Articles | Volume 15, issue 17
Geosci. Model Dev., 15, 6891–6917, 2022
https://doi.org/10.5194/gmd-15-6891-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Geosci. Model Dev., 15, 6891–6917, 2022
https://doi.org/10.5194/gmd-15-6891-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
12 Sep 2022
Model evaluation paper
| 12 Sep 2022
Assessment of the data assimilation framework for the Rapid Refresh Forecast System v0.1 and impacts on forecasts of a convective storm case study
Ivette H. Banos et al.
Related authors
No articles found.
Cheng-Hsuan Lu, Quanhua Liu, Shih-Wei Wei, Benjamin T. Johnson, Cheng Dang, Patrick G. Stegmann, Dustin Grogan, Guoqing Ge, Ming Hu, and Michael Lueken
Geosci. Model Dev., 15, 1317–1329, https://doi.org/10.5194/gmd-15-1317-2022, https://doi.org/10.5194/gmd-15-1317-2022, 2022
Short summary
Short summary
This article is a technical note on the aerosol absorption and scattering calculations of the Community Radiative Transfer Model (CRTM) v2.2 and v2.3. It also provides guidance for prospective users of the CRTM aerosol option and Gridpoint Statistical Interpolation (GSI) aerosol-aware radiance assimilation. Scientific aspects of aerosol-affected BT in atmospheric data assimilation are also briefly discussed.
Luiz F. Sapucci, Luiz A. T. Machado, Eniuce Menezes de Souza, and Thamiris B. Campos
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2016-378, https://doi.org/10.5194/amt-2016-378, 2016
Revised manuscript not accepted
Short summary
Short summary
This study employs precipitable water vapor from a Global Positioning System (GPS-PWV) signal, in high time resolution, to be used as precursor information of intense rainfall events. A typical jump in the GPS-PWV values before the occurrence of more intense rainfalls has been found, it is probably related to humid convergence occurring before intense rainfall events. The results from this manuscript create the physical basis for further development of a nowcasting tool in future studies.
Related subject area
Atmospheric sciences
Downscaling atmospheric chemistry simulations with physically consistent deep learning
A machine learning methodology for the generation of a parameterization of the hydroxyl radical
Large-eddy simulations with ClimateMachine v0.2.0: a new open-source code for atmospheric simulations on GPUs and CPUs
Hybrid ensemble-variational data assimilation in ABC-DA within a tropical framework
OpenIFS/AC: atmospheric chemistry and aerosol in OpenIFS 43r3
Simulations of aerosol pH in China using WRF-Chem (v4.0): sensitivities of aerosol pH and its temporal variations during haze episodes
A daily highest air temperature estimation method and spatial–temporal changes analysis of high temperature in China from 1979 to 2018
TransClim (v1.0): a chemistry–climate response model for assessing the effect of mitigation strategies for road traffic on ozone
A description of the first open-source community release of MISTRA-v9.0: a 0D/1D atmospheric boundary layer chemistry model
Integrated Methane Inversion (IMI 1.0): a user-friendly, cloud-based facility for inferring high-resolution methane emissions from TROPOMI satellite observations
Computationally efficient methods for large-scale atmospheric inverse modeling
Improving the joint estimation of CO2 and surface carbon fluxes using a constrained ensemble Kalman filter in COLA (v1.0)
RAP-Net: Region Attention Predictive Network for precipitation nowcasting
Effects of point source emission heights in WRF–STILT: a step towards exploiting nocturnal observations in models
uDALES 1.0: a large-eddy simulation model for urban environments
Development and evaluation of the Aerosol Forecast Member in the National Center for Environment Prediction (NCEP)'s Global Ensemble Forecast System (GEFS-Aerosols v1)
Assimilation of GPM-retrieved ocean surface meteorology data for two snowstorm events during ICE-POP 2018
A multi-pollutant and multi-sectorial approach to screening the consistency of emission inventories
Evaluation of a forest parameterization to improve boundary layer flow simulations over complex terrain. A case study using WRF-LES V4.0.1
Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): a protocol for investigating the role of stratospheric polar vortex disturbances in subseasonal to seasonal forecasts
Variational inverse modeling within the Community Inversion Framework v1.1 to assimilate δ13C(CH4) and CH4: a case study with model LMDz-SACS
The Comprehensive Automobile Research System (CARS) – a Python-based automobile emissions inventory model
Validation of turbulent heat transfer models against eddy covariance flux measurements over a seasonally ice-covered lake
Regional evaluation of the performance of the global CAMS chemical modeling system over the United States (IFS cycle 47r1)
Order of magnitude wall time improvement of variational methane inversions by physical parallelization: a demonstration using TM5-4DVAR
Simulated microphysical properties of winter storms from bulk-type microphysics schemes and their evaluation in the Weather Research and Forecasting (v4.1.3) model during the ICE-POP 2018 field campaign
A novel method for objective identification of 3-D potential vorticity anomalies
Multiple same-level and telescoping nesting in GFDL's dynamical core
Global, high-resolution mapping of tropospheric ozone – explainable machine learning and impact of uncertainties
Assessing the roles emission sources and atmospheric processes play in simulating δ15N of atmospheric NOx and NO3− using CMAQ (version 5.2.1) and SMOKE (version 4.6)
The Regional Coupled Suite (RCS-IND1): application of a flexible regional coupled modelling framework to the Indian region at kilometre scale
A comparative analysis for a deep learning model (hyDL-CO v1.0) and Kalman filter to predict CO concentrations in China
Earth System Model Aerosol–Cloud Diagnostics (ESMAC Diags) package, version 1: assessing E3SM aerosol predictions using aircraft, ship, and surface measurements
Effects of vertical ship exhaust plume distributions on urban pollutant concentration – a sensitivity study with MITRAS v2.0 and EPISODE-CityChem v1.4
An emergency response model for the formation and dispersion of plumes originating from major fires (BUOYANT v4.20)
Description and evaluation of the community aerosol dynamics model MAFOR v2.0
Modeling the high-mercury wet deposition in the southeastern US with WRF-GC-Hg v1.0
Development of a deep neural network for predicting 6 h average PM2.5 concentrations up to 2 subsequent days using various training data
Chemistry Across Multiple Phases (CAMP) version 1.0: an integrated multiphase chemistry model
An aerosol vertical data assimilation system (NAQPMS-PDAF v1.0): development and application
Earth system modeling of mercury using CESM2 – Part 1: Atmospheric model CAM6-Chem/Hg v1.0
MultilayerPy (v1.0): A Python-based framework for building, running and optimising kinetic multi-layer models of aerosols and films
Conservation laws in a neural network architecture: enforcing the atom balance of a Julia-based photochemical model (v0.2.0)
Repeatable high-resolution statistical downscaling through deep learning
On the application and grid-size sensitivity of the urban dispersion model CAIRDIO v2.0 under real city weather conditions
Development and evaluation of an advanced National Air Quality Forecasting Capability using the NOAA Global Forecast System version 16
Estimating aerosol emission from SPEXone on the NASA PACE mission using an ensemble Kalman smoother: observing system simulation experiments (OSSEs)
An ensemble-based statistical methodology to detect differences in weather and climate model executables
A preliminary evaluation of WRF (ARW v4.1.1)/DART (Manhattan release v9.8.0)-RTTOV (v12.3) in assimilating satellite visible radiance data for a cyclone case
Multiphase processes in the EC-Earth model and their relevance to the atmospheric oxalate, sulfate, and iron cycles
Andrew Geiss, Sam J. Silva, and Joseph C. Hardin
Geosci. Model Dev., 15, 6677–6694, https://doi.org/10.5194/gmd-15-6677-2022, https://doi.org/10.5194/gmd-15-6677-2022, 2022
Short summary
Short summary
This work demonstrates the use of modern machine learning techniques to enhance the resolution of atmospheric chemistry simulations. We evaluate the schemes for an 8 x 10 increase in resolution and find that they perform substantially better than conventional methods. Methods are introduced to target machine learning methods towards this type of problem, most notably by ensuring they do not break known physical constraints.
Daniel C. Anderson, Melanie B. Follette-Cook, Sarah A. Strode, Julie M. Nicely, Junhua Liu, Peter D. Ivatt, and Bryan N. Duncan
Geosci. Model Dev., 15, 6341–6358, https://doi.org/10.5194/gmd-15-6341-2022, https://doi.org/10.5194/gmd-15-6341-2022, 2022
Short summary
Short summary
The hydroxyl radical (OH) is the most important chemical in the atmosphere for removing certain pollutants, including methane, the second-most-important greenhouse gas. We present a methodology to create an easily modifiable parameterization that can calculate OH concentrations in a computationally efficient way. The parameterization, which predicts OH within 5 %, can be integrated into larger climate models to allow for calculation of the interactions between OH, methane, and other chemicals.
Akshay Sridhar, Yassine Tissaoui, Simone Marras, Zhaoyi Shen, Charles Kawczynski, Simon Byrne, Kiran Pamnany, Maciej Waruszewski, Thomas H. Gibson, Jeremy E. Kozdon, Valentin Churavy, Lucas C. Wilcox, Francis X. Giraldo, and Tapio Schneider
Geosci. Model Dev., 15, 6259–6284, https://doi.org/10.5194/gmd-15-6259-2022, https://doi.org/10.5194/gmd-15-6259-2022, 2022
Short summary
Short summary
ClimateMachine is a new open-source Julia-language atmospheric modeling code. We describe its limited-area configuration and the model equations, and we demonstrate applicability through benchmark problems, including atmospheric flow in the shallow cumulus regime. We show that the discontinuous Galerkin numerics and model equations allow global conservation of key variables (up to sources and sinks). We assess CPU strong scaling and GPU weak scaling to show its suitability for large simulations.
Joshua Chun Kwang Lee, Javier Amezcua, and Ross Noel Bannister
Geosci. Model Dev., 15, 6197–6219, https://doi.org/10.5194/gmd-15-6197-2022, https://doi.org/10.5194/gmd-15-6197-2022, 2022
Short summary
Short summary
In this article, we implement a novel data assimilation method for the ABC–DA system which combines traditional data assimilation approaches in a hybrid approach. We document the technical development and test the hybrid approach in idealised experiments within a tropical framework of the ABC–DA system. Our findings indicate that the hybrid approach outperforms individual traditional approaches. Its potential benefits have been highlighted and should be explored further within this framework.
