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Atmosphere
Special Issues
Numerical Weather Prediction, Data Assimilation and Ensemble Forecasting—EGU 2018 Session
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Numerical Weather Prediction, Data Assimilation and Ensemble Forecasting—EGU 2018 Session

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Atmospheric Techniques, Instruments, and Modeling".

Deadline for manuscript submissions: closed (28 February 2019) | Viewed by 18654

Special Issue Editors

Prof. Dr. Haraldur Ólafsson

E-Mail Website
Guest Editor
Department of Physics, University of Iceland and Icelandic Meteorological Office, Bustadavegur 9, IS-150 Reykjavik, Iceland
Interests: orographic flows; mesoscale meteorology and climatology; high-resolution simulations for forecasting and regional climate; dust and snow
Special Issues, Collections and Topics in MDPI journals
Dr. Jian-Wen Bao

E-Mail Website
Guest Editor
National Oceanic and Atmospheric Administration, Washington, USA
Interests: surface processes; parameterization of physical processes, including stochastic physics, air quality and numerical simulations at all scales

Special Issue Information

Dear Colleagues,

This Special Issue will welcome contributions related to numerical weather prediction, including:

1) Forecasting and simulating high-impact weather events—research on the improvement of high-resolution numerical model predictions of severe weather events (such as winter storms, tropical storms, and severe mesoscale convective storms) using data from various observational platforms and the evaluation of the impact of new remote sensing data;

2) Development and improvement of model numerics—basic research on advanced numerical techniques for weather and climate models (such as cloud resolving global model and high-resolution regional models specialized for extreme weather events on sub-synoptic scales);

3) Development and improvement of model physics—progress in research on advanced model physics parametrization schemes (such as stochastic physics, air-wave-oceans coupling physics, turbulent diffusion and interaction with the surface, sub-grid condensation and convection, grid-resolved cloud and precipitation, land-surface parametrization, and radiation);

4) Model evaluation—verification of model components and operational NWP products against theories and observations, regional and global re-analysis of past observations, diagnosis of data assimilation systems;

5) Data assimilation systems—progress in the development of data assimilation systems for operational applications (such as reanalysis and climate services), research on advanced methods for data assimilation on various scales (such as treatment of model and observation errors in data assimilation, and observational network design and experiments);

6) Ensemble forecasts and predictability—strategies in ensemble construction, model resolution and forecast range-related issues, and applications to data assimilation;

7) Advances and challenges in high-resolution simulations and forecasting.

Prof. Dr. Haraldur Ólafsson
Dr. Jian-Wen Bao
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

  • numerical weather forecasts
  • parameterization
  • data assimilation
  • high-resolution simulations
  • ensemble forecasts
  • predictability
  • verification

Published Papers (4 papers)

