Articles | Volume 13, issue 6
https://doi.org/10.5194/gmd-13-2631-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-13-2631-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
RainNet v1.0: a convolutional neural network for radar-based precipitation nowcasting
Institute for Environmental Sciences and Geography, University of Potsdam, Potsdam, Germany
Tobias Scheffer
Department of Computer Science, University of Potsdam, Potsdam, Germany
Maik Heistermann
Institute for Environmental Sciences and Geography, University of Potsdam, Potsdam, Germany
Related authors
Omar Seleem, Georgy Ayzel, Axel Bronstert, and Maik Heistermann
Nat. Hazards Earth Syst. Sci., 23, 809–822, https://doi.org/10.5194/nhess-23-809-2023, https://doi.org/10.5194/nhess-23-809-2023, 2023
Short summary
Short summary
Data-driven models are becoming more of a surrogate that overcomes the limitations of the computationally expensive 2D hydrodynamic models to map urban flood hazards. However, the model's ability to generalize outside the training domain is still a major challenge. We evaluate the performance of random forest and convolutional neural networks to predict urban floodwater depth and investigate their transferability outside the training domain.
Alberto Caldas-Alvarez, Markus Augenstein, Georgy Ayzel, Klemens Barfus, Ribu Cherian, Lisa Dillenardt, Felix Fauer, Hendrik Feldmann, Maik Heistermann, Alexia Karwat, Frank Kaspar, Heidi Kreibich, Etor Emanuel Lucio-Eceiza, Edmund P. Meredith, Susanna Mohr, Deborah Niermann, Stephan Pfahl, Florian Ruff, Henning W. Rust, Lukas Schoppa, Thomas Schwitalla, Stella Steidl, Annegret H. Thieken, Jordis S. Tradowsky, Volker Wulfmeyer, and Johannes Quaas
Nat. Hazards Earth Syst. Sci., 22, 3701–3724, https://doi.org/10.5194/nhess-22-3701-2022, https://doi.org/10.5194/nhess-22-3701-2022, 2022
Short summary
Short summary
In a warming climate, extreme precipitation events are becoming more frequent. To advance our knowledge on such phenomena, we present a multidisciplinary analysis of a selected case study that took place on 29 June 2017 in the Berlin metropolitan area. Our analysis provides evidence of the extremeness of the case from the atmospheric and the impacts perspectives as well as new insights on the physical mechanisms of the event at the meteorological and climate scales.
Georgy Ayzel, Maik Heistermann, and Tanja Winterrath
Geosci. Model Dev., 12, 1387–1402, https://doi.org/10.5194/gmd-12-1387-2019, https://doi.org/10.5194/gmd-12-1387-2019, 2019
Short summary
Short summary
How much will it rain within the next hour? To answer this question, we developed rainymotion – an open source Python software library for precipitation nowcasting. In our benchmark experiments, including a state-of-the-art operational model, rainymotion demonstrated its ability to deliver timely and reliable nowcasts for a broad range of rainfall events. This way, rainymotion can serve as a baseline solution in the field of precipitation nowcasting.
Georgy Ayzel and Alexander Izhitskiy
Proc. IAHS, 379, 151–158, https://doi.org/10.5194/piahs-379-151-2018, https://doi.org/10.5194/piahs-379-151-2018, 2018
Short summary
Short summary
Presented paper is our first step in developing a geoscientific stack of models for an assessment of the Small Aral Sea basin current hydrological conditions within the interdisciplinary SMASHI project (smashiproject.github.io). Based on coupling state-of-the-art physically-based hydrological and machine learning models we have developed the skillful model for the Syr Darya river runoff prediction. This result is the key to understanding water balance trends in vulnerable Aral Sea region.
Yeugeniy M. Gusev, Olga N. Nasonova, Evgeny E. Kovalev, and Georgy V. Ayzel
Proc. IAHS, 379, 293–300, https://doi.org/10.5194/piahs-379-293-2018, https://doi.org/10.5194/piahs-379-293-2018, 2018
Short summary
Short summary
Possible changes in various characteristics of annual river runoff (mean values, standard deviations, frequency of extreme annual runoff) up to 2100 were studied using the land surface model SWAP and meteorological projections simulated by five GCMs according to four RCP scenarios. Obtained results has shown that changes in climatic runoff are different (both in magnitude and sign) for the river basins located in different regions of the planet due to differences in natural (primarily climatic).
Olga N. Nasonova, Yeugeniy M. Gusev, Evgeny E. Kovalev, and Georgy V. Ayzel
Proc. IAHS, 379, 139–144, https://doi.org/10.5194/piahs-379-139-2018, https://doi.org/10.5194/piahs-379-139-2018, 2018
Short summary
Short summary
Projections of climate induced changes in streamflow of 11 large-scale rivers located in five continents were modeled up to 2100 using meteorological projections simulated by five global circulation models (GCMs) for four climatic scenarios. Contribution of different sources of uncertainties into a total uncertainty of river runoff projections was analyzed. It was found that contribution of GCMs into the total uncertainty is, on the average, nearly twice larger than that of climatic scenarios.
Till Francke, Cosimo Brogi, Alby Duarte Rocha, Michael Förster, Maik Heistermann, Markus Köhli, Daniel Rasche, Marvin Reich, Paul Schattan, Lena Scheiffele, and Martin Schrön
Geosci. Model Dev., 18, 819–842, https://doi.org/10.5194/gmd-18-819-2025, https://doi.org/10.5194/gmd-18-819-2025, 2025
Short summary
Short summary
Multiple methods for measuring soil moisture beyond the point scale exist. Their validation is generally hindered by not knowing the truth. We propose a virtual framework in which this truth is fully known and the sensor observations for cosmic ray neutron sensing, remote sensing, and hydrogravimetry are simulated. This allows for the rigorous testing of these virtual sensors to understand their effectiveness and limitations.
Till Francke and Maik Heistermann
EGUsphere, https://doi.org/10.5194/egusphere-2025-222, https://doi.org/10.5194/egusphere-2025-222, 2025
This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
Short summary
Short summary
Brandenburg is among the driest federal states in Germany. The low ground water recharge (GWR) is fundamental to both water supply and the support of natural ecosystems. In this study, we show that the decline of observed discharge and groundwater tables since 1980 can be explained by climate change in combination with an increasing leaf area index. Still, simulated GWR rates remain highly uncertain due to the uncertainty of precipitation trends.
Georgy Ayzel and Maik Heistermann
Nat. Hazards Earth Syst. Sci., 25, 41–47, https://doi.org/10.5194/nhess-25-41-2025, https://doi.org/10.5194/nhess-25-41-2025, 2025
Short summary
Short summary
Forecasting rainfall over the next hour is an essential feature of early warning systems. Deep learning (DL) has emerged as a powerful alternative to conventional nowcasting technologies, but it still struggles to adequately predict impact-relevant heavy rainfall. We think that DL could do much better if the training tasks were defined more specifically and that such specification presents an opportunity to better align the output of nowcasting models with actual user requirements.
Paul Voit and Maik Heistermann
Nat. Hazards Earth Syst. Sci., 24, 4609–4615, https://doi.org/10.5194/nhess-24-4609-2024, https://doi.org/10.5194/nhess-24-4609-2024, 2024
Short summary
Short summary
Floods have caused significant damage in the past. To prepare for such events, we rely on historical data but face issues due to rare rainfall events, lack of data and climate change. Counterfactuals, or
what ifscenarios, simulate historical rainfall in different locations to estimate flood levels. Our new study refines this by deriving more-plausible local scenarios, using the June 2024 Bavaria flood as a case study. This method could improve preparedness for future floods.
Daniel Altdorff, Maik Heistermann, Till Francke, Martin Schrön, Sabine Attinger, Albrecht Bauriegel, Frank Beyrich, Peter Biró, Peter Dietrich, Rebekka Eichstädt, Peter Martin Grosse, Arvid Markert, Jakob Terschlüsen, Ariane Walz, Steffen Zacharias, and Sascha E. Oswald
EGUsphere, https://doi.org/10.5194/egusphere-2024-3848, https://doi.org/10.5194/egusphere-2024-3848, 2024
Short summary
Short summary
The German federal state of Brandenburg is particularly prone to soil moisture droughts. To support the management of related risks, we introduce a novel soil moisture and drought monitoring network based on cosmic-ray neutron sensing technology. This initiative is driven by a collaboration of research institutions and federal state agencies, and it is the first of its kind in Germany to have started operation. In this brief communication, we outline the network design and share first results.
Xiaoxiang Guan, Dung Viet Nguyen, Paul Voit, Bruno Merz, Maik Heistermann, and Sergiy Vorogushyn
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-143, https://doi.org/10.5194/nhess-2024-143, 2024
Revised manuscript under review for NHESS
Short summary
Short summary
We evaluated a multi-site stochastic regional weather generator (nsRWG) for its ability to capture the cross-scale extremity of high precipitation events (HPEs) in Germany. We generated 100 realizations of 72 years of daily synthetic precipitation data. The performance was assessed using WEI and xWEI indices, which measure event extremity across spatio-temporal scales. Results show nsRWG simulates well the extremity patterns of HPEs, though it overestimates short-duration, small-extent events.
Paul Voit and Maik Heistermann
Nat. Hazards Earth Syst. Sci., 24, 2147–2164, https://doi.org/10.5194/nhess-24-2147-2024, https://doi.org/10.5194/nhess-24-2147-2024, 2024
Short summary
Short summary
To identify flash flood potential in Germany, we shifted the most extreme rainfall events from the last 22 years systematically across Germany and simulated the consequent runoff reaction. Our results show that almost all areas in Germany have not seen the worst-case scenario of flood peaks within the last 22 years. With a slight spatial change of historical rainfall events, flood peaks of a factor of 2 or more would be achieved for most areas. The results can aid disaster risk management.