Vincent Huijnen, Philippe Le Sager, Marcus O. Köhler, Glenn Carver, Samuel Rémy, Johannes Flemming, Simon Chabrillat, Quentin Errera, and Twan van Noije
Geosci. Model Dev., 15, 6221–6241, https://doi.org/10.5194/gmd-15-6221-2022, https://doi.org/10.5194/gmd-15-6221-2022, 2022
Short summary
Short summary
We report on the first implementation of atmospheric chemistry and aerosol as part of the OpenIFS model, based on the CAMS global model. We give an overview of the model and evaluate two reference model configurations, with and without the stratospheric chemistry extension, against a variety of observational datasets. This OpenIFS version with atmospheric composition components is open to the scientific user community under a standard OpenIFS license.
Xueyin Ruan, Chun Zhao, Rahul A. Zaveri, Pengzhen He, Xinming Wang, Jingyuan Shao, and Lei Geng
Geosci. Model Dev., 15, 6143–6164, https://doi.org/10.5194/gmd-15-6143-2022, https://doi.org/10.5194/gmd-15-6143-2022, 2022
Short summary
Short summary
Accurate prediction of aerosol pH in chemical transport models is essential to aerosol modeling. This study examines the performance of the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) on aerosol pH predictions and the sensitivities to emissions of nonvolatile cations and NH3, aerosol-phase state assumption, and heterogeneous sulfate production. Temporal evolution of aerosol pH during haze cycles in Beijing and the driving factors are also presented and discussed.
Ping Wang, Kebiao Mao, Fei Meng, Zhihao Qin, Shu Fang, and Sayed M. Bateni
Geosci. Model Dev., 15, 6059–6083, https://doi.org/10.5194/gmd-15-6059-2022, https://doi.org/10.5194/gmd-15-6059-2022, 2022
Short summary
Short summary
In order to obtain the key parameters of high-temperature spatial–temporal variation analysis, this study proposed a daily highest air temperature (Tmax) estimation frame to build a Tmax dataset in China from 1979 to 2018. We found that the annual and seasonal mean Tmax in most areas of China showed an increasing trend. The abnormal temperature changes mainly occurred in El Nin~o years or La Nin~a years. IOBW had a stronger influence on China's warming events than other factors.
Vanessa Simone Rieger and Volker Grewe
Geosci. Model Dev., 15, 5883–5903, https://doi.org/10.5194/gmd-15-5883-2022, https://doi.org/10.5194/gmd-15-5883-2022, 2022
Short summary
Short summary
Road traffic emissions of nitrogen oxides, volatile organic compounds and carbon monoxide produce ozone in the troposphere and thus influence Earth's climate. To assess the ozone response to a broad range of mitigation strategies for road traffic, we developed a new chemistry–climate response model called TransClim. It is based on lookup tables containing climate–response relations and thus is able to quickly determine the climate response of a mitigation option.
Josué Bock, Jan Kaiser, Max Thomas, Andreas Bott, and Roland von Glasow
Geosci. Model Dev., 15, 5807–5828, https://doi.org/10.5194/gmd-15-5807-2022, https://doi.org/10.5194/gmd-15-5807-2022, 2022
Short summary
Short summary
MISTRA-v9.0 is an atmospheric boundary layer chemistry model. The model includes a detailed particle description with regards to the microphysics, gas–particle interactions, and liquid phase chemistry within particles. Version 9.0 is the first release of MISTRA as an open-source community model. This paper presents a thorough description of the model characteristics and components. We show some examples of simulations reproducing previous studies with MISTRA with good consistency.
Daniel J. Varon, Daniel J. Jacob, Melissa Sulprizio, Lucas A. Estrada, William B. Downs, Lu Shen, Sarah E. Hancock, Hannah Nesser, Zhen Qu, Elise Penn, Zichong Chen, Xiao Lu, Alba Lorente, Ashutosh Tewari, and Cynthia A. Randles
Geosci. Model Dev., 15, 5787–5805, https://doi.org/10.5194/gmd-15-5787-2022, https://doi.org/10.5194/gmd-15-5787-2022, 2022
Short summary
Short summary
Reducing atmospheric methane emissions is critical to slow near-term climate change. Globally surveying satellite instruments like the TROPOspheric Monitoring Instrument (TROPOMI) have unique capabilities for monitoring atmospheric methane around the world. Here we present a user-friendly cloud-computing tool that enables researchers and stakeholders to quantify methane emissions across user-selected regions of interest using TROPOMI satellite observations.
Taewon Cho, Julianne Chung, Scot M. Miller, and Arvind K. Saibaba
Geosci. Model Dev., 15, 5547–5565, https://doi.org/10.5194/gmd-15-5547-2022, https://doi.org/10.5194/gmd-15-5547-2022, 2022
Short summary
Short summary
Atmospheric inverse modeling describes the process of estimating greenhouse gas fluxes or air pollution emissions at the Earth's surface using observations of these gases collected in the atmosphere. The launch of new satellites, the expansion of surface observation networks, and a desire for more detailed maps of surface fluxes have yielded numerous computational and statistical challenges. This article describes computationally efficient methods for large-scale atmospheric inverse modeling.
Zhiqiang Liu, Ning Zeng, Yun Liu, Eugenia Kalnay, Ghassem Asrar, Bo Wu, Qixiang Cai, Di Liu, and Pengfei Han
Geosci. Model Dev., 15, 5511–5528, https://doi.org/10.5194/gmd-15-5511-2022, https://doi.org/10.5194/gmd-15-5511-2022, 2022
Short summary
Short summary
We described the application of a constrained ensemble Kalman filter (CEnKF) in a joint CO2 and surface carbon fluxes estimation study. By assimilating the pseudo-surface and OCO-2 observations, the annual global flux estimation is significantly biased without mass conservation. With the additional CEnKF process, the CO2 mass is strictly constrained, and the estimation of annual fluxes is significantly improved.
Zheng Zhang, Chuyao Luo, Shanshan Feng, Rui Ye, Yunming Ye, and Xutao Li
Geosci. Model Dev., 15, 5407–5419, https://doi.org/10.5194/gmd-15-5407-2022, https://doi.org/10.5194/gmd-15-5407-2022, 2022
Short summary
Short summary
In this paper, we develop a model to predict radar echo sequences and apply it in the precipitation nowcasting field. Different from existing models, we propose two new attention modules. By introducing them, the performance of RAP-Net outperforms other models, especially in those regions with moderate and heavy rainfall. Considering that these regions cause more threats to human activities, the research in our work is significant for preventing natural disasters caused by heavy rainfall.
Fabian Maier, Christoph Gerbig, Ingeborg Levin, Ingrid Super, Julia Marshall, and Samuel Hammer
Geosci. Model Dev., 15, 5391–5406, https://doi.org/10.5194/gmd-15-5391-2022, https://doi.org/10.5194/gmd-15-5391-2022, 2022
Short summary
Short summary
We show that the default representation of point source emissions in WRF–STILT leads to large overestimations when modelling fossil fuel CO2 concentrations for a 30 m high observation site during stable atmospheric conditions. We therefore introduce a novel point source modelling approach in WRF-STILT that takes into account their effective emission heights and results in a much better agreement with observations.
Ivo Suter, Tom Grylls, Birgit S. Sützl, Sam O. Owens, Chris E. Wilson, and Maarten van Reeuwijk
Geosci. Model Dev., 15, 5309–5335, https://doi.org/10.5194/gmd-15-5309-2022, https://doi.org/10.5194/gmd-15-5309-2022, 2022
Short summary
Short summary
Cities are increasingly moving to the fore of climate and air quality research due to their central role in the population’s health and well-being, while suitable models remain scarce. This article describes the development of a new urban LES model, which allows examining the effects of various processes, infrastructure and vegetation on the local climate and air quality. Possible applications are demonstrated and a comparison to an experiment is shown.
Li Zhang, Raffaele Montuoro, Stuart A. McKeen, Barry Baker, Partha S. Bhattacharjee, Georg A. Grell, Judy Henderson, Li Pan, Gregory J. Frost, Jeff McQueen, Rick Saylor, Haiqin Li, Ravan Ahmadov, Jun Wang, Ivanka Stajner, Shobha Kondragunta, Xiaoyang Zhang, and Fangjun Li
Geosci. Model Dev., 15, 5337–5369, https://doi.org/10.5194/gmd-15-5337-2022, https://doi.org/10.5194/gmd-15-5337-2022, 2022
Short summary
Short summary
The NOAA’s air quality predictions contribute to protecting lives and health in the US, which requires sustainable development and improvement of forecast systems. GEFS-Aerosols v1 has been developed in a collaboration between the NOAA research laboratories for operational forecast since September 2020 in the NCEP. The predictions demonstrate substantial improvements for both composition and variability of aerosol distributions over those from the former operational system.
Xuanli Li, Jason B. Roberts, Jayanthi Srikishen, Jonathan L. Case, Walter A. Petersen, Gyuwon Lee, and Christopher R. Hain
Geosci. Model Dev., 15, 5287–5308, https://doi.org/10.5194/gmd-15-5287-2022, https://doi.org/10.5194/gmd-15-5287-2022, 2022
Short summary
Short summary
This research assimilated the Global Precipitation Measurement (GPM) satellite-retrieved ocean surface meteorology data into the Weather Research and Forecasting (WRF) model with the Gridpoint Statistical Interpolation (GSI) system. This was for two snowstorms during the International Collaborative Experiments for PyeongChang 2018 Olympic and Paralympic Winter Games' (ICE-POP 2018) field experiments. The results indicated a positive impact of the data for short-term forecasts for heavy snowfall.
Philippe Thunis, Alain Clappier, Enrico Pisoni, Bertrand Bessagnet, Jeroen Kuenen, Marc Guevara, and Susana Lopez-Aparicio
Geosci. Model Dev., 15, 5271–5286, https://doi.org/10.5194/gmd-15-5271-2022, https://doi.org/10.5194/gmd-15-5271-2022, 2022
Short summary
Short summary
In this work, we propose a screening method to improve the quality of emission inventories, which are responsible for large uncertainties in air-quality modeling. The first step of screening consists of keeping only emission contributions that are relevant enough. In a second step, the method identifies large differences that provide evidence of methodological divergence or errors. We used the approach to compare two versions of the CAMS-REG European-scale inventory over 150 European cities.
