Total Precipitable Water Vapor Research Articles

Analysis of Precipitable Water Vapor, Liquid Water Path and Their Variations before Rainfall Event over Northeastern Tibetan Plateau

A ground-based microwave radiometer (MWR) provides continuous atmospheric profiles under various weather conditions. The change in total precipitable water vapor (PWV) and liquid water path (LWP) before rainfall events is particularly important for the improvement in the rainfall forecast. However, the analysis of the PWV and LWP before rainfall event on the plateau is especially worth exploring. In this study, the MWR installed at Xining, a city located over the northeastern Tibetan Plateau, was employed during September 2021 to August 2022. The results reveal that the MWR-retrieved temperature and vapor density demonstrate reliable accuracy, when compared with radiosonde observations; PWV and LWP values during the summer account for over 70% of the annual totals in the Xining area; both PWV and LWP at the initiating time of rainfall events are higher during summer, especially after sunset (during 20-00 local solar time); and notably, PWV and LWP anomalies are enhanced abruptly 8 and 28 min prior to the initiating time, respectively. Furthermore, the mean of LWP anomaly rises after the turning time (the moment rises abruptly) to the initiating time; as the intensity of rainfall events increases, the occurrence of the turning time is delayed, especially for PWV anomalies; while the occurrence of the turning time is similar for both convective cloud and stratiform cloud rainfall events, the PWV and LWP anomalies jump more the initiating time; as the intensity of rainfall events increases, the occurrence of the turning time is delayed, especially for PWV anomalies; while the occurrence of the turning time is similar for both convective cloud and stratiform cloud rainfall events, the PWV and LWP anomalies jump more dramatically after the turning time in convective cloud events. This study aims are to analyze the temporal characteristics of PWV and LWP, and assess their potential in predicting rainfall event.

Understanding flash flooding in the Himalayan Region: a case study

The Himalayan region, characterized by its substantial topographical scale and elevation, exhibits vulnerability to flash floods and landslides induced by natural and anthropogenic influences. The study focuses on the Himalayan region, emphasizing the pivotal role of geographical and atmospheric parameters in flash flood occurrences. Specifically, the investigation delves into the intricate interactions between atmospheric and surface parameters to elucidate their collective contribution to flash flooding within the Nainital region of Uttarakhand in the Himalayan terrain. Pre-flood parameters, including total aerosol optical depth, cloud cover thickness, and total precipitable water vapor, were systematically analyzed, revealing a noteworthy correlation with flash flooding event transpiring on October 17th, 18th, and 19th, 2021. Which resulted in a huge loss of life and property in the study area. Contrasting the October 2021 heavy rainfall with the time series data (2000–2021), the historical pattern indicates flash flooding predominantly during June to September. The rare occurrence of October flash flooding suggests a potential shift in the area's precipitation pattern, possibly influenced by climate change. Robust statistical analyses, specifically employing non-parametric tests including the Autocorrelation function (ACF), Mann–Kendall (MK) test, Modified Mann–Kendall, and Sen's slope (q) estimator, were applied to discern extreme precipitation characteristics from 2000 to 201. The findings revealed a general non-significant increasing trend, except for July, which exhibited a non-significant decreasing trend. Moreover, the results elucidate the application of Meteosat-8 data and remote sensing applications to analyze flash flood dynamics. Furthermore, the research extensively explores the substantial roles played by pre and post-atmospheric parameters with geographic parameters in heavy rainfall events that resulted flash flooding, presenting a comprehensive discussion. The findings describe the role of real time remote sensing and satellite and underscore the need for comprehensive approaches to tackle flash flooding, including mitigation. The study also highlights the significance of monitoring weather patterns and rainfall trends to improve disaster preparedness and minimize the impact of flash floods in the Himalayan region.

