scholarly journals Rainfall Regime of a Mountainous Mediterranean Region: Statistical Analysis at Short Time Steps

2012 ◽  
Vol 51 (3) ◽  
pp. 429-448 ◽  
Author(s):  
Gilles Molinié ◽  
Davide Ceresetti ◽  
Sandrine Anquetin ◽  
Jean Dominique Creutin ◽  
Brice Boudevillain
Keyword(s):  
Daily Rainfall ◽  
Large River ◽  
Rain Gauge ◽  
Heavy Rainfall Event ◽  
Rainfall Regime ◽  
Study Region ◽  
Rain Gauge Data ◽  
Rain Gauges ◽  
Gauge Data ◽  
The Difference

AbstractThis paper presents an analysis of the rainfall regime of a Mediterranean mountainous region of southeastern France. The rainfall regime is studied on temporal scales from hourly to yearly using daily and hourly rain gauge data of 43 and 16 years, respectively. The domain is 200 × 200 km2 with spatial resolution of hourly and daily rain gauges of about 8 and 5 km, respectively. On average, yearly rainfall increases from about 0.5 m yr−1 in the large river plain close to the Mediterranean Sea to up to 2 m yr−1 over the surrounding mountain ridges. The seasonal distribution is also uneven: one-third of the cumulative rainfall occurs during the autumn season and one-fourth during the spring. At finer time scales, rainfall is studied in terms of rain–no-rain intermittency and nonzero intensity. The monthly intermittency (proportion of dry days per month) and the daily intermittency (proportion of dry hours per day) is fairly well correlated with the relief. The higher the rain gauges are, the lower the monthly and daily intermittencies are. The hourly and daily rainfall intensities are analyzed in terms of seasonal variability, diurnal cycle, and spatial pattern. The difference between regular and heavy-rainfall event is depicted by using both central parameters and maximum values of intensity distributions. The relationship between rain gauge altitudes and rainfall intensity is grossly inverted relative to intermittency and is also far more complex. The spatial and temporal rainfall patterns depicted from rain gauge data are discussed in the light of known meteorological processes affecting the study region.

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 Improving Satellite-Based Subhourly Surface Rain Estimates Using Vertical Rain Profile Information

2019 ◽  
Vol 20 (5) ◽  
pp. 1015-1026 ◽  
Author(s):  
Nobuyuki Utsumi ◽  
Hyungjun Kim ◽  
F. Joseph Turk ◽  
Ziad. S. Haddad
Keyword(s):  
Rain Rate ◽  
Tropical Rainfall Measuring Mission ◽  
Rain Gauge ◽  
Rain Gauge Data ◽  
Rain Gauges ◽  
Gauge Data ◽  
Surface Rain Rate ◽  
Trmm Precipitation ◽  
Time Lagged ◽  
Rain Rates

Abstract Quantifying time-averaged rain rate, or rain accumulation, on subhourly time scales is essential for various application studies requiring rain estimates. This study proposes a novel idea to estimate subhourly time-averaged surface rain rate based on the instantaneous vertical rain profile observed from low-Earth-orbiting satellites. Instantaneous rain estimates from the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) are compared with 1-min surface rain gauges in North America and Kwajalein atoll for the warm seasons of 2005–14. Time-lagged correlation analysis between PR rain rates at various height levels and surface rain gauge data shows that the peak of the correlations tends to be delayed for PR rain at higher levels up to around 6-km altitude. PR estimates for low to middle height levels have better correlations with time-delayed surface gauge data than the PR’s estimated surface rain rate product. This implies that rain estimates for lower to middle heights may have skill to estimate the eventual surface rain rate that occurs 1–30 min later. Therefore, in this study, the vertical profiles of TRMM PR instantaneous rain estimates are averaged between the surface and various heights above the surface to represent time-averaged surface rain rate. It was shown that vertically averaged PR estimates up to middle heights (~4.5 km) exhibit better skill, compared to the PR estimated instantaneous surface rain product, to represent subhourly (~30 min) time-averaged surface rain rate. These findings highlight the merit of additional consideration of vertical rain profiles, not only instantaneous surface rain rate, to improve subhourly surface estimates of satellite-based rain products.