Julian Quimbayo-Duarte, Johannes Wagner, Norman Wildmann, Thomas Gerz, and Juerg Schmidli
Geosci. Model Dev., 15, 5195–5209, https://doi.org/10.5194/gmd-15-5195-2022, https://doi.org/10.5194/gmd-15-5195-2022, 2022
Short summary
Short summary
The ultimate objective of this model evaluation is to improve boundary layer flow representation over complex terrain. The numerical model is tested against observations retrieved during the Perdigão 2017 field campaign (moderate complex terrain). We observed that the inclusion of a forest parameterization in the numerical model significantly improves the representation of the wind field in the atmospheric boundary layer.
Peter Hitchcock, Amy Butler, Andrew Charlton-Perez, Chaim I. Garfinkel, Tim Stockdale, James Anstey, Dann Mitchell, Daniela I. V. Domeisen, Tongwen Wu, Yixiong Lu, Daniele Mastrangelo, Piero Malguzzi, Hai Lin, Ryan Muncaster, Bill Merryfield, Michael Sigmond, Baoqiang Xiang, Liwei Jia, Yu-Kyung Hyun, Jiyoung Oh, Damien Specq, Isla R. Simpson, Jadwiga H. Richter, Cory Barton, Jeff Knight, Eun-Pa Lim, and Harry Hendon
Geosci. Model Dev., 15, 5073–5092, https://doi.org/10.5194/gmd-15-5073-2022, https://doi.org/10.5194/gmd-15-5073-2022, 2022
Short summary
Short summary
This paper describes an experimental protocol focused on sudden stratospheric warmings to be carried out by subseasonal forecast modeling centers. These will allow for inter-model comparisons of these major disruptions to the stratospheric polar vortex and their impacts on the near-surface flow. The protocol will lead to new insights into the contribution of the stratosphere to subseasonal forecast skill and new approaches to the dynamical attribution of extreme events.
Joël Thanwerdas, Marielle Saunois, Antoine Berchet, Isabelle Pison, Bruce H. Vaughn, Sylvia Englund Michel, and Philippe Bousquet
Geosci. Model Dev., 15, 4831–4851, https://doi.org/10.5194/gmd-15-4831-2022, https://doi.org/10.5194/gmd-15-4831-2022, 2022
Short summary
Short summary
Estimating CH4 sources by exploiting observations within an inverse modeling framework is a powerful approach. Here, a new system designed to assimilate δ13C(CH4) observations together with CH4 observations is presented. By optimizing both the emissions and associated source signatures of multiple emission categories, this new system can efficiently differentiate the co-located emission categories and provide estimates of CH4 sources that are consistent with isotopic data.
Bok H. Baek, Rizzieri Pedruzzi, Minwoo Park, Chi-Tsan Wang, Younha Kim, Chul-Han Song, and Jung-Hun Woo
Geosci. Model Dev., 15, 4757–4781, https://doi.org/10.5194/gmd-15-4757-2022, https://doi.org/10.5194/gmd-15-4757-2022, 2022
Short summary
Short summary
The Comprehensive Automobile Research System (CARS) is an open-source Python-based automobile emissions inventory model designed to efficiently estimate high-quality emissions. The CARS is designed to utilize the local vehicle activity database, such as vehicle travel distance, road-link-level network information, and vehicle-specific average speed by road type, to generate a temporally and spatially enhanced inventory for policymakers, stakeholders, and the air quality modeling community.
Joonatan Ala-Könni, Kukka-Maaria Kohonen, Matti Leppäranta, and Ivan Mammarella
Geosci. Model Dev., 15, 4739–4755, https://doi.org/10.5194/gmd-15-4739-2022, https://doi.org/10.5194/gmd-15-4739-2022, 2022
Short summary
Short summary
Properties of seasonally ice-covered lakes are not currently sufficiently included in global climate models. To fill this gap, this study evaluates three models that could be used to quantify the amount of heat that moves from and into the lake by the air above it and through evaporation of the ice cover. The results show that the complex nature of the surrounding environment as well as difficulties in accurately measuring the surface temperature of ice introduce errors to these models.
Jason E. Williams, Vincent Huijnen, Idir Bouarar, Mehdi Meziane, Timo Schreurs, Sophie Pelletier, Virginie Marécal, Beatrice Josse, and Johannes Flemming
Geosci. Model Dev., 15, 4657–4687, https://doi.org/10.5194/gmd-15-4657-2022, https://doi.org/10.5194/gmd-15-4657-2022, 2022
Short summary
Short summary
The global CAMS air quality model is used for providing tropospheric ozone information to end users. This paper updates the chemical mechanism employed (CBA) and compares it against two other mechanisms (MOCAGE, MOZART) and a multi-decadal dataset based on a previous version of CBA. We perform extensive validation for the US using multiple surface and aircraft datasets, providing an assessment of biases and the extent of correlation across different seasons during 2014.
Sudhanshu Pandey, Sander Houweling, and Arjo Segers
Geosci. Model Dev., 15, 4555–4567, https://doi.org/10.5194/gmd-15-4555-2022, https://doi.org/10.5194/gmd-15-4555-2022, 2022
Short summary
Short summary
Inversions are used to calculate methane emissions using atmospheric mole-fraction measurements. Multidecadal inversions are needed to extract information from the long measurement records of methane. However, multidecadal inversion computations can take months to finish. Here, we demonstrate an order of magnitude improvement in wall clock time for an iterative multidecadal inversion by physical parallelization of chemical transport model.
Jeong-Su Ko, Kyo-Sun Sunny Lim, Kwonil Kim, Gyuwon Lee, Gregory Thompson, and Alexis Berne
Geosci. Model Dev., 15, 4529–4553, https://doi.org/10.5194/gmd-15-4529-2022, https://doi.org/10.5194/gmd-15-4529-2022, 2022
Short summary
Short summary
This study evaluates the performance of the four microphysics parameterizations, the WDM6, WDM7, Thompson, and Morrison schemes, in simulating snowfall events during the ICE-POP 2018 field campaign. Eight snowfall events are selected and classified into three categories (cold-low, warm-low, and air–sea interaction cases). The evaluation focuses on the simulated hydrometeors, microphysics budgets, wind fields, and precipitation using the measurement data.
Christoph Fischer, Andreas H. Fink, Elmar Schömer, Roderick van der Linden, Michael Maier-Gerber, Marc Rautenhaus, and Michael Riemer
Geosci. Model Dev., 15, 4447–4468, https://doi.org/10.5194/gmd-15-4447-2022, https://doi.org/10.5194/gmd-15-4447-2022, 2022
Short summary
Short summary
Potential vorticity (PV) analysis plays a central role in studying atmospheric dynamics. For example, anomalies in the PV field near the tropopause are linked to extreme weather events. In this study, an objective strategy to identify these anomalies is presented and evaluated. As a novel concept, it can be applied to three-dimensional (3-D) data sets. Supported by 3-D visualizations, we illustrate advantages of this new analysis over existing studies along a case study.
Joseph Mouallem, Lucas Harris, and Rusty Benson
Geosci. Model Dev., 15, 4355–4371, https://doi.org/10.5194/gmd-15-4355-2022, https://doi.org/10.5194/gmd-15-4355-2022, 2022
Short summary
Short summary
The single-nest capability in GFDL's dynamical core, FV3, is upgraded to support multiple same-level and telescoping nests. Grid nesting adds a refined grid over an area of interest to better resolve small-scale flow features necessary to accurately predict special weather events such as severe storms and hurricanes. This work allows concurrent execution of multiple same-level and telescoping multi-level nested grids in both global and regional setups.
Clara Betancourt, Timo T. Stomberg, Ann-Kathrin Edrich, Ankit Patnala, Martin G. Schultz, Ribana Roscher, Julia Kowalski, and Scarlet Stadtler
Geosci. Model Dev., 15, 4331–4354, https://doi.org/10.5194/gmd-15-4331-2022, https://doi.org/10.5194/gmd-15-4331-2022, 2022
Short summary
Short summary
Ozone is a toxic greenhouse gas with high spatial variability. We present a machine-learning-based ozone-mapping workflow generating a transparent and reliable product. Going beyond standard mapping methods, this work combines explainable machine learning with uncertainty assessment to increase the integrity of the produced map.
Huan Fang and Greg Michalski
Geosci. Model Dev., 15, 4239–4258, https://doi.org/10.5194/gmd-15-4239-2022, https://doi.org/10.5194/gmd-15-4239-2022, 2022
Short summary
Short summary
A new emission input dataset that incorporates nitrogen isotopes has been used in the CMAQ (Community Multiscale Air Quality) modeling system simulation to qualitatively analyze the changes in δ15N values, due to the dispersion, mixing, and transport of the atmospheric NOx emitted from different sources. The dispersion, mixing, and transport of the atmospheric NOx were based on the meteorology files generated from the WRF (Weather Research and Forecasting) model.
Juan Manuel Castillo, Huw W. Lewis, Akhilesh Mishra, Ashis Mitra, Jeff Polton, Ashley Brereton, Andrew Saulter, Alex Arnold, Segolene Berthou, Douglas Clark, Julia Crook, Ananda Das, John Edwards, Xiangbo Feng, Ankur Gupta, Sudheer Joseph, Nicholas Klingaman, Imranali Momin, Christine Pequignet, Claudio Sanchez, Jennifer Saxby, and Maria Valdivieso da Costa
Geosci. Model Dev., 15, 4193–4223, https://doi.org/10.5194/gmd-15-4193-2022, https://doi.org/10.5194/gmd-15-4193-2022, 2022
Short summary
Short summary
A new environmental modelling system has been developed to represent the effect of feedbacks between atmosphere, land, and ocean in the Indian region. Different approaches to simulating tropical cyclones Titli and Fani are demonstrated. It is shown that results are sensitive to the way in which the ocean response to cyclone evolution is captured in the system. Notably, we show how a more rigorous formulation for the near-surface energy budget can be included when air–sea coupling is included.
Weichao Han, Tai-Long He, Zhaojun Tang, Min Wang, Dylan Jones, and Zhe Jiang
Geosci. Model Dev., 15, 4225–4237, https://doi.org/10.5194/gmd-15-4225-2022, https://doi.org/10.5194/gmd-15-4225-2022, 2022
Short summary
Short summary
We present an application of a hybrid deep learning (DL) model on prediction of surface CO in China from 2015 to 2020, which utilizes both convolutional neural networks and long short-term memory neural networks. The DL model performance is better than a Kalman filter (KF) system in the training period (2005–2018). Furthermore, the DL model demonstrates good temporal extensibility: the mean bias and correlation coefficients are 95.7 ppb and 0.93 in the test period (2019–2020) over eastern China.
