Tropical Rainfall Measuring Mission Rainfall Estimates Research Articles

Bias correction of radar and satellite rainfall estimates and increasing its accuracy using modified merging

Bias correction in the weather radar and the tropical rainfall measuring mission (TRMM) rainfall estimates are used to improve its accuracy. This correction is usually done separately for both radar and TRMM. Even though the corrections are done separately, the results of these corrections can be further improved using the merging. Among the methods of merging, modified local bias, mean field bias and conditional merging may be suitable methods used to correct rainfall estimates from remote sensing surrounding in the Makassar Strait. The aim of this research corrects radar and TRMM rainfall estimates, then combining them to obtain more accurate rainfall estimates. The performance will be validated using correlation, root mean square error (RMSE) and mean absolute error (MAE). The result shows that modified mean field bias (Mod_MFB) and local bias (LB) can increase accuracy, mainly RMSE and MAE but not in correlation. However, conditional merging (CM) and modified LB can improve accuracy by increasing correlation and decrease RMSE and MAE. The modification of CM, LB modification and original estimation of remote sensing successively are the order of the best methods. Moreover, merging three data types is not automatically better than merging the two types of data. However, combination 3 types of data offer the stability of accuracy.

Comparative analysis of two rainfall retrieval algorithms during extreme rainfall event: a case study on cloudburst, 2010 over Ladakh (Leh), Jammu and Kashmir

Intense rainfall events lead to floods and landslides in the Western Himalayas (WH). These rainfall amounts are considered comparatively moderate over the plains. These events, called ‘cloudbursts,’ are convective triggered followed by orographically locked phenomena producing sudden high-intensity rainfall over a small area. Early warning and prediction of such severe local weather systems is important to mitigate societal impacts arising from the accompanying flash floods, landslides, etc. Due to lack of ground-based observations, cloudbursts over remote and unpopulated hilly areas often go unreported. Present study examines a cloudburst occurred at Ladakh (Leh) in the WH in the early hours of August 5, 2010, using remotely sensed rainfall data from Tropical Rainfall Measuring Mission (TRMM) and Kalpana-1. The storm lasted for 2 days starting from August 3, 2010, followed by flash floods. Rain-band propagation over the region is studied from Kalpana-1 3-hourly rainfall estimates using Indian Satellite (INSAT) multi-spectral rainfall algorithm (IMSRA) and TRMM rainfall estimates using TRMM 3B42 algorithm. Quantitative and qualitative assessment and comparison of these two products is made. It is observed that there is decrease in satellite brightness temperature (BT) during the rainfall event. Initiation of rainfall occurs at about < 255 K. Maximum of 16.75 mm/h rainfall is observed over the Jammu and Kashmir at 21 GMT from TRMM 3B42 estimates. In addition, it is observed that Kalpana-1 IMSRA underestimates the rainfall observations with respect to Indian Meteorological Department (IMD) rainfall estimates. Spatial correlation at 5% significant level is evaluated, and similarities in rainfall estimates based on rainfall retrieval algorithms are made during the cloudburst event. Mean and standard deviations depict that TRMM 3B42 and IMD rainfall estimates are closer in terms of spatial signature, but estimates of rainfall from Kalpana-1 underestimate the mean and standard deviation signature. In view of orographic contribution, it has been observed that linear fit is the better than nonlinear with less rainfall bias at 90% confidence bound over the region.

