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Publication date: 15 May 2024

Source: Advances in Space Research, Volume 73, Issue 10

Author(s): Kah How Teo, Kang Tai, Vincenzo Schena, Luca Simonini

Publication date: 15 May 2024

Source: Advances in Space Research, Volume 73, Issue 10

Author(s): Mohamed Mouafik, Mounir Fouad, Felix Antoine Audet, Ahmed El Aboudi

Publication date: 15 May 2024

Source: Advances in Space Research, Volume 73, Issue 10

Author(s): Mahesh Pathakoti, Mahalakshmi D.V., Kanchana A.L., Rajan K.S., Alok Taori, Rajashree Vinod Bothale, Prakash Chauhan

Publication date: 15 May 2024

Source: Advances in Space Research, Volume 73, Issue 10

Author(s): Erick Montoya, Rafael Carlos

Publication date: 1 June 2024

Source: Advances in Space Research, Volume 73, Issue 11

Author(s): Taralicin Deka, Jay McMahon

Publication date: 15 May 2024

Source: Advances in Space Research, Volume 73, Issue 10

Author(s): Yingkai Cai, Yichao Li, Zhaokui Wang

Publication date: 15 May 2024

Source: Advances in Space Research, Volume 73, Issue 10

Author(s): Nana Jiang, Yueling Cao, Fengyu Xia, He Huang, Yinan Meng, Shanshi Zhou, Weijing Qu, Xiaogong Hu

Publication date: 15 May 2024

Source: Advances in Space Research, Volume 73, Issue 10

Author(s): Chao Zhang, Tao Geng, Xin Xie, Qile Zhao, Tao Li, Zhongxing Li, Yinan Meng

Publication date: 15 May 2024

Source: Advances in Space Research, Volume 73, Issue 10

Author(s): Samuel Fedeler, Marcus Holzinger, William Whitacre

Publication date: 15 May 2024

Source: Advances in Space Research, Volume 73, Issue 10

Author(s): Larry Kepko, Rumi Nakamura, Yoshifumi Saito, Angelos Vourlidas, Matthew G.G.T. Taylor, Cristina H. Mandrini, Xóchitl Blanco-Cano, Dibyendu Chakrabarty, Ioannis A. Daglis, Clezio Marcos De Nardin, Anatoli Petrukovich, Minna Palmroth, George Ho, Louise Harra, Jonathan Rae, Mathew Owens, Eric Donovan, Benoit Lavraud, Geoff Reeves, Durgesh Tripathi

Hurricanes are among the world's most destructive natural hazards. Their ability to cause damage is shaped by their environment; conditions like warm ocean waters, guiding winds, and atmospheric moisture can all dictate storm strength.

Drone-based photogrammetry combined with deep learning to estimate hail size distributions and melting of hail on the ground
Martin Lainer, Killian P. Brennan, Alessandro Hering, Jérôme Kopp, Samuel Monhart, Daniel Wolfensberger, and Urs Germann
Atmos. Meas. Tech., 17, 2539–2557, https://doi.org/10.5194/amt-17-2539-2024, 2024
This study uses deep learning (the Mask R-CNN model) on drone-based photogrammetric data of hail on the ground to estimate hail size distributions (HSDs). Traditional hail sensors' limited areas complicate the full HSD retrieval. The HSD of a supercell event on 20 June 2021 is retrieved and contains > 18 000 hailstones. The HSD is compared to automatic hail sensor measurements and those of weather-radar-based MESHS. Investigations into ground hail melting are performed by five drone flights.

TOLNet validation of satellite ozone profiles in the troposphere: impact of retrieval wavelengths
Matthew S. Johnson, Alexei Rozanov, Mark Weber, Nora Mettig, John Sullivan, Michael J. Newchurch, Shi Kuang, Thierry Leblanc, Fernando Chouza, Timothy A. Berkoff, Guillaume Gronoff, Kevin B. Strawbridge, Raul J. Alvarez, Andrew O. Langford, Christoph J. Senff, Guillaume Kirgis, Brandi McCarty, and Larry Twigg
Atmos. Meas. Tech., 17, 2559–2582, https://doi.org/10.5194/amt-17-2559-2024, 2024
Monitoring tropospheric ozone (O3), a harmful pollutant negatively impacting human health, is primarily done using ground-based measurements and ozonesondes. However, these observation types lack the coverage to fully understand tropospheric O3. Satellites can retrieve tropospheric ozone with near-daily global coverage; however, they are known to have biases and errors. This study uses ground-based lidars to validate multiple satellites' ability to observe tropospheric O3.