Comprehensive Evaluation and Comparison of AIRS, VASS, and VIRR Water Vapor Products Over Antarctica

AbstractThe detection capability of the water vapor mixing ratio (q) and total precipitable water vapor (PWV) products of the Atmospheric Infrared Sounder (AIRS), the Atmospheric Vertical Sounder System (VASS), and Visible and Infrared Radiometer (VIRR) in Antarctica is unclear due to the harsh environment and the scarcity of ground‐based stations. This study compares these products with data from nine radiosonde stations and 10 Global Navigation Satellite Systems Stations and evaluates them using the CCHZ‐DISO method. In the low atmosphere, the average wet bias of about 22% of VASS q is significantly greater than that of AIRS q (<15%), and the dry bias of AIRS is gradually corrected with increasing cloudiness, but the trend of exacerbated wet bias at 700 hPa requires attention. Meanwhile, VASS should optimize the inversion principle in the presence of clouds and refine the utilization weights of the channels heavily influenced by clouds under different cloud fraction conditions. Furthermore, when the atmospheric pressure is less than 500 hPa, VASS q exhibits a significant wet bias, and these data should be used cautiously. Both AIRS and VIRR detect PWV well in coastal areas, but VIRR PWV is significantly overestimated in the dry ice sheet plateau region. The CCHZ‐DISO value intuitively reveals that AIRS outperforms VASS and VIRR in both q and PWV detection. The findings of this study contribute to a more comprehensive understanding of the detection capability of the above satellite water vapor products to promote the wider application of satellite data in the Antarctic region.

A new approach to improve precipitable water vapour estimations of Sentinel-3A satellite data using LST, elevation and NDVI over Iran

ABSTRACT The total precipitable water vapour (TPW) extraction algorithm using the Sentinel-3A Ocean and Land Colour Instrument (OLCI) has the potential to be improved on a regional scale. The aim of this study was to improve the TPW extraction algorithm of OLCI for the first time using environmental variables, including the Normalized Difference Vegetation Index (NDVI), land surface temperature (LST), and elevation from mean sea level, on a regional scale over Iran. A previously developed TPW recovery algorithm was applied to the OLCI data during cloud-free days throughout a one-year period (1 January–29 December 2020). The artificial neural network (ANN) methodology was utilized in eight models. The evaluation results revealed the effectiveness of the models varied based on the topography and climate of each station. The assessment findings demonstrated that model 2, which integrated LST, elevation, and NDVI data in the ANN framework, outperformed other models across the study area.

Regional climate change and possible causes over the Three Gorges Reservoir Area

The Three Gorges Project, the largest hydroelectric project in the world, has attracted widespread attention regarding its impact on regional climate. However, existing studies on the climate effects of the Three Gorges Project construction are not sufficient due to limited data accumulation. In this study, we analyzed the annual and seasonal trend changes in temperature, precipitation, and humidity over the Three Gorges Reservoir Area (TGRA) based on long-term meteorological stations data, remote sensing data, and reanalysis products. Observation minus reanalysis method (OMR) was used to reveal possible impacts of land cover changes on climate changes. Major results indicated that the TGRA experienced an overall warming trend for both annual and seasonal variations, with greater rising trends in the upstream. Except for autumn, the relative humidity of most regions mainly showed significant downward trends, indicating an overall drying trend in the TGRA. There was insignificant change in total precipitation and precipitable water vapor, with the largest variation observed during the summer. Although there were small differences among these datasets, their results of climate changes showed good consistency overall. In addition, the results of OMR indicated that land cover changes mainly had a warming and drying effect on the middle and upper reaches, and a cooling and moistening effect on the lower reaches of the TGRA. This may be due to the impact of land cover changes on the surface energy balance, thus affected temperature and humidity. The study has important reference value for understanding the climate changes in the TGRA and the climate effects brought about by large-scale engineering construction.

