Tropical Rainfall Measuring Mission Research Articles

Long‐term assessment of ERA5 reanalysis rainfall for lightning events over India observed by Tropical Rainfall Measurement Mission Lightning Imaging Sensor

AbstractThe ability of numerical weather prediction models to accurately predict extreme weather events, such as thunderstorms marked by heavy rainfall and lightning activities, has consistently been of great importance for human life. The objective of this study is to assess the long‐term reliability of the European Centre for Medium‐Range Weather Forecasts Reanalysis version 5 (ERA5) rainfall in comparison to the Indian gauge‐adjusted Global Satellite Mapping of Precipitation (GSMaP_ISRO) rainfall at the time of lightning flashes measured by the Lightning Imaging Sensor (LIS) onboard the Tropical Rainfall Measuring Mission satellite over the Indian region during the years 2001–2014. This analysis will provide valuable insights into the intricate relationship between lightning flashes and precipitation under various terrain conditions (low, mid, or high), across oceanic regions (Bay of Bengal, Arabian Sea), and during different monsoon phases (normal, active, or deficit). According to a prolonged examination of LIS data, April–June accounts for ~50% of the total flashes, with the largest number of flashes occurring over the Himalayan and the northeastern part of India. According to hourly GSMaP_ISRO rainfall, the most substantial lightning‐associated rainfall happens an hour prior to lightning flash (T − 1) and within three hours after (T + 3), indicating a robust correlation between heavy rainfall and lightning activity during this time frame. The rainfall in the ERA5 reanalysis misses the intensity as well as duration of the peak rainfall at the time of lightning flashes. Furthermore, the ERA5 reanalysis rainfall depicts under (over)‐estimation of rainfall in plain (orographic) regions. The underestimation of ERA5 rainfall is very pronounced over the Indian Ocean and Bay of Bengal regions, mainly between flash time (T) to two hours after the flash time (T + 2). The results indicate that there is a requirement for additional enhancements in the ERA5 reanalysis rainfall for lightning occurrences.

Rainfall dynamics in an ecologically vulnerable area using applied algebraic topology methods

Applied algebraic topology is employed in this work to shed new light on the rainfall dynamics in the Pernambuco state, Brazil. Historical data from the NASA’s Tropical Rainfall Measuring Mission (TRMM) precipitation processing system, for the period 1998 to 2020 with a spatial resolution of 0.25° and temporal resolution of 3 h is used to construct correlation matrices in different time frames. Matrices are then analyzed in terms of topological constructs of network theory to yield novel insights into this highly complex phenomenon in this semiarid, ecologically vulnerable area. The outcomes of the algebraic topological analysis reveal clustering patterns of areas and are related to natural climate phenomena. Together with the generality of the applied methodology, the results suggest a broad scope of future applications for the extraction of patterns in datasets related to the changes in the climate system.

Satellite-observed precipitation and total column water vapor

This study explores the relationship between water vapor and rainfall intensities over three tropical lands (Amazon Basin, Sahel, southern South America) and three tropical ocean regions (Atlantic Ocean, Indian Ocean, Niño 4). We utilized daily total column water vapor (TCWV) data from the Atmospheric Infrared Sounder (AIRS) and daily precipitation records from the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation. Over tropical land, precipitation shows higher sensitivity to changes in water vapor, with a well-sorted pattern of an increased occurrence of higher daily precipitation as TCWV increases. Precipitation intensity over the Sahel, in particular, is extremely responsive to TCWV change. Over tropical oceans, the precipitation intensity is less sensitive to water vapor, particularly in the Indian Ocean and Niño 4 where precipitation intensities above the 40th percentile are no longer responding to the increasing TCWV. Quantifying water vapor and precipitation intensity aids in forecasting the occurrence of precipitation between tropical land and oceans.

