scholarly journals Impact study of integrated precipitable water estimated from Indian GPS measurements

MAUSAM ◽  
2021 ◽  
Vol 65 (4) ◽  
Author(s):  
SURYAK DUTTA ◽  
V.S. PRASAD ◽  
D. RAJAN

The Global Positioning System – Integrated Precipitable Water (IPW) data from Indian stations namely Chennai, Guwahati, Kolkata, Mumbai and New Delhi have been assimilated in the National Centre for Medium Range Weather Forecasting’s (NCMRWF) Global Data Assimilation System (GDAS). Gridpoint Statistical Interpolation (GSI) Scheme of GDAS analysis is experimented with the global model T254L64. The analyses and forecasts are carried out at triangular truncation of wave number 254 and with 64 levels in vertical. Global analyses are carried four times (0000 UTC, 0600 UTC, 1200 UTC and 1800 UTC) daily with intermittent time scheme. Model integrations are carried up to 168 hours. The present study examines the impact that integrated precipitable water has over various meteorological parameters. The study reveals that the assimilation of IPW data influences the analyses and corresponding forecasts of the weather model T254L64. This is an attempt of assimilation of IPW data of the aforesaid five Indian stations in the global model and examination of corresponding impact on various meteorological parameters over Indian region. It is seen that for the layers above 750 hPa the zonal and meridional wind components for IPW analyses have less biases. Forecasts from IPW simulations are found to have consistently by lower 850 hPa wind vector root mean square error (RMSE) where as at 250 hPa, improvement in IPW runs are seen only for day-1 and day-4 forecasts. For temperature at 850 hPa, IPW forecasts valid for day-4 & day-5 are better. At 250 hPa, temperature RMSE for IPW runs is lower for day-1 forecasts. Mean error of IPW forecasts at 250 hPa is lower for all the days of forecasts. Also, geo-potential RMSE for the IPW runs at 250 hPa is lower for all the days of forecasts. Forecasts vs analyses study shows positive impact of IPW assimilation on the anomaly and pattern correlations.

2021 ◽  
pp. 71-71
Author(s):  
Caner Taniş ◽  
Kadir Karakaya

Background/aim: Air pollution is having a positive impact on the spread of the SARS-COV-2 virus. The effects of meteorological parameters on the spread of SARS-COV-2 are a matter of curiosity. The main purpose of this paper is to determine the association between air quality indexes (PM2.5, PM10, NO2, SO2, CO, and O3) and weather parameters (temperature, humidity, pressure, dew, wind speed) with the number of SARS-COV-2 cases, hospitalizations, hospital discharges. In this paper, we also focused on determining the impact of air pollution and weather parameters on the number of daily hospitalizations and daily discharges. Materials and methods: It is gleaned daily cases, hospitalizations, hospital discharges, meteorological, and air quality data in Istanbul from Turkey between July 15, 2020, and September 30, 2020. We performed the Pearson correlation analysis to evaluate the effects of meteorological parameters and air quality indexes on the variables related to SARS-COV-2. Results: It is determined a statistically significant positive relationship between air quality indexes such as CO, SO2, PM2.5, PM10, NO2, and the number of daily confirmed SARS-COV-2 cases. We also observed a negative association between weather parameters such as temperature and pressure and the number of daily confirmed SARS-COV-2 cases. Conclusion: Our study proposes that high air quality could reduce the number of SARS-COV-2 cases. The empirical findings of this paper might provide key input to prevent the spread of SARS-COV-2 across Turkey.


2008 ◽  
Vol 136 (7) ◽  
pp. 2727-2746 ◽  
Author(s):  
S. R. Macpherson ◽  
G. Deblonde ◽  
J. M. Aparicio ◽  
B. Casati

Abstract Half-hourly GPS zenith tropospheric delay (ZTD) and collocated surface weather observations of pressure, temperature, and relative humidity are available in near–real time from the NOAA Global Systems Division (GSD) research GPS receiver network. These observations, located primarily over the continental United States, are assimilated in a research version of the Environment Canada (EC) regional (North America) analysis and forecast system. The impact of the assimilation on regional analyses and 0–48-h forecasts is evaluated for two periods: summer 2004 and winter 2004/05. Forecasts are verified against radiosonde, rain gauge, and NOAA GPS network observations. The impacts of GPS ZTD and collocated surface weather observations for the summer period are generally positive, and include reductions in forecast errors for precipitable water, surface pressure, and geopotential height. It is shown that the ZTD data are primarily responsible for these forecast error reductions. The impact on precipitation forecasts is mixed, but more positive than negative, especially for the central U.S. region and for forecasts of larger precipitation amounts. Assimilation of the collocated surface weather data with ZTD contributes to the positive impact on precipitation forecasts for the central U.S. region. The NOAA GPS network data also have a positive impact on tropical storm system forecasts over the southeast United States, in terms of both storm track and precipitation. Impacts for the winter case are generally smaller because of the lower precipitable water (PW) over North America, but some positive impacts are observed for precipitation forecasts. The greatest regional impacts in the winter case are observed for the southeast U.S. (the Gulf) region where average PW is highest.


