Geostationary Satellite Images Research Articles

Strong Relationship between Eye Radius and Radius of Maximum Wind of Tropical Cyclones

Abstract Estimation of the radius of maximum wind (RMW) of tropical cyclone (TC) is helpful for the disaster prevention and mitigation. If RMWs are estimated from infrared (IR) imagery taken by geostationary meteorological satellites, their estimation is available densely in time, regardless of the ocean basin. Kossin et al. showed that when TCs have clear eyes, the eye radii estimated from IR images have a high correlation with the RMW estimated from aircraft reconnaissance. The regression of the former onto that latter was shown to have a mean absolute error (MAE) of 4.7 km. We revisit the IR-based RMW estimation by using C-band synthetic aperture radar (SAR) sea surface wind estimates. The criteria for selecting clear-eye cases are simplified. The MAE of the Kossin et al. method is found to be smaller than previously suggested: 3.1 km when the proposed relation is used and 2.7 km when the regression is revised with the SAR-measured RMWs. We further propose an improvement of the IR-based method to estimate the eye radii. The resultant MAE is shown to be 1.7 km, which indicates that the IR-based RMW estimation is more accurate than has been suggested. A strong correlation between eyewall slope and eye size is confirmed. We also investigated cloud features in the eye that may be closely related to RMW and wind structure around RMW. Potential applications of highly accurate RMW estimation are discussed. Significance Statement The radius of maximum wind (RMW) of tropical cyclone (TC) is an important factor for TC intensity estimation and disaster prevention. A previous study suggested that the RMWs of TCs with clear eyes can be estimated from geostationary satellite images at a mean absolute error (MAE) of 4.7 km. Here we improved the method, reducing the MAE by more than one-half. Since the method does not require aircraft or satellite in low Earth orbit, it helps TC monitoring at high frequency. The method can also improve initialization of models used to predict TC hazards and further our physical understanding and the climatology of the wind structures near the centers of TCs.

Assessment on the Diurnal Cycle of Cloud Covers of Fengyun-4A Geostationary Satellite Based on the Manual Observation Data in China

Complicated and regionally representative diurnal cycle characteristics of clouds may introduce some errors into the cloud mask (CLM) algorithm of Geostationary (GEO) meteorological satellite imaging system, which are very difficult to be assessed by using analogous products of fixed-passing polar-orbiting satellites. In this investigation, the diurnal cycle of the performance of CLM algorithm of the Advanced Geosynchronous Radiation Imager onboard the China Fengyun-4A satellite (FY-4A/AGRI) is validated by using manually observed cloud covers (CC) at twenty-five ground-based stations in China. The results indicate that the CCs calculated by the FY-4A/AGRI cloud mask algorithm are overestimated at 11:00 BJT (Beijing Time) and 14:00 BJT (around noon) and underestimated at 08:00 BJT and 20:00 BJT (in the morning and evening) at most stations. In summer, compared with other seasons, the cloud covers obtained from the FY-4A/AGRI over northern China and the Tibetan Plateau are much better consistent with the manual observations, but the situation is the opposite in southern China. However, the CC results retrieved at vegetation surface by FY-4A/AGRI show the best and stable performance. Due to that the two independent cloud tests induce most of overestimations, some sensitivity experiments for the CLM algorithm are conducted. The results show that the best improvement effect is achieved after only closing one cloud test using the 3.8 μm band. Many extremely overestimated cloud cover samples (about 56.3%) are eliminated. After that, the FY-4A/AGRI cloud mask product is more reasonable compared with the corresponding infrared and visible imageries.

The retrieval of aerosol optical properties based on a random forest machine learning approach: Exploration of geostationary satellite images

