Geostationary Meteorological Satellite Data Research Articles

Analysis of Precipitation Process and Operational Precipitation Enhancement in Panxi Region Based on Cloud Parameters Retrievals from China’s Next−Generation Geostationary Meteorological Satellite FY−4A

Geostationary meteorological satellite data with high spatial and temporal resolution can be used to retrieve cloud physical parameters, which has significant advantages in tracking cloud evolution and development. Based on satellite multispectral RGB composite image and cloud physical analysis methods, we quantitatively analyze the evolution characteristics of cloud parameters in the pre-, mid- and post-artificially influenced weather process, explore the application potential benefits of satellite data in monitoring the operating conditions of the artificially influenced weather in the Panxi region, and verify the feasibility analysis of the evaluation of their effects. In this study, cloud parameters such as cloud particle effective radius (Re), cloud liquid water path (LWP), cloud ice water path (IWP), and cloud top height and temperature (CTH and CTT) are retrieved using FY−4A satellite data. For the Panxi region, the evolution characteristics of typical cloud parameters in the affected area before and after two aircraft artificial operational precipitation enhancements are analyzed. The results show that the satellite retrieval of cloud characteristic parameters in the Panxi region has good application value, which can be used to guide the artificial Operational Precipitation Enhancement. In this precipitation process, there are solid particles in the upper layer cloud and supercooled water in the lower layer cloud. After the cold cloud catalysis, the cloud top height, liquid water and ice water content, particle effective radius and ground precipitation in the operational area increased, and the cloud top temperature decreased. Thus, it effectively alleviated the drought in the Panxi region. The satellite retrieval of cloud characteristic parameters in the Panxi region has a good application value, which can provide a basis and guidance for future weather modification operations in the Panxi region.

Towards reliable retrievals of cloud droplet number for non-precipitating planetary boundary layer clouds and their susceptibility to aerosol

Remote sensing has been a key resource for developing extensive and detailed datasets for studying and constraining aerosol-cloud-climate interactions. However, aerosol-cloud collocation challenges, algorithm limitations, as well as difficulties in unraveling dynamic from aerosol-related effects on cloud microphysics, have long challenged precise retrievals of cloud droplet number concentrations. By combining a series of remote sensing techniques and in situ measurements at ground level, we developed a semi-automated approach that can address several retrieval issues for a robust estimation of cloud droplet number for non-precipitating Planetary Boundary Layer (PBL) clouds. The approach is based on satellite retrievals of the PBL cloud droplet number (Ndsat) using the geostationary meteorological satellite data of the Optimal Cloud Analysis (OCA) product, which is obtained by the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT). The parameters of the retrieval are optimized through closure with droplet number obtained from a combination of ground-based remote sensing data and in situ observations at ground level. More specifically, the remote sensing data are used to retrieve cloud-scale vertical velocity, and the in situ aerosol measurements at ground level were used constrain as input to a state-of-the-art droplet activation parameterization to predict the respective Cloud Condensation Nuclei (CCN) spectra, cloud maximum supersaturation and droplet number concentration (Nd), accounting for the effects of vertical velocity distribution and lateral entrainment. Closure studies between collocated Nd and Ndsat are then used to evaluate exising droplet spectral width parameters used for the retrieval of droplet number, and determine the optimal values for retrieval. This methodology, used to study aerosol-cloud interactions for non-precipitating clouds formed over the Athens Metropolitan Area (AMA), Greece from March to May 2020, shows that droplet closure can be achieved to within 30%, comparable to the level of closure obtained in many in situ studies. Given this, the ease of applying this approach with satellite data obtained from SEVIRI with high temporal (15 min) and spatial resolution (3.6 km × 4.6 km), opens the possibility of continuous and reliable Ndsat, giving rise to high value datasets for aerosol-cloud-climate interaction studies.

