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

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.

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Nonstationary Tropospheric Processes in Geodetic Precipitable Water Vapor Time Series

International Association of Geodesy Symposia - Observing our Changing Earth ◽

10.1007/978-3-540-85426-5_72 ◽

2008 ◽

pp. 625-630

Author(s):

O.J Botai ◽

W.L Combrinck ◽

C.J deW Rautenbanch

Keyword(s):

Time Series ◽

Water Vapor ◽

Precipitable Water ◽

Precipitable Water Vapor

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Sentinel-1 Interferometric SAR Mapping of Precipitable Water Vapor Over a Country-Spanning Area

IEEE Transactions on Geoscience and Remote Sensing ◽

10.1109/tgrs.2017.2658342 ◽

2017 ◽

Vol 55 (5) ◽

pp. 2993-2999 ◽

Cited By ~ 23

Author(s):

Pedro Mateus ◽

Joao Catalao ◽

Giovanni Nico

Keyword(s):

Water Vapor ◽

Precipitable Water ◽

Precipitable Water Vapor ◽

Interferometric Sar

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

Atmosphere ◽

10.3390/atmos10040192 ◽

2019 ◽

Vol 10 (4) ◽

pp. 192 ◽

Cited By ~ 2

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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KORELASI MUSIMAN ANTARA ZENITH WET DELAY (ZWD) DAN DEBIT SUNGAI DI DAS CIKAPUNDUNG HULU, KAWASAN BANDUNG UTARA, JAWA BARAT

Seminar Nasional Geomatika ◽

10.24895/sng.2018.3-0.1033 ◽

2019 ◽

Vol 3 ◽

pp. 741

Author(s):

Wedyanto Kuntjoro ◽

Z.A.J. Tanuwijaya ◽

A. Pramansyah ◽

Dudy D. Wijaya

Keyword(s):

Water Vapor ◽

Precipitable Water ◽

Precipitable Water Vapor ◽

Zenith Wet Delay

Kandungan total uap air troposfer (precipitable water vapor) di suatu tempat dapat diestimasi berdasarkan karakteristik bias gelombang elektromagnetik dari satelit navigasi GPS, berupa zenith wet delay (ZWD). Pola musiman deret waktu ZWD sangat penting dalam studi siklus hidrologi khususnya yang terkait dengan kejadian-kejadian banjir. Artikel ini menganalisis korelasi musiman antara ZWD dan debit sungai Cikapundung di wilayah Bandung Utara berdasarkan estimasi rataan pola musimannya. Berdasarkan rekonstruksi sejumlah komponen harmonik ditemukan bahwa pola musiman ZWD memiliki kemiripan dan korelasi yang kuat dengan pola musiman debit sungai. Pola musiman ZWD dan debit sungai dipengaruhi secara kuat oleh fenomena pertukaran Monsun Asia dan Monsun Australia. Korelasi linier di antara keduanya menunjukkan hasil yang sangat kuat, dimana hampir 90% fluktuasi debit sungai dipengaruhi oleh kandungan uap air di troposfer dengan level signifikansi 95%. Berdasarkan spektrum amplitudo silang dan koherensi, kedua kuantitas ini nampak didominasi oleh siklus monsun satu tahunan disertai indikasi adanya pengaruh siklus tengah tahunan dan 4 bulanan.

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HGPT2: An ERA5-Based Global Model to Estimate Relative Humidity

Remote Sensing ◽

10.3390/rs13112179 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2179

Author(s):

Pedro Mateus ◽

Virgílio B. Mendes ◽

Sandra M. Plecha

Keyword(s):

Water Vapor ◽

Relative Humidity ◽

Real Time ◽

Prediction Models ◽

Weather Prediction ◽

Precipitable Water ◽

Precipitable Water Vapor ◽

Global Navigation Satellite Systems ◽

International Gnss Service ◽

Weighted Mean Temperature

The neutral atmospheric delay is one of the major error sources in Space Geodesy techniques such as Global Navigation Satellite Systems (GNSS), and its modeling for high accuracy applications can be challenging. Improving the modeling of the atmospheric delays (hydrostatic and non-hydrostatic) also leads to a more accurate and precise precipitable water vapor estimation (PWV), mostly in real-time applications, where models play an important role, since numerical weather prediction models cannot be used for real-time processing or forecasting. This study developed an improved version of the Hourly Global Pressure and Temperature (HGPT) model, the HGPT2. It is based on 20 years of ERA5 reanalysis data at full spatial (0.25° × 0.25°) and temporal resolution (1-h). Apart from surface air temperature, surface pressure, zenith hydrostatic delay, and weighted mean temperature, the updated model also provides information regarding the relative humidity, zenith non-hydrostatic delay, and precipitable water vapor. The HGPT2 is based on the time-segmentation concept and uses the annual, semi-annual, and quarterly periodicities to calculate the relative humidity anywhere on the Earth’s surface. Data from 282 moisture sensors located close to GNSS stations during 1 year (2020) were used to assess the model coefficients. The HGPT2 meteorological parameters were used to process 35 GNSS sites belonging to the International GNSS Service (IGS) using the GAMIT/GLOBK software package. Results show a decreased root-mean-square error (RMSE) and bias values relative to the most used zenith delay models, with a significant impact on the height component. The HGPT2 was developed to be applied in the most diverse areas that can significantly benefit from an ERA5 full-resolution model.

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