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

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

Deadline for manuscript submissions: closed (15 July 2024) | Viewed by 13780

Special Issue Editors


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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
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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
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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
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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
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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
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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

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Related Special Issue

Published Papers (10 papers)

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Research

33 pages, 31036 KiB  
Article
Enhancing Extreme Precipitation Forecasts through Machine Learning Quality Control of Precipitable Water Data from Satellite FengYun-2E: A Comparative Study of Minimum Covariance Determinant and Isolation Forest Methods
by Wenqi Shen, Siqi Chen, Jianjun Xu, Yu Zhang, Xudong Liang and Yong Zhang
Remote Sens. 2024, 16(16), 3104; https://doi.org/10.3390/rs16163104 - 22 Aug 2024
Viewed by 1099
Abstract
Variational data assimilation theoretically assumes Gaussian-distributed observational errors, yet actual data often deviate from this assumption. Traditional quality control methods have limitations when dealing with nonlinear and non-Gaussian-distributed data. To address this issue, our study innovatively applies two advanced machine learning (ML)-based quality [...] Read more.
Variational data assimilation theoretically assumes Gaussian-distributed observational errors, yet actual data often deviate from this assumption. Traditional quality control methods have limitations when dealing with nonlinear and non-Gaussian-distributed data. To address this issue, our study innovatively applies two advanced machine learning (ML)-based quality control (QC) methods, Minimum Covariance Determinant (MCD) and Isolation Forest, to process precipitable water (PW) data derived from satellite FengYun-2E (FY2E). We assimilated the ML QC-processed TPW data using the Gridpoint Statistical Interpolation (GSI) system and evaluated its impact on heavy precipitation forecasts with the Weather Research and Forecasting (WRF) v4.2 model. Both methods notably enhanced data quality, leading to more Gaussian-like distributions and marked improvements in the model’s simulation of precipitation intensity, spatial distribution, and large-scale circulation structures. During key precipitation phases, the Fraction Skill Score (FSS) for moderate to heavy rainfall generally increased to above 0.4. Quantitative analysis showed that both methods substantially reduced Root Mean Square Error (RMSE) and bias in precipitation forecasting, with the MCD method achieving RMSE reductions of up to 58% in early forecast hours. Notably, the MCD method improved forecasts of heavy and extremely heavy rainfall, whereas the Isolation Forest method demonstrated a superior performance in predicting moderate to heavy rainfall intensities. This research not only provides a basis for method selection in forecasting various precipitation intensities but also offers an innovative solution for enhancing the accuracy of extreme weather event predictions. Full article
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18 pages, 11970 KiB  
Article
Contrasting the Effects of X-Band Phased Array Radar and S-Band Doppler Radar Data Assimilation on Rainstorm Forecasting in the Pearl River Delta
by Liangtao He, Jinzhong Min, Gangjie Yang and Yujie Cao
Remote Sens. 2024, 16(14), 2655; https://doi.org/10.3390/rs16142655 - 20 Jul 2024
Viewed by 528
Abstract
Contrasting the X-band phased array radar (XPAR) with the conventional S-Band dual-polarization mechanical scanning radar (SMSR), the XPAR offers superior temporal and spatial resolution, enabling a more refined depiction of the internal dynamics within convective systems. While both SMSR and XPAR data are [...] Read more.
