scholarly journals Diurnal Variation of Seasonal Precipitation over the CONUS: A Comparison of Gauge Observations with TRMM Data

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Liming Zhu ◽  
Yu Zhao ◽  
Xiaoping Rui ◽  
Qingwei Wei
Keyword(s):  
Diurnal Variation ◽  
Clustering Algorithm ◽  
Tropical Rainfall Measuring Mission ◽  
Precipitation Event ◽  
Periodic Signal ◽  
Seasonal Precipitation ◽  
Local Climate ◽  
Precipitation Gauge ◽  
Trmm 3B42 ◽  
Comprehensive Study

Diurnal variation of precipitation is a fundamental periodic signal of local climate. Comprehensive study of diurnal variation of precipitation is helpful in studying the formation of local climate and validating satellite precipitation products. In this study, a comparison is drawn between precipitation gauge observations and Tropical Rainfall Measuring Mission (TRMM) 3B42 data on diurnal variation of precipitation. First, using the K-means clustering algorithm, stations with gauge observations and pixels with TRMM data are divided into different groups according to the diurnal variation of precipitation, respectively. In each group, the stations have similar diurnal variation of precipitation. Then maps of diurnal variation of precipitation for gauge observations and TRMM data are obtained. According to these maps, the diurnal variation of precipitation over the contiguous United States (CONUS) presents seasonal variability in both gauge observations and TRMM data. In addition, the diurnal variation of precipitation shows clustered features in space. However, the spatial patterns of the obtained maps do not match, and the TRMM satellite data perform poorly in capturing the hourly precipitation event. Finally, the possible mechanism behind the prevailing nocturnal precipitation over the middle of the CONUS is discussed, with the prevailing nocturnal precipitation judged likely to be strongly related to the mountain-plains solenoid (MPS) circulation.

scholarly journals Evaluation and Comparison of Daily GPM/TRMM Precipitation Products over the Tianshan Mountains in China

Water ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3088
Author(s):  
Yin Zhang ◽  
Gulimire Hanati ◽  
Sulitan Danierhan ◽  
Qianqian Liu ◽  
Zhiyuan Xu
Keyword(s):  
Tropical Rainfall Measuring Mission ◽  
Tianshan Mountains ◽  
Probability Of Detection ◽  
Rainfall Rate ◽  
Annual Precipitation ◽  
Seasonal Precipitation ◽  
Climate Conditions ◽  
The North ◽  
Trmm 3B42 ◽  
Better Than

Based on the complex topography and climate conditions over the Tianshan Mountains (TSM) in Xinjiang, China, the new precipitation product, the Global Precipitation Measurement (GPM) (IMERG), and its predecessor, the Tropical Rainfall Measuring Mission (TRMM) 3B42 (TMPA), were evaluated and compared. The evaluation was based on daily-scale data from April 2014 to March 2015 and analyses at annual, seasonal and daily scales were performed. The results indicated that, overall, the annual precipitation in the Tianshan area tends to be greater in the north than in the south and greater in the west than in the east. Compared with the ground reference dataset, GPM and TRMM datasets represent the spatial variation of annual and seasonal precipitation over the TSM well; however, both measurements underestimate the annual precipitation. Seasonal analysis found that the spatial variability of seasonal precipitation has been underestimated. For the daily assessment, the coefficient of variation (CV), correlation coefficient (R) and relative bias (RB) were calculated. It was found that the GPM and TRMM data underestimated the larger CV. The TRMM data performed better on the daily variability of precipitation in the TSM. The R and RB data indicate that the performance of GPM is generally better than that of TRMM. The R value of GPM is generally greater than that of TRMM, and the RB value is closer to 0, indicating that it is closer to the measured value. As for the ability to detect precipitation events, the GPM products have significantly improved the probability of detection (POD) (POD values are all above 0.8, the highest is 0.979, increased by nearly 17%), and the critical success index (CSI) (increased by nearly 9% in the TSM) is also better than TRMM, although it is only slightly weaker than TRMM in terms of the false alarm ratio (FAR) and frequency bias index (FBI). Overall, GPM underestimates the low rainfall rate by 6.4% and high rainfall rate by 22.8% and overestimates middle rain rates by 29.1%. However, GPM is better than TRMM in capturing all types of rainfall events. Based on these results, GPM-IMERG presents significant improvement over its predecessor TRMM 3B42. Considering the performance of GPM in different subregions, a lot of work still needs to be done to improve the performance of the satellite before being used for research.

