Validation of TRMM 3B42V7 Rainfall Product under Complex Topographic and Climatic Conditions over Hexi Region in the Northwest Arid Region of China

Continuous and accurate spatiotemporal precipitation data plays an important role in regional climate and hydrology research, particularly in the arid inland regions where rain gauges are sparse and unevenly distributed. The main objective of this study is to evaluate and bias-correct the Tropical Rainfall Measuring Mission (TRMM) 3B42V7 rainfall product under complex topographic and climatic conditions over the Hexi region in the northwest arid region of China with the reference of rain gauge observation data during 2009–2015. A series of statistical indicators were adopted to quantitatively evaluate the error of 3B42V7 and its ability in detecting precipitation events. Overall, the 3B42V7 overestimates the precipitation with Bias of 11.16%, and its performance generally becomes better with the increasing of time scale. The agreements between the rain gauge data and 3B42V7 are very low in cold season, and moderate in warm season. The 3B42V7 shows better correlation with rain gauges located in the southern mountainous and central oasis areas than in the northern extreme arid regions, and is more likely to underestimate the precipitation in high-altitude mountainous areas and overestimate the precipitation in low-elevation regions. The distribution of the error on the daily scale is more related to the elevation and rainfall than in monthly and annual scale. The 3B42V7 significantly overestimates the precipitation events, and the overestimation mainly focuses on tiny amounts of rainfall (0–1 mm/d), which is also the range of false alarm concentration. Bias correction for 3B42V7 was carried out based on the deviation of the average monthly precipitation data during 2009–2015. The bias-corrected 3B42V7 was significantly improved compared with the original product. Results suggest that regional assessment and bias correction of 3B42V7 rainfall product are of vital importance and will provide substantive reference for regional hydrological studies.

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Quality-Based Combination of Multi-Source Precipitation Data

Remote Sensing ◽

10.3390/rs12111709 ◽

2020 ◽

Vol 12 (11) ◽

pp. 1709 ◽

Cited By ~ 1

Author(s):

Anna Jurczyk ◽

Jan Szturc ◽

Irena Otop ◽

Katarzyna Ośródka ◽

Piotr Struzik

Keyword(s):

Spatial Resolution ◽

Flash Flood ◽

Radar Data ◽

Rain Gauge ◽

Quality Information ◽

Precipitation Data ◽

Spatial Error ◽

Rain Gauges ◽

Precipitation Measurement ◽

Temporal And Spatial

A quantitative precipitation estimate (QPE) provides basic information for the modelling of many kinds of hydro-meteorological processes, e.g., as input to rainfall-runoff models for flash flood forecasting. Weather radar observations are crucial in order to meet the requirements, because of their very high temporal and spatial resolution. Other sources of precipitation data, such as telemetric rain gauges and satellite observations, are also included in the QPE. All of the used data are characterized by different temporal and spatial error structures. Therefore, a combination of the data should be based on quality information quantitatively determined for each input to take advantage of a particular source of precipitation measurement. The presented work on multi-source QPE, being implemented as the RainGRS system, has been carried out in the Polish national meteorological and hydrological service for new nowcasting and hydrological platforms in Poland. For each of the three data sources, different quality algorithms have been designed: (i) rain gauge data is quality controlled and, on this basis, spatial interpolation and estimation of quality field is performed, (ii) radar data are quality controlled by RADVOL-QC software that corrects errors identified in the data and characterizes its final quality, (iii) NWC SAF (Satellite Application Facility on support to Nowcasting and Very Short Range Forecasting) products for both visible and infrared channels are combined and the relevant quality field is determined from empirical relationships that are based on analyses of the product performance. Subsequently, the quality-based QPE is generated with a 1-km spatial resolution every 10 minutes (corresponding to radar data). The basis for the combination is a conditional merging technique that is enhanced by involving detailed quality information that is assigned to individual input data. The validation of the RainGRS estimates was performed taking account of season and kind of precipitation.

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Use of APHRODITE Rain Gauge–Based Precipitation and TRMM 3B43 Products for Improving Asian Monsoon Seasonal Precipitation Forecasts by the Superensemble Method

Journal of Climate ◽

10.1175/jcli-d-13-00332.1 ◽

2014 ◽

Vol 27 (3) ◽

pp. 1062-1069 ◽

Cited By ~ 26

Author(s):

Akiyo Yatagai ◽

T. N. Krishnamurti ◽

Vinay Kumar ◽

A. K. Mishra ◽

Anu Simon

Keyword(s):