scholarly journals Rain-Rate Estimation in the Presence of Hail Using S-Band Specific Differential Phase and Other Radar Parameters

1995 ◽  
Vol 34 (2) ◽  
pp. 404-410 ◽  
Author(s):  
K. Aydin ◽  
V. N. Bringi ◽  
L. Liu
Keyword(s):  
Rain Rate ◽  
Rain Gauge ◽  
Heavy Rainfall Event ◽  
Rain Gauge Data ◽  
Differential Phase ◽  
X Band ◽  
Gauge Data ◽  
Radar Measurements ◽  
Multiparameter Radar ◽  
Good Agreement

Abstract Multiparameter radar measurements were made during a heavy rainfall event accompanied by hail in Colorado. Rainfall rates R and accumulation Σ for this event were estimated using S-band specific differential phase KDP, reflectivity factor ZH, and X-band specific attenuation AH3. These estimates were compared with measurements from a ground-based rain gauge. Both R–KDP and R–AH3 relations were in good agreement with the rain gauge data, that is, less than 10% difference in the rainfall accumulations. The R–Z relation produced similar results only when ZH was truncated at 55 dBZ. This study demonstrates the potential of KDP for estimating rainfall rates in severe storms that may have rain-hail mixtures.

scholarly journals An objective cross-validation framework for mapping rainfall hazard based on rain gauge data

2018 ◽  
Author(s):  
Juliette Blanchet ◽  
Emmanuel Paquet ◽  
Pradeebane Vaittinada Ayar ◽  
David Penot
Keyword(s):  
Goodness Of Fit ◽  
Cumulative Distribution Function ◽  
Cross Validation ◽  
Daily Rainfall ◽  
Rain Gauge ◽  
Cumulative Distribution ◽  
Rain Gauge Data ◽  
Weather Patterns ◽  
Rain Gauges ◽  
Validation Framework

Abstract. We propose an objective framework for estimating rainfall cumulative distribution function within a region when data are only available at rain gauges. Our methodology is based on the evaluation of several goodness-of-fit scores in a cross-validation framework, allowing to assess goodness-of-fit of the full distribution but with a particular focus on its tail. Cross-validation is applied both to select the most appropriate statistical distribution at station locations and to validate the mapping of these distributions. Our methodology is applied to daily rainfall in the Ardèche catchment in South of France, a 2260 km2 catchment with strong disparities in rainfall distribution. Results show preference for a mixture of Gamma distribution over seasons and weather patterns, with parameters interpolated with thin plate spline across this region. However the framework presented in this paper is general and could be likewise applied in any region, with possibly different conclusion depending on the subsequent rainfall processes.

scholarly journals WHU-SGCC: A novel approach for blending daily satellite (CHIRP) and precipitation observations over Jinsha River Basin

2019 ◽  
Author(s):  
Gaoyun Shen ◽  
Nengcheng Chen ◽  
Wei Wang ◽  
Zeqiang Chen
Keyword(s):  
River Basin ◽  
Daily Precipitation ◽  
Rain Gauge ◽  
Jinsha River ◽  
Rain Gauge Data ◽  
Rain Gauges ◽  
Novel Approach ◽  
Gauge Data ◽  
Precipitation Estimates ◽  
Hydrological Applications