scholarly journals Suitability of TRMM Products with Different Temporal Resolution (3-Hourly, Daily, and Monthly) for Rainfall Erosivity Estimation

2020 ◽  
Vol 12 (23) ◽  
pp. 3924
Author(s):  
Xianghu Li ◽  
Zhen Li ◽  
Yaling Lin
Keyword(s):  
Temporal Resolution ◽  
Tropical Rainfall Measuring Mission ◽  
Relative Bias ◽  
Rainfall Erosivity ◽  
Lake Basin ◽  
Monthly Data ◽  
Poyang Lake Basin ◽  
Spatial Performance ◽  
Annual Scale ◽  
Trmm 3B42

Rainfall erosivity (RE) is a significant indicator of erosion capacity. The application of Tropical Rainfall Measuring Mission (TRMM) rainfall products to deal with RE estimation has not received much attention. It is not clear which temporal resolution of TRMM data is most suitable. This study quantified the RE in the Poyang Lake basin, China, based on TRMM 3B42 3-hourly, daily, and 3B43 monthly rainfall data, and investigated their suitability for estimating RE. The results showed that TRMM 3-hourly product had a significant systematic underestimation of monthly RE, especially during the period of April–June for the large values. The TRMM 3B42 daily product seems to have better performance with the relative bias of 3.0% in summer. At the annual scale, TRMM 3B42 daily and 3B43 monthly data had acceptable accuracy, with mean error of 1858 and −85 MJ∙mm/ha∙h and relative bias of 18.3% and −0.85%, respectively. A spatial performance analysis showed that all three TRMM products generally captured the overall spatial patterns of RE, while the TRMM 3B43 product was more suitable in depicting the spatial characteristics of annual RE. This study provides valuable information for the application of TRMM products in mapping RE and risk assessment of soil erosion.

Comparison of TRMM Data with Rain Gauge Observations in the Upper Huaihe River Basin of China

2013 ◽  
Vol 726-731 ◽  
pp. 3385-3390
Author(s):  
Josephine Osei-Kwarteng ◽  
Qiong Fang Li ◽  
Kwaku Amaning Adjei
Keyword(s):  
Hydrological Modeling ◽  
Tropical Rainfall Measuring Mission ◽  
Rain Gauge ◽  
Rain Gauge Station ◽  
Huaihe River ◽  
Resources Management ◽  
Sustainable Water Resources Management ◽  
Rain Gage ◽  
Gauge Station ◽  
Trmm 3B42

In this study, the Tropical Rainfall Measuring Mission (TRMM) version 7 satellite rainfall product, TRMM 3B42 (V7), was validated using rain gauge measurements in the Upper Huaihe Basin, China. This validation was carried out at monthly and annual temporal scales for an 11-year period using four selected grids with six, four, two and one rain gauge station (s) located within the TRMM grid respectively; the rain gage measurements for grids with more than one rain gauge were averaged. This study found that the validation of the TRMM dataset in grids where there were adequate rain gauge were present to capture the distributed and stochastic nature of rainfall with very good correlation (0.87-0.94) and with very little relative bias when the rain gage accumulations were compared with the TRMM estimates. From the study we found that the TRMM dataset can be used as precipitation input for hydrological modeling at monthly and annual scales for sustainable water resources management in the Upper Huaihe River and even in un-gaged or sparsely gaged basins in other parts of the world.

scholarly journals On Using a Clustering Approach for Global Climate Classification

2016 ◽  
Vol 29 (9) ◽  
pp. 3387-3401 ◽  
Author(s):  
Pawel Netzel ◽  
Tomasz Stepinski
Keyword(s):  
Land Surface ◽  
Clustering Algorithm ◽  
Global Climate ◽  
Multivariate Time Series ◽  
Clustering Algorithms ◽  
Skewed Distribution ◽  
Climatic Variables ◽  
Climate Classification ◽  
Local Climate ◽  
Partitioning Around Medoids