Spatiotemporal evaluation of five satellite-based precipitation products under the arid environment of Saudi Arabia

In arid regions like Saudi Arabia, accurate precipitation data are crucial for water resource management and climate studies. However, satellite-based precipitation products (SPPs) can contain uncertainties, impacting their reliability. This study evaluated the accuracy of five high-resolution SPPs [IMERG-V06 variants, Tropical Rainfall Measuring Mission (TRMM)-3B42V7, and Soil Moisture to Rain (SM2RAIN)-Advanced SCATterometer (ASCAT)] over Saudi Arabia. We compared daily, monthly, and yearly precipitation estimates from SPPs with in situ rain gauge data (2010–2022) using both continuous and categorical metrics. The evaluation encompassed point-to-pixel comparisons, regional analysis, and national assessments. All SPPs effectively captured the spatiotemporal patterns of precipitation across the country. Notably, monthly estimates showed stronger agreement with rain gauge data than daily estimates, as indicated by higher correlation coefficients. IMERG products generally outperformed SM2RAIN-ASCAT and TRMM, with IMERG-LR exhibiting superior performance in estimating monthly precipitation. However, underestimation of light precipitation events (<2 mm/day) was observed across all SPPs. In addition, their ability to detect moderate and heavy precipitation events remained uncertain, requiring further investigation. While IMERG-FR showed reduced bias and root mean square error compared to IMERG-ER and IMERG-LR, its capability for precipitation event detection did not exhibit significant improvement. This study highlights the need for bias correction of IMERG-LR and IMERG-FR monthly estimates for improved application in hydrometeorological studies in Saudi Arabia. Our findings contribute valuable insights for both data users and SPP algorithm developers, aiming to enhance the accuracy and reliability of satellite-derived precipitation data in arid environments.

Which Error Components in TRMM-Based Multisatellite Precipitation Estimates Reduce over Chinese Mainland after Official Bias Adjustments: Systematic or Random?

Abstract Climatological calibration algorithm (CCA) and satellite–gauge combination (SG) are two official bias adjustments for satellite precipitation estimates (SPE) in the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA). The CCA is designed for the near-real-time SPEs, while the SG procedure is a final step to merge pure SPEs with gauge observations. This study explored the impacts of CCA and SG on the systematic and random errors of TMPA SPEs. The errors of TMPA version-7 near-real-time products before and after CCA (RT_UC, RT_C), and the research product TMPA 3B42 (V7), were decomposed into systematic and random components, benchmarked by the China Gauge-based Daily Precipitation Analysis (CGDPA). After being calibrated by CCA, RT_C reduced the systematic errors relative to RT_UC over the Chinese mainland, except in the Tibetan Plateau and Tianshan Mountains. However, CCA did not aid in reducing random errors; instead, it even exacerbated the random errors. On the other hand, the SG merging is more effective in reducing systematic errors of SPEs than CCA calibration because of the direct inclusion of simultaneous gauge data from the Global Precipitation Climatology Centre (GPCC). We also found that SG merging reduced the random errors of pure SPEs over regions with relatively higher elevations. Despite lower random errors in V7 over the complex terrain region, the SG unfavorably increased the random errors over southeastern China. The results reported here may offer valuable insights into the effects of CCA and SG techniques drawn from TMPA, with the potential to advance the development of bias-adjusting algorithms for SPEs in the future.

Interannual Lightning Variability within the TRMM LIS Dataset Using an ENSO Perspective

Abstract The Tropical Rainfall Measuring Mission (TRMM) Lightning Imaging Sensor (LIS) was used to investigate interannual variability of lightning from 1998 to 2014 within the 38°S–38°N range. Previous studies have indicated that the El Niño–Southern Oscillation (ENSO) phenomenon is one significant contributor to interannual lightning variability, potentially the dominant mechanism on the global scale. This period of 16 years contained four warm- (El Niño), eight cold- (La Niña), and four neutral-phase ENSO years based on the oceanic Niño index. Large magnitude lightning anomalies were found during the warm phase of ENSO, with mean warm-phase anomalies of >10 flashes (1000 km)−2 min−1 in north-central Africa and Argentina. This includes a +35 flashes (1000 km)−2 min−1 anomaly in Argentina during the 2009 El Niño. In general, large-scale anomalies of thermodynamic properties and upper-atmospheric vertical motion coincided with the lightning anomalies observed in both Africa and South America. The anomaly over north-central Africa, however, was characterized by a 6-week shift in the annual lightning maximum with the warm phase, a result of the more complex environmental response to ENSO over the Sahel. The most consistent ENSO anomalies with appreciable lightning were found in southeastern Africa, northwestern Brazil, central Mexico, and the southern Red Sea. Of these, all but the Mexico region had enhanced lightning with the cold phase and suppressed lightning with the warm phase.