Maik Heistermann, Till Francke, Martin Schrön, and Sascha E. Oswald
Hydrol. Earth Syst. Sci., 28, 989–1000, https://doi.org/10.5194/hess-28-989-2024, https://doi.org/10.5194/hess-28-989-2024, 2024
Short summary
Short summary
Cosmic-ray neutron sensing (CRNS) is a non-invasive technique used to obtain estimates of soil water content (SWC) at a horizontal footprint of around 150 m and a vertical penetration depth of up to 30 cm. However, typical CRNS applications require the local calibration of a function which converts neutron counts to SWC. As an alternative, we propose a generalized function as a way to avoid the use of local reference measurements of SWC and hence a major source of uncertainty.
Gerd Bürger and Maik Heistermann
Nat. Hazards Earth Syst. Sci., 23, 3065–3077, https://doi.org/10.5194/nhess-23-3065-2023, https://doi.org/10.5194/nhess-23-3065-2023, 2023
Short summary
Short summary
Our subject is a new catalogue of radar-based heavy rainfall events (CatRaRE) over Germany and how it relates to the concurrent atmospheric circulation. We classify reanalyzed daily atmospheric fields of convective indices according to CatRaRE, using conventional statistical and more recent machine learning algorithms, and apply them to present and future atmospheres. Increasing trends are projected for CatRaRE-type probabilities, from reanalyzed as well as from simulated atmospheric fields.
Maik Heistermann, Till Francke, Lena Scheiffele, Katya Dimitrova Petrova, Christian Budach, Martin Schrön, Benjamin Trost, Daniel Rasche, Andreas Güntner, Veronika Döpper, Michael Förster, Markus Köhli, Lisa Angermann, Nikolaos Antonoglou, Manuela Zude-Sasse, and Sascha E. Oswald
Earth Syst. Sci. Data, 15, 3243–3262, https://doi.org/10.5194/essd-15-3243-2023, https://doi.org/10.5194/essd-15-3243-2023, 2023
Short summary
Short summary
Cosmic-ray neutron sensing (CRNS) allows for the non-invasive estimation of root-zone soil water content (SWC). The signal observed by a single CRNS sensor is influenced by the SWC in a radius of around 150 m (the footprint). Here, we have put together a cluster of eight CRNS sensors with overlapping footprints at an agricultural research site in north-east Germany. That way, we hope to represent spatial SWC heterogeneity instead of retrieving just one average SWC estimate from a single sensor.
Katharina Lengfeld, Paul Voit, Frank Kaspar, and Maik Heistermann
Nat. Hazards Earth Syst. Sci., 23, 1227–1232, https://doi.org/10.5194/nhess-23-1227-2023, https://doi.org/10.5194/nhess-23-1227-2023, 2023
Short summary
Short summary
Estimating the severity of a rainfall event based on the damage caused is easy but highly depends on the affected region. A less biased measure for the extremeness of an event is its rarity combined with its spatial extent. In this brief communication, we investigate the sensitivity of such measures to the underlying dataset and highlight the importance of considering multiple spatial and temporal scales using the devastating rainfall event in July 2021 in central Europe as an example.
Omar Seleem, Georgy Ayzel, Axel Bronstert, and Maik Heistermann
Nat. Hazards Earth Syst. Sci., 23, 809–822, https://doi.org/10.5194/nhess-23-809-2023, https://doi.org/10.5194/nhess-23-809-2023, 2023
Short summary
Short summary
Data-driven models are becoming more of a surrogate that overcomes the limitations of the computationally expensive 2D hydrodynamic models to map urban flood hazards. However, the model's ability to generalize outside the training domain is still a major challenge. We evaluate the performance of random forest and convolutional neural networks to predict urban floodwater depth and investigate their transferability outside the training domain.
Alberto Caldas-Alvarez, Markus Augenstein, Georgy Ayzel, Klemens Barfus, Ribu Cherian, Lisa Dillenardt, Felix Fauer, Hendrik Feldmann, Maik Heistermann, Alexia Karwat, Frank Kaspar, Heidi Kreibich, Etor Emanuel Lucio-Eceiza, Edmund P. Meredith, Susanna Mohr, Deborah Niermann, Stephan Pfahl, Florian Ruff, Henning W. Rust, Lukas Schoppa, Thomas Schwitalla, Stella Steidl, Annegret H. Thieken, Jordis S. Tradowsky, Volker Wulfmeyer, and Johannes Quaas
Nat. Hazards Earth Syst. Sci., 22, 3701–3724, https://doi.org/10.5194/nhess-22-3701-2022, https://doi.org/10.5194/nhess-22-3701-2022, 2022
Short summary
Short summary
In a warming climate, extreme precipitation events are becoming more frequent. To advance our knowledge on such phenomena, we present a multidisciplinary analysis of a selected case study that took place on 29 June 2017 in the Berlin metropolitan area. Our analysis provides evidence of the extremeness of the case from the atmospheric and the impacts perspectives as well as new insights on the physical mechanisms of the event at the meteorological and climate scales.
Paul Voit and Maik Heistermann
Nat. Hazards Earth Syst. Sci., 22, 2791–2805, https://doi.org/10.5194/nhess-22-2791-2022, https://doi.org/10.5194/nhess-22-2791-2022, 2022
Short summary
Short summary
To better understand how the frequency and intensity of heavy precipitation events (HPEs) will change with changing climate and to adapt disaster risk management accordingly, we have to quantify the extremeness of HPEs in a reliable way. We introduce the xWEI (cross-scale WEI) and show that this index can reveal important characteristics of HPEs that would otherwise remain hidden. We conclude that the xWEI could be a valuable instrument in both disaster risk management and research.
Maik Heistermann, Heye Bogena, Till Francke, Andreas Güntner, Jannis Jakobi, Daniel Rasche, Martin Schrön, Veronika Döpper, Benjamin Fersch, Jannis Groh, Amol Patil, Thomas Pütz, Marvin Reich, Steffen Zacharias, Carmen Zengerle, and Sascha Oswald
Earth Syst. Sci. Data, 14, 2501–2519, https://doi.org/10.5194/essd-14-2501-2022, https://doi.org/10.5194/essd-14-2501-2022, 2022
Short summary
Short summary
This paper presents a dense network of cosmic-ray neutron sensing (CRNS) to measure spatio-temporal soil moisture patterns during a 2-month campaign in the Wüstebach headwater catchment in Germany. Stationary, mobile, and airborne CRNS technology monitored the root-zone water dynamics as well as spatial heterogeneity in the 0.4 km2 area. The 15 CRNS stations were supported by a hydrogravimeter, biomass sampling, and a wireless soil sensor network to facilitate holistic hydrological analysis.
Till Francke, Maik Heistermann, Markus Köhli, Christian Budach, Martin Schrön, and Sascha E. Oswald
Geosci. Instrum. Method. Data Syst., 11, 75–92, https://doi.org/10.5194/gi-11-75-2022, https://doi.org/10.5194/gi-11-75-2022, 2022
Short summary
Short summary
Cosmic-ray neutron sensing (CRNS) is a non-invasive tool for measuring hydrogen pools like soil moisture, snow, or vegetation. This study presents a directional shielding approach, aiming to measure in specific directions only. The results show that non-directional neutron transport blurs the signal of the targeted direction. For typical instruments, this does not allow acceptable precision at a daily time resolution. However, the mere statistical distinction of two rates is feasible.
Maik Heistermann, Till Francke, Martin Schrön, and Sascha E. Oswald
Hydrol. Earth Syst. Sci., 25, 4807–4824, https://doi.org/10.5194/hess-25-4807-2021, https://doi.org/10.5194/hess-25-4807-2021, 2021
Short summary
Short summary
Cosmic-ray neutron sensing (CRNS) is a powerful technique for retrieving representative estimates of soil moisture in footprints extending over hectometres in the horizontal and decimetres in the vertical. This study, however, demonstrates the potential of CRNS to obtain spatio-temporal patterns of soil moisture beyond isolated footprints. To that end, we analyse data from a unique observational campaign that featured a dense network of more than 20 neutron detectors in an area of just 1 km2.
Benjamin Fersch, Till Francke, Maik Heistermann, Martin Schrön, Veronika Döpper, Jannis Jakobi, Gabriele Baroni, Theresa Blume, Heye Bogena, Christian Budach, Tobias Gränzig, Michael Förster, Andreas Güntner, Harrie-Jan Hendricks Franssen, Mandy Kasner, Markus Köhli, Birgit Kleinschmit, Harald Kunstmann, Amol Patil, Daniel Rasche, Lena Scheiffele, Ulrich Schmidt, Sandra Szulc-Seyfried, Jannis Weimar, Steffen Zacharias, Marek Zreda, Bernd Heber, Ralf Kiese, Vladimir Mares, Hannes Mollenhauer, Ingo Völksch, and Sascha Oswald
Earth Syst. Sci. Data, 12, 2289–2309, https://doi.org/10.5194/essd-12-2289-2020, https://doi.org/10.5194/essd-12-2289-2020, 2020
Irene Crisologo and Maik Heistermann
Atmos. Meas. Tech., 13, 645–659, https://doi.org/10.5194/amt-13-645-2020, https://doi.org/10.5194/amt-13-645-2020, 2020
Short summary
Short summary
Archives of radar observations often suffer from errors, one of which is calibration. However, it is possible to correct them after the fact by using satellite radars as a calibration reference. We propose improvements to this calibration method by considering factors that affect the data quality, such that poor quality data gets filtered out in the bias calculation by assigning weights. We also show that the bias can be interpolated in time even for days when there are no satellite data.
Georgy Ayzel, Maik Heistermann, and Tanja Winterrath
Geosci. Model Dev., 12, 1387–1402, https://doi.org/10.5194/gmd-12-1387-2019, https://doi.org/10.5194/gmd-12-1387-2019, 2019
Short summary
Short summary
How much will it rain within the next hour? To answer this question, we developed rainymotion – an open source Python software library for precipitation nowcasting. In our benchmark experiments, including a state-of-the-art operational model, rainymotion demonstrated its ability to deliver timely and reliable nowcasts for a broad range of rainfall events. This way, rainymotion can serve as a baseline solution in the field of precipitation nowcasting.
