Evaluation of the TRMM Product for Spatio-temporal Characteristics of Precipitation over Nepal (1998-2018)

2020 ◽  
Vol 25 (2) ◽  
pp. 39-48
Author(s):  
Kalpana Hamal ◽  
Nitesh Khadka ◽  
Samresh Rai ◽  
Bharat Badayar Joshi ◽  
Jagdish Dotel ◽  
...  
Keyword(s):  
Water Cycle ◽  
Winter Season ◽  
Temporal Distribution ◽  
Tropical Rainfall Measuring Mission ◽  
Good Alternative ◽  
High Elevation ◽  
Large Error ◽  
Rain Gauge ◽  
Mean Annual Precipitation ◽  
Spatio Temporal

Precipitation is a fundamental component of the water cycle and integral to the society and the ecosystem. Further, continuous monitoring of precipitation is essential for predicting severe weather, monitoring droughts, and high-intensity related extremes. The present study evaluated the spatio-temporal distribution of precipitation and trends between 1998– 2018 using Tropical Rainfall Measuring Mission (TRMM) (3B43-V7) with reference to 142-gauge observations over Nepal. TRMM moderately captured precipitation patterns' overall characteristics, although underestimated the mean annual precipitation during the study period. TRMM precipitation product well captured the seasonal variation of the observed precipitation with the highest correlation in the winter season. The decreasing seasonal and annual trend was found in both observed and TRMM products, with the highest (lowest) decreasing trend observed during the monsoon (winter) season. It was also noted that the TRMM product showed a smaller bias before 2007, while a large error was found after 2007, especially in the monsoon months. In general, the TRMM product is a good alternative to observe rain gauge measurement in Nepal. However, there is still space for further improvement in rainfall retrieval algorithms, especially in high-elevation areas during the winter season.

scholarly journals Evaluation of IMERG Level-3 Products in Depicting the July to October Rainfall over Taiwan: Typhoon Versus Non-Typhoon

2021 ◽  
Vol 13 (4) ◽  
pp. 622
Author(s):  
Wan-Ru Huang ◽  
Pin-Yi Liu ◽  
Ya-Hui Chang ◽  
Cheng-An Lee
Keyword(s):  
Real Time ◽  
Tropical Rainfall Measuring Mission ◽  
Temporal Variations ◽  
Rain Gauge ◽  
Advantages And Disadvantages ◽  
Level 3 ◽  
Spatio Temporal ◽  
Typhoon Season ◽  
Early Late ◽  
Better Than

This study assesses the performance of satellite precipitation products (SPPs) from the latest version, V06B, Integrated Multi-satellitE Retrievals for Global Precipitation Mission (IMERG) Level-3 (including early, late, and final runs), in depicting the characteristics of typhoon season (July to October) rainfall over Taiwan within the period of 2000–2018. The early and late runs are near-real-time SPPs, while final run is post-real-time SPP adjusted by monthly rain gauge data. The latency of early, late, and final runs is approximately 4 h, 14 h, and 3.5 months, respectively, after the observation. Analyses focus on the seasonal mean, daily variation, and interannual variation of typhoon-related (TC) and non-typhoon-related (non-TC) rainfall. Using local rain-gauge observations as a reference for evaluation, our results show that all IMERG products capture the spatio-temporal variations of TC rainfall better than those of non-TC rainfall. Among SPPs, the final run performs better than the late run, which is slightly better than the early run for most of the features assessed for both TC and non-TC rainfall. Despite these differences, all IMERG products outperform the frequently used Tropical Rainfall Measuring Mission 3B42 v7 (TRMM7) for the illustration of the spatio-temporal characteristics of TC rainfall in Taiwan. In contrast, for the non-TC rainfall, the final run performs notably better relative to TRMM7, while the early and late runs showed only slight improvement. These findings highlight the advantages and disadvantages of using IMERG products for studying or monitoring typhoon season rainfall in Taiwan.

scholarly journals Precipitation downscaling using a probability-matching approach and geostationary infrared data: An evaluation over six climate regions

