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Atmosphere
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Satellite Data Applications to Atmospheric Study and Numerical Weather Forecast
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Satellite Data Applications to Atmospheric Study and Numerical Weather Forecast

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

Deadline for manuscript submissions: closed (17 July 2021) | Viewed by 5191

Special Issue Editors

Dr. Lin Lin

E-Mail Website
Guest Editor
ESSIC/CISESS, University of Maryland, College Park, MD 20740, USA
Interests: application of machine learning techniques; calibration of satellite data at microwave wavelengths; intercalibration of satellite data using Global Positioning System (GPS) Radio Occultation (RO) data; simulation of microwave instrument using Community Radiative Transfer Model (CRTM), Monochromatic Radiative Transfer Model (MonoRTM) and Line-by-Line Radiative Transfer Model (LBLRTM); development of quality control techniques for improving uses of satellite data in numerical weather prediction models; direct assimilation of microwave radiances in Hurricane Weather Research and Forecasting (HWRF) system; impact of direct assimilation of microwave radiances on hurricane forecast; study of cloudy radiances through advanced radiative transfer models
Special Issues, Collections and Topics in MDPI journals
Dr. Miao Tian

E-Mail Website
Guest Editor
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: passive remote sensing; radiative transfer modelling; instrument cal/val; various applications based on big data and deep learning; target detection; pattern recognition; strategy selection
Prof. Dr. George Kallos

E-Mail Website
Guest Editor
Department of Physics, National and Kapodistrian University of Athens, University Campus, Bldg. PHYS-V, 15784 Athens, Greece
Interests: atmospheric model development and applications; aerosol–radiation–cloud–precipitation interactions; dust modeling; sea waves and sea spraying; extreme weather events; wind and solar energy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Since more than 70% of the Earth’s surface is covered by water (oceans and inland waters) as well as mountains, deserts, and polar ice, it is not possible to realize traditional ground-based observations (e.g., stations, buoys, ships, in situ sensors equipped on airplanes and aircraft dropsondes) of the atmospheric state globally. Even for areas where traditional observations are relatively dense, it is difficult to meet the spatial and temporal requirements of mesoscale weather forecasts. In addition, important atmospheric composition information, including ozone, dust, methane, sulfur dioxide, and carbon monoxide, is often not measured in ground-based observation systems.

The modern growing constellation of polar and geostationary satellites orbiting the Earth provides continuous spectral observations of the global atmosphere and oceans at various spatial and temporal scales. The satellite data can help to improve the regional and global numerical weather forecasts via the data assimilation of radiance in numerical weather prediction models. Satellite radiation measurements in different spectral regions can also be used to derive atmospheric state/compositions at different heights, which are essential for Earth system observation at the microscale, mesoscale, and the synoptic and global scales.

The aim of this Special Issue is to investigate the application of satellite observations across the different portions of the electromagnetic spectrum (microwave, infrared, visible, etc.) onboard both operational and experimental environmental satellites, including, but not limited to, NOAA-15, NOAA-15, -16, -17, -18, -19, SNPP, NOAA-20, Aqua, MetOp-A, -B, and -C, GOES-16,-17, Meteosat, Himawari, and FengYun-series satellites.

In particular, but not exclusively, manuscripts are encouraged addressing the application of satellite data to severe weather events, weather and climate studies, and numerical weather forecasts.

Dr. Lin Lin
Dr. Miao Tian
Prof. Dr. George Kallos
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. 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

  • Polar satellite
  • Geostationary satellite
  • Application
  • Severe weather events
  • Weather
  • Climate
  • Numerical weather forecast

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Published Papers (2 papers)

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Research

29 pages, 10788 KiB  
Article
Spatial and Time Warping for Gauge Adjustment of Rainfall Estimates
by Camille Le Coz, Arnold Heemink, Martin Verlaan and Nick van de Giesen
Atmosphere 2021, 12(11), 1510; https://doi.org/10.3390/atmos12111510 - 16 Nov 2021
Cited by 1 | Viewed by 1573
Abstract
Many satellite-based estimates use gauge information for bias correction. In general, bias-correction methods are focused on the intensity error and do not explicitly correct possible position or timing errors. However, position and timing errors in rainfall estimates can also lead to errors in [...] Read more.
Many satellite-based estimates use gauge information for bias correction. In general, bias-correction methods are focused on the intensity error and do not explicitly correct possible position or timing errors. However, position and timing errors in rainfall estimates can also lead to errors in the rainfall occurrence or the intensity. This is especially true for localized rainfall events such as the convective rainstorms occurring during the rainy season in sub-Saharan Africa. We investigated the use of warping to correct such errors. The goal was to gauge-adjust satellite-based estimates with respect to the position and the timing of the rain event, instead of its intensity. Warping is a field-deformation method that transforms an image into another one. We compared two methods, spatial warping focusing on the position errors and time warping for the timing errors. They were evaluated on two case studies: a synthetic rainfall event represented by an ellipse and a rain event in southern Ghana during the monsoon season. In both cases, the two warping methods reduced significantly the respective targeted (position or timing) errors. In the southern Ghana case, the average position error was decreased by about 45 km by the spatial warping and the average timing error was decreased from more than 1 h to 0.2 h by the time warping. Both warping methods also improved the continuous statistics on the intensity: the correlation went from 0.18 to at least 0.62 after warping in the southern Ghana case. The spatial warping seems more interesting because of its positive impact on both position and timing errors. Full article
(This article belongs to the Special Issue Satellite Data Applications to Atmospheric Study and Numerical Weather Forecast)
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19 pages, 14307 KiB  
Article
Impact of Assimilating Advanced Himawari Imager Channel 16 Data on Precipitation Prediction over the Haihe River Basin
by Hongxiang Ouyang, Zhengkun Qin and Juan Li
Atmosphere 2021, 12(10), 1253; https://doi.org/10.3390/atmos12101253 - 27 Sep 2021
Cited by 1 | Viewed by 2209
Abstract
Assimilation of high-resolution geostationary satellite data is of great value for precise precipitation prediction in regional basins. The operational geostationary satellite imager carried by the Himawari-8 satellite, Advanced Himawari Imager (AHI), has two additional water vapor channels and four other channels compared with [...] Read more.
Assimilation of high-resolution geostationary satellite data is of great value for precise precipitation prediction in regional basins. The operational geostationary satellite imager carried by the Himawari-8 satellite, Advanced Himawari Imager (AHI), has two additional water vapor channels and four other channels compared with its predecessor, MTSAT-2. However, due to the uncertainty in surface parameters, AHI surface-sensitive channels are usually not assimilated over land, except for the three water vapor channels. Previous research showed that the brightness temperature of AHI channel 16 is much more sensitive to the lower-tropospheric temperature than to surface emissivity, which is similar to the three water vapor channels 8–10. As a follow-up work, this paper evaluates the effectiveness of assimilating brightness temperature observations over land from both the three AHI water vapor channels and channel 16 to improve watershed precipitation forecasting through both case analysis (in the Haihe River basin, China) and batch tests. It is found that assimilating AHI channel 16 can improve the upstream near-surface atmospheric temperature forecast, which in turn affects the development of downstream weather systems. The precipitation forecasting test results indicate that adding the terrestrial observations of channel 16 to the assimilation of AHI data can improve short-term precipitation forecasting in the basin. Full article
(This article belongs to the Special Issue Satellite Data Applications to Atmospheric Study and Numerical Weather Forecast)
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