Comparing Global High-Resolution Precipitation Data with Rain Gauge Data in Assam, India

2021 ◽  
pp. 341-348
Author(s):  
Pulendra Dutta ◽  
Dipsikha Devi ◽  
Arup Kr. Sarma
Keyword(s):  
High Resolution ◽  
Rain Gauge ◽  
Precipitation Data ◽  
Rain Gauge Data ◽  
Gauge Data

Evaluation of precipitation products based on rain gauge data and water budget in a mountainous river basin, Eastern Tibetan Plateau

2020 ◽  
Author(s):  
Yaozhi Jiang ◽  
Kun Yang ◽  
Xiaodong Li ◽  
Wenjiang Zhang ◽  
Yan Shen ◽  
...  
Keyword(s):  
High Resolution ◽  
Water Resources ◽  
Tibetan Plateau ◽  
Water Budget ◽  
Rain Gauge ◽  
Precipitation Data ◽  
Mountainous Areas ◽  
Rain Gauge Data ◽  
Gauge Data ◽  
High Precipitation

<p>Precipitation in mountainous areas provides abundant water resources for downstream regions, and reliable precipitation data in these areas is of crucial importance for the management of water resources and water-related disasters. Because in-situ precipitation data are usually scarce in mountainous areas, satellite-based precipitation products are expected to play an important role; however, they should be carefully validated before application. This study evaluated the performance of three high-resolution precipitation products in the mountainous Qingyi River basin, by comparison with both rain gauge-based and water budget-based methods. The basin is located at the eastern margin of the Tibetan Plateau, and has high precipitation leading to high runoff (~1100 mm/year). The three precipitation products are CMPA (the China Merged Precipitation Analysis), IMERG (the Integrated Multi-satellitE Retrievals for GPM) and GSMaP (the Global Satellite Mapping of Precipitation). In general, both rain gauge-based and water budget-based methods showed that CMPA has the highest accuracy and IMERG has the poorest accuracy in this region. In two sub-basins with steep terrain and high precipitation, the rain gauge-based evaluation indicated negative or even positive basin-averaged biases of about 1 mm/day or less, but the water budget analysis indicated that all the products had much larger negative biases, of 2.4 ~ 3.8 mm/day. This difference likely arises because the evaluation based on rain gauge data cannot reflect errors in products at the basin-scale, due to the sparse spatial distribution of rain gauges. Finally, observed altitudinal gradients of precipitation were used to correct the precipitation products. Under this approach the water budget can be better closed but is not always satisfactory. Therefore, developing a high-quality precipitation data set for mountainous regions based only on satellite products and sparse ground observations remains challenging and other data sources (e.g. high-resolution meteorological modeling) should be taken into consideration in future.</p>

scholarly journals Mapping the Daily Rainfall over an Ungauged Tropical Micro-Watershed: A Downscaling Algorithm Using GPM Data

Water ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1661 ◽  
Author(s):  
Mohd. Rizaludin Mahmud ◽  
Aina Afifah Mohd Yusof ◽  
Mohd Nadzri Mohd Reba ◽  
Mazlan Hashim
Keyword(s):  
High Resolution ◽  
Daily Rainfall ◽  
Rain Gauge ◽  
Rainfall Data ◽  
Precipitation Data ◽  
Rain Gauge Data ◽  
Satellite Precipitation ◽  
Daily Rainfall Data ◽  
Gauge Data

In this study, half-hourly Global Precipitation Mission (GPM) satellite precipitation data were downscaled to produce high-resolution daily rainfall data for tropical coastal micro-watersheds (100–1000 ha) without gauges or with rainfall data conflicts. Currently, daily-scale satellite rainfall downscaling techniques rely on rain gauge data as corrective and controlling factors, making them impractical for ungauged watersheds or watersheds with rainfall data conflicts. Therefore, we used high-resolution local orographic and vertical velocity data as proxies to downscale half-hourly GPM precipitation data (0.1°) to high-resolution daily rainfall data (0.02°). The overall quality of the downscaled product was similar to or better than the quality of the raw GPM data. The downscaled rainfall dataset improved the accuracy of rainfall estimates on the ground, with lower error relative to measured rain gauge data. The average error was reduced from 41 to 27 mm/d and from 16 to 12 mm/d during the wet and dry seasons, respectively. Estimates of localized rainfall patterns were improved from 38% to 73%. The results of this study will be useful for production of high-resolution satellite precipitation data in ungauged tropical micro-watersheds.

scholarly journals Automatic Prediction of High-Resolution Daily Rainfall Fields for Multiple Extents: The Potential of Operational Radar