2020 ◽  
Author(s):  
Marcelo C. Santos ◽  
Marlon Moura ◽  
Thalia Nikolaidou ◽  
Kyriakos Balidakis

<p>The World Meteorological Organization (WMO) recommends the use of climate normals for dealing with the analysis of variations and trends of the meteorological parameters or be used as input to predictive climate models. The suggested period is 30 years, but shorter periods can also be employed. We computed zenith total delay (ZTD) and zenith wet delay (ZWD) series for each node of NCEP1 numerical weather model, starting in 1948. We computed climate normals of those two parameters using periods of 1, 5, 10, 15, 20 and 30 years, with and without the annual signature. To assess window size impact, we looked at variations and correlation of trends derived from the various solutions. Results shows the obvious better smoothing using larger windows and the decrease of the impact of annual signature. Regions with positive trends appear to be concentrated in continental masses and the equator line, and the most significant negative trends are in the oceans. ZTD increase is caused primarily by an increase in ZWD and is an indication of variations in ZWD variables. In the case of water vapor, such an increase in ZWD shows us a probable increase in the amount of water vapor in the atmosphere. Comparisons with trends computed from GNSS-derived ZTD and ZWD series are included with the caveat that time period for such comparisons must be shorter.</p>


2016 ◽  
Vol 34 (1) ◽  
pp. 123-132 ◽  
Author(s):  
K. Niranjan Kumar ◽  
D. V. Phanikumar ◽  
T. B. M. J. Ouarda ◽  
M. Rajeevan ◽  
M. Naja ◽  
...  

Abstract. This study examines the link between upper-tropospheric planetary-scale Rossby waves and surface meteorological parameters based on the observations made in association with the Ganges Valley Aerosol Experiment (GVAX) campaign at an extratropical site at Aryabhatta Research Institute of Observational Sciences, Nainital (29.45° N, 79.5° E) during November–December 2011. The spectral analysis of the tropospheric wind field from radiosonde measurements indicates a predominance power of around 8 days in the upper troposphere during the observational period. An analysis of the 200 hPa meridional wind (v200 hPa) anomalies from the Modern-Era Retrospective Analysis for Research and Applications (MERRA) reanalysis shows distinct Rossby-wave-like structures over a high-altitude site in the central Himalayan region. Furthermore, the spectral analysis of global v200 hPa anomalies indicates the Rossby waves are characterized by zonal wave number 6. The amplification of the Rossby wave packets over the site leads to persistent subtropical jet stream (STJ) patterns, which further affects the surface weather conditions. The propagating Rossby waves in the upper troposphere along with the undulations in the STJ create convergence and divergence regions in the mid-troposphere. Therefore, the surface meteorological parameters such as the relative humidity, wind speeds, and temperature are synchronized with the phase of the propagating Rossby waves. Moreover, the present study finds important implications for medium-range forecasting through the upper-level Rossby waves over the study region.


2018 ◽  
Author(s):  
Witold Rohm ◽  
Jakub Guzikowski ◽  
Karina Wilgan ◽  
Maciej Kryza

Abstract. The GNSS data assimilation is currently widely discussed in the literature with respect to the various applications in meteorology and numerical weather models. Data assimilation combines atmospheric measurements with knowledge of atmospheric behavior as codified in computer models. With this approach, the best estimate of current conditions consistent with both information sources is produced. Some approaches allow assimilating also the non-prognostic variables, including remote sensing data from radar or GNSS (Global Navigation Satellite System). These techniques are named variational data assimilation schemes and are based on a minimization of the cost function, which contains the differences between the model state (background) and the observations. This paper shows the results of assimilation of GNSS data into numerical weather prediction (NWP) model WRF (Weather Research and Forecasting). The WRF model offers two different variational approaches: 3DVAR and 4DVAR, both available through WRF Data Assimilation (WRFDA) package. The WRFDA assimilation procedure was modified to correct for bias and observation errors. We assimilated the Zenith Troposphere Delay (ZTD), Precipitable Water (PW), radiosonde (RS) and surface synoptic observations (SYNOP) using 4DVAR assimilation scheme. Three experiments have been performed: (1) assimilation of PW and ZTD for May and June of 2013, (2) assimilation of: PW alone; PW, with RS and SYNOP; ZTD alone; and finally ZTD, with RS and SYNOP for 5–23 May 2013, and (3) assimilation of PW or ZTD during severe weather events in June 2013. Once the initial conditions were established, the forecast was run for 48 hours. The obtained WRF predictions are validated against surface meteorological measurements, including air temperature, humidity, wind speed, and rainfall rate. Results from the first experiment (May and June 2013) show that the assimilation of GNSS data (both ZTD and PW) have positive impact on the rain and humidity forecast. However, the assimilation of ZTD is more successful, and brings substantial reduction of errors in rain forecast by 8 %, and a 20 % improvement in bias of humidity forecast, but it has a slight negative impact on temperature bias and wind speed. Second experiment (5–23 May 2013) reveals that the PW or ZTD assimilation leads to a similar reduction of errors as in the first experiment, moreover, adding SYNOP and RS observations to the assimilation does not improve the humidity or rain forecasts (in the 48 h forecast) but reduces errors in the wind speed and temperature. Furthermore, short term predictions (up to 24 h) of rain and humidity are better when SYNOP and RS observations are assimilated. The impact of assimilation of ZTD and PW in severe weather cases is mixed, one out of three investigated cases shows positive impact of GNSS data, whereas other two neutral or negative.


