The assessment of precipitation variability and uncertainty is crucial in a variety of applications, such as flood risk forecasting, water resource assessments, evaluation of the hydrological impacts of climate change, determination of design floods, and hydrological modelling in general. Within this framework, this session aims to gather contributions on research, advanced applications, and future needs in the understanding and modelling of precipitation variability, and its sources of uncertainty.
Specifically, contributions focusing on one or more of the following issues are particularly welcome:
- Novel studies aimed at the assessment and representation of different sources of uncertainty versus natural variability of precipitation.
- Methods to account for different accuracy in precipitation time series, e.g. due to change and improvement of observation networks.
- Uncertainty and variability in spatially and temporally heterogeneous multi-source precipitation products.
- Estimation of precipitation variability and uncertainty at ungauged sites.
- Precipitation data assimilation.
- Process conceptualization and modelling approaches at different spatial and temporal scales, including model parameter identification and calibration, and sensitivity analyses to parameterization and scales of process representation.
- Modelling approaches based on ensemble simulations and methods for synthetic representation of precipitation variability and uncertainty.
- Scaling and scale invariance properties of precipitation fields in space and/or in time.
- Physically and statistically based approaches to downscale information from meteorological and climate models to spatial and temporal scales useful for hydrological modelling and applications.
vPICO presentations: Fri, 30 Apr
Preparedness to natural hazards in mountainous areas strongly relies on the knowledge of extreme rainfall probability. The presence of mountains influences the motion of air masses, thereby modifying the storms characteristics. Here, we use a novel statistical approach to quantify the orographic impact on the probability of occurrence of extreme rainfall of short duration (10-min to 6-hour). We find that mountains tend to decrease the mean annual maximum intensities at sub-hourly scales, thereby confirming the previously reported “reversed orographic effect”, and tend to decrease the tail heaviness, thereby decreasing the extremely high intensities such as the events occurring on average once in 100 years. The second effect is however non-monotonic, in that it increases between 10 minute and 1 hour and diminishes between 1 and 6 hours. Sub-hourly extremes could thus be higher than what can be estimated from hourly data alone, implying that the scaling assumptions typically adopted for risk assessment may systematically underestimate the risk of short-duration extremes
How to cite: Marra, F., Armon, M., Borga, M., and Morin, E.: Orographic effect on extreme precipitation statistics peaks at hourly times scales, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1109, https://doi.org/10.5194/egusphere-egu21-1109, 2021.
A number of studies have shown that the ability of statistical tests to detect trends in hydrologic extremes is negatively affected by (i) the presence of autocorrelation in the time series, and (ii) field significance. Here, we investigate these two issues and evaluate the power of several trend tests using time series of frequencies (or counts) of precipitation extremes from long-term (100 years) precipitation records of 1087 gauges of the Global Historical Climate Network database. For this aim, we design several Monte Carlo experiments based on simulations of random count time series with different levels of autocorrelation and trend. We find the following. (1) The observed records are consistent with the hypothesis of autocorrelation induced by the presence of trends, indicating that the existence of serial correlation does not significantly affect trend detection. (2) Tests based on the linear and Poisson regressions are more powerful that nonparametric tests, such as Mann Kendall. (3) Accounting for field significance improves the interpretation of the results by limiting the rejection of the false null hypothesis. We then use these results to investigate the presence of trends in the observed records. We find that, depending on the quantiles used to define the frequency of precipitation extremes, 34-47% of the selected gages exhibit a statistically significant trend, of which 70-80% are positive and located mainly in United States and Northern Europe. The significant negative trends are mostly located in Southern Australia.
How to cite: Farris, S., Deidda, R., Viola, F., and Mascaro, G.: How important is accounting for serial correlation and field significance in trend detection of extreme rainfall occurrences? , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10830, https://doi.org/10.5194/egusphere-egu21-10830, 2021.
