Tropical Rainfall Measuring Mission Satellite Research Articles

Bayesian Analysis of the Detection Performance of the Lightning Imaging Sensors

Abstract Identical Lightning Imaging Sensors (LIS) aboard the Tropical Rainfall Measuring Mission satellite (TRMM LIS, 1998–2015) and International Space Station (ISS LIS, 2017–23) have provided over two decades of lightning observations over the global tropics, with ISS LIS extending coverage into the midlatitudes. Quantifying the detection performance of both LIS sensors is a necessary step toward generating a combined LIS climatological record and accurately combining LIS data with lightning detections from other sensors and networks. We compare lightning observations from both LIS sensors with reference sources including the Geostationary Lightning Mapper (GLM) and ground-based Earth Networks Total Lightning Network (ENTLN), Earth Networks Global Lightning Network (ENGLN), National Lightning Detection Network (NLDN), and Global Lightning Dataset (GLD360). Instead of a relative detection efficiency (DE) approach that assumes the perfect performance of the reference sensor, we employ a Bayesian approach to estimate the upper limit of the absolute DE (ADE) of each system being analyzed. The results of this analysis illustrate the geographical pattern of ADE as well as its diurnal cycle and yearly evolution. Reference network ADE increased by ∼15%–30% during the TRMM era, leading to a decline in TRMM LIS ADE. ISS LIS flash ADE has been relatively consistent at 61%–65%, about 4%–5% lower than TRMM LIS at the end of its lifetime.

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.

A Backscatter Model Combing Wind and Rain Effect at Low Incidence Angles

As the Wind blows over the ocean surface, the generated waves are the primary factor affecting the microwave backscatter of the remote sensing radar. In addition, there are also some influencing factors, such as rainfall, which is the best-known atmospheric phenomenon. This paper proposes a wind and rain backscatter model whose angle of incidence is from 1 to 6 deg. The model is developed by considering the influence of ocean surface wind, atmospheric rain attenuation, rain volume backscatter and interactions between raindrops and ocean surface. The wind-induced component of the model adopted a quasi-specular theoretical model for the isotropic rough surface of Gaussian statistics. Rain attenuation is expressed as a coefficient of the model, and rain volume backscatter is expressed as a function of the rain volume backscattering coefficient, the thickness of precipitation layer and the incidence angle. Interactions between raindrops and ocean surface are complicated and deduced from the collocated dataset from the Precipitation Radar onboard the Tropical Rainfall Measuring Mission satellite and the European Centre for Medium-Range Weather Forecasts. Based on the collocated dataset, the result shows the proposed model can estimate reasonably accurate estimates surface backscatter within wind speeds range from 8 to 18 m s−1.

Evaluating and Correcting Temperature and Precipitation Grid Products in the Arid Region of Altay, China

Temperature and precipitation are crucial indicators for investigating climate changes, necessitating precise measurements for rigorous scientific inquiry. While the Fifth Generation of European Centre for Medium-Range Weather Forecasts Atmospheric Reanalysis (ERA5), ERA5 of the Land Surface (ERA5-Land), and China Meteorological Forcing Dataset (CMFD) temperature and precipitation products are widely used worldwide, their suitability for the Altay region of arid and semi-arid areas has received limited attention. Here, we used the Altay region as the study area, utilizing meteorological station data and implementing the residual revision method for temperature and the coefficient revision method for precipitation to rectify inaccuracies in monthly temperature and precipitation records from ERA5-Land, ERA5, and CMFD. We evaluate the accuracy of these datasets before and after correction using bias, Taylor diagrams, and root-mean-square error (RMSE) metrics. Additionally, we employ Tropical Rainfall Measuring Mission satellite precipitation data (TRMM) as a benchmark to assess the performance of ERA5-Land, ERA5, and CMFD monthly precipitation before and after correction. The results revealed significant differences in the temperature and precipitation capture capabilities of ERA5-Land, ERA5, and CMFD in the Altay region. Overall, these data exhibit substantial errors and are not directly suitable for scientific research. However, we applied residual and coefficient revision methods. After this revision, ERA5-Land, ERA5, and CMFD showed significantly improved temperature and precipitation capture capabilities, especially for ERA5-Land. In terms of temperature, post-revision-CMFD (CMFDPR) demonstrated better temperature capture capabilities. All three datasets showed weaker performance in mountainous regions compared to plains. Notably, post-revision-ERA5 (ERA5PR) seemed unsuitable for capturing temperature in the Altay region. Concerning rain, CMFDPR, post-revision-ERA5-Land (ERA5-LandPR) and ERA5PR outperformed TRMM in capturing precipitation. CMFDPR and ERA5-LandPR both outperform ERA5PR. In summary, the revision datasets effectively compensated for the sparse distribution of meteorological stations in the Altay region, providing reliable data support for studying climate change in arid and semi-arid areas.

