A recent study is transforming the field of streamflow prediction. By harnessing the power of transfer learning, researchers have developed a model that significantly boosts the precision of daily streamflow forecasts.
Objective identification of meteorological fronts and climatologies from ERA-Interim and ERA5
Philip G. Sansom and Jennifer L. Catto
Geosci. Model Dev., 17, 6137–6151, https://doi.org/10.5194/gmd-17-6137-2024, 2024
Weather fronts bring a lot of rain and strong winds to many regions of the mid-latitudes. We have developed an updated method of identifying these fronts in gridded data that can be used on new datasets with small grid spacing. The method can be easily applied to different datasets due to the use of open-source software for its development and shows improvements over similar previous methods. We present an updated estimate of the average frequency of fronts over the past 40 years.
ShellSet v1.1.0 parallel dynamic neotectonic modelling: a case study using Earth5-049
Jon B. May, Peter Bird, and Michele M. C. Carafa
Geosci. Model Dev., 17, 6153–6171, https://doi.org/10.5194/gmd-17-6153-2024, 2024
ShellSet is a combination of well-known geoscience software packages. It features a simple user interface and is optimised through the addition of a grid search input option (automatically searching for optimal models within a defined N-dimensional parameter space) and the ability to run multiple models in parallel. We show that for each number of models tested there is a performance benefit to parallel running, while two examples demonstrate a use case by improving an existing global model.
A Bayesian method for predicting background radiation at environmental monitoring stations
Jens Peter K. W. Frankemölle, Johan Camps, Pieter De Meutter, and Johan Meyers
Geosci. Model Dev. Discuss., https//doi.org/10.5194/gmd-2024-137,2024
Preprint under review for GMD (discussion: open, 0 comments)
To detect anomalous radioactivity in the environment, it is paramount that we understand the natural background level. In this work, we propose a statistical model to describe the most likely background level and the associated uncertainty in a network of dose rate detectors. We train, verify and validate the model using real environmental data. Using the model, we show that we can correctly predict the background level in a subset of the detector network during a known `anomalous’ event.
Model calibration and streamflow simulations for the extreme drought event of 2018 on the Rhine River Basin using WRF-Hydro 5.2.0
Andrea L. Campoverde, Uwe Ehret, Patrick Ludwig, and Joaquim G. Pinto
Geosci. Model Dev. Discuss., https//doi.org/10.5194/gmd-2024-134,2024
Preprint under review for GMD (discussion: open, 0 comments)
We looked at how well the model WRF-Hydro performed during the 2018 drought event in the River Rhine basin, even though it is typically used for floods. We used the meteorological ERA5 reanalysis dataset to simulate River Rhine’s streamflow and adjusted the model using parameters and actual discharge measurements. We focused on Lake Constance, a key part of the basin, but found issues with the model’s lake outflow simulation. By removing the lake module, we obtained more accurate results.
Numerical investigations on the modelling of ultrafine particles in SSH-aerosol-v1.3a: size resolution and redistribution
Oscar Jacquot and Karine Sartelet
Geosci. Model Dev. Discuss., https//doi.org/10.5194/gmd-2024-150,2024
Preprint under review for GMD (discussion: open, 0 comments)
As the health impact of ultrafine particles is better understood, modeling the size distribution and the number concentration becomes increasingly important. A new analytic formulation is presented to compute coagulation partition coefficients, allowing to lower down the numerical diffusion associated to the resolution of aerosol dynamics. The significance of this effect is assessed over Greater Paris with a chemistry transport model, using different size resolution of the particle distribution.
Abstract
How forests respond to accelerated climate change will affect the terrestrial carbon cycle. To better understand these responses, more examples are needed to assess how tree growth rates react to abrupt changes in growing-season temperatures. Here we use a natural experiment in which a glacier's fluctuations exposed a temperate rainforest to changes in summer temperatures of similar magnitude to those predicted to occur by 2050. We hypothesized that the onset of glacier-accentuated temperature trends would act to increase the variance in stand-level tree growth rates, a proxy for forest net primary productivity. Instead, dendrochronological records reveal that the growth rates of five, co-occurring conifer species became less synchronous, and this diversification of species responses acted to reduce the variance and to increase the stability of community-wide growth rates. These results warrant further inquiry into how climate-induced changes in tree-growth diversity may help stabilize future ecosystem services like forest carbon storage.
