Spatial verification approaches as a tool to evaluate the performance of high resolution precipitation forecasts

Gofa, F.; Boucouvala, D.; Louka, P.; Flocas, Helena A.

doi:10.1016/j.atmosres.2017.09.021

Cited by 17 publications

(13 citation statements)

References 15 publications

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“…forecasts: eSAL SAL has so far mainly been used in high-resolution NWP model development (Ahijevych et al 2009;Zappa et al 2010;Van Weverberg et al 2010;Wittmann et al 2010;Termonia et al 2011;Haiden et al 2011), evaluation (Zimmer et al 2011;Hardy et al 2016;Vincendon et al 2011;Gofa et al 2017), or model intercomparison (Zimmer 2010) for the verification of deterministic forecasts. For its application on ensemble forecasts there are two main possibilities: 1) the ensemble is treated as a collection of deterministic forecasts and distributions of SAL components are analyzed, or 2) the ensemble is verified as a whole, and thus the quality measures describe the performance of the ensemble as a whole.…”

Section: A Proposed Extension Of Sal For Ensemblementioning

confidence: 99%

Spatial Verification of Ensemble Precipitation: An Ensemble Version of SAL

Radanovics

Vidal

Sauquet

2018

Weather and Forecasting

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these figures, we can see that the return loss is better than 19.5 dB, the gain is between 8.1 and 9.36 dB, and the axial ratio is between 2.30 and 4.41 dB. CONCLUSION AND DISCUSSIONSWe are in the process of designing an economical electronicallyscanned antenna array with quasi-hemispheric coverage for Inmarsat mobile communications. In this paper, we have presented the design of an electromagnetically-coupled patch antenna with circular polarization for such an array application. The Ensemble CAD tool has been used to simulate the antenna. Compared with similar antennas reported, this antenna covers a wider bandwidth from 1.525 to 1.661 GHz. We have chosen a simpler feeding structure in order to save the otherwise limited space for the beam-forming networks. Further, the feeding circuit and antenna are on the same side of the ground plane, which in the array situation leaves the volume underneath the antenna array for the power predivider, the duplexer, and the LNA. The return loss is better than 19.5 dB, the gain is between 8.1 and 9.36 dB, and the axial ratio is between 2.30 and 4.41 dB.The highest axial ratio is about 1.7-dB higher than the highest axial ratio of Karmakar and Bialkowski's sample antenna, which is 2.7 dB at 1.525 GHz. This can lead to at most a 0.3-dB difference in the polarization-mismatch factor. The lowest gain in the operation band is also 0.5-dB lower. A possible reason for this is the different material used. In our case, the dielectric constant is higher and therefore the patch is slightly smaller. The loss tangent of our material might be higher as well. Both cases will cause the gain to decrease. We will consider using a different substrate in our future designs. ACKNOWLEDGMENTPart of this work was finished in Laval University, Quebec, Canada. We would like to thank Prof. Michel Lecours and Mr. Claude Vergnolle for their help. INTRODUCTIONThe boundary conditions which electric and magnetic fields have to satisfy on the surfaces of the object play an important role in electromagnetic scattering problems. One of these conditions is called as the impedance boundary condition (IBC), which gives a relation between tangential electric and magnetic field vectors on a given surface in terms of a coefficient called a surface impedance. Leontovich [1] and Wait [2] used this type of boundary condition firstly. The simplest form of the IBC is the standard impedance boundary condition (SIBC), which is used to model coatings and lossy dielectrics [3]. Generally, the surface impedance appearing in the SIBC is assumed to be independent of the location and associated with a constant coefficient [3,4]. On the other hand, when a more accurate SIBC is considered, the surface impedance may be a function of location [5]. For example, when the inhomogeneous earth surface composed of different parts, such as rocky soil, sand, forest, sea, and so forth, it is modeled by an SIBC. Scattering from canonical structures whose surface satisfy inhomogeneous SIBC have been proposed in [6 -8] and scattering from i...

