product. On average, GLM flashes matched to the cloud‐to‐ground flashes have ∼30% longer duration, 50%–70% more extension, and ≥100% higher optical energy compared to the unmatched flashes (assumed to be intra‐cloud). The results from summer‐long chemical transport simulations using LNOx generated from the emission model agrees with previous studies and shows consistency across the GLM/LIS data sets.

The Global Hydrology Resource Center (GHRC) Distributed Active Archive Center (DAAC), developed the Field Campaign eXplorer (FCX) to address a limitation in available visualization resources. FCX is a cloud-native, open-source system capable of visualizing multiple datasets in three dimensions. This includes data from ground-, airborne-, and satellite-based observations. The open-source nature will further allow users of FCX to develop their own extensions for both visualizations and analyses. This paper will discuss the architecture of FCX, its uses, and future development work.

Mahmood, Rezaul; Pielke, Roger A. Sr.; Hubbard, Kenneth G.; Niyogi, Dev; Bonan, Gordon; Lawrence, Peter; McNider, Richard; McAlpine, Clive; Etter, Andres; Gameda, Samuel; Qian, Budong; Carleton, Andrew; Beltran-Przekurat, Adriana; Chase, Thomas; Quintanar, Arturo I.; Adegoke, Jimmy O.; Vezhapparambu, Sajith; Connor, Glen; Asefi, Salvi; Sertel, Elif; Legates, David R.; Wu, Yuling; Hale, Robert; Frauenfeld, Oliver W.; Watts, Anthony; Shepherd, Marshall; Mitra, Chandana; Anantharaj, Valentine G.; Fall, Souleymane; Lund, Robert; Treviño, Anna; Blanken, Peter D.; Du, Jinyang; Chang, Hsin-I; Leeper, Ronnie; Nair, Udaysankar S.; Dobler, Scott; Deo, Ravinesh; and Syktus, Jozef, "Impacts of Land Use/Land Cover Change on Climate and Future Research Priorities" (2010). Papers in Natural Resources. 395. https://digitalcommons.unl.edu/natrespapers/395

High mixing ratios of ozone along the shores of Lake Michigan have been a recurring theme over the last 40 years. Models continue to have difficulty in replicating ozone behavior in the region. Although emissions and chemistry may play a role in model performance, the complex meteorological setting of the relatively cold lake in the summer ozone season and the ability of the physical model to replicate this environment may contribute to air quality modeling errors. In this paper, several aspects of the physical atmosphere that may affect air quality, along with potential paths to improve the physical simulations, are broadly examined. The first topic is the consistent overwater overprediction of ozone. Although overwater measurements are scarce, special boat and ferry ozone measurements over the last 15 years have indicated consistent overprediction by models. The roles of model mixing and lake surface temperatures are examined in terms of changing stability over the lake. From an analysis of a 2009 case, it is tentatively concluded that excessive mixing in the meteorological model may lead to an underestimate of mixing in offline chemical models when different boundary layer mixing schemes are used. This is because the stable boundary layer shear, which is removed by mixing in the meteorological model, can no longer produce mixing when mixing is rediagnosed in the offline chemistry model. Second, air temperature has an important role in directly affecting chemistry and emissions. Land–water temperature contrasts are critical to lake and land breezes, which have an impact on mixing and transport. Here, satellite-derived skin temperatures are employed as a path to improve model temperature performance. It is concluded that land surface schemes that adjust moisture based on surface energetics are important in reducing temperature errors.

To efficiently perform multiscale analysis of high-resolution, global, multiple-dimensional datasets, the authors have deployed the parallel ensemble empirical mode decomposition (PEEMD) package by implementing three-level parallelism into the ensemble empirical mode decomposition (EMD), achieving a scaled performance of 5,000 cores. In this study, they discuss the implementation of the PEEMD and its application for the analysis of Earth science data, including the solution of the Lorenz model, an idealized terrain-induced flow, and Hurricane Sandy.

