Articles | Volume 14, issue 6
Model description paper 29 Jun 2021
Model description paper | 29 Jun 2021
MSDM v1.0: A machine learning model for precipitation nowcasting over eastern China using multisource data
Dawei Li et al.
Related subject area
Atmospheric sciencesEvaluation of the offline-coupled GFSv15–FV3–CMAQv5.0.2 in support of the next-generation National Air Quality Forecast Capability over the contiguous United StatesA climatology of tropical wind shear produced by clustering wind profiles from the Met Office Unified Model (GA7.0)Surface representation impacts on turbulent heat fluxes in the Weather Research and Forecasting (WRF) model (v.4.1.3)Effects of heterogeneous reactions on tropospheric chemistry: a global simulation with the chemistry–climate model CHASER V4.0Development of a large-eddy simulation subgrid model based on artificial neural networks: a case study of turbulent channel flowWRF-GC (v2.0): online two-way coupling of WRF (v188.8.131.52) and GEOS-Chem (v12.7.2) for modeling regional atmospheric chemistry–meteorology interactionsModifying emissions scenario projections to account for the effects of COVID-19: protocol for CovidMIPTowards an improved treatment of cloud–radiation interaction in weather and climate models: exploring the potential of the Tripleclouds method for various cloud types using libRadtran 2.0.4A model for urban biogenic CO2 fluxes: Solar-Induced Fluorescence for Modeling Urban biogenic Fluxes (SMUrF v1)OpenIFS@home version 1: a citizen science project for ensemble weather and climate forecastingRegional CO2 inversions with LUMIA, the Lund University Modular Inversion Algorithm, v1.0The Detailed Emissions Scaling, Isolation, and Diagnostic (DESID) module in the Community Multiscale Air Quality (CMAQ) modeling system version 5.3.2Evaluation of the dynamic core of the PALM model system 6.0 in a neutrally stratified urban environment: comparison between LES and wind-tunnel experimentsImplementing a sectional scheme for early aerosol growth from new particle formation in the Norwegian Earth System Model v2: comparison to observations and climate impactsLimitations of WRF land surface models for simulating land use and land cover change in Sub-Saharan Africa and development of an improved model (CLM-AF v. 1.0)Simulation of O3 and NOx in São Paulo street urban canyons with VEIN (v0.2.2) and MUNICH (v1.0)A case study of wind farm effects using two wake parameterizations in the Weather Research and Forecasting (WRF) model (V3.7.1) in the presence of low-level jetsRadiative Transfer Model 3.0 integrated into the PALM model system 6.0Development and evaluation of CO2 transport in MPAS-A v6.3Variational regional inverse modeling of reactive species emissions with PYVAR-CHIMERE-v2019A new gas absorption optical depth parameterisation for RTTOV version 13The Community Multiscale Air Quality (CMAQ) model versions 5.3 and 5.3.1: system updates and evaluationDevelopment and evaluation of spectral nudging strategy for the simulation of summer precipitation over the Tibetan Plateau using WRF (v4.0)Combining homogeneous and heterogeneous chemistry to model inorganic compound concentrations in indoor environments: the H2I model (v1.0)Interpol-IAGOS: a new method for assessing long-term chemistry–climate simulations in the UTLS based on IAGOS data, and its application to the MOCAGE CCMI REF-C1SD simulationThe Environment and Climate Change Canada Carbon Assimilation System (EC-CAS v1.0): demonstration with simulated CO observationsWRF4PALM v1.0: a mesoscale dynamical driver for the microscale PALM model system 6.0pyPI (v1.3): Tropical Cyclone Potential Intensity Calculations in PythonComparison of three aerosol representations of NHM-Chem (v1.0) for the simulations of air quality and climate-relevant variablesJlBox v1.1: a Julia-based multi-phase atmospheric chemistry box modelA new Lagrangian in-time particle simulation module (Itpas v1) for atmospheric particle dispersionEffects of black carbon morphology on brown carbon absorption estimation: from numerical aspectsSimulation of the evolution of biomass burning organic aerosol with different volatility basis set schemes in PMCAMx-SRv1.0Novel estimation of aerosol processes with particle size distribution measurements: a case study with the TOMAS algorithm v1.0.0Evaluation of ECMWF IFS-AER (CAMS) operational forecasts during cycle 41r1–46r1 with calibrated ceilometer profiles over GermanyurbanChemFoam 1.0: Large-Eddy Simulation of Non-Stationary Chemical Transport of Traffic Emissions in an Idealized Street CanyonInfluence of biomass burning vapor wall loss correction on modeling organic aerosols in Europe by CAMx v6.50Seasonal and diurnal performance of daily forecasts with WRF V3.8.1 over the United Arab EmiratesMLAir (v1.0) – a tool to enable fast and flexible machine learning on air data time seriessnowScatt 1.0: consistent model of microphysical and scattering properties of rimed and unrimed snowflakes based on the self-similar Rayleigh–Gans approximationEffects of spatial resolution on WRF v3.8.1 simulated meteorology over the central HimalayaOn the suitability of second-order accurate finite-volume solvers for the simulation of atmospheric boundary layer flowAn urban large-eddy-simulation-based dispersion model for marginal grid resolutions: CAIRDIO v1.0Applying a new integrated mass-flux adjustment filter in rapid update cycling of convective-scale data assimilation for the COSMO model (v5.07)On the model uncertainties in Bayesian source reconstruction using an ensemble of weather predictions, the emission inverse modelling system FREAR v1.0, and the Lagrangian transport and dispersion model Flexpart v9.0.2Evaluation of the interactive stratospheric ozone (O3v2) module in the E3SM version 1 Earth system modelDevelopment of an atmospheric chemistry model coupled to the PALM model system 6.0: implementation and first applicationsThe Vertical City Weather Generator (VCWG v1.3.2)Comparison of source apportionment approaches and analysis of non-linearity in a real case model applicationA zero-dimensional view of atmospheric degradation of levoglucosan (LEVCHEM_v1) using numerical chamber simulations
Xiaoyang Chen, Yang Zhang, Kai Wang, Daniel Tong, Pius Lee, Youhua Tang, Jianping Huang, Patrick C. Campbell, Jeff Mcqueen, Havala O. T. Pye, Benjamin N. Murphy, and Daiwen Kang
Geosci. Model Dev., 14, 3969–3993,Short summary
The continuously updated National Air Quality Forecast Capability (NAQFC) provides air quality forecasts. To support the development of the next-generation NAQFC, we evaluate a prototype of GFSv15-CMAQv5.0.2. The performance and the potential improvements for the system are discussed. This study can provide a scientific basis for further development of NAQFC and help it to provide more accurate air quality forecasts to the public over the contiguous United States.
