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Remote Sensing for the Improvement of High-Impact Weather Analyses and Forecasts

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

Deadline for manuscript submissions: closed (31 January 2023) | Viewed by 14942

Special Issue Editors

School of Atmospheric Science, Nanjing University of Information Science and Technology, Nanjing 210044, China
Interests: satellite and radar remote sensing; remote sensing data utilization; data assimilation; numerical weather prediction; nowcasting using machine learning methods
Special Issues, Collections and Topics in MDPI journals
CIRES, University of Colorado Boulder and NOAA/Global Systems Laboratory, Boulder, CO 80305, USA
Interests: radar data assimilation/analysis; lightning data assimilation; satellite data assimilation; meso-scale processes; thunderstorms; supercell; tornado; hurricane; advanced data assimilation; numerical weather analysis/prediction/modeling; OSSEs and OSEs; GPS/GNSS meteorology
College of Information Science and Engineering, Ocean University of China, Qingdao 266100, China
Interests: radar and satellite remote sensing; intelligent information processing and pattern recognition; severe weather nowcasting using machine learning methods
Center for Analysis and Prediction of Storms, University of Oklahoma, Norman, OK 73072, USA
Interests: mesoscale dynamics; numerical weather prediction; severe convective systems; extreme rainfall events; tropical cyclones; data assimilation; cloud microphysics; regional climate

Special Issue Information

Dear Colleagues,
 
High-impact weather, such as extreme rainfall, severe storms, damaging wind, heatwaves, droughts, and so on, has huge impacts on our society and life. It may affect food and water safety, damage infrastructure, and put public life and/or health at risk. Improving the forecasting and communication of high-impact weather events has been identified by the World Meteorological Organization (WMO) as a priority for international weather research. The WMO has established a 10-year High-Impact Weather Project (HIWeather) to address the corresponding global challenges and accelerate progress on scientific and social solutions.

Remote sensing plays key roles in the monitoring, analysis, and prediction of high-impact weather. In the past few decades, there have been various exciting advances in the development of remote sensing technologies, platforms, and algorithms. Revolutionary progress in the field of artificial intelligence also adds new powerful tools for in-depth mining of remote sensing data. These dramatically enhance our understanding of the Earth's environment and boost our ability to simulate and forecast high-impact weather events.

This Special Issue covers a wide range of topics in remote sensing, including but not limited to remote sensing technology, quantitative precipitation estimation (QPE), innovative quality control procedures, data assimilation of radar, satellite, lidar and/or lightning data, GNSS/GPS meteorology, atmospheric monitoring using remote platforms, analyses and forecasts of high-impact weather events utilizing remote sensing data, artificial intelligence to make the best use of remote sensing measurements, etc.

Original research papers and/or review papers that cover developments or applications of remote sensing technology or data for the improvement of analyses and forecasts of high-impact weather events are all welcome contributions.
 
Dr. Guangxin He
Dr. Guoqing Ge
Dr. Lei Han
Dr. Jie Feng
Dr. Yongjie Huang
Guest Editor

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

  • High-impact weather
  • Severe weather
  • Remote Sensing
  • Radar, satellite, lidar, lightning detection
  • Artificial intelligence
  • Data assimilation
  • GNSS meteorology
  • Quantitative precipitation estimation (QPE)
  • Model evaluation

Published Papers (9 papers)