Probing the Atmospheric Precipitable Water Vapor with SOFIA, Part. IV. Water Vapor Estimates from FORCAST Grism Spectra

SOFIA was an airborne observatory for far-infrared astronomy stationed at the Armstrong Flight Research Center in Palmdale, CA, USA. Although SOFIA flew at altitudes of ∼41,000 ft, any far-infrared observations from within the Earth’s atmosphere are nevertheless hampered by water vapor absorbing the astronomical signal. The primary atmospheric parameter governing absorption at far-infrared wavelengths is the total upward precipitable water vapor (PWV). In this paper we present a method of deriving PWV values directly from low resolution (R ∼ 100–200) mid-infrared (5–40 μm) spectroscopic observations and apply it to low resolution grism spectra obtained with the FORCAST instrument on-board SOFIA. We then compare these values with those determined from the fifth European Re-analysis (ERA5) of the global atmospheric parameters provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) for the time and altitude corresponding to the SOFIA observations. In general, we find a very good correlation between the ERA5-ECMWF values and the values derived from the FORCAST grism spectra, especially for high signal-to-noise ratio data. These results indicate that ERA5-ECMWF PWV values can be used to generate the telluric corrections for FORCAST imaging data as well as grism spectra for which the PWV values cannot be determined directly. We also derive the resolving power of the various grism and slit width combinations for FORCAST. Our results will be useful for reprocessing the FORCAST data in the SOFIA archive.

Understanding the impact of assimilating FORMOSAT‐7/COSMIC‐2 radio occultation refractivity on tropical cyclone genesis: Observing system simulation experiments using Hurricane Gordon (2006) as a case study

AbstractStudies have shown that assimilating the radio occultation (RO) observations, including those from the FORMOSAT‐3/COSMIC (constellation observing systems for meteorology, ionosphere, and climate) (FS3‐C), provides positive impacts on tropical cyclone (TC) forecasts. The FS3‐C's successor, the FORMOSAT‐7/COSMIC‐2 (FS7‐C2), provides denser spatial data coverage over the Tropics and Subtropics, where severe weather systems often occur. This study investigates the impact of FS7‐C2 refractivity profiles on the prediction of TC genesis. A quick observing system simulation experiment is conducted for the period when Hurricanes Helene and Gordon (2006) occurred over the North Atlantic Ocean using a regional ensemble data assimilation system. Though assimilating FS3‐C or FS7‐C2 ROs successfully reproduces Helene's development, assimilating FS7‐C2 ROs better captures the genesis and development of Gordon with abundant moisture and vorticity in Gordon's core region, providing conditions favorable for the development of deep convection. A minimum area‐mean total precipitable water vapor of 54 mm, as well as the existence of mid‐level cyclonic vorticity (e.g., 500 hPa), at the storm core region in the initial condition is required for forecasting Gordon's genesis. Also, the assimilation of FS7‐C2 ROs in our experiments reduces the 500 hPa geopotential error by 22% and improves probabilistic quantitative precipitation forecast compared with assimilating FS3‐C ROs. Two sensitivity tests are conducted to evaluate the impact of low‐level negatively biased FS7‐C2 RO profiles and the removal of FS7‐C2 data below 3 km on Gordon's genesis. The former test does not degrade Gordon's genesis forecast skills due to a dipole error correlation between the background ROs and the moisture field over an observed RO profile near Gordon. The latter test does degrade Gordon's forecast skills but is still better than the assimilation of FS3‐C ROs since the features of low‐level moisture and mid‐level vorticity are preserved to some extent.

A New Bottom-of-Atmosphere (BOA) Radiance-Based Hybrid Method for Estimating Clear-Sky Surface Longwave Upwelling Radiation From MODIS Data