Mark R. Muetzelfeldt, Robert S. Plant, Peter A. Clark, Alison J. Stirling, and Steven J. Woolnough
Geosci. Model Dev., 14, 4035–4049,Short summary
Wind shear causes organized convection in the tropics, producing, e.g., squall lines. We have developed a procedure for producing a climatology of sheared wind profiles in a climate model and demonstrated that the profiles are linked with organized convection, both in terms of their structure and their spatio-temporal distribution. The procedure could be used to diagnose organization of convection in a climate model, which could lead to improvements in the model's representation of convection.
Carlos Román-Cascón, Marie Lothon, Fabienne Lohou, Oscar Hartogensis, Jordi Vila-Guerau de Arellano, David Pino, Carlos Yagüe, and Eric R. Pardyjak
Geosci. Model Dev., 14, 3939–3967,Short summary
The type of vegetation (or land cover) and its status influence the heat and water transfers between the surface and the air, affecting the processes that develop in the atmosphere at different (but connected) spatiotemporal scales. In this work, we investigate how these transfers are affected by the way the surface is represented in a widely used weather model. The results encourage including realistic high-resolution and updated land cover databases in models to improve their predictions.
Phuc T. M. Ha, Ryoki Matsuda, Yugo Kanaya, Fumikazu Taketani, and Kengo Sudo
Geosci. Model Dev., 14, 3813–3841,Short summary
Policies to mitigate air pollution require an understanding of tropospheric oxidizing capacity, which is controlled by mechanisms including heterogeneous processes on aerosols and clouds. This study uses a chemistry–climate model CHASER (MIROC) to explore the heterogeneous effects in the troposphere for -2.96 % O3, -2.19 % NOx, +3.28 % CO, and +5.91 % CH4 lifetime. Besides, these processes affect polluted areas and remote areas and can bring challenges to pollution reduction efforts.
Robin Stoffer, Caspar M. van Leeuwen, Damian Podareanu, Valeriu Codreanu, Menno A. Veerman, Martin Janssens, Oscar K. Hartogensis, and Chiel C. van Heerwaarden
Geosci. Model Dev., 14, 3769–3788,Short summary
Turbulent flows are often simulated with the large-eddy simulation (LES) technique, which requires subgrid models to account for the smallest scales. Current subgrid models often require strong simplifying assumptions. We therefore developed a subgrid model based on artificial neural networks, which requires fewer assumptions. Our data-driven SGS model showed high potential in accurately representing the smallest scales but still introduced instability when incorporated into an actual LES.
Xu Feng, Haipeng Lin, Tzung-May Fu, Melissa P. Sulprizio, Jiawei Zhuang, Daniel J. Jacob, Heng Tian, Yaping Ma, Lijuan Zhang, Xiaolin Wang, Qi Chen, and Zhiwei Han
Geosci. Model Dev., 14, 3741–3768,Short summary
WRF-GC is an online coupling of the WRF meteorological model and GEOS-Chem chemical transport model for regional atmospheric chemistry and air quality modeling. In WRF-GC v2.0, we implemented the aerosol–radiation interactions and aerosol–cloud interactions, as well as the capability to nest multiple domains for high-resolution simulations based on the modular framework of WRF-GC v1.0. This allows the GEOS-Chem users to investigate the meteorology–atmospheric chemistry interactions.
Robin D. Lamboll, Chris D. Jones, Ragnhild B. Skeie, Stephanie Fiedler, Bjørn H. Samset, Nathan P. Gillett, Joeri Rogelj, and Piers M. Forster
Geosci. Model Dev., 14, 3683–3695,Short summary
Lockdowns to avoid the spread of COVID-19 have created an unprecedented reduction in human emissions. We can estimate the changes in emissions at a country level, but to make predictions about how this will affect our climate, we need more precise information about where the emissions happen. Here we combine older estimates of where emissions normally occur with very recent estimates of sector activity levels to enable different groups to make simulations of the climatic effects of lockdown.
Nina Črnivec and Bernhard Mayer
Geosci. Model Dev., 14, 3663–3682,Short summary
This study aims to advance the cloud–radiation interplay treatment in global weather and climate prediction, focusing on cloud horizontal inhomogeneity misrepresentation. We explore the potential of the Tripleclouds method for diverse cloud types, namely the stratocumulus, cirrus and cumulonimbus. The validity of global cloud variability estimate with various condensate distribution assumptions is assessed. Optimizations for overcast and extremely heterogeneous cloudiness are further endorsed.
Dien Wu, John C. Lin, Henrique F. Duarte, Vineet Yadav, Nicholas C. Parazoo, Tomohiro Oda, and Eric A. Kort
Geosci. Model Dev., 14, 3633–3661,Short summary
A model (SMUrF) is presented that estimates biogenic CO2 fluxes over cities around the globe to separate out biogenic fluxes from anthropogenic emissions. The model leverages satellite-based solar-induced fluorescence data and a machine-learning technique. We evaluate the biogenic fluxes against flux observations and show contrasts between biogenic and anthropogenic fluxes over cities, revealing urban–rural flux gradients, diurnal cycles, and the resulting imprints on atmospheric-column CO2.
