scholarly journals Precipitable water vapor characterization in the Gulf of Cadiz region (southwestern Spain) based on Sun photometer, GPS, and radiosonde data

2010 ◽  
Vol 115 (D18) ◽  
Author(s):  
B. Torres ◽  
V. E. Cachorro ◽  
C. Toledano ◽  
J. P. Ortiz de Galisteo ◽  
A. Berjón ◽  
...  
Keyword(s):  
Water Vapor ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Radiosonde Data ◽  
Gulf Of Cadiz ◽  
Gulf Of Cádiz ◽  
Sun Photometer

scholarly journals Application of GNSS Methodologies to Obtain Precipitable Water Vapor (PWV) and Its Comparison with Radiosonde Data

Proceedings ◽  
2019 ◽  
Vol 19 (1) ◽  
pp. 24 ◽  
Author(s):  
Raquel Perdiguer-López ◽  
José Luis Berné-Valero ◽  
Natalia Garrido-Villén
Keyword(s):  
Water Vapor ◽  
Precipitable Water ◽  
Weather Conditions ◽  
Precipitable Water Vapor ◽  
Radiosonde Data ◽  
Tropospheric Delay ◽  
The North ◽  
Radiosonde Station ◽  
Observation Method ◽  
North Of Spain

A processing methodology with GNSS observations to obtain Zenith Tropospheric Delay using Bernese GNSS Software version 5.2 is revised in order to obtain Precipitable Water Vapor (PWV). The most traditional PWV observation method is the radiosonde and it is often used as a standard to validate those derived from GNSS. For this reason, a location in the north of Spain, in A Coruña, which has a GNSS station with available data and also a radiosonde station, was chosen. Two GPS weeks, in different weather conditions were calculated. The result of the comparison between the GNSS- retrieved PWV and Radiosonde-PWV is explained in the last section of this paper.

scholarly journals CONSTRUCTION OF REGIONAL WEIGHTED MEAN TEMPERATURE MODEL BASED ON OPTIMIZATION BP NEURAL NETWORK

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 1099-1105
Author(s):  
Z. X. Chen ◽  
L. L. Liu ◽  
L. K. Huang ◽  
Q. T. Wan ◽  
X. Q. Mo
Keyword(s):  
Neural Network ◽  
Water Vapor ◽  
Bp Neural Network ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Radiosonde Data ◽  
Weighted Mean ◽  
Mean Temperature ◽  
Weighted Mean Temperature ◽  
Single Station

Abstract. 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). The precision of Global Navigation Satellite System (GNSS) inversion of PWV can be significantly improved with the accurate calculation of Tm. Due to the strong nonlinear mapping ability of Back Propagation (BP) neural network, the algorithm can be used to excavate the law with massive data. In term of the nonlinear and non-stationary characteristics of GNSS precipitable water vapor, in this paper, we proposes a forecast method of GNSS precipitable water vapor based on BP neural network, which can modelling the weighted mean temperature of troposphere. The traditional BP neural network has some shortcomings, such as large amount of calculation, long training time and easy to appear “over-fitting” phenomenon and so on. In order to optimize the deficiency and numerical simulation, the three characteristic values include water vapor pressure, surface pressure and surface temperature provided are selected as input parameters, named as BP_Tm. The optimal initialization parameters of the model were obtained from the 2016 radiosonde data of 89 radiosonde stations in China, and the modeling and accuracy verification were conducted with the 2017 radiosonde data，and the accuracy of the new model was compared with the common regional Tm model. The results show the BP_Tm model has good simulation accuracy, the average deviation is −0.186K, and the root mean square error is 3.144K. When simulating the weighted mean temperature of a single station, the accuracy of the four models to simulate Tm is compared and analyzed, which the BP_Tm model can obtain good accuracy and reflect better stability and reliability.

Precipitable Water Vapor Retrieval Using Neural Network from Infrared Hyperspectral Soundings

2012 ◽  
Vol 500 ◽  
pp. 390-396 ◽  
Author(s):  
Sheng Lan Zhang ◽  
Li Sheng Xu ◽  
Ji Lie Ding ◽  
Hai Lei Liu ◽  
Xiao Bo Deng
Keyword(s):  
Neural Network ◽  
Water Vapor ◽  
Radiative Transfer ◽  
Rbf Neural Network ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Radiosonde Data ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Rbf Network

A neural network (NN) based algorithm for retrieval of precipitable water vapor (PWV) from the Atmospheric Infrared Sounder (AIRS) observations is proposed. An exact radial basis function (RBF) network is selected, in which the at-sensor brightness temperatures are the input variables, and PWV is the output variable. The training data sets for the RBF network are mainly simulated from the fast radiative transfer model (Community Radiative Transfer Model, CRTM) and the latest global assimilation data. The algorithm is validated by retrieving the PWV over west area in China using AIRS data. Compared with the AIRS PWV products, the RMSE of the PWV retrieved by our algorithm is 0.67 g/cm2, and a comparison between the retrieved PWV and radiosonde data is carried out. The result suggests that the RBF neural network based algorithm is applicable and feasible in actual conditions. Furthermore, spatial resolution of water vapor derived by RBF neural network is superior as compared to that of AIRS-L 2 standard product. Finally a PCA scheme is used for the preliminary investigation of the compression of AIRS high dimension observations.

scholarly journals Variations of precipitable water vapor from 2012 to 2019 over the Philippines using radiosondes

