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 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 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).

scholarly journals Evaluation of the Radar QPE and Rain Gauge Data Merging Methods in Northern China

2020 ◽  
Vol 12 (3) ◽  
pp. 363 ◽  
Author(s):  
Qingtai Qiu ◽  
Jia Liu ◽  
Jiyang Tian ◽  
Yufei Jiao ◽  
Chuanzhe Li ◽  
...  
Keyword(s):  
Northern China ◽  
Rain Gauge ◽  
Bayesian Regression ◽  
Small Scale ◽  
Regression Kriging ◽  
Storm Events ◽  
Rain Gauge Data ◽  
Bias Adjustment ◽  
Gauge Data

Radar-rain gauge merging methods have been widely used to produce high-quality precipitation with fine spatial resolution by combing the advantages of the rain gauge observation and the radar quantitative precipitation estimation (QPE). Different merging methods imply a specific choice on the treatment of radar and rain gauge data. In order to improve their applicability, significant studies have focused on evaluating the performances of the merging methods. In this study, a categorization of the radar-rain gauge merging methods was proposed as: (1) Radar bias adjustment category, (2) radar-rain gauge integration category, and (3) rain gauge interpolation category for a total of six commonly used merging methods, i.e., mean field bias (MFB), regression inverse distance weighting (RIDW), collocated co-kriging (CCok), fast Bayesian regression kriging (FBRK), regression kriging (RK), and kriging with external drift (KED). Eight different storm events were chosen from semi-humid and semi-arid areas of Northern China to test the performance of the six methods. Based on the leave-one-out cross validation (LOOCV), conclusions were obtained that the integration category always performs the best, the bias adjustment category performs the worst, and the interpolation category ranks between them. The quality of the merging products can be a function of the merging method that is affected by both the quality of radar QPE and the ability of the rain gauge to capture small-scale rainfall features. In order to further evaluate the applicability of the merging products, they were then used as the input to a rainfall-runoff model, the Hybrid-Hebei model, for flood forecasting. It is revealed that a higher quality of the merging products indicates a better agreement between the observed and the simulated runoff.

scholarly journals Evaluation of TRMM rainfall estimates over a large Indian river basin (Mahanadi)

2014 ◽  
Vol 18 (7) ◽  
pp. 2493-2502 ◽  
Author(s):  
D. Kneis ◽  
C. Chatterjee ◽  
R. Singh
Keyword(s):  
Real Time ◽  
Stream Flow ◽  
Rain Gauge ◽  
Rainfall Runoff ◽  
Runoff Simulation ◽  
Rain Gauge Data ◽  
Gauge Data ◽  
Precipitation Estimates ◽  
Rainfall Estimates

Abstract. The paper examines the quality of satellite-based precipitation estimates for the lower Mahanadi River basin (eastern India). The considered data sets known as 3B42 and 3B42-RT (version 7/7A) are routinely produced by the tropical rainfall measuring mission (TRMM) from passive microwave and infrared recordings. While the 3B42-RT data are disseminated in real time, the gauge-adjusted 3B42 data set is published with a delay of some months. The quality of the two products was assessed in a two-step procedure. First, the correspondence between the remotely sensed precipitation rates and rain gauge data was evaluated at the sub-basin scale. Second, the quality of the rainfall estimates was assessed by analysing their performance in the context of rainfall–runoff simulation. At sub-basin level (4000 to 16 000 km2) the satellite-based areal precipitation estimates were found to be moderately correlated with the gauge-based counterparts (R2 of 0.64–0.74 for 3B42 and 0.59–0.72 for 3B42-RT). Significant discrepancies between TRMM data and ground observations were identified at high-intensity levels. The rainfall depth derived from rain gauge data is often not reflected by the TRMM estimates (hit rate < 0.6 for ground-based intensities > 80 mm day-1). At the same time, the remotely sensed rainfall rates frequently exceed the gauge-based equivalents (false alarm ratios of 0.2–0.6). In addition, the real-time product 3B42-RT was found to suffer from a spatially consistent negative bias. Since the regionalisation of rain gauge data is potentially associated with a number of errors, the above results are subject to uncertainty. Hence, a validation against independent information, such as stream flow, was essential. In this case study, the outcome of rainfall–runoff simulation experiments was consistent with the above-mentioned findings. The best fit between observed and simulated stream flow was obtained if rain gauge data were used as model input (Nash–Sutcliffe index of 0.76–0.88 at gauges not affected by reservoir operation). This compares to the values of 0.71–0.78 for the gauge-adjusted TRMM 3B42 data and 0.65–0.77 for the 3B42-RT real-time data. Whether the 3B42-RT data are useful in the context of operational runoff prediction in spite of the identified problems remains a question for further research.

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 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 &amp; 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.

scholarly journals Study on quantification of areal mean precipitation using satellite-gauge merging precipitation

2018 ◽  
Vol 12 (5) ◽  
pp. 117-126
Author(s):  
Bui Thi Hieu
Keyword(s):  
Design Method ◽  
Rain Gauge ◽  
Bias Reduction ◽  
Rainfall Runoff ◽  
Sample Design ◽  
Rain Gauge Data ◽  
Rain Gauges ◽  
Precipitation Product ◽  
Gauge Data ◽  
Satellite Rainfall

Satellite based precipitation product (GSMaP-MVK) can be reliably used to estimate the Areal Mean Precipitation error based on “Sample Design method” (Esdd) with the effort to mitigate the problem of sparse data, especially severe in poorly gauged river basins. In addition, the satellite-gauge merging precipitation would reduce significantly the magnitude gaps between the satellite rainfall estimations and the rain gauge data. In this study, the capability of satellite-gauge merging precipitation using GSMaP-MVK and local dense rain gauge data with bias reduction approach to evaluate the AMP is investigated. The main finding is that satellite-gauge blending data which incorporates a dense rain gauge measurements shows the better capability to evaluate AMP using Esdd index than the original satellite only precipitation estimations. However, Esdd quantification performances of satellite-gauge blending precipitation are inferior to the original satellite only precipitation product GSMaP-MVK when the number of blended rain gauges is not large enough. Keywords: areal mean precipitation; remote sensed precipitation product; satellite-gauge merging; rainfall runoff simulations.