2016 ◽  
Vol 29 (14) ◽  
pp. 5061-5081 ◽  
Author(s):  
Ambroise Dufour ◽  
Olga Zolina ◽  
Sergey K. Gulev

Abstract The atmospheric water cycle of the Arctic is evaluated via seven global reanalyses and in radiosonde observations covering the 1979–2013 period. In the regional moisture budget, evaporation and precipitation are the least consistent terms among different datasets. Despite the assimilation of radiosoundings, the reanalyses present a tendency to overestimate the moisture transport. Aside from this overestimation, the reanalyses exhibit a remarkable agreement with the radiosondes in terms of spatial and temporal patterns. The northern North Atlantic, subpolar North Pacific, and Labrador Sea stand out as the main gateways for moisture to the Arctic in all reanalyses. Because these regions correspond to the end of the storm tracks, the link between moisture transports and extratropical cyclones is further investigated by decomposing the moisture fluxes in the mean flow and transient eddy parts. In all reanalyses, the former term tends to cancel out when averaged over a latitude circle, leaving the latter to provide the bulk of the midlatitude moisture imports (89%–94% at 70°N). Although the Arctic warms faster than the rest of the world, the impact of these changes on its water cycle remains ambiguous. In most datasets, evaporation, precipitation, and precipitable water increase in line with what is expected from a warming signal. At the same time, the moisture transports have decreased in all the reanalyses but not in the radiosonde observations, though none of these trends is statistically significant. The fluxes do not scale with the Clausius–Clapeyron relation because the increasing humidity is not correlated with the meridional wind, particularly near the surface.


2008 ◽  
Vol 136 (9) ◽  
pp. 3608-3628 ◽  
Author(s):  
Shu-Hua Chen ◽  
Zhan Zhao ◽  
Jennifer S. Haase ◽  
Aidong Chen ◽  
Francois Vandenberghe

Abstract This study determined the accuracy and biases associated with retrieved Moderate Resolution Imaging Spectroradiometer (MODIS) total precipitable water (TPW) data, and it investigated the impact of these data on severe weather simulations using the Weather Research and Forecast (WRF) model. Comparisons of MODIS TPW with the global positioning system (GPS) TPW and radiosonde-derived TPW were carried out. The comparison with GPS TPW over the United States showed that the root-mean-square (RMS) differences between these two datasets were about 5.2 and 3.3 mm for infrared (IR) and near-infrared (nIR) TPW, respectively. MODIS IR TPW data were overestimated in a dry atmosphere but underestimated in a moist atmosphere, whereas the nIR values were slightly underestimated in a dry atmosphere but overestimated in a moist atmosphere. Two cases, a severe thunderstorm system (2004) over land and Hurricane Isidore (2002) over ocean, as well as conventional observations and Special Sensor Microwave Imager (SSM/I) retrievals were used to assess the impact of MODIS nIR TPW data on severe weather simulations. The assimilation of MODIS data has a slightly positive impact on the simulated rainfall over Oklahoma for the thunderstorm case, and it was able to enhance Isidore’s intensity when the storm track was reasonably simulated. The use of original and bias-corrected MODIS nIR TPW did not show significant differences from both case studies. In addition, SSM/I data were found to have a positive impact on both severe weather simulations, and the impact was comparable to or slightly better than that of MODIS data.


2021 ◽  
Vol 8 ◽  
Author(s):  
Babette C. Tchonang ◽  
Mounir Benkiran ◽  
Pierre-Yves Le Traon ◽  
Simon Jan van Gennip ◽  
Jean Michel Lellouche ◽  
...  

