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The current methods for short-time precipitation nowcasting are based on radar echo extrapolation model, without fully considering the close influence of other meteorological factors on the evolution of precipitation generation and cancellation, thus limiting the accuracy of the forecasts. To address the above issues, this paper produced a short-time precipitation nowcasting dataset, and proposed the MFPNM(multiple factors precipitation nowcasting model). Based on data from the Fengyun4B satellite, the dataset toke quantitative precipitation estimation as the forecast object and contained four background meteorological factors. Taking the TransUNet as the backbone of the model, this model proposed the parallel dual encoder to extract the high-dimensional spatio-temporal features of the forecast object and the background meteorological data, respectively. Besides, it constructed the content coding module to encode the spatial features of the background data as the learnable positional embedding of the high-dimensional feature vectors of the forecast object. It used a Transformer module to construct the global relationship between the high-dimensional features of the sequence data for better sequence prediction. The metrics used in this paper included critical success index, false alarm rate, root-mean-square error, and structural similarity, etc. The MPFNM was evaluated on two datasets (the proposed dataset and an open-source dataset) and outperformed the baseline models, and it was analyzed for explainability through the SHAP technique. The experimental results and explainability analysis show that the model has better forecasting accuracy and reliability. [ABSTRACT FROM AUTHOR]

The accurate detection of clouds is an important first step in the processing of remotely sensed satellite data analyses and subsequent cloud model predictions. While initial cloud retrieval technology began with the exploitation of one or two bands of satellite imagery, it has accelerated rapidly in recent years as sensor and retrieval technology, creating a new era in space observation exploration. Additionally, the initial emphasis in satellite retrieval technology focused on cloud detection for cloud forecast models, but more recently, cloud screening in satellite-acquired data is playing an increasingly critical role in the investigation of cloud-free data for the retrieval of soil moisture, vegetation cover, ocean color concentration and sea surface temperatures, as well as the environmental monitoring of a host of products, e.g., atmospheric aerosol data, to study the Earth's atmospheric and climatic systems. With about 60% of the Earth covered by clouds, on average, it is necessary to accurately detect clouds in remote sensing data to screen cloud contaminate data in remote sensing analyses. In this review, the evolution of cloud-detection methodologies is highlighted with advancement in sensor hardware technology and machine learning algorithmic advances. The review takes into consideration the meteorological sensors usually used for atmospheric parameters estimation (thermodynamic profiles, aerosols, cloud microphysical parameters). Moreover, a discussion is presented on methods for obtaining the cloud-truth data needed to determine the accuracy of these cloud-detection approaches. [ABSTRACT FROM AUTHOR]

Tropical cyclones (TCs), including tropical depressions and different categories of typhoons, hurricanes, and cyclonic storms, mostly originate over the oceans in the absence of direct observations. Thus, detailed monitoring and analysis of TCs has always been an unsolved problem. In the recent 20 years, great changes have taken place in domestic and foreign TC monitoring techniques, imposing a significant impact on TC operations and research. Some new technologies and products gradually emerge to support operations, with improved monitoring accuracy. In this paper, the progress on TC monitoring and analysis via meteorological satellites, radars, and airplanes in China and the world is reviewed, compared, and summarized, with special focuses on multisatellite fusion observations, in situ aircraft measurements, and some unconventional observation equipment such as rockets, saildrones, and underwater gliders. On this basis, the paper points out future directions for improving TC monitoring and analysis in aid of better TC forecast and early warning. [ABSTRACT FROM AUTHOR]

