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Advances in Mesoscale Numerical Weather Prediction and Its Applications (2nd Volume)

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

Deadline for manuscript submissions: closed (25 January 2023) | Viewed by 17770

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Dr. Anastasios Papadopoulos

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Guest Editor

Hellenic Centre for Marine Research, 19013 Athens, Greece
Interests: numerical weather prediction; model evaluation; operational meteorology and hydrometeorology; water and energy cycle; land/sea–air interactions; flash floods; coupling numerical models
Special Issues, Collections and Topics in MDPI journals

Dr. George Varlas

E-Mail Website
Guest Editor

Hellenic Centre for Marine Research, 19013 Athens, Greece
Interests: numerical weather prediction; synoptic and dynamic meteorology; boundary layer; hydrometeorological modelling; extreme weather events; flash floods; coupling numerical models; land/sea-air interaction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Our first volume of Special Issue “Advances in Mesoscale Numerical Weather Prediction and its Applications” was quite a successful undertaking and became popular not only for authors but for our readers as well. It is for this reason that we have decided to launch a second volume intending to collect contributions that report advancements and the current state of the mesoscale numerical weather prediction models including air-sea-land coupled models, forecast skill improvements, the impact of physical parameterizations on forecasts, boundary layer processes modeling, as well as applications of data assimilation techniques, nowcasting methods, hydrometeorological applications etc.

Advances in computer science and information technology facilitates continuing progress in mesoscale numerical weather prediction (NWP) for both research and operational forecasting purposes. Nowadays, mesoscale NWP models are essential for a broad spectrum of applications ranging from driving other numerical models (related to, e.g., ocean, hydrology, and air quality) and downscaling climate simulations to providing forecasts for renewable energy and hydrological purposes.

This Special Issue aims to shed new light on interdisciplinary applications of mesoscale NWPs to reveal weather-related physical processes as well as to mitigate the consequences of high-impact weather events. Therefore, this Special Issue intends to collect contributions that report advancements and the current state of mesoscale NWP models, including forecast skill improvements, the impact of physical parameterizations on forecasts, the validation and intercomparison of forecasts, as well as applications of data assimilation techniques and nowcasting methods. Furthermore, this Special Issue welcomes numerical experiments and case studies regarding strategies designed to couple mesoscale NWP models with hydrological, ocean, wave, and chemical models to improve our understanding of the mesoscale physical and dynamical processes that can trigger natural hazards. Methodological approaches and applications exploiting this knowledge to advance analyses and forecasts as well as to tailor them for the design and refinement of early warning systems and decision support services are of particular interest.

Dr. Anastasios Papadopoulos
Dr. George Varlas
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

mesoscale meteorology
numerical weather prediction (NWP)
convection-permitting NWP
physical parameterizations
ensemble weather forecasting
coupled models
hydrometeorological simulations
land/sea–atmosphere interactions
sensitivity experiments
data assimilation
model verification
tropical-like Mediterranean cyclones
severe local storms
weather-induced wildfires
wind power forecasting
early warning systems
decision support systems

Related Special Issues

Advances in Mesoscale Numerical Weather Prediction and Its Applications (3rd Edition) in Atmosphere (1 article)
Advances in Mesoscale Numerical Weather Prediction and its Applications in Atmosphere (11 articles)

Published Papers (9 papers)