Cloud and Precipitation Variability Associated With the Madden‐Julian Oscillation Over the Maritime Continent

AbstractThis study reexamines cloud and precipitation variability associated with the Madden‐Julian Oscillation (MJO) over the Maritime Continent (MC), mainly using data acquired in 1998–2013 by the Tropical Rainfall Measuring Mission satellite. Infrared data indicated uniform, moderately deep stratiform cloud cover over the MC during MJO active phases, suggesting that an apparent MJO bypass of Borneo and New Guinea, observed in the brightness temperature anomaly distribution, was an artifact of climatologically low average brightness temperatures above the islands. Although previous satellite precipitation products indicated negligible anomalies over Borneo during MJO active phases, we identified positive precipitation anomalies in precipitation radar data and other recent satellite products. We hypothesize that previous satellite precipitation data products underestimated stratiform precipitation over Borneo during MJO active phases because ice‐particle scattering was weak. This is consistent with the moderately deep stratiform cloud layer identified from infrared data.

THE USE OF TROPICAL RAINFALL MEASURING MISSION RAINFALL DATA AS INPUT DATA FOR WATER AVAILABILITY ANALYSIS WITH RAINFALL-RUNOFF MODELS IN THE MELAWI SUB-BASIN

Many rain-to-discharge transformation models have been made, such as the Mock Model and the NRECA Model, which are used to make monthly synthetic discharge data when there is not enough discharge data to analyze water availability in a Sub-Basin or Sub-Basin. Tropical Rainfall Measuring Mission (TRMM) data, created by NASA and JAXA, is one type of satellite-based rainfall data that can help overcome the lack of rainfall data. However, not all areas have rainfall recording stations, and if there are, not all existing rainfall recording stations operate correctly. Such conditions also occur in the Melawi Sub-Basin in West Kalimantan. This research aims to determine how much water is available in the Melawi Sub-Basin by putting satellite-based rainfall data into the NRECA model and the Mock model, which converts rainfall to discharge.This study was done to determine how reliable TRMM satellite rainfall data are as input data for the rainfall-to-discharge transformation model used to determine how much water is available in the Melawi Sub-Basin. The goal of this study is to assess the suitability of TRMM data with observation station rainfall data in the Melawi Sub-Basin by validating TRMM satellite rainfall data with BMKG Susilo and BMKG Nanga Pinoh observation station rainfall data and to choose the appropriate rain-to-flow diversion model used in the Melawi Sub-Basin based on the results of model parameter calibration, with the models whose parameters are calibrated being the Mock Model and the NRECA.The study results show that TRMM rainfall data in the Melawi Sub-Basin that has been validated and corrected with the equation Y = 0.748089283 X + 62.19135368 can be used as input data in the analysis of water availability, and the transformation model of rainfall into discharge that is more suitable is the Mock Model. In general, both the observation station rainfall pattern and the TRMM rainfall pattern tend to have the same pattern as the discharge pattern in the Melawi Sub-Basin. The fact that August has the lowest peak is clear evidence of this. Even though the highest point doesn't happen in the same month, the trend is the same, so TRMM rainfall data can be considered valid and used to replace observational rainfall data in the Melawi Sub-Basin. Similar research can be done on other Sub-Basins in Kalimantan Barat Province to help develop and manage water resources since many observation stations in Kalimantan Barat Province no longer work, and there are many large Sub-Basins in that province.

