scholarly journals Sea Fog Detection Based on Normalized Difference Snow Index Using Advanced Himawari Imager Observations

2020 ◽  
Vol 12 (9) ◽  
pp. 1521
Author(s):  
Han-Sol Ryu ◽  
Sungwook Hong
Keyword(s):  
Near Infrared ◽  
Weather Conditions ◽  
Skill Score ◽  
Probability Of Detection ◽  
The Yellow Sea ◽  
Sea Fog ◽  
Cloudy Weather ◽  
Comparison Results ◽  
Normalized Difference Snow Index ◽  
Fog Detection

Many previous studies have attempted to distinguish fog from clouds using low-orbit and geostationary satellite observations from visible (VIS) to longwave infrared (LWIR) bands. However, clouds and fog have often been misidentified because of their similar spectral features. Recently, advanced meteorological geostationary satellites with improved spectral, spatial, and temporal resolutions, including Himawari-8/9, GOES-16/17, and GeoKompsat-2A, have become operational. Accordingly, this study presents an improved algorithm for detecting daytime sea fog using one VIS and one near-infrared (NIR) band of the Advanced Himawari Imager (AHI) of the Himawari-8 satellite. We propose a regression-based relationship for sea fog detection using a combination of the Normalized Difference Snow Index (NDSI) and reflectance at the green band of the AHI. Several case studies, including various foggy and cloudy weather conditions in the Yellow Sea for three years (2017–2019), have been performed. The results of our algorithm showed a successful detection of sea fog without any cloud mask information. The pixel-level comparison results with the sea fog detection based on the shortwave infrared (SWIR) band (3.9 μm) and the brightness temperature difference between SWIR and LWIR bands of the AHI showed high statistical scores for probability of detection (POD), post agreement (PAG), critical success index (CSI), and Heidke skill score (HSS). Consequently, the proposed algorithms for daytime sea fog detection can be effective in daytime, particularly twilight, conditions, for many satellites equipped with VIS and NIR bands.

scholarly journals Impact of Multivariate Background Error Covariance on the WRF-3DVAR Assimilation for the Yellow Sea Fog Modeling

2020 ◽  
Vol 2020 ◽  
pp. 1-19
Author(s):  
Xiaoyu Gao ◽  
Shanhong Gao
Keyword(s):  
Data Assimilation ◽  
Yellow Sea ◽  
Initial Conditions ◽  
Wrf Model ◽  
Probability Of Detection ◽  
Dominant Negative ◽  
The Yellow Sea ◽  
Background Error ◽  
Moisture Condition ◽  
Sea Fog

Numerical modeling of sea fog is highly sensitive to initial conditions, especially to moisture in the marine atmospheric boundary layer (MABL). Data assimilation plays a vital role in the improvement of initial MABL moisture for sea fog modeling over the Yellow Sea. In this study, the weather research and forecasting (WRF) model and its three-dimensional variational (3DVAR) data assimilation module are employed for sea fog simulations. Two kinds of background error (BE) covariances with different control variables (CV) used in WRF-3DVAR, that is, CV5 and multivariate BE (CV6), are compared in detail by explorative case studies and a series of application experiments. Statistical verification metrics including probability of detection (POD) and equitable threat scores (ETS) of forecasted sea fog area are computed and compared for simulations with the implementations of CV5 and CV6 in the WRF-3DVAR system. The following is found: (1) there exists a dominant negative correlation between temperature and moisture in CV6 near the sea surface, which makes it possible to improve the initial moisture condition in the MABL by assimilation of observed temperature; (2) in general, the performance of the WRF-3DVAR assimilation with CV6 is distinctly better, and the results of 10 additional sea fog cases clearly suggest that CV6 is more suitable than CV5 for sea fog modeling. Compared to those with CV5, the average POD and ETS of forecasted sea fog area using 3DVAR with CV6 can be improved by 27.6% and 21.0%, respectively.

scholarly journals Assimilating MTSAT-Derived Humidity in Nowcasting Sea Fog over the Yellow Sea

