Infrared Satellite Images Research Articles

Tropical Cyclone Intensity Estimation Using RVM and DADI Based on Infrared Brightness Temperature

Abstract An objective technique is presented to estimate tropical cyclone intensity using the relevance vector machine (RVM) and deviation angle distribution inhomogeneity (DADI) based on infrared satellite images of the northwest Pacific Ocean basin from China’s FY-2C geostationary satellite. Using this technique, structures of a deviation-angle gradient co-occurrence matrix, which include 15 statistical parameters nonlinearly related to tropical cyclone intensity, were derived from infrared satellite imagery. RVM was then used to relate these statistical parameters to tropical cyclone intensity. Twenty-two tropical cyclones occurred in the northwest Pacific during 2005–09 and were selected to verify the performance of the proposed technique. The results show that, in comparison with the traditional linear regression method, the proposed technique can significantly improve the accuracy of tropical cyclone intensity estimation. The average absolute error of intensity estimation using the linear regression method is between 15 and 29 m s−1. Compared to the linear regression method, the average absolute error of the intensity estimation using RVM is between 3 and 10 m s−1.

A SIMPLE AND EFFECTIVE RETRIEVAL OF LAND SURFACE TEMPERATURE USING A NEW REFLECTANCE BASED EMISSIVITY ESTIMATION TECHNIQUE

Emissivity is a significant factor in determining land surface temperature (LST) retrieved from the thermal infrared (TIR) satellite images. A new simplified method (reflectance method) for emissivity correction was developed in this study while estimating emissivity values at a spatial resolution of 30 m from the radiance values of the SWIR image. This in turn enables mapping surface temperatures at a much finer spatial resolution (30 m). Temperatures so estimated are validated against surface temperatures measured in the ground by thermocouple data loggers recorded during satellite overpass time. In this study, surface emissivity values are derived directly from the AST_ L1B images. The reflectance method estimates temperature at higher spatial resolution of 30 m when compared to the 90 m spatial resolution of TES and reference channel methods. Temperature determined for the daytime image of 30<sup>th</sup> November 2007 using different emissivity techniques was compared with the temperatures measured on the field using thermocouple data loggers. It is observed that the estimates from the reflectance method are much closer to the field measurements than the TES and reference channel methods. The temperature difference values range from 0.2 to 2.3 °C, 0.15 to 5.6 °C, and 2.6 to 8.6 °C for the reflectance method, normalization method and reference channel method, respectively. The new reflectance emissivity techniques i.e. reflectance method exhibits the least deviation from the field measured temperature values. While considering the accuracy of data logger (1 °C) the reflectance method enables one to map surface temperature precisely than other two methods.

A SIMPLE AND EFFECTIVE RETRIEVAL OF LAND SURFACE TEMPERATURE USING A NEW REFLECTANCE BASED EMISSIVITY ESTIMATION TECHNIQUE

Abstract. Emissivity is a significant factor in determining land surface temperature (LST) retrieved from the thermal infrared (TIR) satellite images. A new simplified method (reflectance method) for emissivity correction was developed in this study while estimating emissivity values at a spatial resolution of 30 m from the radiance values of the SWIR image. This in turn enables mapping surface temperatures at a much finer spatial resolution (30 m). Temperatures so estimated are validated against surface temperatures measured in the ground by thermocouple data loggers recorded during satellite overpass time. In this study, surface emissivity values are derived directly from the AST_ L1B images. The reflectance method estimates temperature at higher spatial resolution of 30 m when compared to the 90 m spatial resolution of TES and reference channel methods. Temperature determined for the daytime image of 30th November 2007 using different emissivity techniques was compared with the temperatures measured on the field using thermocouple data loggers. It is observed that the estimates from the reflectance method are much closer to the field measurements than the TES and reference channel methods. The temperature difference values range from 0.2 to 2.3 °C, 0.15 to 5.6 °C, and 2.6 to 8.6 °C for the reflectance method, normalization method and reference channel method, respectively. The new reflectance emissivity techniques i.e. reflectance method exhibits the least deviation from the field measured temperature values. While considering the accuracy of data logger (1 °C) the reflectance method enables one to map surface temperature precisely than other two methods.