Abstract. Accurate and consistent satellite-based precipitation estimates blended with rain gauge data are important for regional precipitation monitoring and hydrological applications, especially in regions with limited rain gauges. However, existing fusion precipitation estimates often have large uncertainties over mountainous areas with complex topography and sparse rain gauges, and the existing data blending algorithms are very bad at removing the day-by-day random errors. Therefore, the development of effective methods for high-accuracy precipitation estimates over complex terrain and on a daily scale is of vital importance for mountainous hydrological applications. This study aims to offer a novel approach for blending daily precipitation gauge data, gridded precipitation data and the Climate Hazards Group Infrared Precipitation (CHIRP, daily, 0.05°) satellite-derived precipitation estimates over the Jinsha River Basin for the period of June–July–August in 2016. This method is named the Wuhan University Satellite and Gauge precipitation Collaborated Correction (WHU-SGCC). The results show that the WHU-SGCC method is effective in precipitation bias adjustments from point to surface, which is evaluated by categorical indices. Moreover, the accuracy of the spatial distribution of the precipitation estimates derived from the WHU-SGCC method is related to the complexity of the topography. The validation also verifies that the proposed approach is effective in the detection of precipitation events that are less than 20 mm. This study indicates that the WHU-SGCC approach is a promising tool to monitor monsoon precipitation over Jinsha River Basin, the complicated mountainous terrain with sparse rain gauge data, considering the spatial correlation and the historical precipitation characteristics. The daily precipitation estimations at 0.05° resolution over Jinsha River Basin in summer 2016, derived from WHU-SGCC are available at the PANGAEA Data Publisher for Earth & Environmental Science portal (https://doi.pangaea.de/10.1594/PANGAEA.896615).

Calibration of TRMM Derived Rainfall Over Nepal During 1998-2007

2010 ◽  
Vol 4 (1) ◽  
pp. 12-23 ◽  
Author(s):  
Md. Nazrul Islam ◽  
Someshwar Das ◽  
Hiroshi Uyeda
Keyword(s):  
Daily Rainfall ◽  
Tropical Rainfall Measuring Mission ◽  
Calibration Procedure ◽  
Skill Score ◽  
Rain Gauge ◽  
Probability Of Detection ◽  
Similar Distribution ◽  
Rain Gauge Data ◽  
Gauge Data ◽  
Adjustment Factors

In this study rainfall is calculated from Tropical Rainfall Measuring Mission (TRMM) Version 6 (V6) 3B42 datasets and calibrated with reference to the observed daily rainfall by rain-gauge collected at 15 locations over Nepal during 1998-2007. In monthly, seasonal and annual scales TRMM estimated rainfalls follow the similar distribution of historical patterns obtained from the rain-gauge data. Rainfall is large in the Southern parts of the country, especially in the Central Nepal. Day-to-day rainfall comparison shows that TRMM derived trend is very similar to the observed data but TRMM usually underestimates rainfall on many days with some exceptions of overestimation on some days. The correlation coefficient of rainfalls between TRMM and rain-gauge data is obtained about 0.71. TRMM can measure about 65.39% of surface rainfall in Nepal. After using calibration factors obtained through regression expression the TRMM estimated rainfall over Nepal becomes about 99.91% of observed data. TRMM detection of rainy days is poor over Nepal; it can approximately detect, under-detect and over-detect by 19%, 72% and 9% of stations respectively. False alarm rate, probability of detection, threat score and skill score are calculated as 0.30, 0.68, 0.53 and 0.55 respectively. Finally, TRMM data can be utilized in measuring mountainous rainfall over Nepal but exact amount of rainfall has to be calculated with the help of adjustment factors obtained through calibration procedure. This preliminary work is the preparation of utilization of Global Precipitation Measurement (GPM) data to be commencing in 2013.

scholarly journals Performance of daily satellite-based rainfall in groundwater basin of Merapi Aquifer System, Yogyakarta

2021 ◽  
Author(s):  
Novi Rahmawati ◽  
Kisworo Rahayu ◽  
Sukma Tri Yuliasari
Keyword(s):  
Hydrological Modeling ◽  
Daily Rainfall ◽  
Rain Gauge ◽  
Mountain Range ◽  
Modeling Framework ◽  
Rain Gauge Data ◽  
Aquifer System ◽  
Orographic Rainfall ◽  
Gauge Data ◽  
Groundwater Basin

