Products 3B42V7 Research Articles

AVALIAÇÃO DA PRECIPITAÇÃO DO SATÉLITE TRMM 3B43V7 NO PANTANAL DE MATO GROSSO DO SUL NOS ANOS DE 1998 A 2019

Remote sensing can assist in the acquisition of scarce surface data. The analyzes for validation of the precipitation product estimated by the TRMM satellite (Tropical Rainfall Measuring Mission) were carried out with the precipitation data observed on the surface during the period from 1998 to 2019. For this purpose, precipitation data from the Pantanal biome meteorological stations were used, located between the 16 and 22°S parallels and the 55 and 58°W meridians, and compared with the data from the TRMM 3B42 V7 product algorithms. Statistical analysis was performed based on the correlation coefficient, root mean square error (RMSE), and relative bias (BIAS) between the monthly precipitation data observed on the surface and the estimated precipitation data. The results found for product 3B43 V7 indicated that the precipitation estimates were representative when compared to the surface observations. However, when compared for the rainy and dry periods, there was underestimation and overestimation, respectively, of the product. The product 3B42 V7 satisfactorily represents the precipitation that occurs on the surface. Keywords: Tropical Rainfall, Remote Sensing, Conventional Meteorological Observation Precipitation Estimate.

Comparison of Two Multisatellite Algorithms for Estimation of Tropical Cyclone Precipitation in the United States and Mexico: TMPA and IMERG

AbstractTropical cyclone precipitation (TCP) contributes a significant amount of precipitation each year in the contiguous United States and Mexico, and it can cause damaging floods. In this study, we evaluate the ability of two precipitation estimates from the latest Integrated Multisatellite Retrievals for GPM (IMERG Final Run V06, hereafter referred to as IMERG-F) and its predecessor, the TRMM Multisatellite Precipitation Analysis (TMPA research product 3B42V7, hereafter referred to as TMPA), to capture TCP at daily, event, and annual scales by comparing the satellite observations with gauge measurements based on data from 2014 to 2018. The results show that both TMPA and IMERG-F are able to accurately capture the general TCP patterns. IMERG-F provides a noticeable improvement in accuracy over TMPA, especially for times and locations with light and heavy TCP. However, both IMERG-F and TMPA still systematically underestimate TCP during extreme events. At the annual scale, both TMPA and IMERG-F slightly underestimate annual TCP, but IMERG-F to a lesser degree. For individual TC events, IMERG-F has lower bias and a higher Nash–Sutcliffe efficiency than TMPA in the majority of the events. The differences between IMERG-F and TMPA are especially pronounced for extreme TCP events, such as Hurricane Harvey in 2017. At the daily scale, both IMERG-F and TMPA underestimate TCP when daily TCP exceeds ~150 mm. However, IMERG-F shows closer agreements with gauge-based measurements than TMPA. This study demonstrates that IMERG-F can more accurately measure TCP than TMPA. However, there are still systematic biases in IMERG-F when it comes to heavy TCP at all of the time scales.

Evaluation of TRMM 3B42V7 and CHIRPS Satellite Precipitation Products as an Input for Hydrological Model over Eastern Nile Basin

Nowadays, precipitation estimations based on satellite products (PESPs) have become an alternative source of sparse rainfall gauge data for various hydrologic applications, especially in scarce data regions like Africa which suffer from a lack of surface monitoring resources. Hence, their accuracy and performances must be evaluated by climate variance, complex topography, and geographical position due to their variation from one region to another. The Blue Nile is considered the crucial tributary of the Nile River which provides the utmost share of the river flow. So, this study aims to evaluate high-resolution PESPs [Tropical Rainfall Measuring Mission product 3B42V7 (TRMM 3B42V7) and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS)] over the Blue Nile basin (BNB) using categorical metrics [probability of detection (POD), and false alarm ratio (FAR)] and continuous statistical indicators [root mean square error (RMSE), mean absolute error (MAE), relative bias (RE), and correlation coefficient (R)] to assess its capability of rainfall amount detection and event occurrence. Hydrological river basin environmental assessment model (Hydro-BEAM) distributed model is also employed to investigate their capabilities to predict streamflow over the BNB. The hydrological daily and monthly simulations were calibrated and validated at Khartoum and Eldeim station for the period 1998–2007. Further, a hydrological monthly simulation was performed at various stations in the Eastern Nile basin (ENB) within the period 2001–2007 using Hydro-BEAM to appraise the ability of both precipitation products to predict streamflow. Moreover, a trial is made to adopt Hydro-BEAM to consider into the runoff simulation the influence of exiting dams during the simulation periods along the ENB to avoid the uncertainness and inaccuracies in simulation results.