Aerosol optical properties are among the most fundamental parameters in atmospheric environmental studies. Satellite aerosols retrievals that are based on deep learning or machine learning approach have been widely discussed in remote sensing studies, but the flexible random forest (RF) model has not received much attention in the retrieval of geostationary satellite, like Himawari-8. Thus, the Himawari-8 aerosol retrieval achieved by RF model requires further investigation and optimization. Based on the radiative transfer equation, this study proposed a RF model driven by a differential operator, which quantifies a simple linear relationship between aerosol optical depth (AOD) and top-of-atmosphere (TOA) reflectance enhancement. The spectral information of aerosols is achieved by independent TOA reflectance comparison between images rather than one result from multiple band synthesis. The method allows simple feature inputs and shows weak dependence on auxiliary data. It also achieves simultaneous retrievals over different surfaces and maintains mathematical correlation between spectral AODs and Angstrom Exponents (AE). The model performance was evaluated using a series of comprehensive temporal and spatial validation analyses. A sample-based tenfold cross-validation (10-CV) shows that the new method can simultaneously improve the estimation of aerosol properties, with considerably high correlation coefficients (R2) of 0.85 for AODs at the 0.50 μm wavelengths, a mean absolute error (MAE) of 0.08, a root mean square error (RMSE) of 0.13 and >70% of the samples fell within the AOD expected error (EE). The high accuracy of the spectral AOD retrievals also exhibits good performance on AE calculations, with at least 2/3 of the samples falling within the EE. The site based 10-CV also evaluates the spatial predictions on AODs at the 0.50 μm wavelength, with R2 of 0.67, MAE of 0.12 and RMSE of 0.18. It also has outperformed the Himawari operational aerosol products and appeared to be comparable to other popular machine learning models with better AE retrievals in some typical regions. Two typical regional pollution cases also highlight the advantages of the new aerosol monitoring approach. The 5 km resolution aerosol retrievals exhibit good spatial coverage and performance when describing the regional pollution levels and types. The proposed method improves the performance of RF in retrieving aerosol properties from geostationary satellites and also offers a new prospective for aerosol remote sensing using machine learning approaches.

The January 2022 eruption of Hunga Tonga-Hunga Ha'apai volcano reached the mesosphere.

Explosive volcanic eruptions can loft ash, gases, and water into the stratosphere, which affects both human activities and the climate. Using geostationary satellite images of the 15 January 2022 eruption of the Hunga Tonga-Hunga Ha'apai volcano, we find that the volcanic plume produced by this volcano reached an altitude of 57 kilometers at its highest extent. This places the plume in the lower mesosphere and provides observational evidence of a volcanic eruption injecting material through the stratosphere and directly into the mesosphere. We then discuss potential implications of this injection and suggest that the altitude reached by plumes from previous eruptions, such as the eruption of Mount Pinatubo in 1991, may have been underestimated because of a lack of observational data.

FINER-SCALE MONITORING AND ANALYSIS OF 2016–2021 DUST STORMS DISASTERS IN CHINA BASED ON HIMAWARI-8 SATELLITE IMAGERIES

Abstract. Today, remote sensing is one of the important techniques for natural disasters monitoring. Using the Himawari-8 new generation of geostationary meteorological satellite image data, we identified and monitored 2016–2021 dust storm disasters in China at the finer scale, and compared the object-oriented classification, supervised and unsupervised classification methods. Taking the dust storm on May 5, 2016 as example, the average accuracy and kappa coefficient of the supervised classification were 0.78 and 0.68, while the object-oriented classification were 0.81, 0.73. We used the object-oriented classification method to classify the 2016-2021 dust storm incidents across China. In 2021, the largest area affected by sandstorms was about 68.33km2, the smallest area was about 2.08km2, and the average area was about 22.25km2. During 2016–2021, the most severe and wide-spreading sandstorm occurred on March 14–18 2021. The dust storm first appeared in northwestern Mongolia on March 14, and then spread southeast to China. The dust area covered Beijing on the 15th, and then spread from eastern China to the western regions of North China and Northeast China, including most of the Hexi Corridor. On the 16th and 17th, the influence of the dust storms narrowed to central and western Inner Mongolia, northern Gansu and southern Mongolia. The dust storm gradually disappeared on the 18th.

