UMBC Joint Center for Earth Systems Technology (JCET)

Permanent URI for this collectionhttp://hdl.handle.net/11603/7732

The Joint Center for Earth Systems Technology (JCET) was formed under a Cooperative agreement between the Earth Science Division of NASA Goddard Space Flight Center (GSFC) and the University of Maryland, Baltimore County (UMBC) in 1995. The JCET family consists of its business staff, students, research faculty, GSFC Sponsors, appointed fellows and affiliated tenured/tenure-track faculty. JCET is an innovative center where these scientists interact in a seamless fashion with the support of an efficient business and administrative unit.

Browse

Now showing 1 - 20 of 1453

Analyses of Virtual Ship-Tracks Systematically Underestimate Aerosol-Cloud Interactions Signals
(AGU, 2025) Yuan, Tianle; Song, Hua; Oreopoulos, Lazaros; Wood, Robert; Meyer, Kerry; Crawford, Alice; Smith, William; Eastman, Ryan
Ship-tracks are important natural/opportunistic experiments to study aerosol-cloud interactions (ACIs). However, detectable ship-tracks are not produced in many instances. Virtual ship-tracks have been conceived to expand the scale of ACIs analyses. Cloud responses in virtual ship-tracks differ strongly from those of detected ones. Here we show that the current approach of virtual ship-tracks can lead to systematic biases and errors and suggest necessary improvements. Errors in trajectory modeling introduce mismatches between areas actually affected by ship-emissions and virtual ship-track locations, that is, positional errors. Positional errors systematically underestimate ACI signals and the underestimate is severe as indicated by analysis of cloud droplet number concentration changes. The assumption of fixed ship-track width also systematically diminishes resulting aerosol effects by more than 10%, which leads to a forcing difference of around 0.1 Wm⁻². We make suggestions to improve the simulation of virtual ship-tracks so that their full potential for studying ACIs can be unleashed.
A Comparative Study of Data Cleaning Tools
(IGI Global, 2019-10-01) Oni, Samson; Chen, Zhiyuan; Hoban, Susan; Jademi, Onimi Precious
In the information era, data is crucial in decision making. Most data sets contain impurities that need to be weeded out before any meaningful decision can be made from the data. Hence, data cleaning is essential and often takes more than 80 percent of time and resources of the data analyst. Adequate tools and techniques must be used for data cleaning. There exist a lot of data cleaning tools but it is unclear how to choose them in various situations. This research aims at helping researchers and organizations choose the right tools for data cleaning. This article conducts a comparative study of four commonly used data cleaning tools on two real data sets and answers the research question of which tool will be useful based on different scenario.
Substantial Diel Changes of Cloud Adjustments to Aerosols in Ship-tracks
(2025-04-09) Yuan, Tianle; Song, Hua; Wood, Robert; Oreopoulos, Lazaros; Meyer, Kerry; Smith, William; Eastman, Ryan
Human induced changes in atmospheric aerosols have introduced a climate forcing by modifying cloud droplet number concentration, liquid water, and cloud fraction. This forcing is subject to large uncertainties as cloud adjustments have not only complex dependence on background conditions, but also temporal fluctuations, especially those driven by diel variations in solar heating. However, direct observations of such diel changes are still limited. Here, we present observational evidence of substantial diel changes in the cloud adjustments to aerosols within ship tracks, linear lines of polluted clouds captured in satellite images. We developed a novel method to automatically determine the age of each ship-track segment and analyze cloud adjustments to aerosols. We show that more aged polluted clouds with extended nighttime exposure exhibit higher increases in cloud fraction. By contrast, liquid water path adjustments follow a non-monotonic pattern: they generally decrease with time before reversing trend in clouds formed at nighttime. Most of these diel contrasts are statistically significant and likely stem from differences in solar heating and cloud precipitation. The increase in cloud fraction adjustment suggests a larger aerosol effective radiative forcing, -0.1 to -0.4 W per meter squared, than the estimate without considering temporal variations, while the temporal changes in liquid water path adjustments may partially offset it. These findings underscore the importance of diel variations in aerosol cloud interactions. Our approach demonstrates that ship tracks, despite appearing as instantaneous observations, yield valuable insights into the temporal evolution of cloud adjustments.