2017 ◽  
Author(s):  
Ruifang Guo ◽  
Yuanbo Liu ◽  
Han Zhou ◽  
Yaqiao Zhu
Keyword(s):  
Water Cycle ◽  
Rain Gauge ◽  
Cumulative Distribution ◽  
Rainfall Regime ◽  
Precipitation Data ◽  
Main Hypothesis ◽  
Global Water Cycle ◽  
Basin Scale ◽  
Daily Scale ◽  
Spatio Temporal

Abstract. Precipitation is one of the most important components of the global water cycle. Precipitation data at high spatial and temporal resolutions are crucial for basin-scale hydrological and meteorological studies. In this study, we proposed a cumulative distribution of frequency (CDF)-based downscaling method (DCDF) to obtain hourly 0.05° × 0.05° precipitation data. The main hypothesis is that a variable with the same resolution of target data should produce a CDF that is similar to the reference data. The method was demonstrated using the 3 hourly 0.25° × 0.25° Climate Prediction Center Morphing method (CMORPH) dataset and the hourly 0.05° × 0.05° FY2-E Geostationary (GEO) Infrared (IR) temperature brightness (Tb) data. Initially, power function relationships were established between precipitation rate and Tb for each 1° × 1° region. Then the CMORPH data were downscaled to 0.05° × 0.05°. The downscaled results were validated over diverse rainfall regimes in China. Within each rainfall regime, the fitting functions coefficients were able to implicitly reflect the characteristics of precipitation. Qualitatively, the downscaled estimates were able to capture more details about rainfall motions and changes. Quantitatively, the time series of the downscaled estimates were more similar to the rain gauge data than the original CMORPH product at the daily scale. The downscaled estimates not only improved spatio-temporal resolutions, but also performed better (Bias: −7.35 %~10.35 %; correlation coefficient (CC): 0.48~0.60) than the CMORPH product (Bias: 20.82 %~94.19 %; CC: 0.31~0.59) over convective precipitating regions. The downscaled results performed as well as the CMORPH product over regions dominated with frontal rain systems and performed relatively poorly over mountainous or hilly areas where orographic rain systems dominate.

Evaluation of High-Resolution Satellite Precipitation Products over Very Complex Terrain in Ethiopia

2010 ◽  
Vol 49 (5) ◽  
pp. 1044-1051 ◽  
Author(s):  
Feyera A. Hirpa ◽  
Mekonnen Gebremichael ◽  
Thomas Hopson
Keyword(s):  
Neural Networks ◽  
Complex Terrain ◽  
Tropical Rainfall Measuring Mission ◽  
High Elevation ◽  
Rain Gauge ◽  
Satellite Precipitation ◽  
Precipitation Estimation ◽  
Precipitation Analysis ◽  
Climate Prediction Center ◽  
Microwave Data

Abstract This study focuses on the evaluation of 3-hourly, 0.25° × 0.25°, satellite-based precipitation products: the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42RT, the NOAA/Climate Prediction Center morphing technique (CMORPH), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN). CMORPH is primarily microwave based, 3B42RT is primarily microwave based when microwave data are available and infrared based when microwave data are not available, and PERSIANN is primarily infrared based. The results show that 1) 3B42RT and CMORPH give similar rainfall fields (in terms of bias, spatial structure, elevation-dependent trend, and distribution function), which are different from PERSIANN rainfall fields; 2) PERSIANN does not show the elevation-dependent trend observed in rain gauge values, 3B42RT, and CMORPH; and 3) PERSIANN considerably underestimates rainfall in high-elevation areas.

scholarly journals Assessing precipitation distribution impacts on droughts on the island of Crete

2012 ◽  
Vol 12 (4) ◽  
pp. 1159-1171 ◽  
Author(s):  
A.-E. K. Vrochidou ◽  
I. K. Tsanis
Keyword(s):  
Standardized Precipitation Index ◽  
Temporal Distribution ◽  
Mean Annual Precipitation ◽  
Drought Duration ◽  
Precipitation Distribution ◽  
Management Plans ◽  
Drought Pattern ◽  
Spatio Temporal ◽  
Precipitation Records ◽  
High Elevations