Evaluation of products 3B42 v7 and 3IMERG for the hydroclimatic regions of Chile

Data provided by products 3B42 V7 (TRMM) and its successor 3IMERG (GPM) are compared with discrete rainfall information throughout the Chilean territory covering four macro hydroclimatic zones. Precipitation data was obtained from weather stations available on a daily basis from the 1930s to present. A total of 143 stations were chosen and rainfall estimates performed for years 2014 through 2018. Applying the same metrics we showed how GPM performances improve as the temporal aggregation increases. Several drawbacks were detected in the coastal areas, which were characterized by lower accuracy performances than internal areas. However, the 3IMERG product could be a strong source of data to study the impact that climatic disturbances have on the hydrologic cycle in the Central and South zones of Chile. Additionally, its offers a fundamental source of data for remote zones or areas where access is complicated to install weather stations.

Evaluation of satellite-based products for extreme rainfall estimations in the eastern coastal areas of China

ABSTRACTRemotely sensed rainfall plays an important role in providing efficient approaches for global or regional rainfall analysis. However, the accuracy of satellite-based products is mainly affected by the errors in sensor observation and retrieval algorithms, particularly with respect to extreme rainfall estimates. The objective of this study is to evaluate the accuracy of satellite-based products in capturing rainfall extremes. The eastern coastal areas of China were chosen as the case study area to compare the accuracy of three mainstream satellite-based products with respect to extreme rainfall estimates during 2003–2015 period. This included the Tropical Rainfall Measurement Mission (TRMM) rainfall product 3B42V7, the Climate Prediction Centre Morphing technique RAW (CMORPH-RAW), and the CMORPH bias-corrected product (CMORPH-CRT). In general, all satellite-based products demonstrated numerous errors in extreme rainfall estimates. Based on three different indices of extreme rainfall, it was observed that the satellite-based products underestimated the amounts of rainfall extremes and their respective average values. It was noted that CMORPH-RAW demonstrated the largest relative bias (RB) and underestimated the average extreme rainfall by −31% to −35%. Additionally, all satellite-based products exhibited poor capabilities in capturing the variations in hourly extreme rainfall processes. Finally, a simple potential flood index was developed to simulate the potential flood areas in the eastern coastal areas of China. We found that the potential flood areas can be simulated by combining the potential flood index with the amounts of rainfall derived by satellite-based products.

Accuracy Evaluation of Two High-Resolution Satellite-Based Rainfall Products: TRMM 3B42V7 and CMORPH in Shanghai

Satellite-based rainfall products have extensive applications in global change studies, but they are known to contain deviations that require comprehensive verification at different scales. In this paper, we evaluated the accuracies of two high-resolution satellite-based rainfall products: the Tropical Rainfall Measurement Mission (TRMM) rainfall product 3B42V7 and the Climate Prediction Center morphing (CMORPH) technique from January 2010 to December 2011 in Shanghai, by using categorical metrics (Probability of Detection, False Alarm Ratio, and Critical Success Index) and statistical indicators (Mean Absolute Error, Root Mean Square Error, Relative Bias, and Correlation Coefficient). Our findings show that, firstly, CMORPH data has a higher accuracy than 3B42V7 at the daily scale, but it underestimates the occurrence frequency of daily rainfall for some intensity ranges. Most errors of the two products are distributed between −10 and 10 mm/day. Second, at the monthly scale, the total accuracy of 3B42V7 is higher than CMORPH in terms of the value of the Correlation Coefficient (CC) and Relative Bias (RB). Finally, CMORPH brings about daily rainfall detection results from categorical metrics computation better than 3B42V7. Generally, the two satellite-based rainfall products show a high correlation with rain gauge data in Shanghai, particularly in spring and winter. Unfortunately, in summer, both of them do not perform well in detecting the short-duration heavy rainfall events. Overall, the relatively poor data accuracy has limited their further applications in Shanghai and similar urban areas.

Assessing the applicability of TMPA-3B42V7 precipitation dataset in wavelet-support vector machine approach for suspended sediment load prediction

In the present study, the latest Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) research product 3B42V7 has been evaluated over gauge-based India Meteorological Department (IMD) gridded dataset employing statistical and contingency table methods for two South Indian watersheds. A comparative analysis of TMPA-3B42V7 with IMD gauge-based gridded dataset was carried out on daily, monthly, seasonal and yearly basis for 16years (1998–2013). The study revealed that TMPA estimates performed reasonably well with the gauge-based gridded dataset, however, some significant biases were also observed. It has been observed that TMPA overestimates at very light rain, but underestimates at light, moderate, heavy and very heavy rainfall intensities. Further, the TMPA estimates was evaluated for prediction of daily suspended sediment load (SL) employing Support Vector Machine (SVM) with wavelet analysis (WASVM). Initially, 1-day ahead SL prediction was performed using best WASVM model. The results showed that 1-day predictions were very precise and shows a better agreement with the observed SL data. Later, the developed WASVM model was used for the prediction of SL for the higher leads period. The statistical analysis shows that the developed WASVM model could predict the target value successfully up to 6-days lead and found to be not suitable for higher lead specifically in the selected watersheds with similar hydro-climatic conditions like the ones selected in this study. Predictions results of WASVM model is superior to conventional SVM model and could be used as an effective forecasting tool for hydrological applications. The study suggest that the use of TMPA precipitation estimates can be a compensating approach after suitable bias correction and have potential for SL prediction in data-sparse regions.