Surface longwave upwelling radiation (SLUR) is a critical parameter for studying the water-energy balance between the land surface and atmosphere. In this study, we proposed a new hybrid method for estimating the clear-sky SLUR from the bottom-of-atmosphere (BOA) radiance (new BOA-hybrid method) of the Moderate Resolution Imaging Spectroradiometer (MODIS). There are two key parts in the developed new BOA-hybrid method. First, a physical four-channel algorithm was developed to estimate the atmospheric terms (atmospheric upwelling radiance and transmittance), which were used to calculate MODIS BOA radiance. Second, a linear model linking SLUR and MODIS BOA channel radiances was constructed using a large number of samples generated by extensive radiative transfer simulations. In situ measurements from twenty-seven sites in four flux networks were collected to validate the new BOA-hybrid method. The average bias and RMSE were 1.65 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and 16.23 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> for the new BOA-hybrid method, which was superior to the original BOA-hybrid method whose bias and RMSE were 3.52 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and 18.51 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and slightly better than the classical hybrid method that estimates SLUR using the top-of-atmosphere (TOA) radiance (TOA-hybrid method) whose bias and RMSE were -1.64 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and 17.44 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , respectively. The performance of the new and original BOA-hybrid methods was similar when the total precipitable water vapor (TPW) was less than 1.5 cm. However, under a large TPW (i.e., TPW>3 cm), especially in the case of a large viewing zenith angle (i.e., VZA>45°), the accuracy of the original BOA-hybrid method decreased significantly, while the physical four-channel algorithm could effectively improve the accuracy of atmospheric correction and the RMSE of the SLUR estimated by BOA-hybrid could be reduced by more than 10 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . In summary, the developed new BOA-hybrid method can estimate SLUR accurately and has great potential to produce a long-term high spatial resolution environmental data record of SLUR.

New Models for Vertical Distribution and Variation of Tropospheric Water Vapor—A Case Study for China

For better modeling the vertical distribution and variations of water vapor, a 10-year time-series of a newly derived water vapor parameter (termed IRPWV), defined as the ratio of water vapor density (WVD) to the total precipitable water vapor (TPWV), was statistically analyzed. This research showed that the vertical distribution of IRPWV presents a periodic pattern and is highly correlated with the relative magnitude of its corresponding TPWV when compared with the other TPWVs in the same time range. Six TPWV ranges were first chosen to determine the relative magnitude and then used to classify the IRPWV vertical distributions of TPWV. For the periodic variations in each of the six classified IRPWV vertical distribution time-series, a temporal IRPWV model was developed accordingly with six sets of coefficients. The new models were validated by comparing their predictions against the reference values from sounding data at 12 radiosonde stations in China, and their performance was also evaluated against that of the commonly used exponential model. Results showed that, first, the proportions of the height range that had reduced annual root mean square error (RMSE) of WVD in all height ranges within all TPWV ranges were over 75% at the 12 stations. Then, the annual RMSEs of the WVD for all the stations were reduced by at least 11%, 20%, 43%, 48%, 40%, 38%, 32%, 35%, 32%, and 28% in each of the 10 selected height ranges, respectively.

Probing the Atmospheric Precipitable Water Vapor with SOFIA, Part III. Atlas of Seasonal Median PWV Maps from ERA5, Implications for FIFI-LS and in situ Comparison Between the ERA5 and MERRA-2 Atmospheric Re-analyses

The Stratospheric Observatory for Infrared Astronomy (SOFIA) is an airborne observatory for far-infrared astronomy stationed at the Armstrong Flight Research Center (AFRC) in Palmdale, CA, USA. Although SOFIA flies at altitudes of ∼41,000 ft, any far-infrared observations from within the Earth’s atmosphere are nevertheless hampered by water vapor absorbing the astronomical signal. The primary atmospheric parameter governing absorption in the far-infrared is the total upward precipitable water vapor, PWV. In this paper we present global PWV maps derived from re-analyses from the European Centre for Medium-Range Weather Forecasts, ECMWF, with a geographical resolution of 0.°5, for flight altitudes ranging from 37,000 ft to 45,000 ft and each meteorological season. These maps were validated with FIFI-LS PWV measurements on board SOFIA and allow an investigation of the global morphology and seasonal dependence of the total upward PWV in the stratosphere. We additionally investigate the observing conditions, in terms of PWV, at various locations, especially around SOFIA’s home base, Palmdale, but also around sites in the southern hemisphere like Tahiti, Santiago de Chile (Chile), Buenos Aires (Argentina), and Christchurch (New Zealand). From the southern sites investigated Christchurch provides the best conditions in terms of PWV (and efficiency), Tahiti the worst. Using total power sky measurements with FIFI-LS we also derive a mean emissivity of the telescope (primary, secondary and tertiary mirror) of ϵ Tel = 20.5 ± 1.6% around the astronomically significant [C ii] emission line. We finally compare atmospheric re-analyses from GEOS (MERRA-2) and ECMWF (ERA5) to our FIFI-LS PWV measurements. Both re-analyses correlate linearly with our FIFI-LS PWV measurements from all flight altitudes but with different scaling factors. However, MERRA-2 correlates significantly less well than ERA5 especially for flight altitudes below 41,000 ft.