Asian Monsoon ◽

Rain Gauge ◽

Training Data ◽

Monthly Precipitation ◽

Observation Data ◽

Training Phase ◽

Multiple Model ◽

Seasonal Forecasts ◽

Past Performance ◽

Seasonal Climate

Abstract A multimodel superensemble developed by the Florida State University combines multiple model forecasts based on their past performance (training phase) to make a consensus forecast. Because observed precipitation reflects local characteristics such as orography, quantitative high-resolution precipitation products are useful for downscaling coarse model outputs. The Asian Precipitation–Highly-Resolved Observational Data Integration Toward Evaluation of Water Resources (APHRODITE) and Tropical Rainfall Measuring Mission (TRMM) 3B43 products are used for downscaling and as training data in the superensemble training phase. Seven years (1998–2004) of monthly precipitation (June–August) over the Asian monsoon region (0°–50°N, 60°–150°E) and results of four coupled climate models were used. TRMM 3B43 was adjusted by APHRODITE (m-TRMM). For seasonal climate forecasts, a synthetic superensemble technique was used. A cross-validation technique was adopted, in which the year to be forecast was excluded from the calculations for obtaining the regression coefficients. The principal results are as follows: 1) Seasonal forecasts of Asian monsoon precipitation were considerably improved by use of APHRODITE rain gauge–based data or the m-TRMM product. These forecasts are much superior to those from the best model of the suite and ensemble mean. 2) Use of a statistical downscaling and synthetic superensemble method for multimodel forecasts of seasonal climate significantly improved precipitation prediction at higher resolution. This is confirmed by cross-evaluation of superensemble with using other observation data than the data used in the training phase. 3) Availability of a dense rain gauge network–based analysis was essential for the success of this work.

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Regional and Seasonal Precipitation and Drought Trends in Ganga–Brahmaputra Basin

Water ◽

10.3390/w13162218 ◽

2021 ◽

Vol 13 (16) ◽

pp. 2218

Author(s):

Bikram Parajuli ◽

Xiang Zhang ◽

Sudip Deuja ◽

Yingbing Liu

Keyword(s):

Spatial Pattern ◽

Time Scales ◽

Standardized Precipitation Index ◽

Monthly Precipitation ◽

Seasonal Precipitation ◽

Precipitation Data ◽

Rain Gauges ◽

Geographical Regions ◽

Brahmaputra Basin ◽

Different Time Scales

Satellite-based precipitation products can be a better alternative of rain gauges for hydro-meteorological studies in data-poor regions. This study aimed to evaluate how regional and seasonal precipitation and drought patterns had changed in the Ganga–Brahmaputra Basin between 1983 and 2020 with PERSIANN-CDR precipitation data. The spatial pattern of winter drought, monsoon drought, and Standardized Precipitation Index (SPI) calculated for different time scales were evaluated using principal component analysis. Ganga–Brahmaputra is one of the most populated river basins that flows through different geographical regions. Rain gauges are heterogeneously distributed in the basin due to its complex orography, highlighting the significance of gridded precipitation products over gauge observations for climate studies. Annual and monthly precipitation trends between 1983 and 2020 were evaluated using the original and modified Mann–Kendall trend test, and annual precipitation in the basin was found to be declining at a rate of 5.8 mm/year. An increasing trend was observed in pre-monsoon rainfall, whereas precipitation exhibited a decreasing trend for other months. Results of the Pettitt test showed precipitation time series was inhomogeneous and changepoint occurred around 2000. Decreasing trends of SPI indicated increasing frequency and intensity of drought events. Winter drought showed a clear spatial pattern in the basin; however, SPIs calculated for different time scales and monsoon drought had complex spatial patterns. This study demonstrates the applicability of satellite-based PERSIANN-CDR precipitation data in climate research in the Ganga–Brahmaputra Basin.

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Spatially-distributed IDF curves for Center-Southern Chile using IMERG

10.5194/egusphere-egu2020-21091 ◽

2020 ◽

Author(s):

Mauricio Zambrano-Bigiarini ◽

Cristóbal Soto Escobar ◽

Oscar M. Baez-Villanueva

Keyword(s):

