Submit to Atmosphere Review for Atmosphere Propose a Special Issue

Journal Menu

Journal Browser

► Journal Browser

Advances in Hazardous Weather Prediction: Data Assimilation, Numerical Model and Tools

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Published Papers

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Meteorology".

Deadline for manuscript submissions: closed (15 July 2022) | Viewed by 24068

Share This Special Issue

Special Issue Editors

Dr. Yunheng Wang

E-Mail Website
Guest Editor

1. Cooperative Institute for Severe and High-Impact Weather Research and Operations (CIWRO), University of Oklahoma, Norman, OK 73072, USA
2. National Severe Storms Laboratory (NSSL), National Oceanic & Atmospheric Administration, Norman, OK 73072, USA
Interests: radar data assimilation; regional NWP; convective-allowing model; high-performance computing
Special Issues, Collections and Topics in MDPI journals

Dr. Avelino F. Arellano

E-Mail Website
Guest Editor

Department of Hydrology and Atmospheric Sciences, University of Arizona, Tucson, AZ 85721, USA
Interests: data assimilation; inverse modeling; atmospheric chemistry and composition; satellite remote sensing and data analysis; sources and sinks of atmospheric constituents; numerical weather prediction; global and regional air quality

Special Issue Information

Dear Colleagues,

Short-range (0–6 hour) weather forecasts have made significant progress recently for hazardous weather events including tornados, hails, flash flooding and damaging winds, etc. This is highly accredited to the advances in data assimilation (DA) algorithms and the application of radar/satellite observation data, the development with the convective-allowing models (CAMs), the utilization of high-performance computers, and the development of AI techniques. This Special Issue seeks submissions on the following topics that are related to the improvement of forecasts, warnings and decision support for high-impact thunderstorm events:

CAM development and application;
DA algorithms and application for new observation datasets;
high-performance computing in DA and CAMs;
applications of machine learning and AI techniques for hazardous event prediction;
development in verification method and data for hazardous events;
applications of other computing techniques for hazardous weather systems, such as workflow development, software management, etc.

Dr. Yunheng Wang
Dr. Avelino F. Arellano
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Atmosphere is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

radar data assimilation
regional numerical weather prediction
convective-allowing model
probabilistic hazard information
high-performance computing
machine learning and artificial intelligence
objective verification

Benefits of Publishing in a Special Issue

Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (9 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

14 pages, 14549 KiB

Open AccessArticle

Application of Radar Radial Velocity Data Assimilation Based on Different Momentum Control Variables in Forecasting Typhoon Kompasu

by Gangjie Yang, Jinzhong Min and Feifei Shen

Atmosphere 2023, 14(1), 39; https://doi.org/10.3390/atmos14010039 - 26 Dec 2022

Viewed by 1852

Abstract

The study is based on the Weather Research and Forecasting Model (WRF) and the three-dimensional variational (3DVAR) data assimilation system. Based on two different control variables, the effects of radar radial velocity assimilation in forecasting of the tropical cyclone (TC) Kompasu were evaluated. The single observation experiment showed that DA_ψχ produces cyclonic increments, while DA_UV only produces increments in the same direction as the observation. DA_ψχ significantly enhances the wind field at 850 hPa with a large number of unphysical cyclonic increments. On the other hand, DA_UV produces reasonable cyclonic increments to enhance the TC. The assimilation of DA_UV makes the surface wind enhanced and the sea level pressure at the TC center reduced. The circular structure of the DA_ψχ wind field is not clear and neither is the large wind area concentrated. In addition, the DA_ψχ shows spurious convection at the high altitude of the vertical cross section, while the DA_UV presents enhanced large wind area at the bottom. The RMSE of the radial velocity is smaller during the circular assimilation in DA_UV. DA_ψχ does not improve the track forecast of Kompasu, while DA_UV shows a significant improvement by the track forecast. Full article

(This article belongs to the Special Issue Advances in Hazardous Weather Prediction: Data Assimilation, Numerical Model and Tools)