A Rainfall Forecast Model Based on GNSS Tropospheric Parameters and BP-NN Algorithm

The occurrence of rainfall is the result of a combination of various meteorological factors. Traditional rainfall early warning models solely use Global Navigation Satellite System (GNSS)-derived Zenith Total Delay (ZTD) or Precipitable Water Vapor (PWV) to forecast rainfall, resulting in a low true detected rate. While non-linear rainfall early warning models based on the Back-Propagation Neural Network (BP-NN) algorithm consider the influences of various meteorological factors, the forecasts often exhibit a high false rate. To further improve the prediction of rainfall, a short-term rainfall early warning model based on the GNSS and BP-NN algorithms is proposed in this study. The method uses the traditional rainfall forecasting model and utilizes the BP-NN algorithm to combine various meteorological factors for rainfall early warning. The results of GNSS and BP-NN together improve the precision of rainfall early warning. Observation data from eight GNSS stations, the fifth-generation reanalysis of European Centre for Medium-Range Weather Forecast (ECMWF ERA5), and temperature, pressure, and rainfall data from corresponding meteorological stations in Ningbo, China were utilized to verify the rainfall early warning model proposed in this study. The results show that the proposed model can complement the advantages of the traditional linear and non-linear rainfall early warning methods. The model can maintain a high True Detected Rate (TDR) of rainfall early warning while simultaneously reducing the False Forecasted Rate (FFR). The average TDR of the eight GNSS stations is 100% and the FFR is 20.75%, which are both better than those of existing traditional linear and non-linear rainfall early warning models.

Assimilation of GNSS and Synoptic Data in a Convection Permitting Limited Area Model: Improvement of Simulated Tropospheric Water Vapor Content

The assimilation of observations in limited area models (LAMs) allows to find the best possible estimate of a region’s meteorological state. Water vapor is a crucial constituent in terms of cloud and precipitation formation. Its highly variable nature in space and time is often insufficiently represented in models. This study investigates the improvement of simulated water vapor content within the Weather Research and Forecasting model (WRF) in every season by assimilating temperature, relative humidity, and surface pressure obtained from climate stations, as well as geodetically derived Zenith Total Delay (ZTD) and precipitable water vapor (PWV) data from global navigation satellite system (GNSS) ground stations. In four case studies we analyze the results of high-resolution convection-resolving WRF simulations (2.1 km) between 2016 and 2018 each in every season for a 650 × 670 km domain in the tri-border-area Germany, France and Switzerland. The impact of 3D VAR assimilation of different variables and combinations thereof, background error option, as well as the temporal and spatial resolution of assimilation is evaluated. Both column values and profiles derived from radiosondes are addressed. Best outcome was achieved when assimilating ZTD and synoptic data at an hourly resolution and a spatial thinning distance of 10 km. It is concluded that the careful selection of assimilation options can additionally improve simulation results in every season. Clear effects of assimilation on the water budgets can also be seen.

Improving precipitable water vapor estimations of the Moderate Resolution Imaging Spectroradiometer (MODIS) using metaheuristic algorithms