Shuaiqi Tang, Jerome D. Fast, Kai Zhang, Joseph C. Hardin, Adam C. Varble, John E. Shilling, Fan Mei, Maria A. Zawadowicz, and Po-Lun Ma
Geosci. Model Dev., 15, 4055–4076, https://doi.org/10.5194/gmd-15-4055-2022, https://doi.org/10.5194/gmd-15-4055-2022, 2022
Short summary
Short summary
We developed an Earth system model (ESM) diagnostics package to compare various types of aerosol properties simulated in ESMs with aircraft, ship, and surface measurements from six field campaigns across spatial scales. The diagnostics package is coded and organized to be flexible and modular for future extension to other field campaign datasets and adapted to higher-resolution model simulations. Future releases will include comprehensive cloud and aerosol–cloud interaction diagnostics.
Ronny Badeke, Volker Matthias, Matthias Karl, and David Grawe
Geosci. Model Dev., 15, 4077–4103, https://doi.org/10.5194/gmd-15-4077-2022, https://doi.org/10.5194/gmd-15-4077-2022, 2022
Short summary
Short summary
For air quality modeling studies, it is very important to distribute pollutants correctly into the model system. This has not yet been done for shipping pollution in great detail. We studied the effects of different vertical distributions of shipping pollutants on the urban air quality and derived advanced formulas for it. These formulas take weather conditions and ship-specific parameters like the exhaust gas temperature into account.
Jaakko Kukkonen, Juha Nikmo, Kari Riikonen, Ilmo Westerholm, Pekko Ilvessalo, Tuomo Bergman, and Klaus Haikarainen
Geosci. Model Dev., 15, 4027–4054, https://doi.org/10.5194/gmd-15-4027-2022, https://doi.org/10.5194/gmd-15-4027-2022, 2022
Short summary
Short summary
A mathematical model has been developed for the dispersion of plumes originating from major fires. We have refined the model for the early evolution of the fire plumes; such a module has not been previously presented. We have evaluated the model against experimental field-scale data. The predicted concentrations agreed well with the aircraft measurements. We have also compiled an operational version of the model, which can be used for emergency contingency planning in the case of major fires.
Matthias Karl, Liisa Pirjola, Tiia Grönholm, Mona Kurppa, Srinivasan Anand, Xiaole Zhang, Andreas Held, Rolf Sander, Miikka Dal Maso, David Topping, Shuai Jiang, Leena Kangas, and Jaakko Kukkonen
Geosci. Model Dev., 15, 3969–4026, https://doi.org/10.5194/gmd-15-3969-2022, https://doi.org/10.5194/gmd-15-3969-2022, 2022
Short summary
Short summary
The community aerosol dynamics model MAFOR includes several advanced features: coupling with an up-to-date chemistry mechanism for volatile organic compounds, a revised Brownian coagulation kernel that takes into account the fractal geometry of soot particles, a multitude of nucleation parameterizations, size-resolved partitioning of semi-volatile inorganics, and a hybrid method for the formation of secondary organic aerosols within the framework of condensation and evaporation.
Xiaotian Xu, Xu Feng, Haipeng Lin, Peng Zhang, Shaojian Huang, Zhengcheng Song, Yiming Peng, Tzung-May Fu, and Yanxu Zhang
Geosci. Model Dev., 15, 3845–3859, https://doi.org/10.5194/gmd-15-3845-2022, https://doi.org/10.5194/gmd-15-3845-2022, 2022
Short summary
Short summary
Mercury is one of the most toxic pollutants in the environment, and wet deposition is a major process for atmospheric mercury to enter, causing ecological and human health risks. High-mercury wet deposition in the southeastern US has been a problem for many years. Here we employed a newly developed high-resolution WRF-GC model with the capability to simulate mercury to study this problem. We conclude that deep convection caused enhanced mercury wet deposition in the southeastern US.
Jeong-Beom Lee, Jae-Bum Lee, Youn-Seo Koo, Hee-Yong Kwon, Min-Hyeok Choi, Hyun-Ju Park, and Dae-Gyun Lee
Geosci. Model Dev., 15, 3797–3813, https://doi.org/10.5194/gmd-15-3797-2022, https://doi.org/10.5194/gmd-15-3797-2022, 2022
Short summary
Short summary
The predication of PM2.5 has been carried out using a numerical air quality model in South Korea. Despite recent progress of numerical air quality models, accurate prediction of PM2.5 is still challenging. In this study, we developed a data-based model using a deep neural network (DNN) to overcome the limitations of numerical air quality models. The results showed that the DNN model outperformed the CMAQ when it was trained by using observation and forecasting data from the numerical models.
Matthew L. Dawson, Christian Guzman, Jeffrey H. Curtis, Mario Acosta, Shupeng Zhu, Donald Dabdub, Andrew Conley, Matthew West, Nicole Riemer, and Oriol Jorba
Geosci. Model Dev., 15, 3663–3689, https://doi.org/10.5194/gmd-15-3663-2022, https://doi.org/10.5194/gmd-15-3663-2022, 2022
Short summary
Short summary
Progress in identifying complex, mixed-phase physicochemical processes has resulted in an advanced understanding of the evolution of atmospheric systems but has also introduced a level of complexity that few atmospheric models were designed to handle. We present a flexible treatment for multiphase chemical processes for models of diverse scale, from box up to global models. This enables users to build a customized multiphase mechanism that is accessible to a much wider community.
Haibo Wang, Ting Yang, Zifa Wang, Jianjun Li, Wenxuan Chai, Guigang Tang, Lei Kong, and Xueshun Chen
Geosci. Model Dev., 15, 3555–3585, https://doi.org/10.5194/gmd-15-3555-2022, https://doi.org/10.5194/gmd-15-3555-2022, 2022
Short summary
Short summary
In this paper, we develop an online data coupled assimilation system (NAQPMS-PDAF) with the Eulerian atmospheric chemistry-transport model. NAQPMS-PDAF allows efficient use of large computational resources. The application and performance of the system are investigated by assimilating 1 month of vertical aerosol observations. The results show that NAQPMS-PDAF can significantly improve the performance of aerosol vertical structure simulation and reduce the uncertainty to a large extent.
Peng Zhang and Yanxu Zhang
Geosci. Model Dev., 15, 3587–3601, https://doi.org/10.5194/gmd-15-3587-2022, https://doi.org/10.5194/gmd-15-3587-2022, 2022
Short summary
Short summary
Mercury is a global pollutant that can be transported over long distance through the atmosphere. We develop a new online global model for atmospheric mercury. The model reproduces the observed global atmospheric mercury concentrations and deposition distributions by simulating the emissions, transport, and physicochemical processes of atmospheric mercury. And we find that the seasonal variations of atmospheric Hg are the result of multiple processes and have obvious regional characteristics.
Adam Milsom, Amy Lees, Adam M. Squires, and Christian Pfrang
EGUsphere, https://doi.org/10.5194/egusphere-2022-143, https://doi.org/10.5194/egusphere-2022-143, 2022
Short summary
Short summary
MultilayerPy is a Python-based framework facilitating the creation, running and optimisation of state-of-the-art kinetic multi-layer models of aerosol and film processes. Models can be fit to data with local and global optimisation algorithms along with a statistical sampling algorithm, which quantifies the uncertainty in optimised model parameters. This “modelling study in a box” enables more reproducible and reliable results, with model code and outputs produced in a human readable way.
Patrick Obin Sturm and Anthony S. Wexler
Geosci. Model Dev., 15, 3417–3431, https://doi.org/10.5194/gmd-15-3417-2022, https://doi.org/10.5194/gmd-15-3417-2022, 2022
Short summary
Short summary
Large air quality and climate models require vast amounts of computational power. Machine learning tools like neural networks can be used to make these models more efficient, with the downside that their results might not make physical sense or be easy to interpret. This work develops a physically interpretable neural network that obeys scientific laws like conservation of mass and models atmospheric composition more accurately than a traditional neural network.
Dánnell Quesada-Chacón, Klemens Barfus, and Christian Bernhofer
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-14, https://doi.org/10.5194/gmd-2022-14, 2022
Revised manuscript accepted for GMD
Short summary
Short summary
We improved the performance of past perfect prognosis statistical downscaling methods while achieving full model repeatability with GPU calculated deep learning models using the TensorFlow, climate4R and VALUE frameworks. We employed the ERA5 reanalysis as predictors and ReKIS (Eastern Ore Mountains, Germany, 1 km resolution) as precipitation predictand, while incorporating modern deep learning architectures. The achieved repeatability is key to accomplish further milestones with deep learning.
Michael Weger, Holger Baars, Henriette Gebauer, Maik Merkel, Alfred Wiedensohler, and Bernd Heinold
Geosci. Model Dev., 15, 3315–3345, https://doi.org/10.5194/gmd-15-3315-2022, https://doi.org/10.5194/gmd-15-3315-2022, 2022
Short summary
Short summary
Numerical models are an important tool to assess the air quality in cities,
as they can provide near-continouos data in time and space. In this paper,
air pollution for an entire city is simulated at a high spatial resolution of 40 m.
At this spatial scale, the effects of buildings on the atmosphere,
like channeling or blocking of the air flow, are directly represented by diffuse obstacles in the used model CAIRDIO. For model validation, measurements from air-monitoring sites are used.
Patrick C. Campbell, Youhua Tang, Pius Lee, Barry Baker, Daniel Tong, Rick Saylor, Ariel Stein, Jianping Huang, Ho-Chun Huang, Edward Strobach, Jeff McQueen, Li Pan, Ivanka Stajner, Jamese Sims, Jose Tirado-Delgado, Youngsun Jung, Fanglin Yang, Tanya L. Spero, and Robert C. Gilliam
Geosci. Model Dev., 15, 3281–3313, https://doi.org/10.5194/gmd-15-3281-2022, https://doi.org/10.5194/gmd-15-3281-2022, 2022
Short summary
Short summary
NOAA's National Air Quality Forecast Capability (NAQFC) continues to protect Americans from the harmful effects of air pollution, while saving billions of dollars per year. Here we describe and evaluate the development of the most advanced version of the NAQFC to date, which became operational at NOAA on 20 July 2021. The new NAQFC is based on a coupling of NOAA's operational Global Forecast System (GFS) version 16 with the Community Multiscale Air Quality (CMAQ) model version 5.3.1.