Sarah Sparrow, Andrew Bowery, Glenn D. Carver, Marcus O. Köhler, Pirkka Ollinaho, Florian Pappenberger, David Wallom, and Antje Weisheimer
Geosci. Model Dev., 14, 3473–3486,Short summary
This paper describes how the research version of the European Centre for Medium-Range Weather Forecasts’ Integrated Forecast System is combined with climateprediction.net’s public volunteer computing resource to develop OpenIFS@home. Thousands of volunteer personal computers simulated slightly different realizations of Tropical Cyclone Karl to demonstrate the performance of the large-ensemble forecast. OpenIFS@Home offers researchers a new tool to study weather forecasts and related questions.
Guillaume Monteil and Marko Scholze
Geosci. Model Dev., 14, 3383–3406,Short summary
LUMIA is a Python library for atmospheric inversions, originally developed at Lund University to perform regional atmospheric CO2 inversions. The inversions rely on coupling the regional transport model FLEXPART and the global transport model TM5. The paper presents the modeling setup and some first results, and it introduces the LUMIA Python package as a toolbox for inversions beyond the use case presented in the paper.
Benjamin N. Murphy, Christopher G. Nolte, Fahim Sidi, Jesse O. Bash, K. Wyat Appel, Carey Jang, Daiwen Kang, James Kelly, Rohit Mathur, Sergey Napelenok, George Pouliot, and Havala O. T. Pye
Geosci. Model Dev., 14, 3407–3420,Short summary
The algorithms for applying air pollution emission rates in the Community Multiscale Air Quality (CMAQ) model have been improved to better support users and developers. The new features accommodate emissions perturbation studies that are typical in atmospheric research and output a wealth of metadata for each model run so assumptions can be verified and documented. The new approach dramatically enhances the transparency and functionality of this critical aspect of atmospheric modeling.
Tobias Gronemeier, Kerstin Surm, Frank Harms, Bernd Leitl, Björn Maronga, and Siegfried Raasch
Geosci. Model Dev., 14, 3317–3333,Short summary
We demonstrate the capability of the PALM model system version 6.0 to simulate urban boundary layers. The studied situation includes a real-world building setup of the HafenCity area in Hamburg, Germany. We evaluate the simulation results against wind-tunnel measurements utilizing PALM's virtual measurement module. The comparison reveals an overall high agreement between simulation results and wind-tunnel measurements including mean wind speed and direction as well as turbulence statistics.
Sara M. Blichner, Moa K. Sporre, Risto Makkonen, and Terje K. Berntsen
Geosci. Model Dev., 14, 3335–3359,Short summary
Aerosol–cloud interactions are the largest contributor to climate forcing uncertainty. In this study we combine two common approaches to aerosol representation in global models: a sectional scheme, which is closer to first principals, for the smallest particles forming in the atmosphere and a log-modal scheme, which is faster, for the larger particles. With this approach, we improve the aerosol representation compared to observations, while only increasing the computational cost by 15 %.
Timothy Glotfelty, Diana Ramírez-Mejía, Jared Bowden, Adrian Ghilardi, and J. Jason West
Geosci. Model Dev., 14, 3215–3249,Short summary
Land use and land cover change is a major contributor to climate change in Africa. Here we document deficiencies in how a weather model represents the land surface of Africa and how we modify a common land surface model to overcome these deficiencies. Our tests reveal that the default weather model does not accurately predict and transition the properties of different African biomes and growing cycles. This paper demonstrates that our modified model addresses these limitations.
Mario Eduardo Gavidia-Calderón, Sergio Ibarra-Espinosa, Youngseob Kim, Yang Zhang, and Maria de Fatima Andrade
Geosci. Model Dev., 14, 3251–3268,Short summary
The MUNICH model was used to calculate pollutant concentrations inside the streets of São Paulo. The VEIN emission model provided the vehicular emissions and the coordinates of the streets. We used information from an air quality station to account for pollutant concentrations over the street rooftops. Results showed that when emissions are calibrated, MUNICH satisfied the performance criteria. MUNICH can be used to evaluate the impact of traffic-related air pollution on public health.
Xiaoli G. Larsén and Jana Fischereit
Geosci. Model Dev., 14, 3141–3158,Short summary
For the first time, turbulent kinetic energy (TKE) calculated from the explicit wake parameterization (EWP) in WRF is examined using high-frequency measurements over a wind farm and compared with that calculated using the Fitch et al. (2012) scheme. We examined the effect of farm-induced TKE advection in connection with the Fitch scheme. Through a case study with a low-level jet (LLJ), we analyzed the key features of LLJs and raised the issue of interaction between wind farms and LLJs.
Pavel Krč, Jaroslav Resler, Matthias Sühring, Sebastian Schubert, Mohamed H. Salim, and Vladimír Fuka
Geosci. Model Dev., 14, 3095–3120,Short summary
The adverse effects of an urban environment, e.g. heat stress and air pollution, pose a risk to health and well-being. Precise modelling of the urban climate is crucial to mitigate these effects. Conventional atmospheric models are inadequate for modelling the complex structures of the urban environment; in particular, they lack a 3-D model of radiation and its interaction with surfaces and the plant canopy. The new RTM simulates these processes within the PALM-4U urban climate model.
Tao Zheng, Sha Feng, Kenneth J. Davis, Sandip Pal, and Josep-Anton Morguí
Geosci. Model Dev., 14, 3037–3066,Short summary
Carbon dioxide is the most important greenhouse gas. We develop the numerical model that represents carbon dioxide transport in the atmosphere. This model development is based on the MPAS model, which has a variable-resolution capability. The purpose of developing carbon dioxide transport in MPAS is to allow for high-resolution transport model simulation that is not limited by the lateral boundaries. It will also form the base for a future development of MPAS-based carbon inversion system.
