scholarly journals An Evaluation of QPF from the WRF, NAM, and GFS Models Using Multiple Verification Methods over a Small Domain

2016 ◽  
Vol 31 (4) ◽  
pp. 1363-1379 ◽  
Author(s):  
Haifan Yan ◽  
William A. Gallus
Keyword(s):  
High Resolution ◽  
Wrf Model ◽  
Radar Data ◽  
Early Morning ◽  
Small Scale ◽  
Time Interval ◽  
Verification Methods ◽  
Small Domain ◽  
Arw Model ◽  
Neighborhood Scale

Abstract The ARW model was run over a small domain centered on Iowa for 9 months with 4-km grid spacing to better understand the limits of predictability of short-term (12 h) quantitative precipitation forecasts (QPFs) that might be used in hydrology models. Radar data assimilation was performed to reduce spinup problems. Three grid-to-grid verification methods, as well as two spatial techniques, neighborhood and object based, were used to compare the QPFs from the high-resolution runs with coarser operational GFS and NAM QPFs to verify QPFs for various precipitation accumulation intervals and on two grid configurations with different resolutions. In general, NAM had the worst performance not only for model skill but also for spatial feature attributes as a result of the existence of large dry bias and location errors. The finer resolution of NAM did not offer any advantage in predicting small-scale storms compared to the coarser GFS. WRF had a large advantage for high precipitation thresholds. A greater improvement in skill was noted when the accumulation time interval was increased, compared to an increase in the spatial neighborhood size. At the same neighborhood scale, the high-resolution WRF Model was less influenced by the grid on which the verification was done than the other two models. All models had the highest skill from midnight to early morning, because the least wet bias, location, and coverage errors were present then. The lowest skill was shown from late morning through afternoon. The main cause of poor skill during this period was large displacement errors.

A Comparison of Scan Speedup Strategies and Their Effect on Rapid-Scan Weather Radar Data Quality

2020 ◽  
Vol 37 (11) ◽  
pp. 1955-1972
Author(s):  
Andrew Mahre ◽  
Tian-You Yu ◽  
David J. Bodine
Keyword(s):  
High Resolution ◽  
Data Quality ◽  
Wrf Model ◽  
Weather Radar ◽  
Radar Data ◽  
Radar Imaging ◽  
Strong Argument ◽  
Scan Time ◽  
Replacement System ◽  
Scanning Strategies

AbstractAs the existing NEXRAD network nears the end of its life cycle, intense study and planning are underway to design a viable replacement system. Ideally, such a system would offer improved temporal resolution compared to NEXRAD, without a loss in data quality. In this study, scan speedup techniques—such as beam multiplexing (BMX) and radar imaging—are tested to assess their viability in obtaining high-quality rapid updates for a simulated long-range weather radar. The results of this study—which uses a Weather Research and Forecasting (WRF) Model–simulated supercell case—show that BMX generally improves data quality for a given scan time or can provide a speedup factor of 1.69–2.85 compared to NEXRAD while maintaining the same level of data quality. Additionally, radar imaging is shown to improve data quality and/or decrease scan time when selectively used; however, deleterious effects are observed when imaging is used in regions with sharp reflectivity gradients parallel to the beam spoiling direction. Consideration must be given to the subsequent loss of sensitivity and beam broadening. Finally, imaging is shown to have an effect on the radar-derived mesocyclone strength (ΔV) of a simulated supercell. Because BMX and radar imaging are most easily achieved with an all-digital phased array radar (PAR), these results make a strong argument for the use of all-digital PAR for high-resolution weather observations. It is believed that the results from this study can inform decisions about possible scanning strategies and design of a NEXRAD replacement system for high-resolution weather radar data.

scholarly journals Radar-based characterisation of heavy precipitation in the eastern Mediterranean and its representation in a convection-permitting model