Athanasios Tsikerdekis, Nick A. J. Schutgens, Guangliang Fu, and Otto P. Hasekamp
Geosci. Model Dev., 15, 3253–3279, https://doi.org/10.5194/gmd-15-3253-2022, https://doi.org/10.5194/gmd-15-3253-2022, 2022
Short summary
Short summary
In our study we quantify the ability of the future satellite sensor SPEXone, part of the NASA PACE mission, to estimate aerosol emissions. The sensor will be able to retrieve accurate information of aerosol light extinction and most importantly light absorption. We simulate SPEXone spatial coverage and combine it with an aerosol model. We found that SPEXone will be able to estimate species-specific (e.g. dust, sea salt, organic or black carbon, sulfates) aerosol emissions very accurately.
Christian Zeman and Christoph Schär
Geosci. Model Dev., 15, 3183–3203, https://doi.org/10.5194/gmd-15-3183-2022, https://doi.org/10.5194/gmd-15-3183-2022, 2022
Short summary
Short summary
Our atmosphere is a chaotic system, where even a tiny change can have a big impact. This makes it difficult to assess if small changes, such as the move to a new hardware architecture, will significantly affect a weather and climate model. We present a methodology that allows to objectively verify this. The methodology is applied to several test cases, showing a high sensitivity. Results also show that a major system update of the underlying supercomputer did not significantly affect our model.
Yongbo Zhou, Yubao Liu, Zhaoyang Huo, and Yang Li
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-30, https://doi.org/10.5194/gmd-2022-30, 2022
Revised manuscript accepted for GMD
Short summary
Short summary
The study evaluates the performance of the Data Assimilation Research Testbed (DART), equipped with the recently-added forward operator Radiative Transfer for TOVS (RTTOV), in assimilating FY-4 visible image into the Weather Research and Forecasting (WRF) model. The ability of the WRF-DART/RTTOV system to improve the forecasting skills of a cyclone case over East Asia and Western Pacific is demonstrated using Observing System Simulation Experiments (OSSEs).
Stelios Myriokefalitakis, Elisa Bergas-Massó, María Gonçalves-Ageitos, Carlos Pérez García-Pando, Twan van Noije, Philippe Le Sager, Akinori Ito, Eleni Athanasopoulou, Athanasios Nenes, Maria Kanakidou, Maarten C. Krol, and Evangelos Gerasopoulos
Geosci. Model Dev., 15, 3079–3120, https://doi.org/10.5194/gmd-15-3079-2022, https://doi.org/10.5194/gmd-15-3079-2022, 2022
Short summary
Short summary
We here describe the implementation of atmospheric multiphase processes in the EC-Earth Earth system model. We provide global budgets of oxalate, sulfate, and iron-containing aerosols, along with an analysis of the links among atmospheric composition, aqueous-phase processes, and aerosol dissolution, supported by comparison to observations. This work is a first step towards an interactive calculation of the deposition of bioavailable atmospheric iron coupled to the model’s ocean component.
Cited articles
Alexander, C. and Carley, J.: Short-Range Weather in operations, Bulletin of
the UFS Community, p. 9, https://doi.org/10.25923/k3zn-xe66, 2020. a, b
Alpert, J. C., Yudin, V. A., and Strobach, E.: Atmospheric Gravity Wave Sources
Correlated with Resolved-scale GW Activity and Sub-grid Scale
Parameterization in the FV3gfs Model, in: AGU Fall Meeting Abstracts, vol.
2019, SA21A–02, 2019. a
Azevedo, H. B. D., Gonçalves, L. G. G. D., Kalnay, E., and Wespetal, M.:
Dynamically weighted hybrid gain data assimilation: perfect model testing,
Tellus A, 72, 1–11,
https://doi.org/10.1080/16000870.2020.1835310, 2020. a
Bannister, R. N.: A review of operational methods of variational and
ensemble-variational data assimilation, Q. J. Roy.
Meteor. Soc., 143, 607–633, https://doi.org/10.1002/qj.2982,
2017. a
Bannister, R. N., Chipilski, H. G., and Martinez-Alvarado, O.: Techniques and
challenges in the assimilation of atmospheric water observations for
numerical weather prediction towards convective scales, Q. J. Roy. Meteor. Soc., 146, 1–48,
https://doi.org/10.1002/qj.3652, 2020. a, b
Banos, I. H., Mayfield, W. D., Ge, G., Sapucci, L. F., Carley, J. R., and Nance, L.: Rapid Refresh Forecast System (RRFS) v0.1 (0.1), Zenodo [code], https://doi.org/10.5281/zenodo.5546592, 2021a. a
Banos, I. H., Mayfield, W. D., Ge, G., Sapucci, L. F., Carley, J. R., and Nance, L.: Assessment of the data assimilation framework for the prototype Rapid Refresh Forecast System and impacts on forecasts of convective storms, Zenodo [code, data set], https://doi.org/10.5281/zenodo.5226389, 2021b. a
Bathmann, K.: The GSI Minimization Code Structure,
https://github.com/NOAA-EMC/GSI/wiki/GSI_Minimization_Code_Explained.pdf,
2021. 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
Benjamin, S. G., Weygandt, S. S., Devenyi, D., Manikin, J. B. G., Smith, T.,
and Smirnova, T.: Improved moisture and PBL initialization in the RUC using
METAR data, in: Preprints 22th Conf. Severe Local Storms, SPC,
82023, 2004. a
Benjamin, S. G., Jamison, B. D., Moninger, W. R., Sahm, S. R., Schwartz, B. E.,
and Schlatter, T. W.: Relative Short-Range Forecast Impact from Aircraft,
Profiler, Radiosonde, VAD, GPS-PW, METAR, and Mesonet Observations via the
RUC Hourly Assimilation Cycle, Mon. Weather Rev., 138, 1319–1343,
https://doi.org/10.1175/2009MWR3097.1, 2010. a
Benjamin, S. G., Weygandt, S. S., Brown, J. M., Hu, M., Alexander, C. R., Smirnova, T. G., Olson, J. B., James, E. P., Dowell, D. C., Grell, G. A., Lin, H., Peckham, S. E., Smith, T. L., Moninger, W. R., Kenyon, J. S., and Manikin, G. S.: A North American hourly assimilation and model forecast cycle: The
Rapid Refresh, Mon. Weather Rev., 144, 1669–1694,
2016. a, b, c, d, e, f
Benjamin, S. G., James, E. P., Brown, J. M., Szoke, E. J., Kenyon, J. S., and
Ahmadov, R.: Diagnostic fields developed for hourly updated NOAA weather
models, NOAA Technical Memorandum OAR GSL-66,
https://doi.org/10.25923/98fy-xx71, 2020. a
Benjamin, S. G., James, E. P., Hu, M., Alexander, C. R., Ladwig, T. T., Brown, J. M., Weygandt, S. S., Turner, D. D., Minnis, P., Smith, W. L., and Heidinger, A. K.:
Stratiform Cloud-Hydrometeor Assimilation for HRRR and RAP Model Short-Range
Weather Prediction, Mon. Weather Rev., 149,
2673–2694,
https://doi.org/10.1175/MWR-D-20-0319.1, 2021. a
Bernardet, L., Firl, G., Heinzeller, D., Carson, L., Sun, X., Pan,
L., and Zhang, M.: Engaging the Community in the Development of Physics for
NWP Models, in: EGU General Assembly Conference Abstracts, p. 22093,
https://ui.adsabs.harvard.edu/abs/2020EGUGA..2222093B (last access: 14 April 2021), 2020. a
Black, T. L., Abeles, J. A., Blake, B. T., Jovic, D., Rogers, E., Zhang, X., Aligo, E. A., Dawson, L. C., Lin, Y., Strobach, E., Shafran, P. C., and Carley, J. R.: A Limited Area Modeling Capability
for the Finite-Volume Cubed-Sphere (FV3) Dynamical Core and Comparison with a
Global Two-Way Nest, J. Adv. Model. Earth Sy., https://doi.org/10.1029/2021MS002483,
e2021MS002483, 2021. a, b, c, d, e
Brousseau, P., Berre, L., Bouttier, F., and Desroziers, G.: Flow-dependent
background-error covariances for a convective-scale data assimilation system,
Q. J. Roy. Meteor. Soc., 138, 310–322,
https://doi.org/10.1002/qj.920, 2012. a
Brown, A., Milton, S., Cullen, M., Golding, B., Mitchell, J., and Shelly, A.:
Unified modeling and prediction of weather and climate: A 25-year journey,
B. Am. Meteorol. Soc., 93, 1865–1877,
2012. a
Brown, B., Jensen, T., Gotway, J. H., Bullock, R., Gilleland, E., Fowler, T.,
Newman, K., Adriaansen, D., Blank, L., Burek, T., Harrold, M., Hertneky, T.,
Kalb, C., Kucera, P., Nance, L., Opatz, J., Vigh, J., and Wolff, J.: The
Model Evaluation Tools (MET): More than a Decade of Community-Supported
Forecast Verification, B. Am. Meteorol. Soc., 102,
E782–E807, https://doi.org/10.1175/BAMS-D-19-0093.1, 2021. a
Buehner, M.: Ensemble-derived stationary and flow-dependent background-error
covariances: Evaluation in a quasi-operational NWP setting, Q. J. Roy.