Abstract. Precipitation records from 56 stations on the island of Crete (Greece) revealed that areal mean annual precipitation is of a strong orographic type and its magnitude decreases in west-east direction by as much as 400 mm on average. Amongst many parameters that influence precipitation, the elevation and longitude were the most important and provided the highest spatial correlation. It was found that during the year with minimum precipitation, the precipitation shortage was greater at high elevations while the precipitation excess during the year with maximum precipitation was greater in the western part of the island. The assessment of the spatial and temporal distribution of droughts was carried out with the aid of the Spatially Normalized Standardized Precipitation Index (SN-SPI) for the period 1974–2005 in order to compare drought conditions between neighbouring areas of differing precipitation heights. The analysis showed that severe droughts occurred around the year 1992–1993, with a duration of up to 3 yr. Multiple linear regression (MLR) modeling of precipitation in conjunction with cluster analysis of drought duration exhibits the linkage between precipitation, droughts and geographical factors. This connection between spatial precipitation distribution and geographical parameters provides an important clue for the respective spatial drought pattern. The above findings on the spatio-temporal drought distribution will update the current~drought management plans by developing more precise drought warning systems.

scholarly journals Wet and dry spells in Senegal: comparison of detection based on satellite products, reanalysis, and in situ estimates

2021 ◽  
Vol 21 (3) ◽  
pp. 1051-1069
Author(s):  
Cheikh Modou Noreyni Fall ◽  
Christophe Lavaysse ◽  
Mamadou Simina Drame ◽  
Geremy Panthou ◽  
Amadou Thierno Gaye
Keyword(s):  
Measurement Techniques ◽  
West African Monsoon ◽  
Tropical Rainfall Measuring Mission ◽  
Early Warning Systems ◽  
Rain Gauge ◽  
Major Influence ◽  
Dry Spells ◽  
Dry Spell ◽  
Spatio Temporal ◽  
Wet Spells

Abstract. In this study, the detection and characteristics of dry/wet spells (defined as episodes when precipitation is abnormally low or high compared to usual climatology) drawn from several datasets are compared for Senegal. Here, four datasets are based on satellite data (TRMM-3B42 V7, CMORPH V1.0, TAMSAT V3, and CHIRPS V2. 0), two on reanalysis products (NCEP-CFSR and ERA5), and three on rain gauge observations (CPC Unified V1.0/RT and a 65-rain-gauge network regridded by using two kriging methods, namely ordinary kriging, OK, and block kriging, BK). All datasets were converted to the same spatio-temporal resolution: daily cumulative rainfall on a regular 0.25∘ grid. The BK dataset was used as a reference. Despite strong agreement between the datasets on the spatial variability in cumulative seasonal rainfall (correlations ranging from 0.94 to 0.99), there were significant disparities in dry/wet spells. The occurrence of dry spells is less in products using infrared measurement techniques than in products coupling infrared and microwave, pointing to more frequent dry spell events. All datasets show that dry spells appear to be more frequent at the start and end of rainy seasons. Thus, dry spell occurrences have a major influence on the duration of the rainy season, in particular through the “false onset” or “early cessation” of seasons. The amplitude of wet spells shows the greatest variation between datasets. Indeed, these major wet spells appear more intense in the OK and Tropical Rainfall Measuring Mission (TRMM) datasets than in the others. Lastly, the products indicate a similar wet spell frequency occurring at the height of the West African monsoon. Our findings provide guidance in choosing the most suitable datasets for implementing early warning systems (EWSs) using a multi-risk approach and integrating effective dry/wet spell indicators for monitoring and detecting extreme events.

scholarly journals Precipitation downscaling using a probability-matching approach and geostationary infrared data: an evaluation over six climate regions

2018 ◽  
Vol 22 (7) ◽  
pp. 3685-3699 ◽  
Author(s):  
Ruifang Guo ◽  
Yuanbo Liu ◽  
Han Zhou ◽  
Yaqiao Zhu
Keyword(s):  
Water Cycle ◽  
Regional Scale ◽  
Rain Gauge ◽  
Cumulative Distribution ◽  
Rainfall Regime ◽  
Precipitation Data ◽  
Main Hypothesis ◽  
Basin Scale ◽  
Spatio Temporal ◽  
Better Than