2020 ◽  
Vol 24 (3) ◽  
pp. 1227-1249 ◽  
Author(s):  
Moshe Armon ◽  
Francesco Marra ◽  
Yehouda Enzel ◽  
Dorita Rostkier-Edelstein ◽  
Efrat Morin
Keyword(s):  
High Resolution ◽  
Eastern Mediterranean ◽  
Wrf Model ◽  
Heavy Precipitation ◽  
Spatial Location ◽  
Radar Data ◽  
Weather Research And Forecasting ◽  
Model Simulations ◽  
Rain Gauges ◽  
Rainfall Patterns

Abstract. Heavy precipitation events (HPEs) can lead to natural hazards (e.g. floods and debris flows) and contribute to water resources. Spatiotemporal rainfall patterns govern the hydrological, geomorphological, and societal effects of HPEs. Thus, a correct characterisation and prediction of rainfall patterns is crucial for coping with these events. Information from rain gauges is generally limited due to the sparseness of the networks, especially in the presence of sharp climatic gradients. Forecasting HPEs depends on the ability of weather models to generate credible rainfall patterns. This paper characterises rainfall patterns during HPEs based on high-resolution weather radar data and evaluates the performance of a high-resolution, convection-permitting Weather Research and Forecasting (WRF) model in simulating these patterns. We identified 41 HPEs in the eastern Mediterranean from a 24-year radar record using local thresholds based on quantiles for different durations, classified these events into two synoptic systems, and ran model simulations for them. For most durations, HPEs near the coastline were characterised by the highest rain intensities; however, for short durations, the highest rain intensities were found for the inland desert. During the rainy season, the rain field's centre of mass progresses from the sea inland. Rainfall during HPEs is highly localised in both space (less than a 10 km decorrelation distance) and time (less than 5 min). WRF model simulations were accurate in generating the structure and location of the rain fields in 39 out of 41 HPEs. However, they showed a positive bias relative to the radar estimates and exhibited errors in the spatial location of the heaviest precipitation. Our results indicate that convection-permitting model outputs can provide reliable climatological analyses of heavy precipitation patterns; conversely, flood forecasting requires the use of ensemble simulations to overcome the spatial location errors.

Radar-based characterization of heavy precipitation in the eastern Mediterranean and its representation in a convection-permitting model

2020 ◽  
Author(s):  
Efrat Morin ◽  
Moshe Armon ◽  
Francesco Marra ◽  
Yehouda Enzel ◽  
Dorita Rostkier-Edelstein
Keyword(s):  
High Resolution ◽  
Eastern Mediterranean ◽  
Center Of Mass ◽  
Wrf Model ◽  
Heavy Precipitation ◽  
Spatial Location ◽  
Radar Data ◽  
Model Simulations ◽  
Rain Gauges ◽  
Rainfall Patterns

<p>Heavy precipitation events (HPEs) can lead to natural hazards (floods, debris flows) and contribute to water resources. Spatiotemporal rainfall patterns govern the hydrological, geomorphological and societal effects of HPEs. Thus, a correct characterization and prediction of rainfall patterns is crucial for coping with these events. However, information from rain gauges suitable for these goals is generally limited due to the sparseness of the networks, especially in the presence of sharp climatic gradients and small precipitating systems. Forecasting HPEs depends on the ability of weather models to generate credible rainfall patterns. In this study we characterize rainfall patterns during HPEs based on high-resolution weather radar data and evaluate the performance of a high-resolution (1 km<sup>2</sup>), convection-permitting Weather Research and Forecasting (WRF) model in simulating these patterns. We identified 41 HPEs in the eastern Mediterranean from a 24-year long radar record using local thresholds based on quantiles for different durations, classified these events into two synoptic systems, and ran model simulations for them. For most durations, HPEs near the coastline were characterized by the highest rain intensities; however, for short storm durations, the highest rain intensities were characterized for the inland desert. During the rainy season, center of mass of the rain field progresses from the sea inland. Rainfall during HPEs is highly localized in both space (<10 km decorrelation distance) and time (<5 min). WRF model simulations accurately generate the structure and location of the rain fields in 39 out of 41 HPEs. However, they showed a positive bias relative to the radar estimates and exhibited errors in the spatial location of the heaviest precipitation. Our results indicate that convection-permitting model outputs can provide reliable climatological analyses of heavy precipitation patterns; conversely, flood forecasting requires the use of ensemble simulations to overcome the spatial location errors.</p>

scholarly journals New Algorithm for Rain Cell Identification and Tracking in Rainfall Event Analysis