A neural network with spatiotemporal encoding module for tropical cyclone intensity estimation from infrared satellite image

Accurate and instant estimation of tropical cyclone (TC) intensity is crucial for emergency decision making. Although deep neural networks and satellite images have been increasingly adopted for estimating TC intensity, most neural networks that estimate TC intensity from satellite images are largely dependent on the network structure or the quantity and quality of training data, without sufficiently considering the physical characteristics of satellite images and TCs. Therefore, a spatiotemporal encoding module named STE-TC is devised to facilitate estimating TC intensity from the Himawari-8 (H-8) geostationary satellite images, which can enhance spatial features and integrate temporal information of TCs into the satellite images. Furthermore, a TC intensity estimation model named DenseConvMixer is constructed by applying the dense connectivity strategy to an easily stacked ConvMixer network. To demonstrate the effectiveness of the proposed STE-TC module and DenseConvMixer network, a series of experiments and visualization analyses have been conducted. The experimental results show that the STE-TC module can be applied to estimation models based on the convolutional neural network (CNN) to improve their estimation performances. Moreover, the proposed DenseConvMixer model outperforms other CNN-based models on H-8 satellite images, with an average mean absolute error of 6.77 kt and root mean squared error of 8.89 kt.

Solar radiation forecasting with deep learning techniques integrating geostationary satellite images

The prediction of solar radiation allows estimating photovoltaic systems’ power production in advance, guaranteeing a more reliable and stable energy supply. In this work, we present a novel approach for short-term solar radiation forecasting that leverages multi-channel images from the geostationary satellites of the Meteosat series, coupled with GHI values in clear-sky conditions. We propose two distinct deep learning models, a 3D-CNN and a ConvLSTM, to forecast solar radiation in terms of GHI values, up to 6-h ahead with a temporal granularity of 15 min, over a test study area, the city of Turin, Piedmont, Italy. The models have been validated with ground GHI measurements, and the results show that the ConvLSTM consistently outperforms the 3D-CNN for longer forecasting horizons, achieving a MAD of 27.18% and an nRMSE of 0.57 for 6-h ahead predictions. To motivate the use of satellite images, we compared the performance of our approach with a baseline Smart Persistence model and another benchmark model, which previously achieved state-of-the-art performance on the same data set by exploiting various kinds of meteorological inputs. The proposed models outperform the Smart Persistence for predictions farther than 15-min ahead, achieving a Forecast Skill of 0.56 for predictions 6-h ahead. Furthermore, the comparison shows that using raw satellite images overcomes the performance achievable by solely using meteorological variables, reducing the RMSD by more than 3% and the MAD by 1.37% for prediction horizons greater than 4-h ahead.

Impact of tropical convective conditions on solar irradiance forecasting based on cloud motion vectors

Intra-day forecasts of global horizontal solar irradiance (GHI) are widely produced by displacing existing clouds on a geo-stationary satellite image to their future locations with cloud motion vectors (CMVs) derived from preceding images. The CMV estimation methods assume rigid cloud bodies with advective motion, which performs reasonably well in mid-latitudes but can be strained for tropical and sub-tropical climatic zones during prolonged periods of seasonal convection. We study the impact of the South Asian monsoon time convection on the accuracy of CMV based forecasts by analysing 2 years of forecasts from three commonly used CMV methods—Block-match, Farnebäck (Optical flow) and TV-L1 (Optical flow). Forecasted cloud index (CI) maps of the entire image section are validated against analysis CI maps for the period 2018–2019 for forecast lead times from 0 to 5.5 h. Site-level GHI forecasts are validated against ground measured data from two Baseline Surface Radiation Network stations—Gurgaon (GUR) and Tiruvallur (TIR), located in hot semi-arid and tropical savanna climatic zones respectively. The inter-seasonal variation of forecast accuracy is prominent and a clear link is found between the increase in convection, represented by a decrease in outgoing longwave radiation (OLR), and the decrease in forecast accuracy. The GUR site shows the highest forecast error in the southwest monsoon period and exhibits a steep rise of forecast error with the increase in convection. The highest forecast error occurs in the northeast monsoon period of December in TIR. The impact of convection on the number of erroneous time blocks of predicted photovoltaic production is also studied. Our results provide insights into the contribution of convection to errors in CMV based forecasts and shows that OLR can be used as a feature in future forecasting methods to consider the impact of convection on forecast accuracy.