AbstractEvaluation of the performance of daily satellite-based rainfall (CMORPH, CHIRPS, GPM IMERG, and TRMM) was done to obtain applicable satellite rainfall estimates in the groundwater basin of the Merapi Aquifer System (MAS). Performance of satellite data was assessed by applying descriptive statistics, categorical statistics, and bias decomposition on the basis of daily rainfall intensity classification. This classification is possible to measure the performance of daily satellite-based rainfall in much detail. CM (CMORPH) has larger underestimation compared to other satellite-based rainfall assessments. This satellite-based rainfall also mostly has the largest RMSE, while CHR (CHIRPS) has the lowest. CM has a good performance to detect no rain, while IMR (GPM IMERG) has the worst performance. IMR and CHR have a good performance to detect light and moderate rain. Both of them have larger H frequencies and lower MB values compared to other satellite products. CHR mostly has a good performance compared to TR (TRMM), especially on wet periods. CM, IMR, and TR mostly have a good performance on dry periods, while CHR on wet periods. CM mostly has the largest MB and lowest AHB values. CM and CHR have better accuracy to estimate rain amount compared to IMR and TR. All in all, all 4 satellite-based rainfall assessments have large discrepancy compared with rain gauge data along mountain range where orographic rainfall usually occurs in wet periods. Hence, it is recommended to evaluate satellite-based rainfall with time series of streamflow simulation in hydrological modeling framework by merging rain gauge data with more than one satellite-based rainfall than to merge both IMR and TR together.

Experimental 4D-Var Assimilation of SYNOP Rain Gauge Data at ECMWF

2013 ◽  
Vol 141 (5) ◽  
pp. 1527-1544 ◽  
Author(s):  
Philippe Lopez
Keyword(s):  
Data Assimilation ◽  
Surface Pressure ◽  
Positive Impact ◽  
Rain Gauge ◽  
Rain Gauge Data ◽  
Surface Precipitation ◽  
Rain Gauges ◽  
Gauge Data ◽  
Key Aspects

Abstract Four-dimensional variational data assimilation (4D-Var) experiments with 6-hourly rain gauge accumulations observed at synoptic stations (SYNOP) around the globe have been run over several months, both at high resolution in an ECMWF operations-like framework and at lower resolution with the reference observational coverage reduced to surface pressure data only, as would be expected in early twentieth-century periods. The key aspects of the technical implementation of rain gauge data assimilation in 4D-Var are described, which include the specification of observation errors, bias correction procedures, screening, and quality control. Results from experiments indicate that the positive impact of rain gauges on forecast scores remains limited in the operations-like context because of their competition with all other observations already available. In contrast, when only synoptic station surface pressure observations are assimilated in the data-poor control experiment, the additional assimilation of rain gauge measurements substantially improves not only surface precipitation scores, but also analysis and forecast scores of temperature, geopotential, wind, and humidity at most atmospheric levels and for forecast ranges up to 10 days. The verification against Meteosat infrared imagery also shows a slight improvement in the spatial distribution of clouds. This suggests that assimilating rain gauge data available during data-sparse periods of the past might help to improve the quality of future reanalyses and subsequent forecasts.

scholarly journals WHU-SGCC: a novel approach for blending daily satellite (CHIRP) and precipitation observations over the Jinsha River basin

2019 ◽  
Vol 11 (4) ◽  
pp. 1711-1744 ◽  
Author(s):  
Gaoyun Shen ◽  
Nengcheng Chen ◽  
Wei Wang ◽  
Zeqiang Chen
Keyword(s):  
River Basin ◽  
Daily Precipitation ◽  
Rain Gauge ◽  
Jinsha River ◽  
Rain Gauge Data ◽  
Rain Gauges ◽  
Novel Approach ◽  
Gauge Data ◽  
Precipitation Estimates ◽  
Hydrological Applications