The accurate detection of clouds is an important first step in the processing of remotely sensed satellite data analyses and subsequent cloud model predictions. While initial cloud retrieval technology began with the exploitation of one or two bands of satellite imagery, it has accelerated rapidly in recent years as sensor and retrieval technology, creating a new era in space observation exploration. Additionally, the initial emphasis in satellite retrieval technology focused on cloud detection for cloud forecast models, but more recently, cloud screening in satellite-acquired data is playing an increasingly critical role in the investigation of cloud-free data for the retrieval of soil moisture, vegetation cover, ocean color concentration and sea surface temperatures, as well as the environmental monitoring of a host of products, e.g., atmospheric aerosol data, to study the Earth’s atmospheric and climatic systems. With about 60% of the Earth covered by clouds, on average, it is necessary to accurately detect clouds in remote sensing data to screen cloud contaminate data in remote sensing analyses. In this review, the evolution of cloud-detection methodologies is highlighted with advancement in sensor hardware technology and machine learning algorithmic advances. The review takes into consideration the meteorological sensors usually used for atmospheric parameters estimation (thermodynamic profiles, aerosols, cloud microphysical parameters). Moreover, a discussion is presented on methods for obtaining the cloud-truth data needed to determine the accuracy of these cloud-detection approaches.

• The satellite precipitation products with a resolution of 0. 1° (∼10 km) are downscaled to 1 km with relatively high accuracy. • This method can effectively supplement precipitation data in areas with limited observations. • This method enhances the utilization and effectiveness of satellite precipitation data in complex terrain. Precipitation products with high spatial and temporal resolutions are crucial in hydrological monitoring, meteorological forecasting, and disaster warning. However, obtaining such products in regions with complex terrain and limited observation conditions is a challenging task. To address this issue, a statistical downscaling model for satellite precipitation products using an optimized Back Propagation (BP) neural network was developed. The model employed key environmental variables selected by the Mean Impact Value (MIV) index, including enhanced vegetation (EVI), land surface temperature (LST_D, LST_N), evapotranspiration (ET), digital elevation model (DEM), slope, and latitude and longitude (Lon, Lat). The downscaled precipitation products were corrected twice using a combination of the model error correction and observation data merging methods. Finally, the satellite precipitation products with a resolution of 0.1° (∼10 km) were downscaled to 1 km. A comprehensive evaluation of the results showed that: (1) the accuracy of the precipitation products obtained using this method was better than that of the original satellite precipitation products; (2) the method effectively supplemented precipitation data in areas with few ground stations, with better results in areas with a dense distribution of ground stations; (3) this method demonstrated the efficacy of satellite precipitation products for downscaling in areas with complex terrain. [ABSTRACT FROM AUTHOR]

In general, the far-infrared channel in the wavelength range of 10.5–12.0 µm plays an auxiliary role in wildfire detection as its sensitivity to high-temperature targets is far lower than the mid-infrared channel in the wavelength range of 3.5–4.0 µm at the same spatial resolution (1 km, which is the spatial resolution of infrared channels in most satellites used for wildfire monitoring in daily operational mode). The Medium-Resolution Spectral Imager II onboard the Fengyun-3D polar orbiting meteorological satellite (FY-3D/MERSI-II) contains far-infrared channels with a spatial resolution of 250 m at the wavelengths of 10.8 μm and 12.0 μm, which promotes the application of far-infrared channels in wildfire monitoring. In this study, the features of FY-3D/MERSI-II far-infrared channels in fire monitoring are discussed. The sensitivity of 10.8 μm (250 m) to fire spots and the influence of solar radiation reflection on the infrared channels are quantitatively analyzed. The method of using 10.8 μm (250 m) as a major data source to detect fire spots is proposed, and several typical wildfire cases are used to verify the proposed method. The results show that the 10.8 μm (250 m) far-infrared channel has the same advantages as the existing method in wildfire monitoring in terms of a more precise positioning of the detected fire pixel, avoiding interference by solar radiation reflections, and reflecting stronger fire regions in large fire fields. [ABSTRACT FROM AUTHOR]