Contrasting the X-band phased array radar (XPAR) with the conventional S-Band dual-polarization mechanical scanning radar (SMSR), the XPAR offers superior temporal and spatial resolution, enabling a more refined depiction of the internal dynamics within convective systems. While both SMSR and XPAR data are extensively used in monitoring and alerting for severe convective weather, their comparative application in numerical weather prediction through data assimilation remains a relatively unexplored area. This study harnesses the Weather Research and Forecasting Model (WRF) and its data assimilation system (WRFDA) to integrate radial velocity and reflectivity from the Guangzhou SMSR and nine XPARs across Guangdong Province. Utilizing a three-dimensional variational approach at a 1 km convective-scale grid, the assimilated data are applied to forecast a rainstorm event in the Pearl River Delta (PRD) on 6 June 2022. Through a comparative analysis of the results from assimilating SMSR and XPAR data, it was observed that the assimilation of SMSR data led to more extensive adjustments in the lower- and middle-level wind fields compared to XPAR data assimilation. This resulted in an enlarged convergence area at lower levels, prompting an overdevelopment of convective systems and an excessive concentration of internal hydrometeor particles, which in turn led to spurious precipitation forecasts. However, the sequential assimilation of both SMSR and XPAR data effectively reduced the excessive adjustments in the wind fields that were evident when only SMSR data were used. This approach diminished the generation of false echoes and enhanced the precision of quantitative precipitation forecasts. Additionally, the lower spectral width of XPAR data indicates its superior detection accuracy. Assimilating XPAR data alone yields more reasonable adjustments to the low- to middle-level wind fields, leading to the formation of small-to-medium-scale horizontal convergence lines in the lower levels of the analysis field. This enhancement significantly improves the model’s forecasts of composite reflectivity and radar echoes, aligning them more closely with actual observations. Consequently, the Threat Score (TS) and Equitable Threat Score (ETS) for heavy-rain forecasts (>10 mm/h) over the next 5 h are markedly enhanced. This study underscores the necessity of incorporating XPAR data assimilation in numerical weather prediction practices and lays the groundwork for the future joint assimilation of SMSR and XPAR data. Full article
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21 pages, 7739 KiB  
Article
Assimilation of Hyperspectral Infrared Atmospheric Sounder Data of FengYun-3E Satellite and Assessment of Its Impact on Analyses and Forecasts
by Ruixia Liu, Qifeng Lu, Chunqiang Wu, Zhuoya Ni and Fu Wang
Remote Sens. 2024, 16(5), 908; https://doi.org/10.3390/rs16050908 - 4 Mar 2024
Cited by 1 | Viewed by 1084
Abstract
HIRAS-II is the hyperspectral detector carried on FengYun-3E which is the world’s first meteorological satellite in dawn–dusk orbit. It fills the observation gaps during the dawn and dusk periods of polar orbit meteorological satellites, enabling a 100% global data coverage and assimilation of [...] Read more.
HIRAS-II is the hyperspectral detector carried on FengYun-3E which is the world’s first meteorological satellite in dawn–dusk orbit. It fills the observation gaps during the dawn and dusk periods of polar orbit meteorological satellites, enabling a 100% global data coverage and assimilation of polar orbit satellite data within each 6 h window for numerical weather forecasting models. With 3053 vertical detection channels, it provides high-resolution vertical temperature and humidity information, thus playing an important role in improving the forecast skills of the global medium-range weather prediction models. This study assimilated data from 56 CO2 channels of FY-3E HIRAS into the CMA-GFS 4DVAR system. Two sets of experiments, FY3EHIRAS and CTRL, were designed, conducting a one-month cycle assimilation test to evaluate the impact of assimilating FY-3E HIRAS data on CMA-GFS analysis and forecasting. Using the ECMWF reanalysis data ERA5 as a reference, the study demonstrated that after assimilating data from FY-3E HIRAS’s 56 CO2 channels, there was a certain extent of improvement in the temperature field at almost all model levels. The RMSE notably reduced in the southern hemisphere’s temperature analysis field near the surface and at 500 hPa by 3.5% and 2%, respectively. The most significant improvement in the entire temperature analysis field was observed in the tropical region, followed by the southern and then the northern hemisphere. Additionally, there was a reduction in RMSE for the height and wind fields, showing considerable improvement compared to the CTRL experiment. Overall, assimilating the FY-3E HIRAS data led to positive improvements in the forecasting skills for temperature, wind fields, and height fields in both the southern and northern hemispheres. The forecasting effectiveness was slightly lower in the tropical region but displayed an overall neutral-to-positive effect. Full article