Trend of rainfall over Indonesian major lakes from tropical rainfall measuring mission data

Establishing patterns of natural variability of water resources in lakes is important for water management. However, the availability of reliable data often becomes obstacles, especially in ungauged regions. Rainfall products from Tropical Rainfall Measuring Mission (TRMM) potentially fill this gap. The aim of this study is to assess the trend of rainfall over Indonesian major lakes using TRMM rainfall estimates to investigate the natural variability of rainfall over the lakes. TRMM 3B43 monthly data over the period of 1998–2017 obtained from Giovanni data portal maintained by NASA were used to assess the trend of rainfall over Indonesian major lakes. Rainfall correlation with El Niño-Southern Oscillation (ENSO) variability was also investigated using Southern Oscillation Index (SOI) and Multivariate ENSO Index (MEI). Trend analysis was carried out using Mann-Kendall test. Rainfall depths over Lake Laut Tawar in Sumatra tend to gradually decline, which is confirmed by the Mann-Kendall test. Result of the test of rainfall over all other investigated lakes on Kalimantan, Sulawesi, Papua, and Java indicate no trends except for that of Lake Paniai on Papua, which indicates an increasing trend.

Evaluation of TRMM Precipitation Dataset over Himalayan Catchment: The Upper Ganga Basin, India

Satellite based rainfall estimation techniques have emerged as a potential alternative to ground based rainfall measurements. The Tropical Rainfall Measuring Mission (TRMM) precipitation, in particular, has been used in various climate and hydrology based studies around the world. While having wide possibilities, TRMM rainfall estimates are found to be inconsistent with the ground based rainfall measurements at various locations such as the southwest coast and Himalayan region of India, northeast parts of USA, Lake Victoria in Africa, La Plata basin in South America, etc. In this study, the applicability of TRMM estimates is evaluated over the Upper Ganga Basin (Himalayan catchment) by comparing against gauge-based India Meteorological Department (IMD) gridded precipitation records. Apart from temporal evaluation, the ability of TRMM in capturing spatial distribution is also examined using three statistical parameters namely correlation coefficient (r), mean absolute error (MAE) and relative bias (RBIAS). In the results, the dual nature of bias is evident in TRMM precipitation with rainfall magnitude falling in the range from 100 to 370 mm representing positive bias, whereas, rainfall magnitude above 400 mm, approximately, representing negative bias. The Quantile Mapping (QM) approach has been used to correct the TRMM dataset from these biases. The raw TRMM precipitation is found to be fairly correlated with IMD rainfall for post-monsoon and winter season with R2 values of 0.65 and 0.57, respectively. The R2 value of 0.41 is obtained for the monsoon season, whereas least correlation is found for the pre-monsoon season with an R2 value of 0.24. Moreover, spatial distribution of rainfall during post-monsoon and winter season is captured adequately; however, the limited efficiency of TRMM is reflected for pre-monsoon and monsoon season. Bias correction has satisfactorily enhanced the spatial distribution of rainfall obtained from TRMM for almost all the seasons except for monsoon. Overall, the corrected TRMM precipitation dataset can be used for various climate analyses and hydrological water balance based studies in the Himalayan river basins.

Contribution of TRMM 3B42 Data to Improve Knowledge on Rainfall in the Kayanga/Geba River Basin (Republic of Guinea, Senegal and GuineaBissau)

The use of Tropical Rainfall Measuring Mission (TRMM) data is an option for counteracting challenge of the lack of ground based observations, particularly in Kayanga/Gêba. This paper undertakes validation of monthly TRMM rainfall estimates before using it to understand the spatial and temporal variability in the Basin. This validation based on application of statistical study, made it possible to obtain interesting results with correlation coefficients varying from 0.92 to 0.96 and Nash indices close to 1. The analysis of the seasonal rainfall pattern shows consistence with ground based observations. The study of the annual cycle reveals that their interannual variability is similar to that of ground based observations. Finally, the interpolation of average monthly rainfall in the basin highlights the NorthSouth rainfall gradient, which shows that the South is wetter than the North, with differences more pronounced in August and September.

