Establishment and Evaluation of Atmospheric Water Vapor Inversion Model Without Meteorological Parameters Based on Machine Learning [PDF]
Precipitable water vapor (PWV) is an important indicator to characterize the spatial and temporal variability of water vapor. A high spatial and temporal resolution of atmospheric precipitable water can be obtained using ground-based GNSS, but its ...
Ning Liu +3 more
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GLOBAL POSITIONING SYSTEM PRECIPITABLE WATER VAPOR INTERPOLATION USING INVERSE MULTIQUADRIC, ARTIFICIAL NEURAL NETWORK AND INVERSE DISTANCE WEIGHTED [PDF]
Precipitable water vapor (PWV) is one of the most critical data in many meteorological departments. This component has great spatial and temporal changes, so the global positioning system (GPS) always seeks to increase the accuracy of estimating the ...
S. Chamankar +2 more
doaj +1 more source
Satellite imagery and products of the 16–17 February 2020 Saharan Air Layer dust event over the eastern Atlantic: impacts of water vapor on dust detection and morphology [PDF]
On 16–17 February 2020, dust within the Saharan Air Layer (SAL) from western Africa moved over the eastern Atlantic Ocean. Satellite imagery and products from the ABI on GOES-16, VIIRS on NOAA-20, and CALIOP on CALIPSO, along with retrieved values of ...
L. Grasso +11 more
doaj +1 more source
Precipitation Extremes and Water Vapor
AbstractPurpose of Review:Review our current understanding of how precipitation is related to its thermodynamic environment, i.e., the water vapor and temperature in the surroundings, and implications for changes in extremes in a warmer climate.Recent Findings:Multiple research threads have i) sought empirical relationships that govern onset of strong ...
J. David Neelin +7 more
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Tropical precipitation clusters as islands on a rough water‐vapor topography [PDF]
AbstractTropical precipitation clusters exhibit power‐law frequency distributions in area and volume (integrated precipitation), implying a lack of characteristic scale in tropical convective organization. However, it remains unknown what gives rise to the power laws and how the power‐law exponents for area and volume are related to one another.
Ziwei Li +2 more
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GNSS-DERIVED PRECIPITABLE WATER VAPOR MODELING USING MACHINE LEARNING METHODS [PDF]
Atmospheric water vapor plays a vital role in phenomena related to the global hydrological cycle and climate changes, and its Spatio-temporal modeling and prediction help to identify and predict climatic phenomena.
S. Izanlou +2 more
doaj +1 more source
Forecasting Precipitable Water Vapor Using LSTMs [PDF]
Long-Short-Term-Memory (LSTM) networks have been used extensively for time series forecasting in recent years due to their ability of learning patterns over different periods of time. In this paper, this ability is applied to learning the pattern of Global Positioning System (GPS)-based Precipitable Water Vapor (PWV) measurements over a period of 4 ...
Jain, Mayank +4 more
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CONSTRUCTION OF REGIONAL WEIGHTED MEAN TEMPERATURE MODEL BASED ON OPTIMIZATION BP NEURAL NETWORK [PDF]
The tropospheric weighted mean temperature (Tm) is one of the key characteristic parameters in the troposphere, which plays an important role in the conversion of Zenith Wet Delay (ZWD) to atmospheric Precipitable Water Vapor (PWV).
Z. X. Chen +10 more
doaj +1 more source
Calibration of Raman lidar water vapor profiles by means of AERONET photometer observations and GDAS meteorological data [PDF]
We present a practical method to continuously calibrate Raman lidar observations of water vapor mixing ratio profiles. The water vapor profile measured with the multiwavelength polarization Raman lidar PollyXT is calibrated by means of co-located ...
G. Dai +10 more
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Probing the Atmospheric Precipitable Water Vapor with SOFIA, Part I, Measurements of the Water Vapor Overburden with FIFI-LS [PDF]
Abstract We report on the measurements of telluric water vapor made with the instrument FIFI-LS on SOFIA. Since November 2018, FIFI-LS has measured the water vapor overburden with the same measurement setup on each science flight with about 10 data points throughout the flight. This created a large sample of 469 measurements at different
C. Fischer +6 more
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