Forest Fire Monitoring and Positioning Improvement at Subpixel Level: Application to Himawari-8 Fire Products

Forest fires are among the biggest threats to forest ecosystems and forest resources, and can lead to ecological disasters and social crises. Therefore, it is imperative to detect and extinguish forest fires in time to reduce their negative impacts. Satellite remote sensing, especially meteorological satellites, has been a useful tool for forest-fire detection and monitoring because of its high temporal resolution over large areas. Researchers monitor forest fires directly at pixel level, which usually presents a mixture of forest and fire, but the low spatial resolution of such mixed pixels cannot accurately locate the exact position of the fire, and the optimal time window for fire suppression can thus be missed. In order to improve the positioning accuracy of the origin of forest fire (OriFF), we proposed a mixed-pixel unmixing integrated with pixel-swapping algorithm (MPU-PSA) model to monitor the OriFFs in time. We then applied the model to the Japanese Himawari-8 Geostationary Meteorological Satellite data to obtain forest-fire products at subpixel level. In this study, the ground truth data were provided by the Department of Emergency Management of Hunan Province, China. To validate the positioning accuracy of MPU-PSA for OriFFs, we applied the model to the Himawari-8 satellite data and then compared the derived fire results with fifteen reference forest-fire events that occurred in Hunan Province, China. The results show that the extracted forest-fire locations using the proposed method, referred to as forest fire locations at subpixel (FFLS) level, were far closer to the actual OriFFs than those from the modified Himawari-8 Wild Fire Product (M-HWFP). This improvement will help to reduce false fire claims in the Himawari-8 Wild Fire Product (HWFP). We conducted a comparative study of M-HWFP and FFLS products using three accuracy-evaluation indexes, i.e., Euclidean distance, RMSE, and MAE. The mean distances between M-HWFP fire locations and OriFFs and between FFLS fire locations and OriFFs were 3362.21 m and 1294.00 m, respectively. The mean RMSEs of the M-HWFP and FFLS products are 1225.52 m and 474.93 m, respectively. The mean MAEs of the M-HWFP and FFLS products are 992.12 m and 387.13 m, respectively. We concluded that the newly proposed MPU-PSA method can extract forest-fire locations at subpixel level, providing higher positioning accuracy of forest fires for their suppression.

Impact of Space Environment on Geostationary Meteorological Satellite Data Outage

AbstractImpact of space environment changes on geostationary meteorological satellite services, such as data outage, incomplete imagery, or quality degradation were investigated using event logs of Himawari‐8 and Meteosat (MET‐7 and MET‐8) in 2015–2017. The event logs show that such failures were caused by anomalies on spacecraft and in ground system half each. On Himawari‐8, a total of 11 incomplete imagery occurred due to spacecraft anomaly, and among them about 45% (5 anomalies) occurred during energetic electron enhancement and about 9% (1 anomaly) occurred during energetic proton enhancement. In cases of Meteosat, a total of 84 service alerts occurred due to spacecraft anomaly, and among them 35% (29 anomalies) occurred during electron enhancement and 7% (6 anomalies) occurred during both proton and electron enhancement. On the basis of statistical analysis, it is found that the probability of spacecraft anomaly occurrence markedly increases when electron fluence exceeds a threshold. The probability of anomaly is less than 10% when the 1‐MeV electron fluence is less than 104 (/cm2 sr eV), whereas it increases to more than 20% above the fluence. Thresholds are also found for electron fluences at other energies from 200 keV to 1.5 MeV. This study clarifies that changes in the space environment, particularly electron fluence enhancement affect geostationary meteorological satellite services.

Tropopause‐penetrating convection in the extratropics: A satellite‐based investigation for the year 2018

AbstractTropopause‐penetrating convection (or “overshooting convection” [OC]) is defined as a meso‐to‐microscale stratosphere–troposphere exchange process caused by deep convection. OC plays an important role in stratosphere–troposphere exchange and in producing turbulence. Compared with the tropics, the characteristics of OC in the extratropics are poorly understood. To this end, a high‐resolution and objective satellite‐based OC identification and classification algorithm was developed. In the algorithm, a machine learning method was utilized to derive tropopause height directly from satellite observations to ensure a high level of consistency between tropopause and cloud top height variations. By studying the climatology of OCs, including geographic distribution, vertical extent, and seasonal and diurnal variation, as well as their linkage with turbulence in the extratropics in 2018 based on Fengyun‐4A geostationary meteorological satellite data, it was found that most OCs in the extratropics have relatively shallow vertical extents and are more likely to occur over complex topography within the cyclonic curvature side of subtropical jets near the exit region, with a total frequency lower than 0.2%. They showed significant seasonal and diurnal variation. OCs in the Northern Hemisphere occurred most frequently during spring and summer and peak in the afternoon, while those in the Southern Hemisphere were more likely to occur in winter and spring, and with the highest likelihood in the early morning. Typical synoptic conditions for the occurrence of OCs in the mid–high latitudes of the Northern Hemisphere in the summer months included low‐level easterly flows, increased moisture symmetric instability, upper cold trough (or cold vortex), and a subtropical westerly jet. Under this large‐scale flow pattern, moderate‐to‐greater turbulence was often found to occur within or about 200 km away from the core convective area. The stronger the penetrating convection, the greater the probability of moderate‐to‐greater turbulence.

