Atmospheric Soundings in Near Real Time from Combined Satellite and Ground-Based Remotely Sensed Data

1997 ◽  
Author(s):  
James Cogan ◽  
Edward Measure ◽  
Daniel Wolfe
Keyword(s):  
Real Time ◽  
Remotely Sensed ◽  
Remotely Sensed Data

scholarly journals Improved near real time surface wind resolution over the Mediterranean Sea

Ocean Science ◽  
2007 ◽  
Vol 3 (2) ◽  
pp. 259-271 ◽  
Author(s):  
A. Bentamy ◽  
H.-L. Ayina ◽  
P. Queffeulou ◽  
D. Croize-Fillon ◽  
V. Kerbaol
Keyword(s):  
Wind Speed ◽  
Mediterranean Sea ◽  
Real Time ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Remotely Sensed ◽  
Wind Fields ◽  
Remotely Sensed Data ◽  
Space And Time ◽  
The Mediterranean

Abstract. Several scientific programs, including the Mediterranean Forecasting System Toward Environmental Predictions (MFSTEP project), request high space and time resolutions of surface wind speed and direction. The purpose of this paper is to focus on surface wind improvements over the global Mediterranean Sea, based on the blending near real time remotely sensed wind observations and ECMWF wind analysis. Ocean surface wind observations are retrieved from QuikSCAT scatterometer and from SSM/I radiometers available at near real time at Météo-France. Using synchronous satellite data, the number of remotely sensed data available for each analysis epoch (00:00 h; 06:00 h; 12:00 h; 18:00 h) is not uniformly distributed as a function of space and time. On average two satellite wind observations are available for each analysis time period. The analysis is performed by optimum interpolation (OI) based on the kriging approach. The needed covariance matrixes are estimated from the satellite wind speed, zonal and meridional component observations. The quality of the 6-hourly resulting blended wind fields on 0.25° grid are investigated trough comparisons with the remotely sensed observations as well as with moored buoy wind averaged wind estimates. The blended wind data and remotely wind observations, occurring within 3 h and 0.25° from the analysis estimates, compare well over the global basin as well as over the sub-basins. The correlation coefficients exceed 0.95 while the rms difference values are less than 0.30 m/s. Using measurements from moored buoys, the high-resolution wind fields are found to have similar accuracy as satellite wind retrievals. Blended wind estimates exhibit better comparisons with buoy moored in open sea than near shore.

scholarly journals Improved near real time surface wind resolution over the Mediterranean Sea

2006 ◽  
Vol 3 (3) ◽  
pp. 435-470 ◽  
Author(s):  
A. Bentamy ◽  
H.-L. Ayina ◽  
P. Queffeulou ◽  
D. Croize-Fillon
Keyword(s):  
Wind Speed ◽  
Mediterranean Sea ◽  
Real Time ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Remotely Sensed ◽  
Wind Fields ◽  
Remotely Sensed Data ◽  
Space And Time ◽  
The Mediterranean

Abstract. Several scientific programs, including the Mediterranean Forecasting System Toward Environmental Predictions (MFSTEP project), request high space and time resolutions of surface wind speed and direction. The purpose of this paper is to focus on surface wind improvements over the global Mediterranean Sea, based on the blending near real time remotely sensed wind observations and ECMWF wind analysis. Ocean surface wind observations are retrieved from QuikSCAT scatterometer and from SSM/I radiometers available at near real time at Météo-France. Using synchronous satellite data, the number of remotely sensed data available for each analysis epoch (00:00 h; 06:00 h; 12:00 h; 18:00 h) is not uniformly distributed as a function of space and time. On average two satellite wind observations are available for each analysis time period. The analysis is performed by optimum interpolation (OI) based on the kriging approach. The needed covariance matrixes are estimated from the satellite wind speed, zonal and meridional component observations. The quality of the 6-hourly resulting blended wind fields on 0.25° grid are investigated trough comparisons with the remotely sensed observations as well as with moored buoy wind averaged wind estimates. The blended wind data and remotely wind observations, occurring within 3 h and 0.25° from the analysis estimates, compare well over the global basin as well as over the sub-basins. The correlation coefficients exceed 0.95 while the rms difference values are less than 0.30 m/s. Using measurements from moored buoys, the high-resolution wind fields are found to have similar accuracy as satellite wind retrievals. Blended wind estimates exhibit better comparisons with buoy moored in open sea than near shore.