Audrey Fortems-Cheiney, Isabelle Pison, Grégoire Broquet, Gaëlle Dufour, Antoine Berchet, Elise Potier, Adriana Coman, Guillaume Siour, and Lorenzo Costantino
Geosci. Model Dev., 14, 2939–2957,Short summary
Up-to-date and accurate emission inventories for air pollutants are essential for understanding their role in the formation of tropospheric ozone and particulate matter, for anticipating pollution peaks and for identifying the key drivers that could help mitigate their emissions. Complementarily with bottom-up inventories, the system described here aims at updating and improving the knowledge on the high spatiotemporal variability of emissions of air pollutants.
James Hocking, Jérôme Vidot, Pascal Brunel, Pascale Roquet, Bruna Silveira, Emma Turner, and Cristina Lupu
Geosci. Model Dev., 14, 2899–2915,Short summary
RTTOV is a fast radiative transfer model for simulating passive satellite-based observations at visible, infrared, and microwave wavelengths. A core part of the model is a parameterisation of the absorption of radiation by the various gases present in the atmosphere. We present a new parameterisation that performs well compared to the existing one in terms of accuracy and can be developed further more easily. The new parameterisation is implemented in the latest release, RTTOV v13.0.
K. Wyat Appel, Jesse O. Bash, Kathleen M. Fahey, Kristen M. Foley, Robert C. Gilliam, Christian Hogrefe, William T. Hutzell, Daiwen Kang, Rohit Mathur, Benjamin N. Murphy, Sergey L. Napelenok, Christopher G. Nolte, Jonathan E. Pleim, George A. Pouliot, Havala O. T. Pye, Limei Ran, Shawn J. Roselle, Golam Sarwar, Donna B. Schwede, Fahim I. Sidi, Tanya L. Spero, and David C. Wong
Geosci. Model Dev., 14, 2867–2897,Short summary
This paper details the scientific updates in the recently released CMAQ version 5.3 (and v5.3.1) and also includes operational and diagnostic evaluations of CMAQv5.3.1 against observations and the previous version of the CMAQ (v5.2.1). This work was done to improve the underlying science in CMAQ. This article is used to inform the CMAQ modeling community of the updates to the modeling system and the expected change in model performance from these updates (versus the previous model version).
Ziyu Huang, Lei Zhong, Yaoming Ma, and Yunfei Fu
Geosci. Model Dev., 14, 2827–2841,Short summary
Spectral nudging is an effective dynamical downscaling method used to improve precipitation simulations of regional climate models (RCMs). However, the biases of the driving fields over the Tibetan Plateau (TP) would possibly introduce extra biases when spectral nudging is applied. The results show that the precipitation simulations were significantly improved when limiting the application of spectral nudging toward the potential temperature and water vapor mixing ratio over the TP.
Eve-Agnès Fiorentino, Henri Wortham, and Karine Sartelet
Geosci. Model Dev., 14, 2747–2780,Short summary
Indoor air quality (IAQ) is strongly influenced by reactivity with surfaces, which is called heterogeneous reactivity. To date, this reactivity is barely integrated into numerical models due to the strong uncertainties it is subjected to. In this work, an open-source IAQ model, called the H2I model, is developed to consider both gas-phase and heterogeneous reactivity and simulate indoor concentrations of inorganic compounds.
Yann Cohen, Virginie Marécal, Béatrice Josse, and Valérie Thouret
Geosci. Model Dev., 14, 2659–2689,Short summary
Assessing long-term chemistry–climate simulations with in situ and frequent observations near the tropopause is possible with the IAGOS commercial aircraft data set. This study presents a method that distributes the IAGOS data (ozone and CO) on a monthly model grid, limiting the impact of resolution for the evaluation of the modelled chemical fields. We applied it to the CCMI REF-C1SD simulation from the MOCAGE CTM and notably highlighted well-reproduced O3 behaviour in the lower stratosphere.
Vikram Khade, Saroja M. Polavarapu, Michael Neish, Pieter L. Houtekamer, Dylan B. A. Jones, Seung-Jong Baek, Tai-Long He, and Sylvie Gravel
Geosci. Model Dev., 14, 2525–2544,Short summary
A new modeling system has been developed at Environment and Climate Change Canada to ingest observations of carbon monoxide (CO) into a coupled weather and constituent transport model. We show that accounting for the uncertainty in surface flux leads to a better estimate of CO distributions. The benefit of assimilating observations from different simulated networks varies with region. This is the first step towards developing a state and flux estimation system for greenhouse gases.
Dongqi Lin, Basit Khan, Marwan Katurji, Leroy Bird, Ricardo Faria, and Laura E. Revell
Geosci. Model Dev., 14, 2503–2524,Short summary
We present an open-source toolbox WRF4PALM, which enables weather dynamics simulation within urban landscapes. WRF4PALM passes meteorological information from the popular Weather Research and Forecasting (WRF) model to the turbulence-resolving PALM model system 6.0. WRF4PALM can potentially extend the use of WRF and PALM with realistic boundary conditions to any part of the world. WRF4PALM will help study air pollution dispersion, wind energy prospecting, and high-impact wind forecasting.
Daniel M. Gilford
Geosci. Model Dev., 14, 2351–2369,Short summary
Potential intensity (PI) is a tropical cyclone's maximum speed limit given by modeling the storm as a thermal heat engine. pyPI is the first software package fully documenting the PI algorithm and translating it to Python. This study details/validates the underlying PI model and demonstrates its use in tropical cyclone intensity research. pyPI supports open science and transparency in the tropical meteorological community and is ideally suited for ongoing community development and improvement.