Daily States of the March–April East Pacific ITCZ in Three Decades of High-Resolution Satellite Data

Abstract Zonally elongated areas of cloudiness that make up the east Pacific intertropical convergence zone (ITCZ) can take on several configurations in instantaneous observations. A novel statistical model is used to automatically assess the daily state of the east Pacific ITCZ using infrared satellite images from 1980 to 2012. Four ITCZ states are defined based on ITCZ location relative to the equator: north (nITCZ) and south (sITCZ) of the equator, simultaneously north and south of the equator (dITCZ, for double ITCZ), and over the equator (eITCZ). A fifth ITCZ state is used to classify days when no zonally elongated area of cloudiness is present (aITCZ, for absent ITCZ). The ITCZ states can occur throughout the year (except for the eITCZ, which is not present during June–October), with the nITCZ state dominating in terms of frequency of occurrence. Interannual variability of the state distribution is large. The most striking variability in ITCZ states is observed in spring. During March–April, the dITCZ state occurs on average 34% of the time, second only to the nITCZ state (39%). Composites of observed infrared temperature and precipitation by ITCZ state reveal distinct spatial configurations of cloudiness and rainfall. Strong sea surface temperature anomalies are associated only with eITCZ and sITCZ and they correspond to El Niño and La Niña, respectively. However, all five ITCZ states are associated with distinct atmospheric circulation patterns. A connection is found between the ITCZ and the South Pacific convergence zone (SPCZ), such that activity in the SPCZ is enhanced when the ITCZ is absent in the east Pacific.

A novel algorithm for the objective detection of tropical cyclone centres using infrared satellite images

ABSTRACTTropical cyclones (TCs) are one of nature’s most destructive phenomena, and a key element in forecasting TC tracks is the ability to accurately detect TC centres. In this paper, a novel algorithm has been proposed to objectively detect TC centres using infrared satellite images. Pyramid representation and optical flow technique are utilized to construct the cloud motion wind (CMW) field of each cyclone, and thereafter the centre is determined by analysing the constructed CMW field. Ten TCs formed in the Northwestern Pacific Ocean in 2014 have been tested to evaluate the performance of the present method, and TC Halong and Rammasun were analysed in detail as instances. Experimental results comparing with forecast track derived from Unisys Weather show that the proposed method provides an accurate detection of TC centres. The present algorithm has a potential to be employed to assist forecasters to detect TC centres.

A Multiple Linear Regression Model for Tropical Cyclone Intensity Estimation from Satellite Infrared Images

An objectively trained model for tropical cyclone intensity estimation from routine satellite infrared images over the Northwestern Pacific Ocean is presented in this paper. The intensity is correlated to some critical signals extracted from the satellite infrared images, by training the 325 tropical cyclone cases from 1996 to 2007 typhoon seasons. To begin with, deviation angles and radial profiles of infrared images are calculated to extract as much potential predicators for intensity as possible. These predicators are examined strictly and included into (or excluded from) the initial predicator pool for regression manually. Then, the “thinned” potential predicators are regressed to the intensity by performing a stepwise regression procedure, according to their accumulated variance contribution rates to the model. Finally, the regressed model is verified using 52 cases from 2008 to 2009 typhoon seasons. The R2 and Root Mean Square Error are 0.77 and 12.01 knot in the independent validation tests, respectively. Analysis results demonstrate that this model performs well for strong typhoons, but produces relatively large errors for weak tropical cyclones.

