Forecasting GNSS Zenith Troposphere Delay by Improving GPT3 Model with Machine Learning in Antarctica

Antarctica has a significant impact on global climate change. However, to draw climate change scenarios, there is a need for meteorological data, such as water vapor content, which is scarce in Antarctica. Global navigation satellite system (GNSS) networks can play a major role in overcoming this problem as the tropospheric delay that can be derived from GNSS measurements is an important data source for monitoring the variation of water vapor content. This work intends to be a contribution for improving the estimation of the zenith tropospheric delay (ZTD) obtained with the latest global pressure–temperature (GPT3) model for Antarctica through the use of long short-term-memory (LSTM) and radial basis function (RBF) neural networks for modifying GPT3_ZTD. The forecasting ZTD model is established based on the GNSS_ZTD observations at 71 GNSS stations from 1 January 2018 to 23 October 2021. According to the autocorrelation of the bias series between GNSS_ZTD and GPT3_ZTD, we predict the LSTM_ZTD for each GNSS station for period from October 2020 to October 2021 using the LSTM day by day. Based on the bias between LSTM_ZTD and GPT3_ZTD of the training stations, the RBF is adopted to estimate the LSTM_RBF_ZTD of the verified station, where the LSTM_ZTD represents the temporal forecasting ZTD at a single station, and the LSTM_RBF_ZTD represents the predicted ZTD obtained from space. Both the daily and yearly RMSE are calculated against the reference (GNSS_ZTD), and the improvement of predicted ZTD is compared with GPT3_ZTD. The results show that the single-station LSTM_ZTD series has a good agreement with the GNSS_ZTD, and most daily RMSE values are within 20 mm. The yearly RMSE of the 65 stations ranges from 6.4 mm to 32.8 mm, with an average of 10.9 mm. The overall accuracy of the LSTM_RBF_ZTD is significantly better than that of the GPT3_ZTD, with the daily RMSE of LSTM_RBF_ZTD significantly less than 30 mm, and the yearly RMSE ranging from 5.6 mm to 50.1 mm for the 65 stations. The average yearly RMSE is 15.7 mm, which is 10.2 mm less than that of the GPT3_ZTD. The LSTM_RBF_ZTD of 62 stations is more accurate than GPT3_ZTD, with the maximum improvement reaching 76.3%. The accuracy of LSTM_RBF_ZTD is slightly inferior to GPT3_ZTD at three stations located in East Antarctica with few GNSS stations. The average improvement across the 65 stations is 39.6%.

KELEMBABAN UDARA DENGAN ALAT HUMYDIMETER PADA LAHAN SAWAH DI KELURAHAN TANAH MERAH

Humidity is the concentration of water vapor in the air. In agriculture, air humidity is associated with increased productivity and development of cultivated plants, humidity in the environment where it grows can determine the selection of appropriate plant species, the purpose of this study was to determine air humidity in paddy fields between April, May , and June, changes in air humidity at any time describe the water vapor content in the air can be expressed as absolute humidity, relative or vapor pressure deficit, relative humidity compares the actual water vapor content/pressure with its saturation state or the air's capacity to accommodate water vapor. The relationship between air humidity in paddy fields in Tanah Merah Village is relatively low, this is the impact of changes in temperature, quantity and quality of radiation, wind movement, air pressure, vegetation, and availability of water and productivity of irrigated ricefields

Relationship Between Surface Dew Point Temperature and Satellite Based Total Water Vapor Content over North Indian Ocean

The dew point temperature is related to the total water vapor content available in the atmosphere column. In this study, Water Vapor Content (WVC) from Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and Relative humidity from Research Moored Array for African-Asian-Australian Monsoon Analysis (RAMA) buoy data has been utilized to make a relationship between satellite measured WVC and Dew point temperature. This study focuses on the development of an algorithm to estimate the surface dew point temperature from satellite-based WVC. Regression coefficients are established using 9-years (2004-2012) data of Dew point Temperature computed from Relative humidity and satellite measured WVC. 1594 data points are observed weekly, mean monthly collocated data points are considered to examine the relationship between Dew point temperature and WVC.