The on-orbit radiometric calibration is a fundamental task in quantitative remote sensing applications. A widely used calibration method is the cross-calibration based on Simultaneous Nadir Observation (SNO), which involves using high-precision reference instruments to calibrate lower-precision onboard instruments. However, despite efforts to match the observation time, spatial location, field geometry, and instrument spectra, errors can still be introduced during the matching processes and linear regression analysis. This paper focuses on the error generated by sample matching and the error fitting method generated by the sample fitting method. An error propagation analysis is performed to develop a generic model for assessing the uncertainty of the SNO cross-calibration method itself in meteorological satellite infrared channels. The model is validated using the payload parameters of the Hyperspectral Infrared Atmospheric Sounder (HIRAS) and the Medium Resolution Spectral Imager (MERSI) instruments aboard the FengYun-3D (FY-3D). Simulation experiments are performed considering typical bright temperatures, different background fields, and varying matching threshold conditions. The results demonstrate the effectiveness of the proposed model in capturing the error propagation chain in the SNO cross-calibration process. The model provides valuable insight into error analysis in the SNO cross-calibration method and can assist in determining the optimal sample matching threshold for achieving radiometric calibration accuracy. [ABSTRACT FROM AUTHOR]

Abstract Prediction of ash concentrations in volcanic ash clouds for the Kirishima-Shinmoedake eruption on April 4th, 2018 is performed on the basis of an atmospheric transport model and the Japanese meteorological satellite Himawari-8. The retrieval algorithm for Himawari-8 (referred to as the “Optimal Volcanic Ash Algorithm”, OVAA) provides two-dimensional properties of volcanic ash clouds, such as cloud heights and total column mass loading, whereas it does not provide ash cloud thickness which is required to make an initial condition for the atmospheric transport model. To estimate ash cloud thickness immediately after an eruption, here, a wind shear index is introduced. The wind shear index includes an empirical constant parameter $$T_c$$ T c ; a small value of $$T_c$$ T c leads to thick ash clouds, whereas a large value of $$T_c$$ T c leads to thin ash clouds. In this study, the value of $$T_c$$ T c is optimized empirically in the following two ways: (1) a comparison between the total column mass loadings in the prediction and that in the OVAA estimation and (2) a comparison between the estimated ash cloud thickness and the observed ash cloud thickness by lidar measurements. These two comparisons suggest the optimal value of $$T_c$$ T c is 0.5 $$-$$ - 0.6, and then, the uncertainty of the ash clouds thickness estimation to be $$\sim$$ ∼ 700 m. In an operation, this estimation of $$T_c$$ T c can be used as a fixed value to estimate the ash cloud thickness for a future eruption. In this case, the ash concentration predictions can be obtained immediately after the OVAA estimation. The ash concentrations prediction for $$T_c$$ T c =0.6 provides areas of high contamination (>4 mg/m $$^3$$ 3 ) and low contamination (

Abstract The Japanese meteorological satellite Himawari-8 has captured the Earth’s atmosphere and stars, planets, and the Moon in its field of view, enabling us to capture their spectroscopy with 16 bands from visible to mid-infrared wavelengths. The nine infrared bands in the Advanced Himawari Imager (AHI) onboard Himawari-8 are unique in spaceborne observations and are potentially useful for lunar science. In addition, infrared bands of AHI cover wavelengths similar to those of other interplanetary instruments and thus are useful for calibrations. However, infrared AHI data have not yet been investigated in planetary science. To confirm the utility of AHI for planetary science, we develop a procedure to retrieve the lunar infrared spectrum and compare it with thermal conduction simulations. Our analysis shows that lunar brightness temperature curves can be obtained in the morning, evening, and nighttime for all nine bands. Particularly at 8.5 μm, they show a good agreement with previous observations by the Diviner radiometer onboard NASA’s Lunar Reconnaissance Orbiter. As pointed out previously, the brightness temperatures differ between the bands, indicating temperature mixing within a pixel. Our simulation suggests that surface roughness as steep as those measured at the Apollo landing sites significantly contributes to the observed brightness temperature differences in the morning and evening; however, nighttime brightness temperatures are greatly affected by rocks with higher thermal inertia than the regolith. The rock abundances are estimated to be 0.18–0.48% and 6.1–10.3% at the equator and within Tycho crater, respectively. Our estimations from AHI data are consistent with those of Diviner. These results support the idea that AHI potentially serves as a space telescope for future lunar and planetary sciences, for example, for constraining water content on the lunar surface. Graphical Abstract

