Atmosphere

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21 pages, 15539 KiB

Open AccessArticle

The Variations of Outgoing Longwave Radiation in East Asia and Its Influencing Factors

by Chaoli Tang, Dong Liu, Xiaomin Tian, Fengmei Zhao and Congming Dai

Atmosphere 2023, 14(3), 576; https://doi.org/10.3390/atmos14030576 - 17 Mar 2023

Viewed by 1826

Outgoing longwave radiation (OLR) data are one of the key factors in studying the radiation balance of the earth–atmosphere system in East Asia. It is of great significance to explore the influence factors on OLR. This paper processes the data of nearly 19 [...] Read more.

Outgoing longwave radiation (OLR) data are one of the key factors in studying the radiation balance of the earth–atmosphere system in East Asia. It is of great significance to explore the influence factors on OLR. This paper processes the data of nearly 19 years, from September 2002 to February 2022, and conducts in-depth research using the exponential smoothing method, empirical orthogonal decomposition (EOF), correlation analysis, and other methods. We found that the spatial distribution of OLR is zonal symmetry and gradually decreases with the increase of latitude. Using EOF analysis, it is found that the total variance contribution of the first four decomposed spatial features exceeds 70%, and the overall change trend of the four-time coefficients in the past 19 years all show a downward trend. OLR is positively correlated with total column water vapor (TCWV), air temperature (AT), and cloud top temperature (CTT), but negatively correlated with cloud top pressure (CTP). OLR has a similar spatial correlation distribution with TCWV and AT, while the spatial correlation between OLR and CTP is opposite to the first two parameters. In most parts of East Asia, the spatial correlation with CTT exceeds 0.8. The change in OLR value is affected by various meteorological parameters. In East Asia, the positive correlation between 30° N and 60° N is significantly affected by TCWV, AT, and CTT; and the negative correlation is more significantly affected by CTP. At 0–25° N, the positive correlation is significantly affected by CTP and CTT, while the negative correlation is significantly affected by TCWV and AT. Full article

(This article belongs to the Special Issue Meteorological Models: Recent Trends, Current Progress and Future Directions)

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14 pages, 609 KiB

Open AccessArticle

Optimized Wavelength Sampling for Thermal Radiative Transfer in Numerical Weather Prediction Models

by Michael de Mourgues, Claudia Emde and Bernhard Mayer

Atmosphere 2023, 14(2), 332; https://doi.org/10.3390/atmos14020332 - 07 Feb 2023

Cited by 3 | Viewed by 1326

Abstract

In the thermal spectral range, there are millions of individual absorption lines of water vapor, CO₂, and other trace gases. Radiative transfer calculations of wavelength-integrated quantities, such as irradiance and heating rate, are computationally expensive, requiring a high spectral resolution for [...] Read more.

In the thermal spectral range, there are millions of individual absorption lines of water vapor, CO₂, and other trace gases. Radiative transfer calculations of wavelength-integrated quantities, such as irradiance and heating rate, are computationally expensive, requiring a high spectral resolution for accurate numerical weather prediction and climate modeling. This paper introduces a method that could highly reduce the cost of integration in the thermal spectrum by employing an optimized wavelength sampling method. Absorption optical thicknesses for various trace gases were calculated from the HITRAN 2012 spectroscopic dataset using the ARTS line-by-line model as input to a fast Schwarzschild radiative transfer model. Using a simulated annealing algorithm, different optimized sets of wavelengths and corresponding weights were identified, which allowed for accurate integrated quantities to be computed as a weighted sum, reducing the computational time by several orders of magnitude. For each set of wavelengths, a lookup table, including the corresponding weights and absorption cross-sections, is created and can be applied to any atmospheric setups for which it was trained. We applied the lookup table to calculate irradiances and heating rates for a large set of atmospheric profiles from the ECMWF 91-level short-range forecast. Ten wavelength nodes are sufficient to obtain irradiances within an average root mean square error (RMSE) of upward and downward radiation at any height below 1 Wm⁻² while 100 wavelengths allowed for an RSME of below 0.05 Wm⁻². The applicability of this method was confirmed for irradiances and heating rates in clear conditions and for an exemplary cloud at 3.2 km height. Representative spectral gridpoints for integrated quantities in the thermal spectrum (REPINT) is available as absorption parameterization in the libRadtran radiative transfer package, where it can be used as an efficient molecular absorption parameterization for a variety of radiative transfer solvers. Full article