Quantification of the contribution of the Beauce groundwater aquifer to the discharge of the Loire River using thermal infrared satellite imaging

Abstract. Seven Landsat thermal infrared (TIR) images taken over the period 2000–2010 were used to establish longitudinal temperature profiles of the middle Loire River where it flows above the Beauce aquifer. The groundwater discharge along the river course was quantified for each identified groundwater catchment area using a heat budget based on temperature variations of the Loire River estimated from the TIR images. The results showed that 75 % of the temperature differences, between in situ observations and TIR image-based estimations, remained within the ±1 °C interval. The main discharge area of the Beauce aquifer into the Loire River was located between river kilometers 630 and 650, where there was a temperature drop of 1–1.5 °C in the summer and a rise of 0.5 °C in winter. According to the heat budgets, groundwater discharge was higher during the winter period (13.5 m3 s−1) than during the summer period (5.3 m3 s−1). These findings are in line with the results of both a groundwater budget and a process-based distributed hydrogeological model. Groundwater input was also found to be higher during the Loire's flow recession periods.

Characterization of Initiation and Growth of Selected Severe Convective Storms over Central Europe with MSG-SEVIRI

AbstractThe early development of severe convective storms over central Europe was investigated on the basis of nine cases from 2012. Using data from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) imaging radiometer aboard a geostationary Meteosat Second Generation (MSG) satellite, dynamical and microphysical properties of developing storms were monitored and combined. Several satellite-based storm properties, for example, cloud-top temperature, cloud-top cooling rate, and cloud particle effective radius, were investigated following the storm tracks. A framework for quantification of uncertainties of along-track properties resulting from tracking errors was also introduced. The majority of studied storms show a distinct maximum in cloud-top cooling rate; the corresponding time was used for track synchronization. The cloud growth phase was divided into an initial updraft intensification period before the maximum cooling and a continued growth period afterward. The initial updraft intensification period is variable and strongly depends on the convection initiation mechanism and detection conditions. The continued growth period is more confined, lasting between 30 and 45 min. The change in anvil size and the resulting average anvil edge velocity were determined from infrared satellite images. As a consequence of mass transport, the anvil edge velocity shows its highest correlation with the cloud-top vertical velocity approximately 20–30 min after the maximum in the cloud-top cooling rate. Larger effective radii of ice crystals were observed for vertically slower-growing clouds. The largest anticorrelation between cloud-top vertical velocity and effective radius was found at a time lag of 20 min after the maximum in cloud-top cooling.

An extended maxima transform-based region growing algorithm for convective cell detection on satellite images

Convective cell detection is a critical step for tracking and forecasting of Mesoscale Convective Systems from geostationary satellite infrared (IR) data. Conventional threshold methods for identifying convective cell depend on the choice of threshold. Adjacent convective cells cannot be well distinguished because of the influence of anvil cloud. To address this problem, a new algorithm called extended maxima transform–based region growing (EMTRG) is proposed. First, EMTRG algorithm uses extended maxima transform to generate seed points of convective cell, and applies neighbourhood criterion to cluster adjacent seed points. Second, the merger and split times of pair-wise seed clusters are counted, and a merger criterion is utilized to decide whether pair-wise seed clusters should be merged. The algorithm is applied to various case studies over China. Experimental results on geostationary satellite IR images show that the proposed algorithm distinguishes adjacent convective cells region efficiently.

The Tropical Cyclone Diurnal Cycle of Mature Hurricanes

Abstract The diurnal cycle of tropical convection and the tropical cyclone (TC) cirrus canopy has been described extensively in previous studies. However, a complete understanding of the TC diurnal cycle remains elusive and is an area of ongoing research. This work describes a new technique that uses infrared satellite image differencing to examine the evolution of the TC diurnal cycle for all North Atlantic major hurricanes from 2001 to 2010. The imagery reveals cyclical pulses in the infrared cloud field that regularly propagate radially outward from the storm. These diurnal pulses begin forming in the storm’s inner core near the time of sunset each day and continue to move away from the storm overnight, reaching areas several hundreds of kilometers from the circulation center by the following afternoon. A marked warming of the cloud tops occurs behind this propagating feature and there can be pronounced structural changes to a storm as it moves away from the inner core. This suggests that the TC diurnal cycle may be an important element of TC dynamics and may have relevance to TC structure and intensity change. Evidence is also presented showing the existence of statistically significant diurnal signals in TC wind radii and objective Dvorak satellite-based intensity estimates for the 10-yr hurricane dataset that was examined. Findings indicate that TC diurnal pulses are a distinguishing characteristic of the TC diurnal cycle and the repeatability of TC diurnal pulsing in time and space suggests that it may be an unrealized, yet fundamental TC process.