► Show Figures

Figure 1

19 pages, 13665 KiB

Open AccessArticle

Impacts of Multi-Source Microwave Satellite Radiance Data Assimilation on the Forecast of Typhoon Ampil

by Aiqing Shu, Dongmei Xu, Shiyu Zhang, Feifei Shen, Xuewei Zhang and Lixin Song

Atmosphere 2022, 13(9), 1427; https://doi.org/10.3390/atmos13091427 - 2 Sep 2022

Cited by 2 | Viewed by 1944

Abstract

This study investigates the impacts of the joint assimilation of microware temperature sensor, Advanced Microwave Sounding Unit-A (AMSUA), and microware humidity sensors, Microwave Humidity Sounder (MHS) and Microwave Humidity Sounder-2 (MWHS2), on the analyses and forecasts for the tropical cyclone (TC) system. Experiments are conducted using a three-dimensional variation (3DVAR) algorithm in the framework of the weather research and forecasting data assimilation (WRFDA) system for the forecasting of Typhoon Ampil (2018). The results show that the assimilation of MWHS2 radiance data improves the analyses better in terms of TC’s structure and moisture conditions than those of the MHS experiment. To some extent, the experiment with only AMSUA radiance delivers some positive impacts of the precipitation, track, and intensity forecast than the other two experiments do. In addition, the skill of the precipitation forecast is notably enhanced with higher equitable threat score (ETS) by the simultaneous assimilation of the MHS, MWHS2, and AMSUA radiance. Generally, assimilation of radiance from all sources of MHS, MWHS2, and AMSUA could combine the advantages of assimilating each type of sensors rather than individually. The consistent improvement is also confirmed for the TC’s track forecast with reduced error on average, whereas the improvement of intensity forecast is not obvious. Full article

(This article belongs to the Special Issue Advances in Hazardous Weather Prediction: Data Assimilation, Numerical Model and Tools)

► Show Figures

Figure 1

19 pages, 3415 KiB

Open AccessArticle

WWLLN Hot and Cold-Spots of Lightning Activity and Their Relation to Climate in an Extended Central America Region 2012–2020

by Jorge A. Amador and Dayanna Arce-Fernández

Atmosphere 2022, 13(1), 76; https://doi.org/10.3390/atmos13010076 - 1 Jan 2022

Cited by 4 | Viewed by 2844

Abstract

Lightning activity has been recognized to have, historically, social and environmental consequences around the globe. This work analyzes the space-time distribution of lightning-densities (D) in an extended Central America region (ECA). World Wide Lightning Location Network data was analyzed to link D with dominant climate patterns over the ECA for 2012–2020. D associated with cold surges entering the tropics dominate during boreal winter. The highest D (hot-spots) was found to agree well with previously known sites, such as the “Catatumbo” in Venezuela; however, D was lower here due to different detection efficiencies. Previously reported hot-spots showed strong continental signals in CA; however, in this work, they were over the oceans near to coastlines, especially in the eastern tropical Pacific (ETP). Most cold-spots, implying a minimum of vulnerability to human impacts and to some industries, were situated in the Caribbean Sea side of Central America. The Mid-Summer-Drought and the Caribbean-Low-Level-Jet (CLLJ) markedly reduced the D during July-August. The CLLJ in the central CS and across the Yucatan and the southern Gulf of Mexico acts as a lid inhibiting convection due to its strong vertical shear during the boreal summer. The CLLJ vertical wind-shear and its extension to the Gulf of Papagayo also diminished convection and considerably decreased the D over a region extending westward into the ETP for at least 400–450 km. A simple physical mechanism to account for the coupling between the CLLJ, the MSD, and lightning activity is proposed for the latter region. Full article

(This article belongs to the Special Issue Advances in Hazardous Weather Prediction: Data Assimilation, Numerical Model and Tools)