Abstract Classifying the land surface into climate types provides means of diagnosing relations between Earth’s physical and biological systems and the climate. Global climate classifications are also used to visualize climate change. Clustering climate datasets provides a natural approach to climate classification, but the rule-based Köppen–Geiger classification (KGC) is the one most widely used. Here, a comprehensive approach to the clustering-based classification of climates is presented. Local climate is defined as a multivariate time series of mean monthly climatic variables and the authors propose to use dynamic time warping (DTW) as a measure of dissimilarity between local climates. Also discussed are the choice of climatic variables, the importance of their proper normalization, and the advantage of using distance-based clustering algorithms. Using the WorldClim global climate dataset and different combinations of clustering parameters, 32 different clustering-based classifications are calculated. These classifications are compared between themselves and to the KGC using the information-theoretic V measure. It is found that the best classifications are obtained using three climate variables (temperature, precipitation, and temperature range), a data normalization that takes into account the skewed distribution of precipitation values, and the partitioning around medoids clustering algorithm. Two such classifications are compared in detail between each other and to the KGC. About half of the climate types found by clustering can be matched to the familiar KGC classes, but the rest differ in their climatic character and spatial distribution. Finally, it is demonstrated that clustering-based classification results in climate types that are internally more homogeneous and externally more distinct than climate types in the KGC.

scholarly journals Bias Corrections of Precipitation Measurements across Experimental Sites in Different Ecoclimatic Regions of Western Canada

2016 ◽  
Author(s):  
Xicai Pan ◽  
Daqing Yang ◽  
Yanping Li ◽  
Alan Barr ◽  
Warren Helgason ◽  
...  
Keyword(s):  
Regional Climate ◽  
Western Canada ◽  
Monthly Precipitation ◽  
Seasonal Precipitation ◽  
Balance Assessment ◽  
Precipitation Regime ◽  
Western Cordillera ◽  
Operational Networks ◽  
Precipitation Gauge ◽  
Bias Corrections

Abstract. This study assesses a filtering procedure on accumulating precipitation gauge measurements, and quantifies the effects of bias corrections for wind-induced undercatch across four ecoclimatic regions in western Canada, including the permafrost regions of the Sub-arctic, the Western Cordillera, the Boreal Forest, and the Prairies. The bias corrections increased monthly precipitation by up to 163 % at windy sites with short vegetation, and sometimes modified the seasonal precipitation regime, whereas the increases were less than 13 % at sites shielded by forest. On a yearly basis, the increase of total precipitation ranged from 8 to 20 mm (3–4 %) at sites shielded by vegetation, and 60 to 384 mm (about 15–34 %) at open sites. In addition, the bias corrections altered the seasonal precipitation patterns at some windy sites with high snow percentage (> 50 %). This study highlights the need and importance of precipitation bias corrections at both research sites and operational networks for water balance assessment and the validation of global/regional climate/hydrology models.

scholarly journals Contribution of TRMM 3B42 Data to Improve Knowledge on Rainfall in the Kayanga/Geba River Basin (Republic of Guinea, Senegal and GuineaBissau)

2018 ◽  
Vol 14 (9) ◽  
pp. 260
Author(s):  
Saly Sambou ◽  
Honore Dacosta ◽  
Abdoulaye Deme ◽  
Ibrahima Diouf
Keyword(s):  
Temporal Variability ◽  
Statistical Study ◽  
Correlation Coefficients ◽  
Tropical Rainfall Measuring Mission ◽  
Spatial And Temporal Variability ◽  
Rainfall Gradient ◽  
Republic Of Guinea ◽  
The North ◽  
Average Monthly Rainfall ◽  
Trmm 3B42

The use of Tropical Rainfall Measuring Mission (TRMM) data is an option for counteracting challenge of the lack of ground based observations, particularly in Kayanga/Gêba. This paper undertakes validation of monthly TRMM rainfall estimates before using it to understand the spatial and temporal variability in the Basin. This validation based on application of statistical study, made it possible to obtain interesting results with correlation coefficients varying from 0.92 to 0.96 and Nash indices close to 1. The analysis of the seasonal rainfall pattern shows consistence with ground based observations. The study of the annual cycle reveals that their interannual variability is similar to that of ground based observations. Finally, the interpolation of average monthly rainfall in the basin highlights the NorthSouth rainfall gradient, which shows that the South is wetter than the North, with differences more pronounced in August and September.