Magdalena Uber, Jean-Pierre Vandervaere, Isabella Zin, Isabelle Braud, Maik Heistermann, Cédric Legoût, Gilles Molinié, and Guillaume Nord
Hydrol. Earth Syst. Sci., 22, 6127–6146, https://doi.org/10.5194/hess-22-6127-2018, https://doi.org/10.5194/hess-22-6127-2018, 2018
Short summary
Short summary
We investigate how rivers in a flash-flood-prone region in southern France respond to rainfall depending on initial soil moisture. Therefore, high-resolution data of rainfall, river discharge and soil moisture were used. We find that during dry initial conditions, the rivers hardly respond even for heavy rain events, but for wet initial conditions, the response remains unpredictable: for some rain events almost all rainfall is transformed to discharge, whereas this is not the case for others.
Irene Crisologo, Robert A. Warren, Kai Mühlbauer, and Maik Heistermann
Atmos. Meas. Tech., 11, 5223–5236, https://doi.org/10.5194/amt-11-5223-2018, https://doi.org/10.5194/amt-11-5223-2018, 2018
Short summary
Short summary
The calibration of ground-based weather radar (GR) can be improved a posteriori by comparing observed GR reflectivity to well-established spaceborne radar platforms (SR), such as TRMM or GPM. Our study shows that the consistency between GR and SR reflectivity measurements can be enhanced by considering the quality of GR data from areas where signals may have been blocked due to the surrounding terrain, and provides an open-source toolset to carry out corresponding analyses.
Georgy Ayzel and Alexander Izhitskiy
Proc. IAHS, 379, 151–158, https://doi.org/10.5194/piahs-379-151-2018, https://doi.org/10.5194/piahs-379-151-2018, 2018
Short summary
Short summary
Presented paper is our first step in developing a geoscientific stack of models for an assessment of the Small Aral Sea basin current hydrological conditions within the interdisciplinary SMASHI project (smashiproject.github.io). Based on coupling state-of-the-art physically-based hydrological and machine learning models we have developed the skillful model for the Syr Darya river runoff prediction. This result is the key to understanding water balance trends in vulnerable Aral Sea region.
Yeugeniy M. Gusev, Olga N. Nasonova, Evgeny E. Kovalev, and Georgy V. Ayzel
Proc. IAHS, 379, 293–300, https://doi.org/10.5194/piahs-379-293-2018, https://doi.org/10.5194/piahs-379-293-2018, 2018
Short summary
Short summary
Possible changes in various characteristics of annual river runoff (mean values, standard deviations, frequency of extreme annual runoff) up to 2100 were studied using the land surface model SWAP and meteorological projections simulated by five GCMs according to four RCP scenarios. Obtained results has shown that changes in climatic runoff are different (both in magnitude and sign) for the river basins located in different regions of the planet due to differences in natural (primarily climatic).
Olga N. Nasonova, Yeugeniy M. Gusev, Evgeny E. Kovalev, and Georgy V. Ayzel
Proc. IAHS, 379, 139–144, https://doi.org/10.5194/piahs-379-139-2018, https://doi.org/10.5194/piahs-379-139-2018, 2018
Short summary
Short summary
Projections of climate induced changes in streamflow of 11 large-scale rivers located in five continents were modeled up to 2100 using meteorological projections simulated by five global circulation models (GCMs) for four climatic scenarios. Contribution of different sources of uncertainties into a total uncertainty of river runoff projections was analyzed. It was found that contribution of GCMs into the total uncertainty is, on the average, nearly twice larger than that of climatic scenarios.
Berry Boessenkool, Gerd Bürger, and Maik Heistermann
Nat. Hazards Earth Syst. Sci., 17, 1623–1629, https://doi.org/10.5194/nhess-17-1623-2017, https://doi.org/10.5194/nhess-17-1623-2017, 2017
Short summary
Short summary
Rainfall is more intense at high temperatures than in cooler weather, as can be seen in summer thunder storms. The relationship between temperature and rainfall intensity seems to invert at very high temperatures, however. There are some possible meteorological explanations, but we propose that part of the reason might be the low number of observations, due to which the actually possible values are underestimated. We propose a better way to estimate high quantiles from small datasets.
Maik Heistermann
Hydrol. Earth Syst. Sci., 21, 3455–3461, https://doi.org/10.5194/hess-21-3455-2017, https://doi.org/10.5194/hess-21-3455-2017, 2017
Short summary
Short summary
In 2009, the "planetary boundaries" were introduced. They consist of nine global control variables and corresponding "thresholds which, if crossed, could generate unacceptable environmental change". The idea has been very successful, but also controversial. This paper picks up the debate with regard to the boundary on "global freshwater use": it argues that such a boundary is based on mere speculation, and that any exercise of assigning actual numbers is arbitrary, premature, and misleading.
K. Vormoor, D. Lawrence, M. Heistermann, and A. Bronstert
Hydrol. Earth Syst. Sci., 19, 913–931, https://doi.org/10.5194/hess-19-913-2015, https://doi.org/10.5194/hess-19-913-2015, 2015
Short summary
Short summary
Projected shifts towards more dominant autumn/winter events during a future climate correspond to an increasing relevance of rainfall as a flood generating process in six Norwegian catchments. The relative role of hydrological model parameter uncertainty, compared to other uncertainty sources from our applied ensemble, is highest in those catchments showing the largest shifts in flood seasonality which indicates a lack in parameter robustness under non-stationary hydroclimatological conditions.
M. Heistermann, I. Crisologo, C. C. Abon, B. A. Racoma, S. Jacobi, N. T. Servando, C. P. C. David, and A. Bronstert
Nat. Hazards Earth Syst. Sci., 13, 653–657, https://doi.org/10.5194/nhess-13-653-2013, https://doi.org/10.5194/nhess-13-653-2013, 2013
M. Heistermann, S. Jacobi, and T. Pfaff
Hydrol. Earth Syst. Sci., 17, 863–871, https://doi.org/10.5194/hess-17-863-2013, https://doi.org/10.5194/hess-17-863-2013, 2013
Related subject area
Atmospheric sciences
Evaluation of dust emission and land surface schemes in predicting a mega Asian dust storm over South Korea using WRF-Chem
Sensitivity studies of a four-dimensional local ensemble transform Kalman filter coupled with WRF-Chem version 3.9.1 for improving particulate matter simulation accuracy
A Bayesian method for predicting background radiation at environmental monitoring stations in local-scale networks
Inclusion of the ECMWF ecRad radiation scheme (v1.5.0) in the MAR (v3.14), regional evaluation for Belgium, and assessment of surface shortwave spectral fluxes at Uccle
Development of a fast radiative transfer model for ground-based microwave radiometers (ARMS-gb v1.0): validation and comparison to RTTOV-gb
Indian Institute of Tropical Meteorology (IITM) High-Resolution Global Forecast Model version 1: an attempt to resolve monsoon prediction deadlock
Cell-tracking-based framework for assessing nowcasting model skill in reproducing growth and decay of convective rainfall
NeuralMie (v1.0): an aerosol optics emulator
Simulation performance of planetary boundary layer schemes in WRF v4.3.1 for near-surface wind over the western Sichuan Basin: a single-site assessment
FootNet v1.0: development of a machine learning emulator of atmospheric transport
Updates and evaluation of NOAA's online-coupled air quality model version 7 (AQMv7) within the Unified Forecast System
Quantifying the analysis uncertainty for nowcasting application
Improving the ensemble square root filter (EnSRF) in the Community Inversion Framework: a case study with ICON-ART 2024.01
The MESSy DWARF (based on MESSy v2.55.2)
An enhanced emission module for the PALM model system 23.10 with application for PM10 emission from urban domestic heating
Identifying lightning processes in ERA5 soundings with deep learning
Sensitivity of predicted ultrafine particle size distributions in Europe to different nucleation rate parameterizations using PMCAMx-UF v2.2
Explaining neural networks for detection of tropical cyclones and atmospheric rivers in gridded atmospheric simulation data
PALACE v1.0: Paranal Airglow Line And Continuum Emission model
Accurate space-based NOx emission estimates with the flux divergence approach require fine-scale model information on local oxidation chemistry and profile shapes
Exploring a high-level programming model for the NWP domain using ECMWF microphysics schemes
Quantifying uncertainties in satellite NO2 superobservations for data assimilation and model evaluation
ML-AMPSIT: Machine Learning-based Automated Multi-method Parameter Sensitivity and Importance analysis Tool
Coupling the urban canopy model TEB (SURFEXv9.0) with the radiation model SPARTACUS-Urbanv0.6.1 for more realistic urban radiative exchange calculation
Forecasting contrail climate forcing for flight planning and air traffic management applications: the CocipGrid model in pycontrails 0.51.0
Simulation of the heat mitigation potential of unsealing measures in cities by parameterizing grass grid pavers for urban microclimate modelling with ENVI-met (V5)
AI-NAOS: an AI-based nonspherical aerosol optical scheme for the chemical weather model GRAPES_Meso5.1/CUACE
Orbital-Radar v1.0.0: a tool to transform suborbital radar observations to synthetic EarthCARE cloud radar data
The Modular and Integrated Data Assimilation System at Environment and Climate Change Canada (MIDAS v3.9.1)
A new set of indicators for model evaluation complementing to FAIRMODE’s MQO
Modeling of polycyclic aromatic hydrocarbons (PAHs) from global to regional scales: model development (IAP-AACM_PAH v1.0) and investigation of health risks in 2013 and 2018 in China
LIMA (v2.0): A full two-moment cloud microphysical scheme for the mesoscale non-hydrostatic model Meso-NH v5-6
SLUCM+BEM (v1.0): a simple parameterisation for dynamic anthropogenic heat and electricity consumption in WRF-Urban (v4.3.2)
Carbon dioxide plume dispersion simulated at hectometer scale using DALES: model formulation and observational evaluation
NAQPMS-PDAF v2.0: a novel hybrid nonlinear data assimilation system for improved simulation of PM2.5 chemical components
Source-specific bias correction of US background and anthropogenic ozone modeled in CMAQ
Observational operator for fair model evaluation with ground NO2 measurements
Valid time shifting ensemble Kalman filter (VTS-EnKF) for dust storm forecasting
The third Met Office Unified Model-JULES Regional Atmosphere and Land Configuration, RAL3
Atmospheric moisture tracking with WAM2layers v3
An updated parameterization of the unstable atmospheric surface layer in the Weather Research and Forecasting (WRF) modeling system
The impact of cloud microphysics and ice nucleation on Southern Ocean clouds assessed with single-column modeling and instrument simulators
An updated aerosol simulation in the Community Earth System Model (v2.1.3): dust and marine aerosol emissions and secondary organic aerosol formation
Exploring ship track spreading rates with a physics-informed Langevin particle parameterization
Do data-driven models beat numerical models in forecasting weather extremes? A comparison of IFS HRES, Pangu-Weather, and GraphCast
Development of the MPAS-CMAQ coupled system (V1.0) for multiscale global air quality modeling
UA-ICON with NWP physics package (version: ua-icon-2.1): mean state and variability of the middle atmosphere
Assessment of object-based indices to identify convective organization
Diagnosis of winter precipitation types using Spectral Bin Model (SBM): Comparison of five methods using ICE-POP 2018 field experiment data
The Global Forest Fire Emissions Prediction System version 1.0
Ji Won Yoon, Seungyeon Lee, Ebony Lee, and Seon Ki Park
Geosci. Model Dev., 18, 2303–2328, https://doi.org/10.5194/gmd-18-2303-2025, https://doi.org/10.5194/gmd-18-2303-2025, 2025
Short summary
Short summary
This study evaluates the Weather Research and Forecasting Model (WRF) coupled with Chemistry (WRF-Chem) to predict a mega Asian dust storm (ADS) over South Korea on 28–29 March 2021. We assessed combinations of five dust emission and four land surface schemes by analyzing meteorological and air quality variables. The best scheme combination reduced the root mean square error (RMSE) for particulate matter 10 (PM10) by up to 29.6 %, demonstrating the highest performance.