Abstract. Precipitation is one of the most important components of the global water cycle. Precipitation data at high spatial and temporal resolutions are crucial for basin-scale hydrological and meteorological studies. In this study, we propose a cumulative distribution of frequency (CDF)-based downscaling method (DCDF) to obtain hourly 0.05∘ × 0.05∘ precipitation data. The main hypothesis is that a variable with the same resolution of target data should produce a CDF that is similar to the reference data. The method was demonstrated using the 3-hourly 0.25∘ × 0.25∘ Climate Prediction Center morphing method (CMORPH) dataset and the hourly 0.05∘ × 0.05∘ FY2-E geostationary (GEO) infrared (IR) temperature brightness (Tb) data. Initially, power function relationships were established between the precipitation rate and Tb for each 1∘ × 1∘ region. Then the CMORPH data were downscaled to 0.05∘ × 0.05∘. The downscaled results were validated over diverse rainfall regimes in China. Within each rainfall regime, the fitting functions' coefficients were able to implicitly reflect the characteristics of precipitation. Quantitatively, the downscaled estimates not only improved spatio-temporal resolutions, but also performed better (bias: −7.35–10.35 %; correlation coefficient, CC: 0.48–0.60) than the CMORPH product (bias: 20.82–94.19 %; CC: 0.31–0.59) over convective precipitating regions. The downscaled results performed as well as the CMORPH product over regions dominated with frontal rain systems and performed relatively poorly over mountainous or hilly areas where orographic rain systems dominate. Qualitatively, at the daily scale, DCDF and CMORPH had nearly equivalent performances at the regional scale, and 79 % DCDF may perform better than or nearly equivalently to CMORPH at the point (rain gauge) scale. The downscaled estimates were able to capture more details about rainfall motion and changes under the condition that DCDF performs better than or nearly equivalently to CMORPH.

scholarly journals Runoff prediction using rainfall data from microwave links: Tabor case study

2018 ◽  
Vol 2017 (2) ◽  
pp. 351-359 ◽  
Author(s):  
David Stransky ◽  
Martin Fencl ◽  
Vojtech Bares
Keyword(s):  
Temporal Distribution ◽  
Monitoring Network ◽  
Rain Gauge ◽  
Urban Drainage ◽  
Rainfall Runoff ◽  
Water Utility ◽  
Promising Alternative ◽  
Runoff Prediction ◽  
Spatio Temporal

Abstract Rainfall spatio-temporal distribution is of great concern for rainfall-runoff modellers. Standard rainfall observations are, however, often scarce and/or expensive to obtain. Thus, rainfall observations from non-traditional sensors such as commercial microwave links (CMLs) represent a promising alternative. In this paper, rainfall observations from a municipal rain gauge (RG) monitoring network were complemented by CMLs and used as an input to a standard urban drainage model operated by the water utility of the Tabor agglomeration (CZ). Two rainfall datasets were used for runoff predictions: (i) the municipal RG network, i.e. the observation layout used by the water utility, and (ii) CMLs adjusted by the municipal RGs. The performance was evaluated in terms of runoff volumes and hydrograph shapes. The use of CMLs did not lead to distinctively better predictions in terms of runoff volumes; however, CMLs outperformed RGs used alone when reproducing a hydrograph's dynamics (peak discharges, Nash–Sutcliffe coefficient and hydrograph's rising limb timing). This finding is promising for number of urban drainage tasks working with dynamics of the flow. Moreover, CML data can be obtained from a telecommunication operator's data cloud at virtually no cost. That makes their use attractive for cities unable to improve their monitoring infrastructure for economic or organizational reasons.

scholarly journals Avaliação da precipitação baseada em dados do CHIRPS e GPCC com medições na superfície para a sub-bacia do rio Guamá no Nordeste Paraense

2020 ◽  
Vol 42 ◽  
pp. e32
Author(s):  
Ivan Carlos da Costa Barbosa ◽  
Emerson Renato Maciel da Silva ◽  
Helder José Farias da Silva ◽  
Luiz Gonzaga da Silva Costa ◽  
Maria Isabel Vitorino ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Temporal Distribution ◽  
Rain Gauge ◽  
Rainfall Data ◽  
Temporal Behavior ◽  
Large Variability ◽  
Rain Gauges ◽  
Spatio Temporal ◽  
Basin Region ◽  
Climate Studies