A Global Climatology of Diabatic Heating in Tropical Easterly Waves

Abstract Tropical easterly waves (TEWs) play a critical role in regulating convection and precipitation across the global tropics. TEWs act as seed disturbances for tropical cyclogenesis, serve as an essential component in monsoon precipitation, and produce large amounts of rainfall and diabatic heating that can strongly affect the large-scale circulation. To help improve our knowledge of a more elusive type of tropical wave, we use satellite and reanalysis estimates of the diabatic heating associated with TEWs that are identified by a tracking algorithm based on low-level curvature vorticity. This study uses the Tropical Rainfall Measuring Mission (TRMM) version 6 convective–stratiform heating (CSH) and spectral latent heating (SLH) orbital products to create a global climatology (1998–2015) of TEW diabatic heating. TEW-specific composites for the satellite-observed vertical structure of diabatic heating are compared to similar terms from MERRA-2 across a variety of tropical regions. There are striking differences between the reanalysis and satellite heating with MERRA-2 having much stronger background heating, especially at low levels. Both the satellite-observed and reanalysis heating profiles show stronger midlevel heating associated with TEWs relative to the unconditional background. Similar patterns of mid- and bottom-heaviness emerge in two-dimensional composites of TEW latent heating as stronger heating rates and percent contributions to the background are generally higher at 500 hPa than at 850 hPa. Although TEWs only represent a few percent of the background heating across the global tropics, they comprise 30%–50% of the heating in the prominent TEW tracks over the northeastern Atlantic and Pacific Oceans.

Superiority of Dynamic Weights against Fixed Weights in Merging Multi-Satellite Precipitation Datasets over Pakistan

Satellite precipitation products (SPPs) are undeniably subject to uncertainty due to retrieval algorithms and sampling issues. Many research efforts have concentrated on merging SPPs to create high-quality merged precipitation datasets (MPDs) in order to reduce these uncertainties. This study investigates the efficacy of dynamically weighted MPDs in contrast to those using static weights. The analysis focuses on comparing MPDs generated using the “dynamic clustered Bayesian averaging (DCBA)” approach with those utilizing the “regional principal component analysis (RPCA)” under fixed-weight conditions. These MPDs were merged from SPPs and reanalysis precipitation data, including TRMM (Tropical Rainfall Measurement Mission) Multi-satellite Precipitation Analysis (TMPA) 3B42V7, PERSIANN-CDR, CMORPH, and the ERA-Interim reanalysis precipitation data. The performance of these datasets was evaluated in Pakistan’s diverse climatic zones—glacial, humid, arid, and hyper-arid—employing data from 102 rain gauge stations. The effectiveness of the DCBA model was quantified using Theil’s U statistic, demonstrating its superiority over the RPCA model and other individual merging methods in the study area The comparative performances of DCBA and RPCA in these regions, as measured by Theil’s U, are 0.49 to 0.53, 0.38 to 0.45, 0.37 to 0.42, and 0.36 to 0.43 in glacial, humid, arid, and hyper-arid zones, respectively. The evaluation of DCBA and RPCA compared with SPPs at different elevations showed poorer performance at high altitudes (>4000 m). The comparison of MPDs with the best performance of SPP (i.e., TMPA) showed significant improvement of DCBA even at altitudes above 4000 m. The improvements are reported as 49.83% for mean absolute error (MAE), 42.31% for root-mean-square error (RMSE), 27.94% for correlation coefficient (CC), 40.15% for standard deviation (SD), and 13.21% for Theil’s U. Relatively smaller improvements are observed for RPCA at 13.04%, 1.56%, 10.91%, 1.67%, and 5.66% in the above indices, respectively. Overall, this study demonstrated the superiority of DCBA over RPCA with static weight. Therefore, it is strongly recommended to use dynamic variation of weights in the development of MPDs.

Observed Relationships between Sea Surface Temperature, Vertical Wind Shear, Tropical Organized Deep Convection, and Radiative Effects