In this study the parallel ensemble empirical mode decomposition (PEEMD) is applied for an analysis of 10-yr (2004-13) ERA-Interim global reanalysis data in order to explore the role of downscaling processes associated with African easterly waves (AEWs) in tropical cyclone (TC) genesis. The focus of the study was aimed at understanding the downscaling process in multiscale flows during storm intensification. To represent the various length scales of atmospheric systems, intrinsic mode functions (IMFs) were extracted from the reanalysis data using the PEEMD. It was found that the nonoscillatory trend mode can be used to represent large-scale environmental flow and that the third oscillatory mode (IMF3) can be used to represent AEW/TC scale systems. The results 1) identified 42 developing cases from 272 AEWs, where 25 of them eventually developed into hurricanes; 2) indicated that the maximum for horizontal shear largely occurs over the ocean for the IMF3 and over land near the coast for the trend mode for developing cases, suggesting shear transfer between the trend mode and the IMF3; 3) displayed opposite wind shear tendencies for the trend mode and the IMF3 during storm intensification, signifying that the downscaling process was active in 13 hurricane cases along their tracks; and 4) showed that among the 42 developing cases, only 13 of the 25 hurricanes were found to have significant downscaling transfer features, so other processes such as upscaling processes may play an important role in the other developing cases, especially for the remaining 12 hurricane cases. In a future study, the authors intend to investigate the upscaling process between the convection scale and AEWs/TCs, which requires data at a finer grid resolution.

Hurricane Sandy in October 2012 is currently the second costliest tropical cyclone (TC) in the U.S. history, surpassed only by Hurricane Katrina (2005). This paper uses advanced data analysis methods and visualization technology to examine the role of multiscale processes in the initial formation and movement of Hurricane Sandy. To efficiently analyze high-resolution, global, and multiple-dimensional datasets, a parallel ensemble empirical mode decomposition (PEEMD) method is developed by implementing a multi-level parallelism into an ensemble EMD (EEMD). The augmentation resulted in a parallel speedup of 720 using 200 eight-core processors. Here, we discuss performance for the PEEMD in decomposing multiscale signals from data sets that represent: (i) idealized tropical waves and (ii) large-scale environmental flows associated with Hurricane Sandy (2012). Our results indicate that the PEEMD can efficiently reveal major wave characteristics such as wavelengths and periods within the data by sifting out the dominant (wave) components. Visualization tools have been developed to make four-dimensional (4D) visualizations of Sandy. The 4D visualizations help elucidate the following factors which led to the sinuous track of Sandy: (i) the initial steering impact of an upper-level trough (appearing over the Northwestern Caribbean Sea and the Gulf of Mexico); (ii) the blocking impact of systems to the Northeast of Sandy; and (iii) interaction with a mid-latitude, upper-level trough that appeared at 130 degrees West longitude on October 23, moved to the east coast, and intensified from October 29–30 prior to Sandy’s landfall. Both the PEEMD method and advanced visualization technology have been integrated with other modules in order to examine the statistical relationship between tropical waves and TC formation in a hurricane climate study.

Abstract In this study, we discuss the performance of the parallel ensemble empirical mode decomposition (EMD) in the analysis of tropical waves that are associated with tropical cyclone (TC) formation. To efficiently analyze high-resolution, global, multiple-dimensional data sets, we first implement multi-level parallelism into the ensemble EMD (EEMD) and obtain a parallel speedup of 720 using 200 eight-core processors. We then apply the parallel EEMD (PEEMD) to extract the intrinsic mode functions (IMFs) from preselected data sets that represent (1) idealized tropical waves and (2) large-scale environmental flows associated with Hurricane Sandy (2012). Results indicate that the PEEMD is efficient and effective in revealing the major wave characteristics of the data, such as wavelengths and periods, by sifting out the dominant (wave) components. This approach has a potential for Analyzing Tropical Waves Using the Parallel Ensemble Empirical Model Decomposition ...Page 2 of 11http://www.earthzine.org/2013/12/02/analyzing-tropical-waves-using-the-parallel-ensembl... 12/7/2013

