Change In Brightness Temperature Research Articles

A Convolutional Neural Network and Attention-Based Retrieval of Temperature Profile for a Satellite Hyperspectral Microwave Sensor

As numerical weather forecasting advances, there is a growing demand for higher-quality atmospheric data. Hyperspectral instruments can capture more atmospheric information and increase vertical resolution, but there has been limited research into retrieval algorithms for obtaining hyperspectral microwaves in the future. This study proposes an atmospheric temperature profile detection algorithm based on Convolutional Neural Networks (CNN) and Local Attention Mechanisms for local feature extraction, applied to hyperspectral microwave sensors. The study utilizes the method of information entropy to extract more effective channels in the vicinities of 60 GHz, 118 GHz, and 425 GHz. The algorithm uses the brightness temperature as the input of the network. The algorithm addresses common issues encountered in conventional networks, such as overfitting, gradient explosion, and gradient vanishing. Additionally, this method isolates the three oxygen-sensitive frequency bands for modularized local feature extraction training, thereby avoiding abrupt changes in brightness temperature between adjacent frequency bands. More importantly, the algorithm considers the correlation between multiple channels and information redundancy, focusing on variations in local information. This enhances the effectiveness of hyperspectral microwave channel information extraction. We simulated the brightness temperatures of the selected channels through ARTS and divided them into training, validation, and test sets. The retrieval capability of the proposed method is validated on a test dataset, achieving a root mean square error of 1.46 K and a mean absolute error of 1.4 K for temperature profile. Detailed comparisons are also made between this method and other commonly used networks for atmospheric retrieval. The results demonstrate that the proposed method significantly improves the accuracy of temperature profile retrieval, particularly in capturing fine details, and is more adaptable to complex environments. The model also exhibits scalability, extending from one-dimensional (pressure level) to three-dimensional space. The error for each pressure level is controlled within 0.7 K and the average error is within 0.4 K, demonstrating effectiveness across different scales with impressive results. The computational efficiency and accuracy have both been improved when handling a large amount of radiation data.

Characteristics and Mechanisms of Differential Infrared Brightness Temperature Change of Rock Specimen Partly Loaded to Fracturing

Characteristics and Mechanisms of Differential Infrared Brightness Temperature Change of Rock Specimen Partly Loaded to Fracturing

Disturbing Variability in Microwave Emission from a Non-Gaussian Distributed and Correlated Multiscale Rough Surface

In passive microwave remote sensing of the Earth’s surface, it is essential to relate the emission to geophysical parameters. The emissivity ranges between 0 and 1. Hence, a slight emissivity variation leads to a significant change in brightness temperature. Many sources of error contribute to such tiny variations in emission. This paper quantifies microwave emission variability from a rough surface through model simulation due to the non-Gaussianity in height probability density (HPD) and power spectrum density (PSD). We considered Gaussian and exponential distributions for surface height and correlation functions, representing two extremes of asperity and skewness. Additionally, the surface under consideration contains multiscale roughness. The impact of the HPD and multiscale roughness on the polarization index of the emissivity is evaluated as a function of frequency and roughness. In general, assuming that Gaussian-distributed height leads to an underestimation of the emissivity, with V polarization being less sensitive to the non-Gaussian HPD and PSD effects than H polarization, the emissions are enhanced at high roughness with small look angles but are reduced for smooth surfaces at large look angles under non-Gaussian PSD. In a specific scenario, the dynamic range of the difference between exponential and Gaussian HPD is 0~10%, and the difference in emissivity caused by non-Gaussian PSD ranges from −2% to 16%. These results should be helpful in interpreting the radiometric measurements that exhibit fluctuations and differences with model predictions.