Scattering properties and Lidar Characteristics of Asian Dust Particles Based on Realistic Shape Models
(2025-03-26) La Luna, Anthony; Zhang, Zhibo; Zheng, Jianyu; Song, Qianqian; Yu, Hongbin; Ding, Jiachen; Yang, Ping; Saito, Masanori
The lidar backscattering properties of Asian dust particles, namely the lidar ratio (𝑆) and backscattering depolarization ratio (δ), were studied using a discrete dipole approximation (DDA) model. The three-dimensional morphology of the dust particles was reconstructed in fine detail using the focused ion-beam (FIB) tomography technique. An index based on the symmetry of the scattering phase matrix was developed to assess the convergence of random orientation computation using DDA. Both the 𝑆 and δ exhibit an asymptotic trend with dust particle size: the 𝑆 initially decreases while the δ increases with size, before both approach their asymptotic values. The lidar properties were found to have statistically insignificant dependence on effective sphericity. The presence of strongly absorbing minerals, such as magnetite, can greatly reduce the dust's single-scattering albedo and δ. Utilizing the robust asymptotic trend behavior, two parameterization schemes were developed: one to estimate the δ of a single dust particle given its size, and the other to estimate the δ of dust particles with a lognormal particle size distribution given the effective radius. The parameterization scheme was compared with results based on the TAMUdust2020 database, showing hexahedrals to reasonably represent realistic geometries with similar physical properties.
A systematic comparison of ACE-FTS δD retrievals with airborne in situ sampling
(2025-04-04) Clouser, Benjamin Wade; KleinStern, Carly Cyd; Desmoulin, Adrien; Singer, Clare E.; St. Clair, Jason; Hanisco, Thomas F.; Sayres, David S.; Moyer, Elisabeth J.
The isotopic composition of water vapor in the upper troposphere and lower stratosphere (UTLS) can be used to understand and constrain the budget and pathways of water transport into that region of the atmosphere. Measurements of the water isotopic composition help further understanding of the region's chemistry, radiative budget, and the sublimation and growth of polar stratospheric clouds and high-altitude cirrus, both of which are also important to stratospheric chemistry and Earth's radiation budget. Here we present the first intercomparison of water isotopic composition δD using in situ measurements from the ChiWIS, Harvard ICOS, and Hoxotope instruments and satellite retrievals from ACE-FTS. The in situ data comes from the AVE-WIIF, TC4, CR-AVE, StratoClim, and ACCLIP field campaigns, and satellite retrievals of isotopic composition are derived from the ACE-FTS v5.2 data set. We find that in all campaign intervals, the satellite retrievals above about 14 km altitude are depleted by up to 150 ‰ with respect to in situ measurements. We also use in situ measurements from the ChiWIS instrument, which has flown in both the Asian Summer Monsoon (AM) and the North American Monsoon (NAM), to confirm the isotopic enhancement in δD observed in satellite retrievals above the NAM.
Long-term trends in daytime cirrus cloud radiative effects: Analyzing twenty years of Micropulse Lidar Network measurements at Greenbelt, Maryland in eastern North America
(2025-03-25) Lolli, Simone; Dolinar, Erica K.; Lewis, Jasper R; Salcedo-Bosch, Andreu; Campbell, James R.; Welton, Ellsworth J.
This pioneering study elucidates the long-term trends and intricate variability of the radiative impacts and optical characteristics of cirrus clouds over two decades, from 2003 to 2022 at the NASA GSFC in Greenbelt, Maryland, USA, headquarters of the Micropulse Lidar Network (MPLNET) project. Over twenty years, analysis of the net cloud radiative effects (CREs) at both the top-of-the-atmosphere (TOA) and surface (SFC) reveals decreases in radiative flux by -0.0017 and -0.0035 W m⁻² yr⁻¹ and -0.0027 and -0.048 W m⁻² yr⁻¹, respectively (based on the constrained solutions for lidar-derived 523/527/532 nm extinction coefficient (m⁻¹) solved for lidar ratios bounded by both 20 and 30 sr). Concurrently, pivotal attributes such as cloud boundary temperature and altitude and integrated optical depth exhibit noteworthy stability, punctuated only by minor seasonal shifts. This study also uncovers a persistent decline in surface albedo, with a derived trend of -0.00036 yr⁻¹. We further find that the interrelationship between CRE and surface albedo variation intensifies notably during winter months. This leads to speculation that a decrease in the number of days of snow and ice is the main driver of the decrease in surface albedo. The decline in radiative flux at both the TOA and SFC can be perceived as a positive feedback loop that leads to increased atmospheric warming. The unveiled trends underscore the intricate synergy between albedo, radiative flux, and climate dynamics, pressing the need for vigilant monitoring of these shifts, given their profound implications for future climatic and circulatory phenomena.