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Research

19 pages, 10748 KiB

Open AccessArticle

Impact of Hyperspectral Infrared Sounding Observation and Principal-Component-Score Assimilation on the Accuracy of High-Impact Weather Prediction

by Qi Zhang and Min Shao

Atmosphere 2023, 14(3), 580; https://doi.org/10.3390/atmos14030580 - 17 Mar 2023

Cited by 1 | Viewed by 1240

Abstract

Observations from a hyperspectral infrared (IR) sounding interferometer such as the Infrared Atmospheric Sounding Interferometer (IASI) and the Cross-Track Infrared Sounder (CrIS) are crucial to numerical weather prediction (NWP). By measuring radiance at the top of the atmosphere using thousands of channels, these observations convey accurate atmospheric information to the initial condition through data assimilation (DA) schemes. The massive data volume has pushed the community to develop novel approaches to reduce the number of assimilated channels while retaining as much information content as possible. Thus, channel-selection schemes have become widely accepted in every NWP center. Two significant limitations of channel-selection schemes are (1) the deficiency in retaining the observational information content and (2) the higher cross-channel correlation in the observational error (R) matrix. This paper introduces a hyperspectral IR observation DA scheme in the principal component (PC) space. Four-month performance comparison case studies using the Weather Research and Forecasting model (WRF) as a forecast module between PC-score assimilation and the selected-channel assimilation experiment show that the PC-score assimilation scheme can reduce the initial condition’s root-mean-squared error for temperature and water vapor compared to the channel-selection scheme and thus improve the forecasting of precipitation and high-impact weather. Case studies using the Unified Forecast System Short-Range Weather (UFS-SRW) application as forecast module also indicate that the positive impact can be retained among different NWP models. Full article

(This article belongs to the Special Issue Advances in Mesoscale Numerical Weather Prediction and Its Applications (2nd Volume))

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42 pages, 11432 KiB

Open AccessArticle

Simulations of Mesoscale Flow Systems around Dugway Proving Ground Using the WRF Modeling System

by Robert E. Dumais, Jr., Daniela M. Spade and Thomas E. Gill

Atmosphere 2023, 14(2), 251; https://doi.org/10.3390/atmos14020251 - 27 Jan 2023

Viewed by 1389

Abstract

It is widely recognized that regions with complex heterogeneous topography and land-use properties produce a variety of diurnal mesoscale and microscale flows, which can be modified or even masked by significant large-scale synoptic forcing. These flows can be produced through both dynamic and thermal-forcing processes. Recent field programs such as the Terrain-induced Rotor Experiment (T-REX), Mountain Terrain Atmospheric Modeling and Observations Program (MATERHORN), and Perdigao have been used to observe and model flow behaviors under different topographical and large-scale meteorological conditions. Using the Advanced research version of the Weather Research and Forecast (WRF-ARW) model, we applied multi-nesting using an interactive one-way nesting approach to resolve to a sub-kilometer inner-grid spacing (0.452 km). Our interest was in the intensive observation period 6 (IOP6) of the Fall 2012 MATERHORN campaign conducted over Dugway Proving Ground (DPG) in Utah. An initial review of the IOP6 suggested that a range of diurnal flows were present, and that a relatively small subset of model setup configurations would be able to capture the general flows of this period. The review also led us to believe that this same subset would be able to capture differences due to variations in choice of model boundary-layer physics, land surface physics, land use/soil type specifications, and larger-scale meteorological conditions. A high model vertical resolution was used, with 90 vertical sigma levels applied. The IOP6 spanned the period of 2012 0800 UTC 14 October–0800 UTC 15 October. Based upon a lack of deep convection and moist microphysics throughout IOP6, we included comparison of planetary boundary layer (PBL) turbulence parameterization schemes even at the sub-kilometer grid spacing. We focused upon the gross model performance over our inner nest; therefore, a detailed comparison of the effects of model horizontal resolution are excluded. For surface parameters of wind and temperature, we compare mean absolute error and bias scores throughout the period at a number of surface meteorological observing sites. We found that despite attention given to the boundary layer turbulence physics, radiation physics and model vertical resolution, the results seemed to indicate more impact from the choices of thermal soil conductivity parameterization, land surface/soil texture category classification (and associated static property-parameter values), and large-scale forcing model. This finding lends support to what other researchers have found related to how these same forcings can exert a strong influence upon mesoscale flows around DPG. Our findings suggest that the two nights of IOP6 offer a pair of excellent consecutive nights to explore many of the forcing features important to local complex terrain flow. The flows of interest in this case included valley, anabatic/katabatic, and playa breeze systems. Subjective evidence was also found to support an influence provided by the modest synoptic northwesterly flow present within the lower troposphere (mainly on the night of 14 October). Follow-on research using the WRF-ARW capability to nest directly from mesoscale-to-LES can leverage IOP6 further. For example, to uncover more detailed and focused aspects of the dynamic and thermodynamic forcings contributing to the DPG diurnal flows. Full article

