Relationship between topography, tropospheric wind, and frequency of mountain waves in the upper mesosphere over the Kanto area of Japan

Imaging observations of OH airglow were performed at Meiji University, Japan (35.6° N, 139.5° E), from May 2018 to December 2019. Mountainous areas are located to the west of the imager, and westerly winds are dominant in the lower atmosphere throughout the year. Mountain waves (MWs) are generated and occasionally propagate to the upper atmosphere. However, only four likely MW events were identified, which are considerably fewer than expected. There are two possible reasons for the low incidence: (1) MWs do not propagate easily to the upper mesosphere due to background wind conditions, and/or (2) the frequency of MW excitation was low around the observation site. Former possibility is found not to be a main reason to explain the frequency by assuming typical wind profiles in troposphere and upper mesosphere over Japan. Thus, frequency and spatial distribution of orographic wavy clouds were investigated by analyzing images taken by the Himawari-8 geostationary meteorological satellite in 2018. The number of days when wavy clouds were detected in the troposphere around the observation site (Kanto area) was about a quarter of that around the Tohoku area. This result indicates that frequency of over-mountain flow which is thought to be a source of excitation of MWs is low in Kanto area. We also found that the angle between the horizontal wind direction in troposphere and the orientation of the mountain ridge is a good proxy for the occurrence of orographic wavy clouds, i.e., excitation of MWs. We applied this proxy to the topography around the world to investigate regions where MWs are likely to be excited frequently throughout the year to discuss the likelihood of "MW hotspots" at various spatial scale. Graphical Abstract

Single cell and multiple cell clouds analysed with satellite data in and around Bangladesh

Multi-cell and single-cell clouds were analysed using Geostationary Meteorological Satellite (GMS-5) data on 6 and 13 August 1997 in and around Bangladesh. The multi-cell cloud moved NE with a speed of about 6 m/s and lasted approximately 21 hours. The single-cell cloud moved SE with a speed of about 13 m/s and lasted approximately 12 hours. Clouds move faster on oceans than on land. At the mature stage of the cloud, convective component was 40% and the rest was stratiform. The precipitable portion of the cloud was 74% and the rest was non-precipitable which differs from the reported value.

Improved Bi-Angle Aerosol Optical Depth Retrieval Algorithm from AHI Data Based on Particle Swarm Optimization

The Advanced Himawari Imager (AHI) aboard the Himawari-8, a new generation of geostationary meteorological satellite, has high-frequency observation, which allows it to effectively capture atmospheric variations. In this paper, we have proposed an Improved Bi-angle Aerosol optical depth (AOD) retrieval Algorithm (IBAA) from AHI data. The algorithm ignores the aerosol effect at 2.3 μm and assumes that the aerosol optical depth does not change within one hour. According to the property that the reflectivity ratio K of two observations at 2.3 μm does not change with wavelength, we constructed the equation for two observations of AHI 0.47 μm band. Then Particle Swarm Optimization (PSO) was used to solve the nonlinear equation. The algorithm was applied to the AHI observations over the Chinese mainland (80°–135°E, 15°–60°N) between April and June 2019 and hourly AOD at 0.47 μm was retrieved. We validated IBAA AOD against the Aerosol Robotic Network (AERONET) sites observation, including surrounding regions as well as the Chinese mainland, and compared it with the AHI L3 V030 hourly AOD product. Validation with AERONET of 2079 matching points shows a correlation coefficient R = 0.82, root-mean-square error RMSE = 0.27, and more than 62% AOD retrieval results within the expected error of ±(0.05 + 0.2 × AODAERONET). Although IBAA does not perform very well in the case of coarse-particle aerosols, the comparison and validation demonstrate it can estimate AHI AOD with good accuracy and wide coverage over land on the whole.