Availability of precipitation data at fine spatial resolution is highly desirable for hydroclimatic studies. Rain gauges are often considered as the primary source of precipitation data due to its reliability. However, due to either physical, climatic or economic constraints, setting up networks of rain gauges becomes unfeasible in many isolated terrains such as the Himalayan region. In the absence of gauge data, other alternate sources of weather information such as Satellite based Precipitation Products (SPPs) and Reanalysis precipitation Datasets (RPDs) are generally used. In this study, we aim to utilise 18 years of precipitation data (2001-2018) derived from the Integrated Multi-Satellite Retrievals for GPM (IMERG) at 10km spatial resolution as input to a Multiple-Point Statistics (MPS) based statistical model to obtain corresponding data for the year 2019 at 10km over the North-west Himalayan region. MPS is capable of generating fine scale data using the available coarse scale hindcast data by reproducing spatially connected spatial patterns. It requires data to be split into two parts. First part is called the training image and it requires both coarse and fine scale data. Second part is called the conditioning data which requires data only at coarse scale for the year 2019. In the attempt of using MPS as the tool for this study, the spatial field of Original IMERG data at 10 km (O_IMERG) is smoothen (S_IMERG) in order to transform the data features to a coarse scale reference data. The reference data used for this purpose is the High Asia Refined analysis (HAR) available at 30km spatial resolution over the South-Central Asia and Tibetan Plateau region. The variograms of both O_IMERG and S_IMERG are used to evaluate error frequency between the two data at specific distances followed by bias correction of S_IMERG. The bias corrected S_IMERG (BCS_IMERG) acts as the conditioning data for the MPS model. Training Image is composed of both BCS_IMERG and O_IMERG. Both the training image (year 2001-2018) and the conditioning data (2019) are provided to the MPS model. In addition to the variable of precipitation, the model also employs static parameters such as locational and topographical variables to help in identification of true patterns between training image and conditioning data. The study is significant in its ability to generate future precipitation information by utilising the available hindcast data observation data (10 km spatial resolution) by overcoming the spatial heterogeneity involved with observation data.
How to cite: Singhal, A. and Jha, S.: A Statistical Approach to generate ensembles of observation data of precipitation using hindcast data over the Northwest Himalayas, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6991, https://doi.org/10.5194/egusphere-egu21-6991, 2021.
Precipitation is one of the most essential variables within the hydrological system, and accordingly one of the main drivers for terrestrial hydrological processes. The quality of many hydrological applications such as climate prediction, water resource management, and flood forecasting, depends on the correct reproduction of its spatiotemporal distribution. Not only are there a variety of methods for reconstructing precipitation maps, but the reconstruction can also be based on different observation types or its combinations. In our approach we use rain gauge observations and path-averaged rain rates, derived from Commercial Microwave Link (CML) attenuations, as observations. Using these two observation types we apply Random Mixing Whittaker-Shannon (RMWSPy) to stochastically simulate precipitation fields.
The algorithm generates precipitation fields as a linear combination of unconditional spatial random fields, where the spatial dependence structure is described by copulas. The weights of the linear combination are optimized in such a way that the precipitation observations themselves as well as their spatial structure are reproduced. Using RMWSPy allows to simulate a precipitation field ensemble of any size, where each ensemble member is in concordance with the underlying observations.
Here, we apply RMWSPy to the whole of Germany and various catchments of different sizes, covering cases with different amount of available observations and different orographic complexity. The resulting ensembles of precipitation fields are evaluated regarding the quality of the reproduced spatial distribution of the precipitation and its pattern. We show that the reconstructed precipitation fields reproduce the observed spatiotemporal distribution in a quality that is comparable to the gauge-adjusted radar product RADOLAN-RW provided by the German Weather Service (DWD).
How to cite: Haese, B., Blettner, N., Hörning, S., Linder, M., Chwala, C., and Kunstmann, H.: Evaluation of the spatial distribution of a stochastically reconstructed ensemble of precipitation fields using RMWSPy, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13532, https://doi.org/10.5194/egusphere-egu21-13532, 2021.