Jianyu Lin, Tie Dai, Lifang Sheng, Weihang Zhang, Shangfei Hai, and Yawen Kong
Geosci. Model Dev., 18, 2231–2248, https://doi.org/10.5194/gmd-18-2231-2025, https://doi.org/10.5194/gmd-18-2231-2025, 2025
Short summary
Short summary
The effectiveness of this assimilation system and its sensitivity to the ensemble member size and length of the assimilation window are investigated. This study advances our understanding of the selection of basic parameters in the four-dimensional local ensemble transform Kalman filter assimilation system and the performance of ensemble simulation in a particulate-matter-polluted environment.
Jens Peter Karolus Wenceslaus Frankemölle, Johan Camps, Pieter De Meutter, and Johan Meyers
Geosci. Model Dev., 18, 1989–2003, https://doi.org/10.5194/gmd-18-1989-2025, https://doi.org/10.5194/gmd-18-1989-2025, 2025
Short summary
Short summary
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.
Jean-François Grailet, Robin J. Hogan, Nicolas Ghilain, David Bolsée, Xavier Fettweis, and Marilaure Grégoire
Geosci. Model Dev., 18, 1965–1988, https://doi.org/10.5194/gmd-18-1965-2025, https://doi.org/10.5194/gmd-18-1965-2025, 2025
Short summary
Short summary
The MAR (Modèle Régional Atmosphérique) is a regional climate model used for weather forecasting and studying the climate over various regions. This paper presents an update of MAR thanks to which it can precisely decompose solar radiation, in particular in the UV (ultraviolet) and photosynthesis ranges, both being critical to human health and ecosystems. As a first application of this new capability, this paper presents a method for predicting UV indices with MAR.
Yi-Ning Shi, Jun Yang, Wei Han, Lujie Han, Jiajia Mao, Wanlin Kan, and Fuzhong Weng
Geosci. Model Dev., 18, 1947–1964, https://doi.org/10.5194/gmd-18-1947-2025, https://doi.org/10.5194/gmd-18-1947-2025, 2025
Short summary
Short summary
Direct assimilation of observations from ground-based microwave radiometers (GMRs) holds significant potential for improving forecast accuracy. Radiative transfer models (RTMs) play a crucial role in direct data assimilation. In this study, we introduce a new RTM, the Advanced Radiative Transfer Modeling System – Ground-Based (ARMS-gb), designed to simulate brightness temperatures observed by GMRs along with their Jacobians. Several enhancements have been incorporated to achieve higher accuracy.
R. Phani Murali Krishna, Siddharth Kumar, A. Gopinathan Prajeesh, Peter Bechtold, Nils Wedi, Kumar Roy, Malay Ganai, B. Revanth Reddy, Snehlata Tirkey, Tanmoy Goswami, Radhika Kanase, Sahadat Sarkar, Medha Deshpande, and Parthasarathi Mukhopadhyay
Geosci. Model Dev., 18, 1879–1894, https://doi.org/10.5194/gmd-18-1879-2025, https://doi.org/10.5194/gmd-18-1879-2025, 2025
Short summary
Short summary
The High-Resolution Global Forecast Model (HGFM) is an advanced iteration of the operational Global Forecast System (GFS) model. HGFM can produce forecasts at a spatial scale of ~6 km in tropics. It demonstrates improved accuracy in short- to medium-range weather prediction over the Indian region, with notable success in predicting extreme events. Further, the model will be entrusted to operational forecasting agencies after validation and testing.
Jenna Ritvanen, Seppo Pulkkinen, Dmitri Moisseev, and Daniele Nerini
Geosci. Model Dev., 18, 1851–1878, https://doi.org/10.5194/gmd-18-1851-2025, https://doi.org/10.5194/gmd-18-1851-2025, 2025
Short summary
Short summary
Nowcasting models struggle with the rapid evolution of heavy rain, and common verification methods are unable to describe how accurately the models predict the growth and decay of heavy rain. We propose a framework to assess model performance. In the framework, convective cells are identified and tracked in the forecasts and observations, and the model skill is then evaluated by comparing differences between forecast and observed cells. We demonstrate the framework with four open-source models.
Andrew Geiss and Po-Lun Ma
Geosci. Model Dev., 18, 1809–1827, https://doi.org/10.5194/gmd-18-1809-2025, https://doi.org/10.5194/gmd-18-1809-2025, 2025
Short summary
Short summary
Particles in the Earth's atmosphere strongly impact the planet's energy budget, and atmosphere simulations require accurate representation of their interaction with light. This work introduces two approaches to represent light scattering by small particles. The first is a scattering simulator based on Mie theory implemented in Python. The second is a neural network emulator that is more accurate than existing methods and is fast enough to be used in climate and weather simulations.
Qin Wang, Bo Zeng, Gong Chen, and Yaoting Li
Geosci. Model Dev., 18, 1769–1784, https://doi.org/10.5194/gmd-18-1769-2025, https://doi.org/10.5194/gmd-18-1769-2025, 2025
Short summary
Short summary
This study evaluates the performance of four planetary boundary layer (PBL) schemes in near-surface wind fields over the Sichuan Basin, China. Using 112 sensitivity experiments with the Weather Research and Forecasting (WRF) model and focusing on 28 wind events, it is found that wind direction was less sensitive to the PBL schemes. The quasi-normal scale elimination (QNSE) scheme captured temporal variations best, while the Mellor–Yamada–Janjić (MYJ) scheme had the least error in wind speed.
Tai-Long He, Nikhil Dadheech, Tammy M. Thompson, and Alexander J. Turner
Geosci. Model Dev., 18, 1661–1671, https://doi.org/10.5194/gmd-18-1661-2025, https://doi.org/10.5194/gmd-18-1661-2025, 2025
Short summary
Short summary
It is computationally expensive to infer greenhouse gas (GHG) emissions using atmospheric observations. This is partly due to the detailed model used to represent atmospheric transport. We demonstrate how a machine learning (ML) model can be used to simulate high-resolution atmospheric transport. This type of ML model will help estimate GHG emissions using dense observations, which are becoming increasingly common with the proliferation of urban monitoring networks and geostationary satellites.
Wei Li, Beiming Tang, Patrick C. Campbell, Youhua Tang, Barry Baker, Zachary Moon, Daniel Tong, Jianping Huang, Kai Wang, Ivanka Stajner, and Raffaele Montuoro
Geosci. Model Dev., 18, 1635–1660, https://doi.org/10.5194/gmd-18-1635-2025, https://doi.org/10.5194/gmd-18-1635-2025, 2025
Short summary
Short summary
The study describes the updates of NOAA's current UFS-AQMv7 air quality forecast model by incorporating the latest scientific and structural changes in CMAQv5.4. An evaluation during the summer of 2023 shows that the updated model overall improves the simulation of MDA8 O3 by reducing the bias by 8%–12% in the contiguous US. PM2.5 predictions have mixed results due to wildfire, highlighting the need for future refinements.
Yanwei Zhu, Aitor Atencia, Markus Dabernig, and Yong Wang
Geosci. Model Dev., 18, 1545–1559, https://doi.org/10.5194/gmd-18-1545-2025, https://doi.org/10.5194/gmd-18-1545-2025, 2025
Short summary
Short summary
Most works have delved into convective weather nowcasting, and only a few works have discussed the nowcasting uncertainty for variables at the surface level. Hence, we proposed a method to estimate uncertainty. Generating appropriate noises associated with the characteristic of the error in analysis can simulate the uncertainty of nowcasting. This method can contribute to the estimation of near–surface analysis uncertainty in both nowcasting applications and ensemble nowcasting development.
