Thermal Monitoring and Evaluation of Precipitation Data Applying TRMM and GPM Satellites (Case Study: Bandar Abbas City)

Precipitation plays a decisive role in meeting the water needs of various crops, dam reserves, feeding surface and groundwater resources, and the occurrence of floods and droughts. Lack of access to long-term daily rainfall data and the high cost of setting up ground meteorological stations necessitate the replacement of low-cost methods with high-precision, high-volume data such as satellite data. The aim of this study was to evaluate the accuracy of precipitation data predicted by TRMM and GPM satellites in Bandar Abbas metropolis during a 17-year period from 2000 to 2017. Hourly precipitation data of TRMM and GPM satellites were obtained from databases. After analyzing the data format in MATLAB environment, hourly precipitation information was extracted. The results showed that the accuracy of both TRMM and GPM models in precipitation forecasting was appropriate and close to each other; it was often overestimated so that 75% of TRMM model precipitation forecasts and all forecasts GPM models were overestimated. The results showed that the TRMM model was more accurate than the GPM model in accurately predicting the occurrence of rainfall events and had less error in predicting unrealistic rainfall and the highest accuracy of the TRMM and GPM models is on a monthly, annual and daily scale, respectively. The value of EF index in TRMM model varies from -284.52 to 0.71 and in GPM model from -25514 to -1.25. The value of the EF index in the TRMM model predictions was positive in 42% of events, while, in the GPM model, it was not positively predicted in any event. The general conclusion of the research is that TRMM satellite is a suitable tool for monitoring and forecasting precipitation.
Extended Abstract
1-Introduction
Precipitation plays a decisive role in meeting the water needs of various crops, dam reserves, feeding surface and groundwater resources, and the occurrence of floods and droughts. Lack of access to daily and long-term data in different regions, high costs of setting up ground meteorological stations, as well as measurement errors have led researchers to seek new, inexpensive, up-to-date methods, available and accurate. In this regard, one of the practical ways to comprehensively estimate global precipitation is the use of satellites. Research has been done in the field of evaluation of satellites in estimating precipitation in different time scales; different results have been obtained regarding the degree of accuracy and more or less of their estimation. The aim of this study was to evaluate and monitor TRMM and GPM satellite data in Bandar Abbas metropolis in daily, monthly and annual time intervals in the period 2000-2017.
2-Materials and Methods
The coastal city of Bandar Abbas is the capital of Hormozgan province and is located in southern Iran. The coordinates of the area include  to  North and  to  East. For this study, daily precipitation data of Bandar Abbas synoptic station over a 17-year period from 2000 to 2017 were used. Hourly precipitation data of TRMM and GPM satellites were obtained from databases. After analyzing the data format in MATLAB, hourly precipitation information was extracted. TRMM is the first precipitation radar space system still in orbit and uses its information. The TRMM satellite was launched on November 27, 1997 and the GPM satellite on February 28, 2014. The TRMM satellite provides systematic, multi-year, visible, and infrared and microwave rainfall measurements in the tropics as the main inputs for climate and climate projects; the products of this rainfall source are now covered by 60 degrees worldwide. North up to 60 degrees south has been available to everyone since 2000. The GPM signal is also a combination of the two warnings (GMI) and the two-pronged airborne radar (DPR). Mean Absolute Difference (MAD), Root Mean Square Error (RMSE), relative deviation (BIAS), Index of Agreement (IA) and Efficiency Index (EF) ) were applied in order to evaluate the accuracy of satellite data relative to the ground station from the statistical coefficient of determination (). Correlation indices, including Probability of Detection (POD), False Alarm Ratio (FAR), Critical Success Index (CSI) and True Skill Statistic (TSS) were also used to validate the computational results.
3- Results and Discussion
The results showed that the accuracy of both GPM and TRMM models are close to each other and acceptable. According to the findings from this study, both TRMM and GPM satellites can be used to estimate precipitation in Bandar Abbas. The highest accuracy of GPM and TRMM models is related to monthly, annual and daily scales, respectively. The highest value of monthly R coefficient in both TRMM and GPM models was 98%. However, comparison of other statistics showed that the TRMM model has a higher accuracy than the GPM model. According to the results of POD statistics, TRMM model (with POD = 0.638) compared to GPM model (with POD = 0.531) has more accurately predicted rainfall events among all events. False warning ratio (FAR) also showed that TRMM model with FAR = 0.773 has less unrealistic rainfall forecast than GPM model with FAR = 0.897. The TRMM model predicts precipitation at 11% more accurate points, while the GPM model fails to pinpoint precipitation at almost half of the points. The lower accuracy of the TRMM model than the GPM model (13% difference) also confirmed the higher accuracy of the TRMM model. TSS statistics also showed that the accuracy of the TRMM model is better than the GPM model and with more appropriate confidence can be applied to the application of the TRMM model in hot and humid areas of Bandar Abbas metropolis. In the TRMM model, 25% of the data had an  above 0.5, and in the GPM model, in any of the months, the   forecast was not above 0.5. The value of EF index in TRMM model varies from negative 284.52 to positive 0.71 and in GPM model varies from negative 25514 to negative 1.25, the value of this index in TRMM model is positive in 5 months. However, it is not positive in the GPM model in any month. The Index of Agreement (IA) in the TRMM‌ model varies between 0.087 and 0.911 and in the GPM model varies between 0.009 and 0.672. The value of BIAS index in TRMM model varies between negative 12.8 and positive 21.58 and in GPM model varies between positive 22.64 and positive 78.86. In TRMM model, 25% of BIAS negative data was obtained. In other words, 25% of the data are underestimated. Due to the positive BIAS of all GPM model data, the data in this model were overestimated. The highest value of explanation coefficient () equal to 1.54 related to June on TRMM satellite and the lowest value of coefficient of determination () equal to 0.004 related to October on GPM satellite. The TRMM model was overestimated in 75% of the estimates and the GPM model was overestimated in 100% of the estimates, and the accuracy of the satellites in predicting the occurrence of precipitation in the rainy months (winter) decreased.
4- Conclusion
The general conclusion of this study showed that the accuracy of both TRMM and GPM satellites in estimating precipitation in the southern metropolis of the country with suitable hot and humid climate was evaluated. Due to the higher accuracy of TRMM satellite than GPM satellite, it is recommended to use TRMM satellite data on a monthly scale to estimate precipitation.