Precipitation is one of the main inputs for hydrological models. For design purposes observed precipitation at high temporal resolution is often not available. In this case weather generators can be used to simulate realistic precipitation. Synthetic precipitation time series are often produced directly from observed time series using the stochastic methods which are able to reproduce the properties of the observed time series. The main difference and advantage of this research is to generate time series by focusing on the specific properties of the observed time series and trying to obtain these properties indirectly by conducting through investigation on the phases and power spectra and their individual effects using the phase annealing method.
Phase annealing is mainly based on annealing the phases of precipitation time series which are obtained from Fourier transform in order to meet the desired properties. These are obtained from observed time series and defined in the objective function. The outcome is synthetic time series with altered phases while the power spectrum is kept intact yielding new precipitation time series with properties matching those of the observed time series.
How to cite: Mehrvand, M., Bárdossy, A., and Anwar, F.: Conditional Simulation of Precipitation Time Series Using Phase Annealing , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12500, https://doi.org/10.5194/egusphere-egu21-12500, 2021.
A high-resolution rainfall data at a km and sub-hourly scales provides a powerful tool for hydrological risk assessment in the current and the future climate. Global circulation models or regional circulation models generally provide projections at much coarser space-time resolutions of 10-100 kilometres and daily to monthly. In the recent decade, convection-permitting models (CPM) have been developed and enable the projection at a kilometre and sub-hourly scales. CPMs, due to their very high computational demand, are still limited to a small number of ensemble simulations. This limits their skill in hydrology, where quantification of extremes and their variability is essential for risk assessment and design. In this project, we propose the combined use of CPMs with stochastic rainfall generators to simulate ensemble of climate change at hydrologically relevant scales.
To achieve this, we used the STREAP space-time stochastic rainfall generator, a 1 km resolution composite rain radar data and a 2.2km CPM dataset from the UK Met Office. For the mid-land region of the UK, we parameterised STREAP for the present climate using rainfall observations. CPM simulations were used to derive the change of STREAP parameters with a changing climate. These parameters describe the change in weather patterns, the rainfall intensification, and changes in the structure of rainfall. Our results show that by combining a physics-based model and a stochastic weather generator we can simulate robust ensemble of rainfall at a minimal computational cost while preserving all physical attributes from climate change projections.
How to cite: Chen, Y., Paschalis, A., Peleg, N., and Onof, C.: Reparametrizing rainfall generators with convective-permitting models to generate high-resolution rainfall for climate impact studies, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4622, https://doi.org/10.5194/egusphere-egu21-4622, 2021.
Dynamical models are a major tool for studying climate variability and its evolution. But despite the refinements in resolution and efforts to revise the dynamical and physical processes, rainfall extremes are still poorly represented, even at regional scales. Recent studies using convection-permitting simulations have demonstrated the improvement in representing heavy rainfall. In this study, we investigate the impacts of different model resolutions and convection representations (parameterized vs. explicit) in simulating precipitation frequency over Europe and the Mediterranean and try to explain the difference between model ensembles by focusing on triggering processes. For this purpose, we used a multi-model data-set with three different resolutions (0.44°, 0.11° and 0.0275°) produced in the context of the MED and EURO-CORDEX and the CORDEX Flagship Pilot Study on convection (FPSCONV). At 0.0275°, deep convection is explicitly represented while at 0.44° and 0.11°, it is parameterized with different schemes. In addition, to partially separate the impact of the higher resolution and convective schemes, we remapped the outputs of resolution 0.0275° to the 0.11° grid. To explain the difference in simulating precipitation frequency, a multi-variate approach is applied, in which precipitation is considered in the statistical relationship with tropospheric temperature and humidity - derived from colocated observations at the supersite SIRTA near Paris and some GPS stations. The results show that precipitation frequency in the higher resolution simulations is reduced because of a lower probability to exceed the critical value of integrated water vapor (IWVcv) over which precipitation picks up for different temperature bins. At low temperature, the probability decreases mostly due to a different humidity distribution in high resolution simulations, but for the temperature bins where the dominant precipitation type changes to convective precipitation, the decrease of probability to exceed IWVcv is mainly explained by a higher value of IWVcv. In these bins, the differences between 0.0275° and 0.44°, 0.11° resolutions become larger over southern Europe and the Mediterranean. This is not clear over mountain areas, where processes of triggering are more linked to orography than convection. Our results also suggest a decrease of model spread at higher temperature, and a stronger impact of switching off convective schemes than increasing resolution.
