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Atmosphere
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Data Assimilation Development: Theory, Algorithm, and Applications in Meteorology
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Data Assimilation Development: Theory, Algorithm, and Applications in Meteorology

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

Deadline for manuscript submissions: closed (15 August 2023) | Viewed by 12473

Special Issue Editors

Dr. Zhibin Sun

E-Mail Website
Guest Editor
Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO 80523, USA
Interests: data assimilation; remote sensing; climate change; numerical computation; nonlinear system; mathematical modeling; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Yan-An Liu

E-Mail Website
Guest Editor
School of Geographic Sciences, East China Normal University, Shanghai 200241, China
Interests: data assimilation; hyperspectral infrared remote sensing; retrieval of atmospheric parameters; application of meteorological satellite data; extreme weather simulation and prediction
Dr. Zigang Wei

E-Mail Website
Guest Editor
Department of Atmospheric & Oceanic Science, University of Maryland, College Park, MD 20740, USA
Interests: satellite remote sensing; data retrieval; retrieval of trace gases

Special Issue Information

Dear Colleagues,

Used to assimilate observational information into dynamical systems, data assimilation has many successful applications in atmospheric science and oceanic science, while also being utilized in many other fields. With the continuous development of a new generation of meteorological satellite- and ground-based remote sensing data, the development of data assimilation directly affects its applicational benefits in various fields, especially in extreme weather prediction.

With many successful studies on and applications of data assimilation, there are still many important research topics within the study of data assimilation. For example, there is no unique way to analytically or numerically estimate background/forecast error covariance and observational error covariance; therefore, efficient and accurate algorithms are always needed for those estimations. Although developing an appropriate observational operator requires a substantial understanding of model states and observational states, how to propagate observational information into model space as far as possible is often neglected by researchers. The systematic bias analysis and bias correction methods of new remote sensing data directly affect the effects of assimilation. When assimilating trace gases into meteorology models, the retrieval and quality control of trace gases are critical in minimizing the observational error covariance; thus, they can help assimilation processes to quickly constrain model solutions to the truth.

With much challenging research on the theories, algorithms, and meteorology applications of data assimilation, this Special Issue aims to cover the advancing studies in this field. Original studies, from pure theories to algorithm improvements, from assimilating satellite data to coupling data assimilation with machine learning, from Kalman filters with non-Gaussian noise to estimating error covariance via non-ensemble methods, from the combination between sequential assimilation and variational assimilation to data fusion with assimilation techniques, and so on, are all welcome contributions.

Dr. Zhibin Sun
Dr. Yan-An Liu
Dr. Zigang Wei
Guest Editors

Manuscript Submission Information

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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

  • data assimilation
  • new generation of satellite- and ground-based observations
  • data quality control
  • Kalman filter
  • error covariance
  • bias correction
  • extreme weather
  • remote sensing retrieval
  • trace gas

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Published Papers (4 papers)