Mizuo Kajino, Makoto Deushi, Tsuyoshi Thomas Sekiyama, Naga Oshima, Keiya Yumimoto, Taichu Yasumichi Tanaka, Joseph Ching, Akihiro Hashimoto, Tetsuya Yamamoto, Masaaki Ikegami, Akane Kamada, Makoto Miyashita, Yayoi Inomata, Shin-ichiro Shima, Pradeep Khatri, Atsushi Shimizu, Hitoshi Irie, Kouji Adachi, Yuji Zaizen, Yasuhito Igarashi, Hiromasa Ueda, Takashi Maki, and Masao Mikami
Geosci. Model Dev., 14, 2235–2264,Short summary
This study compares performance of aerosol representation methods of the Japan Meteorological Agency's regional-scale nonhydrostatic meteorology–chemistry model (NHM-Chem). It indicates separate treatment of sea salt and dust in coarse mode and that of light-absorptive and non-absorptive particles in fine mode could provide accurate assessments on aerosol feedback processes.
Langwen Huang and David Topping
Geosci. Model Dev., 14, 2187–2203,Short summary
As our knowledge and understanding of atmospheric aerosol particle evolution and impact grows, designing community mechanistic models requires an ability to capture increasing chemical, physical and therefore numerical complexity. As the landscape of computing software and hardware evolves, it is important to profile the usefulness of emerging platforms in tackling this complexity. With this in mind we present JlBox v1.1, written in Julia.
Matthias Faust, Ralf Wolke, Steffen Münch, Roger Funk, and Kerstin Schepanski
Geosci. Model Dev., 14, 2205–2220,Short summary
Trajectory dispersion models are powerful and intuitive tools for tracing air pollution through the atmosphere. But the turbulent nature of the atmospheric boundary layer makes it challenging to provide accurate predictions near the surface. To overcome this, we propose an approach using wind and turbulence information at high temporal resolution. Finally, we demonstrate the strength of our approach in a case study on dust emissions from agriculture.
Jie Luo, Yongming Zhang, and Qixing Zhang
Geosci. Model Dev., 14, 2113–2126,Short summary
In this work, we developed a numerical method to investigate the effects of black carbon (BC) morphology on the estimation of brown carbon (BrC) absorption using the absorption Ångström exponent (AAE) method. We found that BC morphologies have significant impacts on the estimated BrC absorptions. Moreover, we have demonstrated under what conditions the AAE methods can provide good or bad estimations and explored the reasons for why the good or bad estimations were caused.
Georgia N. Theodoritsi, Giancarlo Ciarelli, and Spyros N. Pandis
Geosci. Model Dev., 14, 2041–2055,Short summary
Two schemes based on the volatility basis set were used for the simulation of biomass burning organic aerosol (bbOA) in the continental US. The first is the default scheme of the PMCAMx-SR model, and the second is a recently developed scheme based on laboratory experiments. The alternative bbOA scheme predicts much higher concentrations. The default scheme performed better during summer and fall, while the alternative scheme was a little better during spring.
Dana L. McGuffin, Yuanlong Huang, Richard C. Flagan, Tuukka Petäjä, B. Erik Ydstie, and Peter J. Adams
Geosci. Model Dev., 14, 1821–1839,Short summary
Atmospheric particle formation, emissions, and growth process rates are significant sources of uncertainty in predicting climate change. We aim to reduce that uncertainty by using measurements from several ground-based sites across Europe. We developed an estimation technique to adapt the governing process rates so model–measurement bias decays. The estimation framework developed has potential to improve model predictions while providing insight into the underlying atmospheric particle physics.
Harald Flentje, Ina Mattis, Zak Kipling, Samuel Rémy, and Werner Thomas
Geosci. Model Dev., 14, 1721–1751,Short summary
Atmospheric aerosols crucially impact air quality, climate and weather. Thus, global model forecasts of atmospheric constituents are published daily on the ECMWF website and are regularly verified by the CAMS service team. The IFS-AER model is largely able to reproduce observed 3-D distributions of the important particle types over Germany. The particle concentration is mostly captured within several tens of percent, but quantification of some specific processes still remains a challenge.
Edward C. Chan and Timothy M. Butler
Geosci. Model Dev. Discuss.,
Revised manuscript accepted for GMDShort summary
A large-eddy simulation based chemical transport model is implemented for an idealized street canyon. The dynamics of the model are evaluated using stationary measurements. A transient model run is also conducted over a 24-hour period, where variations of pollutant concentrations indicate dependence on emissions, background concentrations, and solar state. Comparison stationary model runs show changes in flow structures concentrations.
Jianhui Jiang, Imad El Haddad, Sebnem Aksoyoglu, Giulia Stefenelli, Amelie Bertrand, Nicolas Marchand, Francesco Canonaco, Jean-Eudes Petit, Olivier Favez, Stefania Gilardoni, Urs Baltensperger, and André S. H. Prévôt
Geosci. Model Dev., 14, 1681–1697,Short summary
We developed a box model with a volatility basis set to simulate organic aerosol (OA) from biomass burning and optimized the vapor-wall-loss-corrected OA yields with a genetic algorithm. The optimized parameterizations were then implemented in the air quality model CAMx v6.5. Comparisons with ambient measurements indicate that the vapor-wall-loss-corrected parameterization effectively improves the model performance in predicting OA, which reduced the mean fractional bias from −72.9 % to −1.6 %.
Oliver Branch, Thomas Schwitalla, Marouane Temimi, Ricardo Fonseca, Narendra Nelli, Michael Weston, Josipa Milovac, and Volker Wulfmeyer
Geosci. Model Dev., 14, 1615–1637,Short summary
Effective numerical weather forecasting is vital in arid regions like the United Arab Emirates where extreme events like heat waves, flash floods, and dust storms are becoming more severe. This study employs a high-resolution simulation with the WRF-NOAHMP model, and the output is compared with seasonal observation data from 50 weather stations. This type of verification is vital to identify model deficiencies and improve forecasting systems for arid regions.
Lukas Hubert Leufen, Felix Kleinert, and Martin G. Schultz
Geosci. Model Dev., 14, 1553–1574,Short summary
MLAir provides a coherent end-to-end structure for a typical time series analysis workflow using machine learning (ML). MLAir is adaptable to a wide range of ML use cases, focusing in particular on deep learning. The user has a free hand with the ML model itself and can select from different methods during preprocessing, training, and postprocessing. MLAir offers tools to track the experiment conduction, documents necessary ML parameters, and creates a variety of publication-ready plots.
