Short-range Weather Forecasts Research Articles

Impact of different land use data on WRF model short range forecasts during pre-monsoon and monsoon seasons in India

This study focuses to analyse the impact of land use changes on short range weather forecasts over Indian region. Weather Research and Forecasting (WRF) model simulation experiments are conducted by using land use data from Moderate Resolution Imaging Spectroradiometer (MODIS) and Indian Space Research Organization (ISRO) satellites for pre-monsoon and monsoon season. MODIS 2001 land use is used in control (CNT) experiment and updated land use with recent urban class from MODIS (EXP1) and ISRO (EXP2) for the year 2019 is used to generate model lower boundary conditions in other two experiments. Quantitative error measures and skill score computed for rainfall forecast showed that model skill is better with the use of realistic recent land use data from MODIS and ISRO during pre-monsoon and monsoon period. Extreme Dependency Index score computed also revealed that model skill in predicting extreme rare rainfall events is improved with recent landuse data. Model simulated surface meteorological variables and profiles at lower levels also found to be improved with the inclusion of realistic land use class from MODIS and ISRO. Between the two experiments, the one which used ISRO based land use showed larger improvement particularly during the monsoon season.

Using Artificial Neural Networks to Improve CFS Week-3–4 Precipitation and 2-m Air Temperature Forecasts

Abstract Forecast skill from dynamical forecast models decreases quickly with projection time due to various errors. Therefore, postprocessing methods, from simple bias correction methods to more complicated multiple linear regression–based model output statistics, are used to improve raw model forecasts. Usually, these methods show clear forecast improvement over the raw model forecasts, especially for short-range weather forecasts. However, linear approaches have limitations because the relationship between predictands and predictors may be nonlinear. This is even truer for extended range forecasts, such as week-3–4 forecasts. In this study, neural network techniques are used to seek or model the relationships between a set of predictors and predictands, and eventually to improve week-3–4 precipitation and 2-m temperature forecasts made by the NOAA/NCEP Climate Forecast System. Benefitting from advances in machine learning techniques in recent years, more flexible and capable machine learning algorithms and availability of big datasets enable us not only to explore nonlinear features or relationships within a given large dataset, but also to extract more sophisticated pattern relationships and covariabilities hidden within the multidimensional predictors and predictands. Then these more sophisticated relationships and high-level statistical information are used to correct the model week-3–4 precipitation and 2-m temperature forecasts. The results show that to some extent neural network techniques can significantly improve the week-3–4 forecast accuracy and greatly increase the efficiency over the traditional multiple linear regression methods.

Innovative Integration of Severe Weather Forecasts into an Extended Arrival Manager

In the H2020 project “Satellite-borne and INsitu Observations to Predict The Initiation of Convection for ATM” (SINOPTICA), an air traffic controller support system was extended to organize approaching traffic even under severe weather conditions. During project runtime, traffic days with extreme weather events in the Po Valley were analyzed, an arrival manager was extended with a module for 4D diversion trajectory calculation, two display variants for severe weather conditions in an air traffic controller primary display were developed, and the airport Milano Malpensa was modelled for an air traffic simulation. On the meteorological side, three new forecasting techniques were developed to better nowcast weather events affecting tactical air traffic operations and used to automatically organize arrival traffic. Additionally, short-range weather forecasts with high spatial resolution were elaborated using radar-based nowcasting and a numerical weather prediction model with data assimilation. This nowcast information was integrated into the extended arrival manager for the sequencing and guiding of approaching aircraft even in adverse weather situations. The combination of fast and reliable weather nowcasts with a guidance support system enables severe weather diversion coordination in combination with a visualization of its dynamics on traffic situation displays.