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Research

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17 pages, 7295 KiB  
Article
Characteristics Analysis of the Multi-Channel Ground-Based Microwave Radiometer Observations during Various Weather Conditions
by Meng Liu, Yan-An Liu and Jiong Shu
Atmosphere 2022, 13(10), 1556; https://doi.org/10.3390/atmos13101556 - 23 Sep 2022
Cited by 7 | Viewed by 2021
Abstract
Ground-based multi-channel microwave radiometers (MWRs) can continuously detect atmospheric profiles in the tropospheric atmosphere. This makes MWR an ideal tool to supplement radiosonde and satellite observations in monitoring the thermodynamic evolution of the atmosphere and improving numerical weather prediction (NWP) through data assimilation. [...] Read more.
Ground-based multi-channel microwave radiometers (MWRs) can continuously detect atmospheric profiles in the tropospheric atmosphere. This makes MWR an ideal tool to supplement radiosonde and satellite observations in monitoring the thermodynamic evolution of the atmosphere and improving numerical weather prediction (NWP) through data assimilation. The analysis of product characteristics of MWR is the basis for applying its data to real-time monitoring and assimilation. In this paper, observations from the latest generation of ground-based multi-channel MWR RPG-HATPRO-G5 installed in Shanghai, China, are compared with the radiosonde observations (RAOB) observed in the same location. The detection performance, characteristics of various channels, and the accuracy of the retrieval profile products of the MWR RPG are comprehensively evaluated during various weather conditions. The results show that the brightness temperatures (BTs) observed by the ground-based MWR RPG during precipitation conditions were high, which affected its detection performance. The bias and the standard deviation (SD) between the BT observed by MWR RPG and the simulated BT during clear and cloudy sky conditions were slight and large, respectively, and the coefficient of determination (R2) was high and low, respectively. However, when the cloud liquid water (CLW) information was added when simulating BT, the bias and the SD of the observed BT and the simulated BT during cloudy days were reduced and the R2 value improved, which indicated that CLW information should be taken into account when simulating BT during cloudy conditions. The temperature profiles of the MWR retrieval had the same accuracy of RMSEs (root-mean-square error) with heights during both clear-sky and cloudy sky conditions, where the RMSEs were below 2 K when the heights were below 4 km. In addition, the MWR RPG has the potential ability to retrieve the temperature inversion in the boundary layer, which has important application value for fog and air pollution monitoring. Full article
(This article belongs to the Special Issue Data Assimilation Development: Theory, Algorithm, and Applications in Meteorology)
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21 pages, 7752 KiB  
Article
Conversion Coefficient Analysis and Evaporation Dataset Reconstruction for Two Typical Evaporation Pan Types—A Study in the Yangtze River Basin, China
by Ziheng Li, Xuefeng Sang, Siqi Zhang, Yang Zheng and Qiming Lei
Atmosphere 2022, 13(8), 1322; https://doi.org/10.3390/atmos13081322 - 19 Aug 2022
Cited by 4 | Viewed by 1760
Abstract
For the day-by-day evaporation observation data in the Yangtze River Basin from 1951 to 2019, the effects of the gradual shift of observation instruments from 20 cm diameter evaporation pan (D20) to E601 evaporation pan after 1980 are discussed, including inconsistent data series, [...] Read more.
For the day-by-day evaporation observation data in the Yangtze River Basin from 1951 to 2019, the effects of the gradual shift of observation instruments from 20 cm diameter evaporation pan (D20) to E601 evaporation pan after 1980 are discussed, including inconsistent data series, and missing and anomalous data. This study proposes a governance and improvement method for dual-source evaporation data (GIME). The method can accomplish the homogenization of data from different observation series and solve the problem of inconsistent and missing data, and we applied it in practice on data of the Yangtze River Basin. Firstly, the primary and secondary periods of the data were obtained by wavelet periodicity analysis; secondly, we considered the first cycle of observations to be representative of the sample and calculated the conversion relationship between the primary and secondary periods; thirdly, the conversion coefficient between the dual-source observations was calculated, and the results were corrected for stations outside the main cycle; finally, the daily evaporation dataset of E601 pan was established through data fusion and interpolation technology. The study found that the annual average conversion coefficients of the D20 and E601 pans in the Yangtze River Basin are basically between 0.55 and 0.80, and there are obvious differences in different regions. The conversion coefficient is positively correlated with relative humidity, wind speed, minimum temperature and altitude; and negatively correlated with sunshine duration, average temperature and maximum temperature. Evaporation is high in the upper reaches of the basin and low in the middle and lower reaches; in particular, evaporation is highest in the southwest, which is associated with the drought hazards. In addition, the article presents the spatial distribution of the conversion coefficients of D20 and E601 pans in the Yangtze River Basin. The results can realize the rapid correction of the evaporation data of the local meteorological department, and can be extended to the processing of other types of data in similar areas. Full article
(This article belongs to the Special Issue Data Assimilation Development: Theory, Algorithm, and Applications in Meteorology)
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15 pages, 5585 KiB  
Article
Satellite Radiance Data Assimilation Using the WRF-3DVAR System for Tropical Storm Dianmu (2021) Forecasts
by Thippawan Thodsan, Falin Wu, Kritanai Torsri, Efren Martin Alban Cuestas and Gongliu Yang
Atmosphere 2022, 13(6), 956; https://doi.org/10.3390/atmos13060956 - 12 Jun 2022
Cited by 6 | Viewed by 3377
Abstract
This study investigated the impact of the assimilation of satellite radiance observations in a three-dimensional variational data assimilation system (3DVAR) that could improve the tracking and intensity forecasts of the Tropical Storm Dianmu in 2021, which occurred over parts of southeast mainland Asia. [...] Read more.
This study investigated the impact of the assimilation of satellite radiance observations in a three-dimensional variational data assimilation system (3DVAR) that could improve the tracking and intensity forecasts of the Tropical Storm Dianmu in 2021, which occurred over parts of southeast mainland Asia. The weather research and forecasting (WRF) model was used to conduct the assimilation experiments of the storm. Four sets of numerical experiments were performed using the WRF. In the first, the control experiment, only conventional data in Binary Universal Form for the Representation of Meteorological Data (PREPBUFR) observations from the National Centers for Environmental Prediction (NCEP) were assimilated. The second experiment (RDA1) was performed with PREPBUFR observations and satellite radiance data from the Advanced Microwave Unit-A (AMSU-A), and the Advanced Technology Microwave Sounder (ATMS). PREPBUFR observations and the High-resolution Infrared Radiation Sounder (HIRS-4) were used in the third experiment (RDA2). The fourth experiment (ALL-OBS) used the assimilation of PREPBUFR observations and all satellite radiance data (AMSU-A, ATMS, and HIRS-4). The community radiative transfer model was used on the forward operator for the satellite radiance assimilation, along with quality control and bias correction procedures, before assimilating the radiance data. To evaluate the impact of the assimilation experiments, a forecast starting on 00 UTC 23 September 2021, was produced for 72 h. The results showed that the ALL-OBS experiment improved the short-term forecast up to ~24 h lead time, as compared to the assimilation considering only PREPBUFR observations. When all observations were assimilated into the model, the storm’s landfall position, intensity, and structure were accurately predicted. In the deterministic forecast, the tracking errors of the ALL-OBS experiment was consistently less than 40 km within 24 h. The case study of Tropical Storm Dianmu exhibited the significant positive impact of all observations in the numerical model, which could improve updates for initial conditions and storm forecasting. Full article
(This article belongs to the Special Issue Data Assimilation Development: Theory, Algorithm, and Applications in Meteorology)
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Review