Abstract Organized deep convective activity has been routinely monitored by satellite precipitation radar from the Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Mission (GPM). Organized deep convective activity is found to increase not only with sea surface temperature (SST) above 27°C, but also with low-level wind shear. Precipitation shows a similar increasing relationship with both SST and low-level wind shear, except for the highest low-level wind shear. These observations suggest that the threshold for organized deep convection and precipitation in the tropics should consider not only SST, but also vertical wind shear. The longwave cloud radiative feedback, measured as the tropospheric longwave cloud radiative heating per amount of precipitation, is found to generally increase with stronger organized deep convective activity as SST and low-level wind shear increase. Organized deep convective activity, the longwave cloud radiative feedback, and cirrus ice cloud cover per amount of precipitation also appear to be controlled more strongly by SST than by the deviation of SST from its tropical mean. This study hints at the importance of non-thermodynamic factors such as vertical wind shear for impacting tropical convective structure, cloud properties, and associated radiative energy budget of the tropics. Significance Statement This study uses tropical satellite observations to demonstrate that vertical wind shear affects the relationship between sea surface temperature and tropical organized deep convection and precipitation. Shear also affects associated cloud properties and how clouds affect the flow of radiation in the atmosphere. Although how vertical wind shear affects convective organization has long been studied in the mesoscale community, the study attempts to apply mesoscale theory to explain the large-scale mean organization of tropical deep convection, cloud properties, and radiative feedbacks. The study also provides a quantitative observational baseline of how vertical wind shear modifies cloud radiative effects and convective organization, which can be compared to numerical simulations.

Evaluation of satellite rainfall estimates using PERSIANN-CDR and TRMM over three critical cells in Jordan

Abstract Effective management of water resources is heavily dependent on accurate knowledge of rainfall patterns. Satellite rainfall estimates (SREs) have become increasingly popular due to their ability to provide spatial rainfall data. However, the accuracy of SREs is limited by a variety of factors including a lack of observations, inadequate evaluation techniques, and the use of short evaluation durations. To improve our understanding of SREs, this study evaluated the long-term performance of Tropical Rainfall Measuring Mission (TRMM) and PERSIANN-CDR by analyzing their spatiotemporal patterns. Daily, monthly, seasonal, and annual precipitation estimates were evaluated using statistical measures and data from 71 rain gauges across three critical cells in Jordan from 2000 to 2013. The results showed that while both SREs had low accuracy on a daily scale, TRMM 3B43 performed better than PERSIANN-CDR at the monthly level. Additionally, TRMM 3B43 exhibited superior performance during the heavy rainy season, whereas PERSIANN-CDR showed better results during other seasons. In annual studies, TRMM 3B43 was found to be more accurate than PERSIANN-CDR for the north and south cells, while PERSIANN-CDR had a higher correlation coefficient for the middle cell. These findings can contribute to the development of more reliable and accurate SREs, thereby improving water resource management studies.

Spatiotemporal Distributions of the Thunderstorm and Lightning Structures over the Qinghai–Tibet Plateau

Utilizing data from the Tropical Rainfall Measuring Mission (TRMM) satellite’s precipitation radar (PR) and lightning imaging sensor (LIS), this study explores the spatiotemporal distributions of thunderstorm and lightning structures over the Qinghai–Tibet Plateau (QTP), an aspect that has not been explored previously. The structural aspects are crucial when considering the impact of thunderstorm and lightning activity in the atmospheric processes. Thunderstorms over the QTP show clear spatial variations in both vertical height and horizontal extension. In the southern region, the average heights of 20 dBZ and 30 dBZ echo tops typically exceed 11.2 and 9.3 km, respectively. Meanwhile, in the eastern part, the average coverage areas for reflectivity greater than 20 dBZ and 30 dBZ consistently surpass 1000 and 180 km2, respectively. The spatial distribution of thunderstorm vertical development height relative to the surface aligns more closely with the horizontal extension, indicating stronger convection in the eastern QTP. The thunderstorm flash rate shows an eastward and northward prevalence, while the thunderstorm flash density peaks in the western and northeastern QTP, with a minimum in the southeast. Furthermore, in the eastern QTP, lightning duration, spatial expansion, and radiance are more pronounced, with the average values typically exceeding 0.22 s, 14.5 km, and 0.50 J m−2 sr−1 μm−1, respectively. Monthly variations reveal heightened values during the summer season for thunderstorm vertical extension, areas with reflectivity greater than 30 dBZ, and lightning frequency. Diurnal variations highlight an afternoon increase in thunderstorm vertical and horizontal extension, lightning frequency, duration, and spatial scale. From a statistical perspective, under weak convective conditions, lightning length exhibits a positive correlation with thunderstorm convection intensity, contrasting with the opposite relationship suggested by previous studies. This article further analyzes and discusses the correlations between various thunderstorm and lightning structural parameters, enhancing our understanding of the distinctive features of thunderstorm and lightning activities in the QTP.