Abstract. Accurate and consistent satellite-based precipitation estimates blended with rain gauge data are important for regional precipitation monitoring and hydrological applications, especially in regions with limited rain gauges. However, the existing fusion precipitation estimates often have large uncertainties over mountainous areas with complex topography and sparse rain gauges, and most of the existing data blending algorithms are not good at removing the day-by-day errors. Therefore, the development of effective methods for high-accuracy precipitation estimates over complex terrain and at a daily scale is of vital importance for mountainous hydrological applications. This study aims to offer a novel approach for blending daily precipitation gauge data and the Climate Hazards Group Infrared Precipitation (CHIRP; daily, 0.05∘) satellite-derived precipitation developed by UC Santa Barbara over the Jinsha River basin from 1994 to 2014. This method is called the Wuhan University Satellite and Gauge precipitation Collaborated Correction (WHU-SGCC). The results show that the WHU-SGCC method is effective for liquid precipitation bias adjustments from points to surfaces as evaluated by multiple error statistics and from different perspectives. Compared with CHIRP and CHIRP with station data (CHIRPS), the precipitation adjusted by the WHU-SGCC method has greater accuracy, with overall average improvements of the Pearson correlation coefficient (PCC) by 0.0082–0.2232 and 0.0612–0.3243, respectively, and decreases in the root mean square error (RMSE) by 0.0922–0.65 and 0.2249–2.9525 mm, respectively. In addition, the Nash–Sutcliffe efficiency coefficient (NSE) of the WHU-SGCC provides more substantial improvements than CHIRP and CHIRPS, which reached 0.2836, 0.2944, and 0.1853 in the spring, autumn, and winter. Daily accuracy evaluations indicate that the WHU-SGCC method has the best ability to reduce precipitation bias, with average reductions of 21.68 % and 31.44 % compared to CHIRP and CHIRPS, respectively. Moreover, the accuracy of the spatial distribution of the precipitation estimates derived from the WHU-SGCC method is related to the complexity of the topography. The validation also verifies that the proposed approach is effective at detecting major precipitation events within the Jinsha River basin. In spite of the correction, the uncertainties in the seasonal precipitation forecasts in the summer and winter are still large, which might be due to the homogenization attenuating the extreme rain event estimates. However, the WHU-SGCC approach may serve as a promising tool to monitor daily precipitation over the Jinsha River basin, which contains complicated mountainous terrain with sparse rain gauge data, based on the spatial correlation and the historical precipitation characteristics. The daily precipitation estimations at the 0.05∘ resolution over the Jinsha River basin during all four seasons from 1990 to 2014, derived from WHU-SGCC, are available at the PANGAEA Data Publisher for Earth & Environmental Science portal (https://doi.org/10.1594/PANGAEA.905376, Shen et al., 2019).

scholarly journals Temporal and spatial scale and positional effects on rain erosivity derived from contiguous rain data

2018 ◽  
Author(s):  
Franziska K. Fischer ◽  
Tanja Winterrath ◽  
Karl Auerswald
Keyword(s):  
Spatial Scale ◽  
Temporal Resolution ◽  
Rain Gauge ◽  
Point Scale ◽  
Rain Gauge Data ◽  
Rain Gauges ◽  
Intensity Threshold ◽  
Gauge Data ◽  
Temporal And Spatial ◽  
Rain Data

Abstract. Up until now, erosivity required for soil loss predictions has been mainly estimated from rain gauge data at point scale and then spatially interpolated to erosivity maps. Contiguous radar rain data are now available but they differ in temporal and spatial scale from the point scale. We determined how the intensity threshold has to be modified and which temporal and spatial scaling factors have to be applied to account for the differences in scale. Furthermore, a positional effect quantifies heterogeneity of erosivity within 1 km2, which presently is the highest resolution of freely available gauge-adjusted radar rain data. A method effect accounts for differences in measuring peculiarities between rain gauges and weather radars. These effects were analysed using several large data sets with a total of approximately 2 x 106 erosive events (e.g., records of 115 rain gauges for 16 years distributed across Germany and radar rain data for the same locations and events). With decreasing temporal resolution, peak intensities decreased and the intensity threshold of erosive rains was met less often. This became especially pronounced, when time increments became larger than 30 min. With decreasing spatial resolution, intensity peaks were also reduced but additionally large areas without erosive rain were included within one pixel. This was due to the steep spatial gradients in erosivity. Erosivity of single events could be zero or more than twice the mean annual sum within a distance of less than 1 km. We conclude that the resulting large positional effect requires use of contiguous rain data, even over distances of less than 1 km, but at the same time contiguously measured radar data cannot be resolved to point scale. The temporal scale is easier to consider but time increments larger than 30 min should be avoided because the loss of information increases considerably. We provide functions to account for temporal scale (from 1 min to 120 min) and spatial scale (from rain gauge to pixels of 18 km width) that can be applied to rain gauge data of low temporal resolution and to contiguous radar rain data.

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