How to cite: Ha-Truong, M., Bastin, S., Drobinski, P., Fita, L., Chiriaco, M., Polcher, J., and Bock, O. and the model providers from FPSCONV community: Precipitation frequency in MED and EURO-CORDEX ensembles from 0.44° to convective permitting resolution: what explains the differences?, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8843, https://doi.org/10.5194/egusphere-egu21-8843, 2021.
High-resolution precipitation models are essential to forecast urban pluvial floods. Global Numerical Weather Prediction Models (NWPs) are considered too coarse to accurately forecast flooding at the city scale. High-resolution radar nowcasting can be either unavailable or insufficient to forecast at the required lead-times. Downscaling models are used to increase the resolution and extend forecast by several days when initialised with global NWPs. However, resolving weather processes at smaller spatial scales and sub-daily temporal resolutions has its challenges and does not necessarily result in more accurate forecast but instead only increase the computational requirements. Additionally, in ungauged regions, forecast verification is a challenge as in-situ measurements and radar estimates remain scarce or non-existent. This research evaluates the ability of a dynamically downscaled WRF model to capture the spatial and temporal variability of rainfall suitable for an urban drainage flood forecast model and evaluated against IMERG Global Precipitation Model (GPM) Satellite Precipitation Products (SPPs).
A WRF model was set-up with one-way nesting, three nested domains at horizontal grid resolutions 10km, 3.33km and 1km, a 1hourly temporal output, a spin-up time of 12 hours and evaluated at different lead times up to 48 hrs. The analysis was performed for three (3) winter frontal systems during the period 2015-2019 in the highly urbanised coastal Mediterranean city of Alexandria in Egypt which experiences floods from extreme precipitation. The Global Forecast System (GFS), and European Centre for Medium Range (ECMWF) forecast were used as initial and lateral boundary conditions.
Initial results indicate the WRF models could capture extreme rainfall for all events. There is some agreement with the IMERG data and the model correctly forecasted a decrease in rainfall as the systems transition from coastal to inland areas. In general, GFS and ECMWF initialised WRF models overestimated rainfall estimates compared to IMERG data. Differences in GFS and ECMWF initialised models (multi-model approach) highlight the sensitivity of models to initial and boundary conditions and emphasises the need for post-processing and data assimilation when possible to generate accurate small-scale features. A study such as this provides knowledge for understanding, future applications and limitations of using Quantitative Precipitation Forecasts (QPFs) in urban drainage models. Additionally, the potential use of IMERG GPM to verify spatial and temporal variability of forecast in ungauged and data-scarce regions. Future analysis will evaluate the skill of ensembles precipitation systems in characterising forecast uncertainty in such applications.
How to cite: Young, A., Bhattacharya, B., Daniels, E., and Zevenbergen, C.: Evaluation of a WRF model in forecasting extreme rainfall in the urban data-scarce coastal city of Alexandria, Egypt, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9241, https://doi.org/10.5194/egusphere-egu21-9241, 2021.
Stochastic Weather Generators (WG) have been extensively used in recent years for hydrologic modeling, among others. Compared to traditional approaches, the main advantage of using WGs is that they can produce synthetic continuous time series of weather data of unlimited length preserving their spatiotemporal distribution. Synthetic simulations are based on the statistical characteristics of the observed weather, thus, relying upon the length and spatial distribution of the input data series. In most cases, and especially in arid/semiarid regions, these are scarce, which makes it difficult for WGs to obtain reliable quantile estimates, particularly those associated with low-frequency events. The present study aims to explore the importance of the input weather data length in the performance of WGs, focusing on the adequate estimation of the higher quantiles, and quantifying their uncertainty.