Investigating Drought and Flood Evolution Based on Remote Sensing Data Products over the Punjab Region in Pakistan

Over the last five decades, Pakistan experienced its worst drought from 1998 to 2002 and its worst flood in 2010. This study determined the record-breaking impacts of the droughts (1998–2002) and the flood (2010) and analyzed the given 12-year period, especially the follow-on period when the winter wheat crop was grown. We identified the drought, flood, and warm and cold edges over the plain of Punjab Pakistan based on a 12-year time series (2003–2014), using the vegetation temperature condition index (VTCI) approach based on Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data products. During the year 2010, the Global Flood Monitoring System (GFMS) model applied to the real-time Tropical Rainfall Measuring Mission (TRMM) rainfall incorporated data products into the TRMM Multi-Satellite Precipitation Analysis (TMPA) for the flood detection/intensity, stream flow, and daily accumulative precipitation, and presented the plain provisions to wetlands. This study exhibits drought severity, warm and cold edges, and flood levels using the VTCI drought-monitoring approach, which utilizes a combination of the normalized difference vegetation index (NDVI) with land surface temperature (LST) data products. It was found that during the years 2003–2014, the VTCI had a positive correlation coefficient (r) with the cumulative precipitation (r = 0.60) on the day of the year (D-073) in the winter. In the year 2010, at D-201, there was no proportionality (nonlinear), and at D-217, a negative correlation was established. This revealed the time, duration, and intensity of the flood at D-201 and D-217, and described the heavy rainfall, stream flow, and flood events. At D-233 and D-281 during 2010, a significant positive correlation was noticed in normal conditions (r = 0.95 in D-233 and r = 0.97 in D-281 during the fall of 2010), which showed the flood events and normality. Notably, our results suggest that VTCI can be used for drought and wet conditions in both rain-fed and irrigated regions. The results are consistent with anomalies in the GFMS model using the spatial and temporal observations of the MODIS, TRMM, and TMPA satellites, which describe the dry and wet conditions, as well as flood runoff stream flow and flood detection/intensity, in the region of Punjab during 2010. It should be noted that the flood (2010) affected the area, and the production of the winter wheat crop has consistently declined from 19.041 to 17.7389 million tons.

Assessing Impacts of Flood and Drought over the Punjab Region of Pakistan Using Multi-Satellite Data Products

The Punjab region of Pakistan faced significant losses from flash flooding in 2010 and experienced a multiyear drought during 1998–2002. The current study illustrates the drought and flood conditions using the multi-satellite data products derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Tropical Rainfall Measuring Mission (TRMM) as well as the TRMM Multi-satellite Precipitation Analysis (TMPA) satellites with high-quality resolution in the region of Punjab during 2010–2014. To determine the drought and flood events, we used the Vegetation Temperature Condition Index (VTCI) drought monitoring approach combined with the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) to identify the warm and cold edges (WACE) in the provision of soil moisture as well as the VTCI imagery using the MODIS-Aqua data products. We assessed the 2010 flood effect on the four years (2011–2014) of drought conditions during winter wheat crop seasons. The obtained VTCI imagery and precipitation data were utilized to validate the drought and flood conditions in the year 2010 and the drought conditions in the years 2011–2014 during the winter-wheat-crop season. It is worth mentioning that over the four years (2011–2014) of the Julian day~D-041 year, the VTCI shows a stronger link with the accumulative precipitation anomaly (r = 0.77). It was found that for D-201 during the 2010 flood was the relationship was nonlinear, and in D-217, there was a negative relationship which revealed the flood timing, duration, and intensity. For D-281, a correlation (r = 0.97) was noted during fall 2010, which showed the drought and flood extreme conditions for the winter-wheat-crop season in the year 2010–2014. In regard to 2010, the Global Flood Monitoring System (GFMS) model employs the TRMM and TMPA data products to display the study region during the 2010 flood events and validate the VTCI results. This study’s spatial and temporal observations based on the observed results of the MODIS, TRMM, and TMPA satellites are in good agreement with dry and wet conditions as well as the flood runoff stream flow and flood intensity. It demonstrates the flood events with high intensity compared with the normality of flood with the complete establishment of flood events and weather extremes during the year of 2011–2014, thereby highlighting the natural hazards impacts. Our findings show that the winter wheat harvest was affected by the 2010 monsoon’s summer high rain and floods in the plain of Punjab (Pakistan).

