scholarly journals Retrieval of column-integrated water vapour from MODIS and analysis of its monthly and seasonal variability over several typical cities in China

MAUSAM ◽  
2021 ◽  
Vol 67 (3) ◽  
pp. 571-576
Author(s):  
ZHANG JINYE ◽  
CHENG CHUNFU ◽  
ZHU JINRONG ◽  
YU XIULI
Keyword(s):  
Water Vapour ◽  
Global Climate ◽  
Low Cost ◽  
Precipitable Water ◽  
Spatial And Temporal Variability ◽  
Precipitable Water Vapour ◽  
Integrated Water Vapour ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Good Tracer

Column-integrated water vapour also called Precipitable Water Vapour (PWV), is one of the main parameters influencing the global climate change. Due to its high spatial and temporal variability PWV has been found to be a good tracer of atmospheric motions. Retrieving PWV from Moderate Resolution Imaging Spectroradiometer (MODIS) data has the merits of high spatial resolution and low cost. In this paper, an algorithm for retrieving PWV using several MODIS near-IR channels data is first presented. Six typical cities in China with different climate are selected for study. These are Beijing, Shanghai, Guangzhou, Chengdu, Wuhan and Lanzhou. The variations of PWV in recent13 years (2001-2013) over six cities have been analyzed. The study brings out an increasing trend of annual average of water vapour over these cities in recent 13 years. The results also indicate that PWV reaches the highest value in summer, decreases in autumn, further decrease in spring, and is lowest in winter. PWV in summer over the six cities have been increasing in recent 13 years, but PWV in autumn and winter have been decreasing over inland cities, such as Wuhan and Beijing. Possible reasons for such observed trends are given in this paper.  

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 Estimation of precipitable water vapour from GPS during winter season 2003

MAUSAM ◽  
2021 ◽  
Vol 57 (2) ◽  
pp. 323-328
Author(s):  
R. K. GIRI ◽  
B. R. LOE ◽  
N. PUVIARSON ◽  
S. S. BHANDARI ◽  
R. K. SHARMA
Keyword(s):  
Water Vapour ◽  
Winter Season ◽  
Precipitable Water ◽  
New Delhi ◽  
Total Delay ◽  
Precipitable Water Vapour ◽  
Integrated Water Vapour ◽  
Zenith Hydrostatic Delay ◽  
Indian Institute Of Science ◽  
Good Agreement

Lkkj & ok;qeaMy esa ty ok"i dk forj.k LFkkfud :i ls vkSj dkfyd rkSj ij cgqr vf/kd ifjorZu’khy gksrk gSA ty ok"i dk forj.k vusdksa ok;qeaMyh; izfØ;kvksa esa izeq[k Hkwfedk fuHkkrk gSA dqy lekdfyr ty ok"i vFkok le:ih o"kkZ ty ok"i dk vkdyu Xykscy iksft’kfuax flLVe ¼th- ih- ,l-½ tsfuFk VksVy fMys ¼tsM- Vh- Mh-½ ds vk¡dM+ksa dh lgk;rk ls fd;k tk ldrk gSA blesa tsfuFk nzoLFkSfrd fMys ds eku dks funf’kZr fd;k x;k gS vkSj bls tsM- Vh- Mh- ls fudkyus ij tsfuFk vknzZ fMys ds vk¡dM+s izkIr gksaxsA vr% bl izdkj vkdfyr fd, x, tsM- MCY;w- Mh- ds eku ls izk;% yxkrkj ,e- ,e-  esa o"kkZ  ty ok"i dk irk pysxkA bl 'kks/k&i= esa th- ih- ,l- ds vk¡dM+ksa dk mi;ksx djrs gq, ubZ fnYyh ds fy, o"kZ 2003 ds 'khrdkyhu _rq vkSj Hkkjrh; foKku laLFkku ifj"kn] caxykSj ds dsanzksa ds fy, ,e- ,e- esa ih- MCY;w- oh- dk vkdyu djus dk iz;kl fd;k x;k gSA buls izkIr gq, ifj.kkeksa dk jsfM;kslkSUnsa vk¡dM+ksa ds lkFk lgh rkyesy ik;k x;k gSA The distribution of water vapour in atmosphere is highly spatial and temporal variable. It plays a key role in many atmospheric processes. The total integrated water vapour or equivalent precipitable water vapour (PWV) can be estimated with the help of Global Positioning System (GPS) Zenith Total Delay (ZTD) data. The value of Zenith Hydrostatic Delay (ZHD) is modeled and subtracting from ZTD will give Zenith wet delay (ZWD). Consequently, the estimated ZWD values will provide PWV in mm almost in a continuous manner. In this paper an attempt has been made for the estimation of PWV in mm during winter season 2003 for New Delhi and Indian Institute of Science (IISC), Bangalore stations using GPS data. The result shows fairly good agreement with the radio-sonde data. 