2014 ◽  
Vol 29 (2) ◽  
pp. 205-225 ◽  
Author(s):  
Yongming Wang ◽  
Shanhong Gao ◽  
Gang Fu ◽  
Jilin Sun ◽  
Suping Zhang
Keyword(s):  
Yellow Sea ◽  
Marine Boundary Layer ◽  
Three Dimensional ◽  
Horizontal Distribution ◽  
Probability Of Detection ◽  
The Yellow Sea ◽  
Temperature Structure ◽  
Structure Information ◽  
Sea Fog ◽  
Realistic Representation

Abstract An extended three-dimensional variational data assimilation (3DVAR) method based on the Weather Research and Forecasting Model (WRF) is developed to assimilate satellite-derived humidity from sea fog at its initial stage over the Yellow Sea. The sea fog properties, including its horizontal distribution and thickness, are retrieved empirically from the infrared and visible cloud imageries of the Multifunctional Transport Satellite (MTSAT). Assuming a relative humidity of 100% in fog, the MTSAT-derived humidity is assimilated by the extended 3DVAR assimilation method. Two sea fog cases, one spread widely over the Yellow Sea and the other spread narrowly along the coast, are first studied in detail with a suite of experiments. For the widespread-fog case, the assimilation of MTSAT-derived information significantly improves the forecast of the sea fog area, increasing the probability of detection and equitable threat scores by about 20% and 15%, respectively. The improvement is attributed to a more realistic representation of the marine boundary layer (MBL) and better descriptions of moisture and temperature profiles. For the narrowly spread coastal case, the model completely fails to reproduce the sea fog event without the assimilation of MTSAT-derived humidity. The extended 3DVAR assimilation method is then applied to 10 more sea fog cases to further evaluate its effect on the model simulations. The results reveal that the assimilation of MTSAT-derived humidity not only improves sea fog forecasts but also provides better moisture and temperature structure information in the MBL.

scholarly journals A scSE-LinkNet Deep Learning Model for Daytime Sea Fog Detection

2021 ◽  
Vol 13 (24) ◽  
pp. 5163
Author(s):  
Xiaofei Guo ◽  
Jianhua Wan ◽  
Shanwei Liu ◽  
Mingming Xu ◽  
Hui Sheng ◽  
...  
Keyword(s):  
Deep Learning ◽  
Spatial Information ◽  
Probability Of Detection ◽  
Orthogonal Polarization ◽  
Feature Maps ◽  
Threshold Method ◽  
Ground Sample ◽  
Sea Fog ◽  
Skill Scores ◽  
Fog Detection

Sea fog is a precarious weather disaster affecting transportation on the sea. The accuracy of the threshold method for sea fog detection is limited by time and region. In comparison, the deep learning method learns features of objects through different network layers and can therefore accurately extract fog data and is less affected by temporal and spatial factors. This study proposes a scSE-LinkNet model for daytime sea fog detection that leverages residual blocks to encoder feature maps and attention module to learn the features of sea fog data by considering spectral and spatial information of nodes. With the help of satellite radar data from Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), a ground sample database was extracted from Moderate Resolution Imaging Spectroradiometer (MODIS) L1B data. The scSE-LinkNet was trained on the training set, and quantitative evaluation was performed on the test set. Results showed the probability of detection (POD), false alarm rate (FAR), critical success index (CSI), and Heidke skill scores (HSS) were 0.924, 0.143, 0.800, and 0.864, respectively. Compared with other neural networks (FCN, U-Net, and LinkNet), the CSI of scSE-LinkNet was improved, with a maximum increase of nearly 8%. Moreover, the sea fog detection results were consistent with the measured data and CALIOP products.

scholarly journals A Comparison between 3DVAR and EnKF for Data Assimilation Effects on the Yellow Sea Fog Forecast