Monitoring Land Vegetation from Geostationary Satellite Advanced Himawari Imager (AHI)

For many years, the Advanced Very High-Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) instruments have been widely used to monitor the condition of surface vegetation. Since the polar-orbiting satellite provides limited daily samples on surface, a completed spatial coverage of land vegetation is often relied on over multiple days of observations. In this study, observations from the Japanese geostationary satellite imager Advanced Himawari Imagers (AHI) are used to derive the surface vegetation index. The AHI reflectance at visible and near-infrared bands are first corrected to the surface reflectance by using the 6S radiative transfer model. The AHI surface reflectance from various viewing angles and solar geometry is further normalized to form an angular-independent reflectance by using a BRDF model. Finally, the surface vegetation index is calculated and synthesized from the daytime AHI data. It is found that the high-frequency AHI observations can significantly reduce the impact of clouds on compositing land NDVI and require a shorter time for a complete coverage of surface conditions. Also, a single NDVI image from AHI exhibits spatial distribution similar to that from 16 days of MODIS data.

End-to-End Prediction of Lightning Events from Geostationary Satellite Images

While thunderstorms can pose severe risks to property and life, forecasting remains challenging, even at short lead times, as these often arise in meta-stable atmospheric conditions. In this paper, we examine the question of how well we could perform short-term (up to 180 min) forecasts using exclusively multi-spectral satellite images and past lighting events as data. We employ representation learning based on deep convolutional neural networks in an “end-to-end” fashion. Here, a crucial problem is handling the imbalance of the positive and negative classes appropriately in order to be able to obtain predictive results (which is not addressed by many previous machine-learning-based approaches). The resulting network outperforms previous methods based on physically based features and optical flow methods (similar to operational prediction models) and generalizes across different years. A closer examination of the classifier performance over time and under masking of input data indicates that the learned model actually draws most information from structures in the visible spectrum, with infrared imaging sustaining some classification performance during the night.

Classification and Estimation of Typhoon Intensity from Geostationary Meteorological Satellite Images Based on Deep Learning

In this paper, a novel typhoon intensity classification and estimation network (TICAENet) is constructed to recognize typhoon intensity. The TICAENet model is based on the LeNet-5 model, which uses weight sharing to reduce the number of training parameters, and the VGG16 model, which replaces a large convolution kernel with multiple small kernels to improve feature extraction. Satellite cloud images of typhoons over the Northwest Pacific Ocean and the South China Sea from 1995–2020 are taken as samples. The results show that the classification accuracy of this model is 10.57% higher than that of the LeNet-5 model; the classification accuracy of the TICAENet model is 97.12%, with a classification precision of 97.00% for tropical storms, severe tropical storms and super typhoons. The mean absolute error (MAE) and root mean square error (RMSE) of the samples estimation in 2019 are 4.78 m/s and 6.11 m/s, and the estimation accuracy are 18.98% and 20.65% higher than that of the statistical method, respectively. Additionally, the model takes less memory and runs faster due to the weight sharing and multiple small kernels. The results show that the proposed model performs better than other methods. In general, the proposed model can be used to accurately classify typhoon intensity and estimate the maximum wind speed by extracting features from geostationary meteorological satellite images.

Geospatial assessment of rooftop solar photovoltaic potential using multi-source remote sensing data

Rooftop solar photovoltaics (PV) play increasing role in the global sustainable energy transition. This raises the challenge of accurate and high-resolution geospatial assessment of PV technical potential in policymaking and power system planning. To address the challenge, we propose a general framework that combines multi-resource satellite images and deep learning models to provide estimates of rooftop PV power generation. We apply deep learning based inversion model to estimate hourly solar radiation based on geostationary satellite images, and automatic segmentation model to extract building footprint from high-resolution satellite images. The framework enables precise survey of available rooftop resources and detailed simulation of power generation on an hourly basis with a spatial resolution of 100 m. The case study in Jiangsu Province demonstrates that the framework is applicable for large areas and scalable between precise locations and arbitrary regions across multiple temporal scales. Our estimates show that rooftop resources across the province have a potential installed capacity of 245.17 GW, corresponding to an annual power generation of 290.66 TWh. This highlights the huge space for carbon emissions reduction through developing rooftop PVs.