Spatial Distribution ◽

Rain Gauge ◽

Temporal Scales ◽

Rain Gauges ◽

South Central ◽

Idf Curves ◽

The Impact ◽

Term Rain ◽

Precipitation Events

The Intensity-Duration-Frequency (IDF) curves are crucial for urban drainage design and to mitigate the impact of extreme precipitation events and floods. However, many regions lack a high-density network of rain gauges to adequately characterise the spatial distribution of precipitation events. In this work we compute IDF curves for the South-Central Chilean region (26-56&#176;S) using the latest version of the Integrated Multi-satellitE Retrievals for GPM (IMERGv06B) for 2001-2018, with a spatial resolution of 0.10&#176; and half-hourly temporal frequency. First, we evaluated the performance of IMERGv06B against 344 rain gauge stations at daily, monthly and annual temporal scales using a point-to-pixel approach. The modified Kling-Gupta efficiency (KGE&#8217;) and its components (linear correlation, bias, and variability ratio) were selected as continuous indices of performance. Secondly, we fit maximum precipitation intensities from 14 long-term rain gauge stations to three probability density functions (Gumbel, Log-Pearson Type III, and GEV II) to evaluate: i) the impact of using 15-year rainfall time series in the computation of IDF curves instead of using the typical long-term periods (~ 30 years); and ii) to select the best distribution function for the study area. The Gumbel distribution was selected to obtain the maximum annual intensities for each grid-cell within the study area for 12 durations (0.5, 1, 2, 4, 6, 8, 10, 12, 18, 24, 48, and 72 h) and 6 return periods (T=2, 5, 10, 25, 50, and 100 years). The application of the Wilcoxon Mann-Whitney test indicates that differences between IDF curves obtained from 15 years of records at the 14 long-term rain gauges and those derived from 25 years of record (or more) are not statistically significant, and therefore, 15 years of record are enough (although not optimal) to compute the IDF curves. Also, our results show that IMERGv06B is able to represent the spatial distribution of precipitation at daily, monthly and annual temporal scales over the study area. Moreover, the obtained precipitation intensities showed high spatial variability, mainly over the Near North (26.0-32.2&#176;S) and the Far South (43.7-56.0&#176;S). Additionally, the intensities from Central Chile (32.2-36.4&#176;S) to the Near South (36.4-43.7&#176;S) were systematically higher compared to the intensities described in older official governmental reports, suggesting an increase in precipitation intensities during recent decades.

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Geographic segmentation, spatial dependencies, and evaluation of the relative position of rain-gauges based on gridded data of mean monthly precipitation: application in Nigeria

Hydrology Research ◽

10.2166/nh.2016.095 ◽

2016 ◽

Vol 49 (1) ◽

pp. 107-122 ◽

Cited By ~ 2

Author(s):

V. G. Aschonitis ◽

G. O. Awe ◽

T. P. Abegunrin ◽

K. A. Demertzi ◽

D. M. Papamichail ◽

...

Keyword(s):

Relative Position ◽

Principal Component ◽

Spatiotemporal Analysis ◽

Rain Gauge ◽

Spatiotemporal Variability ◽

Monthly Precipitation ◽

Rain Gauges ◽

Spatial Dependencies ◽

The Mean ◽

Rain Gauge Network

Abstract The aim of the study is to present a combination of techniques for (a) the spatiotemporal analysis of mean monthly gridded precipitation datasets and (b) the evaluation of the relative position of the existing rain-gauge network. The mean monthly precipitation (P) patterns of Nigeria using ∼1 km2 grids for the period 1950–2000 were analyzed and the position of existing rain-gauges was evaluated. The analysis was performed through: (a) correlations of P versus elevation (H), latitude (Lat) and longitude (Lon); (b) principal component analysis (PCA); (c) Iso-Cluster and maximum likelihood classification (MLC) analysis for terrain segmentation to regions with similar temporal variability of mean monthly P; (d) use of MLC to create reliability classes of grid locations based on the mean clusters’ characteristics; and (e) analysis to evaluate the relative position of 33 rain-gauges based on the clusters and their reliability classes. The correlations of mean monthly P versus H, Lat, Lon, and PCA highlighted the spatiotemporal effects of the Inter Tropical Discontinuity phenomenon. The cluster analysis revealed 47 clusters, of which 22 do not have a rain-gauge while eight clusters have more than one rain-gauge. Thus, more rain-gauges and a better distribution are required to describe the spatiotemporal variability of P in Nigeria.

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A Novel Electromagnetic Wave Rain Gauge and its Average Rainfall Estimation Method

Remote Sensing ◽

10.3390/rs12213528 ◽

2020 ◽

Vol 12 (21) ◽

pp. 3528

Author(s):

S. Lim

Keyword(s):

Electromagnetic Wave ◽

Short Range ◽

Estimation Method ◽

Rain Gauge ◽

Observation Data ◽

Dual Polarization ◽

Rainfall Events ◽

Rain Gauges ◽

Average Rainfall ◽

Average Observation

It is essential to accurately estimate rainfall to predict and prevent hydrological disasters such as floods. In this paper, an electromagnetic wave rain gauge system and a method to estimate average rainfall using the system’s multiple elevation observation data are presented. The compact electromagnetic wave rain gauge is a small-sized radar that performs very short-range observations using K-band dual-polarization technology. The method to estimate average rainfall is based on the concept of an average observation derived from multiple elevation scans with very short range and dual-polarization information. The proposed method was evaluated by comparing it with ground instruments, including a pit-gauge, tipping-bucket rain gauges, and a Parsivel disdrometer. The evaluation results demonstrated that the new methodology worked fairly well for various rainfall events.