Hydrology of inland tropical lowlands: the Kapuas and Mahakam wetlands

Abstract. Wetlands are important reservoirs of water, carbon and biodiversity. They are typical landscapes of lowland regions that have high potential for water retention. However, the hydrology of these wetlands in tropical regions is often studied in isolation from the processes taking place at the catchment scale. Our main objective is to study the hydrological dynamics of one of the largest tropical rainforest regions on an island using a combination of satellite remote sensing and novel observations from dedicated field campaigns. This contribution offers a comprehensive analysis of the hydrological dynamics of two neighbouring poorly gauged tropical basins; the Kapuas basin (98 700 km2) in West Kalimantan and the Mahakam basin (77 100 km2) in East Kalimantan, Indonesia. Both basins are characterised by vast areas of inland lowlands. Hereby, we put specific emphasis on key hydrological variables and indicators such as discharge and flood extent. The hydroclimatological data described herein were obtained during fieldwork campaigns carried out in the Kapuas over the period 2013–2015 and in the Mahakam over the period 2008–2010. Additionally, we used the Tropical Rainfall Measuring Mission (TRMM) rainfall estimates over the period 1998–2015 to analyse the distribution of rainfall and the influence of El-Niño – Southern Oscillation. Flood occurrence maps were obtained from the analysis of the Phase Array type L-band Synthetic Aperture Radar (PALSAR) images from 2007 to 2010. Drought events were derived from time series of simulated groundwater recharge using time series of TRMM rainfall estimates, potential evapotranspiration estimates and the threshold level approach. The Kapuas and the Mahakam lake regions are vast reservoirs of water of about 1000 and 1500 km2 that can store as much as 3 and 6.5 billion m3 of water, respectively. These storage capacity values can be doubled considering the area of flooding under vegetation cover. Discharge time series show that backwater effects are highly influential in the wetland regions, which can be partly explained by inundation dynamics shown by flood occurrence maps obtained from PALSAR images. In contrast to their nature as wetlands, both lowland areas have frequent periods with low soil moisture conditions and low groundwater recharge. The Mahakam wetland area regularly exhibits low groundwater recharge, which may lead to prolonged drought events that can last up to 13 months. It appears that the Mahakam lowland is more vulnerable to hydrological drought, leading to more frequent fire occurrences than in the Kapuas basin.

Validation of TRMM and FEWS Satellite Rainfall Estimates with Rain Gauge Measurement over Ashanti Region, Ghana

Satellite rainfall estimates have predominantly been used for climate impact studies due to poor rain gauge network in sub-Saharan Africa. However, there are limited microscale studies within the sub-region that have assessed the performance of these satellite products, which is the focus of the present study. This paper therefore considers validation of Tropical Rainfall Measuring Mission (TRMM) and Famine Early Warning System (FEWS) satellite estimates with rain gauge measurements over Ashanti region of Ghana. First, a consistency assessment of the two gauge data products, the Automatic Rain Gauge (ARG) and Ghana Meteorological Agency (GMet) Standard Rain Gauge (SRG) measurements, was performed. This showed a very good agreement with correlation coefficient of 0.99. Secondly, satellite rainfall products from TRMM and FEWS were validated with the two gauge measurements. Validation results showed good agreement with correlation coefficients of 0.6 and 0.7 for TRMM and FEWS with SRG, and 0.87 and 0.86 for TRMM and FEWS with ARG respectively. Probability Of Detection (POD) and Volumetric Hit Index (VHI) were found to be greater than 0.9. Volumetric Critical Success Index (VCSI) was 0.9 and 0.8 for TRMM and FEWS respectively with low False Alarm Ratio (FAR) and insignificant Volumetric Miss Index (VMI). In general, relatively low biases and RMSE values were observed. The biases were less than 1.3 and 0.8 for TRMM and FEWS-RFE respectively. These indicate high rainfall detection capabilities of both satellite products. In addition, both TRMM and FEWS were able to capture the onset, peak and cessation of the rainy season, as well as the dry spells. Although TRMM and FEWS sometimes under/overestimated rainfall, they have the potential to be used for agricultural and other hydro-climatic impact studies over the region. The Dynamic-Aerosol-Cloud-Chemistry Interactions in West Africa (DACCIWA) project will provide an improved spatial gauge network database over the study area to enhance future validation and other climate impact studies.