Analytical Study of the Changes in Brightness Temperature Based on the Tectonic Field Associated With Three Earthquakes in the Eastern Tibetan Plateau

The thermal infrared brightness temperature (BT) of the eastern Tibetan Plateau (TP) was retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) level-1B data. The multiyear averaged BT background field was subtracted from the punctual BT data to yield monthly BT spatial anomaly, and calculated time series of BT for the secondary blocks. Then, the spatial and temporal changes in the BT of the study area before the Menyuan M6.4, Zaduo M6.2, and Jiuzhaigou M7.0 earthquakes were investigated and analyzed based on the tectonic setting. The results show the following. The spatial BT radiation enhancement frequency rose remarkably before strong earthquakes; each of the three earthquakes was preceded by marked spatiotemporal continuous BT anomalies. The tectonic setting significantly influences the BT anomaly feature. The spatial BT anomaly was not notable in the Qaidam and Qilian block before the Menyuan earthquake; the spatial BT anomaly mainly appeared in the Qiangtang and Bayan Har blocks before the Zaduo and Jiuzhaigou earthquakes. The Qiangtang and Bayan Har block’s BT time series curves have similar features. The Qaidam and Qilian block’s BT time series curves have analogous shapes. The three earthquakes may be regarded as one seismic event induced by a stage of tectonic stress enhancement rather than three independent occasions. The spatial BT anomalous behavior before earthquakes is, to a great extent, like the result of the rock stress loading experiment; the rock compression and the lithosphere-atmosphere-ionosphere coupling (LAIC) may be the main reasons for the intensification of the BT radiation.

A Method for Retrieving Cloud-Top Height Based on a Machine Learning Model Using the Himawari-8 Combined with Near Infrared Data

Different cloud-top heights (CTHs) have different degrees of atmospheric heating, which is an important factor for weather forecasting and aviation safety. AHIs (Advanced Himawari Imagers) on the Himawari-8 satellite are a new generation of visible and infrared imaging spectrometers characterized by a wide observation range and a high temporal resolution. In this paper, a cloud-top height retrieval algorithm based on XGBoost is proposed. The algorithm comprehensively utilizes AHI L1 multi-channel radiance data and calculates the input parameters of the generated model according to the characteristics of the cloud phase, texture, and the local brightness temperature change of the cloud. In addition, the latitude, longitude, solar zenith angle and satellite zenith angle are input into the model to further constrain the influence of the geographical and spatial factors such as the sea and land location, on CTH. Compared with the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) cloud-top height data (CTHCAL), the results show that: the algorithm retrieved the cloud-top height (CTHXGB) with a mean error (ME) of 0.3 km, a standard deviation (Std) of 1.72 km, and a root mean square error (RMSE) of 1.74 km. Additionally, it improves the problem of the large systematic deviation in the cloud-top height products released by the Japan Meteorological Agency (CTHJMA), especially for ice clouds and multi-layer clouds with ice clouds on the top layer. For water clouds below 2 km and multi-layer clouds with water clouds at the top, the algorithm solves the systematically serious CTHJMA problem. XGBoost can effectively distinguish between different cloud scenarios within the model, which is robust and suitable for CTH retrieval.

Remote Radio-Physical Harbingers of Drought in Steppes of the South of Western Siberia

Methods for remote sensing of the underlying surface in the microwave range based on moisture dependence of soil emissivity were successfully used in monitoring droughts and assessing water availability of the studied territories. Soil moisture influence on soil cover emissivity calibrated in units of the radio brightness temperature (TB) was studied. We used values of TB derived from SMOS satellite data. This paper presents the results of a comparative analysis of soil, meteorological conditions and physical characteristics of soils in the test territories of the Kulunda Plain. The experimental data were applied in computing trends of TB and physical temperature (T) described by linear dependencies. Volume fractions of water (W) in soil were calculated based on the satellite sensing data, the results of field studies and laboratory measurements of dielectric characteristics of soils. A map scheme of spatial distribution of W was constructed and the influences of snow cover, precipitation and surface wind velocity on drought were analyzed. The comprehensive analysis of remote, field and laboratory data suggest that the rate of change in the brightness temperature (dTBH/dD—up to 17 K per day), which characterizes the rate of fall in volume humidity of soil (ΔW—up to 0.009 cm3/cm3 per day), can be used as a short-term radio-physical harbinger of drought. An experimental dependence of the rate of change in radio brightness temperature on the rate of change in soil moisture was established.