Higher temporal evapotranspiration estimation with improved SEBS model from geostationary meteorological satellite data

Evapotranspiration (ET) is a key variable in hydrologic cycle that directly affects the redistribution of precipitation and surface balance. ET measurements with high temporal resolution are required for coupling with models of highly dynamic processes, e.g., hydrological and land surface processes. The Haihe River Basin is the focus of China’s industrial base and it is one of the three major grain-producing regions within the country. However, this area is facing serious water resource shortages and water pollution problems. The present study used geostationary satellite remote sensing data, in situ meteorological observations, and the surface energy balance system (SEBS) model with a new kB−1 parameterization to estimate 3-hourly and daily energy and water fluxes in the Haihe River Basin. The results of the SEBS model were validated with point-scale data from five observation flux towers. Validation showed that 3-hourly and daily ET derived from the SEBS model performed well (R2 = 0.67, mean bias = 0.027 mm/h, RMSE = 0.1 mm/h). Moreover, factors influencing ET were also identified based on the results of this study. ET varies with land cover type and physical and chemical properties of the underlying surface. Furthermore, ET is also controlled by water availability, radiation, and other atmospheric conditions. It was found that ET had strong correlation with the normalized difference vegetation index (NDVI). Specifically, daily ET fluctuated with the NDVI when the NDVI was <0.29, and ET increased rapidly as the NDVI increased from 0.29 to 0.81. For NDVI values >0.81, indicating a state of saturation, the rate of increase of ET slowed. This research produced reliable information that could assist in sustainable management of the water resources and in improved understanding of the hydrologic cycle of the Haihe River Basin.

A Novel Framework of Detecting Convective Initiation Combining Automated Sampling, Machine Learning, and Repeated Model Tuning from Geostationary Satellite Data

This paper proposes a complete framework of a machine learning-based model that detects convective initiation (CI) from geostationary meteorological satellite data. The suggested framework consists of three main processes: (1) An automated sampling tool; (2) machine learning-based CI detection modelling; (3) repeated model tuning through validation. In this study, the automated sampling tool was able to track the CI objects iteratively, even without ancillary data such as an atmospheric motion vector (AMV). The collected samples were used to train the machine learning model for CI detection. Random forest (RF) was used to classify the CI and non-CI. To enhance the advantages of the machine learning approach, we adopted model tuning to iteratively update the training dataset from each validation result by adding hits and misses to the CI samples, and false alarms and correct negatives to the non-CI samples. Using 12 interest fields from the Himawari-8 Advanced Himawari Imager (AHI) over the Korean Peninsula, this simple and intuitive tuning process increased the overall probability of detection (POD) from 0.79 to 0.82 and decreased the overall false alarm rate (FAR) from 0.46 to 0.37 with around 40 min of the lead-time. Amongst the 12 interest fields, T b (11.2) µm was identified as the most significant predictor in the RF model, followed by T b (8.6—11.2) µm, and T b (6.2–7.3) µm. The effect of model tuning on the CI detection performance was also analyzed using spatiotemporal validation maps. By automatically collecting and updating the machine learning training dataset, the suggested framework is expected to help the maintenance of the CI detection model from an operational perspective.