Thermal physical property-based fusion of geostationary meteorological satellite visible and infrared channel images.

Geostationary meteorological satellite infrared (IR) channel data contain important spectral information for meteorological research and applications, but their spatial resolution is relatively low. The objective of this study is to obtain higher-resolution IR images. One common method of increasing resolution fuses the IR data with high-resolution visible (VIS) channel data. However, most existing image fusion methods focus only on visual performance, and often fail to take into account the thermal physical properties of the IR images. As a result, spectral distortion occurs frequently. To tackle this problem, we propose a thermal physical properties-based correction method for fusing geostationary meteorological satellite IR and VIS images. In our two-step process, the high-resolution structural features of the VIS image are first extracted and incorporated into the IR image using regular multi-resolution fusion approach, such as the multiwavelet analysis. This step significantly increases the visual details in the IR image, but fake thermal information may be included. Next, the Stefan-Boltzmann Law is applied to correct the distortion, to retain or recover the thermal infrared nature of the fused image. The results of both the qualitative and quantitative evaluation demonstrate that the proposed physical correction method both improves the spatial resolution and preserves the infrared thermal properties.

Hilbertian spatial periodically correlated first order autoregressive models

In this article, we consider Hilbertian spatial periodically correlated autoregressive models. Such a spatial model assumes periodicity in its autocorrelation function. Plausibly, it explains spatial functional data resulted from phenomena with periodic structures, as geological, atmospheric, meteorological and oceanographic data. Our studies on these models include model building, existence, time domain moving average representation, least square parameter estimation and prediction based on the autoregressive structured past data. We also fit a model of this type to a real data of invisible infrared satellite images.

An Algorithm for Pre-Processing of Satellite Images of Cyclone Clouds

advances in satellite imaging technologies have made it possible to obtain images of the atmosphere using different modalities and accordingly, make weather predictions. The progress of cyclone storms is one such area where cloud intensity images exhibit characteristic patterns at various stages of evolution. These patterns have been classified using Dvorak's technique, which is based on expert human judgment. Recent research efforts are being made to perform a computer analysis of these intensity patterns in order to make the classification process more objective. However, in order to perform an analysis of these image intensity patterns, the satellite images of different modalities need to be preprocessed to extract the dominant cyclone cloud patterns. This paper describes our algorithm to obtain cloud intensity contours to be used for pattern analysis. Results obtained using Visible (VIS) and Enhanced Infra-Red satellite images of cyclones have been found to be promising.

Diurnal variation of MCSs over Asia and the western Pacific Region

Mesoscale convective systems (MCSs) are severe disaster-producing weather systems. Radar data and infrared satellite image are useful tools in MCS surveillance. The previous method of MCS census is to look through the printed infrared imagery manually. This method is not only subjective and inaccurate, but also inefficient. Different from previous studies, a new automatic MCS identification (AMI) method, which overcomes the above disadvantages, is used in the present study. The AMI method takes three steps: searching potential MCS profiles, tracking the MCS, and assessing the MCS, so as to capture MCSs from infrared satellite images. Finally, 47468 MCSs are identified over Asia and the western Pacific region during the warm seasons (May–October) from 1995 to 2008.

