Tropical Rainfall Measuring Mission Data Research Articles

Application of Tropical Rainfall Measuring Mission (TRMM) Data for Flood Estimation in Lack Data Catchment

Providing real-time hydrological data for the design, development and management of water resources is a fundamental issue that is still being faced by hydrologists and practitioners. This data limitation is not only caused by the uneven distribution of hydrological measuring instruments, but also due to the inability of measuring instruments to present the data in real time. This paper aims to evaluate hydrological data, especially spatial-based rainfall data known as Tropical Rainfall Measuring Mission (TRMM) data as an alternative to rainfall data to predict flood hydrographs in a catchment with limited data. The study was initiated by selecting pairs of automatic rainfall data (ARG) and water level (AWLR) and combined with TRMM rainfall data for the same time series. These two types of rainfall data are then used as input for the HEC-HMS hydrological model involving a number of catchment parameters, such as: basin area, stream network, land use/land cover (LULC), soil characteristics and several other parameters. Three analytical methods are assigned to determine the shape of the hydrograph which include: Snyder UH (transformation method), SCS-CN (loss method), and recession (baseflow method). Discharge data converted from water level data is input at the catchment outlet as a reference for setting calibration parameters which are evaluated with the RMSE error indicator. The simulation results show that the predicted discharge deviation from TRMM rainfall data is higher than the predicted discharge from ARG rainfall data with RMSE 0.979 and 1.731 respectively. However, basically this TRMM data can be an alternative data by validating it with ARG rain data to be applied to lack of catchment data.

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.

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).

GENERATION OF SYNTHETIC RAINFALL DATA IN SEKAYAM SUB-WATERSHED BASED ON TRMM SATELLITE RAINFALL DATA CORRECTION EQUATION

The hydrological analysis is the first stage of the review in waterworks planning. Hydrological analysis necessitates the availability of sufficient data. Data availability tends to have several problems, including a lack of availability, incomplete/empty data, a smaller number of stations, observers, and observation systems, data entry that is still manual, and slow data collection. One possible solution is to use rain satellites. However, TRMM data must be evaluated for field suitability. TRMM (Tropical Rainfall Measurement Mission) rainfall data can help overcome this. TRMM is a NASA mission that uses weather monitoring satellite technology to monitor tropical rainfall. This is also the case for the Sekayam Subwatershed, part of the Kapuas River Basin.In the Sekayam sub-watershed, there are 14 (fourteen) rainfall observation stations managed by the Balai Wilayah Sungai Kalimantan (BWSK) I, but currently only 5 observation stations are still active, namely the SGU-01 Sanggau, SGU-03 Balai Karangan, SGU-06 Entikong, SGU-19 Semuntai and SC-01 Kembayan observation stations with data recorded up to 2019, while the other 9 (nine) observation stations do not have long continuous data, because there are years where rainfall data is not recorded. This is because the recording of rainfall on average stopped until 2005, or even some have stopped operating since the 1990s or early 2000s. This study aims to generate representative synthetic TRMM daily rainfall amount data for the Sekayam sub-watershed based on the correction equations obtained in a series of TRMM rainfall data validation analyses, so that it can be used as alternative daily rainfall data in water resources planning and management in the Sekayam sub-watershed. From the analysis, it can be seen that synthetic rainfall data in the Sekayam sub-watershed will be valid if it is generated with the linear model correction equation Y = 0.6708 X + 139.123 or can be interpreted as TRMM' = 0.6708 TRMM + 139.123.

English

Indonesia lies in a tropical climate area and depends on rainfall as the main source of irrigation. Changes in rainfall patterns and air temperature directly impact the water availability for irrigation purposes, including Danda Besar Swamp Irrigation Area (DIR). DIR Danda Besar is a tidal swamp irrigation field, but the tidal power cannot reach several areas, so it needs to use rainwater to supply water demand and becomes rainfed agriculture. The observation of changes in rainfall patterns and air temperature from 2001 to 2020 was conducted using daily TRMM (Tropical Rainfall Measurement Mission) data split into 2 observation periods: Period I from 2001 to 2010 and period II from 2011 to 2020. The data analysis process consists of validation and correction to the TRMM data before it is used for water balance analysis, calculation of effective rainfall for rice and evapotranspiration, and analysis of water availability and demand. From the comparison of water availability discharge between Period I (2001-2010) and Period II (2011-2020), the highest increase in mid-month water availability occured on December 1 and December 2, and the highest decrease in water availability occured on November 1 and November 2. The results of the water balance analysis are based on water demand according to the cropping scenario with two cropping scenarios. Water balance 1 has a deficit in water availability for Periods I and II; water balance 2 is recommended for cropping patterns due to climate change, so the water balance is calculated based on water availability for Period II. No water deficit in Water balance 2.

