Radar–raingauge data combination techniques: a revision and analysis of their suitability for urban hydrology

2013 ◽  
Vol 68 (4) ◽  
pp. 737-747 ◽  
Author(s):  
Li-Pen Wang ◽  
Susana Ochoa-Rodríguez ◽  
Nuno Eduardo Simões ◽  
Christian Onof ◽  
Čedo Maksimović
Keyword(s):  
Error Variance ◽  
Bias Reduction ◽  
Spatiotemporal Variability ◽  
Urban Hydrology ◽  
Small Scale ◽  
Spatial And Temporal Variability ◽  
Radar Rainfall ◽  
North East ◽  
Raingauge Data ◽  
Rainfall Estimates

The applicability of the operational radar and raingauge networks for urban hydrology is insufficient. Radar rainfall estimates provide a good description of the spatiotemporal variability of rainfall; however, their accuracy is in general insufficient. It is therefore necessary to adjust radar measurements using raingauge data, which provide accurate point rainfall information. Several gauge-based radar rainfall adjustment techniques have been developed and mainly applied at coarser spatial and temporal scales; however, their suitability for small-scale urban hydrology is seldom explored. In this paper a review of gauge-based adjustment techniques is first provided. After that, two techniques, respectively based upon the ideas of mean bias reduction and error variance minimisation, were selected and tested using as case study an urban catchment (∼8.65 km2) in North-East London. The radar rainfall estimates of four historical events (2010–2012) were adjusted using in situ raingauge estimates and the adjusted rainfall fields were applied to the hydraulic model of the study area. The results show that both techniques can effectively reduce mean bias; however, the technique based upon error variance minimisation can in general better reproduce the spatial and temporal variability of rainfall, which proved to have a significant impact on the subsequent hydraulic outputs. This suggests that error variance minimisation based methods may be more appropriate for urban-scale hydrological applications.

scholarly journals Calibration of a rainfall-runoff model using radar and raingauge data

2005 ◽  
Vol 2 ◽  
pp. 41-46 ◽  
Author(s):  
V. Lopez ◽  
F. Napolitano ◽  
F. Russo
Keyword(s):  
Measurement System ◽  
Small Scale ◽  
Rainfall Runoff ◽  
Radar Rainfall ◽  
Measurement Systems ◽  
Raingauge Data ◽  
Temporal And Spatial ◽  
Rainfall Runoff Model ◽  
Runoff Model ◽  
Rainfall Estimates

Abstract. Since raingauges give pointwise measurements the small scale variability of rainfall fields leads to biases on the estimation for the rainfall over the whole basin. In this context meteorological radars have several advantages since a single site is able to obtain coverage over a wide area with high temporal and spatial resolution. The purpose of this study is to compare the capability of the two different measurement systems in order to give correct input to drive rainfall-runoff models. Therefore a geomorphological model was calibrated, using firstly raingauge data and secondly radar rainfall estimates, for the Treja river basin. In this way it is possible to determine different sets of parameters and the influence of measurement system in hydrological modelling. The results shown that radar rainfall data is able to improve significantly hydrographs reconstructions.

A climatological approach for operational radar rainfall bias correction 

2021 ◽  
Author(s):  
Ruben Imhoff ◽  
Claudia Brauer ◽  
Klaas-Jan van Heeringen ◽  
Hidde Leijnse ◽  
Aart Overeem ◽  
...  
Keyword(s):  
The Netherlands ◽  
Real Time ◽  
Grid Cell ◽  
Rain Gauge ◽  
Bias Reduction ◽  
Training Dataset ◽  
Correction Factors ◽  
Spatial And Temporal Patterns ◽  
Radar Rainfall ◽  
Rainfall Estimates