The Far-INfrarEd Spectrometer for Surface Emissivity (FINESSE) – Part 1: Instrument description and level 1 radiances
Jonathan E. Murray, Laura Warwick, Helen Brindley, Alan Last, Patrick Quigley, Andy Rochester, Alexander Dewar, and Daniel Cummins
Atmos. Meas. Tech., 17, 4757–4775, https://doi.org/10.5194/amt-17-4757-2024, 2024
The Far INfrarEd Spectrometer for Surface Emissivity, FINESSE, is designed to measure the ability of natural surfaces to emit infrared radiation. FINESSE combines a commercial instrument with custom-built optics to view a surface from different angles with complementary views of the sky. Its choice of internal components means it can cover a wide range of wavelengths, extending into the far-infrared. We characterize FINESSE’s uncertainty budget and provide examples of its measurement capability.
The Far-INfrarEd Spectrometer for Surface Emissivity (FINESSE) – Part 2: First measurements of the emissivity of water in the far-infrared
Laura Warwick, Jonathan E. Murray, and Helen Brindley
Atmos. Meas. Tech., 17, 4777–4787, https://doi.org/10.5194/amt-17-4777-2024, 2024
We describe a method for measuring the emissivity of natural surfaces using data from the new Far-INfrarEd Spectrometer for Surface Emissivity (FINESSE) instrument. We demonstrate our method by making measurements of the emissivity of water. We then compare our results to the emissivity predicted using a model and find good agreement. The observations from FINESSE are novel because they allow us to determine surface emissivity at longer wavelengths than have been routinely measured before.
Scientists at The University of Manchester have effectively simulated how bubbles grow in volcanic magma thanks to a novel pressure vessel that can mimic the eruption process in a laboratory setting.
Abstract
The precipitation of magnesium oxide (MgO) from the Earth's core has been proposed as a potential energy source to power the geodynamo prior to the inner core solidification. Yet, the stable phase and exact amount of MgO exsolution remain elusive. Here we utilize an iterative learning scheme to develop a unified deep learning interatomic potential for the Mg-Fe-O system valid over a wide pressure-temperature range. This potential enables direct, large-scale simulations of MgO exsolution processes at the Earth's core-mantle boundary. Our results suggest that Mg exsolve in the form of crystalline Fe-poor ferropericlase as opposed to a liquid MgO component presumed previously. The solubility of Mg in the core is limited, and the present-day core is nearly Mg-free. The resulting exsolution rate is small yet nonnegligible, suggesting that MgO exsolution may provide a potentially important energy source, although it alone may be difficult to drive an early geodynamo.
Brief Communication: Rapid high-resolution flood impact-based early warning is possible with RIM2D: a showcase for the 2023 pluvial flood in Braunschweig
Shahin Khosh Bin Ghomash, Heiko Apel, Kai Schröter, and Max Steinhausen
Nat. Hazards Earth Syst. Sci. Discuss., https//doi.org/10.5194/nhess-2024-139,2024
Preprint under review for NHESS (discussion: open, 0 comments)
This work introduces RIM2D, a hydrodynamic model for precise and rapid flood predictions, ideal for early warning systems. We demonstrate RIM2D's ability to deliver detailed and localized flood forecasts using the June 2023 flood in Braunschweig, Germany, as a case study. This research highlights the readiness of RIM2D and the required hardware for integration into operational flood warning and impact-based forecasting systems.
Development of operational decision support tools for mechanized ski guiding using avalanche terrain modelling, GPS tracking, and machine learning
John Sykes, Pascal Haegeli, Roger Atkins, Patrick Mair, and Yves Bühler
Nat. Hazards Earth Syst. Sci. Discuss., https//doi.org/10.5194/nhess-2024-147,2024
Preprint under review for NHESS (discussion: open, 0 comments)
We develop decision support tools to assist professional ski guides in determining safe terrain each day based on current conditions. To understand the decision-making process we collaborate with professional guides and build three unique models to predict their decisions. The models accurately capture the real world decision-making outcomes in 85–93 % of cases. Our conclusions focus on strengths and weaknesses of each model and discuss ramifications for practical applications in ski guiding.