The GRAS-2 Radio Occultation Mission
Joel Rasch, Anders Carlström, Jacob Christensen, and Thomas Liljegren
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-60,2024
Preprint under review for AMT (discussion: open, 0 comments)
Soon the Metop Second Generation (Metop-SG) series of polar orbiting meteorological satellite will be launched. On these satellites the GRAS-2 instrument will be mounted. It will provide GNSS radio occultation measurements with unsurpassed accuracy. The occultation measurements are used routinely for numerical weather prognosis, i.e. predicting the weather. In this paper we describe the design of this new instrument and the novel methods developed to process the data.

A new study presents the first large-scale analysis of fire patterns in West and Central Africa's wet, tropical forests. The number of active fires there typically doubled over 18 years, particularly in the Congo Basin. The increases are primarily due to increasingly hot, dry conditions and humans' impact on the forests, including deforestation. The increase in forest fires is likely to continue given current climate projections, according to the study.

How often do mountains collapse, volcanoes erupt or ice sheets melt?

Modelling the vulnerability of urban settings to wildland–urban interface fires in Chile
Paula Aguirre, Jorge León, Constanza González-Mathiesen, Randy Román, Manuela Penas, and Alonso Ogueda
Nat. Hazards Earth Syst. Sci., 24, 1521–1537, https://doi.org/10.5194/nhess-24-1521-2024, 2024
Wildfires pose a significant risk to property located in the wildland–urban interface (WUI). To assess and mitigate this risk, we need to understand which characteristics of buildings and building arrangements make them more prone to damage. We used a combination of data collection and analysis methods to study the vulnerability of dwellings in the WUI for case studies in Chile and concluded that the spatial arrangement of houses has a substantial impact on their vulnerability to wildfires.

Improving seasonal predictions of German Bight storm activity
Daniel Krieger, Sebastian Brune, Johanna Baehr, and Ralf Weisse
Nat. Hazards Earth Syst. Sci., 24, 1539–1554, https://doi.org/10.5194/nhess-24-1539-2024, 2024
Previous studies found that climate models can predict storm activity in the German Bight well for averages of 5–10 years but struggle in predicting the next winter season. Here, we improve winter storm activity predictions by linking them to physical phenomena that occur before the winter. We guess the winter storm activity from these phenomena and discard model solutions that stray too far from the guess. The remaining solutions then show much higher prediction skill for storm activity.

Identifying atmospheric rivers and their poleward latent heat transport with generalizable neural networks: ARCNNv1
Ankur Mahesh, Travis A. O'Brien, Burlen Loring, Abdelrahman Elbashandy, William Boos, and William D. Collins
Geosci. Model Dev., 17, 3533–3557, https://doi.org/10.5194/gmd-17-3533-2024, 2024
Atmospheric rivers (ARs) are extreme weather events that can alleviate drought or cause billions of US dollars in flood damage. We train convolutional neural networks (CNNs) to detect ARs with an estimate of the uncertainty. We present a framework to generalize these CNNs to a variety of datasets of past, present, and future climate. Using a simplified simulation of the Earth's atmosphere, we validate the CNNs. We explore the role of ARs in maintaining energy balance in the Earth system.

Assessing acetone for the GISS ModelE2.1 Earth system model
Alexandra Rivera, Kostas Tsigaridis, Gregory Faluvegi, and Drew Shindell
Geosci. Model Dev., 17, 3487–3505, https://doi.org/10.5194/gmd-17-3487-2024, 2024
This paper describes and evaluates an improvement to the representation of acetone in the GISS ModelE2.1 Earth system model. We simulate acetone's concentration and transport across the atmosphere as well as its dependence on chemistry, the ocean, and various global emissions. Comparisons of our model’s estimates to past modeling studies and field measurements have shown encouraging results. Ultimately, this paper contributes to a broader understanding of acetone's role in the atmosphere.