Evaluating dynamics of land water storage and its response to climate variation in the Deccan Plateau, India

Inter-state river water disputes are frequent over the rain shadow (RS) region of the Western Ghats (WG), where the annual rainfall is much lower (75.2 cm) than that of overall India (118 cm). This study investigated the TWS (Terrestrial Water Storage) dynamics of the entire Deccan plateau (DP) and RS regions, located at the leeward side of the WG mountain range, with the help of Gravity Recovery and Climate Experiment (GRACE) based TWS anomalies (TWSA) data. Here, the latest TRMM (Tropical Rainfall Measuring Mission) rainfall product was evaluated at different time scales against gauge-based measurements to check its applicability as an alternative to gauge-based observed data. Trends in climate variables and TWSA were estimated to understand their variations during 2003–2016 in both regions. The response of GRACE-based TWSA and its constituent components to rainfall was determined using time-lagged correlation analysis. The contribution of constituent variables to TWSA was estimated using the component contribution ratio (CCR) method. It was observed that the TRMM-3B43 data captured the rainfall pattern over the entire DP with great accuracy. Results showed that the increasing trend of TWS in 2003–2009 had reversed in 2010–2016 due to the reduction of climate moistening in both regions. The warming trend of climate is more than twice in the RS region than that found in the DP, leading to a higher decreasing rate of TWS in this region than in DP. For both DP and RS regions, the time lags of groundwater storage anomalies (GWSA), total runoff anomalies (RA), canopy water storage anomalies (CWSA), and soil moisture storage anomalies (SMSA) to rainfall can be arranged as CWSA ≤ RA ≤ SMSA ≤ GWSA. SMSA and GWSA are the main contributors to the TWSA in both areas. However, SWSA also played a vital role in TWS variability, particularly in the RS region.

Development of intensity – duration – frequency (IDF) curves using combined rain gauge (RG) and remote sense (TRMM) datasets for Weta Traditional Area in Ghana

This research developed IDF curves and derived empirical equations for the Weta Area to estimate design rainfall. Daily rainfall data of Rain Gauge and Tropical Rainfall Measuring Mission (TRMM) 3B42 over 22 years for Weta were obtained from the Ghana Meteorological Services and National Aeronautics and Space Administration, respectively. The Indian Meteorological Department formula was used to disaggregate the daily data into hourly synthetic series to develop the IDF curves. The combined dataset was subjected to frequency analysis to determine the distribution which best characterize the dataset. The results demonstrated that the IDF curves provided higher intensities for shorter durations (0.08-h, 0.17-h, 0.25 h, and 0.5 h) while lower intensities for longer durations (1 h, 2-h, 6-h, 12 h and 24 h). The rainfall intensities and return periods are parallel and high intensities correlated with brief durations for the same return period. TRMM overestimated rainfall compared to Rain Gauge data. The IDF curves and derived empirical equations are useful scientific tools for hydraulic infrastructure design and water resources planning and management.

The Thunderstorms With the Greatest Lightning Densities on Earth

AbstractThe most intense thunderstorms on Earth were surveyed using the comprehensive meteorological instrumentation on the Tropical Rainfall Measuring Mission (TRMM) satellite. Expansive land‐based Mesoscale Convective Systems (MCSs) were consistently identified among the Earth's most intense thunderstorms, with their organization into many convective cells spanning a large areal extent permitting exceptional overall flash rates for these storms. In this study, we identify a new class of extreme thunderstorm. Lightning‐dense thunderstorms are relatively compact convective storms whose concentrated lightning activity hinders our ability to accurately measure their flash rates. The top storms have a flash rate of one flash spanning many seconds, as there is insufficient separation to distinguish one flash from another. While any particularly active convective cell could be capable of producing high lightning densities, we find that thunderstorms with the greatest lightning densities on Earth are found in maritime thunderstorms that have not been appreciated in prior work due to the inaccurate flash rate measurements. These storms that are mostly found throughout the Gulf of Mexico and east of South Africa (among other coastal and oceanic regions) have measured TRMM proxies for convective intensity that rival the top MCS thunderstorms, but their horizontal and vertical dimensions are small by comparison. Thus, the necessary microphysical elements for electrification processes are more highly concentrated, enabling the observed extreme lightning densities.