Short-Term Weather Forecast Skill of Artificial Neural Networks

Abstract We evaluate the short-term weather forecast performance of three flavors of artificial neural networks (NNs): feed forward back propagation, radial basis function, and generalized regression. To prepare the application of the NNs to an operational setting, we tune NN hyperparameters using over two years of historical data. Five objective guidance products serve as predictors to the NNs: North American Mesoscale and Global Forecast System model output statistics (MOS) forecasts, the High-Resolution Rapid Refresh (HRRR) model, National Weather Service forecasts, and the National Blend of Models product. We independently test NN performance using 96 real-time forecasts of temperature, wind, and precipitation across 11 U.S. cities made during the WxChallenge, a weather forecasting competition. We demonstrate that all NNs significantly improve short-range weather forecasts relative to the traditional objective guidance aids used to train the networks. For example, 1-day maximum and minimum temperature forecast error is 20%–30% lower than MOS. However, NN improvement over multiple linear regression for short-term forecasts is not significant. We suggest this may be attributed to the small number of training samples, the operational nature of the experiment, and the short forecast lead times. Regardless, our results are consistent with previous work suggesting that applying NNs to model forecasts can have a positive impact on operational forecast skill and will become valuable tools when integrated into the forecast enterprise. Significance Statement We used approximately two years of historical weather data and objective forecasts for a number of cities to tune a series of artificial neural networks (NNs) to forecast 1-day values of maximum and minimum temperature, maximum sustained wind speed, and quantitative precipitation. We compare forecast error against common objective guidance and multiple linear regression. We found that the NNs exhibit about 25% lower error than common objective guidance for temperature forecasting and 50% lower error for wind speed. Our results suggest that NNs will be a valuable contributor to improving weather forecast skill when adopted into the existing forecast enterprise.

Understanding the sensitivity of WRF hindcast of Beijing extreme rainfall of 21 July 2012 to microphysics and model initial time

The extreme rainfall during 21–22 July 2012 brought the heaviest rain to Beijing (190 mm) and Fangshan (460 mm) in six decades, which caused widespread damage in the city and its surroundings. To understand the evolution of spatio-temporal variability of precipitation and to quantify the inherent uncertainties in short-range weather forecasts of such an urban extreme precipitation event, we use an ensemble of high-resolution urban-aware simulations. In particular, we focus on the sensitivity of WRF hindcasts to microphysics parameterizations since reliable forecasting of extreme precipitation depends heavily on accurate descriptions of the processes of cloud microphysics. Three widely used microphysics schemes (Thompson, WSM6, and WDM6) were examined. Apart from the microphysics schemes, we also evaluated the performance of the simulations to four different model initial times (starting from 1200 UTC 19 July with 12 h-intervals) and two different boundary conditions (ERA-Interim and NCEP FNL). The results indicate that all three microphysics schemes underestimated total rainfall. The Thompson microphysics scheme, when initialized at the beginning of convection intensification (0000 UTC 20 July 2012), captures the intense rain areas. The WSM6 scheme captures the total amount of intense rainfall better than other schemes but with a spatial bias, and the WDM6 captures the location of the heavy precipitation but underpredicts the amount of precipitation compared to other schemes. The simulations starting at 0000 UTC 20 July 2012 perform better compared with other three other initialization times. The simulation using NCEP-FNL captures the location of maximum precipitation better compared with ERA-Interim. Overall, it is found that the results of these hindcasts are most sensitive to the time of initialization.

Very Short-Term Rainfall Prediction Using Ground Radar Observations and Conditional Generative Adversarial Networks

Weather radars play an important role in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> rainfall monitoring owing to their ability to measure instantaneous rain rates and rainfall distributions. Currently, the Korea Meteorological Administration (KMA) provides instantaneous radar observation data and predictions based on the McGill algorithm for precipitation nowcasting by Lagrangian extrapolation (MAPLE) for up to 6 h, for short-term forecasting. This study presents a conditional generative adversarial network (CGAN)-based radar rainfall prediction method for very short-range weather forecasts from 10 min to 4 h. The CGAN-predicted model was trained and tested using KMA’s constant altitude plan position indicator (CAPPI) observation data. The qualitative comparison between the radar observation and the CGAN-predicted rain rates displayed high statistical scores, such as the probability of detection (POD) = 0.8442, false alarm ratio (FAR) = 0.2913, and critical success index (CSI) = 0.6268, in the case of a 1-h prediction for rainfall on September 5, 2019, 15:20 KST. This study demonstrates the capability of the CGAN model for short-term rainfall forecasting. Consequently, the CGAN-generated radar-based rainfall prediction could complement the KMA MAPLE system and be useful in various forecasting applications.