Joël Thanwerdas, Antoine Berchet, Lionel Constantin, Aki Tsuruta, Michael Steiner, Friedemann Reum, Stephan Henne, and Dominik Brunner
Geosci. Model Dev., 18, 1505–1544, https://doi.org/10.5194/gmd-18-1505-2025, https://doi.org/10.5194/gmd-18-1505-2025, 2025
Short summary
Short summary
The Community Inversion Framework (CIF) brings together methods for estimating greenhouse gas fluxes from atmospheric observations. The initial ensemble method implemented in CIF was found to be incomplete and could hardly be compared to other ensemble methods employed in the inversion community. In this paper, we present and evaluate a new implementation of the ensemble mode, building upon the initial developments.
Astrid Kerkweg, Timo Kirfel, Duong H. Do, Sabine Griessbach, Patrick Jöckel, and Domenico Taraborrelli
Geosci. Model Dev., 18, 1265–1286, https://doi.org/10.5194/gmd-18-1265-2025, https://doi.org/10.5194/gmd-18-1265-2025, 2025
Short summary
Short summary
Normally, the Modular Earth Submodel System (MESSy) is linked to complete dynamic models to create chemical climate models. However, the modular concept of MESSy and the newly developed DWARF component presented here make it possible to create simplified models that contain only one or a few process descriptions. This is very useful for technical optimisation, such as porting to GPUs, and can be used to create less complex models, such as a chemical box model.
Edward C. Chan, Ilona J. Jäkel, Basit Khan, Martijn Schaap, Timothy M. Butler, Renate Forkel, and Sabine Banzhaf
Geosci. Model Dev., 18, 1119–1139, https://doi.org/10.5194/gmd-18-1119-2025, https://doi.org/10.5194/gmd-18-1119-2025, 2025
Short summary
Short summary
An enhanced emission module has been developed for the PALM model system, improving flexibility and scalability of emission source representation across different sectors. A model for parametrized domestic emissions has also been included, for which an idealized model run is conducted for particulate matter (PM10). The results show that, in addition to individual sources and diurnal variations in energy consumption, vertical transport and urban topology play a role in concentration distribution.
Gregor Ehrensperger, Thorsten Simon, Georg J. Mayr, and Tobias Hell
Geosci. Model Dev., 18, 1141–1153, https://doi.org/10.5194/gmd-18-1141-2025, https://doi.org/10.5194/gmd-18-1141-2025, 2025
Short summary
Short summary
As lightning is a brief and localized event, it is not explicitly resolved in atmospheric models. Instead, expert-based auxiliary descriptions are used to assess it. This study explores how AI can improve our understanding of lightning without relying on traditional expert knowledge. We reveal that AI independently identified the key factors known to experts as essential for lightning in the Alps region. This shows how knowledge discovery could be sped up in areas with limited expert knowledge.
David Patoulias, Kalliopi Florou, and Spyros N. Pandis
Geosci. Model Dev., 18, 1103–1118, https://doi.org/10.5194/gmd-18-1103-2025, https://doi.org/10.5194/gmd-18-1103-2025, 2025
Short summary
Short summary
The effect of the assumed atmospheric nucleation mechanism on particle number concentrations and size distribution was investigated. Two quite different mechanisms involving sulfuric acid and ammonia or a biogenic organic vapor gave quite similar results which were consistent with measurements at 26 measurement stations across Europe. The number of larger particles that serve as cloud condensation nuclei showed little sensitivity to the assumed nucleation mechanism.
Tim Radke, Susanne Fuchs, Christian Wilms, Iuliia Polkova, and Marc Rautenhaus
Geosci. Model Dev., 18, 1017–1039, https://doi.org/10.5194/gmd-18-1017-2025, https://doi.org/10.5194/gmd-18-1017-2025, 2025
Short summary
Short summary
In our study, we built upon previous work to investigate the patterns artificial intelligence (AI) learns to detect atmospheric features like tropical cyclones (TCs) and atmospheric rivers (ARs). As primary objective, we adopt a method to explain the AI used and investigate the plausibility of learned patterns. We find that plausible patterns are learned for both TCs and ARs. Hence, the chosen method is very useful for gaining confidence in the AI-based detection of atmospheric features.
Stefan Noll, Carsten Schmidt, Patrick Hannawald, Wolfgang Kausch, and Stefan Kimeswenger
EGUsphere, https://doi.org/10.5194/egusphere-2024-3512, https://doi.org/10.5194/egusphere-2024-3512, 2025
Short summary
Short summary
Non-thermal emission from chemical reactions in the Earth's middle und upper atmosphere strongly contributes to the brightness of the night sky below about 2.3 µm. The new Paranal Airglow Line and Continuum Emission model calculates the emission spectrum and its variability with an unprecedented accuracy. Relying on a large spectroscopic data set from astronomical spectrographs and theoretical molecular/atomic data, it is valuable for airglow research and astronomical observatories.
Felipe Cifuentes, Henk Eskes, Enrico Dammers, Charlotte Bryan, and Folkert Boersma
Geosci. Model Dev., 18, 621–649, https://doi.org/10.5194/gmd-18-621-2025, https://doi.org/10.5194/gmd-18-621-2025, 2025
Short summary
Short summary
We tested the capability of the flux divergence approach (FDA) to reproduce known NOx emissions using synthetic NO2 satellite column retrievals from high-resolution model simulations. The FDA accurately reproduced NOx emissions when column observations were limited to the boundary layer and when the variability of the NO2 lifetime, the NOx : NO2 ratio, and NO2 profile shapes were correctly modeled. This introduces strong model dependency, reducing the simplicity of the original FDA formulation.
Stefano Ubbiali, Christian Kühnlein, Christoph Schär, Linda Schlemmer, Thomas C. Schulthess, Michael Staneker, and Heini Wernli
Geosci. Model Dev., 18, 529–546, https://doi.org/10.5194/gmd-18-529-2025, https://doi.org/10.5194/gmd-18-529-2025, 2025
Short summary
Short summary
We explore a high-level programming model for porting numerical weather prediction (NWP) model codes to graphics processing units (GPUs). We present a Python rewrite with the domain-specific library GT4Py (GridTools for Python) of two renowned cloud microphysics schemes and the associated tangent-linear and adjoint algorithms. We find excellent portability, competitive GPU performance, robust execution on diverse computing architectures, and enhanced code maintainability and user productivity.
Pieter Rijsdijk, Henk Eskes, Arlene Dingemans, K. Folkert Boersma, Takashi Sekiya, Kazuyuki Miyazaki, and Sander Houweling
Geosci. Model Dev., 18, 483–509, https://doi.org/10.5194/gmd-18-483-2025, https://doi.org/10.5194/gmd-18-483-2025, 2025
Short summary
Short summary
Clustering high-resolution satellite observations into superobservations improves model validation and data assimilation applications. In our paper, we derive quantitative uncertainties for satellite NO2 column observations based on knowledge of the retrievals, including a detailed analysis of spatial error correlations and representativity errors. The superobservations and uncertainty estimates are tested in a global chemical data assimilation system and are found to improve the forecasts.
Dario Di Santo, Cenlin He, Fei Chen, and Lorenzo Giovannini
Geosci. Model Dev., 18, 433–459, https://doi.org/10.5194/gmd-18-433-2025, https://doi.org/10.5194/gmd-18-433-2025, 2025
Short summary
Short summary
This paper presents the Machine Learning-based Automated Multi-method Parameter Sensitivity and Importance analysis Tool (ML-AMPSIT), a computationally efficient tool that uses machine learning algorithms for sensitivity analysis in atmospheric models. It is tested with the Weather Research and Forecasting (WRF) model coupled with the Noah-Multiparameterization (Noah-MP) land surface model to investigate sea breeze circulation sensitivity to vegetation-related parameters.
Robert Schoetter, Robin James Hogan, Cyril Caliot, and Valéry Masson
Geosci. Model Dev., 18, 405–431, https://doi.org/10.5194/gmd-18-405-2025, https://doi.org/10.5194/gmd-18-405-2025, 2025
Short summary
Short summary
Radiation is relevant to the atmospheric impact on people and infrastructure in cities as it can influence the urban heat island, building energy consumption, and human thermal comfort. A new urban radiation model, assuming a more realistic form of urban morphology, is coupled to the urban climate model Town Energy Balance (TEB). The new TEB is evaluated with a reference radiation model for a variety of urban morphologies, and an improvement in the simulated radiative observables is found.
Zebediah Engberg, Roger Teoh, Tristan Abbott, Thomas Dean, Marc E. J. Stettler, and Marc L. Shapiro
Geosci. Model Dev., 18, 253–286, https://doi.org/10.5194/gmd-18-253-2025, https://doi.org/10.5194/gmd-18-253-2025, 2025
Short summary
Short summary
Contrails forming in some atmospheric conditions may persist and become strongly warming cirrus, while in other conditions may be neutral or cooling. We develop a contrail forecast model to predict contrail climate forcing for any arbitrary point in space and time and explore integration into flight planning and air traffic management. This approach enables contrail interventions to target high-probability high-climate-impact regions and reduce unintended consequences of contrail management.
Nils Eingrüber, Alina Domm, Wolfgang Korres, and Karl Schneider
Geosci. Model Dev., 18, 141–160, https://doi.org/10.5194/gmd-18-141-2025, https://doi.org/10.5194/gmd-18-141-2025, 2025
Short summary
Short summary
Climate change adaptation measures like unsealings can reduce urban heat stress. As grass grid pavers have never been parameterized for microclimate model simulations with ENVI-met, a new parameterization was developed based on field measurements. To analyse the cooling potential, scenario analyses were performed for a densely developed area in Cologne. Statistically significant average cooling effects of up to −11.1 K were found for surface temperature and up to −2.9 K for 1 m air temperature.
