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Application of Remote Sensing to the Weather Prediction

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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: closed (31 January 2021) | Viewed by 22455

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Special Issue Editors

Prof. Dr. Myoung Hwan Ahn

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Guest Editor

Professor at Department of Climate and Energy Systems Engineering, Ewha Womans University, Seoul, Korea
Interests: satellite meteorology; calibration of spaceborne instrument; retrieval algorithm

Prof. Dr. Chian-Yi Liu

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Guest Editor

Center for Space and Remote Sensing Research, National Central University, Taoyuan 32001, Taiwan
Interests: meteorological satellite remote sensing; atmospheric thermodynamic; cloud and precipitation

Special Issue Information

Dear Colleagues,

Remote sensing instruments provide the vital component of global observing systems for planet Earth. Especially with the increasing concerns over the extreme weather events along with global warming, application of remote sensing technology plays a vital role in accelerating the skill of weather prediction, by not only providing data for the monitoring and understanding but also feeding key observation data to the numerical weather prediction (NWP) models. With the advent of next-generation space platforms such as Himawari-8/-9, Fengyun-4, GEO-KOMPSAT-2A/-2B, GOES-16/17, and MTG on GEO and JPSS, Metop-SG, Fengyun-3 series on LEO, we now have the capability of improved remote sensing data in terms of temporal, spatial, spectral and radiometric resolution. To fully utilize these advanced observation data, advanced utilization technology, such as data assimilation and applications, is essential and critical for weather prediction. Additionally, other remote sensing data from ground-based and airborne instruments such as ceilometer, wind profiler, lidar, and microwave radiometer are becoming more common and finding their usability. For the current Special Issue, community members are invited to submit manuscripts dealing with current accomplishments and future advancements of remote sensing in weather prediction, such as analysis and/or assimilation for weather forecasts, quality control and calibration of remote sensing data, and new algorithms for new instrumentation, to name a few.

Prof. Dr. Myoung Hwan Ahn
Prof. Dr. Chian-Yi Liu
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

Weather satellites
Ground-based and airborne remote sensing
Retrieval algorithm
Assimilation of satellite data
Instrument calibration and validation

Published Papers (7 papers)

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Research

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20 pages, 8638 KiB

Open AccessArticle

An Analysis Study of FORMOSAT-7/COSMIC-2 Radio Occultation Data in the Troposphere

by Shu-Ya Chen, Chian-Yi Liu, Ching-Yuang Huang, Shen-Cha Hsu, Hsiu-Wen Li, Po-Hsiung Lin, Jia-Ping Cheng and Cheng-Yung Huang

Remote Sens. 2021, 13(4), 717; https://doi.org/10.3390/rs13040717 - 16 Feb 2021

Cited by 28 | Viewed by 3402

Abstract

This study investigates the Global Navigation Satellite System (GNSS) radio occultation (RO) data from FORMOSAT-7/COSMIC-2 (FS7/C2), which provides considerably more and deeper profiles at lower latitudes than those from the former FORMOSAT-3/COSMIC (FS3/C). The statistical analysis of six-month RO data shows that the rate of penetration depth below 1 km height within ±45° latitudes can reach 80% for FS7/C2, significantly higher than 40% for FS3/C. For verification, FS7/C2 RO data are compared with the observations from chartered missions that provided aircraft dropsondes and on-board radiosondes, with closer observation times and distances from the oceanic RO occultation over the South China Sea and near a typhoon circulation region. The collocated comparisons indicate that FS7/C2 RO data are reliable, with small deviations from the ground-truth observations. The RO profiles are compared with collocated radiosondes, RO data from other missions, global analyses of ERA5 and National Centers for Environmental Prediction (NCEP) final (FNL), and satellite retrievals of NOAA Unique Combined Atmospheric Processing System (NCAPS). The comparisons exhibit consistent vertical variations, showing absolute mean differences and standard deviations of temperature profiles less than 0.5 °C and 1.5 °C, respectively, and deviations of water vapor pressure within 2 hPa in the lower troposphere. From the latitudinal distributions of mean difference and standard deviation (STD), the intertropical convergence zone (ITCZ) is evidentially shown in the comparisons, especially for the NUCAPS, which shows a larger deviation in moisture when compared to FS7/C2 RO data. The sensitivity of data collocation in time departure and spatial distance among different datasets are presented in this study as well. Full article