Retrieval of Rain Rates for Tropical Cyclones From Sentinel-1 Synthetic Aperture Radar Images

The purpose of this study was to develop a method for retrieving the rain rate from C-band (∼5.3 GHz) synthetic aperture radar (SAR) images during tropical cyclones (TCs). Seven dual-polarized (vertical-vertical [VV] and vertical-horizontal [VH]) Sentinel-1 (S-1) SAR images were acquired in the interferometric-wide (IW) swath mode during the Satellite Hurricane Observation Campaign. These images were collocated with rain rates measured by the Stepped-Frequency Microwave Radiometers onboard National Oceanic and Atmospheric Administration aircraft. Wind speeds were retrieved from the VH-polarized SAR images using the geophysical model function (GMF) S1IW.NR. We determined the difference between the measured normalized radar cross section (NRCS) based on VV-polarized SAR and the predicted NRCS derived using the geophysical model function CMOD5.N forced with wind speeds retrieved from VH-polarized SAR images. Rain cells were identified as regions in the images where the NRCS difference was greater than 0.5 dB or smaller than −0.5 dB. We found that the difference in the NRCS decreased and the VH-polarized wind speed increased with increasing rain rate. Based on these findings, we developed an empirical function for S-1 SAR rain retrieval in a TC, naming it CRAIN_S1. The validation of the CRAIN_S1 results with Tropical Rainfall Measuring Mission data resulted in a root mean square error of 0.58 mm/hr and a correlation of 0.89. This study provides an alternate method for rain monitoring utilizing SAR data with a fine spatial resolution.

Monthly Streamflow Prediction by Metaheuristic Regression Approaches Considering Satellite Precipitation Data

In this study, the viability of three metaheuristic regression techniques, CatBoost (CB), random forest (RF) and extreme gradient tree boosting (XGBoost, XGB), is investigated for the prediction of monthly streamflow considering satellite precipitation data. Monthly streamflow data from three measuring stations in Turkey and satellite rainfall data derived from Tropical Rainfall Measuring Mission (TRMM) were used as inputs to the models to predict 1 month ahead streamflow. Such predictions are crucial for decision-making in water resource planning and management associated with water allocations, water market planning, restricting water supply and managing drought. The outcomes of the metaheuristic regression methods were compared with those of artificial neural networks (ANN) and nonlinear regression (NLR). The effect of the periodicity component was also investigated by importing the month number of the streamflow data as input. In the first part of the study, the streamflow at each station was predicted using CB, RF, XGB, ANN and NLR methods and considering TRMM data. In the second part, streamflow at the downstream station was predicted using data from upstream stations. In both parts, the CB and XGB methods generally provided similar accuracy and performed superior to the RF, ANN and NLR methods. It was observed that the use of TRMM rainfall data and the periodicity component considerably improved the efficiency of the metaheuristic regression methods in modeling (prediction) streamflow. The use of TRMM data as inputs improved the root mean square error (RMSE) of CB, RF and XGB by 36%, 31% and 24%, respectively, on average, while the corresponding values were 37%, 18% and 43% after introducing periodicity information into the model’s inputs.

Hydrological evaluation of gridded climate datasets in a texas urban watershed using soil and water assessment tool and artificial neural network

Precipitation is a vital component of the hydrologic cycle, and successful hydrological modeling largely depends on the quality of precipitation input. Gridded precipitation datasets are gaining popularity as a convenient alternative for hydrological modeling. However, many of the gridded precipitation data have not been adequately assessed across a range of conditions. This study compared three gridded precipitation datasets, Tropical Rainfall Measuring Mission (TRMM), Climate Forecast System Reanalysis (CFSR), and Parameter-elevation Relationships on Independent Slopes Model (PRISM). This study used the conventional gauge observation as reference data and evaluated the suitability of the three sources of gridded rainfall data to drive rainfall-runoff simulations. The Soil and Water Assessment Tool (SWAT) and Artificial Neural Network (ANN) were used to create daily streamflow simulations in the Leon Creek Watershed (LCW) in San Antonio, Texas, with the TRMM, CFSR, PRISM, and gauge rainfall data used as inputs. A direct comparison of the gridded data sources showed that the TRMM data underestimates the volume of rainfall, while PRISM data most closely matches the volume of rainfall when compared to the gauge rainfall observations. The hydrological simulation results showed that the PRISM and TRMM rainfall data driven models had preferable results to the CFSR and gauge driven models, in terms of both graphical comparison and goodness-of-fit indicator values. Additionally, no significant discrepancy was found between SWAT and ANN simulation results when the same precipitation data source was used, while SWAT and ANN simulation results varied in an identical pattern when different precipitation data sources were applied.