Differentiating between rain, snow, and glacier contributions to river discharge in the western Himalaya using remote-sensing data and distributed hydrological modeling

Rivers draining the southern Himalaya provide most of the water supply for the densely populated Indo-Gangetic plains. Despite the importance of water resources in light of climate change, the relative contributions of rainfall, snow and glacier melt to discharge are not well understood, due to the scarcity of ground-based data in this complex terrain. Here, we quantify discharge sources in the Sutlej Valley, western Himalaya, from 2000 to 2012 with a distributed hydrological model that is based on daily, ground-calibrated remote-sensing observation. Based on the consistently good model performance, we analyzed the spatiotemporal distribution of hydrologic components and quantified their contribution to river discharge. Our results indicate that the Sutlej River's annual discharge at the mountain front is sourced to 55% by effective rainfall (rainfall reduced by evapotranspiration), 35% by snow melt and 10% by glacier melt. In the high-elevation orogenic interior glacial runoff contributes ∼30% to annual river discharge. These glacier melt contributions are especially important during years with substantially reduced rainfall and snowmelt runoff, as during 2004, to compensate for low river discharge and ensure sustained water supply and hydropower generation. In 2004, discharge of the Sutlej River totaled only half the maximum annual discharge; with 17.3% being sourced by glacier melt. Our findings underscore the importance of calibrating remote-sensing data with ground-based data to constrain hydrological models with reasonable accuracy. For instance, we found that TRMM (Tropical Rainfall Measuring Mission) product 3B42 V7 systematically overestimates rainfall in arid regions of our study area by a factor of up to 5. By quantifying the spatiotemporal distribution of water resources we provide an important assessment of the potential impact of global warming on river discharge in the western Himalaya. Given the near-global coverage of the utilized remote-sensing datasets this hydrological modeling approach can be readily transferred to other data-sparse regions.

Spatial and temporal variability of daily monsoon rainfall in Tunga and Bhadra River basins, Karnataka

The Tropical Rainfall Measuring Mission (TRMM) rainfall product 3B42-V7 and the telemetric station data sets of years 2010 and 2011 for the monsoon season were used to study the spatial and temporal variability of the daily rainfall in Tunga and Bhadra River basins in Karnataka. The daily rain observations of gauge and TRMM estimates show correlation coefficient values of 0.63 for the stations located within the Western Ghats (highland) and 0.40 for the stations located away from the Western Ghats (midland). The threshold of 50% and 75% of the mean surface rainfall gives a good accuracy of 0.89 and 0.4 for midland stations and 0.62 for highland stations. The reliability of the TRMM data varies depending on the root mean square error of 10.74 mm in highland region and 5.25 mm in midland region. The amplitude of the variability is high over the highland stations; this may be due to the orography and the inconsistency in cell resolution of the satellite to that of the point data of the rain gauging stations. The variability in the diurnal time scale is also studied and it is found that high rainfall activity is in the afternoon hours in both the highland and midland regions. A bimodal variation during the active monsoon phase and unimodal variation during rest of the season is noticed in the highland region of the area, and unimodal variation in midland region is noticed throughout the south-west monsoon period.

Statistical and hydrological evaluation of TRMM-based Multi-satellite Precipitation Analysis over the Wangchu Basin of Bhutan: Are the latest satellite precipitation products 3B42V7 ready for use in ungauged basins?

The objective of this study is to quantitatively evaluate the successive Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) products and further to explore the improvements and error propagation of the latest 3B42V7 algorithm relative to its predecessor 3B42V6 using the Coupled Routing and Excess Storage (CREST) hydrologic model in the mountainous Wangchu Basin of Bhutan. First, the comparison to a decade-long (2001–2010) daily rain gauge dataset reveals that: (1) 3B42V7 generally improves upon 3B42V6’s underestimation both for the whole basin (bias from −41.15% to −8.38%) and for a 0.25° × 0.25° grid cell with high-density gauges (bias from −40.25% to 0.04%), though with modest enhancement of correlation coefficients (CC) (from 0.36 to 0.40 for basin-wide and from 0.37 to 0.41 for grid); and (2) 3B42V7 also improves its occurrence frequency across the rain intensity spectrum. Using the CREST model that has been calibrated with rain gauge inputs, the 3B42V6-based simulation shows limited hydrologic prediction NSCE skill (0.23 in daily scale and 0.25 in monthly scale) while 3B42V7 performs fairly well (0.66 in daily scale and 0.77 in monthly scale), a comparable skill score with the gauge rainfall simulations. After recalibrating the model with the respective TMPA data, significant improvements are observed for 3B42V6 across all categories, but not as much enhancement for the already-well-performing 3B42V7 except for a reduction in bias (from −26.98% to −4.81%). In summary, the latest 3B42V7 algorithm reveals a significant upgrade from 3B42V6 both in precipitation accuracy (i.e., correcting the underestimation) thus improving its potential hydrological utility. Forcing the model with 3B42V7 rainfall yields comparable skill scores with in situ gauges even without recalibration of the hydrological model by the satellite precipitation, a compensating approach often used but not favored by the hydrology community, particularly in ungauged basins.