Saturn’s seasonal variability from four decades of ground-based mid-infrared observations

A multi-decade record of ground-based mid-infrared (7–25μm) images of Saturn is used to explore seasonal and non-seasonal variability in thermal emission over more than a Saturnian year (1984–2022). Thermal emission measured by 3-m and 8-m-class observatories (notably NASA’s Infrared Telescope Facility, Subaru, and ESO’s Very Large Telescope) compares favourably with synthetic images based on both Cassini-derived temperature records and the predictions of radiative climate models. We find that 8-m class facilities are capable of resolving thermal contrasts on the scale of Saturn’s belts, zones, polar hexagon, and polar cyclones, superimposed onto large-scale seasonal asymmetries. Seasonal changes in brightness temperatures of ∼30 K in the stratosphere and ∼10 K in the upper troposphere are observed, as the northern and southern polar stratospheric vortices (NPSV and SPSV) form in spring and dissipate in autumn. The timings of the first appearance of the warm polar vortices is successfully reproduced by radiative climate models, confirming them to be radiative phenomena, albeit entrained within sharp boundaries influenced by dynamics. Axisymmetric thermal bands (4–5 per hemisphere) display temperature gradients that are strongly correlated with Saturn’s zonal winds, indicating winds that decay in strength with altitude from the cloud-tops to the ∼1-mbar level, and implying meridional circulation cells in Saturn’s upper troposphere and stratosphere forming the system of cool zones and warm belts. Saturn’s thermal structure is largely repeatable from year to year (via comparison of infrared images in 1989 and 2018), with the exception of low-latitudes. Here we find evidence of inter-annual variations because the equatorial banding at 7.9μm is inconsistent with a ∼15-year period for Saturn’s equatorial stratospheric oscillation, i.e., it is not strictly semi-annual. Either the oscillation has a longer period closer to ∼20 years, or its progression is naturally variable and interrupted by tropospheric meteorology (e.g., storms). Finally, observations between 2017–2022 extend the legacy of the Cassini mission, revealing the continued warming of the NPSV during northern summer in line with predictions of radiative climate models.

Spatio-temporal fire detection based on brightness temperature change in Himawari-8 images

ABSTRACT The geostationary satellite Himawari-8 has become the primary source for fire remote-sensing detection because of its advantages of high frequency, large width and easy access. To solve the problem of difficulty in small fire detection and with a high omission on Himawari-8 image, a spatio-temporal fire detection (STFD) method based on Himawari-8 image brightness temperature change is proposed. Calculate the mean value difference of brightness temperature between multi-time series images before the target time, and use the least square model to fit the ideal mean value difference of brightness temperature between the target time and the image at the previous time. Then, the difference value of the actual brightness temperature between the target time and the last image time is calculated. The initial brightness temperature change pixels are obtained by the comparative analysis method. Based on the spatial statistical characteristics, the potential fire point is determined by the brightness temperature value ratio of the image, temperature mean and standard temperature deviation. Then, combined with the spatio-temporal context information, the persistent fire points are detected according to the threshold conditions to supplement undetected persistent fires. The results of experiments on the Himawari-8 images in Guizhou, China and southwestern Australia indicate that STFD exhibits superior performance on small fire detection points, and the accuracy is better than the several traditional methods.