The preservation of organic carbon in marine sediments has long been a key question remaining unclear in understanding the long-term carbon cycling on Earth.
International researchers from Finland, Germany, South Africa, and Ethiopia report that deforestation during the last two decades induced a higher warming and cloud level rise than that caused by climate change, which threatens biodiversity and water supply in African montane forests.
Statistical and neural network assessment of climatological features of fog and mist at Pula airport in Croatia: from local to synoptic scale
Marko Zoldoš, Tomislav Džoić, Jadran Jurković, Frano Matić, Sandra Jambrošić, Ivan Ljuština, and Maja Telišman Prtenjak
Nonlin. Processes Geophys. Discuss., https//doi.org/10.5194/npg-2024-18,2024
Preprint under review for NPG (discussion: open, 0 comments)
Fog can disrupt aviation by causing accidents and delays due to low visibility, yet it remains under-researched in Croatia. This study examined fog and mist at Pula Airport using 20 years of data and machine learning techniques. There is a declining trend in fog, linked to changing weather patterns. Fog mainly occurs from October to March. These findings enhance knowledge about fog in Croatia and can improve weather forecasts, increasing safety at the airport.
Author(s): Junghyo Jo, Alexandre Wagemakers, and Vipul Periwal
The Newton-Raphson method is a fundamental root-finding technique with numerous applications in physics. In this study, we propose a parameterized variant of the Newton-Raphson method, inspired by principles from physics. Through analytical and empirical validation, we demonstrate that this approach…
[Phys. Rev. E 110, 025305] Published Mon Aug 19, 2024
Cape Cod scientists are delaying a geoengineering project that looks to dump more than 60,000 gallons of sodium hydroxide into the ocean and has caught federal concerns around potential impacts on the ecosystem.
Abstract
When charged particles are accelerated from Earth's magnetosphere and precipitate into the atmosphere, their impact with neutral gas creates the aurora. Structured electric fields drive the acceleration processes but they are also passed down to the ionosphere, meaning that turbulence can in part be embedded into the ionosphere rather than emerge through instability processes locally. Applying a point-cloud analysis technique adapted from observational cosmology, we show how observed turbulence in the ionosphere matches electrical current signatures in the pulsating aurora in a series of conjunctions between space- and ground-based instruments. We propose that the temporal spectrum of pulsations in the pulsating aurora is the driver of a clearly observed energy injection into the ionosphere's unstable bottomside. Precipitating electrons produce electric fields through charge deposition, and we observe wave characteristics that are present in this pattern. Next, the relative electron-ion drifts excite the Farley-Buneman instability, the distribution of whose waves are organized according to the local electric field. It is the temporal characteristics of chorus wave interactions in the magnetosphere that is imparted, via precipitating electrons, to the pulsating aurora, and so we propose that chorus wave interactions are capable of embedding turbulent structure into the ionosphere. This structure (now pressure gradients) dissipate energy in the E-region through turbulent processes, observed by the icebear coherent scatter radar.
Abstract
The frequency change of 100-year flood events is often determined by fitting extreme value distributions to annual maximum discharge from a historical base period. This study demonstrates that this approach may significantly bias the computed flood frequency change. An idealized experiment shows frequency bias exceeding 100% for a 50-year base period. Further analyses using Monte Carlo simulations, mathematical derivations, and hydrological model outputs reveal that bias magnitude inversely relates to base period length and is weakly influenced by the generalized extreme value distribution's shape parameter. The bias, persisting across different estimation methods, implies floods may exceed local defenses designed based on short historical records more often than expected, even without climate change. We introduce a frequency bias adjustment method, which significantly reduces the projected rise in global flood occurrence. This suggests a substantial part of the earlier projected increase in flood occurrence and impacts is not attributable to climate change.