Improved rainfall and temperature satellite dataset in areas with scarce weather stations data: case study in Ancash, Peru

Rainfall and temperature variables play an important role in understanding meteorology at global and regional scales. However, the availability of meteorological information in areas of complex topography is difficult, as the density of weather stations is often very low. In this study, we focused on improving existing satellite products for these areas, using Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Measurement (GPM) data for rainfall and Modern Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2) data for air temperature. Our objective was to propose a model that improves the accuracy and correlation of satellite data with observed data on a monthly scale during 2012-2017. The improvement of rainfall satellite data was performed using 4 regions: region 1 Santa (R1Sn), region 2 Marañón (R2Mr), region 3 Pativilca (R3Pt) and region 4 Pacific (R4Pc). For temperature, a model based on the use of the slope obtained between temperature and altitude data was used. In addition, the reliability of the TRMM, GPM and MERRA-2 data was analyzed based on the ratio of the mean square error, PBIAS, Nash-Sutcliffe efficiency (NSE) and correlation coefficient. The final products obtained from the model for temperature are reliable with R2 ranging from 0.72 to 0.95 for the months of February and August respectively, while the improved rainfall products obtained are shown to be acceptable (NSE≥0.6) for the regions R1Sn, R2Mr and R3Pt. However, in R4Pc it is unacceptable (NSE<0.4), reflecting that the additive model is not suitable in regions with low rainfall values.

Evaluation of GSMaP and Trmm Monthly Rainfall Satellite Data in Wadi Ahin, Sohar Area, Sultante of Oman

This research compares the accuracy of Forecasting Global Precipitation Measurement (GPM)-derived satellite precipitation estimates (SPEs) for Wadi Ahin in Sohar area in Sultanate of Oman with Tropical Rainfall Measurement Mission (TRMM). To do so, GSMaP version 7 and TRMM data sets were evaluated at their regional 0.10 and 0.250 spatial resolution respectively. Both the wet and dry seasons were assessed on a monthly basis for each month. A grid cell with two gauges, one ground and the other GSMAP or TRMM, is calculated with the CC, the %RMSE, and the %B for all the considered datasets. Statistical measurements are provided here in more detail. The monthly analysis takes into account the whole period, dry season, and wet season separately for Mean regional and Mean spatial. Verification of monthly rainfall at a spatial level shows that GSMaP underestimates with a correlation coefficient, 0.704, bias -7.88% and RMSE 117.4%. Verification with the regional level shows that GSMAP performs well with a correlation coefficient of higher than 0.9. On the other hand, analysis of TRMM data sets shows a good verification in the spatial level and low accuracy in the regional level where the selected gauges are far from each other. The results indicate the need for improvement of GSMaP and TRMM estimates by doing some combinations between the datasets before application in this wadi.

Construction and application of comprehensive drought monitoring model considering the influence of terrain factors: a case study of southwest Yunnan, China.