(This article belongs to the Special Issue Application of Remote Sensing to the Weather Prediction)

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Graphical abstract

23 pages, 10050 KiB

Open AccessArticle

Satellite Observation for Evaluating Cloud Properties of the Microphysical Schemes in Weather Research and Forecasting Simulation: A Case Study of the Mei-Yu Front Precipitation System

by Kao-Shen Chung, Hsien-Jung Chiu, Chian-Yi Liu and Meng-Yue Lin

Remote Sens. 2020, 12(18), 3060; https://doi.org/10.3390/rs12183060 - 18 Sep 2020

Cited by 6 | Viewed by 3018

Abstract

Radiative transfer model can be used to convert the geophysical variables (e.g., atmospheric thermodynamic state) to the radiation field. In this study, the Community Radiative Transfer Model (CRTM) is used to connect regional Weather Research and Forecasting (WRF) model outputs and satellite observations. A heavy rainfall event caused by the Mei-Yu front on the June 1, 2017, in the vicinity of Taiwan, was chosen as a case study. The simulated cloud performance of WRF with four microphysics schemes (i.e., Goddard (GCE), WRF single-moment 6 class (WSM), WRF double-moment 6 class (WDM), and Morrison (MOR) schemes) was investigated objectively using multichannel observed satellite radiances from a Japanese geostationary satellite Himawari-8. The results over the East Asia domain (9 km) illustrate that all four microphysics schemes overestimate cloudy pixels, in particular, the high cloud of simulation with MOR when comparing with satellite data. Sensitivity tests reveal that the excess condensation of ice at ≥14 km with MOR might be associated with the overestimated high cloud cover. However, GCE displayed an improved performance on water vapor channel in clear skies. When focusing on Taiwan using a higher (3 km) model resolution, each scheme displayed a decent performance on cloudy pixels. In the grid-by-grid skill score analysis, the distribution of high clouds was the most accurate among the three cloud types. The results also suggested that all schemes required a longer simulation time to describe the low cloud horizontal extend. Full article

(This article belongs to the Special Issue Application of Remote Sensing to the Weather Prediction)

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Graphical abstract

25 pages, 9274 KiB

Open AccessArticle

Evaluating the Performance of a Convection-Permitting Model by Using Dual-Polarimetric Radar Parameters: Case Study of SoWMEX IOP8

by Cheng-Rong You, Kao-Shen Chung and Chih-Chien Tsai

Remote Sens. 2020, 12(18), 3004; https://doi.org/10.3390/rs12183004 - 15 Sep 2020

Cited by 7 | Viewed by 3101

Abstract

In this study, a dual-polarimetric radar observation operator is established and modified for the Taiwan area for the purpose of model verification. A severe squall line case during the Southwest Monsoon Experiment Intensive Observing Period 8 (SoWMEX IOP#8) on 14 June 2008, is selected and examined. Because the operator is adopted from the use of the midlatitude region, sensitivity tests are performed to obtain the optimal setting of the operator in the subtropical region. To accurately capture the dynamic structure of the squall lines, the ensemble-based data assimilation system, which assimilates both radial wind and reflectivity data, is used to obtain the optimal analysis field on the mesoscale for evaluating the performance of model simulation. The characteristics of two microphysics schemes are investigated, and the results obtained using the schemes are compared with the S-band dual-polarimetric radar observations. The horizontal and vertical cross-sections show that the analyses resemble the observations. Both schemes can replicate the polarimetric parameter signature such as