(This article belongs to the Special Issue Meteorological Models: Recent Trends, Current Progress and Future Directions)

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15 pages, 7601 KiB

Open AccessArticle

Spatiotemporal Characteristics and Influencing Factors of Sunshine Duration in China from 1970 to 2019

by Chaoli Tang, Yidong Zhu, Yuanyuan Wei, Fengmei Zhao, Xin Wu and Xiaomin Tian

Atmosphere 2022, 13(12), 2015; https://doi.org/10.3390/atmos13122015 - 30 Nov 2022

Cited by 2 | Viewed by 1762

Abstract

In order to alleviate global warming and the energy crisis, it is of great significance to develop and utilize solar energy resources. Sunshine duration (SD) is considered to be the best substitute for solar radiation and a key factor in evaluating solar energy [...] Read more.

In order to alleviate global warming and the energy crisis, it is of great significance to develop and utilize solar energy resources. Sunshine duration (SD) is considered to be the best substitute for solar radiation and a key factor in evaluating solar energy resources. Therefore, the spatial and temporal characteristics of SD and the reasons for its changes have received extensive attention and discussion. Based on the data of 415 meteorological stations from 1970 to 2019, this paper uses linear trend analysis, Mann–Kendall mutation analysis, the Hurst index, empirical orthogonal decomposition, correlation analysis and partial correlation analysis to analyze the spatiotemporal characteristics of SD and its relationship with influencing factors. The results show that the annual SD in China shows a downward trend, with a climate trend rate of −37.93 h/10a, and a significant decline from 1982 to 2019. The seasonal SD shows a downward trend, and the downward trend is most obvious in summer. The annual and seasonal SD will still show a downward trend in the future. The spatial distribution of SD not only has an overall consistent distribution but also takes the Yellow River from Ningxia to Shandong as the boundary, showing a north–south opposite distribution. Annual SD has a significant positive correlation, a significant negative correlation, a positive correlation and a negative correlation with wind speed, precipitation, temperature and relative humidity, respectively, and it is most closely related to wind speed and precipitation. In addition, the change in SD may also be related to human activities. Full article

(This article belongs to the Special Issue Meteorological Models: Recent Trends, Current Progress and Future Directions)

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16 pages, 6546 KiB

Open AccessArticle

The New Improved ZHD and Weighted Mean Temperature Models Based on GNSS and Radiosonde Data Using GPT3 and Fourier Function

by Li Li, Ying Gao, Siyi Xu, Houxian Lu, Qimin He and Hang Yu

Atmosphere 2022, 13(10), 1648; https://doi.org/10.3390/atmos13101648 - 10 Oct 2022

Cited by 3 | Viewed by 1399

Abstract

Compared to the zenith hydrostatic delay (ZHD) obtained from the Saastamonien model based on in-situ measured meteorological (IMM) data and radiosonde-derived weighted mean temperature (

T_{m}

), the ZHD and

T_{m}

deviations of the GPT3 model have shown obvious periodic trends. [...] Read more.

Compared to the zenith hydrostatic delay (ZHD) obtained from the Saastamonien model based on in-situ measured meteorological (IMM) data and radiosonde-derived weighted mean temperature (

T_{m}

), the ZHD and

T_{m}

deviations of the GPT3 model have shown obvious periodic trends. This article analyzed the seasonal variations of GPT3-ZHD and GPT3-

T_{m}

during the 2016–2020 period in the Yangtze River Delta region, and the new improved ZHD and

T_{m}

models were established by the multi-order Fourier function. The precision of the improved-ZHD model was verified using IMM-ZHD products from 7 GNSS stations during the 2016–2020 period. Furthermore, the precisions of improved

T_{m}

and precipitable water vapor (PWV) were verified by radiosonde-derived

T_{m}

and PWV in the 2016–2019 period. Compared with the IMM-ZHD and GNSS-PWV products, the mean Bias and RMS of GPT3-ZHD are −0.5 mm and 2.1 mm, while those of GPT3-PWV are 2.7 mm and 11.1 mm. Compared to the radiosonde-derived

T_{m}

, the mean Bias and RMS of GPT3-

T_{m}

are −0.8 K and 3.2 K. The mean Bias and RMS of the improved-ZHD model from 2019 to 2020 are −0.1 mm and 0.5 mm, respectively, decreasing by 0.4 mm and 1.6 mm compared to the GPT3-ZHD, while those of the improved-