Droughts in winter and spring are one of the most prominent natural disasters in the Yunnan Province in China. They occur frequently, with long durations and have a wide range of damage, which has a serious impact on social and economic development, as well as agricultural production and, therefore, strongly impacts the lives of the people living in the region. The traditional drought monitoring model does not take terrain into consideration, thereby affecting the comparative nature of results, as baseline conditions are not the same. Therefore, this study proposed a comprehensive drought monitoring model considering the influence of terrain factors to improve the evaluation effect. Firstly, based on NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) and Tropical Rainfall Measurement Mission (TRMM 3B43) data, vegetation condition index (VCI), temperature condition index (TCI), precipitation condition index (TRCI), and three terrain factors ground elevation (DEM), slope (SLOPE), aspect (ASPECT) were selected as model parameters. Then, a comprehensive drought monitoring model without considering terrain factors (Model A) and a comprehensive drought monitoring model of considering terrain factors (Model B) were constructed by using multiple linear regression models. Finally, the effects of the two models were evaluated by using standardized precipitation evapotranspiration index (SPEI) in southwest Yunnan Province, China, and model B was used to analyze the drought in winter and spring in the study area from 2008 to 2019. The results showed that (1) the correlation coefficient of model B was higher than that of model A in winter and spring and the standard error of model B was lower than that of model A. (2) The grade consistency rate of Model A and SPEI was 0.92 in winter and 0.33 in spring; the grade consistency between model B and SPEI was 0.83 in winter and 0.75 in spring, and therefore the monitoring effect of model B was more stable. (3) There were periodic droughts during the study period, and the degree of drought in spring was less than in winter. Medium and severe droughts were observed in winter. Thus, this study concluded that the effect of terrain has an important influence on the evaluation of droughts. The comprehensive drought monitoring model which considers topographic factors can effectively identify the occurrence of drought, and therefore provide significant input with regards to disaster prevention and mitigation policies in southwest Yunnan.

Hydrological Modeling in the Chaohu Lake Basin of China—Driven by Open-Access Gridded Meteorological and Remote Sensing Precipitation Products

This study assessed the performance of two well-known gridded meteorological datasets, CFSR (Climate Forecast System Reanalysis) and CMADS (China Meteorological Assimilation Driving Datasets), and three satellite-based precipitation datasets, TRMM (Tropical Rainfall Measuring Mission), CMORPH (Climate Prediction Center morphing technique), and CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data), in driving the SWAT (Soil and Water Assessment Tool) model for streamflow simulation in the Fengle watershed in the middle–lower Yangtze Plain, China. Eighteen model scenarios were generated by forcing the SWAT model with different combinations of three meteorological datasets and six precipitation datasets. Our results showed that (1) the three satellite-based precipitation datasets (i.e., TRMM, CMORPH, and CHIRPS) generally provided more accurate precipitation estimates than CFSR and CMADS. CFSR and CMADS agreed fairly well with the gauged measurements in maximum temperature, minimum temperature, and relative humidity, but large discrepancies existed for the solar radiation and wind speed. (2) The impact of precipitation data on simulated streamflow was much larger than that of other meteorological variables. Satisfactory simulations were achieved using the CMORPH precipitation data for daily streamflow simulation and the TRMM and CHIRPS precipitation data for monthly streamflow simulation. This suggests that different precipitation datasets can be used for optimal simulations at different temporal scales.

Impact assessment of the West African monsoon on convective precipitations over the far north region of Cameroon

Squall lines observed over the sahelo-sudanese regions are responsible for most of the annual rainfall recorded in these regions. They originate from the development of convective processes embedded in the Mesoscale Convective Systems (MCS). The impact assessment of the West African Monsoon (WAM) on the development of squall lines in the sahelo-sudanese regions, particularly the far north region of Cameroon is studied. In this study, the Tropical Rainfall Measuring Mission data (TRMM 3A12 and 3B43) and ERA5 reanalysis data from 1998 to 2008 have been used. The technique used is based on the use of the Climate Data Operator (CDO) software and the NCAR command language (NCL). Analysis of these data shows that the seasonal distribution of winds from the saharan heat-low and the Saint Helena high is the dominant atmospheric dynamics associated with the disturbances behind the development of squall lines. This distribution makes it possible to specifically draw up the evolution of the InterTropical Discontinuity (ITD) on the African continent, and thus, highlight the contribution of the WAM on precipitation in the far north region of Cameroon. This study is performed to contribute to the prediction of hydro-meteorological phenomena to improve disaster risks management associated with floods and landslides in this region, the most vulnerable in Cameroon.

