scholarly journals Comparison of precipitable water vapor derived from radiosonde, GPS, and Moderate-Resolution Imaging Spectroradiometer measurements

2003 ◽  
Vol 108 (D20) ◽  
Author(s):  
Zhenhong Li
Keyword(s):  
Water Vapor ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer

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 Evaluation of MODIS Water Vapour Products over ALGERIA using Radiosonde Data

2021 ◽  
Vol 44 ◽  
Author(s):  
Houaria Namaoui ◽  
Salem Kahlouche ◽  
Ahmed Hafidh Belbachir
Keyword(s):  
Water Vapour ◽  
Satellite Data ◽  
Correlation Coefficients ◽  
Precipitable Water ◽  
Radiosonde Data ◽  
Precipitable Water Vapour ◽  
Total Precipitable Water ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer

Remote sensing of atmospheric water vapour using GNSS and Satellite data has become an efficient tool in meteorology and climate research. Many satellite data have been increasingly used to measure the content of water vapour in the atmosphere and to characterize its temporal and spatial variations. In this paper, we have used observations from radiosonde data collected from three stations (Algiers, Bechar and Tamanrasset) in Algeria from January to December 2012 to evaluate Moderate Resolution Imaging Spectroradiometer (MODIS) total precipitable water vapour (PWV) products. Results show strong agreement between the total precipitable water contents estimated based on radiosondes observations and the ones measured by the sensor MODIS with the correlation coefficients in the range 0.69 to 0.95 and a mean bias, which does not exceed 1.5.  

scholarly journals A Global MODIS Water Vapor Database for the Operational Atmospheric Correction of Historic and Recent Landsat Imagery

2019 ◽  
Vol 11 (3) ◽  
pp. 257 ◽  
Author(s):  
David Frantz ◽  
Marion Stellmes ◽  
Patrick Hostert
Keyword(s):  
Water Vapor ◽  
Landsat Imagery ◽  
Atmospheric Correction ◽  
Atmospheric Conditions ◽  
Wet Season ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Daily Water ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
The Impact

Analysis Ready Data (ARD) have undergone the most relevant pre-processing steps to satisfy most user demands. The freely available software FORCE (Framework for Operational Radiometric Correction for Environmental monitoring) is capable of generating Landsat ARD. An essential step of generating ARD is atmospheric correction, which requires water vapor data. FORCE relies on a water vapor database obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS). However, two major drawbacks arise from this strategy: (1) The database has to be compiled for each study area prior to generating ARD; and (2) MODIS and Landsat commissioning dates are not well aligned. We have therefore compiled an application-ready global water vapor database to significantly increase the operational readiness of ARD production. The free dataset comprises daily water vapor data for February 2000 to July 2018 as well as a monthly climatology that is used if no daily value is available. We systematically assessed the impact of using this climatology on surface reflectance outputs. A global random sample of Landsat 5/7/8 imagery was processed twice (i) using daily water vapor (reference) and (ii) using the climatology (estimate), followed by computing accuracy, precision, and uncertainty (APU) metrics. All APU measures were well below specification, thus the fallback usage of the climatology is generally a sound strategy. Still, the tests revealed that some considerations need to be taken into account to help quantify which sensor, band, climate, and season are most or least affected by using a fallback climatology. The highest uncertainty and bias is found for Landsat 5, with progressive improvements towards newer sensors. The bias increases from dry to humid climates, whereas uncertainty increases from dry and tropic to temperate climates. Uncertainty is smallest during seasons with low variability, and is highest when atmospheric conditions progress from a dry to a wet season (and vice versa).

scholarly journals Estimation of spatio-temporal distribution of precipitable water using MODIS and AVHRR data: a case study for Cyprus

2011 ◽  
Vol 30 ◽  
pp. 23-29 ◽  
Author(s):  
D. Hadjimitsis ◽  
Z. Mitraka ◽  
I. Gazani ◽  
A. Retalis ◽  
N. Chrysoulakis ◽  
...  
Keyword(s):  
Near Infrared ◽  
Temporal Distribution ◽  
Grid Cell ◽  
Precipitable Water ◽  
Avhrr Data ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Spatio Temporal ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. In this paper, the atmospheric precipitable water (PW) over the area of Cyprus was estimated by means of Advanced Very High Resolution Radiometer (AVHRR) thermal channels brightness temperature difference (ΔT). The AVHRR derived ΔT was calculated in a grid of 5 × 5 km cells; the corresponding PW value in each grid cell was extracted from Moderate Resolution Imaging Spectroradiometer (MODIS) Level 2 product (near-infrared algorithm). Once the PW – ΔT relationship coefficients corresponding to the area of Cyprus were calculated, the relationship was applied to AVHRR data for one month period. Radiosonde derived PW values, as well as MODIS independent PW values were used to validate the estimations and a good agreement was noted.

scholarly journals Biases and Abrupt Shifts of Monthly Precipitable Water from Terra MODIS

2019 ◽  
Vol 11 (11) ◽  
pp. 1315
Author(s):  
Ning Lu
Keyword(s):  
Precipitable Water ◽  
Climatic Trend ◽  
Positioning Systems ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Terra Modis ◽  
Abrupt Shifts ◽  
Increasing Trends ◽  
Terra Satellite

Monthly atmospheric precipitable water (PW) from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Terra satellite was assessed over land at 60°S–60°N. MODIS provides two PW products by using infrared (IR) and near-IR (NIR) algorithms, respectively. An assessment was performed for both MODIS PW data from 2000 to 2014, comparing them with the measurements at international stations of the global positioning systems and with a reanalysis to detect abrupt changes through monthly variations. It is noted that MODIS IR systematically underestimated PW in over 75% of stations, and that PW estimation declines with time. MODIS NIR significantly overestimated PW for tropical land and experienced two abrupt shifts. These data defects result in large spurious decreasing trends in MODIS IR and increasing trends in MODIS NIR. The two MODIS PW products are currently not suitable for a climatic-trend analysis, highlighting the need for data reprocessing and calibration.

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