<p>Most radar quantitative precipitation estimation (QPE) products systematically deviate from the true rainfall amount. This makes radar QPE adjustments unavoidable for operational use in hydro-meteorological (forecasting) models. Most correction methods require a timely available, high-density network of quality-controlled rain gauge observations. Here, we introduce a set of fixed bias reduction factors for the Netherlands, which vary per grid cell and day of the year. With this approach, we aim to provide an alternative to current practice, because the climatological factors are both operationally available and independent of the real-time rain gauge availability.</p><p>The correction factors were based on 10 years of 5-min radar QPE and reference rainfall data. We tested this method on the resulting rainfall estimates and subsequent discharge simulations for twelve Dutch catchment and polder areas. In addition, we compared the results to the operational mean field bias (MFB) corrected rainfall estimates and a reference dataset. This reference consisted of the radar QPE, spatially adjusted with a network of 356 validated rain gauge observations. Of this network, only 31 are automatic gauges. Hence, only these were available in real-time for the operational MFB corrections.</p><p>The climatological correction factors show clear spatial and temporal patterns. The factors are higher far from the radars and higher during winter than in summer. The latter pattern is likely a result of sampling above the melting layer during the months December–March, which causes higher underestimations. Estimated yearly rainfall sums are generally comparable to the reference and outperform the MFB corrected rainfall estimates for catchments far from the radars (south and east of the country). This difference is absent for catchments closer to the radars, where both products tend to marginally overestimate the rainfall sums. The differences amplify when both QPE products are used to force the hydrologic models. Discharge simulations based on the proposed QPE product outperform the MFB corrected rainfall estimates for all but one basin. Moreover, the climatological factor derivation shows little sensitivity to the moving window length and to leaving individual years out of the training dataset. The presented method provides a robust and straightforward operational alternative. It can serve as a benchmark for further QPE algorithm development in the Netherlands and elsewhere.</p>

Spatiotemporal Variability of Summer Precipitation in Southeastern Arizona

2013 ◽  
Vol 14 (6) ◽  
pp. 1944-1951 ◽  
Author(s):  
Susan Stillman ◽  
Xubin Zeng ◽  
William J. Shuttleworth ◽  
David C. Goodrich ◽  
Carl L. Unkrich ◽  
...  
Keyword(s):  
Agricultural Research ◽  
Summer Precipitation ◽  
Summer Rainfall ◽  
Atlantic Multidecadal Oscillation ◽  
Rain Gauge ◽  
Spatiotemporal Variability ◽  
Small Scale ◽  
Spatial And Temporal Variability ◽  
Spatial Coverage ◽  
Monthly Total Precipitation

Abstract The Walnut Gulch Experimental Watershed (WGEW) in southeastern Arizona covers ~150 km2 and receives the majority of its annual precipitation from highly variable and intermittent summer storms during the North American monsoon. In this study, the patterns of precipitation in the U.S. Department of Agriculture–Agricultural Research Service (USDA-ARS) 88-rain-gauge network are analyzed for July through September from 1956 to 2011. Because small-scale convective systems generate most of this summer rainfall, the total (T), intensity (I), and frequency (F) exhibit high spatial and temporal variability. Although subsidiary periods may have apparent trends, no significant trends in T, I, and F were found for the study period as a whole. Observed trends in the spatial coverage of storms change sign in the late 1970s, and the multidecadal variation in I and spatial coverage of storms have statistically significant correlation with the Pacific decadal oscillation and the Atlantic multidecadal oscillation indices. Precipitation has a pronounced diurnal cycle with the highest T and F occurring between 1500 and 2200 LT, and its average fractional coverage over 2- and 12-h periods is less than 40% and 60% of the gauges, respectively. Although more gauges are needed to estimate area-averaged daily precipitation, 5–11 gauges can provide a reasonable estimate of the area-averaged monthly total precipitation during the period from July through September.

scholarly journals Radar subpixel-scale rainfall variability and uncertainty: lessons learned from observations of a dense rain-gauge network

