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

This paper presents a case study comparing the latest algorithm version of Global Satellite Mapping of Precipitation (GSMaP) data with C-band and X-band Multi-Parameter (MP) radar as high-resolution rainfall data in terms of localized heavy rainfall events. The study also obliged us to clarify the spatial and temporal resolution of GSMaP data using high-accuracy ground-based radar, and evaluate the performance and reporting frequency of GSMaP satellites. The GSMaP_Gauge_RNL data with less than 70 mm/day of daily rainfall was similar to the data of both radars, but the GSMaP_Gauge_RNL data with over 70 mm/day of daily rainfall was not, and the calibration by rain-gauge data was poor. Furthermore, both direct/indirect observations by the Global Precipitation Measurement/Microwave Imager (GPM/GMI) and the frequency thereof (once or twice) significantly affected the difference between GPM/GMI data and C-band radar data when the daily rainfall was less than 70 mm/day and the hourly rainfall was less than 20 mm/h. Therefore, it is difficult for GSMaP_Gauge to accurately estimate localized heavy rainfall with high-density particle precipitation.

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A New Land Surface Hydrology within the Noah-WRF Land-Atmosphere Mesoscale Model Applied to Semiarid Environment: Evaluation over the Dantiandou Kori (Niger)

Advances in Meteorology ◽

10.1155/2009/731874 ◽

2009 ◽

Vol 2009 ◽

pp. 1-13 ◽

Cited By ~ 8

Author(s):

B. Decharme ◽

C. Ottlé ◽

S. Saux-Picart ◽

N. Boulain ◽

B. Cappelaere ◽

...

Keyword(s):

Land Surface ◽

Mesoscale Model ◽

West African Monsoon ◽

Radar Data ◽

Rain Gauge ◽

Surface Energy Budget ◽

Surface Hydrology ◽

Land Surface Hydrology ◽

Rain Gauge Network

Land-atmosphere feedbacks, which are particularly important over the Sahel during the West African Monsoon (WAM), partly depend on a large range of processes linked to the land surface hydrology and the vegetation heterogeneities. This study focuses on the evaluation of a new land surface hydrology within the Noah-WRF land-atmosphere-coupled mesoscale model over the Sahel. This new hydrology explicitly takes account for the Dunne runoff using topographic information, the Horton runoff using a Green-Ampt approximation, and land surface heterogeneities. The previous and new versions of Noah-WRF are compared against a unique observation dataset located over the Dantiandou Kori (Niger). This dataset includes dense rain gauge network, surfaces temperatures estimated from MSG/SEVIRI data, surface soil moisture mapping based on ASAR/ENVISAT C-band radar data and in situ observations of surface atmospheric and land surface energy budget variables. Generally, the WAM is reasonably reproduced by Noah-WRF even if some limitations appear throughout the comparison between simulations and observations. An appreciable improvement of the model results is also found when the new hydrology is used. This fact seems to emphasize the relative importance of the representation of the land surface hydrological processes on the WAM simulated by Noah-WRF over the Sahel.

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Impact of the Altitudinal Gradients of Precipitation on the Radar QPE Bias in the French Alps

Atmosphere ◽

10.3390/atmos10060306 ◽

2019 ◽

Vol 10 (6) ◽

pp. 306 ◽

Cited By ~ 1

Author(s):

Dominique Faure ◽

Guy Delrieu ◽

Nicolas Gaussiat

Keyword(s):

Ground Level ◽

Radar Data ◽

Rain Gauge ◽

Altitudinal Gradients ◽

French Alps ◽

Rain Gauges ◽

Quantitative Precipitation Estimation ◽

Precipitation Estimation ◽

Radar Data Processing

In the French Alps the quality of the radar Quantitative Precipitation Estimation (QPE) is limited by the topography and the vertical structure of precipitation. A previous study realized in all the French Alps, has shown a general bias between values of the national radar QPE composite and the rain gauge measurements: a radar QPE over-estimation at low altitude (+20% at 200 m a.s.l.), and an increasing underestimation at high altitudes (until −40% at 2100 m a.s.l.). This trend has been linked to altitudinal gradients of precipitation observed at ground level. This paper analyzes relative altitudinal gradients of precipitation estimated with rain gauges measurements in 2016 for three massifs around Grenoble, and for different temporal accumulations (yearly, seasonal, monthly, daily). Comparisons of radar and rain gauge accumulations confirm the bias previously observed. The parts of the current radar data processing affecting the bias value are pointed out. The analysis shows a coherency between the relative gradient values estimated at the different temporal accumulations. Vertical profiles of precipitation detected by a research radar installed at the bottom of the valley also show how the wide horizontal variability of precipitation inside the valley can affect the gradient estimation.

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A neural network model for short term river flow prediction

Natural Hazards and Earth System Science ◽

10.5194/nhess-6-629-2006 ◽

2006 ◽

Vol 6 (4) ◽

pp. 629-635 ◽

Cited By ~ 17

Author(s):

R. Teschl ◽

W. L. Randeu

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

River Flow ◽

Radar Data ◽

Rain Gauge ◽

Huge Amount ◽

Short Term ◽

Flow Prediction ◽

Spatial Coverage ◽

Radar Measurements

Abstract. This paper presents a model using rain gauge and weather radar data to predict the runoff of a small alpine catchment in Austria. The gapless spatial coverage of the radar is important to detect small convective shower cells, but managing such a huge amount of data is a demanding task for an artificial neural network. The method described here uses statistical analysis to reduce the amount of data and find an appropriate input vector. Based on this analysis, radar measurements (pixels) representing areas requiring approximately the same time to dewater are grouped.

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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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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.

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Precipitation Estimation Methods in Continuous, Distributed Urban Hydrologic Modeling

Water ◽

10.3390/w11071340 ◽

2019 ◽

Vol 11 (7) ◽

pp. 1340

Author(s):

Woodson ◽

Adams ◽

Dymond

Keyword(s):

Hydrological Modeling ◽

Mean Squared Error ◽

Uniform Estimate ◽

Mean Field ◽

Radar Data ◽

Rain Gauge ◽

Hydrologic Model ◽

Estimation Methods ◽

Urban Watersheds ◽

Precipitation Estimation

Quantitative precipitation estimation (QPE) remains a key area of uncertainty in hydrological modeling and prediction, particularly in small, urban watersheds, which respond rapidly to precipitation and can experience significant spatial variability in rainfall fields. Few studies have compared QPE methods in small, urban watersheds, and studies that have examined this topic only compared model results on an event basis using a small number of storms. This study sought to compare the efficacy of multiple QPE methods when simulating discharge in a small, urban watershed on a continuous basis using an operational hydrologic model and QPE forcings. The research distributed hydrologic model (RDHM) was used to model a basin in Roanoke, Virginia, USA, forced with QPEs from four methods: mean field bias (MFB) correction of radar data, kriging of rain gauge data, uncorrected radar data, and a basin-uniform estimate from a single gauge inside the watershed. Based on comparisons between simulated and observed discharge at the basin outlet for a six-month period in 2018, simulations forced with the uncorrected radar QPE had the highest accuracy, as measured by root mean squared error (RMSE) and peak flow relative error, despite systematic underprediction of the mean areal precipitation (MAP). Simulations forced with MFB-corrected radar data consistently and significantly overpredicted discharge, but had the highest accuracy in predicting the timing of peak flows.

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