Combined rainfall estimates from personal weather station and commercial microwave link data in Germany

2021 ◽  
Author(s):  
Maximilian Graf ◽  
Abbas El Hachem ◽  
Micha Eisele ◽  
Jochen Seidel ◽  
Christian Chwala ◽  
...  
Keyword(s):  
Water Resource Management ◽  
Hydrological Cycle ◽  
Spatial Scales ◽  
Rainfall Variability ◽  
Rain Gauge ◽  
Data Sets ◽  
Rain Gauge Data ◽  
Rain Gauges ◽  
Gauge Data ◽  
Rainfall Estimates

&lt;p&gt;Rain gauges and weather radars are the default sources of rainfall information. Rainfall estimates from these sensors improve our understanding of the hydrological cycle and are vital for water-resource management, agriculture, urban planning, as well as for weather, climate, and hydrological modelling. Still, due to the high spatio-temporal variability of rainfall and the specific drawbacks of the individual rainfall sensors, the rainfall variability cannot be captured completely. In the last decade, the number and availability of opportunistic rainfall sensors increased rapidly. These sensors are initially not meant to measure rainfall for scientific or operational purposes, but, if processed carefully, can be used for these cases . Here we present an analysis of two years of data from two opportunistic rainfall sensors, namely personal weather stations (PWS) and commercial microwave links (CMLs). We evaluate the performance of rainfall maps derived from these sensors on different spatial and temporal scales in Germany.&lt;/p&gt;&lt;p&gt;The data from around 15000 PWS tipping bucket-style rain gauges from the Netatmo network were accessed via Netatmos API. The data from around 4000 CMLs, which can be used to derive rainfall estimates from the rain-induced attenuation of the CMLs&amp;#8217; signal, were obtained from Ericsson. As both, PWS and CML data, can suffer from various error sources e.g. from unfavourable positioning and poor maintenance of PWS and from non-rain induced attenuation of the CMLs signal, we used a strict filtering routine. A total of seven gridded rainfall products were derived from different combinations of PWS, CML, and rain gauge data from the German Weather Service (DWD) with a geostatistical interpolation approach. This approach incorporates the uncertainty of the opportunistic sensors and the path-averaging characteristic of the CML observations.&lt;/p&gt;&lt;p&gt;To evaluate the resulting rainfall maps, we used three rain gauge data sets with different temporal and spatial scales covering the whole of Germany, the state of Rhineland-Palatinate and the city of Reutlingen, respectively. For all three reference data sets, rainfall maps from opportunistic sensors provided good agreement, with best results being derived from the combinations with PWS. Rainfall maps including CML data had the lowest bias. In a comparison with gauge adjusted radar products from the DWD, the radar products yielded better results than the rainfall maps from opportunistic sensors for the country-wide comparison of daily rainfall sums, which was carried out using the DWD&amp;#8217;s independent network of manual rain gauges. But for the hourly references covering Rhineland-Palatinate and Reutlingen, the rainfall maps derived from opportunistic sensors outperformed the radar products. These results highlight the capabilities of opportunistic rainfall sensors which could be used in many hydrometeorological applications.&lt;/p&gt;

scholarly journals Precipitation Changes near Three Gorges Dam, China. Part I: A Spatiotemporal Validation Analysis

2012 ◽  
Vol 13 (2) ◽  
pp. 735-745 ◽  
Author(s):  
Fang Zhao ◽  
Marshall Shepherd
Keyword(s):  
Satellite Data ◽  
Water Resource Management ◽  
Rain Gauge ◽  
Empirical Orthogonal Functions ◽  
Three Gorges Dam ◽  
Three Gorges ◽  
Rain Gauge Data ◽  
Rain Gauges ◽  
The Three Gorges ◽  
Gauge Data

Abstract In October 2010, the water level upstream of the Three Gorges Dam (TGD) reached the designated 175-m level. The associated inundation and land use–land cover changes have important implications for water resource management, agriculture, ecosystems, and the hydroclimate. Ultimately, it is important to quantify whether the dam-related changes have altered precipitation patterns. Since rain gauges are limited in the region, satellite-based methods are viable. This study is the first to validate NASA Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) data from 1998 to 2009 using 34 national meteorological rain gauges in the Three Gorges region. Areal average satellite estimates are first verified with areal average rain gauge data both annually and seasonally. Then based on empirical orthogonal functions, the study area is divided into two subregions, and similar validation procedures are performed for both subregions. TMPA data are found to have high correlations with rain gauge data for the whole study area, and correlations for the subregions are only slightly lower. The seasonal analysis yields the lowest correlations for winter. Compared with the gauge data, rainfall is slightly overestimated by about 3 mm month−1. At daily scale, satellite data show good agreement with gauge data for all rain intensity categories except light rain (&lt;1 mm day−1). Spatially, the point-source gauge data are gridded using Thiessen polygons for comparison with satellite data, and the results suggest the satellite-based product may overestimate rainfall in mountainous areas near the reservoir, especially in spring and summer. Overall, the validation results yield strong statistical support for applying satellite rainfall data for hydroclimate studies in this region.

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