Davide Ori, Leonie von Terzi, Markus Karrer, and Stefan Kneifel
Geosci. Model Dev., 14, 1511–1531,Short summary
Snowflakes have very complex shapes, and modeling their properties requires vast computing power. We produced a large number of realistic snowflakes and modeled their average properties by leveraging their fractal structure. Our approach allows modeling the properties of big ensembles of snowflakes, taking into account their natural variability, at a much lower cost. This enables the usage of remote sensing instruments, such as radars, to monitor the evolution of clouds and precipitation.
Jaydeep Singh, Narendra Singh, Narendra Ojha, Amit Sharma, Andrea Pozzer, Nadimpally Kiran Kumar, Kunjukrishnapillai Rajeev, Sachin S. Gunthe, and V. Rao Kotamarthi
Geosci. Model Dev., 14, 1427–1443,Short summary
Atmospheric models often have limitations in simulating the geographically complex and climatically important central Himalayan region. In this direction, we have performed regional modeling at high resolutions to improve the simulation of meteorology and dynamics through a better representation of the topography. The study has implications for further model applications to investigate the effects of anthropogenic pressure over the Himalaya.
Beatrice Giacomini and Marco G. Giometto
Geosci. Model Dev., 14, 1409–1426,Short summary
The present work evaluates the suitability of an important class of second-order finite-volume solvers for the large-eddy simulation of atmospheric boundary- layer flows. Results show that these solvers do not capture the dominant mechanisms responsible for momentum transport in boundary layers, leading to a misprediction of relevant flow statistics and to an enhanced sensitivity of the solution to variations in grid resolution.
Michael Weger, Oswald Knoth, and Bernd Heinold
Geosci. Model Dev., 14, 1469–1492,Short summary
A new numerical air-quality transport model for cities is presented, in which buildings are described diffusively. The used diffusive-obstacles approach helps to reduce the computational costs for high-resolution simulations as the grid spacing can be more coarse than in traditional approaches. The research which led to this model development was primarily motivated by the need for a computationally feasible downscaling tool for urban wind and pollution fields from meteorological model output.
Yuefei Zeng, Alberto de Lozar, Tijana Janjic, and Axel Seifert
Geosci. Model Dev., 14, 1295–1307,Short summary
A new integrated mass-flux adjustment filter is introduced and examined with an idealized setup for convective-scale radar data assimilation. It is found that the new filter slightly reduces the accuracy of background and analysis states; however, it preserves the main structure of cold pools and primary mesocyclone properties of supercells. More importantly, it successfully diminishes the imbalance in the analysis considerably and improves the forecasts.
Pieter De Meutter, Ian Hoffman, and Kurt Ungar
Geosci. Model Dev., 14, 1237–1252,Short summary
Inverse atmospheric transport modelling is an important tool in several disciplines. However, the specification of atmospheric transport model error remains challenging. In this paper, we employ a state-of-the-art ensemble technique combined with a state-of-the-art Bayesian inference algorithm to infer point sources. Our research helps to fill the gap in our understanding of model error in the context of inverse atmospheric transport modelling.
Qi Tang, Michael J. Prather, Juno Hsu, Daniel J. Ruiz, Philip J. Cameron-Smith, Shaocheng Xie, and Jean-Christophe Golaz
Geosci. Model Dev., 14, 1219–1236,
Basit Khan, Sabine Banzhaf, Edward C. Chan, Renate Forkel, Farah Kanani-Sühring, Klaus Ketelsen, Mona Kurppa, Björn Maronga, Matthias Mauder, Siegfried Raasch, Emmanuele Russo, Martijn Schaap, and Matthias Sühring
Geosci. Model Dev., 14, 1171–1193,Short summary
An atmospheric chemistry model has been implemented in the microscale PALM model system 6.0. This article provides a detailed description of the model, its structure, input requirements, various features and limitations. Several pre-compiled ready-to-use chemical mechanisms are included in the chemistry model code; however, users can also easily implement other mechanisms. A case study is presented to demonstrate the application of the new chemistry model in the urban environment.
Mohsen Moradi, Benjamin Dyer, Amir Nazem, Manoj K. Nambiar, M. Rafsan Nahian, Bruno Bueno, Chris Mackey, Saeran Vasanthakumar, Negin Nazarian, E. Scott Krayenhoff, Leslie K. Norford, and Amir A. Aliabadi
Geosci. Model Dev., 14, 961–984,Short summary
The Vertical City Weather Generator (VCWG) is an urban microclimate model developed to predict temporal and vertical variation of potential temperature, wind speed, and specific humidity. VCWG is forced by climate variables at a nearby rural site and coupled to radiation and building energy models. VCWG is evaluated against field observations of the BUBBLE campaign. It is run under exploration mode to assess its performance given urban characteristics, seasonal variations, and climate zones.
Claudio A. Belis, Guido Pirovano, Maria Gabriella Villani, Giuseppe Calori, Nicola Pepe, and Jean Philippe Putaud
Geosci. Model Dev. Discuss.,
Revised manuscript accepted for GMDShort summary
The study presents an in-depth analysis of the implications that using different CTM source apportionment approaches (tagged species and brute force) have on the source allocation of secondary inorganic aerosol, an important component of PM10 and PM2.5. A set of runs combining different emissions levels and models were carried out aiming to describe the situations in which strong non-linearity may lead the two approaches to deliver different results and when they are expected to be comparable.
Loredana G. Suciu, Robert J. Griffin, and Caroline A. Masiello
Geosci. Model Dev., 14, 907–921,Short summary
Understanding the atmospheric degradation of biomass burning tracers such as levoglucosan is essential to decreasing uncertainties in the role of biomass burning in air quality, carbon cycling and paleoclimate. Using a 0-D modeling approach and numerical chamber simulations, we found that the multiphase atmospheric degradation of levoglucosan occurs over timescales of hours to days, can form secondary organic aerosols and affects other key tropospheric gases, such as ozone.
