Evaluation of Precipitation from Numerical Weather Prediction Models and Satellites Using Values Retrieved from Radars

Vasić, Slavko; Lin, Charles A.; Zawadzki, Isztar; Bousquet, Olivier; Chaumont, Diane

doi:10.1175/2007mwr1955.1

Cited by 19 publications

(15 citation statements)

References 34 publications

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“…grid scale for moderate precipitation thresholds. This is also consistent with values found in the literature for the effective model resolution for precipitation (e.g., Vasić et al 2007). By increasing the resolution, this scale decreases correspondingly.…”

Section: Discussionsupporting

confidence: 92%

Assessing the Benefits of Convection-Permitting Models by Neighborhood Verification: Examples from MAP D-PHASE

Weusthoff

Ament

Arpagaus

et al. 2010

Monthly Weather Review

123

101

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High-resolution numerical weather prediction (NWP) models produce more detailed precipitation structures but the real benefit is probably the more realistic statistics gained with the higher resolution and not the information on the specific grid point. By evaluating three model pairs, each consisting of a high-resolution NWP system resolving convection explicitly and its low-resolution-driving model with parameterized convection, on different spatial scales and for different thresholds, this paper addresses the question of whether high-resolution models really perform better than their driving lower-resolution counterparts. The model pairs are evaluated by means of two fuzzy verification methods-upscaling (UP) and fractions skill score (FSS)-for the 6 months of the D-PHASE Operations Period and in a highly complex terrain. Observations are provided by the Swiss radar composite and the evaluation is restricted to the area covered by the Swiss radar stations. The high-resolution models outperform or equal the performance of their respective lowerresolution driving models. The differences between the models are significant and robust against small changes in the verification settings. An evaluation based on individual months shows that high-resolution models give better results, particularly with regard to convective, more localized precipitation events.

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Section: Discussionsupporting

confidence: 92%

Assessing the Benefits of Convection-Permitting Models by Neighborhood Verification: Examples from MAP D-PHASE

Weusthoff

Ament

Arpagaus

et al. 2010

Monthly Weather Review

123

101

View full text Add to dashboard Cite

show abstract

“…∼25 km to eliminate most grid-scale errors as suggested by several recent studies (see e.g. Mittermaier, 2006;Vasić et al, 2007). Figure 4 shows various skill scores for the event.…”

Section: Event Analysismentioning

confidence: 98%

Introducing uncertainty of radar-rainfall estimates to the verification of mesoscale model precipitation forecasts

Mittermaier

2008

Nat. Hazards Earth Syst. Sci.

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Abstract.A simple measure of the uncertainty associated with using radar-derived rainfall estimates as "truth" has been introduced to the Numerical Weather Prediction (NWP) verification process to assess the effect on forecast skill and errors. Deterministic precipitation forecasts from the mesoscale version of the UK Met Office Unified Model for a two-day high-impact event and for a month were verified at the daily and six-hourly time scale using a spatially-based intensity-scale method and various traditional skill scores such as the Equitable Threat Score (ETS) and log-odds ratio. Radar-rainfall accumulations from the UK Nimrod radarcomposite were used.The results show that the inclusion of uncertainty has some effect, shifting the forecast errors and skill. The study also allowed for the comparison of results from the intensity-scale method and traditional skill scores. It showed that the two methods complement each other, one detailing the scale and rainfall accumulation thresholds where the errors occur, the other showing how skillful the forecast is. It was also found that for the six-hourly forecasts the error distributions remain similar with forecast lead time but skill decreases. This highlights the difference between forecast error and forecast skill, and that they are not necessarily the same.

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“…This fact induces a marked bias on the modeled precipitation and is probably directly responsible for the general underestimation registered in both case studies. The underestimation of strong precipitation intensity and the tendency to diffuse the precipitation patterns spatially is a known problem of the meteorological models (Vasić et al 2007), which can be considered mainly a consequence of the numerical diffusion (Tartaglione et al 2002). The same analysis for the 183-WSL algorithm results shows that the low-intensity class 1 is less populated while the result for class 2 is well in agreement with the WRF value.…”

Section: Synoptic Analysis and Rainfall Retrievalmentioning

confidence: 99%

Satellite and Numerical Model Investigation of Two Heavy Rain Events over the Central Mediterranean

Laviola

Moscatello

Miglietta

et al. 2011

Journal of Hydrometeorology

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Two heavy rain events over the Central Mediterranean basin, which are markedly different by genesis, dimensions, duration, and intensity, are analyzed. Given the relative low frequency of this type of severe storms in the area, a synoptic analysis describing their development is included. A multispectral analysis based on geostationary multifrequency satellite images is applied to identify cloud type, hydrometeor phase, and cloud vertical extension. Precipitation intensity is retrieved from (i) surface rain gauges, (ii) satellite data, and (iii) numerical model simulations. The satellite precipitation retrieval algorithm 183-Water vapor Strong Lines (183-WSL) is used to retrieve rain rates and cloud hydrometeor type, classify stratiform and convective rainfall, and identify liquid water clouds and snow cover from the Advanced Microwave Sounding Unit-B (AMSU-B) sensor data. Rainfall intensity is also simulated with the Weather Research and Forecasting (WRF) numerical model over two nested domains with horizontal resolutions of 16 km (comparable to that of the satellite sensor AMSU-B) and 4 km. The statistical analysis of the comparison between satellite retrievals and model simulations demonstrates the skills of both methods for the identification of the main characteristics of the cloud systems with a suggested overall bias of the model toward very low rain intensities. WRF (in the version used for the experiment) seems to classify as low rain intensity regions those areas where the 183-WSL retrieves no precipitation while sensing a mixture of freshly nucleated cloud droplets and a large amount of water vapor; in these areas, especially adjacent to the rain clouds, large amounts of cloud liquid water are detected. The satellite method performs reasonably well in reproducing the wide range of gauge-detected precipitation intensities. A comparison of the 183-WSL retrievals with gauge measurements demonstrates the skills of the algorithm in discriminating between convective and stratiform precipitation using the scattering and absorption of radiation by the hydrometeors.

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Evaluation of Precipitation from Numerical Weather Prediction Models and Satellites Using Values Retrieved from Radars

Cited by 19 publications

References 34 publications

Assessing the Benefits of Convection-Permitting Models by Neighborhood Verification: Examples from MAP D-PHASE

Assessing the Benefits of Convection-Permitting Models by Neighborhood Verification: Examples from MAP D-PHASE

Introducing uncertainty of radar-rainfall estimates to the verification of mesoscale model precipitation forecasts

Satellite and Numerical Model Investigation of Two Heavy Rain Events over the Central Mediterranean

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