2018 ◽  
Author(s):  
Xiaoyu Gao ◽  
Shanhong Gao ◽  
Yue Yang
Keyword(s):  
Data Assimilation ◽  
Yellow Sea ◽  
Initial Conditions ◽  
Surface Wind ◽  
Probability Of Detection ◽  
The Other ◽  
Moisture Distribution ◽  
The Yellow Sea ◽  
Background Error ◽  
Sea Fog

The data assimilation method to improve sea fog forecast over the Yellow Sea is usually three-dimensional variational assimilation (3DVAR), whereas ensemble Kalman filter (EnKF) has not yet been applied on this weather phenomenon. In this paper, two sea fog cases over the Yellow sea, one spread widely and the other spread narrowly along the coastal area, are studied in detail by a series of numerical experiments with 3DVAR and EnKF based on the Grid-point Statistical Interpolation (GSI) system and the Weather Research and Forecasting (WRF) model. The results show that the assimilation effect of EnKF outperforms that of 3DVAR: for the widespread-fog case, the probability of detection and equitable threat scores of the forecasted sea fog area get improved respectively by ~57.9% and ~55.5%; the sea fog of the other case completely mis-forecasted by 3DVAR is produced successfully by EnKF. These improvements of EnKF relative to 3DVAR are benefited from its flow-dependent background error, resulting in more realistic depiction of sea surface wind for the widespread-fog case and better moisture distribution for the other case in the initial conditions. More importantly, the correlation between temperature and humidity in the background error of EnKF plays a vital role in the response of moisture to the assimilation of temperature, which leads to a great improvement on the initial moisture conditions for sea fog forecast.

scholarly journals Ground Fog Detection from Space Based on MODIS Daytime Data—A Feasibility Study

2005 ◽  
Vol 20 (6) ◽  
pp. 989-1005 ◽  
Author(s):  
Jörg Bendix ◽  
Boris Thies ◽  
Jan Cermak ◽  
Thomas Nauß
Keyword(s):  
False Alarm ◽  
Near Infrared ◽  
Time Lag ◽  
Probability Of Detection ◽  
Lookup Table ◽  
Percentage Error ◽  
Detection Scheme ◽  
Retrieval Scheme ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Fog Detection

Abstract The distinction made by satellite data between ground fog and low stratus is still an open problem. A proper detection scheme would need to make a determination between low stratus thickness and top height. Based on this information, stratus base height can be computed and compared with terrain height at a specific picture element. In the current paper, a procedure for making the distinction between ground fog and low-level stratus is proposed based on Moderate Resolution Imaging Spectroradiometer (MODIS, flying on board the NASA Terra and Aqua satellites) daytime data for Germany. Stratus thickness is alternatively derived from either empirical relationships or a newly developed retrieval scheme (lookup table approach), which relies on multiband albedo and radiative transfer calculations. A trispectral visible–near-infrared (VIS–NIR) approach has been proven to give the best results for the calculation of geometrical thickness. The comparison of horizontal visibility data from synoptic observing (SYNOP) stations of the German Weather Service and the results of the ground fog detection schemes reveals that the lookup table approach shows the best performance for both a valley fog situation and an extended layer of low stratus with complex local visibility structures. Even if the results are very encouraging [probability of detection (POD) = 0.76], relatively high percentage errors and false alarm ratios still occur. Uncertainties in the retrieval scheme are mostly due to possible collocation errors and known problems caused by comparing point and pixel data (time lag between satellite overpass and ground observation, etc.). A careful inspection of the pixels that mainly contribute to the false alarm ratio reveals problems with thin cirrus layers and the fog-edge position of the SYNOP stations. Validation results can be improved by removing these suspicious pixels (e.g., percentage error decreases from 28% to 22%).

scholarly journals Using Fractal Downscaling of Satellite Precipitation Products for Hydrometeorological Applications