(This article belongs to the Special Issue Advances in Mesoscale Numerical Weather Prediction and Its Applications (2nd Volume))

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23 pages, 11874 KiB

Open AccessArticle

Medicane Ianos: 4D-Var Data Assimilation of Surface and Satellite Observations into the Numerical Weather Prediction Model WRF

by Paraskevi Vourlioti, Theano Mamouka, Apostolos Agrafiotis and Stylianos Kotsopoulos

Atmosphere 2022, 13(10), 1683; https://doi.org/10.3390/atmos13101683 - 14 Oct 2022

Cited by 3 | Viewed by 1450

Abstract

This work investigates extreme weather events such as the onset of medicanes, which can cause severe socioeconomic impacts, along with their predictability. In order to accurately forecast such events, the Weather Research and Forecasting (WRF) model and its state-of-the-art data assimilation modeling framework (WRFDA) were set up to produce high-resolution forecasts for the case study of Medicane Ianos, which affected Greece between 17 and 19 September 2020. Information from weather stations and the satellite precipitation product IMERG was blended with the background model information from the Global Forecast System (GFS) using the 4D variational data assimilation (4D-Var) technique. New fields in an 18 km spatial resolution domain covering Europe were generated and utilized as improved initial conditions for the forecast model. Forecasts were issued based on these improved initial conditions at two nested domains of 6 km and 2 km spatial resolution, with the 2 km domain enclosing Greece. Denial experiments, where no observational data were assimilated in the initial boundary conditions, showed that the temperature fields benefited throughout the forecasting horizon from the assimilation (ranging from a 5 to 10% reduction in the average MAE values), while neutral to slightly positive (ranging from a 0.4 to 2% reduction in the average MAE values) improvement was found for wind, although not throughout the forecast horizon. The increase in spatial resolution did not significantly reduce the forecast error, but was kept at the same small order of magnitude. A tendency of the model to overpredict precipitation regardless of assimilation was observed. The assimilation of the IMERG data improved the precipitation forecasting ability up to the 18th hour of forecast. When compared to assimilation experiments that excluded IMERG data, the assimilation of IMERG data produced a better representation of the spatial distribution of the precipitation fields. Full article

(This article belongs to the Special Issue Advances in Mesoscale Numerical Weather Prediction and Its Applications (2nd Volume))

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21 pages, 5946 KiB

Open AccessArticle

Evaluation of Urban Canopy Models against Near-Surface Measurements in Houston during a Strong Frontal Passage

by Eric A. Hendricks and Jason C. Knievel

Atmosphere 2022, 13(10), 1548; https://doi.org/10.3390/atmos13101548 - 22 Sep 2022

Cited by 1 | Viewed by 1292

Abstract

Urban canopy models (UCMs) in mesoscale numerical weather prediction models need evaluation to understand biases in urban environments under a range of conditions. The authors evaluate a new drag formula in the Weather Research and Forecasting (WRF) model’s multilayer UCM, the Building Effect Parameterization combined with the Building Energy Model (BEP+BEM), against both in-situ measurements in the urban environment as well as simulations with a simple bulk scheme and BEP+BEM using the old drag formula. The new drag formula varies with building packing density, while the old drag formula is constant. The case study is a strong cold frontal passage that occurred in Houston during the winter of 2017, causing high winds. It is found that both BEP+BEM simulations have lower peak wind speeds, consistent with near-surface measurements, while the bulk simulation has winds that are too strong. The constant-drag BEP+BEM simulation has a near-zero wind speed bias, while the new-drag simulation has a negative bias. Although the focus is on the impact of drag on the urban wind speeds, both BEP+BEM simulations have larger negative biases in the near-surface temperature than the bulk-scheme simulation. Reasons for the different performances are discussed. Full article