Spatiotemporal Interpolation of Rainfall by Combining BME Theory and Satellite Rainfall Estimates

The accurate assessment of spatiotemporal rainfall variability is a crucial and challenging task in many hydrological applications, mainly due to the lack of a sufficient number of rain gauges. The purpose of the present study is to investigate the spatiotemporal variations of annual and monthly rainfall over Fujian province in China by combining the Bayesian maximum entropy (BME) method and satellite rainfall estimates. Specifically, based on annual and monthly rainfall data at 20 meteorological stations from 2000 to 2012, (1) the BME method with Tropical Rainfall Measuring Mission (TRMM) estimates considered as soft data, (2) ordinary kriging (OK) and (3) cokriging (CK) were employed to model the spatiotemporal variations of rainfall in Fujian province. Subsequently, the performance of these methods was evaluated using cross-validation statistics. The results demonstrated that BME with TRMM as soft data (BME-TRMM) performed better than the other two methods, generating rainfall maps that represented the local rainfall disparities in a more realistic manner. Of the three interpolation (mapping) methods, the mean absolute error (MAE) and root mean square error (RMSE) values of the BME-TRMM method were the smallest. In conclusion, the BME-TRMM method improved spatiotemporal rainfall modeling and mapping by integrating hard data and soft information. Lastly, the study identified new opportunities concerning the application of TRMM rainfall estimates.

Evaluation of TRMM rainfall estimates over a large Indian river basin (Mahanadi)

Abstract. The paper examines the quality of satellite-based precipitation estimates for the lower Mahanadi River basin (eastern India). The considered data sets known as 3B42 and 3B42-RT (version 7/7A) are routinely produced by the tropical rainfall measuring mission (TRMM) from passive microwave and infrared recordings. While the 3B42-RT data are disseminated in real time, the gauge-adjusted 3B42 data set is published with a delay of some months. The quality of the two products was assessed in a two-step procedure. First, the correspondence between the remotely sensed precipitation rates and rain gauge data was evaluated at the sub-basin scale. Second, the quality of the rainfall estimates was assessed by analysing their performance in the context of rainfall–runoff simulation. At sub-basin level (4000 to 16 000 km2) the satellite-based areal precipitation estimates were found to be moderately correlated with the gauge-based counterparts (R2 of 0.64–0.74 for 3B42 and 0.59–0.72 for 3B42-RT). Significant discrepancies between TRMM data and ground observations were identified at high-intensity levels. The rainfall depth derived from rain gauge data is often not reflected by the TRMM estimates (hit rate &lt; 0.6 for ground-based intensities &gt; 80 mm day-1). At the same time, the remotely sensed rainfall rates frequently exceed the gauge-based equivalents (false alarm ratios of 0.2–0.6). In addition, the real-time product 3B42-RT was found to suffer from a spatially consistent negative bias. Since the regionalisation of rain gauge data is potentially associated with a number of errors, the above results are subject to uncertainty. Hence, a validation against independent information, such as stream flow, was essential. In this case study, the outcome of rainfall–runoff simulation experiments was consistent with the above-mentioned findings. The best fit between observed and simulated stream flow was obtained if rain gauge data were used as model input (Nash–Sutcliffe index of 0.76–0.88 at gauges not affected by reservoir operation). This compares to the values of 0.71–0.78 for the gauge-adjusted TRMM 3B42 data and 0.65–0.77 for the 3B42-RT real-time data. Whether the 3B42-RT data are useful in the context of operational runoff prediction in spite of the identified problems remains a question for further research.

