scholarly journals On evaluation of ensemble precipitation forecasts with observation-based ensembles

2007 ◽  
Vol 10 ◽  
pp. 139-144 ◽  
Author(s):  
B. Ahrens ◽  
S. Jaun

Abstract. Spatial interpolation of precipitation data is uncertain. How important is this uncertainty and how can it be considered in evaluation of high-resolution probabilistic precipitation forecasts? These questions are discussed by experimental evaluation of the COSMO consortium's limited-area ensemble prediction system COSMO-LEPS. The applied performance measure is the often used Brier skill score (BSS). The observational references in the evaluation are (a) analyzed rain gauge data by ordinary Kriging and (b) ensembles of interpolated rain gauge data by stochastic simulation. This permits the consideration of either a deterministic reference (the event is observed or not with 100% certainty) or a probabilistic reference that makes allowance for uncertainties in spatial averaging. The evaluation experiments show that the evaluation uncertainties are substantial even for the large area (41 300 km2) of Switzerland with a mean rain gauge distance as good as 7 km: the one- to three-day precipitation forecasts have skill decreasing with forecast lead time but the one- and two-day forecast performances differ not significantly.

2012 ◽  
Vol 140 (3) ◽  
pp. 874-888 ◽  
Author(s):  
Emmanuel Roulin ◽  
Stéphane Vannitsem

Abstract Extended logistic regression is used to calibrate areal precipitation forecasts over two small catchments in Belgium computed with the European Centre for Medium-Range Weather Forecasts (ECMWF) Ensemble Prediction System (EPS) between 2006 and 2010. The parameters of the postprocessing are estimated from the hindcast database, characterized by a much lower number of members (5) than the EPS (51). Therefore, the parameters have to be corrected for predictor uncertainties. They have been fitted on the 51-member EPS ensembles, on 5-member subensembles drawn from the same EPS, and on the 5-member hindcasts. For small ensembles, a simple “regression calibration” method by which the uncertain predictors are corrected has been applied. The different parameter sets have been compared, and the corresponding extended logistic regressions have been applied to the 51-member EPS. The forecast probabilities have then been validated using rain gauge data and compared with the raw EPS. In addition, the calibrated distributions are also used to modify the ensembles of precipitation traces. The postprocessing with the extended logistic regression is shown to improve the continuous ranked probability skill score relative to the raw ensemble, and the regression calibration to remove a large portion of the bias in parameter estimation with small ensembles. With a training phase limited to a 5-week moving window, the benefit lasts for the first 2 forecast days in winter and the first 5 or 6 days in summer. In general, substantial improvements of the mean error and of the continuous ranked probability score have been shown.


2010 ◽  
Vol 138 (1) ◽  
pp. 176-189 ◽  
Author(s):  
Felix Fundel ◽  
Andre Walser ◽  
Mark A. Liniger ◽  
Christoph Frei ◽  
Christof Appenzeller

Abstract The calibration of numerical weather forecasts using reforecasts has been shown to increase the skill of weather predictions. Here, the precipitation forecasts from the Consortium for Small Scale Modeling Limited Area Ensemble Prediction System (COSMO-LEPS) are improved using a 30-yr-long set of reforecasts. The probabilistic forecasts are calibrated on the exceedance of return periods, independently from available observations. Besides correcting for systematic model errors, the spatial and temporal variability in the amplitude of rare precipitation events is implicitly captured when issuing forecasts of return periods. These forecast products are especially useful for issuing warnings of upcoming events. A way to visualize those calibrated ensemble forecasts conveniently for end users and to present verification results of the return period–based forecasts for Switzerland is proposed. It is presented that, depending on the lead time and return period, calibrating COSMO-LEPS with reforecasts increases the precipitation forecast skill substantially (about 1 day in forecast lead time). The largest improvements are achieved during winter months. The reasonable choice of the length of the reforecast climatology is estimated for an efficient use of this computational expensive calibration method.


2019 ◽  
Vol 20 (12) ◽  
pp. 2347-2365 ◽  
Author(s):  
Ali Jozaghi ◽  
Mohammad Nabatian ◽  
Seongjin Noh ◽  
Dong-Jun Seo ◽  
Lin Tang ◽  
...  

Abstract We describe and evaluate adaptive conditional bias–penalized cokriging (CBPCK) for improved multisensor precipitation estimation using rain gauge data and remotely sensed quantitative precipitation estimates (QPE). The remotely sensed QPEs used are radar-only and radar–satellite-fused estimates. For comparative evaluation, true validation is carried out over the continental United States (CONUS) for 13–30 September 2015 and 7–9 October 2016. The hourly gauge data, radar-only QPE, and satellite QPE used are from the Hydrometeorological Automated Data System, Multi-Radar Multi-Sensor System, and Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR), respectively. For radar–satellite fusion, conditional bias–penalized Fisher estimation is used. The reference merging technique compared is ordinary cokriging (OCK) used in the National Weather Service Multisensor Precipitation Estimator. It is shown that, beyond the reduction due to mean field bias (MFB) correction, both OCK and adaptive CBPCK additionally reduce the unconditional root-mean-square error (RMSE) of radar-only QPE by 9%–16% over the CONUS for the two periods, and that adaptive CBPCK is superior to OCK for estimation of hourly amounts exceeding 1 mm. When fused with the MFB-corrected radar QPE, the MFB-corrected SCaMPR QPE for September 2015 reduces the unconditional RMSE of the MFB-corrected radar by 4% and 6% over the entire and western half of the CONUS, respectively, but is inferior to the MFB-corrected radar for estimation of hourly amounts exceeding 7 mm. Adaptive CBPCK should hence be favored over OCK for estimation of significant amounts of precipitation despite larger computational cost, and the SCaMPR QPE should be used selectively in multisensor QPE.