Z_{D R}

and

K_{D P}

columns. When comparing model simulation with polarimetric parameters through the drawing of contour frequency by altitude diagrams (CFADs), the results reveal that the single moment microphysics scheme performs better than the double moment scheme in this case. However, the reflectivity field in the stratiform area is more accurately captured when using the double moment scheme. Furthermore, validation with polarimetric variables (

Z_{H}

Z_{D R}

and

K_{D P}

) histograms shows underestimation of the

K_{D P}

field in both schemes. Overall, this study indicates the benefit of assimilating radial wind and reflectivity data for the analyses of severe precipitation systems and the necessity of assimilating polarimetric parameters for the accuracy of microphysical processes, especially complex microphysics schemes in subtropical region. Full article

(This article belongs to the Special Issue Application of Remote Sensing to the Weather Prediction)

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Graphical abstract

21 pages, 4832 KiB

Open AccessArticle

An Evaluation of Radiative Transfer Simulations of Cloudy Scenes from a Numerical Weather Prediction Model at Sub-Millimetre Frequencies Using Airborne Observations

by Stuart Fox

Remote Sens. 2020, 12(17), 2758; https://doi.org/10.3390/rs12172758 - 25 Aug 2020

Cited by 17 | Viewed by 3101

Abstract

The Ice Cloud Imager (ICI) will be launched on the next generation of EUMETSAT polar-orbiting weather satellites and make passive observations between 183 and 664 GHz which are sensitive to scattering from cloud ice. These observations have the potential to improve weather forecasts through direct assimilation using "all-sky" methods which have been successfully applied to microwave observations up to 200 GHz in current operational systems. This requires sufficiently accurate representations of cloud ice in both numerical weather prediction (NWP) and radiative transfer models. In this study, atmospheric fields from a high-resolution NWP model are used to drive radiative transfer simulations using the Atmospheric Radiative Transfer Simulator (ARTS) and a recently released database of cloud ice optical properties. The simulations are evaluated using measurements between 89 and 874 GHz from five case studies of ice and mixed-phase clouds observed by the Facility for Airborne Atmospheric Measurements (FAAM) BAe-146 research aircraft. The simulations are strongly sensitive to the assumed cloud ice optical properties, but by choosing an appropriate ice crystal model it is possible to simulate realistic brightness temperatures over the full range of sub-millimetre frequencies. This suggests that sub-millimetre observations have the potential to be assimilated into NWP models using the all-sky method. Full article

(This article belongs to the Special Issue Application of Remote Sensing to the Weather Prediction)

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Figure 1

16 pages, 6392 KiB

Open AccessArticle

Evaluation of Assimilating FY-3C MWHS-2 Radiances Using the GSI Global Analysis System

by Lipeng Jiang, Chunxiang Shi, Tao Zhang, Yang Guo and Shuang Yao

Remote Sens. 2020, 12(16), 2511; https://doi.org/10.3390/rs12162511 - 05 Aug 2020

Cited by 7 | Viewed by 2447

Abstract

The MicroWave Humidity Sounder 2 (MWHS-2) onboard the FY-3C satellite provides an extra important data source for atmospheric water vapor monitoring besides the Microwave Humidity Sounder (MHS) and the Advanced Technology Microwave Sounder (ATMS). This paper introduces MWHS-2 radiance data into the community Gridpoint Statistical Interpolation (GSI) global analysis system. More than one-year cycling assimilation experiments with and without MWHS-2 data are performed. Results show that MWHS-2 has similar data quality to MHS and ATMS. The biases of MWHS-2 are stable except some sudden jumps that can be removed nicely by the variational bias correction scheme within GSI. Assimilating MWHS-2 makes the 6-h forecasts fit more closely to radiosonde observations, with a reduction of 0.55–1% for the observation-minus-simulation standard deviation of specific humidity. The 500 hPa geopotential height anomaly correlation scores are increased by around 0.006 for the 144-h forecast, indicating that assimilating MWHS-2 may also help to improve 3–8-day forecasts. Full article