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18 pages, 5114 KiB  
Article
Assessing Snow Water Retrievals over Ocean from Coincident Spaceborne Radar Measurements
Remote Sens. 2023, 15(4), 1140; https://doi.org/10.3390/rs15041140 - 19 Feb 2023
Viewed by 1065
Abstract
Spaceborne snow water retrievals over oceans are assessed using a multiyear coincident dataset of CloudSat Cloud Profiling Radar (CPR) and Global Precipitation Mission (GPM) Dual-frequency Precipitation Radar (DPR). Various factors contributing to differences in snow water retrievals between CPR and DPR are carefully [...] Read more.
Spaceborne snow water retrievals over oceans are assessed using a multiyear coincident dataset of CloudSat Cloud Profiling Radar (CPR) and Global Precipitation Mission (GPM) Dual-frequency Precipitation Radar (DPR). Various factors contributing to differences in snow water retrievals between CPR and DPR are carefully considered. A set of relationships between radar reflectivity (Ze) and snow water content (SWC) at Ku- and W-bands is developed using the same microphysical assumptions. It is found that surface snow water contents from CPR are much larger than those from DPR at latitudes above 60°, while surface snow water contents from DPR slightly exceed those from CPR at latitudes below 50°. Coincident snow water content profiles between CPR and DPR are further divided into two conditions. One is that only CPR detects the falling snow. Another is that both CPR and DPR detect the falling snow. The results indicate that about 88% of all snow water content profiles are under the first condition and usually associated with light snowfall events. The remaining snow water content profiles are generally associated with moderate and heavy snowfall events. Moreover, CPR surface snow water contents are larger than DPR ones at high latitudes because most light snowfall events are misdetected by DPR due to its low sensitivity. DPR surface snow water contents exceed CPR ones at low latitudes because CPR may experience a significant reduction in backscattering efficiency of large particles and attenuation in heavy snowfall events. The low sensitivity of DPR also causes a noticeable decrease in detected snow layer depth. The results presented here can help in developing global snowfall retrieval algorithms using multi-radars. Full article
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17 pages, 4779 KiB  
Article
Validation of Nadir SWH and Its Variance Characteristics from CFOSAT in China’s Offshore Waters
Remote Sens. 2023, 15(4), 1005; https://doi.org/10.3390/rs15041005 - 11 Feb 2023
Viewed by 1714
Abstract
The offshore waters of China are a typical monsoon−affected area where the significant wave height (SWH) is strongly influenced by the different seasonal mean flow in winter and summer. However, limited in situ validations of the SWH have been performed on the China–France [...] Read more.
The offshore waters of China are a typical monsoon−affected area where the significant wave height (SWH) is strongly influenced by the different seasonal mean flow in winter and summer. However, limited in situ validations of the SWH have been performed on the China–France Oceanography Satellite (CFOSAT) in these waters. This study focused on validating CFOSAT nadir SWH data with SWH data from in situ buoy observations for China’s offshore waters and the Haiyang−2B (HY−2B) satellite, from July 2019 to December 2021. The validation against the buoy data showed that the relative absolute error has a seasonal cycle, varying in a narrow range near 35%. The RMSE of the CFOSAT nadir SWH was 0.29 m when compared against in situ observations, and CFOSAT was found to be more likely to overestimate the SWH under calm sea conditions. The sea−surface winds play a key role in calm sea conditions. The spatial distributions of the CFOSAT and HY−2B seasonal SWHs were similar, with a two−year mean SWH−field correlation coefficient of 0.98. Moreover, the coherence between the two satellites’ SWH variance increased with SWH magnitude. Our study indicates that, in such typical monsoon−influenced waters, attention should be given to the influence of sea conditions on the accuracy of CFOSAT SWH, particularly in studies that combine data from multiple, long−duration space−based sensors. Full article
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18 pages, 3696 KiB  
Article
Reducing Model Error Effects in El Niño–Southern Oscillation Prediction Using Ensemble Coupled Data Assimilation
Remote Sens. 2023, 15(3), 762; https://doi.org/10.3390/rs15030762 - 28 Jan 2023
Cited by 2 | Viewed by 1066
Abstract