Relationships between intense convection, lightning, and rainfall over the interior Congo Basin using TRMM data

The lack of in situ observations across equatorial Africa creates forecasting challenges for regional populations affected by extreme weather events and makes accurate hydrological monitoring within the Congo Basin difficult. Due to the high mean convective intensity in this region, lightning observations may be particularly useful for improving satellite-based surface precipitation monitoring and forecasting. Therefore, this study aims to quantify the relative frequency of the most intense convective events in the Congo and their associated lightning – precipitation relationships through the analysis of high-resolution Tropical Rainfall Measuring Mission (TRMM) radar and lightning observations for the years 1998–2013. Lightning and precipitation measurements associated with observed echoes were isolated and assigned to one of four categories of intense convective-stratiform echo types. Results show that only 2.7% of observed echoes were classified as intense convective-stratiform, yet they produced 36.6% of observed lightning flashes and 27.4% of estimated rain totals. Significant spatial correlations were also found between total rainfall and intense convective-stratiform rain (coefficient r = 0.56). Linear relationships between lightning and echo rain rates are shown to depend heavily on the convective category. As a result, a simple linear regression cannot be made for all intense convective echoes. However, lightning can be used to retrieve a lower-bound approximation with respect to convective rain rates. Results suggest that if properly implemented, the addition of lightning data may help to improve or constrain satellite derived convective intensity and rainfall estimations.

Clouds’ Microphysical Properties and Their Relationship with Lightning Activity in Northeast Brazil

The Northeast region of Brazil (NEB) has a high rate of deaths from lightning strikes (18% of the country’s total). The region has states, such as Piauí, with high mortality rates (1.8 deaths per million), much higher than the national rate (0.8) and the NEB rate (0.5). In this sense, the present work analyzes the microphysical characteristics of clouds with and without the occurrence of total lightning. For this purpose, data from the Lightning Imaging Sensor (LIS), TRMM Microwave Imager (TMI) and Precipitation Radar (PR), aboard the Tropical Rainfall Measuring Mission (TRMM) satellite from 1998 to 2013 were used. The TRMM data were analyzed to establish a relationship between the occurrence of lightning and the clouds’ microphysical characteristics, comparing them as a function of lightning occurrence classes, spatial location and atmospheric profiles. A higher lightning occurrence is associated with higher values of ice water path (>38.9 kg m−2), rain water path (>2 kg m−2), convective precipitation (>5 mm h−1) and surface precipitation (>7 mm h−1), in addition to slightly higher freezing level height values. Reflectivity observations (>36 dBZ) demonstrated typical convective profile curves, with higher values associated with classes with higher lightning densities (class with more than 6.8 flash km−2 year−1).

Exploring Temporal Rainfall Variability and Trends Over a Tropical Region Using Tropical Rainfall Measurement Mission (TRMM) and Observatory Data

Kerala is the gateway of the Indian southwest monsoon. The Tropical Rainfall Measurement Mission (TRMM) rainfall data is an efficient approach to rainfall measurement. This study explores the temporal variability in rainfall and trends over Kerala from 1998-2019 using TRMM data and observatory data procured from India Meteorological Department (IMD). Direct comparison with observatory data at various time scales proved the reliability of the TRMM data (monthly, seasonal and annual). The temporal rainfall converted by averaging the data on an annual, monthly and seasonal time scale, and the results have confirmed that the rainfall estimated based on satellite data is dependable. The station wise comparison of rainfall in monsoon season provides satisfactory results. However, estimation of rainfall in mountainous areas is challenging task using the TRMM. In the basins of humid tropical regions, TRMM data can be a valuable source of rainfall data for water resource management and monitoring with some vigilance. In Kerala, the study found an insignificant increase in the southwest monsoon and winter season rainfall during last two decades. The rainfall over Kerala showed uncertainty in the distribution of monthly, seasonal and yearly time scales. This study provides a preview of recent weather patterns that would enable us to make better decisions and improve public policy against climate change.

Time Varying Spatial Downscaling of Satellite-Based Drought Index

Drought monitoring is essential to detect the presence of drought, and the comprehensive change of drought conditions on a regional or global scale. This study used satellite precipitation data from the Tropical Rainfall Measuring Mission (TRMM), but refined the data for drought monitoring in Java, Indonesia. Firstly, drought analysis was conducted to establish the standardized precipitation index (SPI) of TRMM data for different durations. Time varying SPI spatial downscaling was conducted by selecting the environmental variables, normalized difference vegetation index (NDVI), and land surface temperature (LST) that were highly correlated with precipitation because meteorological drought was associated with vegetation and land drought. This study used time-dependent spatial regression to build the relation among original SPI, auxiliary variables, i.e., NDVI and LST. Results indicated that spatial downscaling was better than nonspatial downscaling (overall RMSEs: 0.25 and 0.46 in spatial and nonspatial downscaling). Spatial downscaling was more suitable for heterogeneous SPI, particularly in the transition time (R: 0.863 and 0.137 in June 2019 for spatial and nonspatial models). The fine resolution (1 km) SPI can be composed of the environmental data. The fine-resolution SPI captured a similar trend of the original SPI. Furthermore, the detailed SPI maps can be used to understand the spatio-temporal pattern of drought severity.