Improving model-satellite comparisons of sea ice melt onset with a satellite simulator

Abstract. Seasonal transitions in Arctic sea ice, such as the melt onset, have been found to be useful metrics for evaluating sea ice in climate models against observations. However, comparisons of melt onset dates between climate models and satellite observations are indirect. Satellite data products of melt onset rely on observed brightness temperatures, while climate models do not currently simulate brightness temperatures, and must therefore define melt onset with other modeled variables. Here we adapt a passive microwave sea ice satellite simulator, the Arctic Ocean Observation Operator (ARC3O), to produce simulated brightness temperatures that can be used to diagnose the timing of the earliest snowmelt in climate models, as we show here using Community Earth System Model version 2 (CESM2) ocean-ice hindcasts. By producing simulated brightness temperatures and earliest snowmelt estimation dates using CESM2 and ARC3O, we facilitate new and previously impossible comparisons between the model and satellite observations by removing the uncertainty that arises due to definition differences. Direct comparisons between the model and satellite data allow us to identify an early bias across large areas of the Arctic at the beginning of the CESM2 ocean-ice hindcast melt season, as well as improve our understanding of the physical processes underlying seasonal changes in brightness temperatures. In particular, the ARC3O allows us to show that satellite algorithm-based melt onset dates likely occur after significant snowmelt has already taken place.

Forest Fire Detection of FY-3D Using Genetic Algorithm and Brightness Temperature Change

As one of China’s new generation polar-orbiting meteorological satellites, FengYun-3D (FY-3D) provides critical data for forest fire detection. Most of the existing related methods identify fire points by comparing the spatial features and setting thresholds empirically. However, they ignore temporal features that are associated with forest fires. Besides, they are difficult to generalize to multiple areas with different environmental characteristics. A novel method based on FY-3D combining the genetic algorithm and brightness temperature change detection is proposed in this work to improve these problems. After analyzing the spatial features of the FY-3D data, it adaptively detects potential fire points based on these features using the genetic algorithm, then filters the points with contextual information. To address the false alarms resulting from the confusing spectral characteristics between fire pixels and conventional hotspots, temporal information is introduced and the “MIR change rate” based on the multitemporal brightness temperature change is further proposed. In order to evaluate the performance of the proposed algorithm, several fire events occurring in different areas are used for testing. The Moderate-Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies/Fire products (MYD14) is chosen as the validation data to assess the accuracy of the proposed algorithm. A comparison of results demonstrates that the algorithm can identify fire points effectively and obtain a higher accuracy than the previous FY-3D algorithm.

Special Sensor Microwave Imager/Sounder Updates for the Global Precipitation Measurement V07 Data Suite

Observations from the Special Sensor Microwave Imager/Sounder (SSMIS) onboard the Defense Meteorological Satellite Program F16, F17, F18, and F19 spacecrafts provide a significant portion of the microwave radiometer data within the Global Precipitation Measurement (GPM) mission constellation. In preparation for the GPM Version 7 (V07) data release, SSMIS corrections developed over a decade ago and incorporated in GPM Version 5 (V05) are reexamined and updated. The calibration updates presented here include pointing parameters affecting geolocation and viewing geometry, along-scan bias adjustments to account for scan edge falloffs and sun angle corrections to account for heating anomalies including an emissive reflector. To address errors in the V05 geolocation, the sensor roll, pitch, yaw, half cone angle, and timing offsets are reanalyzed and updated. The along-scan bias adjustment is updated in a manner consistent with recent Special Sensor Microwave Imager updates to account for variations in scene temperature. Finally, significant improvements to the SSMIS sun angle correction are made by using data from the entire mission, extending the corrections to include the higher frequency channels, and deriving a more consistent channel-to-channel correction. From V05 to V07, the geolocation adjustment is approximately 5–10 km, the earth incidence angle difference is 0–0.4°, and the average brightness temperature change is 0–2 K, but individual pixels may be up to several kelvin difference depending on sensor and channel. The result of these updates is a significant improvement in the quality and long-term consistency of the SSMIS data that are included in the GPM V07 dataset.

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).