An experimental case study consisting of nine rain gauges from the Spain02-v5 network in a 0.11º resolution covering an approximate area of 180 km2 was implemented. The WG used for the experiment was GWEX, which includes a three-parameter (σ, κ, and ξ) cumulative distribution function (E-GPD) to model de precipitation amounts, being the shape parameter ξ the one directly governing the upper tail of the distribution function. A fictitious climate scenario of 15,000 was simulated fixing the ξ value to 0.11. From this scenario, 50 realizations of 5,000 years with a different sample length (i.e. 30, 60, 90, 120, 150, 200, 300 years) were simulated for four different particular cases: (1) leaving the ξ value as default (i.e. 0.05); (2) estimating the ξ value from the observations; (3) calibrating the ξ value with the T = 100 years quantile from the 15,000 years; and (4) fixing the ξ value to the fictitious scenario value. Relative root mean square error (RRMSE) and coefficient of variation (CV) were calculated for each set of realizations and compared with the obtained from the fictitious climate scenario.
Preliminary results showed a clear reduction in the value of both the CV and the RRMSE with the increase of the sample length for the four particular cases, being this reduction more evident for the higher order quantiles and as we move from particular case (1) to (4). Furthermore, it was observed that there was not any significant improvement in the higher quantile estimates between the 200-yrs and the 300-yrs samples, concluding that there is a sample length threshold from which the estimates do not improve. Finally, even observing a clear improvement in all estimates when increasing the sample length, a systematic underestimation of the higher quantiles in all cases was still observed, which remarks the importance of seeking extra sources of information (e.g. regional max. Pd. studies) for a better parameterization of the WG, especially for arid/semiarid climates.
How to cite: Beneyto, C., Aranda, J. Á., and Francés, F.: Exploring the uncertainty of Weather Generators’ extreme estimates associated with the length of the input data series, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12399, https://doi.org/10.5194/egusphere-egu21-12399, 2021.
Both mean and extreme precipitation are highly relevant and a probability distribution that models the entire precipitation distribution therefore provides important information. Gamma distributions are often used to model low and moderate precipitation amounts and extreme value theory allows to model the upper tail of the distribution. We apply the Extended Generalized Pareto Distribution (EGPD). Thanks to a transition function, this method overcomes the problem of finding a threshold between upper and lower tails. The transition cumulative distribution function of the EGPD is constrained on the upper tail and lower tail to enable a GPD behavior for both small and large extremes.
EGPD is used here to characterize ERA-5 precipitation. ERA-5 is a new ECMWF climate re-analysis dataset that provides a numerical description of the recent climate by combining a numerical weather model with observations. The data set is global with a spatial resolution of 0.25° and currently covers the period from 1979 to present. ERA-5 precipitation is computed from model forecasts and therefore needs validation against observational datasets. ERA-5 daily precipitation is compared to EOBS precipitation, a gridded dataset spatially interpolated from observations over Europe, and to CMORPH precipitation, a global satellite-based dataset. Simultaneous occurrence of extreme events is assessed with a hit rate. An intensity comparison is conducted with quantiles confidence intervals and a Kullback Leibler divergence test, both derived from the EGPD.
Overall, good agreements but also strong mismatches between ERA-5 and the observational datasets can be found, depending on the feature of interest in precipitation data. This work highlights both. For example, extreme event occurrences between ERA5 and the observational datasets appear to agree. The overlap between 95% confidence intervals on quantiles depends on the season and the probability of occurrence. Over Europe, the best agreement results are generally reached in regions with high station density in EOBS. The global intensity comparison between ERA5 and CMORPH shows a good agreement for moderate quantiles, except for some mountainous regions, but presents a large signal of disagreement in the tropics for large quantiles.