Comparison of microphysics parameterization schemes on cloud macrophysics forecasts for mixed convective-stratiform cloud events

In this study, the performances and uncertainties of cloud microphysics parameterization (MP) schemes for simulating cloud macrophysics associated with mixed convective-stratiform cloud (MCSC) events are investigated. MCSC system is the primary object of cloud seeding for the purpose of rain enhancement in North China. Understanding the forecast uncertainties of cloud macrophysics, such as cloud top temperature (CTT), cloud base height (CBH), and cloud optical thickness (COT), is of great significance for cloud seeding operations. Simulations of cloud macrophysics are verified using cloud data retrieved from the Feng-Yun-2F geostationary meteorological satellite, radiosondes and Moderate Resolution Imaging Spectroradiometer (MODIS) products. The performances of several MP schemes to accurately simulate cloud macrophysics within ensemble forecasts is explored using both grid-wise and object-based metrics. Large differences are found among schemes in simulated cloud macrophysics compared with similar performances in simulated precipitation. In most cases, the ensemble mean simulates more realistically than each single scheme. The ensemble spread varies somewhat according to the simulated variables. For CBH, the ensemble shows adequate ensemble spread, while relative underdispersion occurs for CTT and COT. Detailed COT comparisons of reveal that COT bias mainly comes from the contribution of cloud droplets and snow. The causes of COT bias are further investigated by a detailed comparison of the cloud water path (CWP) and cloud effective radius (EFFR). The study establishes a baseline for ensemble forecasting of cloud macrophysics.

Investigation of Atmospheric Dynamic and Thermodynamic Structures of Typhoon Sinlaku (2020) from High-Resolution Dropsonde and Two-Way Rawinsonde Measurements

Profiling the vertical atmospheric structure for typhoons remains challenging. Here, the atmospheric dynamic and thermodynamic structures were investigated during the passage of Typhoon Sinlaku (2020) over Xisha Islands in the South China Sea for the period 28 July to 2 August 2020, mainly based on two-way rawinsonde and dropsonde measurements in combination with surface-based automatic weather station observations, disdrometer measurements, and Himawari-8 geostationary satellite images. The study period was divided to three stages: the formation stage of tropical depression (pre-TD), tropical depression (TD), and tropical storm (TS). The wind speed and local vertical wind shear reached the maximum value at 3 km above mean sea level (AMSL) before the typhoon approached the Xisha islands. Pseudo-equivalent potential temperature (θse) was found to decrease with the altitude below 2 km AMSL; temperature inversions occurred frequently within this altitude range, particularly during the TS stage. This seemed a typical capping inversion that indicated a downward motion above 2 km AMSL. The temperature increased slightly with the development of Typhoon Sinlaku (2020) at altitudes of 8–10 km AMSL. This indicated that our observations presumably captured the air mass warmed by the condensation, which was a good signature of an upper air in the tropical cyclone. In addition, wind speed (particularly in the lower stratosphere), specific humidity, and equivalent potential temperature escalated significantly when the tropical depression strengthened into Typhoon Sinlaku (2020), which indicated that the typhoon constantly obtained energy from the sea surface during its passage over the study region. The thermodynamic and dynamic structures of atmosphere advance our understanding of the inner structure of typhoons during the different evolutionary stages.

A Novel Tropical Cyclone Size Estimation Model Based on a Convolutional Neural Network Using Geostationary Satellite Imagery

A novel tropical cyclone (TC) size estimation model (TC-SEM) in the western North Pacific was developed based on a convolutional neural network (CNN) using geostationary satellite infrared (IR) images. The proposed TC-SEM was tested using three CNN schemes: a single-task regression model that separately estimated the radius of maximum wind (RMW) and the radius of 34 kt wind (R34) of the TC, a multi-task regression model that estimated the RMW and R34 simultaneously, and a multi-task regression model using best-track TC intensity information. For model training, validation, and testing, 29,730, 2505, and 11,624 geostationary satellite images of the region around the center of the TC, respectively, were used, each containing four IR bands: short-wavelength IR (3.7 µm), water vapor (6.7 µm), IR1 (10.8 µm), and IR2 (12.0 µm). The results showed that the multi-task model performed better than the single-task model due to knowledge sharing and its ability to solve multiple interrelated tasks simultaneously. The inclusion of TC intensity information in the multi-task model further improved the performance of the RMW and R34 estimations, with correlations (mean absolute errors) of 0.95 (2.05 nmi) and 0.93 (9.77 nmi), respectively, which represent significant improvements over the performance of existing linear regression statistical methods. The results suggested that this CNN model using geostationary satellite images may be a powerful tool for estimating TC sizes in operational TC forecasts.

