Infrared Cloud Research Articles

IR Target Detection via Lateral Inhibition and Singular Value Decomposition

IR (infrared) target detection has been an important technology in the field of target search and tracking. Generally, due to the influence of IR detector noise, cloud interference and other factors, IR image is blurred, the contrast is low, and the background clutter is heavy. As a result, detecting IR targets from complex background has become a challenging task, especially when the target is small, dim and shapeless. Meanwhile, when detecting and tracking a moving IR target, the method should be able to detect both small target and area target. In this paper, an IR target detection method via LI (lateral inhibition) and SVD (singular value decomposition) is proposed. Firstly, a local structure descriptor based on SVD of gradient domain is constructed, which reflects the basic structures of the local regions of an IR image. Then, combining with the local structure descriptor, a modified LI network is established to enhance target and suppress background. Meanwhile, to calculate lateral inhibition coefficients adaptively, the direction parameters of LI network are determined according to the dominant orientations obtained from SVD. Experimental results show that compared with the typical methods, the proposed method not only can detect small and area target under complex backgrounds, but also has excellent abilities of background suppression and target enhancement.

Satellite-Based Rainfall Retrieval: From Generalized Linear Models to Artificial Neural Networks

In this study, we develop and compare satellite rainfall retrievals based on generalized linear models and artificial neural networks. Both approaches are used in classification mode in a first step to identify the precipitating areas (precipitation detection) and in regression mode in a second step to estimate the rainfall intensity at the ground (rain rate). The input predictors are geostationary satellite infrared (IR) brightness temperatures and Satellite Application Facility (SAF) nowcasting products which consist of cloud properties, such as cloud top height and cloud type. Additionally, a set of auxiliary location-describing input variables is employed. The output predictand is the ground-based instantaneous rain rate provided by the European-scale radar composite OPERA, that was additionally quality-controlled. We compare our results to a precipitation product which uses a single infrared (IR) channel for the rainfall retrieval. Specifically, we choose the operational PR-OBS-3 hydrology SAF product as a representative example for this type of approach. With generalized linear models, we show that we are able to substantially improve in terms of hits by considering more IR channels and cloud property predictors. Furthermore, we demonstrate the added value of using artificial neural networks to further improve prediction skill by additionally reducing false alarms. In the rain rate estimation, the indirect relationship between surface rain rates and the cloud properties measurable with geostationary satellites limit the skill of all models, which leads to smooth predictions close to the mean rainfall intensity. Probability matching is explored as a tool to recover higher order statistics to obtain a more realistic rain rate distribution.

Field trial of an automated ground‐based infrared cloud classification system

ABSTRACTAutomated classification of cloud types using a ground‐based infrared (IR) imager can provide invaluable high‐resolution and localized information for air traffic controllers. Observations can be made consistently, continuously in real time and accurately during both day and night operation. Details of a field trial of an automated, ground‐based IR cloud classification system are presented. The system was designed at Campbell Scientific Ltd. in collaboration with Loughborough University, UK. The main objective of the trial was to assess the performance of an automated IR camera system with a lightning detector in classifying several types of clouds, specifically cumulonimbus and towering cumulus, during continuous day and night operation. Results from the classification system were compared with those obtained from Meteorological Aerodrome Reports (METAR) and with data generated by the UK Meteorological Office from their radar‐ and sferics‐automated cloud reports system. In comparisons with METAR data, a probability of detection of up to 82% was achieved, together with a minimum probability of false detection of 18%.

A physically based satellite rainfall estimation method using fluid dynamics modelling

A cloud motion winds (CMW) method is presented for improving quantitative rainfall estimation advection schemes that use both infrared (IR) and passive microwave (PMW) satellite data. Advection schemes are used to provide quantitative rainfall estimates by combining more direct PMW rainfall estimates with more frequent IR cloud top temperature measures using a two‐step technique: (1) PMW estimates are transported along CMW trajectories calculated with an advection scheme at subpixel resolution; and (2) PMW estimates are calibrated using the IR gradient along those trajectories. These schemes outperform traditional methods of satellite rainfall estimation but no clear physical basis for the procedure has yet been described. Here, the physical basis for the image processing techniques used in advection techniques is described. It is shown that geostationary satellite‐derived CMW from IR sensors can be modelled in terms of fluid dynamics using Navier–Stokes equations. This approach allows for modelling the problem as equivalent to the flow of a brightness temperature field, also providing subpixel resolution and unlimited rotation/deformation possibilities. The method is illustrated with rainfall estimates from a numerical weather prediction (NWP) model and with 3‐hourly PMW products as simulation data, obtaining consistent results.