Prediction of ash concentrations in volcanic ash clouds for the Kirishima-Shinmoedake eruption on April 4th, 2018 is performed on the basis of an atmospheric transport model and the Japanese meteorological satellite Himawari-8. The retrieval algorithm for Himawari-8 (referred to as the “Optimal Volcanic Ash Algorithm”, OVAA) provides two-dimensional properties of volcanic ash clouds, such as cloud heights and total column mass loading, whereas it does not provide ash cloud thickness which is required to make an initial condition for the atmospheric transport model. To estimate ash cloud thickness immediately after an eruption, here, a wind shear index is introduced. The wind shear index includes an empirical constant parameter T c ; a small value of T c leads to thick ash clouds, whereas a large value of T c leads to thin ash clouds. In this study, the value of T c is optimized empirically in the following two ways: (1) a comparison between the total column mass loadings in the prediction and that in the OVAA estimation and (2) a comparison between the estimated ash cloud thickness and the observed ash cloud thickness by lidar measurements. These two comparisons suggest the optimal value of T c is 0.5 - 0.6, and then, the uncertainty of the ash clouds thickness estimation to be ∼ 700 m. In an operation, this estimation of T c can be used as a fixed value to estimate the ash cloud thickness for a future eruption. In this case, the ash concentration predictions can be obtained immediately after the OVAA estimation. The ash concentrations prediction for T c =0.6 provides areas of high contamination (>4 mg/m 3 ) and low contamination (<2 mg/m 3 ). This classification of ash concentration in ash clouds has been required by the aviation industry, and is helpful information to assess safe areas and routes for airline operations. [ABSTRACT FROM AUTHOR]

Copyright of Bearing is the property of Bearing Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Geosynchronous satellite observation images have the advantages of a wide observation range and high temporal resolution, which are critical for understanding atmospheric motion and change patterns. The realization of geosynchronous satellite observation image prediction will provide significant support for short-term forecasting, including precipitation forecasting. Here, this paper proposes a deep learning method for predicting satellite observation images that can perform the task of predicting satellite observation sequences. In the study of predicting the observed images for Band 9 of the FY-4A satellite, the average mean square error of the network's 2-h prediction is 4.77 Kelvin. The network's predictive performance is the best among multiple deep learning models. We also used the model to predict Bands 10–14 of the FY-4A satellite and combined the multi-band prediction results. To test the application potential of the network prediction performance, we ran a precipitation area detection task on the multi-band prediction results. After 2 h of prediction, the detection results from satellite infrared images still achieved an accuracy of 0.855. [ABSTRACT FROM AUTHOR]

O b j e c t i v e s. The task of improving the software package for detecting thermal anomalies based on meteorologicalsatellite data developed by the unitary enterprise "Geoinformation Systems" was solved.M e t h o d s. In the period from 2015 to the present, the work on practical testing and improvement of the software for natural fires detection has been carried out. For this purpose, satellite images of the territory of Belarus obtained from NOAA series spacecraft were used. Special attention was paid to the problem of improving the accuracy of determining the coordinates of fires and reducing the time required for initial data processing.Re s u l t s. A retrospective analysis of the main stages of improving the software for natural fires detection and obtained during practical tests generalized results are provided. The description of the web service developed on the basis of the software for detecting natural fires is presented.Co n c l u s i o n. The information can be useful for the specialists and researchers who are engaged in the detection of thermal anomalies (fires) using remote sensing data from meteorological satellites.

In general, the far-infrared channel in the wavelength range of 10.5–12.0 µm plays an auxiliary role in wildfire detection as its sensitivity to high-temperature targets is far lower than the mid-infrared channel in the wavelength range of 3.5–4.0 µm at the same spatial resolution (1 km, which is the spatial resolution of infrared channels in most satellites used for wildfire monitoring in daily operational mode). The Medium-Resolution Spectral Imager II onboard the Fengyun-3D polar orbiting meteorological satellite (FY-3D/MERSI-II) contains far-infrared channels with a spatial resolution of 250 m at the wavelengths of 10.8 μm and 12.0 μm, which promotes the application of far-infrared channels in wildfire monitoring. In this study, the features of FY-3D/MERSI-II far-infrared channels in fire monitoring are discussed. The sensitivity of 10.8 μm (250 m) to fire spots and the influence of solar radiation reflection on the infrared channels are quantitatively analyzed. The method of using 10.8 μm (250 m) as a major data source to detect fire spots is proposed, and several typical wildfire cases are used to verify the proposed method. The results show that the 10.8 μm (250 m) far-infrared channel has the same advantages as the existing method in wildfire monitoring in terms of a more precise positioning of the detected fire pixel, avoiding interference by solar radiation reflections, and reflecting stronger fire regions in large fire fields.