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16 pages, 4112 KiB  
Communication
A Cloud Detection Algorithm Based on FY-4A/GIIRS Infrared Hyperspectral Observations
by Jieying Ma, Yi Liao and Li Guan
Remote Sens. 2024, 16(3), 481; https://doi.org/10.3390/rs16030481 - 26 Jan 2024
Viewed by 1081
Abstract
Cloud detection is an essential preprocessing step when using satellite-borne infrared hyperspectral sounders for data assimilation and atmospheric retrieval. In this study, we propose a cloud detection algorithm based solely on the sensitivity and detection characteristics of the FY-4A Geostationary Interferometric Infrared Sounder [...] Read more.
Cloud detection is an essential preprocessing step when using satellite-borne infrared hyperspectral sounders for data assimilation and atmospheric retrieval. In this study, we propose a cloud detection algorithm based solely on the sensitivity and detection characteristics of the FY-4A Geostationary Interferometric Infrared Sounder (GIIRS), rather than relying on other instruments. The algorithm consists of four steps: (1) combining observed radiation and clear radiance data simulated by the Community Radiative Transfer Model (CRTM) to identify clear fields of view (FOVs); (2) determining the number of clouds within adjacent 2 × 2 FOVs via a principal component analysis of observed radiation; (3) identifying whether there are large observed radiance differences between adjacent 2 × 2 FOVs to determine the mixture of clear skies and clouds; and (4) assigning adjacent 2 × 2 FOVs as a cloud cluster following the three steps above to select an appropriate classification threshold. The classification results within each cloud detection cluster were divided into the following categories: clear, partly cloudy, or overcast. The proposed cloud detection algorithm was tested using one month of GIIRS observations from May 2022 in this study. The cloud detection and classification results were compared with the FY-4A Advanced Geostationary Radiation Imager (AGRI)’s operational cloud mask products to evaluate their performance. The results showed that the algorithm’s performance is significantly influenced by the surface type. Among all-day observations, the highest recognition performance was achieved over the ocean, followed by land surfaces, with the lowest performance observed over deep inland water. The proposed algorithm demonstrated better clear sky recognition during the nighttime for ocean and land surfaces, while its performance was higher for partly cloudy and overcast conditions during the day. However, for inland water surfaces, the algorithm consistently exhibited a lower cloud recognition performance during both the day and night. Moreover, in contrast to the GIIRS’s Level 2 cloud mask (CLM) product, the proposed algorithm was able to identify partly cloudy conditions. The algorithm’s classification results departed slightly from those of the AGRI’s cloud mask product in areas with clear sky/cloud boundaries and minimal convective cloud coverage; this was attributed to the misclassification of clear sky as partly cloudy under a low-resolution situation. AGRI’s CLM products, temporally and spatially collocated to the GIIRS FOV, served as the reference value. The proportion of FOVs consistently classified as partly cloudy to the total number of partly cloudy FOVs was 40.6%. In comparison with the GIIRS’s L2 product, the proposed algorithm improved the identification performance by around 10%. Full article
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13 pages, 2581 KiB  
Communication
The Impact of Profiles Data Assimilation on an Ideal Tropical Cyclone Case
by Changliang Shao and Lars Nerger
Remote Sens. 2024, 16(2), 430; https://doi.org/10.3390/rs16020430 - 22 Jan 2024
Cited by 3 | Viewed by 1043
Abstract
Profile measurements play a crucial role in operational weather forecasting across diverse scales and latitudes. However, assimilating tropospheric wind and temperature profiles remains a challenging endeavor. This study assesses the influence of profile measurements on numerical weather prediction (NWP) using the weather research [...] Read more.