Cloud top height comparisons from ASTER, MISR, and MODIS for trade wind cumuli

ASTER stereo and ASTER infrared (IR) retrieved cloud top heights (CTHs) at 90 m spatial resolution are compared to operational Multi-Angle Imaging SpectroRadiometer (MISR) stereo and MODerate Resolution Imaging Spectroradiometer (MODIS) thermal IR techniques at 1100 m and 5000 m spatial resolution, respectively. ASTER data availability limits this study to trade wind cumulus clouds only. ASTER IR, MISR stereo and MODIS IR cloud height frequency distributions were derived for 41 trade wind cumulus cloud scenes with no cirrus contamination. All three retrievals produce a bimodal CTH distribution with maxima at 650 and 1500 m. Several scenes are investigated in further details utilizing the ASTER stereo heights. The ASTER stereo-derived CTH frequency distribution is also bimodal and coincides best with a surface-based ceilometer-derived cloud base height distribution, in the sense that it produces the fewest CTH retrievals below the typical lidar-observed cloud base height. Sensitivity to spatial resolution was investigated by degrading ASTER stereo and IR CTH retrievals to the MISR spatial resolution, and also by deriving ASTER stereo heights from degraded radiances. The degraded ASTER stereo CTH retrievals remain bimodal, but showed less high clouds and more lower clouds, whereas the ASTER stereo heights from degraded radiances lose the bimodal structure similarly to the ASTER degraded resolution IR technique. Overall, the stereo technique appears more suitable for retrieving CTH for trade wind cumulus clouds than IR techniques, because it shows less sensitivity to resolution. With either technique CTHs should be produced at the high spatial resolution (finer than 1 km) and with subsampling preferred over spatial averaging, if necessary. Nonetheless, even at a spatial resolution of 1 km (which often exceeds the horizontal size of many trade wind cumulus clouds) both stereo and IR techniques reflect the CTH bimodality characterizing the scenes we studied.

Forecasting Convective Initiation by Monitoring the Evolution of Moving Cumulus in Daytime GOES Imagery

Abstract This study identifies the precursor signals of convective initiation within sequences of 1-km-resolution visible (VIS) and 4–8-km infrared (IR) imagery from the Geostationary Operational Environmental Satellite (GOES) instrument. Convective initiation (CI) is defined for this study as the first detection of Weather Surveillance Radar-1988 Doppler (WSR-88D) reflectivities ≥35 dBZ produced by convective clouds. Results indicate that CI may be forecasted ∼30–45 min in advance through the monitoring of key IR fields for convective clouds. This is made possible by the coincident use of three components of GOES data: 1) a cumulus cloud “mask” at 1-km resolution using VIS and IR data, 2) satellite-derived atmospheric motion vectors (AMVs) for tracking individual cumulus clouds, and 3) IR brightness temperature (TB) and multispectral band-differencing time trends. In effect, these techniques isolate only the cumulus convection in satellite imagery, track moving cumulus convection, and evaluate various IR cloud properties in time. Convective initiation is predicted by accumulating information within a satellite pixel that is attributed to the first occurrence of a ≥35 dBZ radar echo. Through the incorporation of satellite tracking of moving cumulus clouds, this work represents a significant advance in the use of routinely available GOES data for monitoring aspects of cumulus clouds important for nowcasting CI (0–1-h forecasts). Once cumulus cloud tracking is established, eight predictor fields based on Lagrangian trends in IR data are used to characterize cloud conditions consistent with CI. Cumulus cloud pixels for which ≥7 of the 8 CI indicators are satisfied are labeled as having high CI potential, assuming an extrapolation of past trends into the future. Comparison to future WSR-88D imagery then measures the method's predictive skill. Convective initiation predictability is demonstrated using several convective events—one during IHOP_2002—that occur over a variety of synoptic and mesoscale forcing regimes.

Environmental Vertical Wind Shear with Hurricane Bertha (1996)

Abstract Hurricane Bertha (1996) was influenced by vertical wind shear with highly variable direction and magnitude. The paper describes a unique method for determining the vertical tilt of a tropical cyclone vortex using satellite and aircraft data. Hurricane Bertha's vortex tracks at three levels are shown during a period of intensification just prior to landfall. During this period, the hurricane vortex becomes more closely aligned in the vertical. Changes in asymmetries of satellite infrared (IR) cold cloud areas are shown to be related to the vortex alignment. Environmental vertical shear measurements throughout Hurricane Bertha's life cycle are presented using IR cloud asymmetries and numerical model analyses. Intensification periods are associated with more symmetric IR cloud measurements. The directions of the IR cloud asymmetric orientations are compared with numerical-model-derived vertical shear directions. The changes in the vertical shear analyses are discussed with respect to observed intens...

Contribution of Mesoscale Convective Complexes to Rainfall in Sahelian Africa: Estimates from Geostationary Infrared and Passive Microwave Data

Abstract The contribution of mesoscale convective complexes to the July–September rainfall in Sahelian Africa is assessed using passive microwave data from the Special Sensor Microwave Image and infrared (IR) data from the European Geostationary Meteorological Satellite (Meteosat). A simple precipitation-estimation procedure, which takes advantage of the good time resolution of the IR and the strong relationship between the microwave radiance and rainfall, is developed and applied. The microwave technique uses the 37- and 86-GHz brightness temperatures to define the rain areas and the 86-GHz ice scattering signal to determine the rainfall intensity. The IR cloud shield areas are defined by the 219 K threshold. Regression analyses are used to relate the microwave-derived precipitation characteristics of the system and the IR data closest to the time of the SSM/I observation. These relationships are used to compute the precipitation characteristics of the total set of systems and to determine their monthly ...