The Japanese meteorological satellite Himawari-8 has captured the Earth's atmosphere and stars, planets, and the Moon in its field of view, enabling us to capture their spectroscopy with 16 bands from visible to mid-infrared wavelengths. The nine infrared bands in the Advanced Himawari Imager (AHI) onboard Himawari-8 are unique in spaceborne observations and are potentially useful for lunar science. In addition, infrared bands of AHI cover wavelengths similar to those of other interplanetary instruments and thus are useful for calibrations. However, infrared AHI data have not yet been investigated in planetary science. To confirm the utility of AHI for planetary science, we develop a procedure to retrieve the lunar infrared spectrum and compare it with thermal conduction simulations. Our analysis shows that lunar brightness temperature curves can be obtained in the morning, evening, and nighttime for all nine bands. Particularly at 8.5 μm, they show a good agreement with previous observations by the Diviner radiometer onboard NASA's Lunar Reconnaissance Orbiter. As pointed out previously, the brightness temperatures differ between the bands, indicating temperature mixing within a pixel. Our simulation suggests that surface roughness as steep as those measured at the Apollo landing sites significantly contributes to the observed brightness temperature differences in the morning and evening; however, nighttime brightness temperatures are greatly affected by rocks with higher thermal inertia than the regolith. The rock abundances are estimated to be 0.18–0.48% and 6.1–10.3% at the equator and within Tycho crater, respectively. Our estimations from AHI data are consistent with those of Diviner. These results support the idea that AHI potentially serves as a space telescope for future lunar and planetary sciences, for example, for constraining water content on the lunar surface. [ABSTRACT FROM AUTHOR]

The on-orbit radiometric calibration is a fundamental task in quantitative remote sensing applications. A widely used calibration method is the cross-calibration based on Simultaneous Nadir Observation (SNO), which involves using high-precision reference instruments to calibrate lower-precision onboard instruments. However, despite efforts to match the observation time, spatial location, field geometry, and instrument spectra, errors can still be introduced during the matching processes and linear regression analysis. This paper focuses on the error generated by sample matching and the error fitting method generated by the sample fitting method. An error propagation analysis is performed to develop a generic model for assessing the uncertainty of the SNO cross-calibration method itself in meteorological satellite infrared channels. The model is validated using the payload parameters of the Hyperspectral Infrared Atmospheric Sounder (HIRAS) and the Medium Resolution Spectral Imager (MERSI) instruments aboard the FengYun-3D (FY-3D). Simulation experiments are performed considering typical bright temperatures, different background fields, and varying matching threshold conditions. The results demonstrate the effectiveness of the proposed model in capturing the error propagation chain in the SNO cross-calibration process. The model provides valuable insight into error analysis in the SNO cross-calibration method and can assist in determining the optimal sample matching threshold for achieving radiometric calibration accuracy.

Cloud cover is an important factor limiting the earth observation efficiency of optical imaging satellites. Existing solutions include avoiding cloudy observation time windows by onboard cloud detectors and ground monitors, which are difficult to improve satellite observation efficiency in time. In order to solve the problem, firstly, a Geostationary Earth Orbit (GEO) and Low Earth Orbit (LEO) satellites cooperation scheme by using cloud cover information provided by GEO meteorological satellite to guide the imaging of LEO optical satellites is proposed, and the operation flow and key elements in this scheme are analyzed. Secondly, Fengyun-4 GEO meteorological satellite and its cloud mask (CLM) products are analyzed. Thirdly, an autonomous mission planning algorithm based on real-time cloud cover information is proposed. Computational results have demonstrated the effectiveness of the proposed GEO–LEO satellites cooperation scheme by taking the actual orbit and payload data of Fengyun-4 and Gaofen-1/2 satellites as examples. [ABSTRACT FROM AUTHOR]