2021 ◽  
Vol 880 (1) ◽  
pp. 012001
Author(s):  
Y Rivera ◽  
K C Capacete ◽  
S K Rodriguez ◽  
A R David ◽  
E Macalalad
Keyword(s):  
Water Vapor ◽  
Precipitable Water ◽  
Kendall Test ◽  
The Philippines ◽  
Precipitable Water Vapor ◽  
Radiosonde Data ◽  
Upward Trend ◽  
Annual Periodicity ◽  
Increasing Trend ◽  
Moderate Positive Correlation

Abstract Precipitable Water Vapor (PWV) refers to the content of water vapor in the atmosphere which is significant in observing climate changes. The trends and variations of precipitable water vapor in Laoag, Legazpi, Mactan, and Puerto Princesa from 2012-2019, are presented through the use of radiosonde data derived from the database of the Integrated Global Radiosonde Archives (IGRA). These data were analyzed for possible patterns through a time series of its daily, monthly, and annual mean, together with a Lomb-Scargle periodogram, and Mann-Kendall test. The results observed varying trends and variability. Legazpi and Puerto Princesa with a minimum value of 20 mm, observed a gradual downward trend of PWV. Laoag and Mactan detected an upward trend of PWV with a minimum of 10 mm and 20 mm, respectively. It also showed an annual and bi-annual periodicity of PWV. Furthermore, all cities detected an increase of PWV during the wet months of May to September, while the dry months of October to April with slight variations over 8 years. In terms of seasonality, only Laoag observed a slightly different dry season, with January, February, and March experiencing around 5 mm less in monthly PWV variation compared to the other cities. The correlation of surface temperature and relative humidity of PWV observed an overall increasing trend while showing a general moderate positive correlation. This study can be used for future references for meteorologists for upcoming forecasting on the likelihood of different weather phenomena in the Philippines.

scholarly journals Monitoring 2019 Forest Fires in Southeastern Australia with GNSS Technique

2021 ◽  
Vol 13 (3) ◽  
pp. 386
Author(s):  
Jinyun Guo ◽  
Rui Hou ◽  
Maosheng Zhou ◽  
Xin Jin ◽  
Chengming Li ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Water Vapor ◽  
Forest Fire ◽  
Forest Fires ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Pollution Monitoring ◽  
New Method ◽  
Radiosonde Data ◽  
Southeastern Australia

From late 2019 to early 2020, forest fires in southeastern Australia caused huge economic losses and huge environmental pollution. Monitoring forest fires has become increasingly important. A new method of fire detection using the difference between global navigation satellite system (GNSS)-derived precipitable water vapor and radiosonde-derived precipitable water vapor (ΔPWV) is proposed. To study the feasibility of the new method, the relationship is studied between particulate matter 10 (PM10) (2.5 to 10 microns particulate matter) and ΔPWV based on Global Positioning System (GPS) data, radiosonde data, and PM10 data from 1 June 2019 to 1 June 2020 in southeastern Australia. The results show that before the forest fire, ΔPWV and PM10 were smaller and less fluctuating. When the forest fire happened, ΔPWV and PM10 were increasing. Then after the forest fire, PM10 became small with relatively smooth fluctuations, but ΔPWV was larger and more fluctuating. Correlation between the 15-day moving standard deviation (STD) time series of ΔPWV and PM10 after the fire was significantly higher than that before the fire. This study shows that ΔPWV is effective in monitoring forest fires based on GNSS technique before and during forest fires in climates with more uniform precipitation, and using ΔPWV to detect forest fires based on GNSS needs to be further investigated in climates with more precipitation and severe climate change.

scholarly journals Spatio-Temporal Variability of the Precipitable Water Vapor over Peru through MODIS and ERA-Interim Time Series

Atmosphere ◽  
2019 ◽  
Vol 10 (4) ◽  
pp. 192 ◽  
Author(s):  
Katherine Ccoica-López ◽  
Jose Pasapera-Gonzales ◽  
Juan Jimenez
Keyword(s):  
Time Series ◽  
Water Vapor ◽  
Spatial Variability ◽  
Temporal Variability ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Radiosonde Data ◽  
North Coast ◽  
Study Region ◽  
The North

Precipitable water vapor (PWV) is a meteorological variable that influences the main processes that occur in the atmosphere. It is not a homogeneous variable, but varies both temporally and spatially according to local conditions. This study analyzes the spatial and temporal variability of the PWV in Peru using MODIS satellite data (MOD05/MYD05 products) during the period 2000 to 2017. MODIS-derived PWV values were complemented with ERA-Interim reanalysis data to take the study period back to 1979. PWV values extracted from MODIS and ERA-Interim were compared against in situ values obtained from five radiosonde stations between the years of 2003 and 2016 (non-continuous data). The study was performed over nine sub-regions of the Peruvian territory: coastal, highland, and jungle sub-regions, which in turn were classified into northern, central and southern regions. The analysis of spatial variability was performed using monthly semivariograms and influencing parameters such as sill and range, whereas the temporal variation was examined by time series of monthly, seasonal, and multi-annual means. The Mann-Kendall test was also applied to determine the presence of trends. The spatial analysis evidenced the heterogeneity of the PWV over the study region, and in most of the sub-regions there was directional variability during the austral summer and austral winter, with the Northeast (NE) and East (E) directions having the greatest spatial variability. The omnidirectional analysis of the sill and range showed that there was a high spatial variability of PWV mainly over the northern and southern jungle, even exceeding the limit area of these sub-regions. The temporal analysis shows that this variability occurs more in the north and center of the jungle and in the north coast, where the content of PWV is higher in relation to other regions, while the central and southern highlands have the lowest values. In addition, the trend test determines that there is a slight increase in PWV for the coast and jungle regions of Peru. Validation analysis using the radiosonde data showed a similar performance of both datasets (MODIS and ERA), with better results for the case of the MODIS product (RMSE < 0.6 cm and R2 = 0.71).

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