A Novel Detection Algorithm for Impulse Spike Interferences in Cross-Track Infrared Sounder Short-Wave Spectral Radiance

Due to deficiencies in the operational spike detection, many moderate and strong impulse spike interferences constantly remain nondetected in the cross-track infrared sounder (CrIS) sensor data record (SDR) data at the short-wave infrared (SWIR) band over the South Atlantic Anomaly (SAA) region. This study characterizes imaginary radiance spectrum features in the presence of four intensities of spikes at three different hitting locations through radiative transfer model simulations. It is found that periodic ringing patterns appear in the imaginary spectral radiance. Following it, we establish a novel spike detection algorithm based on the spiky feature of power spectral density (PSD) in detrended and normalized imaginary SWIR CrIS spectral radiances. The performance of the algorithm is assessed by applying it to the multiple years of the SNPP and NOAA-20 CrIS full spectral resolution (FSR) radiance data. Approximate 300 and 500 spike events per day are detected for SNPP and NOAA-20 CrIS individually. The spikes can obviously increase the uncertainty in brightness temperature measurements at SWIR window channels that is determined by hitting location of the interferogram arms and the impulse intensities. Strong impulse spikes can trigger spectral radiance uncertainty by several times in SWIR window channels. Even the weakest impulse event can increase spectral radiance uncertainty by 4% in SWIR window channels. In addition, our simulations and one case study of CrIS data show that the spikes can degrade the quality of the CrIS data at the SWIR band with the largest impact at absorbing channels and the smallest at the window channels. The resultant brightness temperature change are 3 K for absorbing channels and up to 1 K for window channels in the SWIR band, demonstrating the significance flagging the CrIS data that are contaminated by the moderate and strong spikes that are missed in the operational data processing.

Analysis of the Seasonal Dependence of the Brightness Temperature of the Glacier Sheet of Antarctica by Microwave Satellite Data

In this paper, an analysis of seasonal dependences of the brightness temperature of different regions of Antarctica according to radiometer data from MIRAS (SMOS satellite) and SSMIS (DMSP series satellites) is presented. Queen Maud Land (Norwegian: Dronning Maud Land) was chosen as the region of study, which includes the main zones of Antarctica: the dome, the zone of runoff winds, and the coastal zone. The interrelation of time dynamics of brightness temperature as a function of the change in climatic characteristics of regions is considered. The main factors influencing the change in brightness temperature in different regions of the Antarctic glacier sheet are revealed.

Analysis of the Seasonal Dependence of the Brightness Temperature of the Ice Sheet of Antarctica by Microwave Satellite Data

The paper presents an analysis of seasonal brightness temperature variations of different regions of Antarctica according to the data of MIRAS (SMOS satellite) and SSMIS (DMSP series satellites) radiometers. As a region of study, the Dronning Maud Land was chosen which includes the main zones of Antarctica: the dome, the zone of runoff winds and the coastal zone. The interrelation of time dynamics of brightness temperature from the change of climatic characteristics of regions is considered. The main factors influencing the change of brightness temperature in different regions of the Antarctic ice sheet are revealed.