Impact 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. Plain Language Summary: Space‐based meteorological observations are an indispensable part of our social infrastructure, but they are always face the risk of malfunction due to space radiation enhancement. To assess the impacts of space radiation on meteorological imagery observation services, we investigated energetic particle variation during meteorological imagery service alerts, particularly on Himawari‐8, MET‐7, and MET‐8 between 2015 and 2017. We found that about forty percent of the geostationary meteorological satellite anomalies occurred when 24‐hr fluence of energetic electrons increased. The occurrence probability of spacecraft anomaly markedly increases when the energetic electron fluence exceeds a threshold value. The results are important for planning safety operation of and developing robust next‐generation spacecraft. Key Points: About half of the geostationary meteorological satellite data outage was caused by spacecraft anomaliesAbout 40% of the spacecraft anomalies on the meteorological satellites occurred during 24‐hr fluence of energetic electron enhancementThresholds of energetic electron fluence, above which the probability of spacecraft anomaly occurrence increases, were found [ABSTRACT FROM AUTHOR]

Geosynchronous satellite observation images have the advantages of a wide observation range and high temporal resolution, which are critical for understanding atmospheric motion and change patterns. The realization of geosynchronous satellite observation image prediction will provide significant support for short-term forecasting, including precipitation forecasting. Here, this paper proposes a deep learning method for predicting satellite observation images that can perform the task of predicting satellite observation sequences. In the study of predicting the observed images for Band 9 of the FY-4A satellite, the average mean square error of the network’s 2-h prediction is 4.77 Kelvin. The network’s predictive performance is the best among multiple deep learning models. We also used the model to predict Bands 10–14 of the FY-4A satellite and combined the multi-band prediction results. To test the application potential of the network prediction performance, we ran a precipitation area detection task on the multi-band prediction results. After 2 h of prediction, the detection results from satellite infrared images still achieved an accuracy of 0.855.

Satellites detect the distribution of meteorological data worldwide. However, due to the orbital constraints, the satellite can only reach the same area again after one orbiting cycle. The interval between two detections in the same area is long, and the variation of meteorological data between the two detections is unknown. Moreover, meteorological satellite data are only located near the orbit in one cycle, while the global distribution of meteorological data is unknown. Our method allows to train a regression model with only few meteorological satellite data by taking advantage of the recent advances in deep learning. In detail, we train a model-agnostic meta-learning (MAML) model with data from ground stations instead of meteorological satellites and get the initial network parameters. Based on the initial network parameters trained by MAML, we train the regression models again for different areas. We sample the regression curves of all areas by time and get a time series of global meteorological data distribution. Through case studies conducted together with domain experts, we validate the effectiveness of our method. [ABSTRACT FROM AUTHOR]

Cloud cover is an important factor limiting the earth observation efficiency of optical imaging satellites. Existing solutions include avoiding cloudy observation time windows by onboard cloud detectors and ground monitors, which are difficult to improve satellite observation efficiency in time. In order to solve the problem, firstly, a Geostationary Earth Orbit (GEO) and Low Earth Orbit (LEO) satellites cooperation scheme by using cloud cover information provided by GEO meteorological satellite to guide the imaging of LEO optical satellites is proposed, and the operation flow and key elements in this scheme are analyzed. Secondly, Fengyun-4 GEO meteorological satellite and its cloud mask (CLM) products are analyzed. Thirdly, an autonomous mission planning algorithm based on real-time cloud cover information is proposed. Computational results have demonstrated the effectiveness of the proposed GEO–LEO satellites cooperation scheme by taking the actual orbit and payload data of Fengyun-4 and Gaofen-1/2 satellites as examples.