Multispectral Land Surface Reflectance Reconstruction Based on Non-Negative Matrix Factorization: Bridging Spectral Resolution Gaps for GRASP TROPOMI BRDF Product in Visible
(MDPI, 2025-03-17) Hou, Weizhen; Liu, Xiong; Wang, Jun; Chen, Cheng; Xu, Xiaoguang
In satellite remote sensing, mixed pixels commonly arise in medium- and low-resolution imagery, where surface reflectance is a combination of various land cover types. The widely adopted linear mixing model enables the decomposition of mixed pixels into constituent endmembers, effectively bridging spectral resolution gaps by retrieving the spectral properties of individual land cover types. This study introduces a method to enhance multispectral surface reflectance data by reconstructing additional spectral information, particularly in the visible spectral range, using the TROPOMI BRDF product generated by the Generalized Retrieval of Atmosphere and Surface Properties (GRASP) algorithm. Employing non-negative matrix factorization (NMF), the approach extracts spectral basis vectors from reference spectral libraries and reconstructs key spectral features using a limited number of wavelength bands. The comprehensive test results show that this method is particularly effective in supplementing surface reflectance information for specific wavelengths where gas absorption is strong or atmospheric correction errors are significant, demonstrating its applicability not only within the 400–800 nm range but also across the broader spectral range of 400–2400 nm. While not a substitute for hyperspectral observations, this approach provides a cost-effective means to address spectral resolution gaps in multispectral datasets, facilitating improved surface characterization and environmental monitoring. Future research will focus on refining spectral libraries, improving reconstruction accuracy, and expanding the spectral range to enhance the applicability and robustness of the method for diverse remote sensing applications.
Key Governance Practices That Facilitate the Use of Remote Sensing Information for Wildfire Management: A Case Study in Spain
(MDPI, 2025-2-14) Prados, Ana; Allen, Mackenzie
We present results from a comprehensive analysis on the use of Earth Observations (EO) in Spain for wildfire risk management. Our findings are based on interviews with scientists, firefighters, forest engineers, and other professionals from government and private sector organizations in nine autonomous regions in Spain. Our aim is to identify the key governance practices facilitating or hindering the use of remote sensing (RS) information and to provide recommendations for improving their integration into landscape management and fire suppression activities to reduce wildfire risk. We share several case studies detailing activities and institutional arrangements facilitating the translation of satellite science and research into decision-making environments, with a focus on how this knowledge flows among the various stakeholder categories. Among the barriers faced by fire management teams in Spain, we identified institutional silos, lack of technical skills in satellite data processing and analysis, and the evolving acceptance of satellite data by decision makers.
Integrating Frequency-Domain Representations with Low-Rank Adaptation in Vision-Language Models
(2025-03-08) Khan, Md Azim; Gangopadhyay, Aryya; Wang, Jianwu; Erbacher, Robert F.
Situational awareness applications rely heavily on real-time processing of visual and textual data to provide actionable insights. Vision language models (VLMs) have become essential tools for interpreting complex environments by connecting visual inputs with natural language descriptions. However, these models often face computational challenges, especially when required to perform efficiently in real environments. This research presents a novel vision language model (VLM) framework that leverages frequency domain transformations and low-rank adaptation (LoRA) to enhance feature extraction, scalability, and efficiency. Unlike traditional VLMs, which rely solely on spatial-domain representations, our approach incorporates Discrete Fourier Transform (DFT) based low-rank features while retaining pretrained spatial weights, enabling robust performance in noisy or low visibility scenarios. We evaluated the proposed model on caption generation and Visual Question Answering (VQA) tasks using benchmark datasets with varying levels of Gaussian noise. Quantitative results demonstrate that our model achieves evaluation metrics comparable to state-of-the-art VLMs, such as CLIP ViT-L/14 and SigLIP. Qualitative analysis further reveals that our model provides more detailed and contextually relevant responses, particularly for real-world images captured by a RealSense camera mounted on an Unmanned Ground Vehicle (UGV).