2013 ◽  
Vol 17 (6) ◽  
pp. 2195-2208 ◽  
Author(s):  
N. Peleg ◽  
M. Ben-Asher ◽  
E. Morin
Keyword(s):  
Temporal Variability ◽  
Rainfall Variability ◽  
Rain Gauge ◽  
Small Scale ◽  
Spatial And Temporal Variability ◽  
Radar Rainfall ◽  
Rain Gauges ◽  
Daily Scale ◽  
Rain Gauge Network ◽  
Hydrological Applications

Abstract. Runoff and flash flood generation are very sensitive to rainfall's spatial and temporal variability. The increasing use of radar and satellite data in hydrological applications, due to the sparse distribution of rain gauges over most catchments worldwide, requires furthering our knowledge of the uncertainties of these data. In 2011, a new super-dense network of rain gauges containing 14 stations, each with two side-by-side gauges, was installed within a 4 km2 study area near Kibbutz Galed in northern Israel. This network was established for a detailed exploration of the uncertainties and errors regarding rainfall variability within a common pixel size of data obtained from remote sensing systems for timescales of 1 min to daily. In this paper, we present the analysis of the first year's record collected from this network and from the Shacham weather radar, located 63 km from the study area. The gauge–rainfall spatial correlation and uncertainty were examined along with the estimated radar error. The nugget parameter of the inter-gauge rainfall correlations was high (0.92 on the 1 min scale) and increased as the timescale increased. The variance reduction factor (VRF), representing the uncertainty from averaging a number of rain stations per pixel, ranged from 1.6% for the 1 min timescale to 0.07% for the daily scale. It was also found that at least three rain stations are needed to adequately represent the rainfall (VRF < 5%) on a typical radar pixel scale. The difference between radar and rain gauge rainfall was mainly attributed to radar estimation errors, while the gauge sampling error contributed up to 20% to the total difference. The ratio of radar rainfall to gauge-areal-averaged rainfall, expressed by the error distribution scatter parameter, decreased from 5.27 dB for 3 min timescale to 3.21 dB for the daily scale. The analysis of the radar errors and uncertainties suggest that a temporal scale of at least 10 min should be used for hydrological applications of the radar data. Rainfall measurements collected with this dense rain gauge network will be used for further examination of small-scale rainfall's spatial and temporal variability in the coming years.

scholarly journals Enhancement of radar rainfall estimates for urban hydrology through optical flow temporal interpolation and Bayesian gauge-based adjustment

2015 ◽  
Vol 531 ◽  
pp. 408-426 ◽  
Author(s):  
Li-Pen Wang ◽  
Susana Ochoa-Rodríguez ◽  
Johan Van Assel ◽  
Rui Daniel Pina ◽  
Mieke Pessemier ◽  
...  
Keyword(s):  
Optical Flow ◽  
Urban Hydrology ◽  
Radar Rainfall ◽  
Temporal Interpolation ◽  
Rainfall Estimates

scholarly journals A climatological benchmark for operational radar rainfall bias reduction

2021 ◽  
Vol 25 (7) ◽  
pp. 4061-4080
Author(s):  
Ruben Imhoff ◽  
Claudia Brauer ◽  
Klaas-Jan van Heeringen ◽  
Hidde Leijnse ◽  
Aart Overeem ◽  
...  
Keyword(s):  
Real Time ◽  
Mean Field ◽  
Grid Cell ◽  
Rain Gauge ◽  
Bias Reduction ◽  
Annual Rainfall ◽  
Spatial And Temporal Patterns ◽  
Radar Rainfall ◽  
Derivation Method ◽  
Rainfall Estimates