Rain is one of the most important variables in climate studies in Amazon because of it is large variability in time and space scales. Many basins and sub-basins in the region are deficient in regular and uniform monitoring of data observed on the surface. Today, the remote sensing products available provide satellite estimated rainfall data for a large spatio-temporal distribution and for almost every globe. Therefore, this study aims to evaluate the performance of rainfall data obtained from remote sensing for the sub-basin region of the Guamá River, Northeastern Pará, compared to data observed on terrestrial rain gauges. In addition to identifying the spatio-temporal behavior of rain in the area. The rainfall data used were: rain measured by rain gauge (Hidroweb) and rain estimated by remote sensing and made available by the high resolution precipitation database of GPCC and CHIRPS products, for the period between 1988 and 2018. The data were compared with a remarkably high correlation (r = 0.99) and a satisfactory agreement index (d = 0.98). The two estimated databases showed an approximate overestimation of the observed precipitation and a spatio-temporal distribution consistent with that expected for the region.

scholarly journals A Space-Filling Algorithm to Extrapolate Narrow-Swath Instantaneous TRMM Microwave Rain-Rate Estimates Using Thermal IR Imagery

2008 ◽  
Vol 25 (11) ◽  
pp. 1901-1920 ◽  
Author(s):  
Ana P. Barros ◽  
Kun Tao
Keyword(s):  
Rain Rate ◽  
Monsoon Season ◽  
Spectral Estimation ◽  
Stage Iv ◽  
Tropical Rainfall Measuring Mission ◽  
High Elevation ◽  
Rain Gauge ◽  
Probability Of Detection ◽  
Space Filling ◽  
Mountainous Regions

Abstract A space-filling algorithm (SFA) based on 2D spectral estimation techniques was developed to extrapolate the spatial domain of the narrow-swath near-instantaneous rain-rate estimates from Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and TRMM Microwave Imager (TMI) using thermal infrared imagery (Meteosat-5) without making use of calibration or statistical fitting. A comparison against rain gauge observations and the original PR 2A25 and TMI 2A12 estimates in the central Himalayas during the monsoon season (June–September) over a 3-yr period of 1999–2001 was conducted to assess the algorithm’s performance. Evaluation over the continental United States was conducted against the NCEP stage IV combined radar and gauge analysis for selected events. Overall, the extrapolated PR and TMI rainfall fields derived using SFA exhibit skill comparable to the original TRMM estimates. The results indicate that probability of detection and threat scores of the reconstructed products are significantly better than the original PR data at high-elevation stations (>2000 m) on mountain ridges, and specifically for rainfall rates exceeding 2–5 mm h−1 and for afternoon convection. For low-elevation stations located in steep narrow valleys, the performance varies from year to year and deteriorates strongly for light rainfall (false alarm rates significantly increase). A preliminary comparison with other satellite products (e.g., 3B42, a TRMM-adjusted merged infrared-based rainfall product) suggests that integrating this algorithm in currently existing operational multisensor algorithms has the potential to improve significantly spatial resolution, texture, and detection of rainfall, especially in mountainous regions, which present some of the greatest challenges in precipitation retrieval from satellites over land, and for hydrological operations during extreme events.

scholarly journals Comparsion of TRMM Precipitation Satellite Data over Central Java Region – Indonesia

2018 ◽  
Vol 37 (3) ◽  
pp. 97-114 ◽  
Author(s):  
Andung Bayu Sekaranom ◽  
Emilya Nurjani ◽  
M. Pramono Hadi ◽  
Muh Aris Marfai
Keyword(s):  
Rainy Season ◽  
Tropical Rainfall Measuring Mission ◽  
Satellite Observation ◽  
High Elevation ◽  
Rain Gauge ◽  
Mean Bias Error ◽  
Bias Error ◽  
Precipitation Data ◽  
Central Java ◽  
Trmm Precipitation

Abstract This research aims to compare precipitation data derived from satellite observation and ground measurements through a dense station network over Central Java, Indonesia. A precipitation estimate from the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) 3B42 Version 7 are compared with precipitation data from interpolated rain gauge stations. Correlation analysis, mean bias error (MBE), and root mean square error (RMSE) were utilized in the analysis for each thee-monthly seasonal statistics. The result shows that the 3B42 products often estimate lower rainfall than observed from weather stations in the peak of the rainy season (DJF). Further, it is revealed that the 3B42 product are less robust in estimating rainfall at high elevation, especially when humid environment, which is typical during the rainy season peak, are involved.

Sign in / Sign up

Export Citation Format

Share Document