Evaluation of gridded rain‐gauge‐based precipitation datasets: Impact of station density, spatial resolution, altitude gradient and climate

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.

Tropical Andes Radar Precipitation Estimates Need High Temporal and Moderate Spatial Resolution

Water ◽

10.3390/w11051038 ◽

2019 ◽

Vol 11 (5) ◽

pp. 1038 ◽

Cited By ~ 1

Author(s):

Mario Guallpa ◽

Johanna Orellana-Alvear ◽

Jörg Bendix

Keyword(s):

Spatial Resolution ◽

Temporal Resolution ◽

Time Resolution ◽

Weather Radar ◽

Radar Data ◽

Rain Gauge ◽

Tropical Andes ◽

The Andes ◽

Significant Difference ◽

The Impact

Weather radar networks are an excellent tool for quantitative precipitation estimation (QPE), due to their high resolution in space and time, particularly in remote mountain areas such as the Tropical Andes. Nevertheless, reduction of the temporal and spatial resolution might severely reduce the quality of QPE. Thus, the main objective of this study was to analyze the impact of spatial and temporal resolutions of radar data on the cumulative QPE. For this, data from the world’s highest X-band weather radar (4450 m a.s.l.), located in the Andes of Ecuador (Paute River basin), and from a rain gauge network were used. Different time resolutions (1, 5, 10, 15, 20, 30, and 60 min) and spatial resolutions (0.5, 0.25, and 0.1 km) were evaluated. An optical flow method was validated for 11 rainfall events (with different features) and applied to enhance the temporal resolution of radar data to 1-min intervals. The results show that 1-min temporal resolution images are able to capture rain event features in detail. The radar–rain gauge correlation decreases considerably when the time resolution increases (r from 0.69 to 0.31, time resolution from 1 to 60 min). No significant difference was found in the rain total volume (3%) calculated with the three spatial resolution data. A spatial resolution of 0.5 km on radar imagery is suitable to quantify rainfall in the Andes Mountains. This study improves knowledge on rainfall spatial distribution in the Ecuadorian Andes, and it will be the basis for future hydrometeorological studies.

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

Advances in Meteorology ◽

10.1155/2018/3491960 ◽

2018 ◽

Vol 2018 ◽

pp. 1-13 ◽

Cited By ~ 7

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.

SATIN–Satellite driven nowcasting system

Advances in Science and Research ◽

10.5194/asr-13-27-2016 ◽

2016 ◽

Vol 13 ◽

pp. 27-35

Author(s):

Ingo Meirold-Mautner ◽

Alexander Kann ◽

Florian Meier

Keyword(s):

Rain Gauge ◽

Superior Performance ◽

Lead Times ◽

Motion Vectors ◽

Radar Observations ◽

Station Density

Abstract. A precipitation nowcasting system (SATIN) is presented which relies entirely on satellite based precipitation products and rain gauge measurements. Thus, the proposed system is most suitable for areas where ground based radar observations are not available, or potentially suffer from low quality. SATIN delivers analyses on a 1 km grid every 15 min and nowcasts (obtained through motion vectors) in 15 min time steps. Nowcasts are gradually merged with NWP precipitation forecasts. An extensive validation including comparisons to different NWP models yields superior performance for SATIN analyses as well as nowcasts for lead times up to 1 h. Reducing the station density still yields better performance than operationally available NWP's.

Geostatistical merging of rain gauge and radar data for high temporal resolutions and various station density scenarios

Journal of Hydrology ◽

10.1016/j.jhydrol.2013.10.028 ◽

2014 ◽

Vol 508 ◽

pp. 88-101 ◽

Cited By ~ 68

Author(s):

Christian Berndt ◽

Ehsan Rabiei ◽

Uwe Haberlandt

Keyword(s):

Radar Data ◽

Rain Gauge ◽

Station Density

Small Catchment Runoff Sensitivity to Station Density and Spatial Interpolation: Hydrological Modeling of Heavy Rainfall Using a Dense Rain Gauge Network

Water ◽

10.3390/w13101381 ◽

2021 ◽

Vol 13 (10) ◽

pp. 1381

Author(s):