Model error is an important source of uncertainty that significantly reduces the accuracy of El Niño–Southern Oscillation (ENSO) prediction. In this study, ensemble coupled data assimilation was employed to estimate the tendency error of the fifth-generation Lamont–Doherty Earth observation (LDEO5) model, which represented [...] Read more.
Model error is an important source of uncertainty that significantly reduces the accuracy of El Niño–Southern Oscillation (ENSO) prediction. In this study, ensemble coupled data assimilation was employed to estimate the tendency error of the fifth-generation Lamont–Doherty Earth observation (LDEO5) model, which represented the comprehensive effect of different sources of errors. Then, the estimated tendency error was applied to an ensemble prediction system for ENSO prediction. Assimilation experiments showed that tendency error estimation yielded better analysis than state estimation only. With tendency error estimation, simulated state variables such as zonal wind stress anomalies and subsurface temperature anomalies in the Niño3.4 region and upper layer depth anomalies along the equator showed good agreement with their reanalyzed counterparts. The ensemble ENSO prediction system with tendency error estimation demonstrated significantly better prediction skill than the ensemble system without tendency error estimation or the original LDEO5 model, especially for long lead times. The tendency error estimation improved the prediction skill for El Niño more than for La Niña. This study provides a promising approach to further improve prediction skill by reducing model error effects in an ensemble prediction. Full article
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27 pages, 8181 KiB  
Article
Evaluating the Value of CrIS Shortwave-Infrared Channels in Atmospheric-Sounding Retrievals
Remote Sens. 2023, 15(3), 547; https://doi.org/10.3390/rs15030547 - 17 Jan 2023
Cited by 2 | Viewed by 1186
Abstract
The Cross-track Infrared Sounder (CrIS), in low Earth orbit since 2011, makes measurements of the top of atmosphere radiance for input into data assimilation (DA) systems as well as the retrieval of geophysical state variables. CrIS measurements have 2211 narrow infrared channels ranging [...] Read more.
The Cross-track Infrared Sounder (CrIS), in low Earth orbit since 2011, makes measurements of the top of atmosphere radiance for input into data assimilation (DA) systems as well as the retrieval of geophysical state variables. CrIS measurements have 2211 narrow infrared channels ranging between 650 and 2550 cm−1 (~3.9–15.4 μm) and capture the variation in profiles of atmospheric temperature, water vapor, and numerous trace gas species. DA systems derive atmospheric temperature by assimilating CO2-sensitive channels in the CrIS longwave (LW) band (650–1095 cm−1). Here, we investigate if CO2-sensitive channels in the shortwave (SW) band (2155–2550 cm−1) can similarly be applied. We first evaluated the information content of the CrIS bands followed by an assessment of the performance degradation of retrievals due to the loss of individual CrIS bands. We found that temperature profile retrievals derived from the CrIS SW band were statistically both well-behaved and as accurate as a retrieval utilizing the CrIS LW band. The one caveat, however, is that the higher CrIS instrument noise in the SW band limited its performance under certain conditions. We conclude with a discussion on the implications our results have for channel selection in retrieval and DA systems as well as the design of future space instruments. Full article
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16 pages, 4459 KiB  
Article
Influence of Assimilating Wind Profiling Radar Observations in Distinct Dynamic Instability Regions on the Analysis and Forecast of an Extreme Rainstorm Event in Southern China
Remote Sens. 2022, 14(14), 3478; https://doi.org/10.3390/rs14143478 - 20 Jul 2022
Cited by 5 | Viewed by 1215
Abstract
This study quantitatively examines the contribution of assimilating observations in the regions with different dynamic instabilities to the analysis and prediction of an extreme rainstorm event in Fujian Province of China. The wind profiling radar (WPR) observations are classified into two groups, i.e., [...] Read more.
This study quantitatively examines the contribution of assimilating observations in the regions with different dynamic instabilities to the analysis and prediction of an extreme rainstorm event in Fujian Province of China. The wind profiling radar (WPR) observations are classified into two groups, i.e., strong and weak instability areas (SIA and WIA), according to their local dynamic instability identified by the ensemble spread. Their performance of assimilation and prediction in terms of the wind and precipitation are evaluated and compared in detail. The results show that the wind analysis error by assimilating all of the WPR observations can be reduced by about 30%. In particular, the wind analysis errors by only assimilating the observations in the SIA are about 12% lower than those in the WIA. They are related to the existence of the low-level horizontal wind shear with strong instability in the SIA. The case study shows that the assimilation of observations in the SIA can effectively correct the wind fields on the two sides of the wind shear line, producing an improved precipitation forecast compared to observation assimilation in the WIA. Full article