Application of Himawari-8 Satellite Data in Daytime Sea Fog Monitoring in South China Coast

Based on the high temporal and spatial resolution of Japanese Himawari-8 geostationary meteorological satellite data and visibility observation data, the band and band combination was selected suitable for daytime sea fog identification in coastal areas of South China, including R0.64, NDVI, BT11.2-BT10.4, BT11.2-BT3.9, BT11.2 etc, and taken into account the influence of solar elevation angle on the reflectivity channel, and constructed a new coastal fog monitoring model (SFM) in the coastal area of South China for the identification of sea fog in the coastal areas of South China And applications. According to the fog judgment standard promulgated by the China Meteorological Administration, the sea fog monitoring model (SFM) was used to remotely monitor the coastal fog events in South China from January to April 2018, and further used the three provinces of South China (Guangxi, Guangdong, Hainan). Visibility data from coastal and island observatories are verified. The results show that the accuracy of sea fog recognition based on SFM model is about 68.6%, which has a good business application prospect.

COMS-Based Retrieval of Daily Actual Evapotranspiration over Korea

Evapotranspiration (ET) from the land surface is an important hydrometeorological factor in the exchange of energy between the atmosphere and land surface. The accurate quantification for management of water resources and understanding of climate change are crucial, requiring continuous temporal and spatial monitoring. The objective of this study is to apply and estimate daily actual ET using semiempirical method, B-method, which is based on surface energy balance over heterogeneous area, Korea. To estimate daily ET, we used geostationary meteorological satellite data (Communication, Ocean and Meteorological Satellite, COMS) and polar-orbiting satellite data (Système Pour ľObservation de la Terre, SPOT). Estimated daily ET using only satellite data was relatively accurate and reflects land surface characteristics. It had high periodicity and spatial resolution over a wide area on clear-sky days. The daily ET was overestimated by about 1 mm/day at the two flux tower measurements sites, but the simulated seasonal variation and pattern were in good agreement with flux tower measurements. In the mixed forest, the root-mean-square error (RMSE) was 0.94 mm/day and the bias was 1.05 mm/day, while, in the rice paddy, RMSE was 1.12 mm/day and bias was 1.21 mm/day.

Analysis of solar radiation on the surface estimated from GWNU solar radiation model with temporal resolution of satellite cloud fraction

Preliminary analysis with a solar radiation model is generally performed for photovoltaic power generation projects. Therefore, model accuracy is extremely important. The temporal and spatial resolutions used in previous studies of the Korean Peninsula were 1 km × 1 km and 1-h, respectively. However, calculating surface solar radiation at 1-h intervals does not ensure the accuracy of the geographical effects, and this parameter changes owing to atmospheric elements (clouds, aerosol, ozone, etc.). Thus, a change in temporal resolution is required. In this study, one-year (2013) analysis was conducted using Chollian geostationary meteorological satellite data from observations recorded at 15-min intervals. Observation data from the intensive solar site at Gangneung-Wonju National University (GWNU) showed that the coefficient of determination (R²), which was estimated for each month and season, increased, whereas the standard error (SE) decreased when estimated in 15-min intervals over those obtained in 1-h intervals in 2013. When compared with observational data from 22 solar sites of the Korean Meteorological Administration (KMA), R2 was 0.9 or higher on average, and over- or under-simulated sites did not exceed 3 sites. The model and 22 solar sites showed similar values of annual accumulated solar irradiation, and their annual mean was similar at 4,998 MJ m−2 (3.87 kWh m−2). These results show a difference of approximately ± 70 MJ m−2 (± 0.05 kWh m−2) from the distribution of the Korean Peninsula estimated in 1-h intervals and a higher correlation at higher temporal resolution.

High resolution surface radiation products for studies of regional energy, hydrologic and ecological processes over Heihe river basin, northwest China

This paper presents a framework to obtain high spatial resolution regional surface photosynthetically active radiation (PAR), solar radiation (SSR) and net radiation (NR) products through combining Geostationary Meteorological Satellite (GMS) data, polar-orbiting satellite Moderate Resolution Imaging Spectrometer (MODIS) products and ground meteorological sites’ observations. Huang et al. (2011) approach was adopted and improved to directly retrieve instantaneous PAR and SSR from GMS data; while for NR the relationship between net radiation and net solar radiation was analyzed and calibrated using ground observations and Normalized Difference Vegetation Index (NDVI). Then these instantaneous estimates with temporal resolution of half hour would be averaged or integrated to acquire hourly and daytime accumulated PAR, SSR and NR. Taking Heihe river basin in northwest China, a typical oasis-desert area, as an example, the methodology was applied to produce year 2012 PAR, SSR and NR products. Ground measurements from Watershed Allied Telemetry Experimental Research (WATER, Li et al. (2009)) and Heihe Watershed Allied Telemetry Experimental Research (HiWATER, Li et al. (2013)) were used to perform the accuracy assessments. The results indicate highly reliable products at 1km resolution have been produced over this basin, and are applicable to the researches of the regional surface energy, hydrological and ecological processes.