► Show Figures

Figure 1

18 pages, 3119 KiB

Open AccessArticle

Downscaling of Future Precipitation in China’s Beijing-Tianjin-Hebei Region Using a Weather Generator

by Yaoming Liao, Deliang Chen, Zhenyu Han and Dapeng Huang

Atmosphere 2022, 13(1), 22; https://doi.org/10.3390/atmos13010022 - 24 Dec 2021

Cited by 4 | Viewed by 3008

Abstract

To project local precipitation at the existing meteorological stations in China’s Beijing-Tianjin-Hebei region in the future, local daily precipitation was simulated for three periods (2006–2030, 2031–2050, and 2051–2070) under RCP 4.5 and RCP 8.5 emission scenarios. These projections were statistically downscaled using a weather generator (BCC/RCG-WG) and the output of five global climate models. Based on the downscaled daily precipitation at 174 stations, eight indices describing mean and extreme precipitation climates were calculated. Overall increasing trends in the frequency and intensity of the mean and extreme precipitation were identified for the majority of the stations studied, which is in line with the GCMs’ output. However, the downscaling approach enables more local features to be reflected, adding value to applications at the local scale. Compared with the baseline during 1961–2005, the regional average annual precipitation and its intensity are projected to increase in all three future periods under both RCP 4.5 and RCP 8.5. The projected changes in the number of days with precipitation are relatively small across the Beijing-Tianjin-Hebei region. The regional average annual number of days with precipitation would increase by 0.2~1.0% under both RCP 4.5 and RCP 8.5, except during 2031–2050 under RCP 8.5 when it would decrease by 0.7%. The regional averages of annual days with precipitation ≥25 mm and ≥40 mm, the greatest one-day and five-day precipitation in the Beijing-Tianjin-Hebei region, are projected to increase by 8~30% during all the three periods. The number of days with daily precipitation ≥40 mm was projected to increase most significantly out of the eight indices, indicating the need to consider increased flooding risk in the future. The average annual maximum number of consecutive days without precipitation in the Beijing-Tianjin-Hebei region is projected to decrease, and the drought risk in this area is expected to decrease. Full article

(This article belongs to the Special Issue Advances in Hazardous Weather Prediction: Data Assimilation, Numerical Model and Tools)

► Show Figures

Figure 1

24 pages, 8686 KiB

Open AccessArticle

Multi-Scale Object-Based Probabilistic Forecast Evaluation of WRF-Based CAM Ensemble Configurations

by Andrew Wilkins, Aaron Johnson, Xuguang Wang, Nicholas A. Gasperoni and Yongming Wang

Atmosphere 2021, 12(12), 1630; https://doi.org/10.3390/atmos12121630 - 6 Dec 2021

Cited by 1 | Viewed by 2514

Abstract

Convection-allowing model (CAM) ensembles contain a distinctive ability to predict convective initiation location, mode, and morphology. Previous studies on CAM ensemble verification have primarily used neighborhood-based methods. A recently introduced object-based probabilistic (OBPROB) framework provides an alternative and novel framework in which to re-evaluate aspects of optimal CAM ensemble design with an emphasis on ensemble storm mode and morphology prediction. Herein, we adopt and extend the OBPROB method in conjunction with a traditional neighborhood-based method to evaluate forecasts of four differently configured 10-member CAM ensembles. The configurations include two single-model/single-physics, a single-model/multi-physics, and a multi-model/multi-physics configuration. Both OBPROB and neighborhood frameworks show that ensembles with more diverse member-to-member designs improve probabilistic forecasts over single-model/single-physics designs through greater sampling of different aspects of forecast uncertainties. Individual case studies are evaluated to reveal the distinct forecast features responsible for the systematic results identified from the different frameworks. Neighborhood verification, even at high reflectivity thresholds, is primarily impacted by mesoscale locations of convective and stratiform precipitation across scales. In contrast, the OBPROB verification explicitly focuses on convective precipitation only and is sensitive to the morphology of similarly located storms. Full article

(This article belongs to the Special Issue Advances in Hazardous Weather Prediction: Data Assimilation, Numerical Model and Tools)

► Show Figures

Figure 1

17 pages, 5518 KiB

Open AccessArticle

A Machine-Learning Approach Combining Wavelet Packet Denoising with Catboost for Weather Forecasting

by Dan Niu, Li Diao, Zengliang Zang, Hongshu Che, Tianbao Zhang and Xisong Chen

Atmosphere 2021, 12(12), 1618; https://doi.org/10.3390/atmos12121618 - 4 Dec 2021

