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Local modeling of weighted mean temperature in Iran and its impact on GNSS meteorology

Rahimi Hassan, Asgari Jamal, Nafsi Vahab

Acta Geophysica

2022

Vol. 70, no 3

1445--1454

Weighted mean temperature (Tm) is used to determine water vapor content, precipitable water vapor, and integrated water vapor (IWV) in GNSS. This parameter is highly correlated with climate conditions as well as the type of the region. The case study is performed in Iran which has diverse climate. ERA5 reanalysis datasets were used at a compact grid of 0.125 ×0.125 between 2007 and the end of 2019 to model the Tm. The data obtained from 12 radiosonde stations along with an IGS station located in Tehran were employed in this research. Five models were examined for Tm. Bevis model, linear grouping model (LGM), and linear nearest grid point model (LNGPM) were considered as Tm linear models, and harmonic model (HM) and GPT2w model were used as nonlinear models. In LGM method the study region was divided into smaller areas with different linear model coefficients using spatial grouping method. The local model in each radiosonde station was considered as a reference. According to the results, the accuracy of linear models (Bevis and LGM model) was between 3 and 8 K (radiosonde data as reference); also 7 out of 12 stations in the LGM had higher accuracy than the Bevis model (based on RMSE). The accuracy of the two GPT2w models and the harmonic model was higher than the previous two models, and it was between 2 and 4 K. The IWV values were obtained using zenith total delay observations of IGS station located in Tehran using 5 models and were compared with the IWV values of the radiosonde station. The accuracy of the values in three linear models, Bevis, LGM, and LNGPM, was, respectively, 0.2, 0.17, and 0.14 kg m−2, and in the two nonlinear models, GPT2w and HM, was 0.13 kg m−2.

Possibility of GPS precipitable water vapour for reservoir inflow forecasting

Uang-aree P., Kingpaiboon S.

Journal of Water and Land Development

2018

no. 36

161--171

We investigated the possibility of using GPS precipitable water vapour (GPS-PWV) for forecasting reservoir inflow. The correlations between monthly GPS-PWV and the inflow of two reservoirs were examined and the relationship tested, using a group method of data handling (GMDH) type neural network algorithm. The daily and monthly reservoir inflows were directly proportional to daily and monthly GPS-PWV trends. Peak reservoir inflow, however, shifted from the peak averages for GPS-PWV. A strong relationship between GPS-PWV and inflow was confirmed by high R2 values, high coefficients of correlation, and acceptable mean absolute errors (MAE) of both the daily and monthly models. The Ubon Ratana reservoir model had a monthly MAE of 54.19∙106 m3 and a daily MAE of 5.40∙106 m3. By comparison, the Lumpow reservoir model had a monthly MAE of 25.65∙106 m3 and a daily MAE of 2.62∙106 m3. The models using GPS-PWV as input data responded to extreme inflow better than traditional variables such that reservoir inflow could be predicted using GPS-PWV without using actual inflow and rainfall data. GPS-PWV, thus, represents a helpful tool for regional and national water management. Further research including more reservoirs is needed to confirm this preliminary finding.

W pracy przedstawiono wyniki badań możliwości użycia danych GPS o zawartości pary wodnej (GPS- -PWV) do prognozowania dopływu do zbiornika. Analizowano korelacje między miesięczną wartością GPS- -PWV a dopływem do dwóch zbiorników; zależność testowano, stosując algorytm sieci neuronowej, zwany metodą grupowania argumentów (GMDH). Dobowe i miesięczne dopływy do zbiorników były proporcjonalne do dobowych i miesięcznych trendów GPS-PWV. Maksymalny dopływ odbiegał jednak od maksymalnych średnich GPS-PWV. Silna zależność między GPS-PWV a dopływem została potwierdzona dużymi wartościami R2, wysokim współczynnikiem korelacji i akceptowalnym średnim błędem bezwzględnym (MAE) zarówno w modelu dobowym, jak i miesięcznym. W modelu dla zbiornika Ubon Ratana miesięczny błąd bezwzględny wynosił 54,19∙106 m3 a dobowy – 5,40∙106 m3. Dla porównania w modelu dla zbiornika Lumpow wartość miesięczna MAE wynosiła 25,65∙106 m3, a dobowa 2,62∙106 m3. Modele z wykorzystaniem GPS-PWV jako danych wejściowych reagowały lepiej niż tradycyjne zmienne na dopływ ekstremalny i dlatego dopływ do zbiornika można przewidzieć bez znajomości rzeczywistego dopływu i danych opadowych. GPS-PWV jest więc pomocnym narzędziem w regionalnej i narodowej gospodarce wodnej. Potrzebne są dalsze badania obejmujące większą liczbę zbiorników, aby potwierdzić prezentowane wyniki wstępne.