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Evaluation of Satellite-Based and Reanalysis Precipitation Data in the Tropical Pacific

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-12-049.1 ◽

2013 ◽

Vol 52 (3) ◽

pp. 634-644 ◽

Cited By ~ 36

Author(s):

Uwe Pfeifroth ◽

Richard Mueller ◽

Bodo Ahrens

Keyword(s):

Atmospheric Model ◽

Rain Gauge ◽

Tropical Pacific ◽

Monthly Precipitation ◽

Precipitation Data ◽

Model Based ◽

Station Data ◽

Global Precipitation ◽

Temporal Coverage ◽

The Tropical Pacific

AbstractGlobal precipitation monitoring is essential for understanding the earth’s water and energy cycle. Therefore, usage of satellite-based precipitation data is necessary where in situ data are rare. In addition, atmospheric-model-based reanalysis data feature global data coverage and offer a full catalog of atmospheric variables including precipitation. In this study, two model-based reanalysis products, the interim reanalysis by the European Centre for Medium-Range Weather Forecasts (ERA-Interim) and NASA’s Modern-Era Retrospective Analysis for Research and Applications (MERRA), as well as two satellite-based datasets obtained by the Global Precipitation Climatology Centre (GPCP) and Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data (HOAPS) are evaluated. The evaluation is based on monthly precipitation in the tropical Pacific Ocean during the time period 1989–2005. Rain-gauge atoll station data provided by the Pacific Rainfall Database (PACRAIN) are used as ground-based reference. It is shown that the analyzed precipitation datasets offer temporal correlations ranging from 0.7 to 0.8 for absolute amounts and from 0.6 to 0.75 for monthly anomalies. Average monthly deviations are in the range of 20%–30%. GPCP offers the highest correlation and lowest monthly deviations with reference to PACRAIN station data. The HOAPS precipitation data perform in the range of the reanalysis precipitation datasets. In high native spatial resolution, HOAPS reveals deficiencies owing to its relatively sparse temporal coverage. This result emphasizes that temporal coverage is critical for controlling the performance of precipitation monitoring. Both reanalysis products show similar systematic behaviors in overestimating small and medium precipitation amounts and underestimating high amounts.

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ESOLIP – estimate of solid and liquid precipitation at sub-daily time resolution by combining snow height and rain gauge measurements

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-10-8683-2013 ◽

2013 ◽

Vol 10 (7) ◽

pp. 8683-8714 ◽

Cited By ~ 4

Author(s):

E. Mair ◽

G. Bertoldi ◽

G. Leitinger ◽

S. Della Chiesa ◽

G. Niedrist ◽

...

Keyword(s):

Time Resolution ◽

Hydrological Cycle ◽

Winter Precipitation ◽

Rain Gauge ◽

High Temporal Resolution ◽

Precipitation Data ◽

Rain Gauges ◽

Alpine Catchments ◽

Liquid Precipitation ◽

Daily Time

Abstract. Measuring precipitation in mountain areas is a demanding task, but essential for hydrological and environmental themes. Especially in small Alpine catchments with short hydrological response, precipitation data with high temporal resolution are required for a better understanding of the hydrological cycle. Since most climate/meteorological stations are situated at the easily accessible bottom of valleys, and the few heated rain gauges installed at higher elevation sites are problematic in winter conditions, an accurate quantification of winter (snow) precipitation at high elevations remains difficult. However, there are an increasing number of micro-meteorological stations and snow height sensors at high elevation locations in Alpine catchments. To benefit from data of such stations, an improved approach to estimate solid and liquid precipitation (ESOLIP) is proposed. ESOLIP allows gathering hourly precipitation data throughout the year by using unheated rain gauge data, careful filtering of snow height sensors as well as standard meteorological data (air temperature, relative humidity, global shortwave radiation, wind speed). ESOLIP was validated at a well-equipped test site in Stubai Valley (Tyrol, Austria), comparing results to winter precipitation measured with a snow pillow and a heated rain gauge. The snow height filtering routine and indicators for possible precipitation were tested at a field site in Matsch Valley (South Tyrol, Italy). Results show a good match with measured data because variable snow density is taken into account, which is important when working with freshly fallen snow. Furthermore, the results show the need for accurate filtering of the noise of the snow height signal and they confirm the unreliability of heated rain gauges for estimating winter precipitation. The described improved precipitation estimate ESOLIP at sub-daily time resolution is helpful for precipitation analysis and for several hydrological applications like monitoring systems and rainfall-runoff models.