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20 pages, 17952 KiB  
Article
Improving the Assimilation of Enhanced Atmospheric Motion Vectors for Hurricane Intensity Predictions with HWRF
Remote Sens. 2022, 14(9), 2040; https://doi.org/10.3390/rs14092040 - 24 Apr 2022
Cited by 3 | Viewed by 1223
Abstract
The initial conditions for hurricanes are difficult to improve due to the lack of inner-core observations over the ocean. An enhanced atmospheric motion vectors (AMVs) dataset from the Cooperative Institute for Meteorological Satellite Studies (CIMSS) has recently become available and covers the inner-core [...] Read more.
The initial conditions for hurricanes are difficult to improve due to the lack of inner-core observations over the ocean. An enhanced atmospheric motion vectors (AMVs) dataset from the Cooperative Institute for Meteorological Satellite Studies (CIMSS) has recently become available and covers the inner-core region of hurricanes. This study tries to find an optimal data assimilation (DA) configuration to better utilize the observations for the Hurricane Weather Research and Forecasting (HWRF) model with hurricane Irma (2017). The results show that (a) without vortex relocation (VR), the hourly three-dimensional ensemble–variational (3DEnVar) outperforms the 6-hourly 3DEnVar DA configuration in almost all aspects, except for long-term track predictions. The assimilation of inner-core AMVs further improves the corresponding intensity forecasts for both hourly and 6-hourly 3DEnVar DA. (b) The 6-hourly 3DEnVar DA predictions with VR can be significantly improved upon their non-VR counterparts. However, VR can be detrimental to hourly 3DEnVar minimum sea level pressure (MSLP) predictions due to the spuriously enhanced upper-level warm core. The improvements from the assimilation of additional inner-core AMVs are thus limited under hourly VR. Reducing VR frequency can reduce the detrimental effects of hourly 3DEnVar. (c) An updated observation error profile for the enhanced AMVs benefits the hourly 3DEnVar DA more than the 6-hourly 3DEnVar DA. Full article
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22 pages, 6345 KiB  
Article
Incremental Learning with Neural Network Algorithm for the Monitoring Pre-Convective Environments Using Geostationary Imager
Remote Sens. 2022, 14(2), 387; https://doi.org/10.3390/rs14020387 - 14 Jan 2022
Cited by 2 | Viewed by 2028
Abstract
Early warning of severe weather caused by intense convective weather systems is challenging. To help such activities, meteorological satellites with high temporal and spatial resolution have been utilized for the monitoring of instability trends along with water vapor variation. The current study proposes [...] Read more.
Early warning of severe weather caused by intense convective weather systems is challenging. To help such activities, meteorological satellites with high temporal and spatial resolution have been utilized for the monitoring of instability trends along with water vapor variation. The current study proposes a retrieval algorithm based on an artificial neural network (ANN) model to quickly and efficiently derive total precipitable water (TPW) and convective available potential energy (CAPE) from Korea’s second geostationary satellite imagery measurements (GEO-KOMPSAT-2A/Advanced Meteorological Imager (AMI)). To overcome the limitations of the traditional static (ST) learning method such as exhaustive learning, impractical, and not matching in a sequence data, we applied an ANN model with incremental (INC) learning. The INC ANN uses a dynamic dataset that begins with the existing weight information transferred from a previously learned model when new samples emerge. To prevent sudden changes in the distribution of learning data, this method uses a sliding window that moves along the data with a window of a fixed size. Through an empirical test, the update cycle and the window size of the model are set to be one day and ten days, respectively. For the preparation of learning datasets, nine infrared brightness temperatures of AMI, six dual channel differences, temporal and geographic information, and a satellite zenith angle are used as input variables, and the TPW and CAPE from ECMWF model reanalysis (ERA5) data are used as the corresponding target values over the clear-sky conditions in the Northeast Asia region for about one year. Through the accuracy tests with radiosonde observation for one year, the INC NN results demonstrate improved performance (the accuracy of TPW and CAPE decreased by approximately 26% and 26% for bias and about 13% and 12% for RMSE, respectively) when compared to the ST learning. Evaluation results using ERA5 data also reveal more stable error statistics over time and overall reduced error distribution compared with ST ANN. Full article