scholarly journals Assessment of an Extreme Rainfall Detection System for Flood Prediction over Queensland (Australia)

Proceedings ◽  
2019 ◽  
Vol 18 (1) ◽  
pp. 1
Author(s):  
Paola Mazzoglio ◽  
Francesco Laio ◽  
Constantin Sandu ◽  
Piero Boccardo
Keyword(s):  
Real Time ◽  
Detection System ◽  
Extreme Rainfall ◽  
Early Warning Systems ◽  
Remotely Sensed ◽  
Flood Event ◽  
Management Service ◽  
Remotely Sensed Data ◽  
The Impact ◽  
Rainfall Detection

Flood events represent some of the most catastrophic natural disasters, especially in localities where appropriate measurement instruments and early warning systems are not available. Remotely sensed data can often help to obtain near real-time rainfall information with a global spatial coverage without the limitations that characterize other instruments. In order to achieve this goal, a freely accessible Extreme Rainfall Detection System (ERDS—erds.ithacaweb.org) was developed and implemented by ITHACA with the aim of monitoring and forecasting exceptional rainfall events and providing information in an understandable way for researchers as well as non-specialized users. The near real-time rainfall monitoring is performed by taking advantage of NASA GPM (Global Precipitation Measurement) IMERG (Integrated Multi-satellite Retrievals for GPM) half-hourly data (one of the most advanced rainfall measurements provided by satellite). This study aims to evaluate ERDS performance in the detection of the extreme rainfall that led to a massive flood event in Queensland (Australia) between January and February 2019. Due to the impressive amount of rainfall that affected the area, Flinders River (one of the longest Australian rivers) overflowed, expanding to a width of tens of kilometers. Several cities were also partially affected and Copernicus Emergency Management Service was activated with the aim of providing an assessment of the impact of the event. In this research, ERDS outputs were validated using both in situ and open source remotely sensed data. Specifically, taking advantage of both NASA MODIS (Moderate-resolution Imaging Spectroradiometer) and Copernicus Sentinel datasets, it was possible to gain a clear look at the full extent of the flood event. GPM data proved to be a reliable source of rainfall information for the evaluation of areas affected by heavy rainfall. By merging these data, it was possible to recreate the dynamics of the event.

scholarly journals Assessment of an Extreme Rainfall Detection System for Flood Prediction over Queensland (Australia)

Proceedings ◽  
2019 ◽  
Vol 18 (1) ◽  
pp. 1
Author(s):  
Paola Mazzoglio ◽  
Francesco Laio ◽  
Constantin Sandu ◽  
Piero Boccardo
Keyword(s):  
Real Time ◽  
Detection System ◽  
Extreme Rainfall ◽  
Early Warning Systems ◽  
Remotely Sensed ◽  
Flood Event ◽  
Management Service ◽  
Remotely Sensed Data ◽  
The Impact ◽  
Rainfall Detection

Flood events represent some of the most catastrophic natural disasters, especially in localities where appropriate measurement instruments and early warning systems are not available. Remotely sensed data can often help to obtain near real-time rainfall information with a global spatial coverage without the limitations that characterize other instruments. In order to achieve this goal, a freely accessible Extreme Rainfall Detection System (ERDS—erds.ithacaweb.org) was developed and implemented by ITHACA with the aim of monitoring and forecasting exceptional rainfall events and providing information in an understandable way for researchers as well as non-specialized users. The near real-time rainfall monitoring is performed by taking advantage of NASA GPM (Global Precipitation Measurement) IMERG (Integrated Multi-satellite Retrievals for GPM) half-hourly data (one of the most advanced rainfall measurements provided by satellite). This study aims to evaluate ERDS performance in the detection of the extreme rainfall that led to a massive flood event in Queensland (Australia) between January and February 2019. Due to the impressive amount of rainfall that affected the area, Flinders River (one of the longest Australian rivers) overflowed, expanding to a width of tens of kilometers. Several cities were also partially affected and Copernicus Emergency Management Service was activated with the aim of providing an assessment of the impact of the event. In this research, ERDS outputs were validated using both in situ and open source remotely sensed data. Specifically, taking advantage of both NASA MODIS (Moderate-resolution Imaging Spectroradiometer) and Copernicus Sentinel datasets, it was possible to gain a clear look at the full extent of the flood event. GPM data proved to be a reliable source of rainfall information for the evaluation of areas affected by heavy rainfall. By merging these data, it was possible to recreate the dynamics of the event.

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