Мониторинг флуктуаций яркостной температуры воды в СВЧ-диапазоне

Цель исследования - в мониторинге флуктуаций Т воды в процессе ее испарения при температуре t = 42°С, критической для человека, с помощью СВЧ-радиометрии. Методика: проводился мониторинг изменения яркостной температуры Т воды в СВЧ-диапазоне частот 3,8-4,2 ГГц в процессе ее испарения при температуре в измерительной конусной полипропиленовой кювете t = 42°С. Измерения яркостной температуры проводились при помощи радиотермометра. Результаты. Обнаружено появление скачка яркостной температуры Т при температуре в измерительной конусной полипропиленовой кювете = 42°С. Скачок Т характеризовался фронтом нарастания яркостной температуры в этом диапазоне частот в диапазоне ~4°С с градиентом ~0,05°С/мин - 15°С/мин в зависимости от условий организации процесса испарения и резким спадом в течение 10 с, после чего наблюдалась следующая серия менее интенсивных флуктуаций. При этом температура воды оставалась постоянной. Заключение. Выявлены существенные изменения яркостной температуры воды в СВЧ-диапазоне при ее испарении при температуре воды в конусной измерительной кювете t = 42°С, наблюдается флуктуация в виде скачка Т порядка DТ ~4°C в исследуемой области температуры флуктуаций. При этом термодинамическая температура практически не изменяется. Наблюдаемые эффекты должны учитываться при разработке диагностических систем патологического состояния человека и при создании аналитических устройств. The purpose of the research consisted in detection of fluctuation of brightness temperature (T) of water in the area of the temperature Т = 42°С (that is critical for human) during its evaporation by SHF radiometry. Methods: Monitoring of the changes in brightness temperature of water in superhigh frequency (SHF) range (3.8-4.2 GHz) near the phase transition temperature of water Т = 42°С during its evaporation in the cone dielectric cell. The brightness temperature measurements were carried out using radiometer. Results: Fluctuation with maximum of brightness temperature was detected in 3.8-4.2 GHz frequency range near at the temperature of water Т = 42°С. It was characteristic for these T fluctuations that brightness temperature rise time in this range of frequencies in ~4°С temperature range with 0.05-15°С/min gradient and a sharp decrease during 10 s connected with measuring vapor conditions. Then nonintensive fluctuation series was observed. At that, the environment temperature remained constant. Conclusion: The significant increasing in brightness temperature of water during its evaporation in SHF range near the temperature of Т ~42°С were detected. It was shown that for water, Т pull with the amplitude DТ ~4°C are observed. At the same time, thermodynamic temperature virtually does not change. The observed effects can be used in the development of the systems for diadnostics of pathologies in human and analytical system.

Simulation of submillimetre atmospheric spectra for characterising potential ground-based remote sensing observations

Abstract. The submillimetre is an understudied region of the Earth's atmospheric electromagnetic spectrum. Prior technological gaps and relatively high opacity due to the prevalence of rotational water vapour lines at these wavelengths have slowed progress from a ground-based remote sensing perspective; however, emerging superconducting detector technologies in the fields of astronomy offer the potential to address key atmospheric science challenges with new instrumental methods. A site study, with a focus on the polar regions, is performed to assess theoretical feasibility by simulating the downwelling (zenith angle = 0°) clear-sky submillimetre spectrum from 30 mm (10 GHz) to 150 µm (2000 GHz) at six locations under annual mean, summer, winter, daytime, night-time and low-humidity conditions. Vertical profiles of temperature, pressure and 28 atmospheric gases are constructed by combining radiosonde, meteorological reanalysis and atmospheric chemistry model data. The sensitivity of the simulated spectra to the choice of water vapour continuum model and spectroscopic line database is explored. For the atmospheric trace species hypobromous acid (HOBr), hydrogen bromide (HBr), perhydroxyl radical (HO2) and nitrous oxide (N2O) the emission lines producing the largest change in brightness temperature are identified. Signal strengths, centre frequencies, bandwidths, estimated minimum integration times and maximum receiver noise temperatures are determined for all cases. HOBr, HBr and HO2 produce brightness temperature peaks in the mK to µK range, whereas the N2O peaks are in the K range. The optimal submillimetre remote sensing lines for the four species are shown to vary significantly between location and scenario, strengthening the case for future hyperspectral instruments that measure over a broad wavelength range. The techniques presented here provide a framework that can be applied to additional species of interest and taken forward to simulate retrievals and guide the design of future submillimetre instruments.

Improving Multiyear Sea Ice Concentration Estimates with Sea Ice Drift

Multiyear ice (MYI) concentration can be retrieved from passive or active microwave remote sensing observations. One of the algorithms that combines both observations is the Environmental Canada Ice Concentration Extractor (ECICE). However, factors such as ridging, snow wetness and metamorphism can cause significant changes in brightness temperature and backscatter, leading to misidentification of FYI as MYI, hence increasing the estimated MYI concentrations suddenly. This study introduces a correction scheme to restore the MYI concentrations under these conditions. The correction utilizes ice drift records to constrain the MYI changes and uses two thresholds of passive microwave radiometric parameters to account for snow wetness and metamorphism. The correction is applied to MYI concentration retrievals from ECICE with inputs from QuikSCAT and AMSR-E observations, acquired over the Arctic region in a series of winter seasons (October to May) from 2002 to 2009. Qualitative comparison with the Radarsat-1 SAR images and quantitative comparison against results from previous studies show that the correction works well by removing the anomalous high MYI concentrations. On average, the correction reduces the estimated MYI area by 5.2 × 105 km2 (14.3%) except for the April–May time frame, when the reduction is larger as the warmer weather prompts the condition of the anomalous snow radiometric signature. Due to the long-lasting (i.e., from one to several weeks) effect of the warm spells on FYI, the correction could be important in climatological research and operational applications.

