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).

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 Variación espacio-temporal del vapor de agua precipitable (PWV) en la costa norte del Perú para el periodo 2001-2017

2018 ◽  
pp. 102-108
Keyword(s):  
Time Series ◽  
Water Vapor ◽  
Temporal Variability ◽  
Extreme Values ◽  
Power Spectral Analysis ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Spatial And Temporal Variability ◽  
Northern Coast ◽  
San Marcos

Variación espacio-temporal del vapor de agua precipitable (PWV) en la costa norte del Perú para el periodo 2001-2017  Jhon Brayan Guerrero Salinas, Rolando Renee Badaracco Meza, Joel Rojas Acuña Universidad Nacional Mayor de San Marcos, Ap. Postal 14-0149, Lima, Perú Recibido 11 de octubre del 2018, Revisado el 7 de diciembre de 2018. Aceptado el 12 de diciembre de 2018 DOI: https://doi.org/10.33017/RevECIPeru2018.0016/ Resumen El objetivo de este estudio fue realizar el análisis de la variabilidad espacial y temporal de la columna de vapor de agua precipitable (PWV, por sus siglas en inglés) en la costa norte del Perú (3°S-7°S). Se analizaron un total de 17 años de datos PWV obtenidas del sensor MODIS/Terra, de las cuales se generaron mapas de climatología provisional y de desviación estándar, para así obtener los patrones de distribución espacial promedio y variabilidad temporal. El mapa climatológico provisional de PWV muestra en general que las zonas de mayor variabilidad de PWV se encuentran en el océano y tierras bajas, mientras que las zonas de menor variabilidad se encuentran en la región de los Andes. Los diagramas de Hovmöller y la serie de tiempo identificaron un ciclo anual y el aumento de los valores extremos en los meses de verano a partir del año 2010. El análisis espectral de potencia de la serie de tiempo aparte de identificar el periodo anual también identifica un periodo semianual que se debe al cambio estacional verano-invierno. Descriptores: Vapor de agua precipitable, Costa norte, MODIS/TERRA, Hovmöller. Abstract The objective of this study was to perform the analysis of the spatial and temporal variability of the precipitable water vapor column (PWV) on the northern coast of Peru (3°S-7°S). A total of 17 years of PWV data obtained from the MODIS/Terra sensor were analyzed, from which maps of provisional climatology and standard deviation were generated, in order to obtain the patterns of average spatial distribution and temporal variability. The provisional climatological map of PWV shows in general that the areas with the greatest variability of PWV are found in the ocean and lowlands, while the areas of least variability are found in the Andes region. The Hovmöller diagrams and the time series identified an annual cycle and the increase of the extreme values in the summer months from the year 2010. The power spectral analysis of the time series apart from identifying the annual period also identifies a period semiannual that is due to the seasonal change summer-winter. Keywords: Precipitable water vapor, Northern coast, MODIS/TERRA, Hovmöller.

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.

scholarly journals A new adaptive absolute method for homogenizing GNSS‐derived precipitable water vapor time series

2021 ◽  
Author(s):  
Dantong Zhu ◽  
Kefei Zhang ◽  
Zhen Shen ◽  
Suqin Wu ◽  
Zhiping Liu ◽  
...  
Keyword(s):  
Time Series ◽  
Water Vapor ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Absolute Method

Mapping Precipitable Water Vapor Time Series From Sentinel-1 Interferometric SAR

2020 ◽  
Vol 58 (2) ◽  
pp. 1373-1379 ◽  
Author(s):  
Pedro Mateus ◽  
Joao Catalao ◽  
Giovanni Nico ◽  
Pedro Benevides
Keyword(s):  
Time Series ◽  
Water Vapor ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Interferometric Sar
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