scholarly journals Observed HIRS and Aqua MODIS Thermal Infrared Moisture Determinations in the 2000s

2021 ◽  
Vol 13 (3) ◽  
pp. 502
Author(s):  
Eva E. Borbas ◽  
Paul W. Menzel
Keyword(s):  
Water Vapor ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Statistical Regression ◽  
Absorption Bands ◽  
Mean Values ◽  
Brightness Temperatures ◽  
Spectral Bands ◽  
Infrared Spectral ◽  
Moderate Resolution

This paper compares the tropospheric moisture data records derived from High-resolution Infrared Radiation Sounder (HIRS) and Moderate Resolution Imaging Spectro-radiometer (MODIS) measurements from the years 2003 through 2013. Total Precipitable Water Vapor (TPW) and Upper Tropospheric Precipitable Water Vapor (UTPW) are derived using the infrared spectral bands in the CO2 and H2O absorption bands as well as in the atmospheric windows. Retrieval of TPW and UTPW uses a statistical regression algorithm performed using clear sky radiances (and Brightness Temperatures) measured over land and ocean for both day and night. The TPW and UTPW seasonal cycles of HIRS and MODIS observations are found to be in synchronization with zonal mean values for one degree latitude bands within 2.0 mm and 0.07 mm, respectively.

scholarly journals An Algorithm to Retrieve Total Precipitable Water Vapor in the Atmosphere from FengYun 3D Medium Resolution Spectral Imager 2 (FY-3D MERSI-2) Data

2020 ◽  
Vol 12 (21) ◽  
pp. 3469
Author(s):  
Bilawal Abbasi ◽  
Zhihao Qin ◽  
Wenhui Du ◽  
Jinlong Fan ◽  
Chunliang Zhao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Vapor ◽  
Near Infrared ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Optical Remote Sensing ◽  
Statistical Regression ◽  
Sensing Applications ◽  
Medium Resolution ◽  
Spectral Imager

The atmosphere has substantial effects on optical remote sensing imagery of the Earth’s surface from space. These effects come through the functioning of atmospheric particles on the radiometric transfer from the Earth’s surface through the atmosphere to the sensor in space. Precipitable water vapor (PWV), CO2, ozone, and aerosol in the atmosphere are very important among the particles through their functioning. This study presented an algorithm to retrieve total PWV from the Chinese second-generation polar-orbiting meteorological satellite FengYun 3D Medium Resolution Spectral Imager 2 (FY-3D MERSI-2) data, which have three near-infrared (NIR) water vapor absorbing channels, i.e., channel 16, 17, and 18. The algorithm was improved from the radiance ratio technique initially developed for Moderate-Resolution Imaging Spectroradiometer (MODIS) data. MODTRAN 5 was used to simulate the process of radiant transfer from the ground surfaces to the sensor at various atmospheric conditions for estimation of the coefficients of ratio technique, which was achieved through statistical regression analysis between the simulated radiance and transmittance values for FY-3D MERSI-2 NIR channels. The algorithm was then constructed as a linear combination of the three-water vapor absorbing channels of FY-3D MERSI-2. Measurements from two ground-based reference datasets were used to validate the algorithm: the sun photometer measurements of Aerosol Robotic Network (AERONET) and the microwave radiometer measurements of Energy’s Atmospheric Radiation Measurement Program (ARMP). The validation results showed that the algorithm performs very well when compared with the ground-based reference datasets. The estimated PWV values come with root mean square error (RMSE) of 0.28 g/cm2 for the ARMP and 0.26 g/cm2 for the AERONET datasets, with bias of 0.072 g/cm2 and 0.096 g/cm2 for the two reference datasets, respectively. The accuracy of the proposed algorithm revealed a better consistency with ground-based reference datasets. Thus, the proposed algorithm could be used as an alternative to retrieve PWV from FY-3D MERSI-2 data for various remote sensing applications such as agricultural monitoring, climate change, hydrologic cycle, and so on at various regional and global scales.