Influence of Aerosols on Lightning Activities in Java Island, Indonesia

Lightning is one of the natural disasters that cause significant financial losses and even fatalities. Therefore, it is necessary to understand the characteristics of lightning and related factors take appropriate preventive and mitigation measures. This work investigated the influence of aerosols and atmospheric thermodynamic factors on lightning in Java Island using 16 years of data (1998 - 2013) from the Tropical Rainfall Measuring Mission (TRMM) satellite. Aerosol data were obtained from the Modern Era Retrospective Analysis for Research and Applications version 2 (MERRA-2). Furthermore, convective available potential energy (CAPE) and potential temperature data were taken from the fifth-generation ECMWF atmospheric reanalysis (ERA-5) data. The intensity of lightning strikes in the western part of Java, such as Jakarta and Banten, is higher than in the eastern part, corresponding to the distribution pattern of aerosols, especially sulfate aerosols, sea salt aerosols, and black carbon aerosols. Sea salt aerosols have an inverse relationship with lightning, as these coarse-sized aerosols tend to inhibit convection. An increase in CAPE and potential temperature generally leads to higher lightning intensity. However, in cases where CAPE values are extremely high, such as in Jakarta, the intensity of lightning decreases. A similar pattern can be observed with potential temperature. An inverse relationship between lightning and potential temperature is observed in regions at higher elevations. The peak time for CAPE and potential temperature coincide with the peak intensity of lightning, which typically occurs during the rainy season, while the peak of AOD occurs earlier, during the pre-monsoon (September-October-November). The highest values of sea salt AOD are observed during the dry season (June-July-August) when lightning is minimal. These variations in peak times are particularly evident in the western regions of Java, where AOD values are high. The findings of this study can aid in lightning disaster mitigation efforts on Java Island. HIGHLIGHTS The intensity of lightning strikes in the western part of Java is higher than in the eastern part Sea salt aerosols have an inverse relationship with lightning, as these coarse-sized aerosols tend to inhibit convection An increase in CAPE and potential temperature generally leads to higher lightning intensity The peak time for CAPE and potential temperature coincide with the peak intensity of lightning The highest values of sea salt AOD are observed during the dry season when lightning is minimal GRAPHICAL ABSTRACT 

Correcting for Undercounting of Lightning Flashes by Space‐Based Optical Sensors

AbstractAccurate measurements of global lightning are essential for understanding present and future atmospheric electricity, composition, and climate. The latest space‐based lightning detector, the Geostationary Lightning Mapper (GLM), was the first to be placed in geostationary orbit, with a continuous view of most of the American continents. Prior to the GLM, the Lightning Imaging Sensor (LIS) on the Tropical Rainfall Measuring Mission (TRMM) satellite collected lightning measurements from which numerous lightning climatologies have been developed, including those used in global models. However, this study finds that both the GLM and a second, similar LIS placed on the International Space Station (ISS) in 2017 detect lightning at similar rates and are undercounting lightning compared to ground‐based Lightning Mapping Arrays (LMAs). The GLM undercounts lightning by an average factor of 7.0, reaching a maximum over 120 as a function of satellite zenith angle, radar reflectivity at a height where the temperature is −10°C, flash height, and thunderstorm polarity. The LIS is estimated to undercount lightning by an average factor of 5.6, reaching a maximum of 75.0 as a function of radar reflectivity at the −10°C level, flash height, and thunderstorm polarity. Preliminary predictive equations for the GLM and LIS lightning undercount factor, or scaling factor (SF), use ice‐water content, equilibrium level, flash height, and satellite zenith angle, all of which can be derived in models. These equations are developed to encourage updating lightning parameterizations within global models and will likely increase modeled lightning's effects on atmospheric electrical circuits, composition, chemistry, and climate change.

Validasi Data Satelit Tropical Rainfall Measurement Mission dengan Menggunakan Pengamatan Curah Hujan

The availability of hydrological data poses a challenge in water infrastructure development. Common issues in this domain often arise due to the lack of comprehensive data availability. Utilizing high-resolution satellite-based rainfall measurements covering extensive areas presents a potential solution. However, variations in the observed rainfall data resolution may impact the accuracy of the data. This study employs Tropical Rainfall Measurement Mission (TRMM) rainfall data and compares it with rainfall data from observation stations to assess the suitability of TRMM as a hydrological data source. Validation analysis is conducted using the Root Mean Squared Error (RMSE), Nash-Sutcliffe Efficiency (NSE), Correlation Coefficient (R), and relative bias (RB) methods. Validation results indicate that corrected TRMM data yields better outcomes compared to uncorrected TRMM data, with lower RMSE, higher NSE, and increased RB values. The most favorable findings occur at the Batu Busuk station, with RMSE = 22.554, NSE = 0.181, R = 0.95, and RB = 0.413. These findings suggest that corrected TRMM data can be effectively used as a hydrological data source in water infrastructure development.