2013 ◽  
Vol 17 (6) ◽  
pp. 2107-2120 ◽  
Author(s):  
S. Davolio ◽  
M. M. Miglietta ◽  
T. Diomede ◽  
C. Marsigli ◽  
A. Montani

Abstract. Numerical weather prediction models can be coupled with hydrological models to generate streamflow forecasts. Several ensemble approaches have been recently developed in order to take into account the different sources of errors and provide probabilistic forecasts feeding a flood forecasting system. Within this framework, the present study aims at comparing two high-resolution limited-area meteorological ensembles, covering short and medium range, obtained via different methodologies, but implemented with similar number of members, horizontal resolution (about 7 km), and driving global ensemble prediction system. The former is a multi-model ensemble, based on three mesoscale models (BOLAM, COSMO, and WRF), while the latter, following a single-model approach, is the operational ensemble forecasting system developed within the COSMO consortium, COSMO-LEPS (limited-area ensemble prediction system). The meteorological models are coupled with a distributed rainfall-runoff model (TOPKAPI) to simulate the discharge of the Reno River (northern Italy), for a recent severe weather episode affecting northern Apennines. The evaluation of the ensemble systems is performed both from a meteorological perspective over northern Italy and in terms of discharge prediction over the Reno River basin during two periods of heavy precipitation between 29 November and 2 December 2008. For each period, ensemble performance has been compared at two different forecast ranges. It is found that, for the intercomparison undertaken in this specific study, both mesoscale model ensembles outperform the global ensemble for application at basin scale. Horizontal resolution is found to play a relevant role in modulating the precipitation distribution. Moreover, the multi-model ensemble provides a better indication concerning the occurrence, intensity and timing of the two observed discharge peaks, with respect to COSMO-LEPS. This seems to be ascribable to the different behaviour of the involved meteorological models. Finally, a different behaviour comes out at different forecast ranges. For short ranges, the impact of boundary conditions is weaker and the spread can be mainly attributed to the different characteristics of the models. At longer forecast ranges, the similar behaviour of the multi-model members forced by the same large-scale conditions indicates that the systems are governed mainly by the boundary conditions, although the different limited area models' characteristics may still have a non-negligible impact.


2013 ◽  
Vol 17 (7) ◽  
pp. 2905-2915 ◽  
Author(s):  
M. Arias-Hidalgo ◽  
B. Bhattacharya ◽  
A. E. Mynett ◽  
A. van Griensven

Abstract. At present, new technologies are becoming available to extend the coverage of conventional meteorological datasets. An example is the TMPA-3B42R dataset (research – v6). The usefulness of this satellite rainfall product has been investigated in the hydrological modeling of the Vinces River catchment (Ecuadorian lowlands). The initial TMPA-3B42R information exhibited some features of the precipitation spatial pattern (e.g., decreasing southwards and westwards). It showed a remarkable bias compared to the ground-based rainfall values. Several time scales (annual, seasonal, monthly, etc.) were considered for bias correction. High correlations between the TMPA-3B42R and the rain gauge data were still found for the monthly resolution, and accordingly a bias correction at that level was performed. Bias correction factors were calculated, and, adopting a simple procedure, they were spatially distributed to enhance the satellite data. By means of rain gauge hyetographs, the bias-corrected monthly TMPA-3B42R data were disaggregated to daily resolution. These synthetic time series were inserted in a hydrological model to complement the available rain gauge data to assess the model performance. The results were quite comparable with those using only the rain gauge data. Although the model outcomes did not improve remarkably, the contribution of this experimental methodology was that, despite a high bias, the satellite rainfall data could still be corrected for use in rainfall-runoff modeling at catchment and daily level. In absence of rain gauge data, the approach may have the potential to provide useful data at scales larger than the present modeling resolution (e.g., monthly/basin).


2007 ◽  
Vol 12 ◽  
pp. 5-18 ◽  
Author(s):  
S. Federico ◽  
E. Avolio ◽  
C. Bellecci ◽  
A. Lavagnini ◽  
R. L. Walko

Abstract. This study investigates the sensitivity of a moderate-intense storm that occurred over Calabria, southern Italy, to upper-tropospheric forcing from a Potential Vorticity (PV) perspective. A prominent mid-troposheric trough can be identified for this event, which occurred between 22–24 May 2002, and serves as the precursor agent for the moderate-intense precipitation recorded. The working hypothesis is that the uncertainty in the representation of the upper-level disturbance has a major impact on the precipitation forecast and we test the hypothesis in a two-step approach. First, we examine the degree of uncertainty by comparing five different scenarios in a Limited area model Ensemble Prediction System (LEPS) framework which utilizes the height of the dynamical tropopause as the discriminating variable. Pseudo water vapour images of different scenarios are compared to the corresponding METEOSAT 7 water vapour image at a specific time, antecedent to the rain occurrence over Calabria, in order to evaluate the reliability of the different precipitation scenarios simulated by the LEPS. Second, we examine the impact of upper tropospheric PV variations on precipitation by comparing model simulations with slightly different initial PV fields. Initial velocity and mass fields in each case are balanced with the chosen PV perturbation using a PV inversion technique. The results of this study support the working hypothesis.


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