Atmosphere ◽  
2019 ◽  
Vol 10 (9) ◽  
pp. 532
Author(s):  
Ting He ◽  
Thomas Einfalt ◽  
Jianxin Zhang ◽  
Jiyao Hua ◽  
Yang Cai
Keyword(s):  
High Resolution ◽  
Radar Data ◽  
Life Cycles ◽  
Federal State ◽  
Event Analysis ◽  
Structure Amplitude ◽  
Matching Rule ◽  
Rainfall Events ◽  
Cell Identification ◽  
Verification Methods

This study proposes a new algorithm termed rain cell identification and tracking (RCIT) to identify and track rain cells from high resolution weather radar data. Previous algorithms have limitations when tracking non-consequent rain cells owing to their use of maximum correlation coefficient methods and their lack of an alternative way to handle the variation stages of rain cells during their life cycles. To address these deficiencies, various methods are implemented in the new algorithm. These include the particle image velocimetry (PIV) method for motion estimation and the rain cell matching rule to obtain the stage changes of rain cells. High resolution (5 min and 1 km) radar data from three rainy days over the German federal state North Rhine Westphalia (NRW) are used in this study. The performance of the identification module for the new algorithm is accessed by two object-oriented verification methods: structure–amplitude–location (SAL) and geometric index, while the performance of the tracking module is compared with TREC and SCOUT tracking algorithms and evaluated by the contingency table verification approach. Results suggest that the performance of the new algorithm is better than reference tracking method. Application of the RCIT algorithm to the selected cases shows that the inner structure of rainfall events in the experimental region present extreme value distributions, with most rainfall events having a short duration with less intensity. The new algorithm can effectively capture the stage changes of rain cells during their life cycles. The proposed algorithm can serve as the basis for further hydro-meteorological applications such as spatial and temporal analysis of rainfall events and short-term flood forecasting.

scholarly journals D- and E-region effects in the auroral zone during a moderately active 24-h period in July 2005

2007 ◽  
Vol 25 (8) ◽  
pp. 1837-1849 ◽  
Author(s):  
J. K. Hargreaves ◽  
M. J. Birch ◽  
B. J. I. Bromage
Keyword(s):  
Energetic Electron ◽  
Radar Data ◽  
Early Morning ◽  
Auroral Zone ◽  
Small Scale ◽  
Absorption Region ◽  
Horizontal Structure ◽  
D Region ◽  
E Region ◽  
Eiscat Radar

Abstract. The effects of energetic electron precipitation into the auroral region at a time of enhanced solar wind have been investigated during a continuous period of 24 h, using the European Incoherent Scatter (EISCAT) radar, an imaging riometer, and particle measurements on an orbiting satellite. The relative effects in the E region (120 km) and D region (90 km) are found to vary during the day, consistent with a gradual hardening of the incoming electron spectrum from pre-midnight to morning. Whereas the night spectra are single peaked, the daytime spectra are found to be double peaked, suggesting the presence of two distinct populations. A comparison between the radiowave absorption observed with the riometer and values estimated from the radar data shows generally good agreement, but with some discrepancies suggesting the occurrence of some small-scale features. The height and thickness of the absorbing region are estimated. Two periods of enhanced precipitation and the related radio absorption, one near magnetic midnight and one in the early morning, are studied in detail, including their horizontal structure and movement of the absorption patches. A sharp reduction of electron flux recorded on a POES satellite is related to the edge of an absorption region delineated by the imaging riometer. The observed particle flux is compared with a value deduced from the radar data during the overpass, and found to be in general agreement.