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20 pages, 1703 KiB  
Review
Kalman Filter and Its Application in Data Assimilation
by Bowen Wang, Zhibin Sun, Xinyue Jiang, Jun Zeng and Runqing Liu
Atmosphere 2023, 14(8), 1319; https://doi.org/10.3390/atmos14081319 - 21 Aug 2023
Cited by 4 | Viewed by 4259
Abstract
In 1960, R.E. Kalman published his famous paper describing a recursive solution, the Kalman filter, to the discrete-data linear filtering problem. In the following decades, thanks to the continuous progress of numerical computing, as well as the increasing demand for weather prediction, target [...] Read more.
In 1960, R.E. Kalman published his famous paper describing a recursive solution, the Kalman filter, to the discrete-data linear filtering problem. In the following decades, thanks to the continuous progress of numerical computing, as well as the increasing demand for weather prediction, target tracking, and many other problems, the Kalman filter has gradually become one of the most important tools in science and engineering. With the continuous improvement of its theory, the Kalman filter and its derivative algorithms have become one of the core algorithms in optimal estimation. This paper attempts to systematically collect and sort out the basic principles of the Kalman filter and some of its important derivative algorithms (mainly including the Extended Kalman filter (EKF), the Unscented Kalman filter (UKF), the Ensemble Kalman filter (EnKF)), as well as the scope of their application, and also to compare their advantages and limitations. In addition, because there are a large number of applications based on the Kalman filter in data assimilation, this paper also provides examples and classifies the applications of both the Kalman filter and its derivative algorithms in the field of data assimilation. Full article
(This article belongs to the Special Issue Data Assimilation Development: Theory, Algorithm, and Applications in Meteorology)
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