Abstract. The presence of significant biases in real-time radar quantitative precipitation estimations (QPEs) limits its use in hydrometeorological forecasting systems. Here, we introduce CARROTS (Climatology-based Adjustments for Radar Rainfall in an OperaTional Setting), a set of fixed bias reduction factors, which vary per grid cell and day of the year. The factors are based on a historical set of 10 years of 5 min radar and reference rainfall data for the Netherlands. CARROTS is both operationally available and independent of real-time rain gauge availability and can thereby provide an alternative to current QPE adjustment practice. In addition, it can be used as benchmark for QPE algorithm development. We tested this method on the resulting rainfall estimates and discharge simulations for 12 Dutch catchments and polders. We validated the results against the operational mean field bias (MFB)-adjusted rainfall estimates and a reference dataset. This reference consists of the radar QPE, that combines an hourly MFB adjustment and a daily spatial adjustment using observations from 32 automatic and 319 manual rain gauges. Only the automatic gauges of this network are available in real time for the MFB adjustment. The resulting climatological correction factors show clear spatial and temporal patterns. Factors are higher away from the radars and higher from December through March than in other seasons, which is likely a result of sampling above the melting layer during the winter months. The MFB-adjusted QPE outperforms the CARROTS-corrected QPE when the country-average rainfall estimates are compared to the reference. However, annual rainfall sums from CARROTS are comparable to the reference and outperform the MFB-adjusted rainfall estimates for catchments away from the radars, where the MFB-adjusted QPE generally underestimates the rainfall amounts. This difference is absent for catchments closer to the radars. QPE underestimations are amplified when used in the hydrological model simulations. Discharge simulations using the QPE from CARROTS outperform those with the MFB-adjusted product for all but one basin. Moreover, the proposed factor derivation method is robust. It is hardly sensitive to leaving individual years out of the historical set and to the moving window length, given window sizes of more than a week.

scholarly journals A climatological benchmark for operational radar rainfall bias reduction

2021 ◽  
Author(s):  
Ruben Imhoff ◽  
Claudia Brauer ◽  
Klaas-Jan van Heeringen ◽  
Hidde Leijnse ◽  
Aart Overeem ◽  
...  
Keyword(s):  
Real Time ◽  
Mean Field ◽  
Grid Cell ◽  
Rain Gauge ◽  
Bias Reduction ◽  
Annual Rainfall ◽  
Spatial And Temporal Patterns ◽  
Radar Rainfall ◽  
Derivation Method ◽  
Rainfall Estimates

Abstract. The presence of significant biases in real-time radar quantitative precipitation estimations (QPE) limits its use in hydro-meteorological forecasting systems. Here, we introduce CARROTS (Climatology-based Adjustments for Radar Rainfall in an OperaTional Setting), a set of fixed bias reduction factors, which vary per grid cell and day of the year. The factors are based on a historical set of 10 years of 5-min radar and reference rainfall data for the Netherlands. CARROTS is both operationally available and independent of real-time rain gauge availability, and can thereby provide an alternative to current QPE adjustment practice. In addition, it can be used as benchmark for QPE algorithm development. We tested this method on the resulting rainfall estimates and discharge simulations for twelve Dutch catchments and polders. We validated the results against the operational mean field bias (MFB) adjusted rainfall estimates and a reference dataset. This reference consists of the radar QPE, that combines an hourly MFB adjustment and a daily spatial adjustment using observations from 31 automatic and 325 manual rain gauges. Only the automatic gauges of this network are available in real-time for the MFB adjustment. The resulting climatological correction factors show clear spatial and temporal patterns. Factors are higher far from the radars and higher from December through March than in other seasons, which is likely a result of sampling above the melting layer during the winter months. Annual rainfall sums from CARROTS are comparable to the reference and outperform the MFB adjusted rainfall estimates for catchments far from the radars. This difference is absent for catchments closer to the radars. QPE underestimations are amplified when used in the hydrological model simulations. Discharge simulations using the QPE from CARROTS outperform those with the MFB adjusted product for all but one basin. Moreover, the proposed factor derivation method is robust. It is hardly sensitive to leaving individual years out of the historical set and to the moving window length, given window sizes of more than a week.

scholarly journals On the short-term variability of turbulence and temperature in the winter mesosphere