Agrawal, S., Barrington, L., Bromberg, C., Burge, J., Gazen, C., and Hickey, J.: Machine Learning for Precipitation Nowcasting from Radar Images [cs, stat], arXiv [preprint], arXiv:1912.12132, December 2019.
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.
Adrianto, I., Trafalis, T. B., and Lakshmanan, V.: Support vector machines for spatiotemporal tornado prediction, Int. J. Gen. Syst., 38, 759–776, https://doi.org/10.1080/03081070601068629, 2009.
Chandra, R. and Kapoor, A.: Bayesian neural multi-source transfer learning, Neurocomputing, 378, 54–64, https://doi.org/10.1016/j.neucom.2019.10.042, 2020.
Chandra, R., Cripps, S., Butterworth, N., and Muller, R. D.: Precipitation reconstruction from climate-sensitive lithologies using Bayesian machine learning, Environ. Model. Softw., 139, 105002, https://doi.org/10.1016/j.envsoft.2021.105002, 2021.
Chen, L., Cao, Y., Ma, L., and Zhang, J.: A Deep Learning-Based Methodology for Precipitation Nowcasting With Radar, Earth Space Sci., 7, e2019EA000812, https://doi.org/10.1029/2019EA000812, 2020.
Foresti, L., Sideris, I. V., Nerini, D., Beusch, L., and Germann, U.: Using a 10-Year Radar Archive for Nowcasting Precipitation Growth and Decay: A Probabilistic Machine Learning Approach, Weather Forecast., 34, 1547–1569, https://doi.org/10.1175/WAF-D-18-0206.1, 2019.
Fox, N. I. and Wikle, C. K.: A Bayesian Quantitative Precipitation Nowcast Scheme, Weather Forecast., 20, 264–275, https://doi.org/10.1175/WAF845.1, 2005.
Gagne, D. J., McGovern, A., and Brotzge, J.: Classification of Convective Areas Using Decision Trees, J. Atmospheric Ocean. Tech., 26, 1341–1353, https://doi.org/10.1175/2008JTECHA1205.1, 2009.
Hill, A. J., Herman, G. R., and Schumacher, R. S.: Forecasting Severe Weather with Random Forests, Mon. Weather Rev., 148, 2135–2161, https://doi.org/10.1175/MWR-D-19-0344.1, 2020.
Huang, B.-J., Tseng, T.-H., and Tsai, C.-M.: Rainfall Estimation in Weather Radar Using Support Vector Machine, in: Intelligent Information and Database Systems, vol. 9011, edited by: Nguyen, N. T., Trawiński, B., and Kosala, R., Springer International Publishing, Cham, 583–592, https://doi.org/10.1007/978-3-319-15702-3_56, 2015.
Hwang, J., Orenstein, P., Cohen, J., Pfeiffer, K., and Mackey, L.: Improving Subseasonal Forecasting in the Western U.S. with Machine Learning [cs, stat], arXiv [preprint], arXiv:1809.07394, May 2019.
Ho, J., Kalchbrenner, N., Weissenborn, D., and Salimans, T.: Axial Attention in Multidimensional Transformers, arXiv [preprint], arXiv:1912.12180, December 2019.
Kuligowski, R. J. and Barros, A. P.: Localized Precipitation Forecasts from a Numerical Weather Prediction Model Using Artificial Neural Networks, Weather Forecast., 13, 1194–1204, https://doi.org/10.1175/1520-0434(1998)013<1194:LPFFAN>2.0.CO;2, 1998.
Lakshmanan, V., Karstens, C., Krause, J., and Tang, L.: Quality Control of Weather Radar Data Using Polarimetric Variables, J. Atmos. Ocean. Tech., 31, 1234–1249, https://doi.org/10.1175/JTECH-D-13-00073.1, 2014.
LeCun, Y., Bengio, Y., and Hinton, G.: Deep learning, Nature, 521, 436–444, https://doi.org/10.1038/nature14539, 2015.
Li, D.: MSDM v1.0: A machine learning model for precipitation nowcasting over East China using multisource data, Zenodo, https://doi.org/10.5281/zenodo.4749183, 2021.
Mao, Y. and Sorteberg, A.: Improving Radar-Based Precipitation Nowcasts with Machine Learning Using an Approach Based on Random Forest, Weather Forecast., 35, 2461–2478, https://doi.org/10.1175/WAF-D-20-0080.1, 2020.
Ran, Y., Wang, H., Tian, L., Wu, J., and Li, X.: Precipitation cloud identification based on faster-RCNN for Doppler weather radar, EURASIP J. Wirel. Commun. Netw., 2021, 19, https://doi.org/10.1186/s13638-021-01896-5, 2021.
Ravuri, S., Lenc, K., Willson, M., Kangin, D., Lam, R., Mirowski, P., Athanassiadou, M., Kashem, S., Madge, S., Prudden, R., Mandhane, A., Clark, A., Brock, A., Simonyan, K., Hadsell, R., Robinson, N., Clancy, E., and Mohamed, S.: Skillful Precipitation Nowcasting using Deep Generative Models of Radar, arXiv [preprint], arXiv:2104.00954, April 2021.
Ren, S., He, K., Girshick, R., and Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks [cs.CV], arXiv [preprint], arXiv:1506.01497v3, January 2016.
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, vol. 9351, edited by: Navab, N., Hornegger, J., Wells, W. M., and Frangi, A. F., Springer International Publishing, Cham, 234–241, https://doi.org/10.1007/978-3-319-24574-4_28, 2015.
Sadeghi, M., Asanjan, A. A., Faridzad, M., Nguyen, P., Hsu, K., Sorooshian, S., and Braithwaite, D.: PERSIANN-CNN: Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Convolutional Neural Networks, J. Hydrometeorol., 20, 2273–2289, https://doi.org/10.1175/JHM-D-19-0110.1, 2019.