Impact of increased anthropogenic Amazon wildfires on Antarctic Sea ice melt via albedo reduction
(Cambridge University Press, 2025-03-10) Chakraborty, Sudip; Devnath, Maloy Kumar; Jabeli, Atefeh; Kulkarni, Chhaya; Boteju, Gehan; Wang, Jianwu; Janeja, Vandana
This study shows the impact of black carbon (BC) aerosol atmospheric rivers (AAR) on the Antarctic Sea ice retreat. We detect that a higher number of BC AARs arrived in the Antarctic region due to increased anthropogenic wildfire activities in 2019 in the Amazon compared to 2018. Our analyses suggest that the BC AARs led to a reduction in the sea ice albedo, increased the amount of sunlight absorbed at the surface, and a significant reduction of sea ice over the Weddell, Ross Sea (Ross), and Indian Ocean (IO) regions in 2019. The Weddell region experienced the largest amount of sea ice retreat (~ 33,000 km²) during the presence of BC AARs as compared to ~13,000 km² during non-BC days. We used a suite of data science techniques, including random forest, elastic net regression, matrix profile, canonical correlations, and causal discovery analyses, to discover the effects and validate them. Random forest, elastic net regression, and causal discovery analyses show that the shortwave upward radiative flux or the reflected sunlight, temperature, and longwave upward energy from the earth are the most important features that affect sea ice extent. Canonical correlation analysis confirms that aerosol optical depth is negatively correlated with albedo, positively correlated with shortwave energy absorbed at the surface, and negatively correlated with Sea Ice Extent. The relationship is stronger in 2019 than in 2018. This study also employs the matrix profile and convolution operation of the Convolution Neural Network (CNN) to detect anomalous events in sea ice loss. These methods show that a higher amount of anomalous melting events were detected over the Weddell and Ross regions.
ER-2 X-band Radar (EXRAD) 3D Winds IMPACTS
(NASA GHRC, 2024-05-04) Guimond, Stephen
The ER-2 X-band Radar (EXRAD) 3D Winds IMPACTS dataset consists of horizontal wind components, uncertainties in the horizontal wind components, and radar reflectivity collected by the EXRAD instrument onboard the NASA ER-2 aircraft. These data were gathered during the Investigation of Microphysics and Precipitation for Atlantic CoastThreatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast (2020-2023, No deployments occurred in 2021 due to COVID-19). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. The EXRAD 3D Winds IMPACTS dataset files are available from January 25 through February 7, 2020 in netCDF-3 format.
Enterprise Risk Magazine - Winter 2024
(Issuu, 2024-12-06) Byatt, Gareth; Kelman, Ilan; Prados, Ana
Issuu is a digital publishing platform that makes it simple to publish magazines, catalogs, newspapers, books, and more online. Easily share your publications and get them in front of Issuu’s millions of monthly readers. Title: Enterprise Risk Magazine - Winter 2024, Author: Institute of Risk Management, Length: 40 pages, Page: 1, Published: 2024-12-06
Dynamic Impact of the Southern Annular Mode on the Antarctic Ozone Hole Area
(MDPI, 2025-02-27) Lee, Jae N.; Wu, Dong L.
This study investigates the impact of dynamic variability of the Southern Hemisphere (SH) polar middle atmosphere on the ozone hole area. We analyze the influence of the southern annular mode (SAM) and planetary waves (PWs) on ozone depletion from 19 years (2005–2023) of aura microwave limb sounder (MLS) geopotential height (GPH) measurements. We employ empirical orthogonal function (EOF) analysis to decompose the GPH variability into distinct spatial patterns. EOF analysis reveals a strong relationship between the first EOF (representing the SAM) and the Antarctic ozone hole area (γ = 0.91). A significant negative lag correlation between the August principal component of the second EOF (PC2) and the September SAM index (γ = -0.76) suggests that lower stratospheric wave activity in August can precondition the polar vortex strength in September. The minor sudden stratospheric warming (SSW) event in 2019 is an example of how strong wave activity can disrupt the polar vortex, leading to significant temperature anomalies and reduced ozone depletion. The coupling of PWs is evident in the lag correlation analysis between different altitudes. A “bottom-up” propagation of PWs from the lower stratosphere to the mesosphere and a potential “top-down” influence from the mesosphere to the lower stratosphere are observed with time lags of 21–30 days. These findings highlight the complex dynamics of PW propagation and their potential impact on the SAM and ozone layer. Further analysis of these correlations could improve one-month lead predictions of the SAM and the ozone hole area.