2018 ◽  
Vol 36 (4) ◽  
pp. 1099-1116
Author(s):  
Gerald A. Lehmacher ◽  
Miguel F. Larsen ◽  
Richard L. Collins ◽  
Aroh Barjatya ◽  
Boris Strelnikov
Keyword(s):  
Attitude Control ◽  
Large Scale ◽  
Lower Thermosphere ◽  
Density Fluctuations ◽  
Large Temperature ◽  
Small Scale ◽  
Spatial And Temporal Variability ◽  
Temperature Structure ◽  
Wind Shears

Abstract. Four mesosphere–lower thermosphere temperature and turbulence profiles were obtained in situ within ∼30 min and over an area of about 100 by 100 km during a sounding rocket experiment conducted on 26 January 2015 at Poker Flat Research Range in Alaska. In this paper we examine the spatial and temporal variability of mesospheric turbulence in relationship to the static stability of the background atmosphere. Using active payload attitude control, neutral density fluctuations, a tracer for turbulence, were observed with very little interference from the payload spin motion, and with high precision (<0.01 %) at sub-meter resolution. The large-scale vertical temperature structure was very consistent between the four soundings. The mesosphere was almost isothermal, which means more stratified, between 60 and 80 km, and again between 88 and 95 km. The stratified regions adjoined quasi-adiabatic regions assumed to be well mixed. Additional evidence of vertical transport and convective activity comes from sodium densities and trimethyl aluminum trail development, respectively, which were both observed simultaneously with the in situ measurements. We found considerable kilometer-scale temperature variability with amplitudes of 20 K in the stratified region below 80 km. Several thin turbulent layers were embedded in this region, differing in width and altitude for each profile. Energy dissipation rates varied between 0.1 and 10 mW kg−1, which is typical for the winter mesosphere. Very little turbulence was observed above 82 km, consistent with very weak small-scale gravity wave activity in the upper mesosphere during the launch night. On the other hand, above the cold and prominent mesopause at 102 km, large temperature excursions of +40 to +70 K were observed. Simultaneous wind measurements revealed extreme wind shears near 108 km, and combined with the observed temperature gradient, isolated regions of unstable Richardson numbers (0<Ri<0.25) were detected in the lower thermosphere. The experiment was launched into a bright auroral arc under moderately disturbed conditions (Kp∼5).

scholarly journals Optimal Rainfall Estimation by Considering Elevation in the Han River Basin, South Korea

2013 ◽  
Vol 52 (4) ◽  
pp. 802-818 ◽  
Author(s):  
Seong-Sim Yoon ◽  
Deg-Hyo Bae
Keyword(s):  
South Korea ◽  
River Basin ◽  
Flood Control ◽  
Rainfall Data ◽  
Spatiotemporal Variability ◽  
Radar Rainfall ◽  
Mountainous Regions ◽  
Han River ◽  
Orographic Rainfall ◽  
Han River Basin

AbstractMore than 70% of South Korea has mountainous terrain, which leads to significant spatiotemporal variability of rainfall. The country is exposed to the risk of flash floods owing to orographic rainfall. Rainfall observations are important in mountainous regions because flood control measures depend strongly on rainfall data. In particular, radar rainfall data are useful in these regions because of the limitations of rain gauges. However, radar rainfall data include errors despite the development of improved estimation techniques for their calculation. Further, the radar does not provide accurate data during heavy rainfall in mountainous areas. This study presents a radar rainfall adjustment method that considers the elevation in mountainous regions. Gauge rainfall and radar rainfall field data are modified by using standardized ordinary cokriging considering the elevation, and the conditional merging technique is used for combining the two types of data. For evaluating the proposed technique, the Han River basin was selected; a high correlation between rainfall and elevation can be seen in this basin. Further, the proposed technique was compared with the mean field bias and original conditional merging techniques. Comparison with kriged rainfall showed that the proposed method has a lesser tendency to oversmooth the rainfall distribution when compared with the other methods, and the optimal mean areal rainfall is very similar to the value obtained using gauges. It reveals that the proposed method can be applied to an area with significantly varying elevation, such as the Han River basin, to obtain radar rainfall data of high accuracy.

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