(This article belongs to the Special Issue Advances in Mesoscale Numerical Weather Prediction and Its Applications (2nd Volume))

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20 pages, 5086 KiB

Open AccessArticle

Offline Diagnostics of Skin Sea Surface Temperature from a Prognostic Scheme and Its Application in Typhoon Forecasting Using the CMA-TRAMS Model over South China

by Yanxia Zhang, Daosheng Xu, Zitong Chen and Weiguang Meng

Atmosphere 2022, 13(8), 1324; https://doi.org/10.3390/atmos13081324 - 19 Aug 2022

Cited by 5 | Viewed by 1647

Abstract

In the Tropical Regional Atmospherical Model System of South China of the China Meteorological Administration (CMA-TRAMS), the skin sea surface temperature (

T_{s}

) remains fixed during the forecast time. This limits the model’s performance in describing interactions between air and sea. [...] Read more.

In the Tropical Regional Atmospherical Model System of South China of the China Meteorological Administration (CMA-TRAMS), the skin sea surface temperature (

T_{s}

) remains fixed during the forecast time. This limits the model’s performance in describing interactions between air and sea. The offline diagnostics and online analysis coupled with the CMA-TRAMS of

T_{s}

prognostic scheme were discussed. The results of the offline diagnostics showed that the profile shape parameter, ν, and initial temperature,

T_{b}

, were sensitive to the forecasted

T_{s}

. Based on our observations, when ν was set to 0.2 and

T_{b}

was the averaged

T_{s}

without obvious diurnal variation, the forecasted

T_{s}

was relatively reasonable. The forecasted

T_{s}

of CMA-TRAMS after coupling with the

T_{s}

scheme had diurnal variations during the overall forecast time, which was different from the fixed

T_{s}

from the uncoupled model. There existed a certain difference of forecasted

T_{s}

between uncoupled and coupled models in those days influenced by typhoons. The biases and Root Mean Square Errors (RMSEs) for the temperature and moisture in the lower layer and those for the wind speed in most layers were reduced and, therefore, the accuracy of environmental field forecasting was improved from the coupled model. The typhoon track errors after 36-h decreased due to the improvement of steering flow on the west side of subtropical high from the coupled model. However, the difference of typhoon intensity errors was insignificant, which might mean that the differences of forecasted

T_{s}

and heat flux between uncoupled and coupled model are small. The reasons for the small difference need to be further investigated. Full article

(This article belongs to the Special Issue Advances in Mesoscale Numerical Weather Prediction and Its Applications (2nd Volume))

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29 pages, 27260 KiB

Open AccessEditor’s ChoiceArticle

Summertime Assessment of an Urban-Scale Numerical Weather Prediction System for Toronto

by Sylvie Leroyer, Stéphane Bélair, Vanh Souvanlasy, Marcel Vallée, Simon Pellerin and David Sills

Atmosphere 2022, 13(7), 1030; https://doi.org/10.3390/atmos13071030 - 28 Jun 2022