A Study on Brightness Reversal of Internal Waves in the Celebes Sea Using Himawari-8 Images

A geostationary meteorological satellite is located at a fixed point above the equator, which can continuously observe internal waves and provides great advantages in research on changes in the generation and propagation of internal waves. The scale of internal waves in the Celebes Sea is large, which is still very obvious in geostationary meteorological satellite images with a lower spatial resolution. This study considers continuous remote sensing images of geostationary meteorological satellite Himawari-8 to analyze the bright and dark features of internal waves in the Celebes Sea in optical remote sensing images. The solar zenith angle, sensor zenith angle and relative azimuth angle of internal waves in six images are calculated, and the changes are 12.45°, 0.20° and 3.44°, respectively, within 50 min. Moreover, based on the normalized sunglint radiance theory, the critical solar viewing angle is proposed and verified. The results indicate that the bright and dark features of internal waves when passing through sunglint and non-sunglint areas are greatly reversed, and the critical solar viewing angles are 18.73° and 27.41°, respectively. In this study, geostationary meteorological satellite Himawari-8 images are analyzed to study on the brightness reversal phenomenon of internal waves for the first time, and a unique brightness change in internal waves during the propagation process is revealed, which has not been reported in existing research.

Applications of the Advanced Radiative Transfer Modeling System (ARMS) to Characterize the Performance of Fengyun–4A/AGRI

This study applies the Advanced Radiative Transfer Modeling System (ARMS), which was developed to accelerate the uses of Fengyun satellite data in weather, climate, and environmental applications in China, to characterize the biases of seven infrared (IR) bands of the Advanced Geosynchronous Radiation Imager (AGRI) onboard the Chinese geostationary meteorological satellite, Fengyun–4A. The AGRI data are quality controlled to eliminate the observations affected by clouds and contaminated by stray lights during the mid–night from 1600 to 1800 UTC during spring and autumn. The mean biases, computed from AGRI IR observations and ARMS simulations from the National Center for Environmental Prediction (NCEP) Final analysis data (FNL) as input, are within −0.7–1.1 K (0.12–0.75 K) for all seven IR bands over the oceans (land) under clear–sky conditions. The biases show seasonal variation in spatial distributions at bands 11–13, as well as a strong dependence on scene temperatures at bands 8–14 and on satellite zenith angles at absorption bands 9, 10, and 14. The discrepancies between biases estimated using FNL and the European Center for Medium–Range Weather Forecasts Reanalysis–5 (ERA5) are also discussed. The biases from water vapor absorption bands 9 and 10, estimated using ERA5 over ocean, are smaller than those from FNL. Such discrepancies arise from the fact that the FNL data are colder (wetter) than the ERA5 in the middle troposphere (upper–troposphere).

Frequency of Mountain Waves Over Kanto Area Revealed by Imaging Observations of OH Airglow

Imaging observations of OH airglow were conducted at Meiji University, Japan (IN, mE), from May 2018 to December 2019. Mountainous areas, including Mt. Fuji, are located to the west of the imager, and westerly winds are dominant in the lower atmosphere throughout the year. Mountain waves (MWs) are generated on the leeward sides of mountains and occasionally propagate to the upper atmosphere. However, during the observation period (about 1 year and 8 months), only four possible MW events were identified. Based on previous reports, this incidence is considerably lower than expected. There are two possible reasons for the low incidence of MW events: (1) The frequency of MW excitation is small in the lower layers of the atmosphere, and/or (2) MWs do not propagate easily to the upper mesosphere due to background wind conditions. This study verified the likelihood of the former case. Under over-mountain airflow conditions, wavy clouds are often generated on the leeward side. Since over-mountain airflow is essential for the excitation of MWs, the frequency of wavy clouds in the lower atmosphere can be regarded as a measure of the occurrence of MWs. The frequency and spatial distribution of MWs around Japan were investigated by detecting the wavy clouds from color images taken by the Himawari-8 geostationary meteorological satellite (GSM-8) for one year in 2018. The wavy clouds were detected on more than 70 days a year around the Tohoku region, but just 20 days a year around Mt. Fuji. This suggests that few MWs are generated around Mt. Fuji. The differences between these two regions were examined focusing on the relationship between the local topography and dominant horizontal wind fields in the lower atmosphere. Specifically, the findings showed that the angle between the dominant horizontal wind direction and the orientation of the mountain ridge is a good proxy of the occurrence of wavy clouds, i.e., excitation of MWs in mountainous areas. We have also applied this proxy to topography in other areas of the world to investigate areas where MWs would be occurring frequently. Finally, we discuss the likelihood of "MW hotspots" at various spatial scales in the world.