Augmenting satellite precipitation estimation with lightning information

We have used lightning information to augment the precipitation estimation from remotely sensed imagery using an artificial neural network cloud classification system (PERSIANN-CCS). Co-located lightning data are used to segregate cloud patches, segmented from Geostationary Operational Environmental Satellite (GOES)-12 infrared (IR) data, into either electrified patches (ECPs) or nonelectrified patches (NECPs). A set of features is extracted separately for the ECPs and NECPs. Features for the ECPs include a new feature corresponding to the number of flashes that occur within a 15 minute window around the time of the nominal scan of the satellite IR images of the cloud patches. The cloud patches are classified and clustered using a self-organizing maps (SOM) neural network. Then, brightness temperature and rain rate (T–R) relationships are derived for different clusters. Rain rates are estimated for the cloud patches based on their representative (T–R) relationship. The equitable threat scores (ETS) of the daily and hourly precipitation estimates at a range of rain rate thresholds show that incorporating lightning information can improve categorical precipitation estimation in the winter and fall seasons. In the winter, the ETS improvement is almost 15% for the daily and 12% for the hourly rainfall estimates (at thresholds below 15 mm hour−1). During the same period, there is also a drop in the false alarm ratio (FAR) and a corresponding increase in the probability of detection (POD) at most threshold levels. During the summer and spring seasons, no categorical significant improvements have been noted, except for the BIAS scores for the hourly rainfall estimates at higher thresholds (above 5 mm hour−1) in the summer months. A quantitative evaluation in terms of the root mean squared error (RMSE) and correlation coefficient (CORR) shows that the incorporation of lightning data does improve rainfall estimation over all seasons with the most improvement (around 11–13% CORR improvement) occurring during the winter. We speculate that during the winter, more of the ice processes are packed into a thinner stratiform layer with lower cloud tops and freezing levels. Hence, more of the ice contributes to precipitation on the ground. We also expect that information from lightning, related to the ice microphysics processes, provides surrogate information about the rain rate.

Simulation of SEVIRI infrared channels: a case study from the Eyjafjallajökull April/May 2010 eruption

Abstract. Infrared satellite images are widely and successfully used to detect and follow atmospheric ash from erupting volcanoes. We describe a new radiative transfer model framework for the simulation of infrared radiances, which can be compared directly with satellite images. This can be helpful to get insight into the processes that affect the satellite retrievals. As input to the radiative transfer model, the distribution of ash is provided by simulations with the FLEXPART Lagrangian particle dispersion model, meteorological cloud information is adopted from the ECMWF analysis and the radiative transfer modelling is performed with the MYSTIC 3-D radiative transfer model. The model framework is used to study an episode during the Eyjafjallajökull eruption in 2010. It is found that to detect ash by the reverse absorption retrieval technique, accurate representation of the ash particle size distribution is required. Detailed investigation of individual pixels displays the radiative effects of various combinations of ash, liquid water and ice clouds. In order to be clearly detectable, the ash clouds need to be located at some distance above other clouds. If ash clouds are mixed with water clouds or are located only slightly above water clouds, detection of the ash becomes difficult. Simulations were also made using the so-called independent pixel approximation (IPA) instead of the fully 3-D radiative transfer modelling. In the two simulations, different clouds (or different parts of the clouds) or the ground are effectively emitting radiation towards the instrument, thus causing differences in the brightness temperature of up to ± 25 K. The presented model framework is useful for further studies of the processes that affect satellite imagery and may be used to test both new and existing ash retrieval algorithms.

Evaluation of BRAMS´ Turbulence Schemes during a Squall Line Occurrence in São Paulo, Brazil

This work evaluates the performance of four turbulence parameterization schemes available in the BRAMS model during the simulation of a Squall Line occurrence in the Metropolitan Area of São Paulo, Brazil, with the goal to select the parameterization that possible works better for this kind of phenomena. Simulation results were compared to infrared satellite images provided by GOES and to precipitation estimates from TRMM. Through the analysis of some meteorological fields and by using some statistical parameters such as BIAS and MSE, the anisotropic deformation scheme, based on Smagorinsky (1963) horizontal diffusion coefficients and its one-dimensional analog for the vertical coefficients, was considered the best scheme for the simulated case.