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Section: A Proposed Extension Of Sal For Ensemblementioning

confidence: 99%

Spatial Verification of Ensemble Precipitation: An Ensemble Version of SAL

Radanovics

Vidal

Sauquet

2018

Weather and Forecasting

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“…Traditional verification metrics are necessary to provide an evaluation of the accuracy of a forecasting system, but additional measures are needed to describe to what extent a nowcast is successful (e.g., Gofa et al ., ). An obvious example is when a small‐scale feature in the forecast appears displaced by a few kilometres from the position of the observation; then pixel‐based verification imposes a double penalty (Ebert, ) and the higher the resolution, the worse the situation.…”

Section: Verificationmentioning

confidence: 97%

NowPrecip: localized precipitation nowcasting in the complex terrain of Switzerland

Sideris

Foresti

Nerini

et al. 2020

Quart J Royal Meteoro Soc

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The new precipitation nowcasting system “NowPrecip” is introduced and its methodology is described in detail. NowPrecip is an area‐tracking application, able to generate ensembles of precipitation nowcasts. It supports the paradigm of seamless forecasting: nowcasts start from the most recent radar observation and evolve smoothly, through merging, towards the numerical weather prediction (NWP) ensemble members. NowPrecip relies on the following three pillars. (a) A geostatistics‐based optical flow algorithm, named “NowTrack”, able to determine the scales of interest automatically and equipped with an outlier control scheme. Such a scheme is particularly useful in capturing situations like fine‐scale rotation accurately. (b) A localized architecture motivated by the need to address scenarios where distinct rainfall patterns characterize different parts of the domain. This is mostly relevant for complex terrains like Switzerland, but is also useful for areal nowcasting operating on extensive regions. (c) Techniques to compute and incorporate localized growth and decay into the computation of nowcasts. This is especially useful for Alpine terrain, where there is often systematic growth and decay associated with orographic features. The growth and decay factors are based on analysis of the available NWP product, allowing for a more efficient coupling between the nowcasting sequence and the numerical model output. The main scope of this work is to present the methodology of NowPrecip in detail, but a verification of several representative events is also provided, using both deterministic and probabilistic measures.

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“…In fact, the original release notes from the National Centers for Environmental Prediction (NCEP) stated, "The HRRR provides forecasts, in high detail, of critical weather events such as severe thunderstorms, flash flooding, and localized bands of heavy winter precipitation" [19]. Yet, as of 2018, only a few studies [20][21][22][23] have investigated its skill in representing the mesoscale and convective processes that lead to flash flooding.…”

Section: Introductionmentioning

confidence: 99%

Quantifying Extreme Precipitation Forecasting Skill in High-Resolution Models Using Spatial Patterns: A Case Study of the 2016 and 2018 Ellicott City Floods

Zick

2020

Atmosphere

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Recent historic floods in Ellicott City, MD, on 30 July 2016 and 27 May 2018 provide stark examples of the types of floods that are expected to become more frequent due to urbanization and climate change. Given the profound impacts associated with flood disasters, it is crucial to evaluate the capability of state-of-the-art weather models in predicting these hydrometeorological events. This study utilizes an object-based approach to evaluate short range (<12 h) hourly forecast precipitation from the High-Resolution Rapid Refresh (HRRR) versus observations from the National Centers for Environmental Prediction (NCEP) Stage IV precipitation analysis. For both datasets, a binary precipitation field is delineated using thresholds that span trace to extreme precipitation rates. Next, spatial metrics of area, perimeter, solidity, elongation, and fragmentation, as well as centroid positions for the forecast and observed fields are calculated. A Mann–Whitney U-test reveals biases (using a confidence level of 90%) related to the spatial attributes and locations of model forecast precipitation. Results indicate that traditional pixel-based precipitation verification metrics are limited in their ability to quantify and characterize model skill. In contrast, an object-based methodology offers encouraging results in that the HRRR can skillfully predict the extreme precipitation rates that are anticipated with anthropogenic climate change. Yet, there is still room for improvement, since model forecasts of extreme convective rainfall tend to be slightly too numerous and fragmented compared with observations. Lastly, results are sensitive to the HRRR model’s representation of synoptic-scale and mesoscale processes. Therefore, detailed surface analyses and an “ingredients-based” approach should remain central to the process of forecasting excessive rainfall.

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Spatial verification approaches as a tool to evaluate the performance of high resolution precipitation forecasts

Cited by 17 publications

References 15 publications

Spatial Verification of Ensemble Precipitation: An Ensemble Version of SAL

Spatial Verification of Ensemble Precipitation: An Ensemble Version of SAL

NowPrecip: localized precipitation nowcasting in the complex terrain of Switzerland

Quantifying Extreme Precipitation Forecasting Skill in High-Resolution Models Using Spatial Patterns: A Case Study of the 2016 and 2018 Ellicott City Floods

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