Cited by 24 | Viewed by 3728

Abstract

Accurate forecasting of future meteorological elements is critical and has profoundly affected human life in many aspects from rainstorm warning to flight safety. The conventional numerical weather prediction (NWP) sometimes leads to unsatisfactory performance due to inappropriate initial state settings. In this paper, a short-term weather forecasting model based on wavelet packet denoising and Catboost is proposed, which takes advantage of the fusion information combining the historical observation data with the prior knowledge from NWP. The feature selection and spatiotemporal feather addition are also explored to further improve performance. The proposed method is evaluated on the datasets provided by Beijing weather stations. Experimental results demonstrate that compared with many deep-learning or machine-learning methods such as LSTM, Seq2Seq, and random forest, the proposed Catboost model incorporated with wavelet packet denoising can achieve shorter convergence time and higher prediction accuracy. Full article

(This article belongs to the Special Issue Advances in Hazardous Weather Prediction: Data Assimilation, Numerical Model and Tools)

► Show Figures

Figure 1

21 pages, 5763 KiB

Open AccessArticle

An Object-Based Method for Tracking Convective Storms in Convection Allowing Models

by Fan Han and Xuguang Wang

Atmosphere 2021, 12(11), 1535; https://doi.org/10.3390/atmos12111535 - 21 Nov 2021

Cited by 1 | Viewed by 1770

Abstract

The steady-state assumption commonly used in object-based tracking algorithms may be insufficient to determine the right track when a convective storm goes through a complicated evolution. Such an issue is exacerbated by the relatively coarse output frequency of current convection allowing model (CAM) forecasts (e.g., hourly), giving rise to many spatially well resolved but temporally not well resolved storms that steady-state assumption could not account for. To reliably track simulated storms in CAM outputs, this study proposed an object-based method with two new features. First, the method explicitly estimated the probability of each probable track based on either its immediate past and future motion or a reliable “first-guess motion” derived from storm climatology or near-storm environmental variables. Second, object size was incorporated into the method to help identify temporally not well resolved storms and minimize false tracks derived for them. Parameters of the new features were independently derived from a storm evolution analysis using 2-min Multi-Radar Multi-Sensor (MRMS) data and hourly CAM forecasts produced by the University of Oklahoma (OU) Multiscale data Assimilation and Predictability Laboratory (MAP) from May 2019. The performance of the new method was demonstrated with hourly MRMS and CAM forecast examples from May 2018. A systematic evaluation of four severe weather events indicated 99% accuracy achieved for over 600 hourly MRMS tracks derived with the proposed tracking method. Full article

(This article belongs to the Special Issue Advances in Hazardous Weather Prediction: Data Assimilation, Numerical Model and Tools)

► Show Figures

Figure 1

20 pages, 22579 KiB

Open AccessArticle

Impact of the Assimilation of Multi-Platform Observations on Heavy Rainfall Forecasts in Kong-Chi Basin, Thailand

by Thippawan Thodsan, Falin Wu, Kritanai Torsri, Thakolpat Khampuenson and Gongliu Yang

Atmosphere 2021, 12(11), 1497; https://doi.org/10.3390/atmos12111497 - 12 Nov 2021

