Application of LPW and SAI SAFNWC/MSG satellite products in pre-convective environments

Martínez, Miguel Á.; Velázquez, Mercedes Andrade; Manso, M.; Mas, Irene

doi:10.1016/j.atmosres.2005.10.022

Cited by 11 publications

(7 citation statements)

References 1 publication

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“…The TPW from the physical retrieval is also compared to that from AMSR‐E over ocean using a large number of samples. Relative to other similar research [ Martinez et al , 2007], the results are encouraging in terms of the high correlation coefficient (0.96) and small RMSE (<10%). Moreover, the SEVIRI TPW product is insensitive to surface types and does not show noticeable biases with the variation of LZA.…”

Section: Discussionsupporting

confidence: 49%

Retrieving clear‐sky atmospheric parameters from SEVIRI and ABI infrared radiances

Jin

Schmit

et al. 2008

J. Geophys. Res.

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[1] The algorithm for the current Geostationary Operational Environmental Satellite (GOES) Sounders is adapted to produce atmospheric temperature and moisture legacy profiles from simulated infrared radiances of the Advanced Baseline Imager (ABI) on board the next generation GOES-R. Since the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board the Meteosat Second Generation (MSG) Meteosat-8/9 has many of the same spectral and spatial features as ABI, it is used as proxy to test the algorithm. Because as imagers, SEVIRI and ABI do not have enough CO 2 absorption spectral bands relative to the current GOES Sounders, the legacy profile algorithm for the current GOES Sounders needs to be modified. Both simulations and analysis with radiance measurements indicate that the single temperature-sensitive infrared band (13.4 mm) of SEVIRI cannot provide enough temperature profile information. However, SEVIRI's two H 2 O absorption spectral bands (6.2 and 7.2 mm) are able to provide useful information on water vapor content above 700 hPa. Because of their high spatial (approximately 3 km for SEVIRI and 2 km for ABI IR bands) and high temporal (15 min full disk coverage) resolutions, SEVIRI and ABI will provide useful profile products with a quality similar to that from the current GOES Sounder prior to the availability of a hyperspectral IR sounding system in geostationary orbit.

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Section: Discussionsupporting

confidence: 49%

Retrieving clear‐sky atmospheric parameters from SEVIRI and ABI infrared radiances

Jin

Schmit

et al. 2008

J. Geophys. Res.

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“…Machine learning-based approaches have been widely applied in the field of remote sensing for both classification and regression [4,6,19,20,23,24,[31][32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47]. Here, three machine learning models (i.e., RF, XGB, and DNN) for TPW retrieval using AHI data are assessed.…”

Section: Machine Learning Approachesmentioning

confidence: 99%

“…Unlike low earth orbit (LEO) satellites, which have limited temporal resolution, GEO satellites can produce data more timely and frequently. The retrieved high temporal resolution TPW from GEO satellite sensor data can be utilized to monitor pre-convective environments and predict heavy rainfall, convective storms, and clouds that may cause serious damage to human life and infrastructure [6][7][8]. For example, Lee et al [8] showed that the 10-min interval measurements from the AHI sensor successfully provided information about the pre-landfall environment for typhoon Nangka that occurred in 2015.…”

Section: Introductionmentioning

confidence: 99%

Retrieval of Total Precipitable Water from Himawari-8 AHI Data: A Comparison of Random Forest, Extreme Gradient Boosting, and Deep Neural Network