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Research

19 pages, 2391 KiB  
Article
Generalized Z-Grid Model for Numerical Weather Prediction
by Yuanfu Xie
Atmosphere 2019, 10(4), 179; https://doi.org/10.3390/atmos10040179 - 03 Apr 2019
Cited by 5 | Viewed by 3021
Abstract
Z-grid finite volume models conserve all-scalar quantities as well as energy and potential enstrophy and yield better dispersion relations for shallow water equations than other finite volume models, such as C-grid and C-D grid models; however, they are more expensive to implement. During [...] Read more.
Z-grid finite volume models conserve all-scalar quantities as well as energy and potential enstrophy and yield better dispersion relations for shallow water equations than other finite volume models, such as C-grid and C-D grid models; however, they are more expensive to implement. During each time integration, a Z-grid model must solve Poisson equations to convert its vorticity and divergence to a stream function and velocity potential, respectively. To optimally utilize these conversions, we propose a model in which the stability and possibly accuracy on the sphere are improved by introducing more stencils, such that a generalized Z-grid model can utilize longer time-integration steps and reduce computing time. Further, we analyzed the proposed model’s dispersion relation and compared it to that of the original Z-grid model for a linearly rotating shallow water equation, an important property for numerical models solving primitive equations. The analysis results suggest a means of balancing stability and dispersion. Our numerical results also show that the proposed Z-grid model for a shallow water equation is more stable and efficient than the original Z-grid model, increasing the time steps by more than 1.4 times. Full article
(This article belongs to the Special Issue Numerical Weather Prediction, Data Assimilation and Ensemble Forecasting—EGU 2018 Session)
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11 pages, 17669 KiB  
Article
An Alternative Bilinear Interpolation Method Between Spherical Grids
by Ki-Hwan Kim, Pyoung-Seop Shim and Seoleun Shin
Atmosphere 2019, 10(3), 123; https://doi.org/10.3390/atmos10030123 - 06 Mar 2019
Cited by 20 | Viewed by 6664
Abstract
In geoscientific studies, conventional bilinear interpolation has been widely used for remapping between logically rectangular grids on the surface of a sphere. Recently, various spherical grid systems including geodesic grids have been suggested to tackle the singularity problem caused by the traditional latitude–longitude [...] Read more.
In geoscientific studies, conventional bilinear interpolation has been widely used for remapping between logically rectangular grids on the surface of a sphere. Recently, various spherical grid systems including geodesic grids have been suggested to tackle the singularity problem caused by the traditional latitude–longitude grid. We suggest an alternative to pre-existing bilinear interpolation methods for remapping between any spherical grids, even for randomly distributed points on a sphere. This method supports any geometrical configuration of four source points neighboring a target point for interpolation, and provides remapping accuracy equivalent to the conventional bilinear method. In addition, for efficient search of neighboring source points, we use the linked-cell mapping method with a cubed-sphere as a reference frame. As a result, the computational cost is proportional to NlogN instead of N 2 (N being the number of grid points), even for the remapping of randomly distributed points on a sphere. Full article
(This article belongs to the Special Issue Numerical Weather Prediction, Data Assimilation and Ensemble Forecasting—EGU 2018 Session)
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15 pages, 4195 KiB  
Article
Study on the Construction of Initial Condition Perturbations for the Regional Ensemble Prediction System of North China
by Hanbin Zhang, Min Chen and Shuiyong Fan
Atmosphere 2019, 10(2), 87; https://doi.org/10.3390/atmos10020087 - 19 Feb 2019
Cited by 2 | Viewed by 2931
Abstract
The regional ensemble prediction system (REPS) of North China is currently under development at the Institute of Urban Meteorology, China Meteorological Administration, with initial condition perturbations provided by global ensemble dynamical downscaling. To improve the performance of the REPS, a comparison of two [...] Read more.
The regional ensemble prediction system (REPS) of North China is currently under development at the Institute of Urban Meteorology, China Meteorological Administration, with initial condition perturbations provided by global ensemble dynamical downscaling. To improve the performance of the REPS, a comparison of two initial condition perturbation methods is conducted in this paper: (i) Breeding, which was specifically designed for the REPS, and (ii) Dynamical downscaling. Consecutive tests were implemented to evaluate the performances of both methods in the operational REPS environment. The perturbation characteristics were analyzed, and ensemble forecast verifications were conducted. Furthermore, a heavy precipitation case was investigated. The main conclusions are as follows: the Breeding perturbations were more powerful at small scales, while the downscaling perturbations were more powerful at large scales; the difference between the two perturbation types gradually decreased with the forecast lead time. The downscaling perturbation growth was more remarkable than that of the Breeding perturbations at short forecast lead times, while the perturbation magnitudes of both schemes were similar for long-range forecasts. However, the Breeding perturbations contained more abundant small-scale components than downscaling for the short-range forecasts. The ensemble forecast verification indicated a slightly better downscaling ensemble performance than that of the Breeding ensemble. A precipitation case study indicated that the Breeding ensemble performance was better than that of downscaling, particularly in terms of location and strength of the precipitation forecast. Full article
(This article belongs to the Special Issue Numerical Weather Prediction, Data Assimilation and Ensemble Forecasting—EGU 2018 Session)
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18 pages, 8336 KiB  
Article
Gap-Filling of MODIS Time Series Land Surface Temperature (LST) Products Using Singular Spectrum Analysis (SSA)
by Hamid Reza Ghafarian Malamiri, Iman Rousta, Haraldur Olafsson, Hadi Zare and Hao Zhang
Atmosphere 2018, 9(9), 334; https://doi.org/10.3390/atmos9090334 - 23 Aug 2018
Cited by 44 | Viewed by 5494
Abstract
Land surface temperature (LST) is a basic parameter in energy exchange between the land and the atmosphere, and is frequently used in many sciences such as climatology, hydrology, agriculture, ecology, etc. Time series of satellite LST data have usually deficient, missing, and unacceptable [...] Read more.
Land surface temperature (LST) is a basic parameter in energy exchange between the land and the atmosphere, and is frequently used in many sciences such as climatology, hydrology, agriculture, ecology, etc. Time series of satellite LST data have usually deficient, missing, and unacceptable data caused by the presence of clouds in images, the presence of dust in the atmosphere, and sensor failure. In this study, the singular spectrum analysis (SSA) algorithm was used to resolve the problem of missing and outlier data caused by cloud cover. The region studied in the present research included an image frame of the Moderate Resolution Imaging Spectroradiometer (MODIS) with horizontal number 22 and vertical number 05 (h22v05). This image involved a large part of Iran, Turkmenistan, and the Caspian Sea. In this study, MODIS LST products (MOD11A1) were used during 2015 with approximately 1 km × 1 km spatial resolution and day/night LST data (daily temporal resolution). On average, the data have 36.37% gaps in each pixel profile with 730 day/night LST data. The results of the SSA algorithm in the reconstruction of LST images indicated a root mean square error (RMSE) of 2.95 Kelvin (K) between the original and reconstructed LST time series data in the study region. In general, the findings showed that the SSA algorithm using spatio-temporal interpolation can be effectively used to resolve the problem of missing data caused by cloud cover. Full article
(This article belongs to the Special Issue Numerical Weather Prediction, Data Assimilation and Ensemble Forecasting—EGU 2018 Session)
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