How to cite: Rivoire, P., Martius, O., and Naveau, P.: A comparison of moderate and extreme ERA-5 daily precipitation with two observational data sets, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-666, https://doi.org/10.5194/egusphere-egu21-666, 2021.
Evaluating the future of surface water availability in the western United States requires a robust analysis of the projected trends in precipitation variability within the new generation of global climate model (GCM) simulations. To understand the reliability of future projections, we first construct a historical baseline (1950-2014) of the precipitation climatology and contribution of heavy precipitation events to the total annual precipitation from an ensemble of in-situ (Global Historical Climatology Network (GHCN)) and gridded precipitation products (Abatzoglou, 2013; Livneh et al., 2015; Newman et al., 2015). This historical baseline is used to evaluate the representation of precipitation variability during the historical period of GCM simulations from the CMIP6 HighResMIP and ScenarioMIP ensembles as well as the multi-resolution, factual-counterfactual ensemble of CAM5 simulations. We frame our analysis in the context of water resources by using a collection of large basins across the western US to demonstrate that the role of GCM resolution in the representation of precipitation variability is highly dependent on regional differences in topographical controls and dominant climatological drivers of precipitation. In most regions, we find that the highest-resolution GCM simulations (25-50 km) portray realistic occurrences of heavy precipitation events when compared to gridded historical precipitation at the same spatial resolution, whereas coarser GCM simulations (100-200 km) tend to distribute precipitation more evenly throughout the year than expected. When compared to the historical period (1950-2014), future projections (2014-2050) from both HighResMIP and ScenarioMIP ensembles produce more variable precipitation with a higher fraction of the annual precipitation falling in heavy precipitation events. Furthermore, we explore methods for constraining uncertainty in the projection of future precipitation variability across the Western US using a statistical assessment of the historical GCM simulations compared to the historical baseline.
Abatzoglou, J. T. (2013). Development of gridded surface meteorological data for ecological applications and modelling. International Journal of Climatology, 33(1), 121–131. https://doi.org/10.1002/joc.3413
Livneh, B., Bohn, T. J., Pierce, D. W., Munoz-Arriola, F., Nijssen, B., Vose, R., Cayan, D. R., & Brekke, L. (2015). A spatially comprehensive, hydrometeorological data set for Mexico, the U.S., and Southern Canada 1950–2013. Scientific Data, 2(1), 1–12. https://doi.org/10.1038/sdata.2015.42
Newman, A. J., Clark, M. P., Sampson, K., Wood, A., Hay, L. E., Bock, A., Viger, R. J., Blodgett, D., Brekke, L., Arnold, J. R., Hopson, T., & Duan, Q. (2015). Development of a large-sample watershed-scale hydrometeorological data set for the contiguous USA: Data set characteristics and assessment of regional variability in hydrologic model performance. Hydrology and Earth System Sciences, 19(1), 209–223. https://doi.org/10.5194/hess-19-209-2015
How to cite: Bjarke, N., Livneh, B., Barsugli, J., Quan, X. W., and Hoerling, M.: A multi-resolution analysis of historical and future precipitation variability across the western United States , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16396, https://doi.org/10.5194/egusphere-egu21-16396, 2021.
The Santa River basin has a climatology that is characterized by strong spatial gradients in precipitation. The influence of topography becomes increasingly important when smaller time scales are considered and convective and orographic processes have a more profound influence. This makes its estimation complex and of relevance for research on precipitation estimation in high mountain environments.
This study focused on estimating precipitation for the Santa basin located north of the capital of Peru, assessing spatial patterns and temporal variation. Precipitation products were used at a daily temporal resolution obtained from remote sensing datasets, including CHIRPS, PERSIANN-CCS, GPM and PISCO, altitude and vegetation products as NDVI-BOKU and GDEM. Also ground-based precipitation data from weather stations were collected from 35 meteorological stations (2012 -2019).