Influence of water vapor distribution on the simulated track of Typhoon Hato (2017)

Predicting tropical cyclone (TCs) tracks is a primary concern in TC forecasting. Some TCs appear to move in a direction favorable for their development, beyond the influence of the steering flow. Thus, we hypothesize that TCs move toward regions with high water-vapor content in the lower atmosphere. In this study, four numerical experiments, including a control experiment and three sensitivity experiments, were performed using the Weather Research and Forecasting Model, to analyze the relationship between water vapor distribution and the track of Severe Typhoon Hato (2017). Observations validated the features reproduced in the control experiment. The sensitivity experiments were conducted to explore variations in the TC track under different water vapor environments. Results indicate that the horizontal distribution of water-vapor content exerted a greater impact on the TC track than the steering flow when both factors were significant. Further analysis revealed that the TC’s movement vector was between the direction of the steering flow and the direction toward the peak of vorticity increasing area. The peaks of vorticity increasing area were close to the peaks of water vapor increasing area, which also proved the effect of water vapor distribution on the TC track. These results are expected to improve TC track analysis and forecasting.

Spatiotemporal Variations of Water Vapor Content and Its Relationship with Meteorological Elements in the Third Pole

Water vapor content plays an important role in climate change and the ecosystem in the Tibetan Plateau (TP) through its complicated interaction with the meteorological elements. However, due to the complex topography of the Tibetan Plateau, it is unreliable to attempt to understand the variation pattern of water vapor content using only observational data. Satellite and reanalysis data can be a good substitute for observational data, but their accuracy still needs to be evaluated. Therefore, based on radiosonde stations data, comprehensive assessment of water vapor content on the TP and surrounding areas derived from ERA-5, Second Modern-Era Retrospective analysis for Research and Applications (MERRA2), Atmospheric Infrared Sounder (AIRS)-only, and weighted ensemble data was performed in the context of spatial and temporal distribution at the annual and seasonal scale. Based on precipitation from Gauge V3.0 and Tropical Rainfall Measuring Mission satellite (TRMM) and temperature from ERA-5, the relationship between water vapor content and temperature and precipitation was analyzed. The results show that water vapor content decreases from southeast to northwest, and ERA-5, MERRA2, and AIRS-only can reasonably reproduce the spatial distribution of annual and seasonal water vapor content, with ERA-5 being more reliable in reproducing the spatial distribution. Over the past 50 years, the water vapor content has shown a gradual increasing trend. The variation trends of AIRS-only, MERRA2, ERA-5, and weighted ensemble data are almost consistent with the radiosonde stations data, with MERRA2 being more reliable in capturing water vapor content over time. Weighted ensemble data is more capable of capturing water vapor content characteristics than simple unweighted products. The empirical orthogonal function (EOF) analysis shows that the first spatial mode values of water vapor content and temperature are positive over the TP, while the values of precipitation present a “negative-positive-negative” distribution from south to north over the TP. In the second spatial mode of EOF analysis, the values of water vapor content, air temperature, and precipitation are all negative. The first temporal modes of EOF analysis, water vapor content, air temperature, and precipitation all show an increasing trend. In conclusion, there is a clear relationship of water vapor content with temperature and precipitation.