(This article belongs to the Special Issue Application of Remote Sensing to the Weather Prediction)

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Graphical abstract

Other

Jump to: Research

14 pages, 3167 KiB

Open AccessLetter

Effects of CO₂ Changes on Hyperspectral Infrared Radiances and Its Implications on Atmospheric Temperature Profile Retrieval and Data Assimilation in NWP

by Di Di, Yunheng Xue, Jun Li, Wenguang Bai and Peng Zhang

Remote Sens. 2020, 12(15), 2401; https://doi.org/10.3390/rs12152401 - 27 Jul 2020

Cited by 3 | Viewed by 2109

Abstract

Although atmospheric CO₂ is a trace gas, it has seasonal variations and has increased over the last decade. Its seasonal variation and increase have substantial radiative effects on hyperspectral infrared (IR) radiance calculations in both longwave (LW) and shortwave (SW) CO₂ absorption spectral regions that are widely used for weather and climate applications. The effects depend on the spectral coverage and spectral resolution. The radiative effect caused by the increase of CO₂ has been calculated to be greater than 0.5 K within 5 years, whereas a radiative effect of 0.1–0.5 K is introduced by the seasonal variation in some CO₂ absorption spectral regions. It is important to take into account the increasing trend and seasonal variation of CO₂ in retrieving the atmospheric temperature profile from hyperspectral IR radiances and in the radiance assimilation in numerical weather prediction (NWP) models. The simulation further indicates that it is very difficult to separate atmospheric temperature and CO₂ information from hyperspectral IR sounder radiances because the atmospheric temperature signal is much stronger than that of CO₂ in the CO₂ absorption IR spectral regions. Full article

(This article belongs to the Special Issue Application of Remote Sensing to the Weather Prediction)

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Graphical abstract

9 pages, 1365 KiB

Open AccessLetter

Skills of Thunderstorm Prediction by Convective Indices over a Metropolitan Area: Comparison of Microwave and Radiosonde Data

by Mikhail Yu. Kulikov, Mikhail V. Belikovich, Natalya K. Skalyga, Maria V. Shatalina, Svetlana O. Dementyeva, Vitaly G. Ryskin, Alexander A. Shvetsov, Alexander A. Krasil’nikov, Evgeny A. Serov and Alexander M. Feigin

Remote Sens. 2020, 12(4), 604; https://doi.org/10.3390/rs12040604 - 11 Feb 2020

Cited by 115 | Viewed by 3204

Abstract

In this work, we compare the values of 15 convective indices obtained from radiosonde and microwave temperature and water vapor profiles simultaneously measured over Nizhny Novgorod (56.2°N, 44°E) during 5 convective seasons of 2014–2018. A good or moderate correlation (with coefficients of ~0.7–0.85) is found for most indices. We assess the thunderstorm prediction skills with a lead time of 12 h for each radiosonde and microwave index. It is revealed that the effectiveness of thunderstorm prediction by microwave indices is much better than by radiosonde ones. Moreover, a good correlation between radiosonde and microwave values of a certain index does not necessarily correspond to similar prediction skills. Eight indices (Showalter Index, Maximum Unstable Convective Available Potential Energy (CAPE), Total Totals index, TQ index, Jefferson Index, S index, K index, and Thompson index) are regarded to be the best predictors from both the true skill statistics (TSS) maximum and Heidke skill score (HSS) maximum points of view. In the case of radiosonde data, the best indices are the Jefferson Index, K index, S index, and Thompson index. Only TSS and HSS maxima for these indices are close to the microwave ones, whereas the prediction skills of other radiosonde indices are essentially worse than in the case of microwave data. The analysis suggests that the main possible reason of this discrepancy is an unexpectedly low quality of radiosonde data. Full article

(This article belongs to the Special Issue Application of Remote Sensing to the Weather Prediction)

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