Search Results
1. Introduction Operational oceanographic systems have been developed to accurately predict both present and continuous future conditions for the marginal seas of the northwestern Pacific Ocean, including the Yellow Sea, the East and South China Seas, and the East/Japan Sea ( Kagimoto et al. 2008 ; Miyazawa et al. 2008 ; You 2010 ; Dombrowsky 2011 ; Lim et al. 2011 ; Zhu 2011 ). Ocean forecast information obtained from operational forecasting systems is provided to marine industries
1. Introduction Operational oceanographic systems have been developed to accurately predict both present and continuous future conditions for the marginal seas of the northwestern Pacific Ocean, including the Yellow Sea, the East and South China Seas, and the East/Japan Sea ( Kagimoto et al. 2008 ; Miyazawa et al. 2008 ; You 2010 ; Dombrowsky 2011 ; Lim et al. 2011 ; Zhu 2011 ). Ocean forecast information obtained from operational forecasting systems is provided to marine industries
Limb Correction of Geostationary Infrared Imagery in Clear and Cloudy Regions to Improve Interpretation of RGB Composites for Real-Time Applicationslimb correction of MODIS and VIIRS infrared channels led to improved RGB composites, allowing for accurate interpretation by operational forecasters. The research described in this paper extends Elmer et al. (2016) to demonstrate that the same limb correction algorithm can be applied to SEVIRI, AHI, ABI, and FCI infrared imagery in order to create limb-corrected Air Mass RGB composites. 2. Data and methodology This study uses satellite imagery from the Meteosat-10 SEVIRI, Himawari-8 AHI, and
limb correction of MODIS and VIIRS infrared channels led to improved RGB composites, allowing for accurate interpretation by operational forecasters. The research described in this paper extends Elmer et al. (2016) to demonstrate that the same limb correction algorithm can be applied to SEVIRI, AHI, ABI, and FCI infrared imagery in order to create limb-corrected Air Mass RGB composites. 2. Data and methodology This study uses satellite imagery from the Meteosat-10 SEVIRI, Himawari-8 AHI, and
Limb Correction of MODIS and VIIRS Infrared Channels for the Improved Interpretation of RGB Composites1. Introduction Real-time visible and infrared satellite imagery provide timely information about atmospheric processes and surface conditions, making them useful tools for enhancing situational awareness in an operational forecast setting. However, the limited number of spectral channels on most operational weather satellites precludes their use for more comprehensive applications conducted with multispectral imagery from research satellites such as the Moderate Resolution Imaging
1. Introduction Real-time visible and infrared satellite imagery provide timely information about atmospheric processes and surface conditions, making them useful tools for enhancing situational awareness in an operational forecast setting. However, the limited number of spectral channels on most operational weather satellites precludes their use for more comprehensive applications conducted with multispectral imagery from research satellites such as the Moderate Resolution Imaging
1. Introduction The U.S. National Oceanic and Atmospheric Administration (NOAA) Environmental Modeling Center provides real-time tropical cyclone (TC) track and intensity forecast guidance to NOAA’s National Hurricane Center. NOAA’s Hurricane Weather Research and Forecast Model (HWRF), which is a regional, dynamical TC model, became operational in 2007 after 5 years of development at the Environmental Modeling Center, in collaboration with NOAA’s Geophysical Fluid Dynamics Laboratory (GFDL) and
1. Introduction The U.S. National Oceanic and Atmospheric Administration (NOAA) Environmental Modeling Center provides real-time tropical cyclone (TC) track and intensity forecast guidance to NOAA’s National Hurricane Center. NOAA’s Hurricane Weather Research and Forecast Model (HWRF), which is a regional, dynamical TC model, became operational in 2007 after 5 years of development at the Environmental Modeling Center, in collaboration with NOAA’s Geophysical Fluid Dynamics Laboratory (GFDL) and
such as the following: Will spotter network input be included? How timely will the PRs be? How will PRs time match with radar, satellite, and observations? How will AWIPS display performance be affected during high PR-volume severe weather events? The scope of detailed feedback suggests that there exists a genuine forecaster interest for such a PR system as an operational decision aid. Previously utilized visual, photographic, and crowdsourced weather information in forecasting, decision
such as the following: Will spotter network input be included? How timely will the PRs be? How will PRs time match with radar, satellite, and observations? How will AWIPS display performance be affected during high PR-volume severe weather events? The scope of detailed feedback suggests that there exists a genuine forecaster interest for such a PR system as an operational decision aid. Previously utilized visual, photographic, and crowdsourced weather information in forecasting, decision