T_{m}

are −0.6 K and 2.7 K, respectively, decreasing by 0.2 K and 0.5 K compared to GPT3-

T_{m}

. The mean Bias and RMS of PWV calculated by GNSS-ZTD, improved-ZHD, and improved-

T_{m}

are 0.5 mm and 0.6 mm, respectively, compared to the GNSS-PWV, decreasing by 2.2 mm and 10.5 mm compared to the GPT3-PWV. It indicates that the improved ZHD and

T_{m}

models can be used to obtain the high-precision PWV. It can be applied effectively in the retrieval of high-precision PWV in real-time in the Yangtze River Delta region. Full article

(This article belongs to the Special Issue Meteorological Models: Recent Trends, Current Progress and Future Directions)

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10 pages, 3504 KiB

Open AccessArticle

A Temperature Refinement Method Using the ERA5 Reanalysis Data

by Zhicai Li, Xu Gong, Mingjia Liu, Hui Tang, Yifan Yao, Mengfan Liu and Fei Yang

Atmosphere 2022, 13(10), 1622; https://doi.org/10.3390/atmos13101622 - 05 Oct 2022

Viewed by 1315

Abstract

Air temperature is an important parameter in the research of meteorology, environment, and ecology. Obtaining accurate temperature values with high spatial–temporal resolution is the premise for regional climate monitoring and analysis and is also the basis for the calculation of various ecological and [...] Read more.

Air temperature is an important parameter in the research of meteorology, environment, and ecology. Obtaining accurate temperature values with high spatial–temporal resolution is the premise for regional climate monitoring and analysis and is also the basis for the calculation of various ecological and environmental factors. In this study, we proposed a temperature refinement method using the ERA5 reanalysis data, which constructed the correlation between the measured temperature derived from weather stations and the interpolated temperature based on the artificial neutral network (ANN) model. Experiments in a high-intensity coal mining area in China were conducted, and the root mean square error (RMSE) and compound relative error (CRE) were adopted as the statistical values in the internal and external accuracy tests. Numerical results showed that the proposed temperature refinement method outperformed the traditional interpolated method with an approximately 42% and 33% RMSE improvement in the internal and external accuracy test, respectively. Moreover, the proposed method effectively improved the geographic differences of the traditional method and obtained temperature estimates with high accuracy at arbitrary sites. Full article

(This article belongs to the Special Issue Meteorological Models: Recent Trends, Current Progress and Future Directions)

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31 pages, 8910 KiB

Open AccessArticle

Temporal and Spatial Modal Analysis and Prediction of Tropospheric CO Concentration over the World and East Asia

by Yourui Huang, Le Sun, Yuanyuan Wei and Chaoli Tang

Atmosphere 2022, 13(9), 1476; https://doi.org/10.3390/atmos13091476 - 12 Sep 2022

Cited by 1 | Viewed by 1411

Abstract

Spatiotemporal modal analysis and prediction of tropospheric atmospheric CO concentration in the world and East Asia from 2002 to 2021 using the inversion data of airs sounder. The results show that: The CO concentration in the northern hemisphere is higher than that in [...] Read more.

Spatiotemporal modal analysis and prediction of tropospheric atmospheric CO concentration in the world and East Asia from 2002 to 2021 using the inversion data of airs sounder. The results show that: The CO concentration in the northern hemisphere is higher than that in the southern hemisphere; from the upper troposphere to the lower troposphere, the CO concentration changes from “

\land

“ to “√”; the fluctuation range of near surface CO concentration in the northern hemisphere is relatively intense, and the fluctuation range in the southern hemisphere is relatively small. Using MK, Sen slope estimation, and EOF analysis, it is found that CO concentration in the convective middle layer tends to decline in more than 90% of the global area, and the decline rate in the northern hemisphere is significantly higher than that in the southern hemisphere. In East Asia, the CO concentration in the lower tropospheric marine area is significantly lower than that in the land area. The average concentration and decline rate of CO in East Asia is always higher than that in the world; the CO concentration in East Asia is the highest in spring and winter in the lower troposphere; and the CO concentration in East Asia is lower in the northeast and higher in the southeast in the upper troposphere in spring, autumn, and winter, and higher in the northeast and Central Plains in summer. Compared with the three-exponential smoothing model, the prediction error of the VMD-LSTM hybrid model for atmospheric CO concentration is significantly reduced, which indicates that the improved neural network prediction model has higher prediction accuracy. The factors affecting the change of tropospheric CO concentration are not only affected by the ground factors, but also related to indirect factors such as water vapor, methane, and atmospheric temperature in the atmosphere. Full article