Calibration of satellite-borne radiometer is a key issue for quantitative remote sensing. Its accuracy depends on the stability of the calibration source. Because of no atmosphere and biological activity, the Moon surface keeps stable in the long term and may be a good candidate for thermal calibration. Observation of microwave humidity sounder (MHS) onboard the NOAA-18 made measurements of the disk-integrated brightness temperature (TB) of the Moon for the phase angle between −80° and 50°. The measurement of NOAA-18 has been studied to validate the TB model of lunar surface. In this article, the near side of the Moon surface is divided into 900 subregions with a span of $6^{\circ }\times 6^{\circ }$ in longitude and latitude. By solving 1-D heat conductive equation with the thermophysical parameters validated by the Diviner data of the Lunar Reconnaissance Orbiter (LRO), the temperature profiles of the regolith media in all 900 subregions are obtained. The loss tangents are inversed from the Chang’e-2 (CE-2) 37-GHz microwave TB data at noontime. Employing the fluctuation–dissipation theorem and the Wentzel–Kramer–Brillouin (WKB) approach, the microwave and millimeter-wave TBs of each subregion are simulated. Then, the weighted average TB can be disk-integrated from 900 TBs of all subregions versus the phase angle. These simulations well demonstrate diurnal TB variation and its dependence upon the frequency channels. It is found that the disk-integrated TB of the Moon in MHS channels is sensitive to the full-width at half-maximum (FWHM) of the deep space view (DSV), which is corrected in our simulation, where the Moon is now taken as an extended target, instead of a point-like object. Simulated integrated TBs are compared with the corrected MHS TB data at 89, 157, and 183 GHz. The simulated TB is well consistent with these MHS TB data at 89 and 183 GHz at various phase angles. But the maximum TB of MHS data at 157 GHz is unusually lower than that of 89 GHz. The influence of the loss tangent, emissivity, and the pointing error is analyzed. Some more careful design to observe the Moon TB and technical parameters, especially the FWHM should be well determined. Our model and numerical simulation provides a tool for TB calibration and validation. [ABSTRACT FROM AUTHOR]

Located in the Asian monsoon region, China frequently experiences severe convective weather (SCW), such as short-duration heavy rainfall (SDHR), thunderstorm high winds, hails, and occasional tornadoes. Progress in SCW forecasting in China is closely related to the construction and development of meteorological observation networks, especially weather radar and meteorological satellite networks. In the late 1950s, some county-level meteorological bureaus began to conduct empirical hail forecasting based on observations of clouds and surface meteorological variables. It took over half a century to develop a modern comprehensive operational monitoring and warning system for SCW forecast nationwide since the setup of the first weather radar in 1959. The operational SCW forecasting, including real-time monitoring, warnings valid for tens of minutes, watches valid for several hours, and outlooks covering lead times of up to three days, was established in 2009. Operational monitoring and forecasting of thunderstorms, SDHR, thunderstorm high winds, and hails have been carried out. The performance of operational SCW forecasting will be continually improved in the future with the development of convection-resolving numerical models (CRNMs), the upgrade of weather radar networks, the launch of new-generation meteorological satellites, better understanding of meso-γ and microscale SCW systems, and further application of artificial intelligence technology and CRNM predictions. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Thunderstorms are the most hazardous meteorological phenomena in Latvia in the summer season, and the assessment of their characteristics is essential for the development of an effective national climate and weather prediction service. However, the complex nature of convective processes sets specific limitations to their observation, analysis and forecasting. Therefore, the aim of this study is to analyse thunderstorm features associated with severe thunderstorms observed in weather radar and satellite data in Latvia over the period 2006–2015. The obtained results confirm the applicability of the selected thunderstorm features for thunderstorm nowcasting and analysis in Latvia. The most frequent features observed on days with thunderstorm were maximum radar reflectivities exceeding 50 dBZ and the occurrence of overshooting tops and tilted updrafts, while the occurrence of gravity waves, V-shaped storm structures and small ice particles have been found to be useful indicators of increased thunderstorm severity potential.