1. Introduction Forecasters today are faced with many sources of data. What they need is meteorologically significant data fields blended from all available data sources, not numerous maps of observations from individual sources. In this paper we detail our process for blending data for one such meteorological parameter, the total precipitable water (TPW), which is the amount of water vapor in a column from the surface of the earth to space (in kilograms per square meter or, equivalently, in
1. Introduction Forecasters today are faced with many sources of data. What they need is meteorologically significant data fields blended from all available data sources, not numerous maps of observations from individual sources. In this paper we detail our process for blending data for one such meteorological parameter, the total precipitable water (TPW), which is the amount of water vapor in a column from the surface of the earth to space (in kilograms per square meter or, equivalently, in
CASA Prediction System over Dallas–Fort Worth Urban Network: Blending of Nowcasting and High-Resolution Numerical Weather Prediction Modelwithin precipitation type and cumulative lead time. 8. Summary and conclusions This research study attempted to extend the CASA’s prediction system to 6-h lead time through blending DARTS nowcast with the WRF Model forecast. Nine precipitation events were simulated and analyzed over the DFW urban area and classified into three categories of supercell, line, and multicell events. A detailed evaluation of CASA’s real-time operational reflectivity nowcast system was presented. The results showed the
within precipitation type and cumulative lead time. 8. Summary and conclusions This research study attempted to extend the CASA’s prediction system to 6-h lead time through blending DARTS nowcast with the WRF Model forecast. Nine precipitation events were simulated and analyzed over the DFW urban area and classified into three categories of supercell, line, and multicell events. A detailed evaluation of CASA’s real-time operational reflectivity nowcast system was presented. The results showed the
A Hybrid Multivariate Deep Learning Network for Multistep Ahead Sea Level Anomaly Forecasting1. Introduction The South China Sea (SCS) is a large semienclosed sea where typhoons, mesoscale ocean eddies, internal waves, and other weather and marine phenomena occur frequently ( Wang et al. 2014 ; Tuo et al. 2018 ). The population around the SCS is dense, and hence, the requirements for environmental and operational ocean forecasting are high. In 1992, ERS-1 and T / P remote sensing satellites observed near-global sea level anomaly (SLA) distributions through accurate
1. Introduction The South China Sea (SCS) is a large semienclosed sea where typhoons, mesoscale ocean eddies, internal waves, and other weather and marine phenomena occur frequently ( Wang et al. 2014 ; Tuo et al. 2018 ). The population around the SCS is dense, and hence, the requirements for environmental and operational ocean forecasting are high. In 1992, ERS-1 and T / P remote sensing satellites observed near-global sea level anomaly (SLA) distributions through accurate
the hurricane boundary layer ( Emanuel 1986 ; Cione and Uhlhorn 2003 ). The previous operational climatology for the Atlantic basin, developed by Mainelli-Huber (2000) , blended older versions of the World Ocean Atlas ( WOA ) and the Generalized Digital Environmental Model (GDEM) at 0.5° resolution. Mainelli et al. (2008) used OHC as a predictor of intensity in the Statistical Hurricane Intensity Prediction Scheme (SHIPS; DeMaria et al. 2005 ), which improved intensity forecasts of category
the hurricane boundary layer ( Emanuel 1986 ; Cione and Uhlhorn 2003 ). The previous operational climatology for the Atlantic basin, developed by Mainelli-Huber (2000) , blended older versions of the World Ocean Atlas ( WOA ) and the Generalized Digital Environmental Model (GDEM) at 0.5° resolution. Mainelli et al. (2008) used OHC as a predictor of intensity in the Statistical Hurricane Intensity Prediction Scheme (SHIPS; DeMaria et al. 2005 ), which improved intensity forecasts of category
. Hydraul. Eng. , 134 , 403 – 415 . Brent, R. P. , 1973 : An algorithm with guaranteed convergence for finding a zero of a function . Algorithms for Minimization without Derivatives, Prentice Hall, 47–60 . Bruno, M. S. , Blumberg A. F. , and Harrington T. O. , 2006 : The urban ocean observatory—Coastal ocean observations and forecasting in the New York Bight . J. Mar. Sci. Environ. , C4 , 31 – 39 . Chua, B. , and Bennett A. F. , 2001 : An inverse ocean modeling system . Ocean
. Hydraul. Eng. , 134 , 403 – 415 . Brent, R. P. , 1973 : An algorithm with guaranteed convergence for finding a zero of a function . Algorithms for Minimization without Derivatives, Prentice Hall, 47–60 . Bruno, M. S. , Blumberg A. F. , and Harrington T. O. , 2006 : The urban ocean observatory—Coastal ocean observations and forecasting in the New York Bight . J. Mar. Sci. Environ. , C4 , 31 – 39 . Chua, B. , and Bennett A. F. , 2001 : An inverse ocean modeling system . Ocean