Meteor. Soc., 131, 1013–1043,
https://doi.org/10.1256/qj.04.15, 2005. a
Campbell, W. F., Bishop, C. H., and Hodyss, D.: Vertical covariance
localization for satellite radiances in ensemble Kalman filters, Mon. Weather Rev., 138, 282–290, 2010. a
Carley, J. R., Matthews, M., Morris, M. T., De Pondeca, M. S. F. V., Colavito,
J., and Yang, R.: Variational assimilation of web camera-derived estimates of
visibility for Alaska aviation, Experimental Results, 2, e14,
https://doi.org/10.1017/exp.2020.66, 2021. a
CCPP: CCPP v5.0.0 Scientific Documentation. RRFS_v1alpha Suite,
https://dtcenter.ucar.edu/GMTB/v5.0.0/sci_doc/RRFS_v1alpha_page.html (last access: 18 August 2021),
2021. a
Chen, L., Liu, C., Xue, M., Zhao, G., Kong, R., and Jung, Y.: Use of Power
Transform Mixing Ratios as Hydrometeor Control Variables for Direct
Assimilation of Radar Reflectivity in GSI En3DVar and Tests with Five
Convective Storm Cases, Mon. Weather Rev., 149, 645–659, 2021. a
CIMSS: CIMSS Cooperative Agreement Annual Report, Tech. Rep. April, Cooperative
Institute for Meteorological Satellite Studies University of
Wisconsin-Madison,
https://cimss.ssec.wisc.edu/reports/CIMSS-CA-Report_2014_Final.pdf (last access: 11 August 2022),
2014. a
Davis, C. A., Brown, B. G., Bullock, R., and Halley-Gotway, J.: The Method for
Object-Based Diagnostic Evaluation (MODE) Applied to Numerical Forecasts from
the 2005 NSSL/SPC Spring Program, Weather Forecast., 24, 1252–1267,
https://doi.org/10.1175/2009WAF2222241.1, 2009. a
Derber, J. and Rosati, A.: A global oceanic data assimilation system, J. Phys. Oceanogr., 19, 1333–1347, 1989. a
Dixon, M., Li, Z., Lean, H., Roberts, N., and Ballard, S.: Impact of Data
Assimilation on Forecasting Convection over the United Kingdom Using a
High-Resolution Version of the Met Office Unified Model, Mon. Weather Rev., 137, 1562–1584, https://doi.org/10.1175/2008MWR2561.1, 2009. a
Dong, J., Liu, B., Zhang, Z., Wang, W., Mehra, A., Hazelton, A. T., Winterbottom, H. R., Zhu, L., Wu, K., Zhang, C., Tallapragada, V., Zhang, Xu., Gopalakrishnan, S., and Marks, F.: The evaluation of
real-time Hurricane Analysis and Forecast System (HAFS) Stand-Alone Regional
(SAR) model performance for the 2019 Atlantic hurricane season, Atmosphere,
11, 617, https://doi.org/10.3390/atmos11060617, 2020. a
EMC: Strategic Implementation Plan for evolution of NGGPS to a national Unified
Modeling System (First Annual Update), Tech. Rep. November, NOAA, U.S,
https://www.weather.gov/media/sti/nggps/UFS SIP FY19-21_20181129.pdf (last access: 9 July 2021),
2018. a
Gallo, B. T., Wolff, J. K., Clark, A. J., Jirak, I., Blank, L. R., Roberts, B., Wang, Y., Zhang, C., Xue, M., Supinie, T., Harris, L., Zhou, L., and Alexander, C.: Exploring
Convection-Allowing Model Evaluation Strategies for Severe Local Storms Using
the Finite-Volume Cubed-Sphere (FV3) Model Core, Weather Forecast., 36,
3–19, 2021. a, b, c
Gao, S., Du, N., Min, J., and Yu, H.: Impact of assimilating radar data using a
hybrid 4DEnVar approach on prediction of convective events, Tellus A, 73, 1–19, 2021. a
Gilleland, E., Hering, A. S., Fowler, T. L., and Brown, B. G.: Testing the
Tests: What Are the Impacts of Incorrect Assumptions When Applying Confidence
Intervals or Hypothesis Tests to Compare Competing Forecasts?, Mon. Weather Rev., 146, 1685–1703, https://doi.org/10.1175/MWR-D-17-0295.1, 2018. a
Gustafsson, N., Janji, T., Schraff, C., Leuenberger, D., Weissman, M., Reich,
H., Brousseau, P., Montmerle, T., Wattrelot, E., Bučánek, A.,
Mile, M., Hamdi, R., Lindskog, M., Barkmeijer, J., Dahlbom, M., Macpherson,
B., Ballard, S., Inverarity, G., Carley, J., Alexander, C., Dowell, D., Liu,
S., Ikuta, Y., and Fujita, T.: Survey of data assimilation methods for
convective-scale numerical weather prediction at operational centres,
Q. J. Roy. Meteor. Soc., 144, 1218–1256,
https://doi.org/10.1002/qj.3179, 2018. a, b, c, d, e
Harris, L., Chen, X., Zhou, L., and Chen, J.-H.: The Nonhydrostatic Solver of
the GFDL Finite-Volume Cubed-Sphere Dynamical Core, NOAA Technical Memorandum
OAR GFDL, 2020-003, https://doi.org/10.25923/9wdt-4895,
2020a. a
Harris, L., Zhou, L., Lin, S.-J., Chen, J.-H., Chen, X., Gao, K., Morin, M., Rees, S., Sun, Y., Tong, M., Xiang, B., Bender, M., Benson, R., Cheng, K.-Y., Clark, S., Elbert, O. D., Hazelton, A., Huff, J. J., Kaltenbaugh, A., Liang, Z., Marchok, T., Shin, H. H., and Stern, W.: GFDL SHiELD: A unified system for
weather-to-seasonal prediction, J. Adv. Model. Earth
Sy., 12, e2020MS002223, https://doi.org/10.1029/2020MS002223, 2020b. a
Harris, L. M., Lin, S.-J., and Tu, C.: High-Resolution Climate Simulations
Using GFDL HiRAM with a Stretched Global Grid, J. Climate, 29,
4293–4314, https://doi.org/10.1175/JCLI-D-15-0389.1, 2016. a
Harris, L. M., Rees, S. L., Morin, M., Zhou, L., and Stern, W. F.: Explicit
prediction of continental convection in a skillful variable resolution global
model, J. Adv. Model. Earth Sy., 11, 1847–1869,
https://doi.org/10.1029/2018MS001542, 2019. a
Harrold, M., Hertneky, T., Kalina, E., Newman, K., Ketefian, G., Grell, E. D.,
Lybarger, N. D., and Nelson, B.: Investigating the Scalability of Convective
and Microphysics Parameterizations in the Unified Forecast System Short-Range
Weather (UFS-SRW) Application, in: 101st American Meteorological Society
Annual Meeting, AMS, New Orleans, LA, USA, 10–15 January 2021, 384306,
https://ams.confex.com/ams/101ANNUAL/meetingapp.cgi/Paper/384306 (last access: 28 August 2022), 2021. a
Hazeleger, W., Severijns, C., Semmler, T., Stefanescu, S., Yang, S., Wang, X., Wyser, K., Dutra, E., Baldasano, J. M., Bintanja, R., Bougeault, P., Caballero, R., Ekman, A. M. L., Christensen, J. H., van den Hurk, B., Jimenez, P., Jones, C., Kållberg, P.,Koenigk, T., McGrath, R., Miranda, P., van Noije, T., Palmer, T., Parodi, J. A., Schmith, T., Selten, F., Storelvmo, T., Sterl, A., Tapamo, H., Vancoppenolle, M., Viterbo, P., and Willén, U.:
EC-Earth: a seamless earth-system prediction approach in action, B. Am. Meteorol. Soc., 91, 1357–1364,
2010. a
Heinzeller, D., Bernardet, L., Firl, G., Carson, L., Schramm, J.,
Zhang, M., Dudhia, J., Gill, D., Duda, M., Goldhaber, S., Craig,
C., Vitt, F., and Vertenstein, M.: The Common Community Physics Package
CCPP: unifying physics across NOAA and NCAR models using a common software
framework, in: EGU General Assembly Conference Abstracts, p. 223,
https://ui.adsabs.harvard.edu/abs/2019EGUGA..21..223H (last access: 15 July 2021), 2019. a
Holm, E., Andersson, E., Beljaars, A., Lopez, P., Mahfouf, J.-F., Simmons, A.,
and Thepaut, J.-N.: Assimilation and modelling of the hydrologic cycle:
ECMWF's status and plans, ECMWF Tech. Memo., 383, 55, 2002. a
Houtekamer, P. L. and Mitchell, H. L.: A sequential ensemble Kalman filter for
atmospheric data assimilation, Mon. Weather Rev., 129, 123–137, 2001. a
Hu, M., Xue, M., and Brewster, K.: 3DVAR and Cloud Analysis with WSR-88D
Level-II Data for the Prediction of the Fort Worth, Texas, Tornadic
Thunderstorms. Part I: Cloud Analysis and Its Impact, Mon. Weather Rev.,
134, 675–698, https://doi.org/10.1175/mwr3092.1, 2006a. a
Hu, M., Xue, M., Gao, J., and Brewster, K.: 3DVAR and Cloud Analysis with
WSR-88D Level-II Data for the Prediction of the Fort Worth, Texas, Tornadic
Thunderstorms. Part II: Impact of Radial Velocity Analysis via 3DVAR, Mon. Weather Rev., 134, 699–721, https://doi.org/10.1175/mwr3093.1, 2006b. a
Hu, M., Benjamin, S. G., Ladwig, T. T., Dowell, D. C., Weygandt, S. S.,
Alexander, C. R., and Whitaker, J. S.: GSI three-dimensional
ensemble–variational hybrid data assimilation using a global ensemble for
the regional Rapid Refresh model, Mon. Weather Rev., 145, 4205–4225,
2017. a, b, c, d, e, f, g, h, i, j
Hu, M., Ge, G., Chunhua, Z., Stark, D., Shao, H., Newman, K., Beck, J., and
Zhang, X.: Grid-point Statistical Interpolation (GSI) User’s Guide version
3.7,
https://dtcenter.org/sites/default/files/GSIUserGuide_v3.7_0.pdf (last access: 10 December 2021),
2018. a
Hu, M., Li, R., Trahan, S., Holt, C., Weygandt, S., and Alexander, C. R.:
Initial Development Testing and Evaluation of the RAPHRRR Similar Data
Assimilation Functions for FV3 LAM-Based RRFs, in: 101st American
Meteorological Society Annual Meeting, AMS, 10–15 January 2021, New Orleans, LA, USA, 379264,
https://ams.confex.com/ams/101ANNUAL/meetingapp.cgi/Paper/379264 (last access: 28 August 2022), 2021. a
Huang, B., Wang, X., Kleist, D. T., and Lei, T.: A Simultaneous Multiscale Data
Assimilation Using Scale-Dependent Localization in GSI-Based Hybrid 4DEnVar
for NCEP FV3-Based GFS, Mon. Weather Rev., 149, 479–501,
https://doi.org/10.1175/MWR-D-20-0166.1, 2021. a
Iacono, M. J., Delamere, J. S., Mlawer, E. J., Shephard, M. W., Clough, S. A.,
and Collins, W. D.: Radiative forcing by long-lived greenhouse gases:
Calculations with the AER radiative transfer models, J. Geophys.