et al. 2019

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Total precipitable water (TPW), a column of water vapor content in the atmosphere, provides information on the spatial distribution of moisture. The high-resolution TPW, together with atmospheric stability indices such as convective available potential energy (CAPE), is an effective indicator of severe weather phenomena in the pre-convective atmospheric condition. With the advent of high performing imaging instrument onboard geostationary satellites such as Advanced Himawari Imager (AHI) onboard Himawari-8 of Japan and Advanced Meteorological Imager (AMI) onboard GeoKompsat-2A of Korea, it is expected that unprecedented spatiotemporal resolution data (e.g., AMI plans to provide 2 km resolution data at every 2 min over the northeast part of East Asia) will be provided. To derive TPW from such high-resolution data in a timely fashion, an efficient algorithm is highly required. Here, machine learning approaches—random forest (RF), extreme gradient boosting (XGB), and deep neural network (DNN)—are assessed for the TPW retrieved from AHI over the clear sky in Northeast Asia area. For the training dataset, the nine infrared brightness temperatures (BT) of AHI (BT8 to 16 centered at 6.2, 6.9, 7.3, 8.6, 9.6, 10.4, 11.2, 12.4, and 13.3 μ m , respectively), six dual channel differences and observation conditions such as time, latitude, longitude, and satellite zenith angle for two years (September 2016 to August 2018) are used. The corresponding TPW is prepared by integrating the water vapor profiles from InterimEuropean Centre for Medium-Range Weather Forecasts Re-Analysis data (ERA-Interim). The algorithm performances are assessed using the ERA-Interim and radiosonde observations (RAOB) as the reference data. The results show that the DNN model performs better than RF and XGB with a correlation coefficient of 0.96, a mean bias of 0.90 mm, and a root mean square error (RMSE) of 4.65 mm when compared to the ERA-Interim. Similarly, DNN results in a correlation coefficient of 0.95, a mean bias of 1.25 mm, and an RMSE of 5.03 mm when compared to RAOB. Contributing variables to retrieve the TPW in each model and the spatial and temporal analysis of the retrieved TPW are carefully examined and discussed.

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“…Studies of the pre‐convective environment (e.g. Martinez et al , 2007; Koenig and De Coning, 2009; Goodman et al , 2012), the convective initiation phase (e.g. Mecikalski and Bedka, 2006; Mecikalski et al , 2008, 2010, 2013) and the mature stage (e.g.…”

Section: Introductionmentioning

confidence: 99%

Nowcasting severe convection in the Alpine region: the COALITION approach

Nisi

Ambrosetti

Clementi

2013

Quart J Royal Meteoro Soc

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The purpose of the operationally oriented system named the Context and Scale Oriented Thunderstorm Satellite Predictors Development (COALITION) is automatically to detect severe thunderstorms early in their development and consequently help weather forecasters to increase lead times when issuing severe weather warnings. This new object‐oriented system integrates data provided by different sources. Data from the Meteosat Second Generation Rapid Scan Service, weather radar and numerical weather prediction, as well as climatology, are utilized by the system. One of its primary purposes is to use all the best operationally available information about convective processes and to integrate it into a heuristic model. Furthermore the orographic forcing, which is often neglected in heuristic nowcasting models, is taken into account and included in the system as an additional convective triggering mechanism. This is particularly important for areas characterized by complex orography like the Alpine region. The COALITION algorithm merges evolving thunderstorm properties with selected predictors. The forecast evolution of the storm is the result of the interaction between convective signatures and surrounding storm environment. Eight different ‘object‐environment’ interactions are analyzed in eight modules, providing ensemble nowcasts of thunderstorm attributes (satellite‐ and radar‐based) for the following 60 min. All ensemble nowcasts are then combined through a weighting and thresholding scheme and the results are summarized into a single graphical map in order to facilitate user interpretation. The COALITION nowcast system has an update frequency of 5 min. The output highlights the cells having a high probability of severe thunderstorm development within the next 30 min. Verification statistics confirm that COALITION is able to nowcast the intensity of developing convective cells with sufficient skill up to a lead time of about 20 min.

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Application of LPW and SAI SAFNWC/MSG satellite products in pre-convective environments

Cited by 11 publications

References 1 publication

Retrieving clear‐sky atmospheric parameters from SEVIRI and ABI infrared radiances

Retrieving clear‐sky atmospheric parameters from SEVIRI and ABI infrared radiances

Retrieval of Total Precipitable Water from Himawari-8 AHI Data: A Comparison of Random Forest, Extreme Gradient Boosting, and Deep Neural Network

Nowcasting severe convection in the Alpine region: the COALITION approach

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