2007 ◽  
Vol 8 (6) ◽  
pp. 1204-1224 ◽  
Author(s):  
J. M. Schuurmans ◽  
M. F. P. Bierkens ◽  
E. J. Pebesma ◽  
R. Uijlenhoet
Keyword(s):  
High Resolution ◽  
Hydrological Modeling ◽  
Daily Rainfall ◽  
Rain Gauge ◽  
Added Value ◽  
Rain Gauge Data ◽  
Geostatistical Methods ◽  
Gauge Data ◽  
Event Based ◽  
Better Than

Abstract This study investigates the added value of operational radar with respect to rain gauges in obtaining high-resolution daily rainfall fields as required in distributed hydrological modeling. To this end data from the Netherlands operational national rain gauge network (330 gauges nationwide) is combined with an experimental network (30 gauges within 225 km2). Based on 74 selected rainfall events (March–October 2004) the spatial variability of daily rainfall is investigated at three spatial extents: small (225 km2), medium (10 000 km2), and large (82 875 km2). From this analysis it is shown that semivariograms show no clear dependence on season. Predictions of point rainfall are performed for all three extents using three different geostatistical methods: (i) ordinary kriging (OK; rain gauge data only), (ii) kriging with external drift (KED), and (iii) ordinary collocated cokriging (OCCK), with the latter two using both rain gauge data and range-corrected daily radar composites—a standard operational radar product from the Royal Netherlands Meteorological Institute (KNMI). The focus here is on automatic prediction. For the small extent, rain gauge data alone perform better than radar, while for larger extents with lower gauge densities, radar performs overall better than rain gauge data alone (OK). Methods using both radar and rain gauge data (KED and OCCK) prove to be more accurate than using either rain gauge data alone (OK) or radar, in particular, for larger extents. The added value of radar is positively related to the correlation between radar and rain gauge data. Using a pooled semivariogram is almost as good as using event-based semivariograms, which is convenient if the prediction is to be automated. An interesting result is that the pooled semivariograms perform better in terms of estimating the prediction error (kriging variance) especially for the small and medium extent, where the number of data points to estimate semivariograms is small and event-based semivariograms are rather unstable.

scholarly journals Spatial Downscaling of TRMM Precipitation Data Using an Optimal Subset Regression Model with NDVI and Terrain Factors in the Yarlung Zangbo River Basin, China

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Jinping Liu ◽  
Wanchang Zhang ◽  
Ning Nie
Keyword(s):  
River Basin ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Rain Gauge ◽  
Precipitation Data ◽  
Rain Gauge Data ◽  
Optimal Subset ◽  
Yarlung Zangbo River ◽  
Gauge Data ◽  
Subset Regression

High accuracy, high spatial resolution precipitation data is important for understanding basin-scale hydrology and the spatiotemporal distributions of regional precipitation. The objective of this study was to develop a reliable statistical downscaling algorithm to produce high quality, high spatial resolution precipitation products from Tropical Rainfall Monitoring Mission (TRMM) 3B43 data over the Yarlung Zangbo River Basin using an optimal subset regression (OSR) model combined with multiple topographical factors, the Normalized Difference Vegetation Index (NDVI), and observational data from rain gauge stations. After downscaling, the bias between TRMM 3B43 and rain gauge data decreased considerably from 0.397 to 0.109, the root-mean-square error decreased from 235.16 to 124.60 mm, and the r2 increased from 0.54 to 0.61, indicating significant improvement in the spatial resolution and accuracy of the TRMM 3B43 data. Moreover, the spatial patterns of both precipitation rates of change and their corresponding p value statistics were consistent between the downscaled results and the original TRMM 3B43 during the 2001–2014 period, which verifies that the downscaling method performed well in the Yarlung Zangbo River Basin. Its high performance in downscaling precipitation was also proven by comparing with other models. All of these findings indicate that the proposed approach greatly improved the quality and spatial resolution of TRMM 3B43 rainfall products in the Yarlung Zangbo River Basin, for which rain gauge data is limited. The potential of the post-real-time Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) downscaled precipitation product was also demonstrated in this study.

The climate of daily precipitation in the Alps: development and analysis of a high-resolution grid dataset from pan-Alpine rain-gauge data

2013 ◽  
Vol 34 (5) ◽  
pp. 1657-1675 ◽  
Author(s):  
Francesco A. Isotta ◽  
Christoph Frei ◽  
Viktor Weilguni ◽  
Melita Perčec Tadić ◽  
Pierre Lassègues ◽  
...  
Keyword(s):  
High Resolution ◽  
Daily Precipitation ◽  
Rain Gauge ◽  
Rain Gauge Data ◽  
Gauge Data ◽  
The Alps

scholarly journals High-Resolution Spatiotemporal Trend Analysis of Precipitation Using Satellite-Based Products over the United Arab Emirates