Extracting Total Precipitable Water Vapor (TPW) and its variations from the Moderate Resolution Imaging Spectroradiometer (MODIS) data on a local scale is challenging. Meta-heuristic algorithms have a high ability to solve complex engineering problems of hydrology and climatic studies. Hence, the present study extracts TPW values using meta-heuristic algorithms for MODIS surface reflectance data. In this regard, radiosonde observations of six stations in the western part of Iran during 2019 and 2020 were used as the ground truth data for training algorithms and evaluating the results. The results were also validated with Global Positioning System (GPS) TPW data of Tehran-Mehrabad station. Moreover, MODIS surface reflectance (MOD021) and cloud mask (MOD35) products were prepared to distinguish cloudy days from non-cloudy, along with MODIS TPW (MOD05) for corresponding dates. Meta-heuristic algorithms, including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Harmony Search (HS), Invasive Weed Optimization (IWO), Gray Wolf Optimization (GWO), and Grasshopper Optimization Algorithm (GOA), were applied to improve the performance of MOD05 algorithm. The results showed the proper performance of all the selected algorithms. Nevertheless, the GOA algorithm acquired the best result. The Root Mean Square Error (RMSE) and R values obtained from comparing GOA results with radiosonde data were 2.96 mm and 0.82. Those values for comparing GOA results with GPS data were 2.94 mm and 0.83, respectively. However, MODIS TPW, RMSE, and R showed 6.53 mm and 0.69, respectively, compared to radiosonde data and 6.55 mm and 0.68 compared to GPS data. Overall, the results proved the proper performance of the used meta-heuristic algorithms in extracting TPW from MODIS measurements on a local scale.

A New Cumulative Anomaly-Based Model for the Detection of Heavy Precipitation Using GNSS-Derived Tropospheric Products

In recent years, tropospheric products obtained from ground-based global navigation satellite system (GNSS) measurements, especially the zenith total delay (ZTD) and precipitable water vapor (PWV) estimates, have advanced their usages in meteorological applications such as the detection of precipitation events. Generally, a cumulative anomaly (CA) time series of any atmospheric variable, which represents the long-term departure of the variable from its “normal” cycle, is widely used for quantitatively estimating the variable’s variations in response to a weather event. In this study, a new cumulative anomaly-based model (NCAM) containing 14 variables, including not only PWV and ZTD values but also their respective six types of derivatives, for detecting heavy precipitation was developed. The 6-h CA time series of the variables were calculated based on the data of hourly precipitation records and time series of ZTD and PWV collected at the co-located HKSC–King’s Park (KP) stations over the eight-year period 2010–2017. The model was evaluated using the 14 variables’ CA time series to detect heavy precipitation events happened in the summer months over the period 2018–2019, and precipitation records in the same period were used as the reference. Results demonstrated that 99.1% of heavy precipitation was correctly detected by the NCAM with a lead time of 2.87 h, and the false alarm ratio (FAR) score resulting from the model was reduced to 22.4%. In addition, two case studies were also conducted to verify the effectiveness of the NCAM. These results all provide a promising direction for the application of using the CA time series of GNSS tropospheric products to the detection of heavy precipitation events.

Improving AMSR2 total precipitable water vapour model using metaheuristic algorithms

ABSTRACT Estimating total precipitable water vapour (TPWV) from advanced microwave scanning radiometer 2 (AMSR2) measurements with reasonable accuracy is challenging. Therefore, this study aims to improve the AMSR2 TPWV algorithm using metaheuristic algorithms for the first time. Radiosonde observations of six stations in the western part of Iran during 2020 were used as reference data. Moderate Resolution Imaging Spectroradiometer (MODIS) products, including land surface temperature and cloud mask, were used in the TPWV extracting algorithm and in selecting clear-sky days, respectively. Five metaheuristic methods – genetic algorithm (GA), particle swarm optimization (PSO), grey wolf optimizer (GWO), whale optimization algorithm (WOA), and bat algorithm (BA) – were used, due to the ability of metaheuristic methods in modelling complicated issues such as AMSR2 TPWV estimation. The WOA, GWO, and PSO showed the best performance. Therefore, metaheuristic algorithms can be used to improve the AMSR2 TPWV algorithm with reasonable performance on a local scale.

Evaluation of MODIS Water Vapour Products over ALGERIA using Radiosonde Data

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.