A first attempt was made to quantify the impact of the assimilation of Surface Water Ocean Topography (SWOT) swath altimeter data in a global 1/12° high resolution analysis and forecasting system through a series of Observing System Simulation Experiments (OSSEs). The OSSE framework (Nature Run and Free Run) and data assimilation scheme have been described in detail in a companion article (Benkiran et al., 2021). The impact of assimilating data from SWOT and three nadir altimeters was quantified by estimating analysis and forecast error variances for sea surface height (SSH), temperature, salinity, zonal, and meridional velocities. Wave-number spectra and coherence analyses of SSH errors were also computed. SWOT data will significantly improve the quality of ocean analyses and forecasts. Adding SWOT observations to those of three nadir altimeters globally reduces the variance of SSH and surface velocities in analyses and forecasts by about 30 and 20%, respectively. Improvements are greater in high-latitude regions where space/time coverage of SWOT is much denser. The combination of SWOT data with data from three nadir altimeters provides a better resolution of wavelengths between 50 and 200 km with a more than 40% improvement outside tropical regions with respect to data from three nadir altimeters alone. The study has also highlighted that the impact of using SWOT data is likely to be very different depending on geographical areas. Constraining smaller spatial scales (wavelengths below 100 km) remains challenging as they are also associated with small time scales. Although this is only a first step, the study has demonstrated that SWOT data could be readily assimilated in a global high-resolution analysis and forecasting system with a positive impact at all latitudes and outstanding performances.


2009 ◽  
Vol 3 (1) ◽  
pp. 104-123 ◽  
Author(s):  
Surya K. Dutta ◽  
Someshwar Das ◽  
S.C. Kar ◽  
U.C. Mohanty ◽  
P.C. Joshi

A global model (T80L18; Triangular Truncation at wave number 80 with 18 vertical layers) and a mesoscale model MM5 (nested at 90 and 30 km resolutions) are integrated for 5 monsoon years 1998-2002. The impact of dynamical downscaling from global to mesoscale in the simulations of Indian summer monsoon rainfall is studied. Comparisons between the global and the mesoscale models show that, though the global model has an edge over the mesoscale model in simulating the all-India mean rainfall closer to the observation, the T80L18 model lacks in simulating the spatial variations in rainfall. The effect of downscaling is better represented in the rainfall variations produced by MM5 both quantitatively and qualitatively over the foothills of the Himalayas and along Nepal to North-eastern India. It is also seen that the mesoscale model is able to represent the dispersion (standard deviation) present in the observed rainfall over India. In the five monsoon seasons, RMSE of mean rainfall (monthly and seasonal) of T80L18 forecasts are mostly lower than that of MM5 forecasts. However, synoptic features like the Somali Jet and Tibetan anticyclone are better represented by MM5. This model has also simulated the regions of convection better than the T80L18 model. However, the MM5 simulations produced an anomalous circulation over the Saudi Arabian region (15-200 N and 45-500 E) in many cases. The mesoscale model simulates better wind fields than the global model in general. Over peninsular India T80L18 model showed higher temperature gradient but, over Central India this model has better temperature field as compared to MM5. Over southern and north-eastern India, the temperature field of T80L18 and MM5 are very similar.


2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Zahin Ansari ◽  
Syed Hameedur Rahman Zaini ◽  
Monizah Parwez ◽  
Asif Akhtar

Purpose The outbreak of the new coronavirus has caused tremendous concerns to public health, which are impacting human lives both physically and psychologically. The rise in coronavirus cases has led to the propagation of control measures for its prevention. This study aims to investigate the factors enhancing the coronavirus preventive behavior among the respondents. Design/methodology/approach To understand the coronavirus preventive behavior, the study is based on the value–belief–norm (VBN) theory. Data for the study has been collected through a survey of 319 respondents in New Delhi, India. The study uses structural equation modeling (SEM) to understand the factors impacting preventive behavior. For analysis, the study uses SEM to examine direct and indirect relationships and Hayes’ PROCESS macro SPSS module for moderating effects. Findings The results show that egoistic values have a negative impact on belief while altruistic values have a positive impact on the belief about the coronavirus outbreak. Belief is recorded to have a positive and significant impact on preventive behavior. Also, personal norms positively mediate the relationship between belief and preventive behavior. Additionally, the impact of awareness of preventive behavior is positively moderated by the symptomatic profile. Furthermore, the interaction effect is found to be conditioned positively with age and level of education. Originality/value To the best of the authors’ knowledge, no other work in the existing literature was found to apply the VBN theory to determine coronavirus preventive behavior. Further, the extensive moderation analysis done in this study is expected to be a significant contribution to the literature.


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