scholarly journals High-Resolution COSMO-CLM Modeling and an Assessment of Mesoscale Features Caused by Coastal Parameters at Near-Shore Arctic Zones (Kara Sea)

Atmosphere ◽  
2020 ◽  
Vol 11 (10) ◽  
pp. 1062
Author(s):  
Vladimir Platonov ◽  
Alexander Kislov
Keyword(s):  
High Resolution ◽  
Atmospheric Model ◽  
Atmospheric Modeling ◽  
Kara Sea ◽  
The Arctic ◽  
Small Scale ◽  
Mesoscale Circulation ◽  
Dynamic Features ◽  
Wind Speeds ◽  
Verification Methods

Coastal Arctic regions are characterized by severe mesoscale weather events that include extreme wind speeds, and the rugged shore conditions, islands, and mountain ranges contribute to mesoscale event formation. High-resolution atmospheric modeling is a suitable tool to reproduce and estimate some of these events, and so the regional non-hydrostatic climate atmospheric model COSMO-CLM (Consortium for Small-scale Modeling developed within the framework of the international science group CLM-Community) was used to reproduce mesoscale circulation in the Arctic coast zone under various surface conditions. Mid-term experiments were run over the Arctic domain, especially over the Kara Sea region, using the downscaling approach, with ≈12 km and ≈3 km horizontal grid sizes. The best model configuration was determined using standard verification methods; however, the model run verification process raised questions over its quality and aptness based on the high level of small-scale coastline diversity and associated relief properties. Modeling case studies for high wind speeds were used to study hydrodynamic mesoscale circulation reproduction, and we found that although the model could not describe the associated wind dynamic features at all scales using ≈3 km resolution, it could simulate different scales of island wind shadow effects, tip jets, downslope winds, vortex chains, and so on, quite realistically. This initial success indicated that further research could reveal more about the detailed properties of mesoscale circulations and extreme winds by applying finer resolution modeling.

scholarly journals Characterising patterns of heavy precipitation events in the eastern Mediterranean using a weather radar and convection-permitting WRF simulations

2019 ◽  
Author(s):  
Moshe Armon ◽  
Francesco Marra ◽  
Yehouda Enzel ◽  
Dorita Rostkier-Edelstein ◽  
Efrat Morin
Keyword(s):  
High Resolution ◽  
Eastern Mediterranean ◽  
Wrf Model ◽  
Weather Radar ◽  
Heavy Precipitation ◽  
Radar Data ◽  
Weather Research And Forecasting ◽  
Rain Gauges ◽  
Rainfall Patterns ◽  
Precipitation Events

Abstract. Heavy precipitation events (HPEs) can lead to natural hazards (floods, debris flows) and contribute to water resources. Rainfall patterns govern HPEs effects. Thus, a correct characterisation and prediction of rainfall patterns is crucial for coping with HPEs. Information from rain gauges is generally limited due to the sparseness of the networks, especially in presence of sharp climatic gradients. Forecasting HPEs depends on the ability of weather models to generate credible rainfall patterns. This paper characterises rainfall patterns during HPEs based on high-resolution weather radar data and evaluates the performance of a high-resolution, convection-permitting, Weather Research and Forecasting (WRF) model in simulating these patterns. We identified 41 HPEs in the eastern Mediterranean from a 24-year radar record using local thresholds based on quantiles for different durations, and we ran model simulations of these events. For most durations, HPEs near the coastline are characterised by the highest rain intensities, however, for short durations, the highest rain intensities characterise the inland desert. During the rainy season, the centre-of-mass of the rain field progresses from the sea inland. Rainfall during HPEs is highly localised both in space (

scholarly journals Investigating the Eddy Diffusivity Concept in the Coastal Ocean