A Dynamic Threshold Cloud Detecting Approach based on the Brightness Temperature from FY-2 VISSR Data

Abstract. The traditional statistical methods and radiation transfer theory methods for cloud detecting have a high adaptability just only in those areas with a uniform surface coverage and noncomplex terrain. Therefore, adapted to large spatial and temporal scales, in this work a cloud detection method is developed, seeking the main influencing factors of the change of Brightness Temperature(BT) of clear sky and their relationships, researching the change regularity and normal fluctuation range of BT on the basis of function fitting, setting the cloud detecting dynamic threshold depending on the cloud spectral characteristics, and making accuracy assessment in order to ensure higher adaptability and accuracy of this cloud detecting method. In this paper, a dynamic threshold algorithm is presented for cloud detection using daytime imagery from the VISSR sensor on board FY-2C/D/E, which is the first generation geostationary satellite. And the land surface/brightness temperature influence functions are analysis and established, including latitude, longitude, altitude, time, land cover. The theoretical temperature value of clear sky can be calculated through these influence functions. Then, the dynamic threshold cloud detection model is proposed based on the high temporal resolution of VISSR data. Meanwhile, the land surface emissivity is considered as the main factor to the change range of brightness temperature which determines the dynamic threshold for cloud detection. Finally, the dynamic threshold cloud detecting model is evaluated using FY-2C/D/E VISSR data covering China, and the Kappa of dynamic method is maximum, equalling 0.6195, which is much higher than the indexes for the reflectivity and BT fixed methods, equalling 0.4511 and 0.403, respectively. Consequently, the dynamic threshold cloud detecting method provides an important improvement because the spatial, temporal and geographic characteristics were considered into the model.

MSG SEVIRI Image Segmentation Using a Method Based on Spectral, Temporal and Textural Features

A new scheme for the classification of rainfall areas in convective and stratiform rain using MSG/SEVIRI (Spinning Enhanced Visible and Infrared) data is developed in this paper. The technique is based on spectral, temporal and textural properties of clouds. The introduced classification method uses as spectral parameters: brightness temperature BT(TIR10.8), brightness temperature differences BTDs (TIR10.8-TIR12.1, TIR8.7 -TIR10.8,TWV6.2-TIR10.8) during daytime and nighttime, temperature differences (TIR3.9 -TIR10.8 and TIR3.9-TWV7.3) during nighttime, and reflectances( RVIS0.6, RNIR1.6) during daytime. The textural information is based on the grey level rank approach where each pixel of the brightness temperature in the 10.8µm channel image is represented by a code which takes into account the relations between the spatial positions and the grey level ranks of the neighborhood pixels. The textural parameter of each pixel correspond to the occurrence frequency vector of a 24 pixel code possibilities computed within a window analysis of 31 x 31 pixels. The temporal parameter (RCT10.8) is the rate of change of brightness temperature over two consecutive images. The developed daytime and nighttime rain area classification technique (RACT-DN) is based on two multilayer perceptron neural networks (MLP-D for daytime and MLP-N for nighttime) which relies on the correlation of satellite data with convective and stratiform rain. The two algorithms (MLP-D and MLP-N) are trained using as reference convective and stratiform classification data from ground meteorological radar over northern Algeria. It was found that the introduction of temporal and textural parameters improved the results of discrimination between convective and stratiform areas