Assimilation of Himawari-8 imager radiance data with the WRF-3DVAR system for the prediction of Typhoon Soudelor

Himawari-8 is a next-generation geostationary meteorological satellite launched by the Japan Meteorological Agency. It carries the Advanced Himawari Imager (AHI) on board, which can continuously monitor high-impact weather events with high frequency in space and time. The assimilation of AHI radiance data was implemented with the three-dimensional variational data assimilation system (3DVAR) of the Weather Research and Forecasting Model for the analysis and prediction of Typhoon Soudelor (2015) in the Pacific typhoon season. The effective assimilation of AHI radiance data in improving the forecast of the tropical cyclone during its rapid intensification has been realized. The results show that, after assimilating the AHI radiance data under clear-sky conditions, the typhoon position in the background field of the model was effectively corrected compared with the control experiment without AHI radiance data assimilation. It is found that the assimilation of AHI radiance data is able to improve the analyses of the water vapor and wind in a typhoon's inner-core region. The analyses and forecasts of the minimum sea level pressure, the maximum surface wind, and the track of the typhoon are further improved.

A simple method for surface radiation estimating using FY-4A data

FY-4A is a geostationary meteorological satellite with four advanced payloads, which can be used to quantitatively detect the earth's atmospheric system with multi spectral and high spatial-temporal resolution. However, the applicable model limits the application of the FY-4A satellite data. In this paper, the empirical statistical model developed for the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor is extended for FY-4A Advanced Geosynchronous Radiation Imager (AGRI), and it is applied to observed data to evaluate the applicability of the model for AGRI measurements. To improve the accuracy of radiation estimation, the artificial intelligent particle swarm optimization (PSO) algorithm was used for model optimizing. Results show that the estimated radiation has diurnal variation, which accords with the characteristics of radiation variation. The estimated net surface shortwave radiation (Sn) and observed values show good correlation. However, large deviations from observations are found in the estimated values when the empirical model based on MODIS is directly used to process AGRI data. Thus, the empirical statistical model based on MODIS can be applied to AGRI data, but the empirical parameters need to be revised. Optimization of the empirical statistical model by the PSO algorithm can effectively improve the accuracy of radiation estimate. The Mean absolute percentage error (MAPE) of Sn estimated by optimized models is reduced to 15%. The MAPE of the net surface long-wave radiation (Ln) estimated by optimized models is reduced to 31%, and the MAPE of the net radiation (Rn) estimated by optimized models is reduced to 27%. However, for the uncertainty caused by error accumulation effect, the influence of PSO optimization on Rn is not as obvious as that of Ln. However, from the analysis of error distribution, it shows that PSO optimization does improve the estimation results of Rn. Based on AGRI data, the surface radiation can be estimated simply, and the regional or larger scale surface radiation retrieval can quickly realize by this method which has large application potential and popularization value.

Estimating Ground-Level Hourly PM2.5 Concentrations Over North China Plain with Deep Neural Networks

Fine particulate matter (PM2.5) has a considerable impact on the environment, climate change, and human health. Herein, we introduce a deep neural network model for deriving ground-level, hourly PM2.5 concentrations by Himawari-8 aerosol optical depth, meteorological variables, and land cover information. A total of 151,726 records were collected from 313 ground-level PM2.5 monitoring stations (spread across the North China Plain) to calibrate and test the proposed model. The sample- and site-based cross-validation yielded satisfactory performance, with correlation coefficients > 0.8 (R = 0.86 and 0.83, respectively). Furthermore, the variation in mean ground-level hourly PM2.5 concentrations, using 2017 data, showed that the proposed method could be applied for spatiotemporal continuous PM2.5 monitoring. This study will serve as a reference for the application of geostationary meteorological satellite to perform ground-level PM2.5 estimation and the utilization in atmospheric monitoring.