Clara Hohmann ◽

Gottfried Kirchengast ◽

Sungmin O ◽

Wolfgang Rieger ◽

Ulrich Foelsche

Keyword(s):

Hydrological Modeling ◽

Nearest Neighbor ◽

Heavy Precipitation ◽

Rain Gauge ◽

Large Set ◽

Small Catchment ◽

Long Duration ◽

Runoff Modeling ◽

Thiessen Polygons ◽

Station Density

Precipitation is the most important input to hydrological models, and its spatial variability can strongly influence modeled runoff. The highly dense station network WegenerNet (0.5 stations per km2) in southeastern Austria offers the opportunity to study the sensitivity of modeled runoff to precipitation input. We performed a large set of runoff simulations (WaSiM model) using 16 subnetworks with varying station densities and two interpolation schemes (inverse distance weighting, Thiessen polygons). Six representative heavy precipitation events were analyzed, placing a focus on small subcatchments (10–30 km2) and different event durations. We found that the modeling performance generally improved when the station density was increased up to a certain resolution: a mean nearest neighbor distance of around 6 km for long-duration events and about 2.5 km for short-duration events. However, this is not always true for small subcatchments. The sufficient station density is clearly dependent on the catchment area, event type, and station distribution. When the network is very dense (mean distance < 1.7 km), any reasonable interpolation choice is suitable. Overall, the station density is much more important than the interpolation scheme. Our findings highlight the need to study extreme precipitation characteristics in combination with runoff modeling to decompose precipitation uncertainties more comprehensively.

Assessing the performance of radar-based and satellite precipitation products in hydrological modelling with SWAT in Vils Basin, Germany

10.5194/egusphere-egu2020-11018 ◽

2020 ◽

Author(s):

Zheng Duan ◽

Edward Duggan ◽

Ye Qing ◽

Ye Tuo

Keyword(s):

Spatial Resolution ◽

Swat Model ◽

Rain Gauge ◽

Hydrological Modelling ◽

Satellite Precipitation ◽

Precipitation Product ◽

Monthly Streamflow ◽

Precipitation Estimates ◽

Temporal And Spatial ◽

Streamflow Simulation

Hydrological modelling is an important tool to improve our understanding of hydrological processes of river basins and to predict impacts of climate change and environmental change on water resources. Precipitation is a key component of the hydrological cycle, and the most important driver/input data for hydrological models. Accurate precipitation measurements at desirable temporal and spatial resolution are essential for achieving reasonable performance of hydrological modelling. Compared to the conventional measurements from point-based rain gauge stations, remote sensing of precipitation with satellite sensors and ground-based radar can expand observational coverage and provide regional precipitation at varying temporal and spatial resolutions. Radars can provide sampling at very high resolution but also tend to contain significant errors in precipitation estimates. The Deutscher Wetterdienst (DWD; German Weather Service) developed the RADOLAN (RADar-OnLine-ANeichung) method (a real-time, gauge-adjustment and correction procedure) to generate precipitation estimates (termed as RADOLAN product) from the German Doppler radar network. More recently (2017), the DWD published a reanalysis of radar data to generate RADKLIM (RADarKLIMatologie) precipitation product using upgraded correction algorithms and additional offline gauge adjustment.&#160;&#160;This study presents the first assessment of the performance of two high spatial resolution (1 km) radar-based precipitation products (RADOLAN and RADKLIM) in streamflow simulation using the hydrological model SWAT (Soil and Water Assessment Tool) in Germany. We also evaluate the performance of conventional point-based rain gauge data and a satellite precipitation product in driving SWAT for streamflow simulation. The selected satellite product is CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) because of its well reported good performance and the relative higher spatial resolution (0.05&#176;). The Vils Basin located in Bavaria, Germany is chosen as the study area. Performance of investigated precipitation product is assessed by comparing simulated streamflow using calibrated SWAT model against measured streamflow at basin outlet at both daily and monthly time scales. The model calibration is performed using the SWAT-CUP program with measured streamflow. Different calibration procedures are also investigated to analyze the influence on model performance. This study presents and discusses the accuracy and uncertainty of using ground-based radar and satellite precipitation products in driving SWAT model for daily and monthly streamflow simulation. Our findings are expected to provide beneficial feedback to product developers for further improvements, and to inform local end-users about the quality of investigated precipitation products.