scholarly journals Global hydro-climatic biomes identified via multitask learning

2018 ◽  
Vol 11 (10) ◽  
pp. 4139-4153 ◽  
Author(s):  
Christina Papagiannopoulou ◽  
Diego G. Miralles ◽  
Matthias Demuzere ◽  
Niko E. C. Verhoest ◽  
Willem Waegeman
Keyword(s):  
Land Cover ◽  
Clustering Algorithm ◽  
Climate Extremes ◽  
Climatic Conditions ◽  
Multitask Learning ◽  
Dimensional Representation ◽  
Local Climate ◽  
Global Land Cover ◽  
Global Land ◽  
Low Dimensional

Abstract. The most widely used global land cover and climate classifications are based on vegetation characteristics and/or climatic conditions derived from observational data. However, these classification schemes do not directly stem from the characteristic interaction between the local climate and the biotic environment. In this work, we model the dynamic interplay between vegetation and local climate in order to delineate ecoregions that share a coherent response to hydro-climate variability. Our novel framework is based on a multitask learning approach that discovers the spatial relationships among different locations by learning a low-dimensional representation of predictive structures. This low-dimensional representation is combined with a clustering algorithm that yields a classification of biomes with coherent behaviour. Experimental results using global observation-based datasets indicate that, without the need to prescribe any land cover information, the identified regions of coherent climate–vegetation interactions agree well with the expectations derived from traditional global land cover maps. The resulting global hydro-climatic biomes can be used to analyse the anomalous behaviour of specific ecosystems in response to climate extremes and to benchmark climate–vegetation interactions in Earth system models.

scholarly journals Evaluation of Global Satellite Rainfall Products over Continental Europe

2012 ◽  
Vol 13 (2) ◽  
pp. 588-603 ◽  
Author(s):  
Dimitrios Stampoulis ◽  
Emmanouil N. Anagnostou
Keyword(s):  
Tropical Rainfall Measuring Mission ◽  
Error Variance ◽  
Temporal Variations ◽  
Cold Season ◽  
Global Scale ◽  
Error Statistics ◽  
Extensive Evaluation ◽  
Significant Difference ◽  
Satellite Rainfall ◽  
Trmm 3B42

Abstract An extensive evaluation of two global-scale high-resolution satellite rainfall products is performed using 8 yr (2003–10) of reference rainfall data derived from a network of rain gauges over Europe. The comparisons are performed at a daily temporal scale and 0.25° spatial grid resolution. The satellite rainfall techniques investigated in this study are the Tropical Rainfall Measuring Mission (TRMM) 3B42 V6 (gauge-calibrated version) and the Climate Prediction Center morphing technique (CMORPH). The intercomparison and validation of these satellite products is performed both qualitatively and quantitatively. In the qualitative part of the analysis, error maps of various validation statistics are shown, whereas the quantitative analysis provides information about the performance of the satellite products relative to the rainfall magnitude or ground elevation. Moreover, a time series analysis of certain error statistics is used to depict the temporal variations of the accuracy of the two satellite techniques. The topographical and seasonal influences on the performance of the two satellite products over the European domain are also investigated. The error statistics presented herein indicate that both orography and seasonal variability affect the efficiency of the satellite rainfall retrieval techniques. Specifically, both satellite techniques underestimate rainfall over higher elevations, especially during the cold season, and their performance is subject to seasonal changes. A significant difference between the two satellite products is that TRMM 3B42 V6 generally overestimates rainfall, while CMORPH underestimates it. CMORPH’s mean error is shown to be of higher magnitude than that of 3B42 V6, while in terms of random error variance, CMORPH exhibits lower (higher) values than those of 3B42 V6 in the winter (summer) months.

scholarly journals Unshielded precipitation gauge collection efficiency with wind speed and hydrometeor fall velocity