Estatística para redução de escala nas estimativas de chuva pelo satélite TRMM para o estado de Goiás e Distrito Federal, Brasil

ABSTRACT Rainfall is a fundamental component of agricultural production, and knowing its potential and variability can ensure the success of this activity. However, the number of meteorological stations is still small, even in states with agricultural aptitude, such as Goiás. Geoprocessing techniques can be used to overcome this problem. Thus, this study aimed to evaluate the products of the Tropical Rainfall Measuring Mission (TRMM) satellite to describe the annual and monthly rainfall variability in the Goiás state and the Federal District (Brazil). Interpolations were carried out to increase the spatial resolution by means of ordinary kriging and cluster analysis for spatial and temporal distribution. It was observed that the evaluated territory can be classified into three regions with differentiated water regimes up to 500 mm annually, with seasonality of accumulated precipitation from November to March. Even though the regression evaluation showed limitations for a monthly precipitation above 200 mm, the analysis of the TRMM satellite products demonstrated that this tool allows forecasts of provisional normals with a higher spatial resolution than the Brazilian National Institute of Meteorology (INMET) stations network, with known measurement errors for each evaluation period, allowing the data application in forecast models for agricultural planning involving water management.

Convective Couplings with Equatorial Rossby Waves and Equatorial Kelvin Waves. Part II: Coupled Precipitation Characteristics

Abstract Detailed precipitation characteristics coupled with equatorial Rossby and Kelvin waves are investigated. We prepare a rainfall event dataset using the Tropical Rainfall Measurement Mission (TRMM) Precipitation Radar (PR) level 2 data. Utilizing three indices, area size, maximum echo-top height, and stratiform precipitation ratio, rainfall events are classified into mesoscale convective system (MCS), deep, congestus, and shallow convective events, and “other” type. We perform composite analyses based on the wave phase defined in Part I. Precipitation amount in Rossby waves is in phase with column water vapor (CWV) anomalies and is mainly contributed by MCSs, which are simultaneously activated with deep convection. The large CWV can support deep development and organization of convection. Shallow and congestus convective events indicate their peaks just before the active phase on 10°N or in the later part of the convectively suppressed phase on the equator. A five-step evolution is shown in Kelvin waves. In the first stage, shallow convective events are triggered by high SST, followed by a dominance of congestus convective events. Then, in the developing stage, deep convective events become dominant. In the mature stage, heavy precipitation is contributed by MCSs, and mostly stratiform rain is maximized at later stages. Kelvin waves indicate relatively weak connection to CWV fluctuation. Although contrasted evolutions are indicated, large contributions by MCSs to precipitation amount are common among the two coupled waves. This is considered to result in the commonality of the equivalent depths with their top-heavy heating. Significance Statement A coupling mechanism between equatorial waves and convective activity is a key issue in the tropical meteorology. While many previous idealized studies suggested some instability mechanisms, detailed precipitation characteristics is not enough investigated. We prepare rainfall-event dataset observed from a spaceborne Precipitation Radar on board the TRMM satellite to quantify detailed precipitation characteristics statistically and compare equatorial Rossby waves and equatorial Kelvin waves. We found that organized convective systems and deep convection are simultaneously activated in the Rossby waves and a clear transition of convective activity is shown in the Kelvin waves. These characteristics highly correspond to waves’ synoptic-scale structures. Our observational results of detailed evolutions of rainfall events will improve understanding of coupling processes.