Res.-Atmos., 113, D13, https://doi.org/10.1029/2008JD009944, 2008. a
Janjić, T., McLaughlin, D., Cohn, S. E., and Verlaan, M.: Conservation of
Mass and Preservation of Positivity with Ensemble-Type Kalman Filter
Algorithms, Mon. Weather Rev., 142, 755–773,
https://doi.org/10.1175/MWR-D-13-00056.1, 2014. a, b
Janjić, T., Ruckstuhl, Y., and Toint, P. L.: A data assimilation algorithm
for predicting rain, Q. J. Roy. Meteor. Soc.,
147, 1949–1963, 2021. a
Jensen, T., Brown, B., Bullock, R., Fowler, T., Gotway, J. H., and Newman, K.:
The Model Evaluation Tools v9.0 (METv9.0) User's Guide., Developmental
Testbed Center,
https://dtcenter.org/sites/default/les/community-code/met/docs/user-guide/MET_Users_Guide_v9.0.pdf (last access: 13 April 2021),
2020. a, b
Ji, M. and Toepfer, F.: Dynamical Core Evaluation Test Report for NOAA’s Next
Generation Global Prediction System (NGGPS), Tech. Rep. September, NOAA, U.S,
https://doi.org/10.25923/ztzy-qn82, 2016. a
Kalina, E., Grell, E. D., Harrold, M., Hertneky, T., and Newman, K.: Evaluating
Hydrometeor Type and Amount in the Unified Forecast System, in: 101st
American Meteorological Society Annual Meeting, AMS, 10–15 January 2021,
New Orleans, LA, USA, 383651,
https://ams.confex.com/ams/101ANNUAL/meetingapp.cgi/Paper/383651 (last access: 28 August 2022), 2021. a
Kleist, D. and Ide, K.: An OSSE-Based Evaluation of Hybrid
Variational–Ensemble Data Assimilation for the NCEP GFS. Part II: 4DEnVar
and Hybrid Variants, Mon. Weather Rev., 143, 452–470,
https://doi.org/10.1175/MWR-D-13-00350.1, 2015a. a, b
Kleist, D. T. and Ide, K.: An OSSE-based evaluation of hybrid
variational-ensemble data assimilation for the NCEP GFS. Part I: System
description and 3D-hybrid results, Mon. Weather Rev., 143, 433–451,
https://doi.org/10.1175/MWR-D-13-00351.1, 2015b. a
Kleist, D. T. and Ide, K.: An OSSE-Based Evaluation of Hybrid
Variational–Ensemble Data Assimilation for the NCEP GFS. Part II: 4DEnVar
and Hybrid Variants, Mon. Weather Rev., 143, 452–470,
https://doi.org/10.1175/mwr-d-13-00350.1, 2015c. a
Lean, H. W., Clark, P. A., Dixon, M., Roberts, N. M., Fitch, A., Forbes, R.,
and Halliwell, C.: Characteristics of high-resolution versions of the Met
Office Unified Model for forecasting convection over the United Kingdom,
Mon. Weather Rev., 136, 3408–3424, 2008. a
Li, X. and Derber, J.: Near Sea Surface Temperatures (NSST). Analysis in
NCEP GFS, in: JCSDA 6th Workshop on Satellite Data Assimilation, JCSDA
Workshop on Satellite Data Assimilation,
http://data.jcsda.org/Workshops/6th-workshop-onDA/Session-4/JCSDA_2008_Li.pdf (last access: 12 April 2022),
2008. a
Li, X., Derber, J., and Moorthi, S.: An atmosphere-ocean partially
coupled data assimilation and prediction system developed within the NCEP
GFS/CFS, in: EGU General Assembly Conference Abstracts, EGU General Assembly
Conference Abstracts, 12–17 April 2015,
Vienna, Austria, 2855,
https://ui.adsabs.harvard.edu/abs/2015EGUGA..17.2855L (last access: 25 August 2022), 2015. a
Lin, S.-J.: A finite-volume integration method for computing pressure gradient
force in general vertical coordinates, Q. J. Roy. Meteor. Soc., 123, 1749–1762, https://doi.org/10.1002/qj.49712354214, 1997. a
Lin, S.-J.: A “Vertically Lagrangian” Finite-Volume Dynamical Core for
Global Models, Mon. Weather Rev., 132, 2293–2307,
https://doi.org/10.1175/1520-0493(2004)132<2293:AVLFDC>2.0.CO;2, 2004. a, b
Lin, S.-J. and Rood, R. B.: Multidimensional Flux-Form Semi-Lagrangian
Transport Schemes, Mon. Weather Rev., 124, 2046–2070,
https://doi.org/10.1175/1520-0493(1996)124<2046:Mffslt>2.0.Co;2, 1996. a
Lin, S.-J. and Rood, R. B.: An explicit flux-form semi-lagrangian shallow-water
model on the sphere, Q. J. Roy. Meteor. Soc.,
123, 2477–2498, https://doi.org/10.1002/qj.49712354416, 1997. a
Lin, Y. and Mitchell, K. E.: The NCEP stage II/IV hourly precipitation
analyses: Development and applications, in: 19th Conf. on Hydrology, 1.2,
Amer. Meteor. Soc.,
http://ams.confex.com/ams/pdfpapers/83847.pdf (last access: 20 July 2021), 2005. a
Link, J. S., Tolman, H. L., Bayler, E., Holt, C., Brown, C. W., Burke, P. B.,
Carman, J. C., Cross, S. L., Dunne, J. P., Lipton, D. W., Mariotti, A.,
Methot, R. D., Myers, E. P., Schneider, T. L., Grasso, M., and Robinson, K.:
High-level NOAA unified modeling overview, NOAA,
https://doi.org/10.7289/V5GB2248, 2017. a
Lippi, D. E., Carley, J. R., and Kleist, D. T.: Improvements to the
Assimilation of Doppler Radial Winds for Convection-Permitting Forecasts of a
Heavy Rain Event, Mon. Weather Rev., 147, 3609–3632,
https://doi.org/10.1175/MWR-D-18-0411.1, 2019. a
Long, P. E.: An economical and compatible scheme for parameterizing the stable
surface layer in the medium range forecast model, NOAA,
https://repository.library.noaa.gov/view/noaa/11489 (last access: 20 July 2021),
miscellaneous, 1986. a
Lorenc, A. C.: The potential of the ensemble Kalman filter for NWP – A
comparison with 4D‐Var, Q. J. Roy. Meteor. Soc., 129, 3183–3203, 2003. a
McCaul, E. W. and Weisman, M. L.: The Sensitivity of Simulated Supercell
Structure and Intensity to Variations in the Shapes of Environmental Buoyancy
and Shear Profiles, Mon. Weather Rev., 129, 664–687,
https://doi.org/10.1175/1520-0493(2001)129<0664:TSOSSS>2.0.CO;2, 2001. a
McCormack, J. P., Eckermann, S. D., Siskind, D. E., and McGee, T. J.: CHEM2D-OPP: A new linearized gas-phase ozone photochemistry parameterization for high-altitude NWP and climate models, Atmos. Chem. Phys., 6, 4943–4972, https://doi.org/10.5194/acp-6-4943-2006, 2006. a
McCormack, J. P., Hoppel, K. W., and Siskind, D. E.: Parameterization of middle atmospheric water vapor photochemistry for high-altitude NWP and data assimilation, Atmos. Chem. Phys., 8, 7519–7532, https://doi.org/10.5194/acp-8-7519-2008, 2008. a, b
Miyakoda, K. and Sirutis, J.: Manual of the E-physics, Princeton University,
97, 1986. a
Mlawer, E. J., Taubman, S. J., Brown, P. D., Iacono, M. J., and Clough, S. A.:
Radiative transfer for inhomogeneous atmospheres: RRTM, a validated
correlated-k model for the longwave, J. Geophys. Res.-Atmos., 102, 16663–16682, https://doi.org/10.1029/97JD00237,
1997. a
Morris, M. T., Carley, J. R., Colón, E., Gibbs, A., Pondeca, M. S. F. V. D.,
and Levine, S.: A Quality Assessment of the Real-Time Mesoscale Analysis
(RTMA) for Aviation, Weather Forecast., 35, 977–996,
https://doi.org/10.1175/WAF-D-19-0201.1, 2020. a
Nakanishi, M. and Niino, H.: Development of an Improved Turbulence Closure
Model for the Atmospheric Boundary Layer, J. Meteorol.
Soc. Japan Ser. II, 87, 895–912,
https://doi.org/10.2151/jmsj.87.895, 2009. a, b
National Research Council: A National Strategy for Advancing Climate
Modeling, chap. Synergies Between Weather and Climate Modeling, The National
Academies Pres, Washington, D.C., https://doi.org/10.17226/13430, 2012. a
Newman, K., Grell, E. D., Kalina, E., Harrold, M., Ketefian, G., Hertneky, T.,
and Lybarger, N. D.: Investigation of Land–Atmosphere Interactions in the
Unified Forecast System Short-Range Weather (UFS-SRW) Application, in: 101st
American Meteorological Society Annual Meeting, AMS, 10–15 January 2021,
New Orleans, LA, USA, 384122,
https://ams.confex.com/ams/101ANNUAL/meetingapp.cgi/Paper/384122 (last access: 28 August 2022),
2021. a
Niu, G.-Y., Yang, Z.-L., Mitchell, K. E., Chen, F., Ek, M. B., Barlage, M., Kumar, A., Manning, K., Niyogi, D., Rosero, E., Tewari, M., and Xia, Y.: The community Noah
land surface model with multiparameterization options (Noah-MP): 1. Model
description and evaluation with local-scale measurements, J.
