Detection of deterministic and probabilistic convection initiation using Himawari-8 Advanced Himawari Imager data

The detection of convective initiation (CI) is very important because convective clouds bring heavy rainfall and thunderstorms that typically cause severe socio-economic damage. In this study, deterministic and probabilistic CI detection models based on decision trees (DT), random forest (RF), and logistic regression (LR) were developed using Himawari-8 Advanced Himawari Imager (AHI) data obtained from June to August 2016 over the Korean Peninsula. A total of 12 interest fields that contain brightness temperature, spectral differences of the brightness temperatures, and their time trends were used to develop CI detection models. While, in our study, the interest field of 11.2 mu m T-b was considered the most crucial for detecting CI in the deterministic models and the probabilistic RF model, the trispectral difference, i.e. (8.6-11.2 mu m)-(11.2-12.4 mu m), was determined to be the most important one in the LR model. The performance of the four models varied by CI case and validation data. Nonetheless, the DT model typically showed higher probability of detection (POD), while the RF model produced higher overall accuracy (OA) and critical success index (CSI) and lower false alarm rate (FAR) than the other models. The CI detection of the mean lead times by the four models were in the range of 20-40 min, which implies that convective clouds can be detected 30 min in advance, before precipitation intensity exceeds 35 dBZ over the Korean Peninsula in summer using the Himawari-8 AHI data

A novel framework of detecting convective initiation combining automated sampling, machine learning, and repeated model tuning from geostationary satellite data

This paper proposes a complete framework of a machine learning-based model that detects convective initiation (CI) from geostationary meteorological satellite data. The suggested framework consists of three main processes: (1) An automated sampling tool; (2) machine learning-based CI detection modelling; (3) repeated model tuning through validation. In this study, the automated sampling tool was able to track the CI objects iteratively, even without ancillary data such as an atmospheric motion vector (AMV). The collected samples were used to train the machine learning model for CI detection. Random forest (RF) was used to classify the CI and non-CI. To enhance the advantages of the machine learning approach, we adopted model tuning to iteratively update the training dataset from each validation result by adding hits and misses to the CI samples, and false alarms and correct negatives to the non-CI samples. Using 12 interest fields from the Himawari-8 Advanced Himawari Imager (AHI) over the Korean Peninsula, this simple and intuitive tuning process increased the overall probability of detection (POD) from 0.79 to 0.82 and decreased the overall false alarm rate (FAR) from 0.46 to 0.37 with around 40 min of the lead-time. Amongst the 12 interest fields, Tb(11.2) ??m was identified as the most significant predictor in the RF model, followed by Tb(8.6-11.2) ??m, and Tb(6.2-7.3) ??m. The effect of model tuning on the CI detection performance was also analyzed using spatiotemporal validation maps. By automatically collecting and updating the machine learning training dataset, the suggested framework is expected to help the maintenance of the CI detection model from an operational perspective

Retrieval of Melt Ponds on Arctic Multiyear Sea Ice in Summer from TerraSAR-X Dual-Polarization Data Using Machine Learning Approaches: A Case Study in the Chukchi Sea with Mid-Incidence Angle Data

Melt ponds, a common feature on Arctic sea ice, absorb most of the incoming solar radiation and have a large effect on the melting rate of sea ice, which significantly influences climate change. Therefore, it is very important to monitor melt ponds in order to better understand the sea ice-climate interaction. In this study, melt pond retrieval models were developed using the TerraSAR-X dual-polarization synthetic aperture radar (SAR) data with mid-incidence angle obtained in a summer multiyear sea ice area in the Chukchi Sea, the Western Arctic, based on two rule-based machine learning approachesdecision trees (DT) and random forest (RF)in order to derive melt pond statistics at high spatial resolution and to identify key polarimetric parameters for melt pond detection. Melt ponds, sea ice and open water were delineated from the airborne SAR images (0.3-m resolution), which were used as a reference dataset. A total of eight polarimetric parameters (HH and VV backscattering coefficients, co-polarization ratio, co-polarization phase difference, co-polarization correlation coefficient, alpha angle, entropy and anisotropy) were derived from the TerraSAR-X dual-polarization data and then used as input variables for the machine learning models. The DT and RF models could not effectively discriminate melt ponds from open water when using only the polarimetric parameters. This is because melt ponds showed similar polarimetric signatures to open water. The average and standard deviation of the polarimetric parameters based on a 15 x 15 pixel window were supplemented to the input variables in order to consider the difference between the spatial texture of melt ponds and open water. Both the DT and RF models using the polarimetric parameters and their texture features produced improved performance for the retrieval of melt ponds, and RF was superior to DT. The HH backscattering coefficient was identified as the variable contributing the most, and its spatial standard deviation was the next most contributing one to the classification of open water, sea ice and melt ponds in the RF model. The average of the co-polarization phase difference and the alpha angle in a mid-incidence angle were also identified as the important variables in the RF model. The melt pond fraction and sea ice concentration retrieved from the RF-derived melt pond map showed root mean square deviations of 2.4% and 4.9%, respectively, compared to those from the reference melt pond maps. This indicates that there is potential to accurately monitor melt ponds on multiyear sea ice in the summer season at a local scale using high-resolution dual-polarization SAR data.open