A neural network-based approach for the detection of heavy precipitation using GNSS observations and surface meteorological data

Recent years have witnessed a growing interest in using GNSS observations to detect heavy precipitation. In this study, a neural network-based (NN-based) approach taking seven meteorological variables as input data was developed based on the back propagation (BP) algorithm for detecting heavy precipitation. Apart from the surface meteorological variables of temperature, pressure and relative humidity, the model has also adopted other information such as day-of-year, hour-of-day and GNSS-derived zenith total delay and precipitable water vapor (PWV) as input variables. The feasibility of using these variables for developing the BP-NN-based model was elaborated by conducting the feature analysis of the seven input variables. In addition, the criterion for selecting a proper size of training sample was also briefly investigated by studying the impact and sensibility of the sample lengths in the model. The proposed model was developed using a sample size of an 8-year (2010–2017) period in the summer at a pair of co-located GNSS/weather stations−HKSC-KP in Hong Kong. The use of a long-term data is to “reliably” capture the characteristics of the selected variables. The detection results for the summer months in 2018 and 2019 were then compared against corresponding precipitation records to valid the effectiveness of the newly proposed model. Results of the correct detection and false alarm rates were 94.5 % and 20.8 %, respectively, which were significant improvements compared with the existing models.

Comparison of GNSS and INSAT-3D sounder retrieved precipitable water vapour and validation with the GPS Sonde data over Indian Subcontinent

Precipitable water vapour (PWV) plays a key role in the atmospheric processes from climate change to micrometeorology. Its distribution and quantity are critical for the description of state and evaluation of the atmosphere in NWP model. Lack of precise and continuous water vapour data is one of the major error sources in short term forecast of precipitation. The task of accurately measuring atmospheric water vapour is challenging. Conventional in situ measurements of atmospheric water vapour is provided by GPS Sonde humidity sensors profile twice a day at 0000 and 1200 UTC mainly from limited land regions. In recent years India Meteorological Department (IMD) is computing PWV from 19 channel sounder of INSAT-3D in three layers 1000-900 hPa, 900-700 hPa and 700-300 hPa and total PWV in the vertical column of atmosphere stretching from surface to about 100 hPa under cloud free condition. These data most commonly were validated using spatially and temporally collocated GPS Sonde measurements. In this paper, INSAT-3D satellite retrieved PWV data are validated with column integrated PWV estimates from a network of ground based Global Navigation Satellite System (GNSS) over Indian subcontinent. The PWV retrieved by INSAT-3D sounder platform is very promising, being in a good agreement with the GNSS data recorded over India for the period June, 2017 to May, 2018. The root-mean-square (rms) differences of 5.4 to 7.1 mm, bias of -4.7 to +2.1 mm and correlations coefficient of 0.79 to 0.92 was observed between INSAT-3D and GNSS PWV. The correlations coefficient between GPS Sonde and GNSS derived PWV ranges from 0.85 to 0.98.

The Moving Speed of Typhoons of Recent Years (2018-2020) and Changes in Total Precipitable Water Vapor Around the Korean Peninsula

The Moving Speed of Typhoons of Recent Years (2018-2020) and Changes in Total Precipitable Water Vapor Around the Korean Peninsula

Retrieval of total columnar precipitable water vapour using radio occultation technique over the Indian region

Total column precipitable water vapour (PWV) is retrieved using radio occultation retrievals of water vapour from COSMIC satellites over four regions of India. This analysis is carried out to assess the accuracy of the radio occultation technique for estimating PWV and to observe the PWV variation over these regions. The impact points of radio occultation are investigated for one year to select those points which are very near to the surface for the four regions. Total column PWV is estimated using these points data and further compared to the PWV values obtained from AIRS instrument and reanalysis data from MERRA2. The findings obtained using the radio occultation technique show that the vertical profile of PWV from COSMIC provides almost similar results at different pressure levels with AIRS and MERRA2 PWVs. The PWV values obtained from COSMIC are 16 mm, 17 mm, 27 mm, and 52 mm approximately for region1, region2, region3, and region4 respectively having differences in the range of 0.2 mm–7 mm with respect to PWV values estimated from AIRS and MERRA2. On the surface level, the average difference is estimated to be less than 1 mm. It is inferred that a good estimation of total column PWV is obtained using the radio occultation technique which can be further used for rainfall prediction, its impact on climate change and to study hydrological cycle more efficiently.