Issues of spatial correlation arising from the use of TRMM rainfall estimates in the Brazilian Amazon

This paper discusses issues arising when Tropical Rainfall Measuring Mission (TRMM) estimates of rainfall characteristics are calculated for the sparsely gauged Brazilian Amazon. The particular issue considered is the calculation of statistical uncertainty in (1) comparisons between TRMM estimates of rainfall characteristics and their corresponding rain gauge estimates and (2) interpolated estimates of rainfall characteristics, which are conditional on local TRMM estimates near ungauged sites. Three characteristics selected for particular comparison are (1) mean annual rainfall, 1998–2005, (2) mean value, over these years, of the 95% quantile of within‐year daily rainfall, and (3) mean number of days in the year with rainfall of 2 mm or more. The paper demonstrates how positive spatial correlation leads to underestimation of the uncertainty in the comparisons (i.e., the standard errors of differences), so that the significance of differences, when observed, is likely to be overestimated if spatial correlation is ignored. For the three characteristics mentioned, the Matérn spatial correlation function with shape parameter κ = 1/2, giving an exponential correlation function, was found to be adequate for modeling spatial correlation. It is also argued that the statistic 1 − b, where b is the slope of the regression of a rain gauge estimate of a rainfall characteristic on its counterpart derived from satellite instrumentation, is a measure of the errors in the satellite (spatially averaged) estimates. Finally, although issues of spatial correlation are discussed with particular reference to rainfall, it is argued that the same issues arise whenever satellite‐derived estimates of a component of the water cycle must be combined with ground‐based measurements.

Daily hydrological modeling in the Amazon basin using TRMM rainfall estimates

Summary Rainfall measurements by conventional raingauges provide relatively accurate estimates at a few points of a region. The actual rainfield can be approximated by interpolating the available raingauge data to the remaining of the area of interest. In places with relatively low gauge density such interpolated rainfields will be very rough estimates of the actual events. This is especially true for tropical regions where most rainfall has a convective origin with high spatial variability at the daily level. Estimates of rainfall by remote sensing can be very useful in regions such as the Amazon basin, where raingauge density is very low and rainfall highly variable. This paper evaluates the rainfall estimates of the Tropical Rainfall Measuring Mission (TRMM) satellite over the Tapajos river basin, a major tributary of the Amazon. Three-hour TRMM rainfall estimates were aggregated to daily values and were compared with catch of ground-level precipitation gauges on a daily basis after interpolating both data to a regular grid. Both daily TRMM and raingauge-interpolated rainfields were then used as input to a large-scale hydrological model for the whole basin; the calculated hydrographs were then compared to observations at several streamgauges along the river Tapajos and its main tributaries. Results of the rainfield comparisons showed that satellite estimates can be a practical tool for identifying damaged or aberrant raingauges at a basin-wide scale. Results of the hydrological modeling showed that TRMM-based calculated hydrographs are comparable with those obtained using raingauge data.

Validation of TRMM and Other Rainfall Estimates with a High-Density Gauge Dataset for West Africa. Part II: Validation of TRMM Rainfall Products

Gauge data from a West African network of 920 stations are used to assess Tropical Rainfall Measuring Mission (TRMM) satellite and blended rainfall products for 1998. In this study, mean fields, scattergrams, and latitudinal transects for the months of May‐September and for the 5-month season are presented. Error statistics are also calculated. This study demonstrates that both the TRMM-adjusted Geostationary Observational Environmental Satellite precipitation index (AGPI) and TRMM-merged rainfall products show excellent agreement with gauge data over West Africa on monthly-to-seasonal timescales and 2.5 83 2.58 latitude/longitude space scales. The root-mean-square error of both is on the order of 0.6 mm day 21 at seasonal resolution and 1 mm day21 at monthly resolution. The bias of the AGPI is only 0.2 mm day21, whereas the TRMM-merged product shows no bias over West Africa. Performance at 1.0 83 1.08 latitude/longitude resolution is also excellent at the seasonal scale and good for the monthly scale. A comparison with standard rainfall products that predate TRMM shows that AGPI and the TRMM-merged product perform as well as, or better than, those products. The AGPI shows marked improvement when compared with the GPI, in reducing the bias and in the scatter of the estimates. The TRMM satellite-only products from the precipitation radar and the TRMM Microwave Imager do not perform well over West Africa. Both tend to overestimate gauge measurements.