Developing Advanced Cloud Retrievals for PACE: Building a Joint Spectro-Polarimetric Cloud Microphysics Retrieval
(NASA, 2024-12) Miller, Daniel J.; Meyer, Kerry; Platnick, Steven E.; Zhang, Zhibo; Ademakinwa, Adeleke; Sinclair, Kenneth; Alexandrov, Mikhail; Geogdzhayev, Igor; van Diedenhoven, Bastiaan
Developing a Lagrangian Frame Transformation on Satellite Data to Study Cloud Microphysical Transitions in Arctic Marine Cold Air Outbreaks
(2025-03-13) Seppala, Hannah; Zhang, Zhibo; Zheng, Xue
Arctic marine cold air outbreaks (CAOs) generate distinct and dynamic cloud regimes due to intense air-sea interactions. To understand the temporal evolution of CAO cloud properties and compare different CAO events, a Lagrangian perspective is particularly useful. We developed a novel technique that enables the conversion of inherently Eulerian satellite data into a Lagrangian framework, combining the broad spatiotemporal coverage of satellite observations with the advantages of Lagrangian tracking. This technique was applied to eight CAO cases associated with a recent field campaign. Our results reveal a striking contrast among the cases in terms of cloud-top phase transitions, providing new insights into the evolution of CAO cloud properties.
Correlation to Causation: A Causal Deep Learning Framework for Arctic Sea Ice Prediction
(2025-03-03) Hossain, Emam; Ferdous, Muhammad Hasan; Wang, Jianwu; Subramanian, Aneesh; Gani, Md Osman
Traditional machine learning and deep learning techniques rely on correlation-based learning, often failing to distinguish spurious associations from true causal relationships, which limits robustness, interpretability, and generalizability. To address these challenges, we propose a causality-driven deep learning framework that integrates Multivariate Granger Causality (MVGC) and PCMCI+ causal discovery algorithms with a hybrid deep learning architecture. Using 43 years (1979-2021) of daily and monthly Arctic Sea Ice Extent (SIE) and ocean-atmospheric datasets, our approach identifies causally significant factors, prioritizes features with direct influence, reduces feature overhead, and improves computational efficiency. Experiments demonstrate that integrating causal features enhances the deep learning model's predictive accuracy and interpretability across multiple lead times. Beyond SIE prediction, the proposed framework offers a scalable solution for dynamic, high-dimensional systems, advancing both theoretical understanding and practical applications in predictive modeling.
Using Earth observations to avoid disasters
(UNDRR, 2024-10-11) Kelman, Ilan; Byatt, Gareth; Prados, Ana
Words Into Action requires evidence of disaster risk reduction (DRR) approaches and solid communication of that evidence through effective engagement with everyone in the science-policy-society ecosystem: policy makers, scientists and researchers, the private and non-profit sectors, the media, and ordinary people. Through our Disasters Avoided project, we are demonstrating the benefits of proactive DRR for everyone, including through the effective use of Earth observations, so that no one is left behind. Examples profiled here are wildland fires in Australia, cyclones in Bangladesh, and earthquakes across the east coast of the U.S.A. They show the importance of compiling, verifying, and sharing compelling good news of potential disasters which could have happened, but did not, because action was proactively taken based on knowledge and sound Earth observations data—as well as work remaining. We hope that these examples will inspire continuing action, based on repeated words, across all parts of society, including collaborative activities between Earth scientists and policy makers.