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ANALYSIS OF TREND OF THE PRECIPITATION DATA: A CASE STUDY OF KANGRA DISTRICT, HIMACHAL PRADESH

International Journal of Research -GRANTHAALAYAH ◽

10.29121/granthaalayah.v3.i9.2015.2948 ◽

2015 ◽

Vol 3 (9) ◽

pp. 87-95

Author(s):

Arijit Ganguly ◽

Ranjana Ray Chaudhuri ◽

Prateek Sharma

Keyword(s):

Flood Control ◽

Rain Gauge ◽

Himachal Pradesh ◽

Flood Frequency ◽

Negative Trend ◽

Annual Rainfall ◽

Monthly Precipitation ◽

Precipitation Data ◽

Level Of Significance

The current study is carried out to determine the potential trend of rainfall and assess its significance in Kangra district of Himachal Pradesh. Rainfall is a key characteristic of any watershed which plays a significant role in flood frequency, flood control studies and water planning and management. In this case study,mean monthly rainfall has been analysed to determine the variability in magnitude over the period 1950-2005. Trend in mean monthly precipitation data and mean seasonal trends are analysed using Mann-Kendall test and Sen’s slope estimation for the data period 1950-2005. Analysis of monthly trend in precipitation shows negative trend for the months of July, August, September and October in all the rain gauge stations. However, the falling trend is significant for the month of August for Dharamshala(0.05 level of significance). Interestingly the month of June shows rising trend of rainfall in all the stations, however, at Dharamshala the trend is significant (0.01 level of significance). The winter rainfall in the month of January and February record decreasing trend, with DeraGobipur and Kangra recording significant decreasing trend for the month of January at 0.01 level of significance and 0.05 level of significance respectively. Trend analysis for annual rainfall data shows significant negative trend for Dharamshala.

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A Statistical Approach to generate ensembles of observation data of precipitation using hindcast data over the Northwest Himalayas

10.5194/egusphere-egu21-6991 ◽

2021 ◽

Author(s):

Akshay Singhal ◽

Sanjeev Jha

Keyword(s):

Spatial Resolution ◽

Reference Data ◽

Training Image ◽

Observation Data ◽

Precipitation Data ◽

Fine Scale ◽

Coarse Scale ◽

Rain Gauges ◽

Conditioning Data ◽

Scale Data

Availability of precipitation data at fine spatial resolution is highly desirable for hydroclimatic studies. Rain gauges are often considered as the primary source of precipitation data due to its reliability. However, due to either physical, climatic or economic constraints, setting up networks of rain gauges becomes unfeasible in many isolated terrains such as the Himalayan region. In the absence of gauge data, other alternate sources of weather information such as Satellite based Precipitation Products (SPPs) and Reanalysis precipitation Datasets (RPDs) are generally used. In this study, we aim to utilise 18 years of precipitation data (2001-2018) derived from the Integrated Multi-Satellite Retrievals for GPM (IMERG) at 10km spatial resolution as input to a Multiple-Point Statistics (MPS) based statistical model to obtain corresponding data for the year 2019 at 10km over the North-west Himalayan region. MPS is capable of generating fine scale data using the available coarse scale hindcast data by reproducing spatially connected spatial patterns. It requires data to be split into two parts. First part is called the training image and it requires both coarse and fine scale data. Second part is called the conditioning data which requires data only at coarse scale for the year 2019. In the attempt of using MPS as the tool for this study, the spatial field of Original IMERG data at 10 km (O_IMERG) is smoothen (S_IMERG) in order to transform the data features to a coarse scale reference data. The reference data used for this purpose is the High Asia Refined analysis (HAR) available at 30km spatial resolution over the South-Central Asia and Tibetan Plateau region. The variograms of both O_IMERG and S_IMERG are used to evaluate error frequency between the two data at specific distances followed by bias correction of S_IMERG. The bias corrected S_IMERG (BCS_IMERG) acts as the conditioning data for the MPS model. Training Image is composed of both BCS_IMERG and O_IMERG. Both the training image (year 2001-2018) and the conditioning data (2019) are provided to the MPS model. In addition to the variable of precipitation, the model also employs static parameters such as locational and topographical variables to help in identification of true patterns between training image and conditioning data. The study is significant in its ability to generate future precipitation information by utilising the available hindcast data observation data (10 km spatial resolution) by overcoming the spatial heterogeneity involved with observation data.

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