Shi, X., Chen, Z., Wang, H., Yeung, D.-Y., Wong, W., and Woo, W.: Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting, arXiv [preprint], arXiv:1506.04214, September 2015.
Shi, X., Gao, Z., Lausen, L., Wang, H., Yeung, D.-Y., Wong, W., and Woo, W.: Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model, arXiv [preprint], arXiv:1706.03458 October 2017.
Sønderby, C. K., Espeholt, L., Heek, J., Dehghani, M., Oliver, A., Salimans, T., Agrawal, S., Hickey, J., and Kalchbrenner, N.: MetNet: A Neural Weather Model for Precipitation Forecasting, arXiv [preprint], arXiv:2003.12140, March 2020.
Tao, Y., Gao, X., Hsu, K., Sorooshian, S., and Ihler, A.: A Deep Neural Network Modeling Framework to Reduce Bias in Satellite Precipitation Products, J. Hydrometeorol., 17, 931–945, https://doi.org/10.1175/JHM-D-15-0075.1, 2016.
Tao, Y., Gao, X., Ihler, A., Sorooshian, S., and Hsu, K.: Precipitation Identification with Bispectral Satellite Information Using Deep Learning Approaches, J. Hydrometeorol., 18, 1271–1283, https://doi.org/10.1175/JHM-D-16-0176.1, 2017.
Todini, E.: A Bayesian technique for conditioning radar precipitation estimates to rain-gauge measurements, Hydrol. Earth Syst. Sci., 5, 187–199, https://doi.org/10.5194/hess-5-187-2001, 2001.
Tran, Q.-K. and Song, S.: Computer Vision in Precipitation Nowcasting: Applying Image Quality Assessment Metrics for Training Deep Neural Networks, Atmosphere, 10, 244, https://doi.org/10.3390/atmos10050244, 2019.
Veillette, M. S., Samsi, S., and Mattioli, C. J.: SEVIR: A Storm Event Imagery Dataset for Deep Learning Applications in Radar and Satellite Meteorology, 34th Conference on Neural Information Processing Systems (NeurIPS 2020), Vancouver, Canada, 6-12 December 2020, 11 pp., 2020.
Vislocky, R. L. and Young, G. S.: The use of perfect prog forecasts to improve model output statistics forecasts of precipitation probability, Weather Forecast., 4, 202–209, 1989.
Wang, Y., Gao, Z., Long, M., Wang, J., and Yu, P. S.: PredRNN: Towards A Resolution of the Deep-in-Time Dilemma in Spatiotemporal Predictive Learning [cs, stat], arXiv [preprint], arXiv:1804.06300, November 2018.
Wang, Y., Jiang, L., Yang, M.-H., Li, L.-J., Long, M., and Fei-Fei, L.: Eidetic 3D Lstm: A Model For Video Prediction And Beyond, International Conference on Learning Representations, Ernest N. Morial Convention Center, New Orleans, 6–9 May 2019, 14 pp., 2019a.
Wang, Y., Zhang, J., Zhu, H., Long, M., Wang, J., and Yu, P. S.: Memory In Memory: A Predictive Neural Network for Learning Higher-Order Non-Stationarity from Spatiotemporal Dynamics [cs, stat], arXiv [preprint], arXiv:1811.07490, April 2019b.
Wang, Z., Bovik, A. C., Sheikh, H. R., and Simoncelli, E. P.: Image Quality Assessment: From Error Visibility to Structural Similarity, IEEE Trans. Image Process., 13, 600–612, https://doi.org/10.1109/TIP.2003.819861, 2004.
Wei, C.-C.: Wavelet Support Vector Machines for Forecasting Precipitation in Tropical Cyclones: Comparisons with GSVM, Regression, and MM5, Weather Forecast., 27, 438–450, https://doi.org/10.1175/WAF-D-11-00004.1, 2012.
Woo, W. and Wong, W.: Operational Application of Optical Flow Techniques to Radar-Based Rainfall Nowcasting, Atmosphere, 8, 48, https://doi.org/10.3390/atmos8030048, 2017.
Wu, Y., Tang, Y., Yang, X., Zhang, W., and Zhang, G.: Graph Convolutional Regression Networks for Quantitative Precipitation Estimation, IEEE Geosci. Remote Sensing Lett., 1–5, https://doi.org/10.1109/LGRS.2020.2994087, 2020.
Yan, B.-Y., Yang, C., Chen, F. Takeda, K. and Wang, C.: FDNet: A Deep Learning Approach with Two Parallel Cross Encoding Pathways for Precipitation Nowcasting [cs.LG], arXiv [preprint], arXiv:2105.02585, 22 pp., 2021.
Yang, L. and Deng, M.: Based on k-Means and Fuzzy k-Means Algorithm Classification of Precipitation, in: 2010 International Symposium on Computational Intelligence and Design (ISCID), Hangzhou, China, 29–31 October 2010, 218–221, https://doi.org/10.1109/ISCID.2010.72, 2010.
Yo, T.-S., Su, S.-H., Chu, J.-L., Chang, C.-W., and Kuo, H.-C.: A deep learning approach to radar-based QPE, Earth Space Sci., 8, e2020EA001340, https://doi. org/10.1029/2020EA001340, 2021.
Yu, B., Yin, H., and Zhu, Z.: Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting [cs.LG], arXiv [preprint], arXiv:1709.04875, July 2018.
In the daily weather forecast business, numerical weather prediction is mainly used to forecast precipitation, but its performance for nowcasting tasks within 0–2 h is very poor. Hence, we hope to use machine learning to improve the accuracy and resolution of quantitative precipitation nowcasting (QPN) tasks. Previous works focused on the extrapolation of radar echo without using abundant meteorological data. Therefore, we designed a model using three kinds of data for QPN in eastern china.
In the daily weather forecast business, numerical weather prediction is mainly used to forecast...