Xuan Wang, Lei Bi, Hong Wang, Yaqiang Wang, Wei Han, Xueshun Shen, and Xiaoye Zhang
Geosci. Model Dev., 18, 117–139, https://doi.org/10.5194/gmd-18-117-2025, https://doi.org/10.5194/gmd-18-117-2025, 2025
Short summary
Short summary
The Artificial-Intelligence-based Nonspherical Aerosol Optical Scheme (AI-NAOS) was developed to improve the estimation of the aerosol direct radiation effect and was coupled online with a chemical weather model. The AI-NAOS scheme considers black carbon as fractal aggregates and soil dust as super-spheroids, encapsulated with hygroscopic aerosols. Real-case simulations emphasize the necessity of accurately representing nonspherical and inhomogeneous aerosols in chemical weather models.
Lukas Pfitzenmaier, Pavlos Kollias, Nils Risse, Imke Schirmacher, Bernat Puigdomenech Treserras, and Katia Lamer
Geosci. Model Dev., 18, 101–115, https://doi.org/10.5194/gmd-18-101-2025, https://doi.org/10.5194/gmd-18-101-2025, 2025
Short summary
Short summary
The Python tool Orbital-Radar transfers suborbital radar data (ground-based, airborne, and forward-simulated numerical weather prediction model) into synthetic spaceborne cloud profiling radar data, mimicking platform-specific instrument characteristics, e.g. EarthCARE or CloudSat. The tool's novelty lies in simulating characteristic errors and instrument noise. Thus, existing data sets are transferred into synthetic observations and can be used for satellite calibration–validation studies.
Mark Buehner, Jean-Francois Caron, Ervig Lapalme, Alain Caya, Ping Du, Yves Rochon, Sergey Skachko, Maziar Bani Shahabadi, Sylvain Heilliette, Martin Deshaies-Jacques, Weiguang Chang, and Michael Sitwell
Geosci. Model Dev., 18, 1–18, https://doi.org/10.5194/gmd-18-1-2025, https://doi.org/10.5194/gmd-18-1-2025, 2025
Short summary
Short summary
The Modular and Integrated Data Assimilation System (MIDAS) software is described. The flexible design of MIDAS enables both deterministic and ensemble prediction applications for the atmosphere and several other Earth system components. It is currently used for all main operational weather prediction systems in Canada and also for sea ice and sea surface temperature analysis. The use of MIDAS for multiple Earth system components will facilitate future research on coupled data assimilation.
Alexander de Meij, Cornelis Cuvelier, Philippe Thunis, and Enrico Pisoni
EGUsphere, https://doi.org/10.5194/egusphere-2024-3690, https://doi.org/10.5194/egusphere-2024-3690, 2025
Short summary
Short summary
We assess the relevance and utility indicators developed within FAIRMODE by evaluating 9 CAMS models in calculated air pollutant values. For NO2, the results highlight difficulties at traffic stations. For PM2.5 and PM10 the bias and Winter-Summer gradients reveal issues. O3 evaluation shows that e.g. seasonal gradients are useful. Overall, the indicators provide valuable insights into model limitations, yet there is a need to reconsider the strictness of some indicators for certain pollutants.
Zichen Wu, Xueshun Chen, Zifa Wang, Huansheng Chen, Zhe Wang, Qing Mu, Lin Wu, Wending Wang, Xiao Tang, Jie Li, Ying Li, Qizhong Wu, Yang Wang, Zhiyin Zou, and Zijian Jiang
Geosci. Model Dev., 17, 8885–8907, https://doi.org/10.5194/gmd-17-8885-2024, https://doi.org/10.5194/gmd-17-8885-2024, 2024
Short summary
Short summary
We developed a model to simulate polycyclic aromatic hydrocarbons (PAHs) from global to regional scales. The model can reproduce PAH distribution well. The concentration of BaP (indicator species for PAHs) could exceed the target values of 1 ng m-3 over some areas (e.g., in central Europe, India, and eastern China). The change in BaP is lower than that in PM2.5 from 2013 to 2018. China still faces significant potential health risks posed by BaP although the Action Plan has been implemented.
Marie Taufour, Jean-Pierre Pinty, Christelle Barthe, Benoît Vié, and Chien Wang
Geosci. Model Dev., 17, 8773–8798, https://doi.org/10.5194/gmd-17-8773-2024, https://doi.org/10.5194/gmd-17-8773-2024, 2024
Short summary
Short summary
We have developed a complete two-moment version of the LIMA (Liquid Ice Multiple Aerosols) microphysics scheme. We have focused on collection processes, where the hydrometeor number transfer is often estimated in proportion to the mass transfer. The impact of these parameterizations on a convective system and the prospects for more realistic estimates of secondary parameters (reflectivity, hydrometeor size) are shown in a first test on an idealized case.
Yuya Takane, Yukihiro Kikegawa, Ko Nakajima, and Hiroyuki Kusaka
Geosci. Model Dev., 17, 8639–8664, https://doi.org/10.5194/gmd-17-8639-2024, https://doi.org/10.5194/gmd-17-8639-2024, 2024
Short summary
Short summary
A new parameterisation for dynamic anthropogenic heat and electricity consumption is described. The model reproduced the temporal variation in and spatial distributions of electricity consumption and temperature well in summer and winter. The partial air conditioning was the most critical factor, significantly affecting the value of anthropogenic heat emission.
Arseniy Karagodin-Doyennel, Fredrik Jansson, Bart van Stratum, Hugo Denier van der Gon, Jordi Vilà-Guerau de Arellano, and Sander Houweling
EGUsphere, https://doi.org/10.5194/egusphere-2024-3721, https://doi.org/10.5194/egusphere-2024-3721, 2024
Short summary
Short summary
We introduce a new simulation platform based on the Dutch Large-Eddy Simulation (DALES) to simulate carbon dioxide (CO2) emissions and their dispersion in the turbulent environments with hectometer resolution. This model incorporates both anthropogenic emission inventory and ecosystem exchanges. Simulation results for the main urban area in the Netherlands demonstrate a strong potential of DALES to enhance CO2 emission modeling, which is important for refining their reduction strategies.
Hongyi Li, Ting Yang, Lars Nerger, Dawei Zhang, Di Zhang, Guigang Tang, Haibo Wang, Yele Sun, Pingqing Fu, Hang Su, and Zifa Wang
Geosci. Model Dev., 17, 8495–8519, https://doi.org/10.5194/gmd-17-8495-2024, https://doi.org/10.5194/gmd-17-8495-2024, 2024
Short summary
Short summary
To accurately characterize the spatiotemporal distribution of particulate matter <2.5 µm chemical components, we developed the Nested Air Quality Prediction Model System with the Parallel Data Assimilation Framework (NAQPMS-PDAF) v2.0 for chemical components with non-Gaussian and nonlinear properties. NAQPMS-PDAF v2.0 has better computing efficiency, excels when used with a small ensemble size, and can significantly improve the simulation performance of chemical components.
T. Nash Skipper, Christian Hogrefe, Barron H. Henderson, Rohit Mathur, Kristen M. Foley, and Armistead G. Russell
Geosci. Model Dev., 17, 8373–8397, https://doi.org/10.5194/gmd-17-8373-2024, https://doi.org/10.5194/gmd-17-8373-2024, 2024
Short summary
Short summary
Chemical transport model simulations are combined with ozone observations to estimate the bias in ozone attributable to US anthropogenic sources and individual sources of US background ozone: natural sources, non-US anthropogenic sources, and stratospheric ozone. Results indicate a positive bias correlated with US anthropogenic emissions during summer in the eastern US and a negative bias correlated with stratospheric ozone during spring.
Li Fang, Jianbing Jin, Arjo Segers, Ke Li, Ji Xia, Wei Han, Baojie Li, Hai Xiang Lin, Lei Zhu, Song Liu, and Hong Liao
Geosci. Model Dev., 17, 8267–8282, https://doi.org/10.5194/gmd-17-8267-2024, https://doi.org/10.5194/gmd-17-8267-2024, 2024
Short summary
Short summary
Model evaluations against ground observations are usually unfair. The former simulates mean status over coarse grids and the latter the surrounding atmosphere. To solve this, we proposed the new land-use-based representative (LUBR) operator that considers intra-grid variance. The LUBR operator is validated to provide insights that align with satellite measurements. The results highlight the importance of considering fine-scale urban–rural differences when comparing models and observation.
Mijie Pang, Jianbing Jin, Arjo Segers, Huiya Jiang, Wei Han, Batjargal Buyantogtokh, Ji Xia, Li Fang, Jiandong Li, Hai Xiang Lin, and Hong Liao
Geosci. Model Dev., 17, 8223–8242, https://doi.org/10.5194/gmd-17-8223-2024, https://doi.org/10.5194/gmd-17-8223-2024, 2024
Short summary
Short summary
The ensemble Kalman filter (EnKF) improves dust storm forecasts but faces challenges with position errors. The valid time shifting EnKF (VTS-EnKF) addresses this by adjusting for position errors, enhancing accuracy in forecasting dust storms, as proven in tests on 2021 events, even with smaller ensembles and time intervals.
Mike Bush, David L. A. Flack, Huw W. Lewis, Sylvia I. Bohnenstengel, Chris J. Short, Charmaine Franklin, Adrian P. Lock, Martin Best, Paul Field, Anne McCabe, Kwinten Van Weverberg, Segolene Berthou, Ian Boutle, Jennifer K. Brooke, Seb Cole, Shaun Cooper, Gareth Dow, John Edwards, Anke Finnenkoetter, Kalli Furtado, Kate Halladay, Kirsty Hanley, Margaret A. Hendry, Adrian Hill, Aravindakshan Jayakumar, Richard W. Jones, Humphrey Lean, Joshua C. K. Lee, Andy Malcolm, Marion Mittermaier, Saji Mohandas, Stuart Moore, Cyril Morcrette, Rachel North, Aurore Porson, Susan Rennie, Nigel Roberts, Belinda Roux, Claudio Sanchez, Chun-Hsu Su, Simon Tucker, Simon Vosper, David Walters, James Warner, Stuart Webster, Mark Weeks, Jonathan Wilkinson, Michael Whitall, Keith D. Williams, and Hugh Zhang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-201, https://doi.org/10.5194/gmd-2024-201, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
RAL configurations define settings for the Unified Model atmosphere and Joint UK Land Environment Simulator. The third version of the Regional Atmosphere and Land (RAL3) science configuration for kilometre and sub-km scale modelling represents a major advance compared to previous versions (RAL2) by delivering a common science definition for applications in tropical and mid-latitude regions. RAL3 has more realistic precipitation distributions and improved representation of clouds and visibility.