Tide-corrected flow velocity and mass balance of Campbell Glacier Tongue, East Antarctica, derived from interferometric SAR

Accurate measurement of ice-flow velocity of floating glaciers is required to estimate ice mass balance from volume flux thinning/thickening, basal melting/freezing, and surface accumulation/ablation. We derived a tide-corrected ice velocity map and mass budget of Campbell Glacier Tongue (CGT) in East Antarctica by using 14 COSMO-SkyMed one-day tandem differential interferometric SAR (DInSAR) pairs obtained from January to November 2011. The vertical tidal deflection of CGT was estimated and removed from the DInSAR images by using a tide deflection ratio map generated by double-differential InSAR (DDInSAR) method. We then generated averaged ice-flow velocity (v map) and its standard deviation (?? v map). Ice-flow velocity increased from the upper part of the grounding line (~0.20md-1) to the seaward edge of CGT (~0.67md-1) with ?? v less than ~0.04md-1 in the main stream of CGT. The eastern part of CGT flows slower than the western part because it is grounded along the flow line and thus experiences severe basal drag. Flux mass balance (FMB), i.e., ice thickness change by volume flux divergence, of CGT was obtained by combining the v map and an ice thickness value of 340??18m estimated by the ICESat GLAS data. We used mass conservation assumption in which total mass balance (TMB, -6.29??1.37ma-1 observed by ICESat GLAS data) is attributed to FMB, basal mass balance (BMB) and surface mass balance (SMB, 0.24??0.02ma-1). Mass loss in the freely floating zone of CGT is mainly caused by basal melting (BMB =-144.5??39.9Mta-1) while thinning by volume flux (FMB =-67.2??8.9Mta-1) is relatively small. In the hinge zone of CGT, mass change is contributed to FMB of -147.3??25.2Mta-1 and BMB of 15.3??39.8Mta-1. However, basal freezing derived for the hinge zone may be erroneous as a result of the constant ice thickness assumption extrapolated from the freely floating zoneclose1

MASS BALANCE OF CAMPBELL GLACIER, EAST ANTARCTICA, DERIVED FROM COSMO-SKYMED INTERFEROMETRIC SAR IMAGES

ABSTRACT We derived tide-corrected ice-flow velocity map ( v -map) of Campbell Glacier Tongue (CGT) in East Antarctica from 14 COSMO-SkyMed one-day tandem interferometric SAR image pairs obtained in 2011. Ice-flow velocity was measured to increase from the upper part of Campbell Glacier (~20 cm da

Experiments on a Ground-Based Tomographic Synthetic Aperture Radar

This paper presents the development and experiment of three-dimensional image formation by using a ground-based tomographic synthetic aperture radar (GB-TomoSAR) system. GB-TomoSAR formulates two-dimensional synthetic aperture by the motion of antennae, both in azimuth and vertical directions. After range compression, three-dimensional image focusing is performed by applying Deramp-FFT (Fast Fourier Transform) algorithms, both in azimuth and vertical directions. Geometric and radiometric calibrations were applied to make an image cube, which is then projected into range-azimuth and range-vertical cross-sections for visualization. An experiment with a C-band GB-TomoSAR system with a scan length of 2.49 m and 1.86 m in azimuth and vertical-direction, respectively, shows distinctive three-dimensional radar backscattering of stable buildings and roads with resolutions similar to the theoretical values. Unstable objects such as trees and moving cars generate severe noise due to decorrelation during the eight-hour image-acquisition time

Detection of probabilistic Convective Initiation (CI) using Himawari-8 AHI, weather radar, and lightning data

Convective rainfall can cause flash flooding with significant human and economic losses. In order to prevent such damages, monitoring and prediction of convective rainfall have been conducted with Automatic Weather System (AWS) and ground based weather radar data. However, these measurements cannot cover vast areas limiting spatial continuity. Geostationary satellite sensors observe clouds and storms over vast areas at very high temporal resolution (~ 10 minutes). Thus, geostationary satellite remote sensing is an alternative way to predict and monitor convective rainfall. In general, interest fields such as brightness temperature at a specific spectral channel or the difference of brightness temperatures between two channels are considered important to identify Convective Initiation (CI). Existing CI algorithms use simple interest fields and their associated thresholds. However, such a simple thresholding approach might not be ideal to consider complicated characteristics of convective clouds. In this study, logistic regression and probabilistic random forest were evaluated to provide CI probability associated with various characteristics of convective clouds. Himawari-8 Advanced Himawari-8 Imager (AHI) data collected between June and August 2015 were used to detect CI. A quantitative validation of CI was conducted using weather radar and lightning data. Results show that an overall accuracy of CI detection by logistic regression is 84.5% when radar data was used as reference data and 0.5 was applied to the probability data as a threshold to make a binary classification, which is higher than that by probabilistic random forest (87.4%). The validation using lightning data produced a similar result with the radar-based assessment. However, the probability of detection (POD) of the logistic regression model was a bit lower than that of the random forest model due to the relatively large number of missed CI objects