GIS-based flash flooding susceptibility analysis and water management in arid mountain ranges: Safaga Region, Red Sea Mountains, Egypt

The Safaga Region (SR) is part of the Red Sea mountain range in Egypt. Catastrophic flash flooding is now an inescapable event, wreaking havoc and causing massive loss of life and property. The majority of the floodwater, however, has been wasted as runoff to the Red Sea, which, if used wisely, could meet a fraction of the water demands for a variety of applications in this area. The current work aims to use GIS techniques to integrate remote sensing data for evaluating, mitigating, and managing flash floods in SR. The data set comprised Tropical Rainfall Measuring Mission (TRMM) thematic rainfall data, 1:50,000 scale topographical map sheets, geological maps, the ASTER Digital Elevation Model (ASTER GDEM), Landsat 7 Enhanced Thematic Mapper” (ETM7+), and Landsat 8 Operational Land Imager. The flash flood risk model of SR is developed using ArcGIS-10.3 geoprocessing tools integrating all the causal factors thematic maps. The final flood risk model for the SR suggests that 57% of the total basins in the SR are at high risk of flooding. Almost 38% of all basins are at moderate flood risk. The remaining 5% of basins are less prone to flooding. Flood-prone zones were identified, suitable dam-building sites were located, and extremely probable areas for water recharge were recognized. On the basis of reliable scientific data, structural and non-structural mitigation strategies that might reduce the damage susceptibility, alleviate the sensitivity of the flash flood, and best utilize its water supply were recommended.

Vertical structure of North Indian Ocean tropical cyclones: A composite analysis using TRMM and GPM

The present study deals with the vertical structure of Tropical Cyclones (TC) from 2010 to 2020 over the North Indian Ocean (NIO) using the Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Measurement Mission (GPM). We analyzed 31 TC which were characterized as severe cyclonic storms, very severe cyclonic storms, and super cyclones out of which 18 occurred over the Bay of Bengal and 13 over the Arabian Sea. The normalized intensity difference method of brightness temperature is used to distinguish the regions of the TC into eyewall, inner rainband, outer rainband, and outer region after identifying the eye. The eye radius (Re) of the cyclone is used to define the annular regions. The vertical structure is explored using the reflectivity in each region of the cyclone and also with respect to convective precipitation, stratiform precipitation, and sea surface temperature. The intensity of the convection in the eyewall and the rainbands is strong and reaches up to an altitude of 14 km. The outer rainbands were more of stratiform nature and a distinct bimodal distribution is observed with a peak below and above the bright band. The outermost region exhibited the characteristics of both the convective and stratiform, however, the intensity is observed to be less. The convection is observed to be strong in the eyewall for high SST compared to the marginal, however, the vertical extent is large in the marginal SST. The vertical structure of the TC over the NIO contained a wider distribution of reflectivity and vertically extended compared to that of the cyclones in the other oceanic basins.

Remote sensing-based drought hazard monitoring and assessment in a coastal plain: A principal component approach

Accurate drought information is essential for preventing agricultural and societal losses. The indicators of how severe a drought is the deficiency in precipitation, soil moisture, and vegetation stress. The indicators were evaluated using the Precipitation Condition Index (PCI), Vegetation Condition Index (VCI), and Temperature Condition Index (TCI).The indices were combined using Principal Component Analysis to create the Synthetic Drought Index (SDI) for the evaluation of drought severity. The indices were estimated using multi-source remote sensing data from the Tropical Rainfall Measuring Mission (TRMM) and Operational Land Imager (OLI) of various years. Temporal analysis showed that the district is drought-prone and deficiency of 65% of precipitation in northeast monsoon of 2016 and below average non-monsoon rainfall in 2017, caused drought and affected 223.5 Km2 in 2017. Below average precipitation in northeast monsoon of 2018 and below average non-monsoon rainfall in 2019, caused drought and affected 423 Km2 in 2019. The northeast coastal regions of Ottapidaram, Thoothukudi, and Vilathikulam taluks of the district were more severely prone to drought. Failure of monsoon is the root cause of water deficit in water bodies. The semi-arid coastal climate accelerates the evaporation of water in water bodies and causes soil moisture deficit that leads to drought in the coastal district. A sequential evaluation of this index can be used to identify the onset of drought and mitigate the effect of drought.