Geophys. Res.-Atmos., 116, D12,
https://doi.org/10.1029/2010JD015139, 2011. a
NWS: Service Change Notice 21-20 Updated: Upgrade NCEP Global Forecast Systems
(GFS) to v16: Effective 22 March 2021,
https://www.weather.gov/media/notification/scn_21-20_gfsv16.0_aaa_update.pdf (last access: 12 April 2022),
2021. a
Parrish, D. F. and Derber, J. C.: The National Meteorological Center's Spectral
Statistical-Interpolation Analysis System, Mon. Weather Rev., 120,
1747–1763, https://doi.org/10.1175/1520-0493(1992)120<1747:Tnmcss>2.0.Co;2, 1992. a
Poterjoy, J., Sobash, R. A., and Anderson, J. L.: Convective-scale data
assimilation for the weather research and forecasting model using the local
particle filter, Mon. Weather Rev., 145, 1897–1918, 2017. a
Potvin, C. K., Carley, J. R., Clark, A. J., Wicker, L. J., Skinner, P. S.,
Reinhart, A. E., Gallo, B. T., Kain, J. S., Romine, G. S., Aligo, E. A.,
Brewster, K. A., Dowell, D. C., Harris, L. M., Jirak, I. L., Kong, F.,
Supinie, T. A., Thomas, K. W., Wang, X., Wang, Y., and Xue, M.: Systematic
Comparison of Convection-Allowing Models during the 2017 NOAA HWT Spring
Forecasting Experiment, Weather Forecast., 34, 1395–1416,
https://doi.org/10.1175/WAF-D-19-0056.1, 2019. a
Putman, W. M. and Lin, S.-J.: Finite-volume transport on various cubed-sphere
grids, J. Comput. Phys., 227, 55–78,
https://doi.org/10.1016/j.jcp.2007.07.022, 2007. a, b
Roberts, B., Gallo, B. T., Jirak, I. L., Clark, A. J., Dowell, D. C., Wang, X.,
and Wang, Y.: What Does a Convection-Allowing Ensemble of Opportunity Buy Us
in Forecasting Thunderstorms?, Weather Forecast., 35, 2293–2316,
https://doi.org/10.1175/WAF-D-20-0069.1, 2020. a
Schwartz, C. S. and Sobash, R. A.: Revisiting sensitivity to horizontal grid
spacing in convection-allowing models over the central and eastern United
States, Mon. Weather Rev., 147, 4411–4435, 2019. a
Schwartz, C. S., Poterjoy, J., Carley, J. R., Dowell, D. C., Romine, G. S., and
Ide, K.: Comparing Partial and Continuously Cycling Ensemble Kalman Filter
Data Assimilation Systems for Convection-Allowing Ensemble Forecast
Initialization, Weather Forecast., 37, 85–112,
https://doi.org/10.1175/WAF-D-21-0069.1, 2022. a
Shao, H., Derber, J., Huang, X.-Y., Hu, M., Newman, K., Stark, D., Lueken, M.,
Zhou, C., Nance, L., Kuo, Y.-H., et al.: Bridging research to operations
transitions: Status and plans of community GSI, B. Am. Meteorol. Soc., 97, 1427–1440,
https://doi.org/10.1175/BAMS-D-13-00245.1, 2016. a
Shen, F., Xue, M., and Min, J.: A comparison of limited-area 3DVAR and
ETKF-En3DVAR data assimilation using radar observations at convective scale
for the prediction of Typhoon Saomai (2006), Meteorol. Appl., 24,
628–641, https://doi.org/10.1002/met.1663, 2017. a
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D. M., Wang,
W., and Powers, J. G.: A description of the Advanced Research WRF version 3.
NCAR Technical note-475+ STR, Tech. rep., National Center For Atmospheric
Research, Boulder CO. Mesoscale and Microscale Meteorology Laboratory,
https://doi.org/10.5065/D68S4MVH, 2008. a, b
Smith, T. L., Benjamin, S. G., Gutman, S. I., and Sahm, S.: Short-range
forecast impact from assimilation of GPS-IPW observations into the Rapid
Update Cycle, Mon. Weather Rev., 135, 2914–2930,
https://doi.org/10.1175/MWR3436.1, 2007. a
Snook, N., Kong, F., Brewster, K. A., Xue, M., Thomas, K. W., Supinie, T. A.,
Perfater, S., and Albright, B.: Evaluation of convection-permitting
precipitation forecast products using WRF, NMMB, and FV3 for the 2016–17
NOAA hydrometeorology testbed flash flood and intense rainfall experiments,
Weather Forecast., 34, 781–804, 2019. a
Thompson, G. and Eidhammer, T.: A study of aerosol impacts on clouds and
precipitation development in a large winter cyclone, J.
Atmos. Sci., 71, 3636–3658,
https://doi.org/10.1175/JAS-D-13-0305.1, 2014. a
Tong, C.-C., Jung, Y., Xue, M., and Liu, C.: Direct Assimilation of Radar Data
With Ensemble Kalman Filter and Hybrid Ensemble-Variational Method in the
National Weather Service Operational Data Assimilation System GSI for the
Stand-Alone Regional FV3 Model at a Convection-Allowing Resolution,
Geophys. Res. Lett., 47, e2020GL090179, https://doi.org/10.1029/2020GL090179, 2020. a, b, c, d, e
Tong, W., Li, G., Sun, J., Tang, X., and Zhang, Y.: Design Strategies of an
Hourly Update 3DVAR Data Assimilation System for Improved Convective
Forecasting, Weather Forecast., 31, 1673–1695,
https://doi.org/10.1175/WAF-D-16-0041.1, 2016. a, b, c
UFS Development Team: Unified Forecast System (UFS) Short-Range Weather
(SRW) Application, Zenodo, https://doi.org/10.5281/zenodo.4534994, 2021. a, b
UFS-R2O: Unified Forecast System Research-to-Operations (UFS-R2O) Project
Proposal,
https://www.weather.gov/media/sti/UFS-R2O-Project-Proposal-Public.pdf (last access: 20 June 2021),
2020. a
UPP: UPP Users Guide V4,
https://dtcenter.org/sites/default/files/community-code/upp-users-guide-v4.pdf (last access: 16 August 2021),
2021. a
Wang, X.: Incorporating Ensemble Covariance in the Gridpoint Statistical
Interpolation Variational Minimization: A Mathematical Framework, Mon. Weather Rev., 138, 2990–2995, https://doi.org/10.1175/2010mwr3245.1, 2010. a, b
Wang, X. and Lei, T.: GSI-Based Four-Dimensional Ensemble–Variational
(4DEnsVar) Data Assimilation: Formulation and Single-Resolution Experiments
with Real Data for NCEP Global Forecast System, Mon. Weather Rev., 142,
3303–3325, https://doi.org/10.1175/mwr-d-13-00303.1, 2014. a
Wang, X., Parrish, D., Kleist, D., and Whitaker, J.: GSI 3DVar-Based
Ensemble – Variational Hybrid Data Assimilation for NCEP Global Forecast
System: Single-Resolution Experiments, Mon. Weather Rev., 141,
4098–4117, https://doi.org/10.1175/mwr-d-12-00141.1, 2013. a
Wang, Y. and Wang, X.: Direct Assimilation of Radar Reflectivity without
Tangent Linear and Adjoint of the Nonlinear Observation Operator in the
GSI-Based EnVar System: Methodology and Experiment with the 8 May 2003
Oklahoma City Tornadic Supercell, Mon. Weather Rev., 145, 1447–1471,
https://doi.org/10.1175/MWR-D-16-0231.1, 2017.
a
Weisman, M. L. and Klemp, J. B.: The Dependence of Numerically Simulated
Convective Storms on Vertical Wind Shear and Buoyancy, Mon. Weather Rev., 110, 504–520,
https://doi.org/10.1175/1520-0493(1982)110<0504:TDONSC>2.0.CO;2, 1982. a
Wolff, J. and Beck, J.: The UFS Short-Range Weather App, Bulletin of the UFS
Community, p. 9, https://doi.org/10.25923/k3zn-xe66, 2020. a
Wong, M., Romine, G., and Snyder, C.: Model Improvement via Systematic
Investigation of Physics Tendencies, Mon. Weather Rev., 148, 671–688,
https://doi.org/10.1175/MWR-D-19-0255.1, 2020. a
Yano, J.-I., Ziemiański, M. Z., Cullen, M., Termonia, P., Onvlee, J.,
Bengtsson, L., Carrassi, A., Davy, R., Deluca, A., Gray, S. L., Homar, V.,
Kohler, M., Krichak, S., Michaelides, S., Phillips, V. T. J., Soares, P.
M. M., and Wyszogrodzki, A. A.: Scientific Challenges of Convective-Scale
Numerical Weather Prediction, B. Am. Meteorol. Soc., 99, 699–710, https://doi.org/10.1175/BAMS-D-17-0125.1, 2018. a, b
Zhang, C., Xue, M., Supinie, T. A., Kong, F., Snook, N., Thomas, K. W.,
Brewster, K., Jung, Y., Harris, L. M., and Lin, S.-J.: How well does an
FV3-based model predict precipitation at a convection-allowing resolution?
Results from CAPS forecasts for the 2018 NOAA hazardous weather test bed with
different physics combinations, Geophys. Res. Lett., 46, 3523–3531,
2019. a
Zhang, J., Howard, K., Langston, C., Kaney, B., Qi, Y., Tang, L., Grams, H.,
Wang, Y., Cocks, S., Martinaitis, S., Arthur, A., Cooper, K., Brogden, J.,
and Kitzmiller, D.: Multi-Radar Multi-Sensor (MRMS) Quantitative
Precipitation Estimation: Initial Operating Capabilities, B. Am. Meteorol. Soc., 97(4), 621–638,
https://doi.org/10.1175/BAMS-D-14-00174.1, 2016. a
Zhou, L., Lin, S.-J., Chen, J.-H., Harris, L. M., Chen, X., and Rees, S. L.:
Toward Convective-Scale Prediction within the Next Generation Global
Prediction System, B. Am. Meteorol. Soc., 100,
1225–1243, https://doi.org/10.1175/BAMS-D-17-0246.1, 2019. a, b
Zhu, Y., Derber, J., Collard, A., Dee, D., Treadon, R., Gayno, G., and Jung,
J. A.: Enhanced radiance bias correction in the National Centers for
Environmental Prediction's Gridpoint Statistical Interpolation data
assimilation system, Q. J. Roy. Meteor. Soc.,
140, 1479–1492, 2014. a
Short summary
A prototype data assimilation system for NOAA’s next-generation rapidly updated, convection-allowing forecast system, or Rapid Refresh Forecast System (RRFS) v0.1, is tested and evaluated. The impact of using data assimilation with a convective storm case study is examined. Although the convection in RRFS tends to be overestimated in intensity and underestimated in extent, the use of data assimilation proves to be crucial to improve short-term forecasts of storms and precipitation.
A prototype data assimilation system for NOAA’s next-generation rapidly updated,...