Peter Kalverla, Imme Benedict, Chris Weijenborg, and Ruud J. van der Ent
EGUsphere, https://doi.org/10.5194/egusphere-2024-3401, https://doi.org/10.5194/egusphere-2024-3401, 2024
Short summary
Short summary
We introduce a new version of WAM2layers, a computer program that tracks how the weather brings water from one place to another. It uses data from weather and climate models, whose resolution is steadily increasing. Processing the latest data became a challenge, and the updates presented here ensure that WAM2layers runs smoothly again. We also made it easier to use the program and to understand its source code. This makes it more transparent and reliable, and easier to maintain.
Prabhakar Namdev, Maithili Sharan, Piyush Srivastava, and Saroj Kanta Mishra
Geosci. Model Dev., 17, 8093–8114, https://doi.org/10.5194/gmd-17-8093-2024, https://doi.org/10.5194/gmd-17-8093-2024, 2024
Short summary
Short summary
Inadequate representation of surface–atmosphere interaction processes is a major source of uncertainty in numerical weather prediction models. Here, an effort has been made to improve the Weather Research and Forecasting (WRF) model version 4.2.2 by introducing a unique theoretical framework under convective conditions. In addition, to enhance the potential applicability of the WRF modeling system, various commonly used similarity functions under convective conditions have also been installed.
Andrew Gettelman, Richard Forbes, Roger Marchand, Chih-Chieh Chen, and Mark Fielding
Geosci. Model Dev., 17, 8069–8092, https://doi.org/10.5194/gmd-17-8069-2024, https://doi.org/10.5194/gmd-17-8069-2024, 2024
Short summary
Short summary
Supercooled liquid clouds (liquid clouds colder than 0°C) are common at higher latitudes (especially over the Southern Ocean) and are critical for constraining climate projections. We compare a single-column version of a weather model to observations with two different cloud schemes and find that both the dynamical environment and atmospheric aerosols are important for reproducing observations.
Yujuan Wang, Peng Zhang, Jie Li, Yaman Liu, Yanxu Zhang, Jiawei Li, and Zhiwei Han
Geosci. Model Dev., 17, 7995–8021, https://doi.org/10.5194/gmd-17-7995-2024, https://doi.org/10.5194/gmd-17-7995-2024, 2024
Short summary
Short summary
This study updates the CESM's aerosol schemes, focusing on dust, marine aerosol emissions, and secondary organic aerosol (SOA) . Dust emission modifications make deflation areas more continuous, improving results in North America and the sub-Arctic. Humidity correction to sea-salt emissions has a minor effect. Introducing marine organic aerosol emissions, coupled with ocean biogeochemical processes, and adding aqueous reactions for SOA formation advance the CESM's aerosol modelling results.
Lucas A. McMichael, Michael J. Schmidt, Robert Wood, Peter N. Blossey, and Lekha Patel
Geosci. Model Dev., 17, 7867–7888, https://doi.org/10.5194/gmd-17-7867-2024, https://doi.org/10.5194/gmd-17-7867-2024, 2024
Short summary
Short summary
Marine cloud brightening (MCB) is a climate intervention technique to potentially cool the climate. Climate models used to gauge regional climate impacts associated with MCB often assume large areas of the ocean are uniformly perturbed. However, a more realistic representation of MCB application would require information about how an injected particle plume spreads. This work aims to develop such a plume-spreading model.
Leonardo Olivetti and Gabriele Messori
Geosci. Model Dev., 17, 7915–7962, https://doi.org/10.5194/gmd-17-7915-2024, https://doi.org/10.5194/gmd-17-7915-2024, 2024
Short summary
Short summary
Data-driven models are becoming a viable alternative to physics-based models for weather forecasting up to 15 d into the future. However, it is unclear whether they are as reliable as physics-based models when forecasting weather extremes. We evaluate their performance in forecasting near-surface cold, hot, and windy extremes globally. We find that data-driven models can compete with physics-based models and that the choice of the best model mainly depends on the region and type of extreme.
David C. Wong, Jeff Willison, Jonathan E. Pleim, Golam Sarwar, James Beidler, Russ Bullock, Jerold A. Herwehe, Rob Gilliam, Daiwen Kang, Christian Hogrefe, George Pouliot, and Hosein Foroutan
Geosci. Model Dev., 17, 7855–7866, https://doi.org/10.5194/gmd-17-7855-2024, https://doi.org/10.5194/gmd-17-7855-2024, 2024
Short summary
Short summary
This work describe how we linked the meteorological Model for Prediction Across Scales – Atmosphere (MPAS-A) with the Community Multiscale Air Quality (CMAQ) air quality model to form a coupled modelling system. This could be used to study air quality or climate and air quality interaction at a global scale. This new model scales well in high-performance computing environments and performs well with respect to ground surface networks in terms of ozone and PM2.5.
Markus Kunze, Christoph Zülicke, Tarique Adnan Siddiqui, Claudia Christine Stephan, Yosuke Yamazaki, Claudia Stolle, Sebastian Borchert, and Hauke Schmidt
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-191, https://doi.org/10.5194/gmd-2024-191, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
We present the Icosahedral Nonhydrostatic (ICON) general circulation model with upper atmosphere extension with the physics package for numerical weather prediction (UA-ICON(NWP)). The parameters for the gravity wave parameterizations were optimized, and realistic modelling of the thermal and dynamic state of the mesopause regions was achieved. UA-ICON(NWP) now shows a realistic frequency of major sudden stratospheric warmings and well-represented solar tides in temperature.
Giulio Mandorli and Claudia J. Stubenrauch
Geosci. Model Dev., 17, 7795–7813, https://doi.org/10.5194/gmd-17-7795-2024, https://doi.org/10.5194/gmd-17-7795-2024, 2024
Short summary
Short summary
In recent years, several studies focused their attention on the disposition of convection. Lots of methods, called indices, have been developed to quantify the amount of convection clustering. These indices are evaluated in this study by defining criteria that must be satisfied and then evaluating the indices against these standards. None of the indices meet all criteria, with some only partially meeting them.
Wonbae Bang, Jacob Carlin, Kwonil Kim, Alexander Ryzhkov, Guosheng Liu, and Gyuwon Lee
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-179, https://doi.org/10.5194/gmd-2024-179, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Microphysics model-based diagnosis such as the spectral bin model (SBM) recently has been attempted to diagnose winter precipitation types. In this study, the accuracy of SBM-based precipitation type diagnosis is compared with other traditional methods. SBM have relatively higher accuracy about snow and wetsnow events whereas lower accuracy about rain event. When microphysics scheme in the SBM was optimized for the corresponding region, accuracy about rain events was improved.
Kerry Anderson, Jack Chen, Peter Englefield, Debora Griffin, Paul A. Makar, and Dan Thompson
Geosci. Model Dev., 17, 7713–7749, https://doi.org/10.5194/gmd-17-7713-2024, https://doi.org/10.5194/gmd-17-7713-2024, 2024
Short summary
Short summary
The Global Forest Fire Emissions Prediction System (GFFEPS) is a model that predicts smoke and carbon emissions from wildland fires. The model calculates emissions from the ground up based on satellite-detected fires, modelled weather and fire characteristics. Unlike other global models, GFFEPS uses daily weather conditions to capture changing burning conditions on a day-to-day basis. GFFEPS produced lower carbon emissions due to the changing weather not captured by the other models.
Cited articles
Austin, G. L. and Bellon, A.: The use of digital weather radar records for
short-term precipitation forecasting, Q. J. Roy.