The in situ precipitation data was reviewed, cleaned and quality-checked for processing. The following operations were applied to the raster data: Gaussian filter, resampling at 1km, temporal homogenization (monthly) by accumulating the precipitation products until obtaining the monthly values, and averaging. Afterwards, a linear regression model was built based in which various of the remote sensing datasets served as predictions. The model was validated using the mean square error and the coefficient of determination.
The developed regression model provides a better estimate of the precipitation than the individual precipitation dataset. Overall, the resulting model performs relatively low in the dry season (May-September) but improves considerably in the wet season (October-April), with correlations that go up to 0.95. The outcomes of this research can be used to improve the estimation of precipitation patterns in high mountain regions with complex orography.
How to cite: Loarte, E., Medina, K., Villavicencio, E., León, H., Lavado, W., Rabatel, A., Jacome, G., Hunink, J., and Lopez-Baeza, E.: Analysis of remote sensing and in situ datasets to estimate spatial precipitation in high mountain areas: case study Cordillera Blanca, Peru, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8996, https://doi.org/10.5194/egusphere-egu21-8996, 2021.
It is well known that the performance of radar-derived quantitative precipitation estimates greatly relies on the physical model of the raindrop size distribution (DSD) and the relation between the physical model and radar parameters. However, incorporating changing precipitation microphysics to dynamically adjust the radar reflectivity (Z) and rain rate (R) relations can be challenging for real-time applications. In this study, two adaptive radar rainfall approaches are developed based on the radar-gauge feedback mechanism using 16 S-band Doppler weather radars and 4579 surface rain gauges deployed over the Eastern JiangHuai River Basin (EJRB) in China. Although the Z–R relations in both approaches are dynamically adjusted within a single precipitation system, one is using a single global optimal (SGO) Z–R relation, whereas the other is using different Z–R relations for different storm cells identified by a storm cell identification and tracking (SCIT) algorithm. Four precipitation events featured by different rainfall microphysical characteristics are investigated to demonstrate the performances of these two rainfall mapping methodologies. In addition, the short-term vertical profile of reflectivity (VPR) clusters are extensively analyzed to resolve the storm-scale characteristics of different storm cells. The verification results based on independent gauge observations show that both rainfall estimation approaches with dynamic Z–R relations perform much better than fixed Z–R relations. The adaptive approach incorporating the SCIT algorithm and real-time gauge measurements performs best since it can better capture the spatial variability and temporal evolution of precipitation.
How to cite: Gou, Y. and Chen, H.: Dynamic rainfall mapping using multi-radar multi-gauge observations in changing synoptic environments, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7298, https://doi.org/10.5194/egusphere-egu21-7298, 2021.
Convective rainfall events represent one of the most critical issues in urban areas, where numerical weather prediction models are affected by a large uncertainty related to the short temporal and spatial scales involved, thus making early warning systems ineffective. Conversely, radar-based nowcasting models may be a useful tool to guarantee short-term forecasts, through the extrapolation of most recent properties in observed precipitation fields, for lead times ranging from minutes to few hours.
In this study we develop a procedure for merging relevant information from two radar products with different resolutions and scales: (i) high-resolution observations retrieved by an X-band weather radar in a small domain (the metropolitan area of Cagliari, located in Sardinia, Italy), and (ii) the mosaic data provided by the Italian Civil Protection national radar network (the whole region of Sardinia). Specifically, we here adapt some STEPS procedures to merge the large-scale advection from the latter radar network, and the small-scale statistical properties for the former X-band weather radar. We thus combine the corresponding forecasts preserving the higher resolution scale. In details, for each time step we (i) evaluate the power spectra of the two forecasts (ii) merge the two spectra taking the power of the large (small) frequencies from the high (low) resolution data spectrum and (iii) achieve optimal downscaling by reconstructing the high-resolution nowcast from the blend of the two spectra.