Regional soil moisture prediction system based on Long Short-Term Memory network

In the context of climate change, drought has been recognised as one of the most severe threats for agricultural production since absence of water is one of the most limiting factors for the growth of plants. In this study, a regional system for soil moisture prediction based on ERA5 climate reanalysis dataset, an open-source meteorological dataset issued by Copernicus Climate Change Service, was developed. It consisted of the relevant meteorological parameters for Serbia, during the period 2011–2020. Daily values of maximum and minimum air temperature, precipitation and vapour pressure deficit were used as features, and they were fed to recurrent neural network in order to predict volumetric soil moisture for three days ahead. A Long Short-Term Memory (LSTM) network was designed and trained at a regional scale using the data from the 2011–2016 period, at 28 locations that cover four major soil types. Validation was done on 2017–2018 data and LSTM was compared with a statistical forecasting technique and a classical machine learning approach. Evaluation was done with error measures commonly used in literature. The resulting network yielded the lowest errors and proved to have good generalisation properties. The system was tested from September 2019 to April 2020 using short-range weather forecasts of Yr service. It will present the cornerstone of the irrigation scheduling service in AgroSense.rs, the Serbian national platform for digital agriculture.

Sensitivity of the Short-Range Precipitation Forecast in South China Region to Sea Surface Temperature Variations

Ocean variability plays an essential role in the climate system at different time scales through air–sea interactions. Recent studies have addressed the importance of the ocean mixed layer in cooling feedback to tropical cyclones (TCs). However, using constant sea surface temperature (SST) in short-range weather forecasts remains common, especially in high-resolution regional models. This study investigates the role of subsurface ocean mixing in the short-range forecast of non-TC extreme rainfall with the Weather Research and Forecast (WRF) model. In the simulations of 26 heavy rainfall cases, we found that using a one-dimensional mixed layer model leads to a 15% enhancement (reduction) of rainfall maximum in six (two) cases compared to using constant SST. When the initial depth of the mixed layer model is perturbed by the amount of daily variability, 13 cases exhibit larger than 15% increases or decreases. A detailed analysis of one case suggests that the radiative process dominates the overall response of SST. The warming and moistening of boundary layer air cause significant strengthening of updrafts in convection. Although the SST change in most cases due to varying mixed layer model setups is less than 0.5 K, convective motions in some cases are surprisingly sensitive to small changes.

Impact of the assimilation of DWR-derived precipitation rates through latent heat nudging on simulation of rainfall events over Indian region using NCUM-R

Radar estimate of precipitation is an important observation which can be used to initialize the high-resolution numerical weather prediction models for improved forecast of high impact weather. Impact of Indian Doppler Weather Radar (DWR) derived precipitation rates on short-range weather forecasts is studied here. The present work is one of the first-of-its-kind studies to assimilate the DWR derived rain rate into a high resolution model over Indian region. Chennai DWR derived surface precipitation rates are included in the high resolution regional version of NCMRWF Unified Model (NCUM-R) through latent heat nudging to study its impact on the simulations of two convective rainfall events that occurred over east-peninsular India. The high resolution initial condition for NCUM-R is prepared using 4D-VAR data assimilation system in which various conventional and satellite observations are used. In this study, two sets of numerical experiments are carried out, one experiment named as control experiment (CNTL) uses only the analysis created by 4D-VAR and in the second (LHN), latent heat nudging is also performed with DWR derived precipitation rates in addition to the use of the 4D-VAR analysis.The results of the study show that the assimilation of DWR derived precipitation rates has a positive impact on the simulation of convective rainfall events. The root mean square errors (RMSE) of the analysed meteorological fields, mainly the mass fields are improved in the LHN compared to CNTL in both cases. The error of Convective Available Potential Energy (CAPE) in the LHN analyses is considerably reduced compared to the CNTL analyses for both cases. The stability indices also suggested that the condition for convection is properly reproduced in the LHN analysis in both cases. When evaluated against rainfall observations, the amount and orientation of the main rainfall pattern showed noteworthy improvements in the LHN relative to CNTL. The rainfall forecasts are further verified based on the object oriented Contiguous Rain Area (CRA) technique. The vector displacement errors and total mean square error are reduced considerably in LHN simulations compared to CNTL. The vertical structure and time evolution of moisture flux and moisture convergence are also well simulated in the LHN. This preliminary study revealed encouraging results which shows that the assimilation of Indian DWR derived rain rates in the high resolution regional NWP system can improve the forecast of convective rainfall events over Indian region.