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16 pages, 6204 KiB  
Article
Spatial and Temporal Distribution of Geologic Hazards in Shaanxi Province
Remote Sens. 2021, 13(21), 4259; https://doi.org/10.3390/rs13214259 - 23 Oct 2021
Cited by 6 | Viewed by 2101
Abstract
The spatio-temporal distribution of geological hazards, including collapses, landslides, and debris flows, in Shaanxi province, China was studied based on data from 1951 to 2018. The potential impact factors, including the geomorphologic types, rivers, roads, rainfall, and earthquakes, were analyzed using Random Forests. [...] Read more.
The spatio-temporal distribution of geological hazards, including collapses, landslides, and debris flows, in Shaanxi province, China was studied based on data from 1951 to 2018. The potential impact factors, including the geomorphologic types, rivers, roads, rainfall, and earthquakes, were analyzed using Random Forests. The results indicated that most hazards occurred in summer (i.e., July–September) and were triggered by rainstorms. The freeze–thaw effect had a considerable contribution to hazards in the north. Spatially, most hazards in the north occurred in valley terraces of the Loess Plateau, while medium-relief terrane (relief ranged from 500 to 1000 m) in the southern Qinling Mountains were hazard-prone areas. The collapses and landslides were mainly affected by human factors in Northern Shaanxi, whereas in Southern Shaanxi geomorphology was the primary factor. Permeability was a dominant factor for debris flows. In addition, the 2008 Wenchuan earthquake had a remarkable influence on the spatial distribution of hazards. In contrast, for the situation in the Sichuan province, which was close to the earthquake epicenter, the Wenchuan earthquake triggered many collapse and landslide events in the southwest regions of Shaanxi province only on 12 May 2008. The thresholds for the three hazard types in the north and south regions were almost the same despite their distinctly different geologic characteristics. Through a sensitivity analysis, we found an appropriate dry period of 12 h for the area. Full article
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12 pages, 4541 KiB  
Technical Note
Daytime Sea Fog Detection Based on a Two-Stage Neural Network
Remote Sens. 2022, 14(21), 5570; https://doi.org/10.3390/rs14215570 - 04 Nov 2022
Cited by 2 | Viewed by 1359
Abstract
Sea fog detection has received widespread attention because it plays a vital role in maritime activities. Due to the lack of sea observation data, meteorological satellites with high temporal and spatial resolution have become an essential means of sea fog detection. However, the [...] Read more.
Sea fog detection has received widespread attention because it plays a vital role in maritime activities. Due to the lack of sea observation data, meteorological satellites with high temporal and spatial resolution have become an essential means of sea fog detection. However, the performance is unsatisfactory because low clouds and sea fog are hard to distinguish on satellite images because they have similar spectral radiance characteristics. To address this difficulty, a new method based on a two-stage deep learning strategy was proposed to detect daytime sea fog in the Yellow Sea and Bohai Sea. We first utilized a fully connected network to separate the clear sky from sea fog and clouds. Then, a convolutional neural network was used to extract the differences between low clouds and sea fog on 16 Advanced Himawari Imager (AHI) observation bands. In addition, we built a Yellow and Bohai Sea Fog (YBSF) dataset by pixel-wise labelling AHI images into three categories (i.e., clear sky, cloud, and sea fog). Five comparable methods were used on the YBSF dataset to appraise the performance of our method. The vertical feature mask (VFM) generated by Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) was also used to verify the detection accuracy. The experimental results demonstrate the effectiveness of the proposed method for sea fog detection. Full article
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