2016 ◽  
Vol 46 (7) ◽  
pp. 2201-2218 ◽  
Author(s):  
I. I. Rypina ◽  
A. Kirincich ◽  
S. Lentz ◽  
M. Sundermeyer
Keyword(s):  
High Resolution ◽  
High Frequency ◽  
Eddy Diffusivity ◽  
Coastal Ocean ◽  
Coastal Environment ◽  
Radar System ◽  
Small Scale ◽  
Time Interval ◽  
High Frequency Radar ◽  
Diffusivity Tensor

AbstractThis paper aims to test the validity, utility, and limitations of the lateral eddy diffusivity concept in a coastal environment through analyzing data from coupled drifter and dye releases within the footprint of a high-resolution (800 m) high-frequency radar south of Martha’s Vineyard, Massachusetts. Specifically, this study investigates how well a combination of radar-based velocities and drifter-derived diffusivities can reproduce observed dye spreading over an 8-h time interval. A drifter-based estimate of an anisotropic diffusivity tensor is used to parameterize small-scale motions that are unresolved and underresolved by the radar system. This leads to a significant improvement in the ability of the radar to reproduce the observed dye spreading.

Evaluation of a WRF model in forecasting extreme rainfall in the urban data-scarce coastal city of Alexandria, Egypt

2021 ◽  
Author(s):  
Adele Young ◽  
Biswa Bhattacharya ◽  
Emma Daniels ◽  
Chris Zevenbergen
Keyword(s):  
High Resolution ◽  
Boundary Conditions ◽  
Temporal Variability ◽  
Weather Prediction ◽  
Wrf Model ◽  
Extreme Rainfall ◽  
Small Scale ◽  
Lead Times ◽  
Spatial And Temporal Variability ◽  
Urban Drainage

<p>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).<br> 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. <br>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. </p>

scholarly journals Assimilating C-Band Radar Data for High-Resolution Simulations of Precipitation: Case Studies over Western Sumatra

2021 ◽  
Vol 14 (1) ◽  
pp. 42
Author(s):  
Bojun Zhu ◽  
Zhaoxia Pu ◽  
Agie Wandala Putra ◽  
Zhiqiu Gao
Keyword(s):  
High Resolution ◽  
Data Assimilation ◽  
Doppler Radar ◽  
Weather Prediction ◽  
Wrf Model ◽  
Active Phase ◽  
Radar Data ◽  
Skill Score ◽  
Moist Convection ◽  
Simulated Precipitation

Accurate high-resolution precipitation forecasts are critical yet challenging for weather prediction under complex topography or severe synoptic forcing. Data fusion and assimilation aimed at improving model forecasts, as one possible approach, has gained increasing attention in past decades. This study investigates the influence of the observations from a C-band Doppler radar over the west coast of Sumatra on high-resolution numerical simulations of precipitation around its vicinity under the Madden–Julian oscillation (MJO) in January and February 2018. Cases during various MJO phases were selected for simulations with an advanced research version of the weather research and forecasting (WRF) model at a cloud-permitting scale (~3 km). A 3-dimensional variational (3DVAR) data assimilation method and a hybrid three-dimensional ensemble–variational data assimilation (3DEnVAR) method, based on the NCEP Gridpoint Statistical Interpolation (GSI) assimilation system, were used to assimilate the radar reflectivity and the radial velocity data. The WRF-simulated precipitation was validated with the Integrated Multi-satellitE Retrievals for GPM (IMERG) precipitation data, and the fractions skill score (FSS) was calculated in order to evaluate the radar data impacts objectively. The results show improvements in the simulated precipitation with hourly radar data assimilation 6 h prior to the simulations. The modifications with assimilation were validated through the observation departure and moist convection. It was found that forecast improvements are relatively significant when precipitation is more related to local-scale convection but rather small when the background westerly wind is strong under the MJO active phase. The additional simulation experiments, under a 1- or 2-day assimilation cycle, indicate better improvements in the precipitation simulation with 3DEnVAR radar assimilation than those with the 3DVAR method.

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