Accuracy assessment and error cause analysis of GPM (V06) in Xiangjiang river catchment

Abstract Application potential and development prospect of satellite precipitation products such as Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Mission (GPM) have promising implications. This study discusses causes of spatiotemporal differences on GPM data through the following steps: Initially, calculate bias between satellite-based data and rain gauge data of Xiangjiang river catchment to assess the accuracy of GPM (06E, 06 L, and 06F) products. Second, total errors of satellite precipitation data are divided into hit bias (HBIAS: precipitation detected by both GPM and rain gauge station), missed precipitation (MBIAS: precipitation detected only by rain gauge station), and false precipitation (FBIAS: precipitation detected only by GPM). Third, evaluate the impact of precipitation intensity and total precipitation on accuracy of GPM data and their influence on three error components. Several conclusions are drawn from the results above. (1) Satellite-based precipitation measurements perform better on a larger temporal-spatial scale. (2) The accuracy of TRMM and GPM data displays significant variances on space and time. Season, precipitation intensity, and total precipitation are main factors influencing the accuracy of TRMM and GPM data. (3) The detection capability of satellite products change with seasonal variation and different precipitation intensity level.

The impact of convective overshooting on the thermal structure over the Tibetan Plateau in summer based on TRMM, COSMIC, radiosonde and reanalysis data

AbstractIn this paper, we examine convective overshooting and its effects on the thermal structure of the troposphere and lower stratosphere in the Tibetan Plateau in summer by matching the Tropical Rainfall Measuring Mission (TRMM) with Integrated Global Radiosonde Archive (IGRA), Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC), European Centre for Medium-Range Weather Forecasts 5th Reanalysis (ERA-5), the Japanese Meteorological Association 55-year reanalysis (JRA-55) and the National Aeronautics and Space Administration Modern-Era Retrospective analysis for Research and Applications, Versions2 (MERRA-2). It was found that convective overshooting mainly occurs in the central and eastern part of the Tibetan Plateau, and its frequency varies from 0.01 × 10−4 to 0.91 × 10−4. The convective overshooting warms the low middle tropopause and cools the tropopause nearby significantly, which can also makes air get wetter. The tropopause of the convective overshooting is substantially lower than the mean tropopause. Statistical results calculated from the five datasets are generally consistent; however, each dataset has its own strengths and weaknesses. The high spatiotemporal resolution temperature profiles from ERA-5 along with the high vertical resolution temperature profiles from COSMIC can be combined to accurately study convective overshooting in the Tibetan Plateau.

Mapping soil erosion–prone sites through GIS and remote sensing for the Tifnout Askaoun watershed, southern Morocco

The Moroccan High Atlas is very sensitive to soil erosion due to its steep slopes, torrential rains, and degraded plant cover. The degradation of fertile soils in this mountainous watershed influences negatively upon agricultural productivity. The objective of this study is to quantify soil erosion in the Tifnout Askaoun watershed in southern Morocco. The Revised Universal Soil Loss Equation (RUSLE), the geographic information system (GIS) techniques, and the Tropical Rainfall Measuring Mission (TRMM) data were adopted for mapping the annual rate of soil loss in this watershed area of around 1488 km2. The spatial distribution of annual soil erosion rates was obtained by integrating the geo-environmental variables into a GIS. These variables are the rainfall erosivity (R) generated from the TRMM data, the soil erodibility factor (K), the length and slope inclination (LS), the vegetation and management factor (C), and the practice support factor (P). Results reveal an average annual soil erosion rate of 14.44 t/ha/year and a good correlation with the slope length and steepness factor (r = 0.72) and in a lesser extent with the rainfall erosivity factor (r = 0.63). The sub-catchments of the study area were mapped and grouped into five classes of vulnerability to soil erosion risk, with results indicating that the Toubkal sub-catchment is the most threatened by water erosion risk as reflected by an average erosion rate of 48.05 t/ha/year. Approaches and results from this study, which was conducted between 2017 and 2019, may benefit researchers and decision-makers concerned with soil management primarily in mountainous areas where soil degradation impacts the activities of the rural population.