Application of data simulation procedure for troposphere GNSS tomography tasks

The content and distribution of water vapor in the Earth’s atmosphere are related to various weather conditions and climatic processes, and are therefore important for understanding many meteorological phenomena. At the current stage of development and formation of Global Navigation Satellite Systems (GNSS), the distribution of water vapor content can be established using such observations from GNSS tomography, which, in turn, allows to study changes in the vertical profile of water vapor content in the Earth’s troposphere. In troposphere GNSS tomography, accurate information on the distribution of water vapor is obtained using integrated measurements, such as the water vapor content value in the slant direction (Slant Water Vapor, SWV). The essence of the problem of troposphere GNSS tomography is the solution of equations system, the number of which is limited by the number of satellites involved in observations. In this case, the functional relationship between observations and unknowns, in the pathways of GNSS signals through the troposphere, must be known in sufficient numbers. However, today there is a problem of lack of such information, which leads to the main problem of the troposphere GNSS-tomography method – overcoming the deficit of rank in the inversion of the original equation. This problem can be solved by increasing the number of satellite signals in a wide range of positions. The purpose of this work is to maximize the use ofGNSS signals inmodeling tomographic solutions based on data simulation. Method. Based on the developed method of multi-GNSS observations data processing by the PPP method, an algorithm of the procedure of simulation of additional satellites in tomographic modeling in order to overcome the problems of rank deficit is proposed. Results. The results of application of the data simulation procedure for the vertical profile of water vapor content in the Earth’s troposphere are presented based on the results of processingGNSS observations at the GANP station (Poprad, Slovakia) in the period from 31.05.2019 to 1.06.2019. Scientific novelty and practical significance. For the first time, an algorithm for the procedure of additional satellites simulation was proposed in order to overcome the problems of rank deficit in the tomographic modeling.

Towards the integration of GNSS, SAR and NWP for heavy rainfall forecast in sub-Saharan Africa within the TWIGA project

<p>The H2020 TWIGA - Transforming Weather Water data into value-added Information services for sustainable Growth in Africa - project aims to establish various services in sub-Saharan Africa for a better management of water resources by linking satellite, in-situ and modelled information. The delivery of timely and accurate weather forecasts is one of the envisaged services. GNSS (Global Navigation Satellite Systems) and SAR (Synthetic Aperture Radar) data provide information on the atmospheric water vapor content, which can be assimilated into Numerical Weather Prediction (NWP) models. The assimilation enables these models to exploit observations for a better simulation of the atmospheric dynamics and the subsequent improvement of the forecasts. The activities related to GNSS, SAR and NWP integration are presented in what follows.</p><p>As for GNSS, the modeling of ionospheric errors was investigated for the recently deployed single-frequency low-cost sensors in Uganda. A quality assessment of three different algorithms (ANGBAS, SEID, goSEID) for synthetic L2 observations reconstruction, evaluating the impact on the Zenith Total Delay (ZTD) estimation, was carried out. The three methods show good performances with an overall accuracy ranging between 0.1 and 1 cm when the corrections are computed from geodetic stations at distances up to 65 km from the target receiver. Additionally, an operational system for the retrieval of near real-time GNSS ZTD was implemented. It shows a precision lower than 1 cm, compatible with the target requirements for the assimilation into NWP models.</p><p>GNSS is also used to perform the orbital corrections of the SAR products, reducing the large-scale errors like phase trends and biases. The merging of multiple Sentinel-1 frames to cover extended areas requires large computational resources. Work is ongoing to deal with the computationally intensive unwrapping of large interferograms. Moreover, the removal of ionospheric delays, which are not related to the water vapor content, is under development. </p><p>Concerning NWP, the Weather Research and Forecasting (WRF) model has been used, at cloud-resolving scales, to test the sensitivity of the simulations of three heavy rainfall events (in Uganda and in South Africa) to the Planetary Boundary Layer (PBL) and the microphysical numerical schemes. Non-local PBL schemes are found to outperform the local PBL scheme considered in the study, because they better describe the vertical atmospheric mixing. In parallel, by exploiting a multiphysics set of numerical simulations in West Africa, it was found that the spatial variability of the surface heat fluxes significantly affects the lower atmospheric dynamics. This happens through a differential heating of the atmosphere across soil moisture gradients. Experiments on the assimilation of water vapor data are ongoing.</p>