scholarly journals The CM SAF ATOVS tropospheric water vapour and temperature data record: overview of methodology and evaluation

2015 ◽  
Vol 8 (1) ◽  
pp. 127-171
Author(s):  
N. Courcoux ◽  
M. Schröder
Keyword(s):  
Water Vapour ◽  
Interpolation Method ◽  
Global Climate ◽  
Precipitable Water ◽  
Temperature Data ◽  
Specific Humidity ◽  
Radiosonde Data ◽  
Precipitable Water Vapour ◽  
Infrared Observation ◽  
Data Record

Abstract. Recently, the reprocessed Advanced Television Infrared Observation Satellite (TIROS)-N Operational Vertical Sounder (ATOVS) tropospheric water vapour and temperature data record has been released by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Climate Monitoring (CM SAF). ATOVS observations from the National Oceanic and Atmospheric Agency (NOAA)-15 through NOAA-19 and EUMETSAT's Meteorological operational (Metop-A) satellites have been consistently reprocessed to generate 13 years (1999–2011) of global water vapour and temperature daily and monthly means with a spatial resolution of 90 km × 90 km. After pre-processing, an optimal estimation scheme has been applied to the observations to simultaneously infer temperature and water vapour profiles. In a post-processing step an objective interpolation method (Kriging) has been applied to allow for gap filling. The product suite includes total precipitable water vapour (TPW), layer integrated water vapour (LPW) and layer mean temperature for five tropospheric layers, as well as specific humidity and temperature at six tropospheric levels and is referenced under doi:10.5676/EUM_SAF_CM/WVT_ATOVS/V001. To our knowledge this is the first time that the ATOVS record (1998–now) has been consistently reprocessed (1999–2011) to retrieve water vapour and temperature products. TPW and LPW products were compared to corresponding products from the Global Climate Observing System (GCOS) Upper-Air Network (GUAN) radiosonde observations and from the Atmospheric InfraRed Sounder (AIRS) version 5 satellite data record. The TPW shows a good agreement with the GUAN radiosonde data: average bias and root mean square error (RMSE) are −0.2 and 3.3 kg m−2, respectively. The maximum absolute (relative) bias and RMSE values decrease (increase) strongly with height. While the RMSE relative to AIRS is generally smaller, the TPW bias relative to AIRS is larger with dominant contributions from precipitating areas. The consistently reprocessed ATOVS data record exhibits an improved quality and an improved stability relative to the operational CM SAF ATOVS products when compared to the TPW from GUAN radiosonde data over the period 2004–2011. Finally, it became evident that the change in the number of satellites used for the retrieval combined with the use of the Kriging leads to breakpoints in the ATOVS data record so that a variability analysis of the data record is not recommended for the time period from January 1999 to January 2001.

scholarly journals Integrated water vapour content retrievals from ship-borne GNSS receivers during EUREC<sup>4</sup>A

2021 ◽  
Vol 13 (4) ◽  
pp. 1499-1517
Author(s):  
Pierre Bosser ◽  
Olivier Bock ◽  
Cyrille Flamant ◽  
Sandrine Bony ◽  
Sabrina Speich
Keyword(s):  
Water Vapour ◽  
Satellite System ◽  
Water Vapour Content ◽  
Weather Forecasts ◽  
Integrated Water Vapour ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Gnss Measurements ◽  
Global Navigation Satellite ◽  
Gnss Data

Abstract. In the framework of the EUREC4A (Elucidating the role of clouds–circulation coupling in climate) campaign that took place in January and February 2020, integrated water vapour (IWV) contents were retrieved over the open tropical Atlantic Ocean using Global Navigation Satellite System (GNSS) data acquired from three research vessels (R/Vs): R/V Atalante, R/V Maria S. Merian and R/V Meteor. This paper describes the GNSS processing method and compares the GNSS IWV retrievals with IWV estimates from the European Centre for Medium-range Weather Forecasts (ECMWF) fifth reanalysis (ERA5), from the Moderate Resolution Imaging Spectroradiometer (MODIS) infrared products and from terrestrial GNSS stations located along the tracks of the ships. The ship-borne GNSS IWV retrievals from R/V Atalante and R/V Meteor compare well with ERA5, with small biases (−1.62 kg m−2 for R/V Atalante and +0.65 kg m−2 for R/V Meteor) and a root mean square (rms) difference of about 2.3 kg m−2. The results for the R/V Maria S. Merian are found to be of poorer quality, with an rms difference of 6 kg m−2, which is very likely due to the location of the GNSS antenna on this R/V prone to multipath effects. The comparisons with ground-based GNSS data confirm these results. The comparisons of all three R/V IWV retrievals with MODIS infrared products show large rms differences of 5–7 kg m−2, reflecting the enhanced uncertainties in these satellite products in the tropics. These ship-borne IWV retrievals are intended to be used for the description and understanding of meteorological phenomena that occurred during the campaign, east of Barbados, Guyana and northern Brazil. Both the raw GNSS measurements and the IWV estimates are available through the AERIS data centre (https://en.aeris-data.fr/, last access: 20 September 2020). The digital object identifiers (DOIs) for R/V Atalante IWV and raw datasets are https://doi.org/10.25326/71 (Bosser et al., 2020a) and https://doi.org/10.25326/74 (Bosser et al., 2020d), respectively. The DOIs for the R/V Maria S. Merian IWV and raw datasets are https://doi.org/10.25326/72 (Bosser et al., 2020b) and https://doi.org/10.25326/75 (Bosser et al., 2020e), respectively. The DOIs for the R/V Meteor IWV and raw datasets are https://doi.org/10.25326/73 (Bosser et al., 2020c) and https://doi.org/10.25326/76 (Bosser et al., 2020f), respectively.