Profile measurements play a crucial role in operational weather forecasting across diverse scales and latitudes. However, assimilating tropospheric wind and temperature profiles remains a challenging endeavor. This study assesses the influence of profile measurements on numerical weather prediction (NWP) using the weather research and forecasting (WRF) model coupled to the parallel data assimilation framework (PDAF) system. Utilizing the local error-subspace transform Kalman filter (LESTKF), observational temperature and wind profiles generated by WRF are assimilated into an idealized tropical cyclone. The coupled WRF-PDAF system is adopted to carry out the twin experiments, which employ varying profile densities and localization distances. The results reveal that high-resolution observations yield significant forecast improvements compared to coarser-resolution data. A cost-effective balance between observation density and benefit is further explored through the idealized tropical cyclone case. According to diminishing marginal utility and increasing marginal costs, the optimal observation densities for U and V are found around 26–27%. This may be useful information to the meteorological agencies and researchers. Full article
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19 pages, 4849 KiB  
Article
Evaluation of Temperature and Humidity Profiles Retrieved from Fengyun-4B and Implications for Typhoon Assimilation and Forecasting
by Weiyu Yang, Yaodeng Chen, Wenguang Bai, Xin Sun, Hong Zheng and Luyao Qin
Remote Sens. 2023, 15(22), 5339; https://doi.org/10.3390/rs15225339 - 13 Nov 2023
Cited by 2 | Viewed by 1553
Abstract
Fengyun-4B (FY-4B) is the first operational satellite from China’s latest generation of geostationary meteorological satellites. It is equipped with the Geostationary Interferometric Infrared Sounder (GIIRS), which is able to obtain highly accurate atmospheric temperature and humidity profiles through hyperspectral detection in long- and [...] Read more.
Fengyun-4B (FY-4B) is the first operational satellite from China’s latest generation of geostationary meteorological satellites. It is equipped with the Geostationary Interferometric Infrared Sounder (GIIRS), which is able to obtain highly accurate atmospheric temperature and humidity profiles through hyperspectral detection in long- and mid-wave infrared spectral bands. In this study, the accuracy of the FY-4B/GIIRS temperature and humidity profile retrievals over two months is evaluated using radiosonde observations and ERA5 reanalysis data. We go a step further to investigate the impact of the satellite retrievals on assimilation and forecasts for Typhoons Chaba and Ma-on in 2022. Results reveal that the root-mean-square difference (RMSD) for the FY-4B/GIIRS temperature and humidity profile retrievals were within 1 K and 1.5 g/kg, respectively, demonstrating high overall accuracy. Moreover, assimilating temperature and humidity profiles from FY-4B/GIIRS positively impacts model analysis and prediction, improving typhoon track and intensity forecasts. Additionally, improvements have been discovered in predicting precipitation, particularly with high-magnitude rainfall events. Full article
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17 pages, 9819 KiB  
Article
Direct Assimilation of Ground-Based Microwave Radiometer Clear-Sky Radiance Data and Its Impact on the Forecast of Heavy Rainfall
by Yujie Cao, Bingying Shi, Xinyu Zhao, Ting Yang and Jinzhong Min
Remote Sens. 2023, 15(17), 4314; https://doi.org/10.3390/rs15174314 - 1 Sep 2023
Cited by 1 | Viewed by 1166
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 [...] Read more.
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
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18 pages, 15518 KiB  
Article
Assimilating FY-3D MWHS2 Radiance Data to Predict Typhoon Muifa Based on Different Initial Background Conditions and Fast Radiative Transfer Models
by Lizhen Huang, Dongmei Xu, Hong Li, Lipeng Jiang and Aiqing Shu
Remote Sens. 2023, 15(13), 3220; https://doi.org/10.3390/rs15133220 - 21 Jun 2023
Cited by 1 | Viewed by 1769
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 [...] Read more.
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
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21 pages, 9373 KiB  
Article
Assimilating All-Sky Infrared Radiance Observations to Improve Ensemble Analyses and Short-Term Predictions of Thunderstorms
by Huanhuan Zhang, Qin Xu, Thomas A. Jones and Lingkun Ran
Remote Sens. 2023, 15(12), 2998; https://doi.org/10.3390/rs15122998 - 8 Jun 2023
Cited by 1 | Viewed by 1160
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 [...] Read more.
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
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21 pages, 7009 KiB  
Article
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)
by Jiajun Chen, Dongmei Xu, Aiqing Shu and Lixin Song
Remote Sens. 2023, 15(10), 2592; https://doi.org/10.3390/rs15102592 - 16 May 2023
Cited by 1 | Viewed by 1474
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 [...] Read more.
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
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