Evaluation of TRMM satellite dataset for monitoring meteorological drought in northeastern Brazil

ABSTRACT Although droughts can inflict extreme damage on populations, few studies have characterized drought events in arid and semiarid regions around the globe due to the lack of reliable data. Therefore, precipitation estimates from remote sensing satellites have been increasingly used. In this study, the performance of the Tropical Rainfall Measuring Mission (TRMM) satellite rainfall estimates in monitoring the spatiotemporal patterns of drought at multiple time scales over Paraíba state is evaluated. The drought events are characterized by their quantity, duration, severity, intensity, frequency, and percentage of the area affected, and analyses are performed both for individual points and for four mesoregions at eight different time scales. The results indicate that short-term drought events occur more frequently but are less long-lasting and severe than long-term drought events. On the other hand, relevant changes in the average intensity of events cannot be identified among the time scales.

Impact of Quasi-Biweekly Oscillation on Southeast Asian Cold Surge Rainfall Monitored by TRMM Satellite Observation

Based on the Tropical Rainfall Measuring Mission (TRMM) satellite observation and ERA5 re-analysis dataset, this paper studies the influence of the northwestward-propagating quasi-biweekly oscillation (QBWO) over the western North Pacific on cold surge rainfall (CSR) over Southeast Asia. Cold surges are the most important driver affecting Southeast Asian rainfall on a synoptic scale. The presence of the QBWO during phases 6–8, in which the associated active convection coupling with a cyclonic circulation reaches Southeast Asia, provides a favorable environment for the increase of CSR. The increase in CSR primarily occurs east of the Philippines, leading to a high likelihood of triggering extreme rainfall. The effects from the QBWO are independent of those from the active MJO phases over Southeast Asia. Additionally, cold surge activity could also be influenced by the QBWO. An examination of the QBWO and MJO indicates that the most preferred phases for the occurrence of cold surges are the time when phase 1 of the QBWO co-exists with phase 7 of the MJO or the time when phase 7 of the QBWO couples with phase 5 of the MJO. Accordingly, about 40% of the total cold surge days would fall in either combination.

Preliminary Application of a Multi-Physical Ensemble Transform Kalman Filter in Cloud and Precipitation Forecasts

In this study, based on the retrieval data from the Fengyun geostationary meteorological satellite and the Tropical Rainfall Measuring Mission satellite, a large-scale precipitation case in eastern China is selected to address the systematic deviations of deterministic forecasts for clouds and precipitation. A multi-physical ensemble transform Kalman filter (ETKF) is constructed in this research based on the Weather Research and Forecast model version 3.6, and its forecasting ability in terms of cloud-top height and temperature, hydrometeors, and precipitation is evaluated by quantitatively comparing three microphysical parameterization schemes (Lin, Morrison, and CAM5.1 schemes) and their corresponding multi-physical ensemble mean. The results show that the Lin, Morrison, and CAM5.1 schemes all underestimate the range of cloud systems and have different advantages and disadvantages in forecasting different elements, while the forecasting improvement of the multi-physical ensemble mean is limited. However, the multi-physical ETKF can effectively improve the forecast accuracy of the cloud system range. In addition, the multi-physical ETKF has the advantages of different physical parameterization schemes, which can dramatically improve the forecast accuracy of cloud hydrometeors, reduce precipitation forecast errors, and improve threat scores.