Cited by 7 | Viewed by 1943

Abstract

Urban-scale Numerical Weather Prediction (NWP) systems will be important tools for decision-making in and around large cities in a changing climate exposed to more extreme weather events. Such a state-of-the-art real-time system down to 250-m grid spacing was implemented in the context of the Toronto 2015 Panamerican games, Canada (PanAm). Combined with the Global Environmental Multiscale (GEM) model, attention was brought to the representation of the detailed urban landscape, and to the inclusion of sub-daily variation of the Great Lakes surface temperature. Results show a refined representation of the urban coastal environment micro-meteorology with a strong anisotropy of the urban heat island reaching about 2 °C on average for the summer season, coastal upwelling, and mesoscale features such as cumulus clouds and lake-breeze flow. Objective evaluation at the surface with a dense observational network reveals an overall good performance of the system and a clear improvement in comparison to reference forecasts at 2.5-km grid spacing in particular for standard deviation errors in urban areas up to 0.3 °C for temperature and dew point temperature, and up to 0.5 m s

^{- 1}

for the wind speed, as well as for precipitation with an increased Equitable Threat Score (ETS) by up to 0.3 for the evening accumulation. The study provides confidence in the capacity of the new system to improve weather forecasts to be delivered to urban dwellers although further investigation of the initialization methods in urban areas is needed. Full article

(This article belongs to the Special Issue Advances in Mesoscale Numerical Weather Prediction and Its Applications (2nd Volume))

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Graphical abstract

17 pages, 8274 KiB

Open AccessArticle

Comparison of the Forecast Performance of WRF Using Noah and Noah-MP Land Surface Schemes in Central Asia Arid Region

by Chenxiang Ju, Huoqing Li, Man Li, Zonghui Liu, Yufen Ma, Ali Mamtimin, Mingjing Sun and Yating Song

Atmosphere 2022, 13(6), 927; https://doi.org/10.3390/atmos13060927 - 07 Jun 2022

Cited by 3 | Viewed by 1971

Abstract

The land surface scheme (LSS) plays a very important role in the forecast of surface meteorological elements in the Central Asia arid region. Therefore, two LSSs viz. Noah and Noah-MP were evaluated over the Central Asia arid region in January and July 2017 using the Weather Research and Forecasting (WRF) model at a 3-km horizontal grid resolution. The objective was to assess the performance of WRF LSSs by calculated the mean error (ME) and the root mean squared error (RMSE) of simulated hourly meteorological elements, such as 2-m air temperature, 10-m wind speed, soil temperature, soil moisture at 5 and 25 cm thickness, and surface soil heat flux at 5 cm thickness. The results showed that, compared to Noah, Noah-MP modeled less surface sensible heat flux in the northern Xinjiang (15~20 W∙m⁻²) and surface latent heat flux in most areas of Xinjiang (<10 W∙m⁻²) in January, and mainly generated less sensible heat flux in most areas of north Xinjiang and the mountainous regions of southern Xinjiang (≤20 W·m⁻²) which on the contrary, generated more surface latent heat flux in most parts of Xinjiang (15~20 W·m⁻²) in July. Meanwhile, the surface soil heat flux generated from Noah-MP was closer to the observations at Hongliuhe and Kelameili stations in January, the ME increased by 17.5% and reduced by 80.7%, respectively, the RMSE decreased by 44.4% and 61.7%, respectively, and closer to the observations at Xiaotang station in July, the ME and RMSE reduced by 19.1% and 20.5%, respectively. Compared to Noah, Noah-MP improved the overall simulation of soil temperature and soil moisture over the northern and eastern Xinjiang (at 10 cm thickness), the ME and RMSE of simulated soil temperature reduced by 85.0% and 13.4% in January, decreased by 78.6% and increased by 6.2% in July, respectively, and the ME and RMSE of simulated soil moisture reduced by 67.2% and 14.9% in January, reduced by 33.3% and 2.8% in July, respectively. Compared to Noah, Noah-MP’s results were lowered for the simulated 10-m wind speed and 2-m air temperature, especially the simulated 2-m air temperature over the cold climate regions of northern Xinjiang, was improved significantly, the ME and RMSE of simulated 10-m wind speed reduced by 0.8% and 4.9% in January, decreased by 6.7% and 2.8% in July, respectively, the ME and RMSE of simulated 2-m air temperature reduced by 2.8% and 1.0% in July, respectively. This study demonstrated the advantage of coupled Noah-MP over the Central Asia arid region, providing the basis for WRF/Noah-MP in future operational applications in the Central Asia arid region. Full article