(This article belongs to the Special Issue Meteorological Models: Recent Trends, Current Progress and Future Directions)

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15 pages, 4387 KiB

Open AccessArticle

A Rainfall Forecast Model Based on GNSS Tropospheric Parameters and BP-NN Algorithm

by Huanian Fu, Wenfeng Zhang, Chunjin Li and Zaihuang Hu

Atmosphere 2022, 13(7), 1045; https://doi.org/10.3390/atmos13071045 - 29 Jun 2022

Cited by 1 | Viewed by 1294

Abstract

The occurrence of rainfall is the result of a combination of various meteorological factors. Traditional rainfall early warning models solely use Global Navigation Satellite System (GNSS)-derived Zenith Total Delay (ZTD) or Precipitable Water Vapor (PWV) to forecast rainfall, resulting in a low true [...] Read more.

The occurrence of rainfall is the result of a combination of various meteorological factors. Traditional rainfall early warning models solely use Global Navigation Satellite System (GNSS)-derived Zenith Total Delay (ZTD) or Precipitable Water Vapor (PWV) to forecast rainfall, resulting in a low true detected rate. While non-linear rainfall early warning models based on the Back-Propagation Neural Network (BP-NN) algorithm consider the influences of various meteorological factors, the forecasts often exhibit a high false rate. To further improve the prediction of rainfall, a short-term rainfall early warning model based on the GNSS and BP-NN algorithms is proposed in this study. The method uses the traditional rainfall forecasting model and utilizes the BP-NN algorithm to combine various meteorological factors for rainfall early warning. The results of GNSS and BP-NN together improve the precision of rainfall early warning. Observation data from eight GNSS stations, the fifth-generation reanalysis of European Centre for Medium-Range Weather Forecast (ECMWF ERA5), and temperature, pressure, and rainfall data from corresponding meteorological stations in Ningbo, China were utilized to verify the rainfall early warning model proposed in this study. The results show that the proposed model can complement the advantages of the traditional linear and non-linear rainfall early warning methods. The model can maintain a high True Detected Rate (TDR) of rainfall early warning while simultaneously reducing the False Forecasted Rate (FFR). The average TDR of the eight GNSS stations is 100% and the FFR is 20.75%, which are both better than those of existing traditional linear and non-linear rainfall early warning models. Full article

(This article belongs to the Special Issue Meteorological Models: Recent Trends, Current Progress and Future Directions)

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17 pages, 3297 KiB

Open AccessArticle

An Investigation of Near Real-Time Water Vapor Tomography Modeling Using Multi-Source Data

by Laga Tong, Kefei Zhang, Haobo Li, Xiaoming Wang, Nan Ding, Jiaqi Shi, Dantong Zhu and Suqin Wu

Atmosphere 2022, 13(5), 752; https://doi.org/10.3390/atmos13050752 - 06 May 2022

Cited by 2 | Viewed by 2029

Abstract

Global Navigation Satellite Systems (GNSS) tomography is a well-recognized modeling technique for reconstruction, which can be used to investigate the spatial structure of water vapor with a high spatiotemporal resolution. In this study, a refined near real-time tomographic model is developed based on [...] Read more.

Global Navigation Satellite Systems (GNSS) tomography is a well-recognized modeling technique for reconstruction, which can be used to investigate the spatial structure of water vapor with a high spatiotemporal resolution. In this study, a refined near real-time tomographic model is developed based on multi-source data including GNSS observations, Global Forecast System (GFS) products and surface meteorological data. The refined tomographic model is studied using data from Hong Kong from 2 to 11 October 2021. The result is compared with the traditional model with physical constraints and is validated by the radiosonde data. It is shown that the root mean square error (RMSE) values of the proposed model and traditional model are 0.950 and 1.763 g/m³, respectively. The refined model can decrease the RMSE by about 46%, indicating a better performance than the traditional one. In addition, the accuracy of the refined tomographic model is assessed under both rainy and non-rainy conditions. The assessment shows that the RMSE in the rainy period is 0.817 g/m³, which outperforms the non-rainy period with the RMSE of 1.007 g/m³. Full article

(This article belongs to the Special Issue Meteorological Models: Recent Trends, Current Progress and Future Directions)

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