Remote sensing of rainfall at high spatial-temporal resolution through soil moisture

10.5194/egusphere-egu2020-20275 ◽

2020 ◽

Author(s):

Paolo Filippucci ◽

Luca Brocca ◽

Angelica Tarpanelli ◽

Christian Massari ◽

Luca Ciabatta ◽

...

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Spatial Resolution ◽

Temporal Resolution ◽

Temporal Frequency ◽

Recent Decade ◽

Rain Gauge ◽

Fusion Algorithm ◽

Space Applications ◽

Soil Moisture Data

In order to enhance our understanding of the hydrologic cycle, frequent, reliable and detailed information on precipitation are fundamental. In-situ measurements are the traditional source of this information, but they have limited spatial representativeness and the number of stations worldwide is declining and their access is often troublesome. Satellite products are able to overcome these issues and actually are the main, if not the only, source of information over many areas of the world. Notwithstanding this, the spatial resolution is still limited to tens or hundreds of kilometers, limiting their usefulness for hydrological applications. In the recent decade, a new approach for estimating rainfall from satellite-derived soil moisture observations has been proposed, named SM2RAIN (Brocca et al., 2014) and based on the inversion of the soil water balance equation. The application of SM2RAIN to Sentinel-1 satellites carrying a C-band Synthetic Aperture Radar (CSAR) sensor can provide rainfall data at unprecedented spatial and temporal resolution.In this study, we combined the soil moisture data retrieved from backscatter observations of Sentinel-1 (1.5/4 days temporal frequency over Europe, 500 m sampling) with the soil moisture data obtained from ASCAT sensor, onboard of METOP satellites (8-24 h temporal frequency, 12.5 km sampling) through a data fusion algorithm. The result is an innovative soil moisture dataset with a temporal resolution of 1 day and a spatial resolution of 1 km (Bauer-Marschallinger et al., 2018). These data are used as input for SM2RAIN, obtaining as output a rainfall product with temporal and spatial sampling of 1 day and 1 km, respectively.The approach was applied over test regions in Italy and Austria obtaining promising results. Specifically, the comparison with high density observations from raingauges and meteorological radars has allowed the assessment of the method at high spatial resolution and varying temporal resolution. Results show that good quality rainfall estimates at 1 km of spatial resolution can be obtained in reproducing 3- to 5-day rainfall accumulations. Further testing will be carried out in the next months and presented at the conference.AcknowlodgmentsThe activity is funded by DWC radar project, Austrian Space Applications Programme, FFG Project 873658.ReferenceBauer-Marschallinger, B., Paulik, C., Mistelbauer, T., Hochst&#246;ger, S., Modanesi, S., Ciabatta, L., Massari, C., Brocca, L. & Wagner, W. (2018). Soil Moisture from Fusion of Scatterometer and SAR: Closing the Scale Gap with Temporal Filtering. Remote Sensing, 10(7), 1030. doi:10.3390/rs10071030Brocca L., Ciabatta L., Massari C., Moramarco T., Hahn S., Hasenauer S., Kidd R., Dorigo W., Wagner W., Levizzani V. &#8211; &#8220;Soil as a natural rain gauge: Estimating global rainfall from satellite soil moisture data&#8221;. J. Geophys. Res. Atmos. vol. 119, pp. 5128&#8211;5141, 2014. doi: 10.1002/2014JD021489

Five years of commercial microwave link network derived rainfall research in Sweden

10.5194/egusphere-egu2020-7969 ◽

2020 ◽

Author(s):

Remco (C.Z.) van de Beek ◽

Jafet Andersson ◽

Jonas Olsson ◽

Jonas Hansryd

Keyword(s):