scholarly journals GPS Precipitable Water Vapor Estimations over Costa Rica: A Comparison against Atmospheric Sounding and Moderate Resolution Imaging Spectrometer (MODIS)

Climate ◽  
2019 ◽  
Vol 7 (5) ◽  
pp. 63 ◽  
Author(s):  
Polleth Campos-Arias ◽  
Germain Esquivel-Hernández ◽  
José Francisco Valverde-Calderón ◽  
Stephanie Rodríguez-Rosales ◽  
Jorge Moya-Zamora ◽  
...  
Keyword(s):  
Water Vapor ◽  
Costa Rica ◽  
Central America ◽  
Multiple Linear Regression Model ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Imaging Spectrometer ◽  
Atmospheric Sounding ◽  
Moderate Resolution ◽  
Resolution Imaging

The quantification of water vapor in tropical regions like Central America is necessary to estimate the influence of climate change on its distribution and the formation of precipitation. This work reports daily estimations of precipitable water vapor (PWV) using Global Positioning System (GPS) delay data over the Pacific region of Costa Rica during 2017. The GPS PWV measurements were compared against atmospheric sounding and Moderate Resolution Imaging Spectrometer (MODIS) data. When GPS PWV was calculated, relatively small biases between the mean atmospheric temperatures (Tm) from atmospheric sounding and the Bevis equation were found. The seasonal PWV fluctuations were controlled by two of the main circulation processes in Central America: the northeast trade winds and the latitudinal migration of the Intertropical Convergence Zone (ITCZ). No significant statistical differences were found for MODIS Terra during the dry season with respect GPS-based calculations (p > 0.05). A multiple linear regression model constructed based on surface meteorological variables can predict the GPS-based measurements with an average relative bias of −0.02 ± 0.19 mm/day (R2 = 0.597). These first results are promising for incorporating GPS-based meteorological applications in Central America where the prevailing climatic conditions offer a unique scenario to study the influence of maritime moisture inputs on the seasonal water vapor distribution.

scholarly journals High Spatial Resolution mapping of Precipitable Water Vapor using SAR interferograms, GPS observations and ERA-Interim reanalysis

2016 ◽  
Author(s):  
W. Tang ◽  
M. S. Liao ◽  
L. Zhang ◽  
W. Li ◽  
W. M. Yu
Keyword(s):  
Water Vapor ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Weather Forecasting ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Spatial Correlations ◽  
Mean Values ◽  
Envisat Asar ◽  
The Difference

Abstract. A high spatial resolution of the Precipitable Water Vapor (PW V) in the atmosphere is a key requirement for the short-scale weather forecasting and climate research. The aim of this work is to derive temporally-differenced maps of the spatial distribution of PWV by analyzing the atmospheric delay "noise" in Interferometric Synthetic Aperture Radar (InSA R). A time series maps of differential PW V were obtained by processing a set of ENVISAT ASAR images cover the area of Southern California, USA from 06 October 2007 to 29 November 2008. To get a more accurate PW V, the component of dry delay is calculated by using ERA-Interim reanalysis products. In addition, the ERA-Interim was used to compute the conversion factors required to convert the zenith wet delay to water vapor. The InSA R-derived differential PW V maps were calibrated by means of the GPS PW V measurements over the study area. We validated our results against the measurements of PW V derived from the MEdium Resolution Imaging Spectrometer (MERIS) which is located together with ASAR sensor onboard the ENVISAT satellite. Our comparative results show strong spatial correlations between the two data sets. The difference maps have Gaussian distributions with mean values close to zero and standard deviations below 2 mm. The advantages of the InSAR technique is that it provides water vapor distribution with a spatial resolution as fine as 20 m and an accuracy of ~2 mm. Such a high spatial resolution maps of PW V could lead to much greater accuracy in meteorological understanding and quantitative precipitation forecasts.

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

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.

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

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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