2010 ◽  
Vol 27 (3) ◽  
pp. 409-427 ◽  
Author(s):  
Kun Tao ◽  
Ana P. Barros
Keyword(s):  
Spatial Resolution ◽  
Stage Iv ◽  
Tropical Rainfall Measuring Mission ◽  
Skill Score ◽  
Probability Of Detection ◽  
Grid Spacing ◽  
Satellite Precipitation ◽  
Target Field ◽  
Skill Scores ◽  
Central United States

Abstract The objective of spatial downscaling strategies is to increase the information content of coarse datasets at smaller scales. In the case of quantitative precipitation estimation (QPE) for hydrological applications, the goal is to close the scale gap between the spatial resolution of coarse datasets (e.g., gridded satellite precipitation products at resolution L × L) and the high resolution (l × l; L ≫ l) necessary to capture the spatial features that determine spatial variability of water flows and water stores in the landscape. In essence, the downscaling process consists of weaving subgrid-scale heterogeneity over a desired range of wavelengths in the original field. The defining question is, which properties, statistical and otherwise, of the target field (the known observable at the desired spatial resolution) should be matched, with the caveat that downscaling methods be as a general as possible and therefore ideally without case-specific constraints and/or calibration requirements? Here, the attention is focused on two simple fractal downscaling methods using iterated functions systems (IFS) and fractal Brownian surfaces (FBS) that meet this requirement. The two methods were applied to disaggregate spatially 27 summertime convective storms in the central United States during 2007 at three consecutive times (1800, 2100, and 0000 UTC, thus 81 fields overall) from the Tropical Rainfall Measuring Mission (TRMM) version 6 (V6) 3B42 precipitation product (∼25-km grid spacing) to the same resolution as the NCEP stage IV products (∼4-km grid spacing). Results from bilinear interpolation are used as the control. A fundamental distinction between IFS and FBS is that the latter implies a distribution of downscaled fields and thus an ensemble solution, whereas the former provides a single solution. The downscaling effectiveness is assessed using fractal measures (the spectral exponent β, fractal dimension D, Hurst coefficient H, and roughness amplitude R) and traditional operational scores statistics scores [false alarm rate (FR), probability of detection (PD), threat score (TS), and Heidke skill score (HSS)], as well as bias and the root-mean-square error (RMSE). The results show that both IFS and FBS fractal interpolation perform well with regard to operational skill scores, and they meet the additional requirement of generating structurally consistent fields. Furthermore, confidence intervals can be directly generated from the FBS ensemble. The results were used to diagnose errors relevant for hydrometeorological applications, in particular a spatial displacement with characteristic length of at least 50 km (2500 km2) in the location of peak rainfall intensities for the cases studied.

scholarly journals City-Scale Distance Sensing via Bispectral Light Extinction in Bad Weather

2020 ◽  
Vol 12 (9) ◽  
pp. 1401
Author(s):  
Dong Zhao ◽  
Yuta Asano ◽  
Lin Gu ◽  
Imari Sato ◽  
Huixin Zhou
Keyword(s):  
Extinction Coefficient ◽  
Near Infrared ◽  
Imaging System ◽  
Weather Conditions ◽  
Light Extinction ◽  
Selection Strategy ◽  
Infrared Wavelengths ◽  
The Difference ◽  
Bad Weather ◽  
Almost All

In this paper, we propose a novel city-scale distance sensing algorithm based on atmosphere optics. The suspended particles, especially in bad weather, would attenuate the light at almost all wavelengths. Observing this fact and starting from the light scattering mechanism, we derive a bispectral distance sensing algorithm by leveraging the difference of extinction coefficient between two specifically selected near infrared wavelengths. The extinction coefficient of the atmosphere is related to both wavelength and meteorological conditions, also known as visibility, such as the fog and haze day. To account for different bad weather conditions, we explicitly introduce visibility into our algorithm by incorporating it into the calculation of extinction coefficient, making our algorithm simple yet effective. To capture the data, we build a bispectral imaging system that is able to take a pair of images with a monochrome camera and two narrow band-pass filters. We also present a wavelength selection strategy that allows us to accurately sense distance regardless of material reflectance and texture. Specifically, this strategy determines two distinct near infrared wavelengths by maximising the extinction coefficient difference while minimizing the influence of building’s reflectance variance. The experiments empirically validate our model and its practical performance on the distance sensing for the city-scale buildings.