How to cite: Deidda, R., Farris, S., Badas, M. G., Marrocu, M., Massidda, L., Seoni, A., Urru, S., and Viola, F.: A downscaling framework for precipitation nowcasting by merging radar retrievals at different scales and resolutions, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11062, https://doi.org/10.5194/egusphere-egu21-11062, 2021.
Precipitation - highly variable in space and time - is the most important input for many hydrological models. As these models become more and more detailed in space and time, high-resolution input data are required. Especially for modeling and prediction in fast reacting catchments, such as urban catchment areas, a higher space-time resolution is needed than the current ground measurement networks operated by national weather services usually provide. With the increasing number and availability of opportunistic sensors such as commercial microwave links (CMLs) and personal weather stations (PWS) in recent years, new opportunities for measuring meteorological data are emerging.
We developed a geostatistical interpolation framework which allows a combination of different opportunistic sensors and their specific features and geometric properties, e.g. point and line information. In this framework, a combined kriging approach is introduced, taking into account not only the point information of a reliable primary network, e.g., from national weather services, but also the higher uncertainty of the PWS- and CML-based precipitation. The path-averaged information of the CMLs is included through a block kriging-type approach.
The methodology was applied for two 7-months periods in Germany using an hourly temporal and a 1x1 km spatial resolution. By incorporating CMLs and PWS, the Pearson correlation could be increased from 0.56 to 0.73 compared to using only primary network for interpolation. The resulting precipitation maps also provided good agreement compared to gauge adjusted radar products.
How to cite: Eisele, M., Graf, M., El Hachem, A., Seidel, J., Chwala, C., Kunstmann, H., and Bárdossy, A.: Rainfall estimates from opportunistic sensors in Germany across spatio-temporal scales – Geostatistical interpolation framework, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12415, https://doi.org/10.5194/egusphere-egu21-12415, 2021.
In the field of hydrology, there is a significant demand for high spatial-temporal resolution of rainfall information that can be met by commercial microwave links (CMLs). CMLs are commonly used as a backhaul of telecommunications network with favourable spatial coverage especially in urbanized areas. CMLs are point-to-point radio connections operating at frequencies where attenuation of electromagnetic waves can be related to the rainfall intensity.
The ability of CMLs to assess rainfall intensity is determined by hardware parameters and path lengths of CMLs. The CML operates at various frequencies with horizontal or vertical polarization, moreover, link paths have lengths ranging from hundreds of meters up to kilometres. The characteristics of the rainfall needs to be reflected as they have impact on the errors (de Vos et al., 2019). Even collocated CMLs can detect considerably dissimilar rainfall information. To increase effectivity of rainfall information retrieval it is crucial to understand uncertainties arising from diversity of CML characteristics.
This study evaluates collocated CMLs that are assumed to be affected by the same weather condition. Having identical CML characteristics (as well as the propagations of the signals), it is expected to observe the same response patterns in the attenuated signals. Any disagreement could be caused by random error, sensitivity to the rainfall intensities, and/or hardware reaction to the condition (e.g. sensitivity of the antenna radome to the rainfall splash). Therefore, the role of arrangement of the direction of rainfall field advection and position of the collocated link paths is considered. The magnitude of disagreement between different groups of collocated links could be specified based on their characteristics. Oppositely, for collocated links under the same conditions but with different characteristics, the attributes of the individual CMLs are suspected for the disagreement.
de Vos, L. W., Overeem, A., Leijnse, H., and Uijlenhoet, R. (2019). Rainfall Estimation Accuracy of a Nationwide Instantaneously Sampling Commercial Microwave Link Network: Error Dependency on Known Characteristics. Journal of Atmospheric and Oceanic Technology 36, 7, 1267-1283. https://doi.org/10.1175/JTECH-D-18-0197.1
This study was supported by the project SpraiLINK 20-14151J of the Czech Science Foundation.
How to cite: Špačková, A., Bareš, V., and Fencl, M.: Variability in rainfall information derived from collocated microwave links, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8409, https://doi.org/10.5194/egusphere-egu21-8409, 2021.
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