Pemodelan Debit dengan Data Curah Hujan dari Rain Gauges dan Data TRMM pada DAS Temef di Pulau Timor - NTT

Rainfall data collection based on the TRMM (Tropical Rainfall Measuring Mission) satellite provides a good alternative in estimating rainfall. TRMM technology can minimize manual rainfall recording errors and increase rainfall accuracy for hydrological analysis. From the results of the statistical relationship analysis, the most accurate results obtained are 9 years of calibration and 1 year of validation, the selected equation is a polynomial with the following equation y = 0.0112 x2 - 0.2564x + 4,1293, so that validation analysis is carried out with the selected equation so that the validation results are obtained against 1 year rainfall data for the RMSE value of 7.022. The NSE value is 0.923, the Correlation Coefficient is 0.94 and the relative error value is 5.48 and the results of the equation for discharge are modeling return periods of discharge 2, 5, 10, 20 and 25 years for discharge observations of 2 years return period; Q = 84.44, m3/s at 5 years return period; Q = 105.95 m3/s, 10 year return period; Q = 120.86 m3/s, 20 years return period; Q = 135.16 m3/s, 25 years return period; Q = 139.69 m3/s. Meanwhile, for the simulation discharge, the return period is 2 years; Q =51,84 m3/s at 5 year return period; Q = 65,35 m3/s, 10 year return period; Q = 74,15 m3/s, 20 years return period; Q = 84,89 m3/s, return period of 25 years; Q = 94,65 m3/s.

Spatio-temporal variability of lightning climatology and its association with thunderstorm indices over India

Lightning is an electrical discharge — a ‘spark’ or ‘flash’ as charged regions in the atmosphere instantly balance themselves through this discharge. It is a beautiful and deadly naturally occurring phenomenon. In June 2020, more than a hundred people died in the state Bihar of India only in 3 days’ span due to lightning events. In this study, Lightning Imaging Sensor (LIS) from the Tropical Rainfall Measuring Mission (TRMM) satellite with a very high spatial resolution of 0.1° has been utilized to create the lightning climatology of India for 16 years during the period 1998 to 2013. Diurnal, monthly, and seasonal variations in the occurrence of lightning flash rate density have also been analyzed. TRMM satellite Low-Resolution Monthly Time Series (LRMTS) with 2.5° resolution datasets have been used for lightning trend analysis. The diurnal lightning event mainly occurs in the afternoon/evening time duration (1400–1900 h) around 0.001 flashes/km2/h. The highest lightning occurred in the month of May (0.04 flashes/km2/day) and the least in December (0.005 flashes/km2/day). The distribution of lightning flash counts by season over India landmass is mainly in pre-monsoon months (MAM) which ranges from 0.248 to 0.491 flashes/km2/day, and monsoon (JJA) ranges from 0.284 to 0.451 flashes/km2/day and decreases afterward. Spatially, the distribution of lightning flash density is seen over the North-Eastern region along with Bangladesh, Bihar, Jharkhand, Orissa, and Jammu and Kashmir region. The pre-monsoon (MAM) season shows a positive trend of lightning around 0.04 flashes in the North-Eastern region and the post-monsoon (SON) shows a negative trend of − 0.021 in central India. The CAPE and K-Index have positively correlated with the flash rate density seasonally but CAPE is more significantly correlated around 0.83 during pre-monsoon and monsoon season. This study also focused on finding out the district wise lightning hotspots regions of India. Rajouri and Reasi district in Jammu and Kashmir has the highest lightning with 121 and 115 flashes/km2/yr, respectively.

Lightning Climatology and Human Vulnerability to Lightning Hazards in a School Community: A Case Study in Sri Lanka using LIS Data from TRMM Satellite

This study reported lightning climatology and human vulnerability to lightning in a 20 km × 20 km high-density school area in Colombo city in Sri Lanka from 1998 to 2014 using Lightning Imaging Sensor (LIS) flash data of NASA’s Tropical Rainfall Measuring Mission (TRMM). An average annual flash density recorded over the study area was 9.43 flashes km-2 year-1. A maximum of 49% lightning flashes happened during the first inter-monsoon season. There were only 4% lightning flashes that occurred during 06.00-12.00 LT and during 18.00-24.00 LT, it was 67%, whereas 94% of lightning flashes within a day had occurred after 14.00 LT. It is recommended that, without having proper lightning hazard preventive measures, schools in the study area should avoid or minimalize scheduling their outdoor activities in high lightning risk months of April and November. Especially, after-school outdoor activities should be planned with proper safety measures during the aforementioned months as per the diurnal analysis. Moreover, May to September and December to February were the months with the least lightning risk levels. It is recommended to follow the proposed five-level lightning safety guideline which includes, schedule outdoor activities by considering the variation of lightning activities, follow the 30-30 rule whenever required, avoid staying at the most hazardous locations which are vulnerable to lightning accidents, crouching action if required and providing first-aid whenever necessary. Not only for the Sri Lankan context but also the study is crucial and highly applicable for all schools and other institutes especially in other tropical countries.

Application of Different Modelling Methods to Arbitrate Various Hydrological Attributes Using CMORPH and TRMM Satellite Data in Upper Omo-Gibe Basin of Ethiopia

Rainfall is a basic input parameter for hydrological modelling that exerts a great influence on the dependability of hydrological simulations. Limited availability of accurate and reliable precipitation data in Abelti watershed of Omo Gibe basin of Ethiopia coerces to use satellite rainfall data to design watershed management practices. The primary objective of this research is to find a better output by comparing and evaluating Climate Prediction Centre Morphing techniques (CMORPH) and Tropical Rainfall Measuring Mission (TRMM). Satellite precipitation products (SPPs) and inputs were incorporated to simulate stream flow. Sensitivity and uncertainty analysis, calibration, and validation of the model were conducted using Soil and Water Assessment Tool (SWAT), Calibration and Uncertainty Program 2012 (SWAT-CUP-2012), particularly the Sequential/Uncertainty Fitting (SUFI-2) algorithm for all rainfall inputs independently. The calibration and validation period was taken as 2003–2010 and 2011–2018, respectively. On the basis of the modelling results of SWAT and uncertainty analysis, TRRM relatively performed well than that of CMORPH. The result illustrated that the SWAT model thoroughly predicted the catchment runoff simulation for all SPPs. However, TRMM-based simulations capture the shape of the observed stream flow hydrograph, and there was slight under and overestimation of the stream flow volume simulated SPPs followed by the reduction of model performance statistics. Bias-corrected satellite rainfall-based simulations significantly improved the model performance as well as the volume of stream flow simulated. The detail study exhibited that the in situ-based simulation outperformed satellite products in terms of the objective functions in the study area.

Landslides Triggered by the May 2017 Extreme Rainfall Event in the East Coast Northeast of Brazil

Given the increasing occurrence of landslides on the East Coast Northeast of Brazil (ECNEB), it is essential to understand its conditions and triggering factors because meteorological anomalies triggered by a landslide will threaten life and property in the region. In this sense, this research aimed to diagnose the meteorological conditions that triggered landslides in the ECNEB in May 2017, evaluate the terrain’s intrinsic conditions using elevation, slope, and susceptibility parameters and determine critical precipitation thresholds for the city with the highest number of landslide risk areas in the region. A dynamic downscaling experiment was carried out using the Regional Climate Model (RegCM) to verify the ability of this model to represent rainfall over the ECNEB. The results from the intrinsic factors showed that the ECNEB is highly susceptible to landslides with various high-risk sectors for landslides to the population. The extreme rainfall event was associated with the convergence of humidity at low levels over the ocean, which contributed to landslides in the ECNEB, mainly in the State of Pernambuco, where 67 landslides were registered. The RegCM numerical simulation underestimated the high daily rainfall signal seen on the Tropical Rainfall Measuring Mission satellite. It is suggested that sensitivity tests can be performed using other physical parameters to find the best model configuration for the ECNEB. This work recommends that exploring the relationship between precipitation and landslides will provide objective criteria for assessing risk areas by contributing to the predictability of disasters in this region.

Spatiotemporal Variations of Water Vapor Content and Its Relationship with Meteorological Elements in the Third Pole

Water vapor content plays an important role in climate change and the ecosystem in the Tibetan Plateau (TP) through its complicated interaction with the meteorological elements. However, due to the complex topography of the Tibetan Plateau, it is unreliable to attempt to understand the variation pattern of water vapor content using only observational data. Satellite and reanalysis data can be a good substitute for observational data, but their accuracy still needs to be evaluated. Therefore, based on radiosonde stations data, comprehensive assessment of water vapor content on the TP and surrounding areas derived from ERA-5, Second Modern-Era Retrospective analysis for Research and Applications (MERRA2), Atmospheric Infrared Sounder (AIRS)-only, and weighted ensemble data was performed in the context of spatial and temporal distribution at the annual and seasonal scale. Based on precipitation from Gauge V3.0 and Tropical Rainfall Measuring Mission satellite (TRMM) and temperature from ERA-5, the relationship between water vapor content and temperature and precipitation was analyzed. The results show that water vapor content decreases from southeast to northwest, and ERA-5, MERRA2, and AIRS-only can reasonably reproduce the spatial distribution of annual and seasonal water vapor content, with ERA-5 being more reliable in reproducing the spatial distribution. Over the past 50 years, the water vapor content has shown a gradual increasing trend. The variation trends of AIRS-only, MERRA2, ERA-5, and weighted ensemble data are almost consistent with the radiosonde stations data, with MERRA2 being more reliable in capturing water vapor content over time. Weighted ensemble data is more capable of capturing water vapor content characteristics than simple unweighted products. The empirical orthogonal function (EOF) analysis shows that the first spatial mode values of water vapor content and temperature are positive over the TP, while the values of precipitation present a “negative-positive-negative” distribution from south to north over the TP. In the second spatial mode of EOF analysis, the values of water vapor content, air temperature, and precipitation are all negative. The first temporal modes of EOF analysis, water vapor content, air temperature, and precipitation all show an increasing trend. In conclusion, there is a clear relationship of water vapor content with temperature and precipitation.

Distribution of Lightning Accidents in Sri Lanka from 1974 to 2019 Using the DesInventar Database

The reported lightning accidents that are available in the DesInventar database—which consist of 549 deaths, 498 injured people, 39 destroyed houses, and 741 damaged houses—were analyzed in terms of their geographical and temporal variation. The average lightning flash densities were calculated using zonal statistics using the geographic information system (GIS), referring to the respective raster maps generated based on Lightning Imaging Sensor (LIS) data from the Tropical Rainfall Measurement Mission (TRMM) Satellite. Hence, the variations of the lightning accidents—monthly and climate season-wise—in response to the lightning flash density were also reported. The calculated average lightning flash density in Sri Lanka is 8.26 flashes km−2 year−1, and the maximum average lightning flash density of 31.33 flashes km−2 year−1 is observed in April in a calendar year. April seems to be more vulnerable to lightning accidents, as the maximum number of deaths (150 deaths) and injuries (147 injuries) were recorded in this month. Most of the high-risk lightning accident regions that were identified in Sri Lanka are well known for agricultural activities, and those activities will eventually create the platform for lightning victims. In Sri Lanka, in a year, 12 people were killed and 11 people were injured, based on the reported accidents from 1974 to 2019. Conversely, a substantial increase in the number of deaths, injuries, and incidents of property damage has been observed in the last two decades (2000–2019). On average, for the period from 2000 to 2019, 18 people were killed and 16 people were injured per year. Furthermore, considering the population of the country, 0.56 people per million per year were killed, and 0.51 people per million per year were injured due to lightning accidents based on the reported accidents from 1974 to 2019. Moreover, for the 2000–2019 period, these estimated values are significantly higher; 0.86 people per million per year were killed, and 0.77 people per million per year were injured.