scholarly journals Improving Hydrologic Modeling Using Cloud-Free MODIS Flood Maps

2019 ◽  
Vol 20 (11) ◽  
pp. 2203-2214 ◽  
Author(s):  
Hoang Tran ◽  
Phu Nguyen ◽  
Mohammed Ombadi ◽  
Kuolin Hsu ◽  
Soroosh Sorooshian ◽  
...  
Keyword(s):  
Optical Sensors ◽  
Large Scale ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Skill Score ◽  
Hydrologic Model ◽  
Probability Of Detection ◽  
Landsat 8 ◽  
Inundation Maps ◽  
Flood Maps

Abstract Flood mapping from satellites provides large-scale observations of flood events, but cloud obstruction in satellite optical sensors limits its practical usability. In this study, we implemented the Variational Interpolation (VI) algorithm to remove clouds from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) Snow-Covered Area (SCA) products. The VI algorithm estimated states of cloud-hindered pixels by constructing three-dimensional space–time surfaces based on assumptions of snow persistence. The resulting cloud-free flood maps, while maintaining the temporal resolution of the original MODIS product, showed an improvement of nearly 70% in average probability of detection (POD) (from 0.29 to 0.49) when validated with flood maps derived from Landsat-8 imagery. The second part of this study utilized the cloud-free flood maps for calibration of a hydrologic model to improve simulation of flood inundation maps. The results demonstrated the utility of the cloud-free maps, as simulated inundation maps had average POD, false alarm ratio (FAR), and Hanssen–Kuipers (HK) skill score of 0.87, 0.49, and 0.84, respectively, compared to POD, FAR, and HK of 0.70, 0.61, and 0.67 when original maps were used for calibration.

scholarly journals Very Short-Term Surface Solar Irradiance Forecasting Based On FengYun-4 Geostationary Satellite

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2606 ◽  
Author(s):  
Liwei Yang ◽  
Xiaoqing Gao ◽  
Jiajia Hua ◽  
Pingping Wu ◽  
Zhenchao Li ◽  
...  
Keyword(s):  
Solar Irradiance ◽  
Near Infrared ◽  
Motion Vector ◽  
Skill Score ◽  
Geostationary Satellite ◽  
Short Term ◽  
Direct Normal Irradiance ◽  
Skill Scores ◽  
Meteorological Observatory ◽  
Using Data

An algorithm to forecast very short-term (30–180 min) surface solar irradiance using visible and near infrared channels (AGRI) onboard the FengYun-4A (FY-4A) geostationary satellite was constructed and evaluated in this study. The forecasting products include global horizontal irradiance (GHI) and direct normal irradiance (DNI). The forecast results were validated using data from Chengde Meteorological Observatory for four typical months (October 2018, and January, April, and July 2019), representing the four seasons. Particle Image Velocimetry (PIV) was employed to calculate the cloud motion vector (CMV) field from the satellite images. The forecast results were compared with the smart persistence (SP) model. A seasonal study showed that July and April forecasting is more difficult than during October and January. For GHI forecasting, the algorithm outperformed the SP model for all forecasting horizons and all seasons, with the best result being produced in October; the skill score was greater than 20%. For DNI, the algorithm outperformed the SP model in July and October, with skill scores of about 12% and 11%, respectively. Annual performances were evaluated; the results show that the normalized root mean square error (nRMSE) value of GHI for 30–180 min horizon ranged from 26.78% to 36.84%, the skill score reached a maximum of 20.44% at the 30-min horizon, and the skill scores were all above 0 for all time horizons. For DNI, the maximum skill score was 6.62% at the 180-min horizon. Overall, compared with the SP model, the proposed algorithm is more accurate and reliable for GHI forecasting and slightly better for DNI forecasting.

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