In this paper, the latest progress, major achievements and future plans of Chinese meteorological satellites and the core data processing techniques are discussed. First, the latest three FengYun (FY) meteorological satellites (FY-2H, FY-3D, and FY-4A) and their primary objectives are introduced. Second, the core image navigation techniques and accuracies of the FY meteorological satellites are elaborated, including the latest geostationary (FY-2/4) and polar-orbit (FY-3) satellites. Third, the radiometric calibration techniques and accuracies of reflective solar bands, thermal infrared bands, and passive microwave bands for FY meteorological satellites are discussed. It also illustrates the latest progress of real-time calibration with the onboard calibration system and validation with different methods, including the vicarious China radiance calibration site calibration, pseudo invariant calibration site calibration, deep convective clouds calibration, and lunar calibration. Fourth, recent progress of meteorological satellite data assimilation applications and quantitative science produce are summarized at length. The main progress is in meteorological satellite data assimilation by using microwave and hyper-spectral infrared sensors in global and regional numerical weather prediction models. Lastly, the latest progress in radiative transfer, absorption and scattering calculations for satellite remote sensing is summarized, and some important research using a new radiative transfer model are illustrated. [ABSTRACT FROM AUTHOR]

In this research, the design and survey of the satellite (Kensett) has been addressed. The main mission is to study the atmosphere and eventually land on the air bag, and new designs and ideas were designed. The motivation for this design is to be native and affordable in comparison with foreign ones. [ABSTRACT FROM AUTHOR]

The accuracy of geolocation on meteorological satellite data is critical for its application.The geolocation error sources of polar-orbit satellite are introduced,and then the research status on geolocation error estimation and correction is reviewed.Three mathematical methods of geolocation error correction,including Coastline Crossing Method (CCM),Node Differential Method (NDM) and Land Sea Fraction Method (LFM) are compared and discussed.Future researches in geolocation error correction are prospected. [ABSTRACT FROM AUTHOR]

A quantitative precipitation estimate (QPE) provides basic information for the modelling of many kinds of hydro-meteorological processes, e.g., as input to rainfall-runoff models for flash flood forecasting. Weather radar observations are crucial in order to meet the requirements, because of their very high temporal and spatial resolution. Other sources of precipitation data, such as telemetric rain gauges and satellite observations, are also included in the QPE. All of the used data are characterized by different temporal and spatial error structures. Therefore, a combination of the data should be based on quality information quantitatively determined for each input to take advantage of a particular source of precipitation measurement. The presented work on multi-source QPE, being implemented as the RainGRS system, has been carried out in the Polish national meteorological and hydrological service for new nowcasting and hydrological platforms in Poland. For each of the three data sources, different quality algorithms have been designed: (i) rain gauge data is quality controlled and, on this basis, spatial interpolation and estimation of quality field is performed, (ii) radar data are quality controlled by RADVOL-QC software that corrects errors identified in the data and characterizes its final quality, (iii) NWC SAF (Satellite Application Facility on support to Nowcasting and Very Short Range Forecasting) products for both visible and infrared channels are combined and the relevant quality field is determined from empirical relationships that are based on analyses of the product performance. Subsequently, the quality-based QPE is generated with a 1-km spatial resolution every 10 minutes (corresponding to radar data). The basis for the combination is a conditional merging technique that is enhanced by involving detailed quality information that is assigned to individual input data. The validation of the RainGRS estimates was performed taking account of season and kind of precipitation.

It has been almost four decades since the first launch of geostationary meteorological satellite by Japan Meteorological Agency (JMA). The specifications of the geostationary meteorological satellites have shown tremendous progresses along with the generations, which are now entering their third generation. The third-generation geostationary meteorological satellites not only yield basic data for weather monitoring, but also globally observe the Earth’s environment. The development of multi-band imagers with improved spatial resolution onboard the third-generation geostationary meteorological satellites brings us meteorological data in larger size than those of the second-generation ones. Thus, new techniques for domestic and world-wide dissemination of the observational big data are needed. In this paper, we develop a web-based data visualization for Himawari-8 satellite sensed images in real time and with full resolution. This data visualization is supported by the ecosystems, which uses a tiled pyramid representation and parallel processing technique for terrain on an academic cloud system. We evaluate the performance of our techniques for domestic and international users on laboratory experiments. The results show that our data visualization is suitable for practical use on a temporal preview of observation image data for the domestic users. [ABSTRACT FROM AUTHOR]