위성영상을 통한 서울시 지표온도 분석

Abstract The propose of this study is to analyze the optimum spatial resolution of the urban spatialthermal environment structure and to evaluate of the possibility detection using aerialphotographs and thermal satellite images. The proper techniques of the optimum spatialresolution for the urban spatial thermal environment structure were analyzed. Thermal infraredsatellite image of Seoul city were used for the change rate of surface temperature variation andsuggested to the spatial extent and effects of urban surface characteristics and spatial data wasinterpreted as regions. To extract the surface temperature, Landsat thermal infrared satelliteimage compared with an automatic weather station data and in the field of the measuredtemperature and surface temperature by thermal environment affects, the spatial domain hasbeen verified. The surface temperature of the satellite images to extract after adjusting surfacetemperature isotherms were constructed. The changes in surface temperature from 2008 to 2012the average surface temperature observation images of changing areas were divided into space.The results of this study are as follows: Through analysis of satellite imagery, Seoul city surfacetemperature change due to extraction comfort indices were classified into four grades. Thecomfort index indicative of the temperature of Gangnam-gu, 23.7 ~ 27.2 (C˚) range and Songpa-gu, a 22.7 ~ 30.6 (C˚) respectively, the surface temperature of Yeouido 25.8 ~ 32.6 (C˚) were inthe range.Keywords : Land Surface Temperature, Thermal infrared satellite image, optimum spatialresolution, Land cover

Tracing the origins and propagation of pre-tropical storm Debby (2006) mesoscale convective systems using pattern recognition and image fusion

This study is focused on developing pattern recognition and image fusion techniques to trace the origins and propagation of the pre-tropical storm (pre-TS) Debby (2006) mesoscale convective systems (MCSs) and African easterly waves (AEWs) using satellite imagery. These MCSs could be generating over mountains in North Africa and going through complicated splitting and merging processes. Therefore, an objectively analyzed MCS movement is essential. This study presents a technique which traces extracted features to find the origin of TS Debby. This technique produces a fused image with the most relevant information from water vapor and infrared satellite images, segments the cloud top height satellite images by clustering clouds, and tracks clouds to determine the origin of TS Debby (2006). The presented technique could be applied to other AEWs and MCSs which lead to tropical cyclogenesis to improve the numerical weather prediction over data sparse areas, such as over eastern and central North Africa.

Use of satellite derived cloud properties to quantify growing cumulus beneath cirrus clouds

The accurate prediction of convective cloud development in advance of thunderstorm formation (so‐called “convective initiation,” CI) is a challenging forecast problem, one in which the processing of 5–15min interval imagery from geostationary satellites (e.g., Meteosat Second Generation) offers considerable promise. A present drawback to using sequences of visible or infrared (IR) satellite images to monitor growing cumulus clouds is that higher altitude cirrus clouds often obscure the view of the low‐level cumulus in the pre‐convective environment. In particular, cirrus anvils from pre‐existing convection, and cirrus caused by deep layer quasi‐geostrophic ascent, are very common in pre‐CI environments. Cloud derived parameters from GOES are used here to demonstrate how quantities like visible optical depth (τ), emittance, liquid water path, and effective particle size can be used to quantify cumulus cloud growth in advance of CI. Time rates of change of these derived quantities, as well as IR interest fields that describe cumulus cloud development rates beneath cirrus, are analyzed as τ of the cirrus are binned from 1 to >50. Results confirm that if cirrus possess τ<20, up to >90% of the “signal” in the IR interest field remains, compared to clear‐sky values, and it is proposed that CI can still be adequately nowcasted using IR channel data similar to what is done in the absence of cirrus. Similarly, cloud derived parameters become valuable as their time rates of change measure cumulus cloud growth beneath the higher clouds. In contrast, once τ values increase beyond ∼20, cumulus cloud growth signals decrease significantly through cirrus, and as τ becomes >40, little information from the cumulus remains.