Monitoring Sea Fog of Zhejiang from Geostationary Meteorological Satellite Data

Monitoring Sea Fog of Zhejiang from Geostationary Meteorological Satellite Data

Rainfall estimation for real time flood monitoring using geostationary meteorological satellite data

Rainfall estimation by geostationary meteorological satellite data provides good spatial and temporal resolutions. This is advantageous for real time flood monitoring and warning systems. However, a rainfall estimation algorithm developed in one region needs to be adjusted for another climatic region. This work proposes computationally-efficient rainfall estimation algorithms based on an Infrared Threshold Rainfall (ITR) method calibrated with regional ground truth. Hourly rain gauge data collected from 70 stations around the Chao-Phraya river basin were used for calibration and validation of the algorithms. The algorithm inputs were derived from FY-2E satellite observations consisting of infrared and water vapor imagery. The results were compared with the Global Satellite Mapping of Precipitation (GSMaP) near real time product (GSMaP_NRT) using the probability of detection (POD), root mean square error (RMSE) and linear correlation coefficient (CC) as performance indices. Comparison with the GSMaP_NRT product for real time monitoring purpose shows that hourly rain estimates from the proposed algorithm with the error adjustment technique (ITR_EA) offers higher POD and approximately the same RMSE and CC with less data latency.

An assessment of three satellite-based precipitation data sets as applied to the Thailand region

The study examines three satellite-based data sets to estimate long-term precipitation for the Thailand region: the Tropical Rainfall Mapping Mission (TRMM) 3B43, the Climate Prediction Centre morphing technique (CMORPH), and a locally developed regression model using the geostationary meteorological satellite (GMS) covering the Thailand region. Data for the first two sets were available at a spatial resolution of 0.25° × 0.25°, while the local regression model used data from the GMS at a resolution of 5 km × 5 km. The statistical regression model was developed by relating long-term monthly average precipitation from 27 rain gauge stations with concurrent satellite data in the visible and thermal infrared bands. The model was then tested against independent data from 27 rain gauge stations. Satellite/rain gauge ratios were estimated, and a smooth spline surface was used to correct the error from the model. Data from the three approaches were compared with a rain gauge network. The TRMM relation performed better than CMORPH, and the performance for GMS was comparable to TRMM with root mean square different and mean bias difference of 33.6 and 4.2%, respectively. The locally developed regression model was used to produce monthly and yearly total rainfall maps from the GMS data for the entire country.

A combined deficit index for regional agricultural drought assessment over semi-arid tract of India using geostationary meteorological satellite data

The untimely onset and uneven distribution of south-west monsoon rainfall lead to agricultural drought causing reduction in food-grain production with high vulnerability over semi-arid tract (SAT) of India. A combined deficit index (CDI) has been developed from tri-monthly sum of deficit in antecedent rainfall and deficit in monthly vegetation vigor with a lag period of one month between the two. The formulation of CDI used a core biophysical (e.g., NDVI) and a hydro-meteorological (e.g., rainfall) variables derived using observation from Indian geostationary satellites. The CDI was tested and evaluated in two drought years (2009 and 2012) within a span of five years (2009–2013) over SAT. The index was found to have good correlation (0.49–0.68) with standardized precipitation index (SPI) computed from rain-gauge measurements but showed lower correlation with anomaly in monthly land surface temperature (LST). Significant correlations were found between CDI and reduction in agricultural carbon productivity (0.67–0.83), evapotranspiration (0.64–0.73), agricultural grain yield (0.70–0.85). Inconsistent correlation between CDI and ET reduction was noticed in 2012 in contrast to consistent correlation between CDI and reduction in carbon productivity both in 2009 and 2012. The comparison of CDI-based drought-affected area with those from existing operational approach showed 75% overlapping regions though class-to-class matching was only 40–45%. The results demonstrated that CDI is a potential indicator for assessment of late-season regional agricultural drought based on lag-response between water supply and crop vigor.