Development of the new ensemble weather prediction system at the Hydrometcentre of Russia

The new ensemble weather prediction system developed at Hydrometcenter of Russia is presented. It is based on the global atmospheric model with the resolution of 0.9×0.72 degrees in longitude and latitude, respectively, 96 vertical levels, and the Local Ensemble Transform Kalman Filter (LETKF) algorithm implemented by the authors. The key feature of this system is the centering of the initial data ensemble onto the operational analysis of the Hydrometcenter of Russia, which allows to take into account some satellite observations not assimilated in LETKF. Some improvements in assimilating radiosounding data has increased the accuracy of short range weather forecasts. The first results of ensemble medium-range forecasts are presented. Now this ensemble prediction system is ready for a quasi-operational use.

Cloud-Resolving Model Applied to Nowcasting: An Evaluation of Radar Data Assimilation and Microphysics Parameterization

AbstractThis research explores the benefits of radar data assimilation for short-range weather forecasts in southeastern Brazil using the Weather Research and Forecasting (WRF) Model’s three-dimensional variational data assimilation (3DVAR) system. Different data assimilation options are explored, including the cycling frequency, the number of outer loops, and the use of null-echo assimilation. Initially, four microphysics parameterizations are evaluated (Thompson, Morrison, WSM6, and WDM6). The Thompson parameterization produces the best results, while the other parameterizations generally overestimate the precipitation forecast, especially WDSM6. Additionally, the Thompson scheme tends to overestimate snow, while the Morrison scheme overestimates graupel. Regarding the data assimilation options, the results deteriorate and more spurious convection occurs when using a higher cycling frequency (i.e., 30 min instead of 60 min). The use of two outer loops produces worse precipitation forecasts than the use of one outer loop, and the null-echo assimilation is shown to be an effective way to suppress spurious convection. However, in some cases, the null-echo assimilation also removes convective clouds that are not observed by the radar and/or are still not producing rain, but have the potential to grow into an intense convective cloud with heavy rainfall. Finally, a cloud convective mask was implemented using ancillary satellite data to prevent null-echo assimilation from removing potential convective clouds. The mask was demonstrated to be beneficial in some circumstances, but it needs to be carefully evaluated in more cases to have a more robust conclusion regarding its use.

Improving Near-Surface Short-Range Weather Forecasts Using Strongly Coupled Land–Atmosphere Data Assimilation with GSI-EnKF

Abstract Strongly coupled land–atmosphere data assimilation has not yet been implemented into operational numerical weather prediction (NWP) systems. Up to now, upper-air measurements have been assimilated mainly in atmospheric analyses, while land and near-surface data have been assimilated mainly into land surface models. Thus, this study aims to explore the benefits of assimilating atmospheric and land surface observations within the framework of strongly coupled data assimilation. Specifically, we added soil moisture as a control state within the ensemble Kalman filter (EnKF)-based Gridpoint Statistical Interpolation (GSI) and conducted a series of numerical experiments through the assimilation of 2-m temperature/humidity and in situ surface soil moisture data along with conventional atmospheric measurements such as radiosondes into the Weather Research and Forecasting (WRF) Model with the Noah land surface model. The verification against in situ measurements and analyses show that compared to the assimilation of conventional data, adding soil moisture as a control state and assimilating 2-m humidity can bring additional benefits to analyses and forecasts. The impact of assimilating 2-m temperature (surface soil moisture) data is positive mainly on the temperature (soil moisture) analyses but on average marginal for other variables. On average, below 750 hPa, verification against the NCEP analysis indicates that the respective RMSE reduction in the forecasts of temperature and humidity is 5% and 2% for assimilating conventional data; 10% and 5% for including soil moisture as a control state; and 16% and 11% for simultaneously adding soil moisture as a control state and assimilating 2-m humidity data.

Parameter Sensitivity of the WRF–LETKF System for Assimilation of Radar Observations: Imperfect-Model Observing System Simulation Experiments