Water ◽  
2021 ◽  
Vol 13 (17) ◽  
pp. 2376
Author(s):  
Khalid A. Hussein ◽  
Tareefa S. Alsumaiti ◽  
Dawit T. Ghebreyesus ◽  
Hatim O. Sharif ◽  
Waleed Abdalati
Keyword(s):  
High Resolution ◽  
Water Resources ◽  
United Arab Emirates ◽  
21St Century ◽  
Rain Gauge ◽  
Accurate Information ◽  
Late 20Th Century ◽  
Rain Gauge Data ◽  
Gauge Data

Current water demands are adequately satisfied in the United Arab Emirates (UAE) with the available water resources. However, the changing climate and growing water demand pose a great challenge for water resources managers in the country. Hence, there is a great need for management strategies and policies to use the most accurate information regarding water availability. Understanding the frequency and the short- and long-term trends of the precipitation by employing high-resolution data in both the spatial and temporal domains can provide invaluable information. This study examines the long-term precipitation trends over the UAE using 17 years of data from three of the most highly cited satellite-based precipitation products and rain gauge data observed at 18 stations. The UAE received, on average, 42, 51, and 120 wet hours in a year in the 21st century as recorded by CMORPH, PERSIANN, and IMERG, respectively. The results show that the areal average annual precipitation of the UAE is significantly lower in the early 21st century than that of the late 20th century, even though it shows an increasing trend by all the products. The Mann–Kendall trend test showed positive trends in six rain gauge stations and negative trends in two stations out of 18 stations, all of which are located in the wetter eastern part of the UAE. Results indicate that satellite products have great potential for improving the spatial aspects of rainfall frequency analysis and can complement rain gauge data to develop rainfall intensity–duration–frequency curves in a very dry region, where the installation of dense rain gauge networks is not feasible.

Evaluation of high-resolution satellite rainfall products using rain gauge data over complex terrain in southwest China

2014 ◽  
Vol 119 (1-2) ◽  
pp. 203-219 ◽  
Author(s):  
Meixian Liu ◽  
Xianli Xu ◽  
Alexander Y. Sun ◽  
Kelin Wang ◽  
Yuemin Yue ◽  
...  
Keyword(s):  
High Resolution ◽  
Complex Terrain ◽  
Southwest China ◽  
Rain Gauge ◽  
Rain Gauge Data ◽  
Gauge Data ◽  
Satellite Rainfall

scholarly journals Which Precipitation Product Works Best in the Qinghai-Tibet Plateau, Multi-Source Blended Data, Global/Regional Reanalysis Data, or Satellite Retrieved Precipitation Data?

2020 ◽  
Vol 12 (4) ◽  
pp. 683 ◽  
Author(s):  
Lei Bai ◽  
Yuanqiao Wen ◽  
Chunxiang Shi ◽  
Yanfen Yang ◽  
Fan Zhang ◽  
...  
Keyword(s):  
Spatial Pattern ◽  
Complex Terrain ◽  
Daily Precipitation ◽  
Rain Gauge ◽  
The Other ◽  
Tibet Plateau ◽  
Precipitation Data ◽  
Rain Gauge Data ◽  
Gauge Data ◽  
Qinghai Tibet Plateau

Precipitation serves as a crucial factor in the study of hydrometeorology, ecology, and the atmosphere. Gridded precipitation data are available from a multitude of sources including precipitation retrieved by satellites, radar, the output of numerical weather prediction models, and extrapolation by ground rain gauge data. Evaluating different types of products in ungauged regions with complex terrain will not only help researchers in applying scientific data, but also provide useful information that can be used to improve gridded precipitation products. The present study aims to evaluate comprehensively 12 precipitation datasets made by raw retrieved products, blended with rain gauge data, and blended multiple source datasets in multi-temporal scales in order to develop a suitable method for creating gridded precipitation data in regions with snow-dominated regions with complex terrain. The results show that the Multi-Source Weighted-Ensemble Precipitation (MSWEP), Global Satellite Mapping of Precipitation with Gauge Adjusted (GSMaP_GAUGE), Tropical Rainfall Measuring Mission (TRMM_3B42), Climate Prediction Center Morphing Technique blended with Chinese observations (CMORPH_SUN), and Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) can represent the spatial pattern of precipitation in arid/semi-arid and humid/semi-humid areas of the Qinghai-Tibet Plateau on a climatological spatial pattern. On interannual, seasonal, and monthly scales, the TRMM_3B42, GSMaP_GAUGE, CMORPH_SUN, and MSWEP outperformed the other products. In general, the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN_CCS) has poor performance in basins of the Qinghai-Tibet Plateau. Most products overestimated the extreme indices of the 99th percentile of precipitation (R99), the maximal of daily precipitation in a year (Rmax), and the maximal of pentad accumulation of precipitation in a year (R5dmax). They were underestimated by the extreme index of the total number of days with daily precipitation less than 1 mm (dry day, DD). Compared to products blended with rain gauge data only, MSWEP blended with more data sources, and outperformed the other products. Therefore, multi-sources of blended precipitation should be the hotspot of regional and global precipitation research in the future.

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