Cited by 4 | Viewed by 2668

Abstract

Data assimilation with a Numerical Weather Prediction (NWP) model using an observation system in a regional area is becoming more prevalent for local weather forecasting activities to reduce the risk of disasters. In this study, we evaluated the predictive capabilities of multi-platform observation assimilation based on a WRFDA (Weather Research and Forecasting model data assimilation) system with 9 km grid spacing over the Kong-Chi basin (KCB), where tropical storms and heavy rainfall occur frequently. Data assimilation experiments were carried out with two assimilation schemes: (1) assimilating the combined multi-platform observations of PREPBUFR data from the National Centers for Environmental Prediction (NCEP) and Automatic Weather Stations (AWS) data from the National Hydroinformatics Data Center in Thailand, and (2) assimilating the AWS data only, which are referred to as DAALL and DAAWS, respectively. Assimilation experiments skill scores with lead times of 48 h and 72 h were evaluated by comparing their accumulated rainfall and mean temperatures every three hours in the AWS for heavy rainfall events that occurred on 28 July 2017 and 30 August 2019. The results show that the DAALL improved the statistical skill scores by improving the pattern and intensity of heavy rainfall events, and DAAWS also improved the model results of near-surface location forecasts. The accuracy of the two assimilations for 3 h of accumulated rainfall with a 5 mm threshold, was only above 70%, but the threat score was acceptable. Temperature observations and assimilation experiments fitted a significant correlation with a coefficient greater than 0.85, while the mean absolute errors, even at the 48 h lead times remained below 1.75 °C of the mean temperature. The variables of the AWS observations in real-time after combining them with the weather forecasting model were evaluated for unprecedented rain events in the KCB. The scores suggested that the assimilation of the multi-platform observations at the 48 h lead times has an impact on heavy rainfall prediction in terms of the threat score, compared to the assimilation of AWS data only. The reason for this could be that fewer observations of the AWS data affected the WRFDA model. Full article

(This article belongs to the Special Issue Advances in Hazardous Weather Prediction: Data Assimilation, Numerical Model and Tools)

► Show Figures

Figure 1

21 pages, 24258 KiB

Open AccessArticle

Impact of Ensemble-Variational Data Assimilation in Heavy Rain Forecast over Brazilian Northeast

by João Pedro Gonçalves Nobre, Éder Paulo Vendrasco and Carlos Frederico Bastarz

Atmosphere 2021, 12(9), 1201; https://doi.org/10.3390/atmos12091201 - 16 Sep 2021

Viewed by 2250

Abstract

The Brazilian Northeast (BNE) is located in the tropical region of Brazil. It is bounded by the Atlantic Ocean, and its climate and vegetation are strongly affected by continental plateaus. The plateaus keep the humid air masses to the east and are responsible for the rain episodes, and at the west side the northeastern hinterland and dry air masses are observed. This work is a case study that aims to evaluate the impact of updating the model initial condition using the 3DEnVar (Three-Dimensional Ensemble Variational) system in heavy rain episodes associated with Mesoscale Convective Systems (MCS). The results were compared to 3DVar (Three-Dimensional Variational) and EnSRF (Ensemble Square Root Filter) systems and with no data assimilation. The study enclosed two MCS cases occurring on 14 and 24 January 2017. For that purpose, the RMS (Regional Modeling System) version 3.0.0, maintained by the Center for Weather Forecasting and Climate Studies (CPTEC), used two components: the Weather Research and Forecasting (WRF) mesoscale model and the GSI (Gridpoint Statistical Interpolation) data assimilation system. Currently, the RMS provides the WRF initial conditions by using 3DVar data assimilation methodology. The 3DVar uses a climatological covariance matrix to minimize model errors. In this work, the 3DEnVar updates the RMS climatological covariance matrix through the forecast members based on the errors of the day. This work evaluated the improvements in the detection and estimation of 24 h accumulated precipitation in MCS events. The statistic index RMSE (Root Mean Square Error) showed that the hybrid data assimilation system (3DEnVar) performed better in reproducing the precipitation in the MCS occurred on 14 January 2017. On 24 January 2017, the EnSRF was the best system for improving the WRF forecast. In general, the BIAS showed that the WRF initialized with different initial conditions overestimated the 24 h accumulated precipitation. Therefore, the viability of using a hybrid system may depend on the hybrid algorithm that can modify the weights attributed to the EnSRF and 3DVar matrix in the GSI over the assimilation cycles. Full article

(This article belongs to the Special Issue Advances in Hazardous Weather Prediction: Data Assimilation, Numerical Model and Tools)

► Show Figures

Journal Menu

Journal Browser

Advances in Hazardous Weather Prediction: Data Assimilation, Numerical Model and Tools

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (9 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI