Tropical Rainfall Measuring Mission Research Articles

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.

Flash Flood Hazard Mapping Using Remote Sensing and GIS Techniques in Southwestern Saudi Arabia

Flash flooding is one of the most significant natural disasters in arid/hyperarid regions and causes vast property damage and a large number of deaths. For mitigating and reducing flood risks, data from several remote sensing satellite images—Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM), Landsat 8 Operational Land Imager (OLI), and Tropical Rainfall Measuring Mission (TRMM)—were prepared and combined through a GIS-based multicriteria decision-making technique to test and delineate the flash flood vulnerable areas of Wadi Hali in southwestern Saudi Arabia. Several flash flood thematic layers representing topographic, geomorphic, climatic, and hydrological conditions were prepared, normalized, and combined through a GIS- based analytic hierarchy process (AHP) technique to obtain flash flood hazard zones (FFHs). This method successfully presented a satisfactory output map that revealed six zones of flood risk, and areas of extreme hazard covered about 13% of the entire basin. Landsat 8 band composite 7, 5, and 3 and field data validated the FFHs. This map considered a key requirement for sustaining safe settlements downstream of Wadi Hali. Overall, the integration of remote sensing and GIS techniques revealed significant areas of flash flood zones in an arid region.

Uncertainties in Prediction of Streamflows Using SWAT Model—Role of Remote Sensing and Precipitation Sources

Watershed modelling is crucial for understanding fluctuations in water balance and ensuring sustainable water management. The models’ strength and predictive ability are heavily reliant on inputs such as topography, land use, and climate. This study mainly focuses on quantifying the uncertainty associated with the input sources of the Digital Elevation Model (DEM), Land Use Land Cover (LULC), and precipitation using the Soil and Water Assessment Tool (SWAT) model. Basin-level modelling is being carried out to analyze the impact of source uncertainty in the prediction of streamflow. The sources for DEM used are National Elevation Dataset (NED)-United States Geological Survey (USGS), Shuttle Radar Topographic Mission (SRTM), and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), whereas for LULC the sources were the National Land Cover Database (NLCD), Continuous Change Detection Classification (CCDC), and GAP/LANDFIRE National Terrestrial Ecosystems dataset. Observed monitoring stations (Gage), Climate Forecast System Reanalysis (CFSR), and Tropical Rainfall Measuring Mission (TRMM) satellites are the respective precipitation sources. The Nash-Sutcliffe Efficiency (NSE), Coefficient of Determination (R2), Percent Bias (PBIAS), and the ratio of Root Mean Square Error to the standard deviation (RSR) are used to assess the model’s predictive performance. The results indicated that TRMM yielded better performance compared to the CFSR dataset. The USGS DEM performs best in all four case studies with the NLCD and CCDC LULC for all precipitation datasets except Gage. Furthermore, the results show that using a DEM with an appropriate combination can improve the model’s prediction ability by simulating streamflows with lower uncertainties. TheVIKOR MCDM method is used to rank model combinations. It is observed from MCDM analysis that USGS DEM combinations with NLCD/CCDC LULC attained top priority with all precipitation datasets. Furthermore, the rankings obtained from VIKOR MCDM are in accordance with the validation analysis using SWAT.

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.

Potential of Satellite Rainfall Estimates as Inputs for Flood Forecasting: Case Study Gash River, Sudan

This study is an attempt to test the potential of using real time Satellite Rainfall Estimates (SRE) data for hydrological modeling. Tropical Rainfall Measurement Mission (TRMM-3B42RT V7) SRE was evaluated against observed rain gauge data in Gash river catchment. The TRMM was evaluated against intensity as well as elevation dependency. The Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS) software of the Army Corps of Engineering of the USA was used to simulate the rainfall - runoff process. The performance of TRMM was found to underestimate the rainfall for most of the events, the underestimation increases with the increase in elevation. TRMM data set was biased corrected and used as input to derive the hydrological model. Observed hydrographs at the catchment outlet were compared to the simulated flow hydrographs using events and continuous modeling. The results of hydrological modeling showed that events based modeling performed better, the coefficient of determination (R2) vary between 0.87 to 0.96 while Nash-Sutcliffe Efficiency (NSE), vary from 0.84 to 0.96 and Root Mean Square Error (RMSE) vary from 45 to 118.3 m3/s. While the same statistics for continuous modeling showed, (NSE = 0.65) and (RMSE 44.5 m3/s). These results reflect the high potential of TRMM data set as inputs for hydrological modeling and flood forecasting in the Gash river and other basins with similar characteristics.

Interdecadal change in autumn rainfall over Southeast China and its association with tropical Pacific SST

In this paper, Japan Meteorological Agency (JRA-55) reanalysis and observational rainfall datasets from the National Climate Center (NCC) of China, as well as satellite datasets from the Tropical Rainfall Measuring Mission (TRMM), Global Precipitation Climatology Project (GPCP), and International Satellite Cloud Climatology Project (ISCCP), are used. The correlation coefficient and fast Fourier transform (FFT) low-pass filter are also used, in order to reveal the interdecadal decrease in autumn rainfall in Southeast China (SEC) after 1990. The close and robust relationship between the interdecadal variation in autumn rainfall in SEC and sea surface temperature (SST) in the tropical Pacific is investigated. The most significant and stable region of correlation is located in 10° S–10° N, 160° E–160° W, in which there also exists interdecadal warming after 1990. Furthermore, the interdecadal warming of SST can induce Gill responses of the atmosphere: a cyclone anomaly is produced on each side of the equator in the lower troposphere, with a westerly anomaly to the west of the dateline, and an anticyclone anomaly is produced in the upper troposphere. In particular, the cyclone anomaly on the northern side of the equator is located in the Northwest Pacific (NWP), and its ambient northerly airflow weakens meridional water vapor transport, as well as the local descending motion and low-troposphere divergence, in favor of the interdecadal decrease in SEC rainfall after 1990. In addition, the sensitive experiments with ECHAM-5.4 model also confirm that the interdecadal warming in the region (10° S–10° N, 160° E–160° W) would motivate the atmospheric Gill response and thereby cause the sinking motion in SEC and support the interdecadal decrease in autumn rainfall in SEC.

Adaptation Analysis in IMERG Precipitation Estimation for the Dongting Lake Basin, China

Precipitation data from ground-based observatories in the Dongting Lake basin are often missing, resulting in large errors in surface precipitation data obtained by interpolation, which affects the accuracy of hydro-meteorological studies. Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG) is the main high-resolution precipitation product, which is available to supplement measured missing data. To evaluate the applicability of this product in the Dongting Lake basin at multiple spatial and temporal scales, this paper analyzes daily, monthly, seasonal, annual, and extreme precipitation events of the three latest IMERG precipitation products (IPPs) (IMERG-F, IMERG-E, and IMERG-L) using eight statistical evaluation metrics. We find that the spatial and temporal performance of IMERG precipitation products varies over different time scales and topographic conditions. However, all three metrics (CC, RMSE, and RB) of the IMERG-F precipitation products outperform the IMERG-E and IMERG-L precipitation products for the same period. In the comparison of IMERG and TRMM (Tropical Rainfall Measuring Mission) precipitation products on monthly and seasonal scales, IMERG-F performed the best. IPPs can capture precipitation more accurately on seasonal scales and perform better in winter, indicating good detection of trace precipitation. Both high and low altitudes are not favorable for the satellite detection of extreme precipitation in both general and extreme precipitation events. Overall, the accuracy of IMERG-F with correction delay is slightly better than that of IMERG-E and IMERG-L without correction under near-real-time conditions, which is applicable in the Dongting Lake basin. However, the correction process also exacerbates overestimation of the precipitation extent.

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.

Relationship between Lightning and Aerosol Optical Depth over the Uttarakhand Region in India: Thermodynamic Perspective

The current study is mainly focused on the monthly variation in the lightning flash rate (LFR) and related thermodynamic parameters using the data for the years 2000–2013, and the trend of lightning variation is explored. Lightning data are used from a lightning imaging sensor (LIS) and an optical transient detector (OTP) boarded on the tropical rainfall measuring mission (TRMM). Additionally, aerosol optical depth (AOD) data at 550 nm for the same period were considered from a Moderate Resolution Imaging Spectroradiometer (MODIS). The assessment of lightning and AOD using monthly data makes it difficult to study seasonal contributions, and higher-resolution (hourly) data may be more appropriate, but unfortunately, no data were available with a higher resolution than monthly. The dependency of LFR is also investigated using thermodynamic/dynamic parameters. The LFR shows a moderate correlation with a correlation coefficient of 0.56, 0.62, and 0.63 for AOD, CAPE, and vertical velocity, respectively. The increasing AOD in the pre-monsoon season is associated with higher lightning flash rates over this region. The possible sources of aerosols that cause an increase in lightning activities are identified from the classification of aerosols based on the characteristic values of the AOD and the Ångström exponent. The thermodynamic relation of the Product of Bowen ratio with the sum of the precipitation rate and evaporation rate has been used as a proxy to evaluate the lightning flash rate density over Srinagar, Uttarakhand region (78.55° E–79.05° E, 29.97° N–30.47° N), with nine models from the Coupled Model Inter-comparison Project-Phase 5 (CMIP5). The model-simulated LFR has also been used for the projection of lightning in the late 21st century, and the projected LFR over the study area shows a 7.41% increase during the (2079–2088) period as compared to the historic period (1996–2005). The results of the study region indicate caution in using any single climate variable as a proxy for projecting a change in the lightning–climate relationships in the scenario of global warming.

Differences in Satellite‐Based Latent Heating Profiles Between the 2015/2016 Disruption and Westerly Phase of the Quasi‐Biennial Oscillation

AbstractLatent heating (LH) profiles from the Tropical Rainfall Measuring Mission (TRMM) and the Global Precipitation Measurement mission (GPM) were employed to examine the LH distribution during the quasi‐biennial oscillation (QBO) disruption in the 2015/2016 Northern Hemispheric (NH) winter. We used the LH anomalies from the observation era climatology to compensate for the discontinuity between the TRMM and GPM LH. The difference in the LH anomalies (LHd) between the 2015/2016 winter and the typical winter coinciding with the westerly QBO phase revealed that deeper convective systems in the 2015/2016 winter shifted to the equator, releasing more latent heat relative to the convective systems in the winters coinciding with the westerly QBO phase. Comparison of the LHds calculated for each winter of 1998–2019 shows that the changes in the convective systems in the 2015/2016 winter were statistically exceptional among the changes in other winters. Inside the convective systems, stronger latent heat release and more frequent occurrences of deep convective and deep‐stratiform rain cells made up the total LHd during the 2015/2016 winter. Specifically, convective rain cells mainly derived the LHd of the convective systems in the 2015/2016 winter, except in February 2016. The results suggest that the LH increase from the typical winter with the westerly QBO phase may be considered an unusual equatorial wave source during the 2015/2016 QBO disruption. The signatures of the LHd profiles in the 10°S–10°N zonal band are sourced from the abnormal convective systems that were shifted from the western Pacific to the central‐to‐eastern Pacific.

Climatologies of Mesoscale Convective Systems over China Observed by Spaceborne Radars

Abstract This study investigates the characteristics of mesoscale convective systems (MCSs) as a function of MCS organizational mode over China, using long-term precipitation radar observations from the Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Measurement Mission (GPM). The spaceborne radar-based MCS climatology shows maximum population over low-elevation regions, marked decrease over the foothills, and minimum frequency over the Tibetan Plateau. Linear and nonlinear MCSs account for 17% and 83% of the MCSs over China, respectively. Linear MCSs have much stronger convective intensity and heavier precipitation than nonlinear MCSs, as indicated by TRMM convective proxies. Interestingly, though broad-stratiform MCSs have the weakest convection, they produce the heaviest (maximum) rain rate and the largest amount of heavy rainfall among nonlinear MCSs. Among various types of linear MCSs, bow echoes (BEs) and no-stratiform (NS) systems exhibit the strongest convective intensity, embedded lines exhibit the weakest, and convective lines with trailing/leading stratiform in between. BEs and NSs share the most vertically extended structure, strongest microwave ice scattering, and highest lightning flash rates, but NS systems have a much lower surface rain rate likely due to a drier environment. Vertical radar reflectivity profiles suggest that both ice-based and warm-rain processes play an important role in the precipitation processes of linear MCSs over China, including the most intense BE storms. In short, this study helps to better understand the convective organization, precipitation structure, and ensemble microphysical properties of MCSs over China, and potentially provides guidelines for evaluating high-resolution model simulations and satellite rainfall retrievals for monsoonal MCSs.

The deluge of peninsular India during Northeast Monsoon 2015: Observational aspects of thermodynamical, dynamical and microphysical features

During the period 1st November 2015 to 4th December 2015, Southern India especially the city of Chennai was battered by heavy rainfall owing to the passage of a Deep Depression during 8-11 November 2015 and low-pressure areas during 13-18 November 2015, 19-24 Nov. 2015 and 29 November 2015 - 4 December 2015. The deluge due to heavy rainfall associated with these systems resulted in enormous loss of life and property. A wave perturbation in deep zonal flow moved from east to west in November 2015. The observational, synoptic, dynamical and microphysical aspects of these systems have been analysed using in-situ surface observations, satellite observations and reanalysed datasets. 
 The results show the existence of a dominant trough in the easterlies off the southeast coast of peninsular India at 850 hPa and anomalous anticyclonic circulation over central parts of the country at 500 hPa extending up to the upper troposphere were conducive to the northward movement of these low-pressure systems and copious rainfall over NMR. The surface observational features of the deluge were explored using the Automatic Weather Stations (AWS), High Wind Speed Recording (HWSR) systems, Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Measurement (GPM) satellites. The microphysical aspects of these low-pressure systems are also analysed using TRMM and ERA-Interim datasets.

Flood Hazard Analysis Based on Rainfall Fusion: A Case Study in Dazhou City, China

In recent years, extreme weather events caused by global climate change have occurred frequently, intensifying the frequency of flood disasters. For flood hazard analysis, high-quality data and a reasonable weight assignment of the relevant factors are critical. This study conducts four rainfall fusion methods, to fuse the Tropical Rainfall Measuring Mission (TRMM) 3B42 and the observations in Dazhou City, China. Then, the random forest was applied to obtain the weights of various factors to facilitate a comprehensive flood hazard analysis under four rainfall durations. The results show that (1) the linear regression performs best out of the four fusion methods, with a correlation coefficient of 0.56; (2) the Digital Elevation Model (DEM) is the most impact factor with a weight of more than 0.2; and (3) the proposed flood analysis system performs well, as 70% of historical flood points are distributed in high and sub-high hazard areas and more than 93% of historical flood points are distributed in medium hazard areas. This study identified the flood hazard grade and distribution in Dazhou City, which could provide a valuable methodology to contribute to flood hazard analysis and disaster management with satellite rainfall. Furthermore, the results of this paper are profound for future work on the high-resolution flood risk assessment and management in Dazhou City.

Reconstruction and application of the temperature-vegetation-precipitation drought index in mainland China based on remote sensing datasets and a spatial distance model

In recent years, remote sensing drought monitoring indices have been gradually developed and have been widely used for global or regional drought monitoring due to their strong drought-monitoring capabilities and easy implementation advantages. However, some defects of remote sensing drought indices stand to be improved due to certain errors in the inversion of surface characteristics by remote sensing datasets. The temperature-vegetation-precipitation drought index (TVPDI) was taken as the research object, and the leaf area index (LAI), the difference between the land surface temperature (LST) and monthly average temperature, and Global Precipitation Measurement (GPM) precipitation data were selected instead of the normalized difference vegetation index (NDVI), LST and tropical rainfall measuring mission (TRMM) data to improve TVPDI. The improved remote sensing drought index was named the improved temperature-vegetation-precipitation drought index (iTVPDI). The drought-monitoring accuracy of iTVPDI was verified by the gross primary productivity (GPP), soil moisture, and crop yield per unit. The drought-monitoring ability of iTVPDI was verified with traditional drought indices, including the standardized precipitation index (SPI), standardized precipitation evapotranspiration index (SPEI), Palmer drought severity index (PDSI), temperature-vegetation drought index (TVDI), drought severity index (DSI) and crop water stress index (CWSI). The drought-monitoring accuracy of iTVPDI was verified by selecting sample areas. iTVPDI was applied to monitor drought in mainland China over the 2001–2020 period and obtained four main results. First, the correlation analyses of iTVPDI and TVPDI with GPP, crop yield per unit area, and soil moisture showed that iTVPDI had a stronger monitoring ability in Northeast, North, and Southwest China; the R2 value obtained with soil moisture was 0.62 (p < 0.05), and this value was higher than that of TVPDI. Then, the correlation analyses of iTVPDI and TVPDI with SPI, SPEI, PDSI, CWSI, DSI and TVDI showed that the correlation coefficients of iTVPDI and TVPDI with these six indicators were basically consistent, which indicated that the drought-monitoring capability of iTVPDI was consistent with that of TVPDI. In local areas such as the Qinghai-Tibet Plateau in China, the monitoring ability of iTVPDI was stronger than that of TVPDI. Third, through the sample area analysis, iTVPDI was found to moderate the NDVI-characterized vegetation factors in TVPDI in low-vegetation-cover areas affected by soil disturbances and in high-vegetation-cover areas affected by oversaturation. Finally, the results obtained from the application of iTVPDI in mainland China showed that during the warm-dry to warm-wet climate transition between 2001 and 2021, in 2010 and 2018, and in other special drought years, iTVPDI had the best response.

Synergy between satellite observations of soil moisture and water storage anomalies for runoff estimation

Abstract. This paper presents an innovative approach, STREAM – SaTellite-based Runoff Evaluation And Mapping – to derive daily river discharge and runoff estimates from satellite observations of soil moisture, precipitation, and total water storage anomalies (TWSAs). Within a very simple model structure, precipitation and soil moisture data are used to estimate the quick-flow river discharge component while TWSAs are used for obtaining its complementary part, i.e., the slow-flow river discharge component. The two are then added together to obtain river discharge estimates. The method is tested over the Mississippi River basin for the period 2003–2016 by using precipitation data from the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), soil moisture data from the European Space Agency's Climate Change Initiative (ESA CCI), and total water storage data from the Gravity Recovery and Climate Experiment (GRACE). Despite the model simplicity, relatively high-performance scores are obtained in river discharge estimates, with a Kling–Gupta efficiency (KGE) index greater than 0.64 both at the basin outlet and over several inner stations used for model calibration, highlighting the high information content of satellite observations on surface processes. Potentially useful for multiple operational and scientific applications, from flood warning systems to the understanding of water cycle, the added value of the STREAM approach is twofold: (1) a simple modeling framework, potentially suitable for global runoff monitoring, at daily timescale when forced with satellite observations only, and (2) increased knowledge of natural processes and human activities as well as their interactions on the land.

Implementation of a Rainfall Normalization Module for GSMaP Microwave Imagers and Sounders

This paper introduces the Method of Microwave Rainfall Normalization (MMN) for the Global Satellite Mapping of Precipitation (GSMaP) algorithm in its latest version (V05, algorithm version 8), released in December 2021. The method aims to mitigate the discrepancy of GSMaP rainfall estimates among passive microwave (PMW) imagers/sounders (MWIs/MWSs) due to differences in sensor specifications and retrieval algorithms. The basic idea of the MMN module is to calibrate target PMW sensors with reference sensors (the Global Precipitation Measurement (GPM) Microwave Imager (GMI) and the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI)) using the cumulative distribution function (CDF) of the rain rate. Differences between the CDF and normalization table for MWSs are greater than MWIs due to different rain retrieval algorithms. More (less) MWS rainfall is detected over the ocean (land) than GMI rainfall. Matchup rainfall data between GMI and a target PMW sensor are compared to evaluate MMN performance. The monthly mean rainfall and mean bias error were improved for almost all PMW sensors. This study leaves open the possibility for further inter-calibration and improvement of rain detection and heavy rainfall retrievals.

Relative Importance of Large‐Scale Environmental Variables to the World‐Wide Variability of Thunderstorms

AbstractThis study uses a 16‐yr Tropical Rainfall Measuring Mission (TRMM) Convective Features (CFs) and ERA‐Interim reanalysis data to investigate the relative importance of four large‐scale environmental variables to thunderstorms with random forest models. These four variables include Convective Available Potential Energy (CAPE), Convective Inhibition (CIN), low‐level wind shear, and warm cloud depth (WCD). First, these selected four environmental variables show a distinguished difference between CFs with and without lightning flashes. Specifically, CFs with at least one flash have higher CAPE, CIN, and lower WCD than those without lightning. Then, using these four variables, the geographical distribution of thunderstorms, especially the land‐ocean contrast in the occurrence of thunderstorms, is closely reproduced with a global random forest model. Such results suggest that a random forest model with key large‐scale environmental variables can be a useful tool to estimate the occurrence of global lightning thunderstorms. The study also investigates the relative importance of the selected variables to the occurrence of thunderstorms regionally. Relatively higher skill scores in the regional random forest model than the global one indicate the variation of roles of large scale environment variables over different regions. Though the data‐driven models can be utilized to estimate the occurrence of global thunderstorms, how to link the regional relative importance of these variable to the physical processes of thunderstorms needs further investigation.

Observed and predicted precipitation variability across Pakistan with special focus on winter and pre-monsoon precipitation.

This study utilises ground, satellite and model data to investigate the observed and future precipitation changes in Pakistan. Pakistan Meteorological Department's (PMD) monthly precipitation data set along with the Tropical Rainfall Measuring Mission (TRMM) monthly dataset TRMM_3B43 (0.25° × 0.25° resolution) and European Centre for Medium-Range Weather Forecasts's (ECMWF) monthly reanalysis product ERA5 have been used to evaluate rainfall trends over the climatic zones of Pakistan through Mann-Kendall test, Sen's slope estimator and innovative trend analysis for the time period 1978-2018. Community Climate System Model (CCSM4) projections have been employed to explore the projected changes in precipitation until 2099. Furthermore, TRMM and CCSM4 projections have been correlated and validated using root mean square error (RMSE) and mean bias error (MBE). There is a good correlation between TRMM and PMD ground observation at all stations of the country for all seasons, with correlation coefficient values ranging from 0.89 (November) to 0.97 (July and August). However, ERA5 monthly precipitation tends to overestimate rainfall in the winter months. The study shows a decreasing trend in winter precipitation in all zones of the country with a significant decrease over western mountains, i.e. zone C of the country. During 2008-2018, a sharp decrease in winter precipitation is observed as compared to the baseline value of 1978-2007 in all climatic zones. Rainy days have also shown a decrease in winter and pre-monsoon seasons. There seems to be a shift in precipitation from winter towards pre-monsoon season as pre-monsoon precipitation in the last 11years increased in all zones except for zone C. Coherently, there is a decrease in an area affected by winter precipitation and an increase in area for pre-monsoon precipitation. Future precipitation estimates from the CCSM4 model for RCP 4.5 and RCP 8.5 overestimate precipitation in most parts of the country for the first 9 observed years (2010-2018) and predict a rise in precipitation by 2099 which is more pronounced in the northern and western Pakistan while a decrease is predicted for the plains of the country, which might have negative consequences for agriculture.

Performance evaluation of satellite-based rainfall estimates for hydrological modeling over Bilate river basin, Ethiopia

PurposeThe purpose of this study is to address a highly heterogeneous rift margin environment and exhibit considerable spatiotemporal hydro-climatic variations. In spite of limited, random and inaccurate data retrieved from rainfall gauging stations, the recent advancement of satellite rainfall estimate (SRE) has provided promising alternatives over such remote areas. The aim of this research is to take advantage of the technologies through performance evaluation of the SREs against ground-based-gauge rainfall data sets by incorporating its applicability in calibrating hydrological models.Design/methodology/approachSelected multi satellite-based rainfall estimates were primarily compared statistically with rain gauge observations using a point-to-pixel approach at different time scales (daily and seasonal). The continuous and categorical indices are used to evaluate the performance of SRE. The simple scaling time-variant bias correction method was further applied to remove the systematic error in satellite rainfall estimates before being used as input for a semi-distributed hydrologic engineering center's hydraulic modeling system (HEC-HMS). Runoff calibration and validation were conducted for consecutive periods ranging from 1999–2010 to 2011–2015, respectively.FindingsThe spatial patterns retrieved from climate hazards group infrared precipitation with stations (CHIRPS), multi-source weighted-ensemble precipitation (MSWEP) and tropical rainfall measuring mission (TRMM) rainfall estimates are more or less comparably underestimate the ground-based gauge observation at daily and seasonal scales. In comparison to the others, MSWEP has the best probability of detection followed by TRMM at all observation stations whereas CHIRPS performs the least in the study area. Accordingly, the relative calibration performance of the hydrological model (HEC-HMS) using ground-based gauge observation (Nash and Sutcliffe efficiency criteria [NSE] = 0.71; R2 = 0.72) is better as compared to MSWEP (NSE = 0.69; R2 = 0.7), TRMM (NSE = 0.67, R2 = 0.68) and CHIRPS (NSE = 0.58 and R2 = 0.62).Practical implicationsCalibration of hydrological model using the satellite rainfall estimate products have promising results. The results also suggest that products can be a potential alternative source of data sparse complex rift margin having heterogeneous characteristics for various water resource related applications in the study area.Originality/valueThis research is an original work that focuses on all three satellite rainfall estimates forced simulations displaying substantially improved performance after bias correction and recalibration.

Performance evaluation of IMERG and TMPA daily precipitation products over CONUS (2000–2019)

Ground validation of satellite-based precipitation products is necessary to evaluate the performance of satellite observation at local, regional, and global scales. The evaluation of the rainfall data accuracy will reveal both its strengths and weaknesses, thereby determining its suitability for hydrometeorological applications. To reach this goal, this study comprehensively evaluated and characterized the systematic bias in the Global Precipitation Measurement (GPM) Integrated Multi-SatellitE Retrievals for GPM (IMERG) precipitation product, to be compared with its predecessor Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), based on the wide-spread ground-based observation data, PRISM (Parameter-Elevation Regression on Independent Slopes Model) over the Conterminous United States. The evaluation was conducted for daily datasets from June 2000 to the end of December 2019, in which IMERG and TMPA overlap using statistical error metrics both in space and time. While some statistical measures do not show significant improvement in daily IMERG compared to TMPA, systematic bias is substantially diminished. We detected systematic bias related to the vertical relief for both IMERG and TMPA but with a substantial decrease in the IMERG. The systematic bias in both satellite products distinctively differs in the summer and winter seasons, but, both products showed high performance in summer. According to our findings, the spatiotemporal performance of the IMERG precipitation product varies in different seasons, regions, time scales, and topographic conditions but with superiority over the TMPA daily product.