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Special Issue "Remote Sensing Data Application, Data Reanalysis and Advances for Mesoscale Numerical Weather Models"

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Atmospheric Remote Sensing".

Deadline for manuscript submissions: 25 February 2024 | Viewed by 2368

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Special Issue Editors

Dr. Yunheng Wang
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Guest Editor

Cooperative Institute for Severe and High-Impact Weather Research and Operations (CIWRO), University of Oklahoma, Norman, OK, USA
Interests: radar data assimilation for short-term severe weather forecasting; high performance computing in data assimilation and numerical weather prediction

Dr. Feifei Shen

E-Mail Website
Guest Editor

Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)/Joint International Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science & Technology, Nanjing, China
Interests: Doppler weather radar data assimilation; satellite remote sensing observation data assimilation; integrated variational hybrid assimilation system development; wind, solar and other renewable energy research
Special Issues, Collections and Topics in MDPI journals

Dr. Xin Li

E-Mail Website
Guest Editor

Nanjing Joint Institute for Atmospheric Sciences, Nanjing, China
Interests: satellite data assimilation; radar data assimilation; ensemble–variational data assimilation; satellite data application; numerical model prediction; severe weather simulation

Dr. Lipeng Jiang

E-Mail Website
Guest Editor

CMA Earth System Modeling and Prediction Centre, China Meteorological Administration (CMA), Beijing, China
Interests: global and regional reanalysis; satellite remote sensing data assimilation; coupled chemistry-meteorology data assimilation

Dr. Yuyan Cui

E-Mail Website
Guest Editor

Regional Air Quality Modeling Section, Air Quality Planning and Science Division, California Air Resources Board (CARB), Sacramento, CA, USA
Interests: atmospheric numerical and statistical modeling (application and development); boundary layer and turbulence; earth-atmosphere interactions; atmospheric composition; trace gas (greenhouse gas) emissions; machine learning application of atmospheric sciences

Dr. Dongmei Xu

E-Mail Website
Guest Editor

School of Atmospheric Sciences, Nanjing University of Information Science & Technology, Nanjing 211544, China
Interests: satellite remote sensing observation data assimilation; radiance data application for cloud retrievals; ensemble–variational data assimilation; radar data assimilation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent progress in computer technology and computing capabilities has facilitated more advanced applications of remote sensing data in mesoscale numerical weather models. Furthermore, the developments of remote sensing technology continuously provide new data types. Such advances will benefit both numerical weather prediction (NWP) for severe and high-impact weather events and the quality of regional/global data reanalysis. This Special Issue seeks innovative submissions that are related to improving the accuracy of mesoscale weather models through remote sensing data assimilations, new remote sensing networks, or other remote sensing data applications that improve the prediction of high-impact weather events, air quality research, land & water monitoring, and the decision making involved in such predictions, as well as applications of and enhancements in regional or global data reanalysis with remote sensing data.

Dr. Yunheng Wang
Dr. Feifei Shen
Dr. Xin Li
Dr. Lipeng Jiang
Dr. Yuyan Cui
Dr. Dongmei Xu
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. Remote Sensing is an international peer-reviewed open access semimonthly 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 2700 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

advances in remote sensing data assimilation
new types of remote sensing observations, network design or data analysis with numerical models
convective-allowing and/or regional numerical model developments
probabilistic prediction methods
verification methods and statistical modelling
new developments in artificial intelligence for numerical models
regional and global data reanalysis techniques
coupled data assimilation
air quality research

Published Papers (4 papers)

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Research

Open AccessArticle

Direct Assimilation of Ground-Based Microwave Radiometer Clear-Sky Radiance Data and Its Impact on the Forecast of Heavy Rainfall

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Remote Sens. 2023, 15(17), 4314; https://doi.org/10.3390/rs15174314 (registering DOI) - 01 Sep 2023

Abstract

Ground-based microwave radiometer (GMWR) data with high spatial and temporal resolution can improve the accuracy of weather forecasts when effectively assimilated into numerical weather prediction. Nowadays, the major method to assimilate these data is via indirect assimilation by assimilating the retrieved profiles, which introduces large retrieval errors and cannot easily be represented by an error covariance matrix. Direct assimilation, on the other hand, can avoid this issue. In this study, the ground-based version of the Radiative Transfer for the TIROS Operational Vertical Sounder (RTTOV-gb) was selected as the observation operator, and a direct assimilation module for GMWR radiance data was established in the Weather Research and Forecasting Model Data Assimilation (WRFDA). Then, this direct assimilation module was applied to assimilate GMWR data. The results were compared to the indirect assimilation experiment and demonstrated that direct assimilation can more effectively improve the model’s initial fields in terms of temperature and humidity than indirect assimilation while avoiding the influence of retrieval errors. In addition, direct assimilation performed better in the precipitation forecast than indirect assimilation, making the main precipitation center closer to the observation. In particular, the improvement in the precipitation forecast with a threshold of 60 mm/6 h was obvious, and the corresponding TS score was significantly enhanced. Full article

(This article belongs to the Special Issue Remote Sensing Data Application, Data Reanalysis and Advances for Mesoscale Numerical Weather Models)

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Open AccessArticle

Assimilating FY-3D MWHS2 Radiance Data to Predict Typhoon Muifa Based on Different Initial Background Conditions and Fast Radiative Transfer Models

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Remote Sens. 2023, 15(13), 3220; https://doi.org/10.3390/rs15133220 - 21 Jun 2023

