جستجوی مقالات مرتبط با کلیدواژه "persiann-cdr" در نشریات گروه "آب و خاک"

In the analysis of climatic and hydrological events, precipitation is considered as a main parameter and therefore, measuring precipitation data with high Spatio-temporal resolution is very important in predicting weather patterns. Accurate measurement of precipitation on the land surface is very challenging due to the scattering of rain gauge networks, temporal and spatial diversity, wind effects, and topography. In recent decades, the use and development of satellite products and remote sensing techniques have become widespread which is used in precipitation estimation. The aim of this study was to evaluate the satellite precipitation data of TRMM, CHIRPS, Persiann-CDR and GPM-IMERG and compare them with rain gauge data in the north and northwestern region of the country (including Gilan, Ardabil, East Azerbaijan, and West Azerbaijan provinces). For this purpose, after receiving the satellite data series and pre-processing them, an evaluation was performed between the satellite data on a daily, monthly and seasonal time scale with the observational data. Evaluation of the results is performed using definite indicators including POD, CSI, FAR, Bias and statistical criteria including correlation coefficient (Corr) and Normalized Root Mean Square Error (nRMSE). The study period was selected from January 1, 2017 to December 31, 2021 on 56 synoptic stations. The results of most indicators and statistical criteria (Corr, nRMSE, POD, and CSI) showed that in all products the lowest error is related to the southwest of the study area which increases (south of West Azerbaijan province) by moving toward the east of the region and the Caspian coast.  In assessing the regional average precipitation, the results of IMERG, CHIRPS and Persiann-CDR were close to each other and with a slight difference (except in the nRMSE criterion) the IMERG product is superior. Also, in the study of seasonal estimates, the results of CHIRPS and Persiann-CDR were more reliable, but in order to use IMERG and TRMM, it is suggested that the estimates be accurized using different error correction methods. Finally, according to the results of this study, each product based on the type of topography and climate of the region provides a different result in estimating rainfall and there is a need for further studies according to the type of events in each region and a more detailed study of each product.

Climate changes and global warming have caused changes in weather patterns and the occurrence of serious events in different regions of the earth. Therefore, it is necessary to predict destructive floods, especially in coastal areas, to announce flood warnings and control them. Satellite precipitation estimation systems often generate data with global coverage that can provide information in areas where data from other sources are not available. Satellite retrieval and estimation systems, rain gauges, and radar networks complement each other regarding coverage and rainfall monitoring capabilities. Satellite precipitation estimation systems often produce data with global coverage. One of the main advantages of satellite-based precipitation products is the ease of real-time data availability and high spatial-temporal resolution. Currently, various satellite precipitation products such as GPM, TRMM, GSMap, CMORPH, CHIRPS, etc. are available to the public.

In this study, the southwest coastline of Caspian Sea, Gilan province is considered as the study area and the accuracy of four satellite-based products namely CHIRPS, GPIM-IMERG, PERSIANN CDR, TRMM-3B42V7 is compared in order to estimate the heavy precipitation from 2017 to 2021. Estimates are performed using categorical indices including PC, CSI, BIAS, and HSS and statistical criteria of correlation coefficient (Corr) and normalized root mean square error (nRMSE).

Evaluations and statistical criteria are conducted by comparing estimated satellite precipitation with ground stations through definitive indicators as well as the height of each station and the average of all stations respectively. The results show that in the PC index in eight stations, all of which are located at an altitude of fewer than 40 m above sea level and on the Caspian coast, the product IMERG scores higher than other products. The weakest performance in the PC index at the threshold of more than 5 mm belongs to TRMM and PERSIANN-CDR, which have the lowest scores jointly in eight stations. According to the results obtained from CSI, IMERG product had better performance in all stations except the Manjil station. Additionally, evaluation of the height of stations and the results obtained from this index demonstrate the fact no relationship was found between station height and product performance. In the Bias quality index, in IMERG and CHIRPS products, the bias of each station is directly related to its height, so that in higher stations, rainfall is overestimated, and vice versa in lower altitudes, the precipitations have been underestimated less than the amount of observation.

The IMERG product has performed better than the other three products in all categorical indices and correlation criteria. Finally, to evaluate and analyze heavy precipitation in the coastal strip of Gilan province and studies related to precipitation estimation, it is suggested that the IMERG product be used as a priority. The analysis carried out in this study, from the collection of satellite precipitation bases in estimating heavy precipitation in coastal areas, reveals the need to study and investigate this issue as much as possible in future research.