The airborne LUnar Spectral Irradiance (air-LUSI) Mission
(NASA, 2018-10-08) Turpie, Kevin; Brown, Steve; Woodward, John; Maxwell, Steve; Larason, Thomas; Zarobila, Clarence; Grantham, Steve; Gadsden, Andrew; Cataford, Andrew; Stone, Tom
The airborne LUnar Spectral Irradiance (air-LUSI) mission is a NASA Airborne Instrument Technology Transition (AITT) project. The goal of the AITT program is to mature airborne instruments from the demonstration phase to science-capable instruments.The USGS RObotic Lunar Observatory (ROLO) model represents the most precise knowledge of lunar spectral irradiance and is used frequently as a relative calibration standard for Earth observation by space-borne sensors (Keiffer and Stone, 2005). However, apparent phase-dependent biases in ROLO limits its application for absolute radiometric calibration. The objective of air-LUSI is to provide NASA a capability to improve ROLO by measuring exo-atmospheric lunar spectral irradiance with unprecedented accuracy. Careful characterization of the Moon from above the atmosphere will make it a stable and consistent SI-traceable absolute calibration reference. This could revolutionize lunar calibration for some Earth observing satellites and would be especially beneficial to ocean color missions. Because of the high sensitivity of aquatic remote sensing to calibration (Turpie et al., 2015), improvement of lunar calibration could directly affect upcoming PACE and JPSS (VIIRS) missions, and retrospectively for the SeaWiFS, EOS (MODIS), and S-NPP (VIIRS) data records.
COSP-RTTOV-1.0: Flexible radiation diagnostics to enable new science applications in model evaluation, climate change detection, and satellite mission design
(45692) Shaw, Jonah K.; Swales, Dustin J.; Desouza-Machado, Sergio; Turner, David D.; Kay, Jennifer E.; Schneider, David P.
Infrared spectral radiation fields observed by satellites make up an information-rich, multi-decade record with continuous coverage of the entire planet. As direct observations, spectral radiation fields are also largely free from uncertainties that accumulate during geophysical retrieval and data assimilation processes. Comparing these direct observations with earth system models (ESMs), however, is hindered by definitional differences between the radiation fields satellites observe and those generated by models. Here, we present a flexible, computationally efficient tool called COSP-RTTOV for simulating satellitelike radiation fields within ESMs. Outputs from COSP-RTTOV are consistent with instrument spectral response functions and orbit sampling, as well as the physics of the host model. After validating COSP-RTTOV's performance, we demonstrate new constraints on model performance enabled by COSP-RTTOV. We show additional applications in climate change detection using the NASA AIRS instrument, and observing system simulation experiments using the NASA PREFIRE mission. In summary, COSP-RTTOV is a convenient tool for directly comparing satellite radiation observations with ESMs. It enables a wide range of scientific applications, especially when users desire to avoid the assumptions and uncertainties inherent in satellite-based retrievals of geophysical variables or in atmospheric reanalysis.
Utilizing PBL Height Data from Multiple Observing Systems in the GEOS System (I): Assimilation Framework
(AMS, 2025-01-31) Zhu, Y.; Arnold, N. P.; Yang, E.-G.; Ganeshan, M.; Salmun, H.; Palm, S.; Santanello, J.; McGrath-Spangler, E. L.; Lewis, Jasper; Molod, A.; Wu, D.; Lei, T.; Akkraoui, A. El; Sienkiewicz, M.
In this study, a strategy and framework are developed to build a global Planetary Boundary Layer (PBL) height (PBLH) analysis and monitoring capability from multiple observing systems in the NASA Global Earth Observing System (GEOS) data assimilation system. To facilitate this effort, PBLH are derived from radiosonde and Global Navigation Satellite System Radio Occultation (GNSS-RO) refractivity data. As PBLH can be sensitive to potentially disparate observables and retrieval algorithms, new model PBLH definitions consistent with each observation type are added to the forecast model for the calculation of first guess departures from observations (OmF). These model definitions are augmented to the control variable vector, interacting with other control variables through flow-dependent ensemble background error covariance component. Moreover, to capture capping inversions, methods are explored using PBLH data to improve background error covariance through inflation of ensemble spread and adjustment of vertical localization length scale for virtual temperature and relative humidity variables. Experiments are conducted to assess the separate and combined impacts of these methods and the correlation relationships between PBLH and other control variables in the background error covariance. Preliminary results show that these changes are beneficial to the assimilation of other observations to improve the PBL thermodynamic structure.

Browse

Recent Submissions