Observations from near‐simultaneous atmospheric soundings released over contrasting land surfaces in the southwest of Western Australia during December 2005 (austral summer) and August 2007 (late austral winter or early spring) have shown higher planetary boundary layer (PBL) heights over native vegetation as compared to agricultural land. The large‐eddy simulation technique is used to investigate the drivers behind these observed differences in PBL, and sensitivity tests are carried out with modified soil moisture and vegetation cover. It is shown that the differences in PBL for the December case are mainly driven by the change in vegetation cover, while a soil moisture gradient also played a role for the August case. The mixing diagram approach is used to further quantify the relative contributions of surface and entrainment fluxes on the growth of the PBL and it is shown that, while dry‐air entrainment plays an important role in PBL development, it is the higher surface Bowen ratio which drives the more vigorous PBL development over the native vegetation. It is also shown that the enhanced PBL development over the native vegetation leads to the preferential formation of shallow convective clouds for the August case. Copyright © 2012 Royal Meteorological Society

Observations for the 2005-2008 time period from three Southeastern Aerosol Research and Characterization (SEARCH) air quality monitoring sites are examined for diurnal and seasonal variation in concentrations of gaseous elemental mercury (GEM), gaseous oxidized mercury (GOM), and particle bound mercury (HgP < 2.5 mu m). The sites are located at 1) a suburban-coastal location near Pensacola, Florida (OLF), 2) an urban location in Birmingham, Alabama (BHM), and 3) a rural location west-northwest of Atlanta, Georgia (YRK). Average concentrations of GEM at both OLF and YRK are 1.35 ng m(-3), whereas at BHM it is 2.12 ng m(-3). All sites show increase in GEM concentration during the morning hours (0.023 and 0.011 ng m(-3) hr(-1) at OLF and YRK between 6 and 10 AM, 0.038 ng m(-3) hr(-1) at BHM between 5 and 10 AM) due to downward mixing of higher concentrations from the residual layer, after which OLF and YRK show negligible variation compared to decrease in concentration at BHM (23-1.9 ng m(-3) from 10 AM to 6 PM). All sites show seasonal variation of GEM with enhanced concentrations found in winter and spring. Average GOM concentrations are 4.26, 8.55, and 78.2 pg m(-3) at OLF, YRK, and BHM, respectively. Seasonally, GOM values are enhanced during fall and spring. All sites undergo a sinusoidal daytime variation of GOM that peaks in the afternoon, while BHM additionally exhibits an early morning enhancement likely caused by vertical mixing. The average HgP concentrations at OLF, YRK, and BHM are 2.49, 4.43, and 39.5 pg m-3, respectively. At OLF, vertical mixing causes an early morning increase in HgP concentration followed by an afternoon decline during all seasons. A daytime increase in HgP is found at YRK for all seasons, while at BHM, nocturnal accumulation followed by a daytime decline is also found for most seasons except winter. In winter, concentrations increase due to vertical mixing in the morning and then decline as the boundary layer grows. Boundary layer processes appear to play an important role in the seasonal and diurnal variation of Hg species and further investigation utilizing a boundary layer process model is warranted. (C) 2011 Elsevier Ltd. All rights reserved.

Several recommendations have been proposed for detecting land use and land cover change (LULCC) on the environment from, observed climatic records and to modeling to improve its understanding and its impacts on climate. Researchers need to detect LULCCs accurately at appropriate scales within a specified time period to better understand their impacts on climate and provide improved estimates of future climate. The US Climate Reference Network (USCRN) can be helpful in monitoring impacts of LULCC on near-surface atmospheric conditions, including temperature. The USCRN measures temperature, precipitation, solar radiation, and ground or skin temperature. It is recommended that the National Climatic Data Center (NCDC) and other climate monitoring agencies develop plans and seek funds to address any monitoring biases that are identified and for which detailed analyses have not been completed.