Estimation of global radiation in China and comparison with satellite product

A modified solar radiation model, incorporating into several satellites remote sensing information such as NCEP/NCAR data, EOS-AURA satellite data, and Chinese FY-2C geo-stationary meteorological satellite data, is presented. The model is an attempt to modify Chen’s radiation model and examine its estimation accuracy at various places in six different climatic zones of China. The verification of model is also carried out by comparing between calculated radiation value using modified model and radiation product of FY-2C satellites. According to the NSE values, the adaptability of model is reasonably high in Mid-Temperate Zone (MTZ), Warm Temperate Zone (WTZ), Tibetan Plateau Zone (TPZ), and Cold Temperature Zone (CTZ) climate regimes and slightly low in Subtropical Zone (SZ) and Tropical Zone (TZ) climate regimes. The comparison between modeled radiation values and FY-2C radiation product values shows that the radiation product of FY-2C satellites is superior to the modified model in SZ and TZ climate regimes.

Component Temperature Estimation from Simulated Geostationary Meteorological Satellite Data Based on MAP Criterion and Markov Models

Component Temperature Estimation from Simulated Geostationary Meteorological Satellite Data Based on MAP Criterion and Markov Models

A New Visible Albedo Normalization Method: Quasi-Lambertian Surface Adjustment

Abstract A new visible (VIS; 0.55–0.9 μm) albedo normalization method, that is, the quasi-Lambertian surface adjustment (QLSA), is developed herein by using the geostationary meteorological satellite data and radiative transfer model. Taking the variation of relative locations between the sun, satellite, and clouds into account, the QLSA effectively reduces the inconsistencies in the VIS image brightness caused by the Lambertian surface approximation to cloud tops (i.e., the reflection characteristic is isotropic). The evaluation, using Chinese and Japanese geostationary satellite data, shows that the QLSA is more effective and accurate than three other albedo normalization methods currently in use. The new algorithm is applicable in regions with solar zenith angle and satellite zenith angle less than 60°, which, in the summertime, approximately corresponds to the time range from 0800 to 1600 local time (LT).

Mesoscale grid rainfall estimation from AVHRR and GMS data

Areal rainfall averages derived from rain-gauge observations suffer from limitations not only due to sampling but also because gauges are usually distributed with a spatial bias towards populated areas and against areas with high elevation and slope. For a large river basin, however, heavy rainfall in the mountain upstream can result in severe flooding downstream. In this study, cloud-indexing and cloud model-based techniques were applied to Advanced Very High Resolution Radiometer (AVHRR) and Geostationary Meteorological Satellite (GMS) imager data based on the cloud-top brightness temperature (T B) and processed for estimating mesoscale grid rainfall. This study aims to improve and refine rainfall estimation in Malaysian monsoons based on cloud model techniques for operational pre-flood forecasting using readily available near-real-time satellite data such as the National Oceanic and Atmospheric Administration (NOAA)-AVHRR and GMS imager. Rain rates between 3 and 12 mm h−1 were assigned to cloud pixels of hourly coverage AVHRR or GMS data over the Langat Basin area for the duration of the monsoon rainfall event of 27 September to 8 October 2000 in Malaysia. The observed rainfall and quantitative precipitation forecast (QPF) showed an R 2 value of 0.9028, while the observed rainfall run-off (RR; recorded) and its simulated data had an R 2 value of 0.9263 and the QPF run-off and its simulated data had an R 2 value of 0.815. The rainfall estimate was used to simulate the flood event of the catchment. The estimated rainfall over the catchment showed similar flood area coverage to the observed flood event.

Geostationary Meteorological Satellite Data Processing System

In order to design a set of geostationary meteorological satellite data processing system，which have common data processing，practical remote sensing products and rich visual stylet，used VC++6.0 MFC and dynamic link library and the mature remote sensing products processing algorithms, to design it. Multi-satellite source geostationary meteorological satellite data are integrated, remote sensing products are generated, for example, cloud detection, cloud classification, cloud top height. The original cloud image, remote sensing and geographic information products are displayed vividly. The three-dimensional cloud image processing retrieval based on cloud detection product takes into account the visual effect, and also has a clear physical meaning, practicality is strong. The system makes geostationary meteorological satellite information play a more important role in the current weather forecast.