Spatial Resolution ◽

Temporal Resolution ◽

Rain Rate ◽

Ground Level ◽

Rain Gauge ◽

Professional Services ◽

High Temporal Resolution ◽

Rain Gauges ◽

Microwave Link ◽

Rain Rates

Accurate rainfall measurements are very important in hydrology, meteorology, agriculture and other fields. Traditionally rain gauges combined with radar have been used to measure rain rates. However, these instruments are not always available. Also combining point measurements at the ground with measured reflectivities of volumes in the air to an accurate rain rate map at ground level poses challenges. Commercial microwave link networks can help in these areas as these can provide measurements at a high temporal resolution and tend to be available wherever people live, with highest network densities where most people are. They also measure precipitation along a path near ground level and offer a way to close the gap between rain gauge measurements and radar.In this study we highlight the work SMHI has performed on deriving rain rates from commercial microwave links since 2015. This started with a pilot study in Gothenburg. The signal strengths of 364 microwave links were sampled every ten seconds and were used to create rainfall maps at a one-minute temporal resolution and 500m spatial resolution. These rain maps were then applied in a hydrological experiment and compared to rain gauge and radar measurements. The results were very promising, not only due to the high temporal and spatial resolution, but also with the accuracy of the actual measurements. The correlation was found to be equal to those of the rain gauges, while links were found to overestimate rainfall volumes on average. A demo site was created showing the one-minute rain rate maps and can be found at: https://www.smhi.se/en/services/professional-services/microweather/. Since then the methodology has been further improved and also applied within Stockholm in a new hydrological experiment. Currently new regions are being considered, as well as novel ways to merge data sources to create high quality precipitation maps. This contribution summarizes the progress to date.

Spatial and Temporal Downscaling of TRMM Precipitation with Novel Algorithms

Journal of Hydrometeorology ◽

10.1175/jhm-d-19-0289.1 ◽

2020 ◽

Vol 21 (6) ◽

pp. 1259-1278 ◽

Cited By ~ 1

Author(s):

Huihui Zhang ◽

Hugo A. Loáiciga ◽

Da Ha ◽

Qingyun Du

Keyword(s):

Spatial Resolution ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Principal Component ◽

Tropical Rainfall Measuring Mission ◽

Rain Gauge ◽

Low Resolution ◽

Spatial And Temporal Scales ◽

Geographically Weighted Regression Model ◽

Trmm Precipitation

AbstractTropical Rainfall Measuring Mission (TRMM) satellite products constitute valuable precipitation datasets over regions with sparse rain gauge networks. Downscaling is an effective approach to estimating the precipitation over ungauged areas with high spatial resolution. However, a large bias and low resolution of original TRMM satellite images constitute constraints for practical hydrologic applications of TRMM precipitation products. This study contributes two precipitation downscaling algorithms by exploring the nonstationarity relations between precipitation and various environment factors [daytime surface temperature (LTD), terrain slope, normalized difference vegetation index (NDVI), altitude, longitude, and latitude] to overcome bias and low-resolution constraints of TRMM precipitation. Downscaling of precipitation is achieved with the geographically weighted regression model (GWR) and the backward-propagation artificial neural networks (BP_ANN). The probability density function (PDF) algorithm corrects the bias of satellite precipitation data with respect to spatial and temporal scales prior to downscaling. The principal component analysis algorithm (PCA) provides an alternative method of obtaining accurate monthly rainfall estimates during the wet rainfall season that minimizes the temporal uncertainties and upscaling effects introduced by direct accumulation (DA) of precipitation. The performances of the proposed downscaling algorithms are assessed by downscaling the latest version of TRMM3B42 V7 datasets within Hubei Province from 0.25° (about 25 km) to 1-km spatial resolution at the monthly scale. The downscaled datasets are systematically evaluated with in situ observations at 27 rain gauges from the years 2005 through 2010. This paper’s results demonstrate the bias correction is necessary before downscaling. The high-resolution precipitation datasets obtained with the proposed downscaling model with GWR relying on the NDVI and slope are shown to improve the accuracy of precipitation estimates. GWR exhibits more accurate downscaling results than BP_ANN coupled with the genetic algorithm (GA) in most dry and wet seasons.