Prior numerical modelling studies show that atmospheric dispersion is sensitive to surface heterogeneities, but past studies do not consider the impact of a realistic distribution of surface heterogeneities on mesoscale atmospheric dispersion. While these focussed on dispersion in the convective boundary layer, the present work also considers dispersion in the nocturnal boundary layer and above. Using a Lagrangian particle dispersion model (LPDM) coupled to the Eulerian Regional Atmospheric Modeling System (RAMS), the impact of topographic, vegetation, and soil moisture heterogeneities on daytime and nighttime atmospheric dispersion is examined. In addition, the sensitivity to the use of Moderate Resolution Imaging Spectroradiometer (MODIS)-derived spatial distributions of vegetation characteristics on atmospheric dispersion is also studied. The impact of vegetation and terrain heterogeneities on atmospheric dispersion is strongly modulated by soil moisture, with the nature of dispersion switching from non-Gaussian to near-Gaussian behaviour for wetter soils (fraction of saturation soil moisture content exceeding 40%). For drier soil moisture conditions, vegetation heterogeneity produces differential heating and the formation of mesoscale circulation patterns that are primarily responsible for non-Gaussian dispersion patterns. Nighttime dispersion is very sensitive to topographic, vegetation, soil moisture, and soil type heterogeneity and is distinctly non-Gaussian for heterogeneous land-surface conditions. Sensitivity studies show that soil type and vegetation heterogeneities have the most dramatic impact on atmospheric dispersion. To provide more skilful dispersion calculations, we recommend the utilisation of satellite-derived vegetation characteristics coupled with data assimilation techniques that constrain soil-vegetation-atmosphere transfer (SVAT) models to generate realistic spatial distributions of surface energy fluxes.

During April and May 2007, several hundred fires burned uncontrollably in Georgia and Florida. The smoke from these fire events were visible throughout the Southeastern United States and had a major impact on particulate matter (PM) air quality near the surface. In this study, we show the strength of polar orbiting and geostationary satellite data in capturing the spatial distribution and diurnal variability of columnar smoke aerosol optical depth from these fires. We quantitatively evaluate PM air quality from satellites and ground-based monitors, near and far away (> 300 km) from fire source regions. We also show the changes in organic carbon concentrations (a tracer for smoke aerosols) before, during and after these fire events. Finally, we use fire locations and emissions retrieved and estimated from satellite observations as input to a regional mesoscale transport model to forecast the spatial distribution of aerosols and their impact on PM air quality. During the fire events, near the source regions, total column 550 nm aerosol optical thickness (AOT) exceeded 1.0 on several days and ground-based PM2.5 mass (particles less than 2.5 mum in aerodynamic diameter) reached unhealthy levels ( > 65.5 mug m-3). Since the aerosols were reasonably well mixed in the first 1-2 km (as estimated from meteorology), the column AOT values derived from both geostationary and polar orbiting satellites and the surface PM2.5 were well correlated (linear correlation coefficient, r > 0.7). Several hundred miles away from the fire sources, in Birmingham, AL, the impact of the fires were also seen through the high AOT's and PM2.5 values. Correspondingly, PM2.5 mass due to organic carbon obtained from ground-based monitors showed a three fold increase during fire events when compared to background values. Satellite data were especially useful in capturing PM2.5 air quality in areas where there were no ground-based monitors. Although the mesoscale transport model captured the timing and location of aerosols, when compared to observations, the simulated mass concentrations are underestimated by nearly 70% due to various reasons including uncertainties in fire emission estimates, lack of chemistry in the model, and assumptions on vertical distribution of aerosols. Satellite products such as AOT, fire locations, and emissions from space-borne sensors are becoming a vital tool for assessing extreme events such as fires, smoke, and particulate matter air quality.