(This article belongs to the Special Issue Advances in Mesoscale Numerical Weather Prediction and Its Applications (2nd Volume))

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17 pages, 3698 KiB

Open AccessArticle

Development of a Dust Source Map for WRF-Chem Model Based on MODIS NDVI

by Christos Spyrou, Stavros Solomos, Nikolaos S. Bartsotas, Kostas C. Douvis and Slobodan Nickovic

Atmosphere 2022, 13(6), 868; https://doi.org/10.3390/atmos13060868 - 25 May 2022

Cited by 6 | Viewed by 3550

Abstract

We present the development of a physically-based dust source map for the GOCART-AFWA dust module in WRF-Chem model. The new parameterization is based on MODIS-NDVI and an updated emission strength map is computed every 15 days from the latest satellite observations. Modeling simulations for the period April–May 2017 over the Mediterranean, north Africa, and the Middle East are compared with observations of AOD at 31 AERONET stations. The new module is capable of reproducing the dust sources at finer detail. The overall performance of the model is improved, especially for stronger dust episodes with AOD > 0.25. For this threshold the model BIAS decreases from −0.20 to −0.02, the RMSE from 0.38 to 0.30, the Correlation Coefficient improves from 0.21 to 0.47, the fractional gross error (FGE) from 0.62 to 0.40, and the mean fractional bias (MFB) from −0.49 to −0.08. Similar improvement is also found for the lower AOD thresholds (>0.0 and >0.1), especially for the stations in Europe, the Mediterranean, Sahel, the Middle East, and Arabian Peninsula, which are mostly affected by dust transport during the experimental period. An overprediction of AOD, compared to the original dust-source scheme, is found for some stations in the Sahara desert, the Atlantic Ocean, and the Iberian Peninsula. In total, 124 out of the 170 statistical scores that are calculated indicate improvement of model performance. Full article

(This article belongs to the Special Issue Advances in Mesoscale Numerical Weather Prediction and Its Applications (2nd Volume))

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24 pages, 7995 KiB

Open AccessArticle

A Polarimetric Radar Operator and Application for Convective Storm Simulation

by Xuanli Li, John R. Mecikalski, Jason A. Otkin, David S. Henderson and Jayanthi Srikishen

Atmosphere 2022, 13(5), 645; https://doi.org/10.3390/atmos13050645 - 19 Apr 2022

Cited by 2 | Viewed by 1924

Abstract

In this study, a polarimetric radar forward model operator was developed for the Weather Research and Forecasting (WRF) model that was based on a scattering algorithm using the T-matrix methodology. Three microphysics schemes—Thompson, Morrison 2-moment, and Milbrandt-Yau 2-moment—were supported in the operator. This radar forward operator used the microphysics, thermodynamic, and wind fields from WRF model forecasts to compute horizontal reflectivity, radial velocity, and polarimetric variables including differential reflectivity (Z_DR) and specific differential phase (K_DP) for S-band radar. A case study with severe convective storms was used to examine the accuracy of the radar operator. Output from the radar operator was compared to real radar observations from the Weather Surveillance Radar–1988 Doppler (WSR-88D) radar. The results showed that the radar forward operator generated realistic polarimetric signatures. The distribution of polarimetric variables agreed well with the hydrometer properties produced by different microphysics schemes. Similar to the observed polarimetric signatures, radar operator output showed Z_DR and K_DP columns from low-to-mid troposphere, reflecting the large amount of rain within strong updrafts. The Thompson scheme produced a better simulation for the hail storm with a Z_DR hole to indicate the existence of graupel in the low troposphere. Full article

(This article belongs to the Special Issue Advances in Mesoscale Numerical Weather Prediction and Its Applications (2nd Volume))

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