AbstractSpecification of suitable initial conditions to accurately forecast high-impact weather events associated with intense thunderstorms still poses a significant challenge for convective-scale forecasting. Radar data assimilation has been showing encouraging results to produce an accurate estimate of the state of the atmosphere at the mesoscale, as it combines high-spatiotemporal-resolution observations with convection-permitting numerical weather prediction models. However, many open questions remain regarding the configuration of state-of-the-art data assimilation systems at the mesoscale and their potential impact upon short-range weather forecasts. In this work, several observing system simulation experiments of a mesoscale convective system were performed to assess the sensitivity of the local ensemble transform Kalman filter to both relaxation-to-prior spread (RTPS) inflation and horizontal localization of the error covariance matrix. Realistic large-scale forcing and model errors have been taken into account in the simulation of reflectivity and Doppler velocity observations. Overall, the most accurate analyses in terms of RMSE were produced with a relatively small horizontal localization cutoff radius (~3.6–7.3 km) and large RTPS inflation parameter (~0.9–0.95). Additionally, the impact of horizontal localization on short-range ensemble forecast was larger compared to inflation, almost doubling the lead times up to which the effect of using a more accurate state to initialize the forecast persisted.

Agrometeorological Forecast for Smallholder Farmers: A Powerful Tool for Weather-Informed Crops Management in the Sahel

Agriculture production in Nigerien rural areas mainly depends on weather variability. Weather forecasts produced by national or international bodies have very limited dissemination in rural areas and even if broadcast by local radio, they remain generic and limited to short-term information. According to several experiences in West Africa, weather and climate services (WCSs) have great potential to support farmers’ decision making. The challenge is to reach local communities with tailored information about the future weather to support strategic and tactical crop management decisions. WCSs, in West Africa, are mainly based on short-range weather forecasts and seasonal climate forecasts, while medium-range weather forecasts, even if potentially very useful for crop management, are rarely produced. This paper presents the results of a pilot initiative in Niger to reach farming communities with 10-day forecasts from the National Oceanic and Atmospheric Administration—Global Forecast System (NOAA-GFS) produced by the National Centers for Environmental Prediction (NCEP). After the implementation of the download and treatment chain, the Niger National Meteorological Directorate can provide 10-day agrometeorological forecasts to the agricultural extension services in eight rural municipalities. Exploiting the users’ evaluation of the forecasts, an analysis of usability and overall performance of the service is described. The results demonstrate that, even in rural and remote areas, agrometeorological forecasts are valued as powerful and useful information for decision-making processes. The service can be implemented at low cost with effective technologies making it affordable and sustainable even in developing countries. Nonetheless, the service’s effectiveness depends on several aspects mainly related to the way information is communicated to the public.

Retrieval and application of high-resolution low-level visible winds from INSAT-3DR imager

ABSTRACTAtmospheric motion vectors (AMVs) are estimated by selecting and tracking the cloud and water vapour features from geostationary satellite images. Satellite-derived AMVs are extremely helpful in understanding synoptic-scale atmospheric dynamics, circulations, and also potential input parameters of the numerical weather prediction (NWP) models. In India, till now, operational derivation of low-level visible (VIS) AMVs are retrieved using a coarser spatial resolution (4 km) images from INSAT-3D and INSAT-3DR satellites. In this study, high-resolution (HR) low-level visible AMVs are derived from INSAT-3DR HR (1 km) images. The operational VIS AMVs retrieval technique, with some fine-tuning in the algorithm for the use of HR images, is followed for HR VIS wind derivation. The accuracy of the new HR VIS winds is improved significantly compared with corresponding coarser resolution operational VIS winds against numerical model analysed winds. The statistical comparison has been carried out by calculating root mean square vector difference (RMSVD), speed bias, and percentage improvement. These newly retrieved high- and operationally available coarse-resolution VIS winds are further assimilated in the numerical model to assess their impact on short-range weather forecast during 1 to 15 July 2017. Assimilation of HR VIS winds has improved the forecast, especially at upper levels.

An improved forecast of precipitation type using correlation-based feature selection and multinomial logistic regression

Accurate prediction of precipitation type is an important part of weather forecasting. But using meteorological insight to make such predictions from a small set of weather variables achieves only limited success. We use correlation-based feature selection to assemble an effective subset of the large number of weather variables available in short-range weather forecasts, and from these we obtain the coefficients for multinomial regression, which can then be used to predict precipitation type. We applied this approach to data for significant locations in South Korea, obtained from the European Centre for Medium-Range Weather Forecasts and from the Regional Data Assimilation and Prediction System, and achieved predictions which are respectively 15% and 13% more accurate than those contained in the original forecasts. • We improved the forecast of winter precipitation types in South Korea. • Feature selection was used to assemble an effective subset of weather variables. • A comparative analysis was conducted on various forecasting schemes. • Logistic regression with feature selection works well on precipitation forecast.