Daily monitoring algorithms to detect geostationary imager visible radiance anomalies

The NASA Clouds and the Earth’s Radiant Energy System (CERES) project provides observed flux and cloud products for the climate science community. Geostationary satellite (GEO) imager measured clouds and broadband derived fluxes are incorporated in the CERES SYN1deg product to provide regional diurnal information in between Sun-synchronous Terra and Aqua CERES measurements. The recently launched GEO imagers with onboard calibration systems have active calibration teams that incrementally update the calibration in order to mitigate calibration drifts. However, short-term L1B radiance anomalies and calibration adjustment discontinuities may still exist in the record. To avoid any GEO cloud and flux artifacts in the CERES SYN1deg product, these calibration events must be addressed while scaling the GEO imagers to the Aqua-moderate resolution imaging spectroradiometer (MODIS) calibration reference. All-sky tropical ocean ray-matching (ATO-RM) and deep convective cloud invariant target (DCC-IT)-based monitoring algorithms are presented to detect calibration-driven daily anomalies in the GOES-16 Advanced Baseline Imager L1B visible (0.65 μm) radiance measurements. Sufficient daily ATO-RM sampling was obtained both by ray-matching GOES-16 with multiple MODIS and visible-infrared imaging radiometer suite imagers as well as by increasing the grid resolution. Optimized angular matching and outlier filtering were most effective in reducing the ATO-RM daily gain algorithm noise. The DCC-IT daily calibration algorithm utilized a larger domain and included more GOES-16 scan times. The DCC-IT daily gain uncertainty was reduced by normalizing the DCC regional reflectance on a regional, seasonal, and diurnal basis. The combination of ATO-RM and DCC-IT daily monitoring algorithms is shown to detect, with a high degree of confidence, daily GOES-16 L1B calibration-driven radiance anomalies >2.4 % , while keeping false positives at a minimum. Remarkably, the ATO-RM and DCC-IT daily gains are mostly within 0.5%. The ATO-RM and DCC-IT daily monitoring algorithms can be easily adapted to other GEO imagers and visible channels.

Research on the Geometric Precision Correction Method for Three-Axis-Stabilized Geostationary Meteorological Satellite Images Based on Landmarks

To improve the positioning accuracy of the three-axis-stabilized geostationary meteorological satellite (GSMS) images, we developed a geometric precision correction process based on landmarks. For the problem that GSMS images and binary landmark images are difficult to match accurately, we made full use of the local and global spatial information of the image pairs and proposed a dual-threshold matching algorithm based on the misalignment matrix to screen the initial matching results generated by the maximum normalized cross correlation. Under the premise of ensuring matching precision, the number of correctly matched point pairs is effectively increased to provide more sufficient samples for subsequent processing. Finally, we used a shallow neural network to establish the transformation relationship between the GSMS image and the reference image and resampled the GSMS image. We selected observations from five different periods of FY-4A/Advanced Geosynchronous Radiation Imager (AGRI) on-orbit and conducted two verification experiments: 1) in the image matching, compared with several commonly used algorithms, we found that the proposed matching algorithm has obvious advantages in matching precision and relative recall; and 2) in the image correction, the positioning error corrected by our methods is about 1.26 pixels <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\left ({3\sigma }\right)$ </tex-math></inline-formula> in the central area and about 1.30 pixels <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\left ({3\sigma }\right)$ </tex-math></inline-formula> in the edge area, which is about 14.9% and 9.1% better than the weighted iterative cubic polynomial fitting algorithm, respectively, with better robustness. In conclusion, the landmark-based geometric precision correction process proposed effectively improves the geometric accuracy of GSMS images, which is an important foundation for subsequent quantitative applications.