Meteor. Soc., 100, 658–664, https://doi.org/10.1002/qj.49710042612,
1974. a
Ayzel, G.: hydrogo/rainnet: RainNet v1.0-gmdd, Zenodo, https://doi.org/10.5281/zenodo.3631038,
2020a. a, b, c
Ayzel, G.: RainNet: pretrained model and weights, Zenodo, https://doi.org/10.5281/zenodo.3630429,
2020b. a, b, c
Ayzel, G.: RYDL: the sample data of the RY product for deep learning
applications, Zenodo, https://doi.org/10.5281/zenodo.3629951, 2020c. a, b
Ayzel, G.: RainNet: a convolutional neural network for radar-based precipitation nowcasting, available at: https://github.com/hydrogo/rainnet, last access: 10 June 2020. a
Ayzel, G., Heistermann, M., and Winterrath, T.: Optical flow models as an open benchmark for radar-based precipitation nowcasting (rainymotion v0.1), Geosci. Model Dev., 12, 1387–1402, https://doi.org/10.5194/gmd-12-1387-2019, 2019. a, b, c, d
Badrinarayanan, V., Kendall, A., and Cipolla, R.: SegNet: A Deep
Convolutional Encoder-Decoder Architecture for Image Segmentation, IEEE
T. Pattern Anal., 39, 2481–2495,
https://doi.org/10.1109/TPAMI.2016.2644615, 2017. a
Bai, S., Kolter, J. Z., and Koltun, V.: An Empirical Evaluation of Generic
Convolutional and Recurrent Networks for Sequence Modeling,
available at: https://arxiv.org/abs/1803.01271 (last access: 28 January
2020), 2018. a
Bauer, P., Thorpe, A., and Brunet, G.: The quiet revolution of numerical
weather prediction, Nature, 525, 47–55, https://doi.org/10.1038/nature14956, 2015. a
Boureau, Y.-L., Ponce, J., and LeCun, Y.: A Theoretical Analysis of Feature
Pooling in Visual Recognition, in: Proceedings of the 27th International
Conference on International Conference on Machine Learning, ICML'10, Omnipress, Madison, WI, USA, 21–24 June 2010, Haifa, Israel,
111–118, 2010. a
Chen, P., Chen, G., and Zhang, S.: Log Hyperbolic Cosine Loss Improves
Variational Auto-Encoder,
available at: https://openreview.net/forum?id=rkglvsC9Ym (last access: 28 January 2020), 2018. a
Chollet, F. et al.: Keras, https://keras.io (last access: 10 June 2020),
2015. a
Dahl, G. E., Sainath, T. N., and Hinton, G. E.: Improving deep neural
networks for LVCSR using rectified linear units and dropout, in: 2013 IEEE
International Conference on Acoustics, Speech and Signal Processing, 26–31 May 2013, Vancouver, Canada, 8609–8613, https://doi.org/10.1109/ICASSP.2013.6639346, 2013. a
Dueben, P. D. and Bauer, P.: Challenges and design choices for global weather and climate models based on machine learning, Geosci. Model Dev., 11, 3999–4009, https://doi.org/10.5194/gmd-11-3999-2018, 2018. a
Gehring, J., Auli, M., Grangier, D., Yarats, D., and Dauphin, Y. N.:
Convolutional Sequence to Sequence Learning, in: Proceedings of the 34th
International Conference on Machine Learning – Volume 70, ICML'17, 6–11 August 2017,Sydney, Australia, 1243–1252, JMLR.org, 2017. a
Gentine, P., Pritchard, M., Rasp, S., Reinaudi, G., and Yacalis, G.: Could
Machine Learning Break the Convection Parameterization Deadlock?,
Geophys. Res. Lett., 45, 5742–5751, https://doi.org/10.1029/2018GL078202, 2018. a
Germann, U. and Zawadzki, I.: Scale-Dependence of the Predictability of
Precipitation from Continental Radar Images. Part I: Description of the
Methodology, Mon. Weather Rev., 130, 2859–2873,
https://doi.org/10.1175/1520-0493(2002)130<2859:SDOTPO>2.0.CO;2,
2002. a
Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang,
X., Wang, G., Cai, J., and Chen, T.: Recent advances in convolutional neural
networks, Pattern Recogn., 77, 354–377,
https://doi.org/10.1016/j.patcog.2017.10.013,
2018. a
Kingma, D. P. and Ba, J.: Adam: A Method for Stochastic Optimization, in: 3rd
International Conference on Learning Representations, ICLR 2015, San Diego,
CA, USA, 7–9 May 2015, Conference Track Proceedings, edited by: Bengio, Y.
and LeCun, Y., available at: http://arxiv.org/abs/1412.6980 (last access: 10 June 2020),
2015. a, b
Krizhevsky, A., Sutskever, I., and Hinton, G. E.: ImageNet Classification with Deep Convolutional Neural Networks, in: Advances in Neural Information Processing Systems 25, NIPS 2012, Lake Tahoe, Nevada, USA, 3–9 December 2012, Curran Associates, Inc. Red Hook, NY, USA, edited by: Pereira, F., Burges, C. J. C., Bottou, L., and Weinberger, K. Q., 1097–1105, available at: http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf (last access: 10 June 2020), 2012. a
LeCun, Y., Bengio, Y., and Hinton, G.: Deep learning, Nature, 521, 436–444,
https://doi.org/10.1038/nature14539, 2015. a, b
Lin, C., Vasić, S., Kilambi, A., Turner, B., and Zawadzki, I.:
Precipitation forecast skill of numerical weather prediction models and
radar nowcasts, Geophys. Res. Lett., 32, L14801,
https://doi.org/10.1029/2005GL023451,
2005. a
Long, J., Shelhamer, E., and Darrell, T.: Fully Convolutional Networks for
Semantic Segmentation, in: The IEEE Conference on Computer Vision and Pattern
Recognition (CVPR), 8–12 June 2015, Boston, Massachusetts, USA, 2015. a
Mittermaier, M. and Roberts, N.: Intercomparison of Spatial Forecast
Verification Methods: Identifying Skillful Spatial Scales Using the Fractions
Skill Score, Weather Forecast., 25, 343–354,
https://doi.org/10.1175/2009WAF2222260.1, 2010. a
Nair, V. and Hinton, G. E.: Interpersonal Informatics: Making Social Influence
Visible, in: Proceedings of the 27th International Conference on
International Conference on Machine Learning, ICML'10,
Omnipress, Madison, WI, USA, 21–24 June 2010, Haifa, Israel, 807–814, 2010. a
Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J.,
Carvalhais, N., and Prabhat: Deep learning and process understanding for
data-driven Earth system science, Nature, 566, 195–204,
https://doi.org/10.1038/s41586-019-0912-1, 2019. a
Reyniers, M.: Quantitative precipitation forecasts based on radar observations:
Principles, algorithms and operational systems, Institut Royal
Météorologique de Belgique,
available at: https://www.meteo.be/meteo/download/fr/3040165/pdf/rmi_scpub-1261.pdf (last access: 10 June 2020),
2008. a
Ronneberger, O., Fischer, P., and Brox, T.: U-Net: Convolutional Networks for
Biomedical Image Segmentation, in: Medical Image Computing and
Computer-Assisted Intervention – MICCAI 2015, edited by: Navab, N.,
Hornegger, J., Wells, W. M., and Frangi, A. F., Springer
International Publishing, Cham, pp. 234–241, https://doi.org/10.1007/978-3-319-24574-4_28, 2015. a
Shi, E., Li, Q., Gu, D., and Zhao, Z.: A Method of Weather Radar Echo
Extrapolation Based on Convolutional Neural Networks, in: MultiMedia
Modeling, edited by: Schoeffmann, K., Chalidabhongse, T. H., Ngo, C. W.,
Aramvith, S., O'Connor, N. E., Ho, Y.-S., Gabbouj, M., and Elgammal, A., Springer International Publishing, Cham, pp. 16–28,
https://doi.org/10.1007/978-3-319-73603-7_2,
2018. a, b, c
Shi, X., Chen, Z., Wang, H., Yeung, D.-Y., Wong, W.-k., and Woo, W.-c.:
Convolutional LSTM Network: A Machine Learning Approach for Precipitation
Nowcasting, in: Advances in Neural Information Processing Systems 28, edited
by: Cortes, C., Lawrence, N. D., Lee, D. D., Sugiyama, M., and Garnett, R., Curran Associates, Inc.,
Red Hook, NY, USA, 802–810,
available at: http://papers.nips.cc/paper/5955-convolutional-lstm-network-a-machine-learning-approach-for-precipitation-nowcasting.pdf (last access: 10 June 2020), 2015. a, b, c, d
Shi, X., Gao, Z., Lausen, L., Wang, H., Yeung, D.-Y., Wong, W.-k., and Woo,
W.-c.: Deep Learning for Precipitation Nowcasting: A Benchmark and A New
Model, in: Advances in Neural Information Processing Systems 30, edited by:
Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R., Curran Associates, Inc., Red Hook, NY, USA, 5617–5627,
available at: http://papers.nips.cc/paper/7145-deep-learning-for-precipitation-nowcasting-a-benchmark-and-a-new-model.pdf (last access: 10 June 2020),
2017. a, b
Singh, S., Sarkar, S., and Mitra, P.: Leveraging Convolutions in Recurrent
Neural Networks for Doppler Weather Radar Echo Prediction, in: Advances in
Neural Networks – ISNN 2017, edited by: Cong, F., Leung, A., and Wei, Q., Springer International Publishing, Cham, pp. 310–317,
https://doi.org/10.1007/978-3-319-59081-3_37,
2017. a
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov,
R.: Dropout: A Simple Way to Prevent Neural Networks from
Overfitting, J.
Mach. Learn. Res., 15, 1929–1958, 2014. a
Srivastava, R. K., Greff, K., and Schmidhuber, J.: Training Very Deep Networks,
in: Advances in Neural Information Processing Systems 28, edited by: Cortes,
C., Lawrence, N. D., Lee, D. D., Sugiyama, M., and Garnett, R., Curran Associates, Inc., Red Hook, NY, USA, 2377–2385,
available at: http://papers.nips.cc/paper/5850-training-very-deep-networks.pdf (last access: 10 June 2020),
2015. a, b
Sun, J., Xue, M., Wilson, J. W., Zawadzki, I., Ballard, S. P.,
Onvlee-Hooimeyer, J., Joe, P., Barker, D. M., Li, P.-W., Golding, B., Xu, M.,
and Pinto, J.: Use of NWP for Nowcasting Convective Precipitation: Recent
Progress and Challenges, B. Am. Meteorol. Soc., 95,
409–426, https://doi.org/10.1175/BAMS-D-11-00263.1, 2014.
a
Sutskever, I., Vinyals, O., and Le, Q. V.: Sequence to Sequence Learning with
Neural Networks, in: Advances in Neural Information Processing Systems 27,
edited by: Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N. D., and
Weinberger, K. Q., Curran Associates, Inc., Red Hook, NY, USA, 3104–3112,
available at: http://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf (last access: 10 June 2020),
2014. a
Wilson, J. W., Crook, N. A., Mueller, C. K., Sun, J., and Dixon, M.:
Nowcasting Thunderstorms: A Status Report, B. Am.
Meteorol. Soc., 79, 2079–2099,
https://doi.org/10.1175/1520-0477(1998)079<2079:NTASR>2.0.CO;2,
1998. a
Short summary
In this study, we present RainNet, a deep convolutional neural network for radar-based precipitation nowcasting, which was trained to predict continuous precipitation intensities at a lead time of 5 min. RainNet significantly outperformed the benchmark models at all lead times up to 60 min. Yet, an undesirable property of RainNet predictions is the level of spatial smoothing. Obviously, RainNet learned an optimal level of smoothing to produce a nowcast at 5 min lead time.
In this study, we present RainNet, a deep convolutional neural network for radar-based...