2021 ◽  
Vol 25 (10) ◽  
pp. 5473-5491
Author(s):  
Jeffery Hoover ◽  
Michael E. Earle ◽  
Paul I. Joe ◽  
Pierre E. Sullivan
Keyword(s):  
Wind Speed ◽  
Transfer Functions ◽  
Collection Efficiency ◽  
Threshold Function ◽  
Precipitation Event ◽  
Bias Error ◽  
Velocity Dependence ◽  
Fall Velocity ◽  
Dynamics Modelling ◽  
Precipitation Gauge

Abstract. Collection efficiency transfer functions that compensate for wind-induced collection loss are presented and evaluated for unshielded precipitation gauges. Three novel transfer functions with wind speed and precipitation fall velocity dependence are developed, including a function from computational fluid dynamics modelling (CFD), an experimental fall velocity threshold function (HE1), and an experimental linear fall velocity dependence function (HE2). These functions are evaluated alongside universal (KUniversal) and climate-specific (KCARE) transfer functions with wind speed and temperature dependence. Transfer function performance is assessed using 30 min precipitation event accumulations reported by unshielded and shielded Geonor T-200B3 precipitation gauges over two winter seasons. The latter gauge was installed in a Double Fence Automated Reference (DFAR) configuration. Estimates of fall velocity were provided by the Precipitation Occurrence Sensor System (POSS). The CFD function reduced the RMSE (0.08 mm) relative to KUniversal (0.20 mm), KCARE (0.13 mm), and the unadjusted measurements (0.24 mm), with a bias error of 0.011 mm. The HE1 function provided a RMSE of 0.09 mm and bias error of 0.006 mm, capturing the collection efficiency trends for rain and snow well. The HE2 function better captured the overall collection efficiency, including mixed precipitation, resulting in a RMSE of 0.07 mm and bias error of 0.006 mm. These functions are assessed across solid and liquid hydrometeor types and for temperatures between −22 and 19 ∘C. The results demonstrate that transfer functions incorporating hydrometeor fall velocity can dramatically reduce the uncertainty of adjusted precipitation measurements relative to functions based on temperature.

scholarly journals A comparison between high-resolution satellite precipitation estimates and gauge measured data: case study of Gorganrood basin, Iran

2018 ◽  
Vol 67 (3) ◽  
pp. 236-251 ◽  
Author(s):  
Donya Dezfooli ◽  
Banafsheh Abdollahi ◽  
Seyed-Mohammad Hosseini-Moghari ◽  
Kumars Ebrahimi
Keyword(s):  
Pearson Correlation ◽  
Absolute Error ◽  
Probability Of Detection ◽  
Critical Success ◽  
Precipitation Estimates ◽  
Seasonal Basis ◽  
Precipitation Gauge ◽  
Critical Success Index ◽  
Trmm 3B42

Abstract The aim of this paper is to evaluate the accuracy of the precipitation data gathered from satellites including PERSIANN, TRMM-3B42V7, TRMM-3B42RTV7, and CMORPH, over Gorganrood basin, Iran. The data collected from these satellites (2003–2007) were then compared with precipitation gauge observations at six stations, namely, Tamar, Ramiyan, Bahlakeh-Dashli, Sadegorgan, Fazel-Abad, and Ghaffar-Haji. To compare these two groups, mean absolute error (MAE), bias, root mean square error (RMSE), and Pearson correlation coefficient criteria were calculated on daily, monthly, and seasonal basis. Furthermore, probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI) were calculated for these datasets. Results indicate that, on a monthly scale, the highest correlation between observed and satellite-gathered data calculated is 0.404 for TRMM-3B42 at Bahlakeh-Dashli station. At a seasonal scale, the highest correlation is calculated for winter data and using PERSIANN data, while for the other seasons, TRMM-3B42 data showed the best correlation with observed data. The high values of RMSE and MAE for winter data showed that the satellites provided poor estimations at this season. The best and the worst values of RMSE for studied satellites belonged to Sadegorgan and Ramiyan stations, respectively. Furthermore, the PERSIANN gains a better CSI and POD while TRMM-3B42V7 showed a better FAR.

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