Assessment of Arctic Cloud Cover Anomalies in Atmospheric Reanalysis Products Using Satellite Data

2016 ◽  
Vol 29 (17) ◽  
pp. 6065-6083 ◽  
Author(s):  
Yinghui Liu ◽  
Jeffrey R. Key
Keyword(s):  
Cloud Cover ◽  
Climate Models ◽  
Global Climate ◽  
Correlation Coefficients ◽  
Cloud Amount ◽  
Satellite Observation ◽  
Skill Score ◽  
Global Climate Models ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract Cloud cover is one of the largest uncertainties in model predictions of the future Arctic climate. Previous studies have shown that cloud amounts in global climate models and atmospheric reanalyses vary widely and may have large biases. However, many climate studies are based on anomalies rather than absolute values, for which biases are less important. This study examines the performance of five atmospheric reanalysis products—ERA-Interim, MERRA, MERRA-2, NCEP R1, and NCEP R2—in depicting monthly mean Arctic cloud amount anomalies against Moderate Resolution Imaging Spectroradiometer (MODIS) satellite observations from 2000 to 2014 and against Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) observations from 2006 to 2014. All five reanalysis products exhibit biases in the mean cloud amount, especially in winter. The Gerrity skill score (GSS) and correlation analysis are used to quantify their performance in terms of interannual variations. Results show that ERA-Interim, MERRA, MERRA-2, and NCEP R2 perform similarly, with annual mean GSSs of 0.36/0.22, 0.31/0.24, 0.32/0.23, and 0.32/0.23 and annual mean correlation coefficients of 0.50/0.51, 0.43/0.54, 0.44/0.53, and 0.50/0.52 against MODIS/CALIPSO, indicating that the reanalysis datasets do exhibit some capability for depicting the monthly mean cloud amount anomalies. There are no significant differences in the overall performance of reanalysis products. They all perform best in July, August, and September and worst in November, December, and January. All reanalysis datasets have better performance over land than over ocean. This study identifies the magnitudes of errors in Arctic mean cloud amounts and anomalies and provides a useful tool for evaluating future improvements in the cloud schemes of reanalysis products.

scholarly journals On the inclusion of GPS precipitable water vapour in the nowcasting of rainfall

2015 ◽  
Vol 3 (6) ◽  
pp. 3861-3895 ◽  
Author(s):  
P. Benevides ◽  
J. Catalao ◽  
P. M. A. Miranda
Keyword(s):  
Water Vapour ◽  
Meteorological Data ◽  
Simple Algorithm ◽  
Precipitable Water ◽  
False Alarms ◽  
Precipitable Water Vapour ◽  
Temporal Behaviour ◽  
Nearby Region ◽  
Single Station ◽  
Rain Events

Abstract. The temporal behaviour of Precipitable Water Vapour (PWV) retrieved from GPS delay data is analysed in a number of case studies of intense precipitation in the Lisbon area, in the period 2010–2012, and in a continuous annual cycle of 2012 observations. Such behaviour is found to correlate positively with the probability of precipitation, especially in cases of severe rainfall. The evolution of the GPS PWV in a few stations is analysed by a least-squares fitting of a broken line tendency, made by a temporal sequence of ascents and descents over the data. It is found that most severe rainfall event occurs in descending trends after a long ascending period, and that the most intense events occur after steep ascents in PWV. A simple algorithm, forecasting rain in the 6 h after a steep ascent of the GPS PWV in a single station is found to produce reasonable forecasts of the occurrence of precipitation in the nearby region, without significant misses in what concerns larger rain events, but with a substantial amount of false alarms. It is suggested that this method could be improved by the analysis of 2-D or 3-D time varying GPS PWV fields, or by its joint use with other meteorological data relevant to nowcast precipitation.

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