Configuring LES Based on Dropsonde Data in Sparsely Sampled Areas in the Subtropical Atlantic

AbstractThis study explores the question of how field campaign data, gathered over a large area that is poorly sampled, can be used to make large-eddy simulation (LES) realizations more representative of the local conditions. To this purpose, dropsonde data recorded during the first Next-Generation Aircraft Remote Sensing for Validation Studies (NARVAL)-South campaign, in the marine subtropical North Atlantic, are blended into the forcing data. Control simulations are driven by time-dependent forcings derived from a combination of analyses and short-range weather forecasts, using weak nudging to prevent excessive model drift. A second set of simulations is driven by forcing data with dropsonde profiles included at the time points of their release. Metrics are designed to (i) quantify the impact on the boundary layer vertical profiles as a result of nudging toward the dropsondes and (ii) use a probabilistic method to allow a fair comparison of the inversion strengths in the simulations and observations. The simulations show strong time variation in the cloud-layer depth on relatively short time scales, which is commensurate with recent observational studies in the area. Nudging toward dropsondes improves the representation of the atmospheric profiles throughout the depth of the boundary layer in all simulations. However, the impact on the inversion strength is less pronounced. All impacts persist for some time after the dropsonde time point, depending on the intensity of the nudging and the nudging time window. The sensitivity of the results to nudging details and vertical resolution is assessed.

A Review on GRAPES-TMM Operational Model System at Guangzhou Regional Meteorological Center

This review summarizes the general developments of the operational mesoscale model system based on the Global/Regional Assimilation and Prediction System-Tropical Monsoon Model (GRAPES-TMM) at the Guangzhou Regional Meteorological Center. GRAPES-TMM consists of the Tropical Regional Atmospheric Model System for the South China Sea (TRAMS, a typhoon model with a horizontal resolution of 9 km), the Mesoscale Atmospheric Regional Model System (MARS, 3km) and the fine-scale Rapid Update Cycling (RUC, 1km) forecasting system. The main advances of model dynamical core and physical processes are summarized, including the development of the 3D reference atmosphere scheme, the coupling scheme between dynamics and model physics, the calculation of nonlinear terms by fractional steps, the gravity wave drag scheme induced by sub-grid orography and a simplified model for landsurface scheme. The progress of model applications is reviewed and evaluated. The results show that the updated 9-3-1 forecasting system provides an overall improved performance on the weather forecasting in south China, especially for typhoon-genesis and typhoon-track forecasting as well as short-range weather forecasting. Capabilities and limitations as well as the future development of the forecasting system are also discussed.

Impact of GNSS Signal Delay Assimilation on Short Range Weather Forecasts Over the Indian Region

AbstractRadio signals transmitted from a satellite constellation and gathered by a ground‐based Global Navigation Satellite System (GNSS) receiver allow computation of zenith tropospheric delay (ZTD), which is related to the atmospheric moisture. This study investigates the impact of assimilating ZTD obtained from a ground‐based GNSS network on a numerical weather prediction model analyses and subsequent forecasts quality. The numerical data assimilation experiments are performed using three‐dimensional variational data assimilation system in the Weather Research and Forecasting model, at 10‐km horizontal grid spacing for the entire month of July 2017. A comparison with European Centre for Medium‐Range Weather Forecast analyses shows a clear positive impact of ZTD assimilation on the lower to middle tropospheric moisture, upper air temperature, and middle and upper tropospheric wind; errors are reduced by as large as 4%, when compared to the model run without ZTD assimilation. The impact on the analyses and forecast quality of surface meteorological variables is mostly neutral with some indication of positive impact on surface pressure. An improvement in the rainfall forecasts is also noticed when model assimilates ZTD observations. In addition, the impact of the formulation of forward model, which calculates model equivalent of the GNSS ZTD, has been assessed on ZTD assimilation. A revised forward model has been implemented within the Weather Research and Forecasting assimilation system. The revised forward model outperforms the original model for ZTD assimilation. Overall result implies that GNSS ZTD data has a good potential for improving the weather prediction and advocates the strengthening of the ground‐based GNSS network over the Indian region, which is currently very sparse.

О роли синоптика в технологии подготовки краткосрочных прогнозов погоды

О роли синоптика в технологии подготовки краткосрочных прогнозов погоды