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Abstract

In this study, the impact of assimilating MWHS2 radiance data under different background conditions on the analyses and deterministic prediction of the Super Typhoon Muifa case, which hit China in 2022, was explored. The fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) data and the Global Forecast System (GFS) analysis data from the National Centers for Environmental Prediction (NCEP) were used as the background fields. To assimilate the Microwave Humidity Sounder II (MWHS2) radiance data into the numerical simulation experiments, the Weather Research and Forecasting (WRF) model and its three-dimensional variational data assimilation system were employed. The results show that after the data assimilation, the standard deviation and root-mean-square error of the analysis significantly decrease relative to the observation, indicating the effectiveness of the assimilation process with both background fields. In the MWHS_GFS experiment, a subtropical high-pressure deviation to the east is observed around the typhoon, resulting in its northeast movement. In the differential field of the MWHS_ERA experiment, negative sea-level pressure differences around the typhoon are observed, which increases its intensity. In the deterministic predictions, assimilating the FY3D MWHS2 radiance data reduces the typhoon track error in the MWHS_GFS experiment and the typhoon intensity error in the MWHS_ERA experiment. In addition, it is found that the Community Radiative Transfer Model (CRTM) and the Radiative Transfer for Tovs (RTTOV) model show similar performance in assimilating MWHS2 radiance data for this typhoon case. It seems that the data assimilation experiment with the CRTM significantly reduces the typhoon track error than the experiment with the RTTOV model does, while the intensity error of both experiments is rather comparable. Full article

(This article belongs to the Special Issue Remote Sensing Data Application, Data Reanalysis and Advances for Mesoscale Numerical Weather Models)

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Open AccessArticle

Assimilating All-Sky Infrared Radiance Observations to Improve Ensemble Analyses and Short-Term Predictions of Thunderstorms

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Remote Sens. 2023, 15(12), 2998; https://doi.org/10.3390/rs15122998 - 08 Jun 2023

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Abstract

The experimental rapid-cycling Ensemble Kalman Filter (EnKF) in the convection-allowing ensemble-based Warn-on-Forecast System (WoFS) at the National Severe Storms Laboratory (NSSL) is used to assimilate all-sky infrared radiance observations from the GOES-16 7.3 μm water vapor channel in combination with radar wind and reflectivity observations to improve the analysis and subsequent forecast of severe thunderstorms (which occurred in Oklahoma on 2 May 2018). The method for radiance data assimilation is based primarily on the version used in WoFS. In addition, the methods for adaptive observation error inflation and background error inflation and the method of time-expanded sampling are also implemented in two groups of experiments to test their effectiveness and examine the impacts of radar observations and all-sky radiance observations on ensemble analyses and predictions of severe thunderstorms. Radar reflectivity observations and brightness temperature observations from the GOES-16 6.9 μm mid-level troposphere water vapor channel and 11.2 μm longwave window channel are used to evaluate the assimilation statistics and verify the forecasts in each experiment. The primary findings from the two groups of experiments are summarized: (i) Assimilating radar observations improves the overall (heavy) precipitation forecast up to 5 (4) h, according to the improved composite reflectivity forecast skill scores. (ii) Assimilating all-sky water vapor infrared radiance observations from GOES-16 in addition to radar observations improves the brightness temperature assimilation statistics and subsequent cloud cover forecast up to 6 h, but the improvements are not significantly affected by the adaptive observation and background error inflations. (iii) Time-expanded sampling can not only reduce the computational cost substantially but also slightly improve the forecast. Full article

(This article belongs to the Special Issue Remote Sensing Data Application, Data Reanalysis and Advances for Mesoscale Numerical Weather Models)

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Open AccessArticle

The Impact of Radar Radial Velocity Data Assimilation Using WRF-3DVAR System with Different Background Error Length Scales on the Forecast of Super Typhoon Lekima (2019)

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Remote Sens. 2023, 15(10), 2592; https://doi.org/10.3390/rs15102592 - 16 May 2023

Viewed by 646

Abstract

This study explores the impact of assimilating radar radial velocity (RV) on the forecast of Super Typhoon Lekima (2019) using the Weather Research and Forecasting (WRF) model and three-dimensional variational (3DVAR) assimilation system with different background error length scales. The results of two single observation tests show that the smaller background error length scale is able to constrain the spread of radar observation information within a relatively reasonable range compared with the larger length scale. During the five data assimilation cycles, the position and structure of the near-land typhoon are found to be significantly affected by the setting of the background error length scale. With a reduced length scale, the WRF-3DVAR system could effectively assimilate the radar RV to produce more accurate analyses, resulting in an enhanced typhoon vortex with a dynamic and thermal balance. In the forecast fields, the experiment with a smaller length scale not only reduces the averaged track error for the 24-h forecasts to less than 20 km, but it also more accurately captures the evolutions of the typhoon vortex and rainband during typhoon landing. In addition, the spatial distribution and intensity of heavy precipitation are corrected. For the 24-h quantitative precipitation forecasts, the equitable threat scores of the experiment with a reduced length scale are greater than 0.4 for the threshold from 1 to 100 mm and not less than 0.2 until the threshold increases to 240 mm. The enhanced prediction performances are probably due to the improved TC analysis. Full article

(This article belongs to the Special Issue Remote Sensing Data Application, Data Reanalysis and Advances for Mesoscale Numerical Weather Models)

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