Nonnegligible Diurnal and Long-Term Variation Characteristics of the Calibration Biases in Fengyun-4A/AGRI Infrared Channels Based on the Oceanic Drifter Data

This study mainly focuses on the diurnal and long-term variation characteristics of the calibration performance of the geostationary (GEO) meteorological satellite Fengyun-4A/ Advanced Geostationary Radiation Imager (FY-4A/AGRI) infrared (IR) channels from June 1, 2017 to December 31, 2020. An improved algorithm is developed in this research based on the <i>in situ</i> observations from ocean drifters, which can capture the diurnal variations of the calibration performance of FY-4A/AGRI IR channels. The results suggest that there are significant and nonnegligible diurnal variations of the uncertainties and biases for the brightness temperature (TB) observed by the FY-4A/AGRI, especially the IR channels at 3.71, 8.61, 10.83, and <inline-formula> <tex-math notation="LaTeX">$12.07 \mu \text{m}$ </tex-math></inline-formula>. Among them, the calibration performance from 16:00 to 18:00 UTC (around the time of local midnight at the subsatellite point of FY-4A) is worse at <inline-formula> <tex-math notation="LaTeX">$3.71 \mu \text{m}$ </tex-math></inline-formula>, while TB biases from 04:00 to 16:00 UTC are relatively large at <inline-formula> <tex-math notation="LaTeX">$12.07 \mu \text{m}$ </tex-math></inline-formula>. Moreover, after the operational calibration and update in June 2020, the long-term TB biases at channel 08 (<inline-formula> <tex-math notation="LaTeX">$3.71 \mu \text{m}$ </tex-math></inline-formula>) obviously decrease from about 5&#x2013;2.5 K, and the TB biases at channel 14 (<inline-formula> <tex-math notation="LaTeX">$13.54 \mu \text{m}$ </tex-math></inline-formula>) increase from about 0.5&#x2013;3 K, implying a possible positive or negative impact of the calibration update on the calibration performance of IR channels. Overall, this method based on the <i>in situ</i> drifter data can well monitor the on-orbit calibration performance of GEO satellite imaging sensor&#x2019;s IR channels such as the FY-4A/AGRI, allowing an objective assessment on the effect of calibration update events.

DAGDU: center location of tropical cyclones using Deviation Angle Gradient Distribution Uniformity from infrared satellite images

ABSTRACT The tropical cyclone (TC) is a strong weather system that forms on the surface of the ocean. It is important to locate the TC centre when monitoring and forecasting. Infrared geostationary satellite images are reliable sources of TC location due to their high temporal resolution and wide regional coverage. A novel method for locating the centre of tropical cyclones by deviation angle gradient distribution uniformity is proposed in this paper. A region of interest is first intercepted from an infrared image with a spatial resolution of 5 km per pixel derived from the China FY-2 C and FY-2E geostationary satellites. The Bezier histogram segmentation method and K-means clustering segmentation method are then used to obtain the binary image in which the infrared brightness temperature changes drastically. Next, Hough transformation is utilized to reduce the search range of the centre location of the tropical cyclone. Finally, the deviation angle gradient distribution uniformity matrix is calculated from each pixel in the detection area as the reference centre, and the maximum value location of the matrix is considered to be the corresponding tropical cyclone centre. The methodology of this study could achieve good performance compared with the best track data of the China Meteorological Administration (CMA), the Japan Meteorological Agency (JMA), and the United States Joint Typhoon Warning Center (JTWC).

Impact of Assimilating Advanced Himawari Imager Channel 16 Data on Precipitation Prediction over the Haihe River Basin

Assimilation of high-resolution geostationary satellite data is of great value for precise precipitation prediction in regional basins. The operational geostationary satellite imager carried by the Himawari-8 satellite, Advanced Himawari Imager (AHI), has two additional water vapor channels and four other channels compared with its predecessor, MTSAT-2. However, due to the uncertainty in surface parameters, AHI surface-sensitive channels are usually not assimilated over land, except for the three water vapor channels. Previous research showed that the brightness temperature of AHI channel 16 is much more sensitive to the lower-tropospheric temperature than to surface emissivity, which is similar to the three water vapor channels 8–10. As a follow-up work, this paper evaluates the effectiveness of assimilating brightness temperature observations over land from both the three AHI water vapor channels and channel 16 to improve watershed precipitation forecasting through both case analysis (in the Haihe River basin, China) and batch tests. It is found that assimilating AHI channel 16 can improve the upstream near-surface atmospheric temperature forecast, which in turn affects the development of downstream weather systems. The precipitation forecasting test results indicate that adding the terrestrial observations of channel 16 to the assimilation of AHI data can improve short-term precipitation forecasting in the basin.