Using Temporal Modes of Rainfall to Evaluate the Performance of a Numerical Weather Prediction Model

Knievel, Jason C.; Ahijevych, David; Manning, Kevin W.

doi:10.1175/mwr2828.1

Cited by 46 publications

(35 citation statements)

References 41 publications

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“…During the model spinup time, approximately 0000-0600 UTC, the peak in the diurnal mode of observed rainfall frequency typically occurs from about 105°to 95°W in the central high plains (Knievel et al 2004). This is also the time and location of the strongest propagating signal of rainfall frequency in the United States.…”

Section: A Diurnally Averaged Hovmöller Diagramsmentioning

confidence: 99%

Comparison of the Diurnal Precipitation Cycle in Convection-Resolving and Non-Convection-Resolving Mesoscale Models

Clark

Gallus

Chen

2007

Monthly Weather Review

118

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The diurnal cycles of rainfall in 5-km grid-spacing convection-resolving and 22-km grid-spacing nonconvection-resolving configurations of the Weather Research and Forecasting (WRF) model are compared to see if significant improvements can be obtained by using fine enough grid spacing to explicitly resolve convection. Diurnally averaged Hovmöller diagrams, spatial correlation coefficients computed in Hovmöller space, equitable threat scores (ETSs), and biases for forecasts conducted from 1 April to 25 July 2005 over a large portion of the central United States are used for the comparisons. A subjective comparison using Hovmöller diagrams of diurnally averaged rainfall show that the diurnal cycle representation in the 5-km configuration is clearly superior to that in the 22-km configuration during forecast hours 24-48. The superiority of the 5-km configuration is validated by much higher spatial correlation coefficients than in the 22-km configuration. During the first 24 forecast hours the 5-km model forecasts appear to be more adversely affected by model "spinup" processes than the 22-km model forecasts, and it is less clear, subjectively, which configuration has the better diurnal cycle representation, although spatial correlation coefficients are slightly higher in the 22-km configuration. ETSs in both configurations have diurnal oscillations with relative maxima occurring in both configurations at forecast hours corresponding to 0000-0300 LST, while biases also have diurnal oscillations with relative maxima (largest errors) in the 22-km (5-km) configuration occurring at forecast hours corresponding to 1200 (1800) LST. At all forecast hours, ETSs from the 22-km configuration are higher than those in the 5-km configuration. This inconsistency with some of the results obtained using the aforementioned spatial correlation coefficients reinforces discussion in past literature that cautions against using "traditional" verification statistics, such as ETS, to compare high-to low-resolution forecasts. ABSTRACTThe diurnal cycles of rainfall in 5-km grid-spacing convection-resolving and 22-km grid-spacing nonconvection-resolving configurations of the Weather Research and Forecasting (WRF) model are compared to see if significant improvements can be obtained by using fine enough grid spacing to explicitly resolve convection. Diurnally averaged Hovmöller diagrams, spatial correlation coefficients computed in Hovmöller space, equitable threat scores (ETSs), and biases for forecasts conducted from 1 April to 25 July 2005 over a large portion of the central United States are used for the comparisons. A subjective comparison using Hovmöller diagrams of diurnally averaged rainfall show that the diurnal cycle representation in the 5-km configuration is clearly superior to that in the 22-km configuration during forecast hours 24-48. The superiority of the 5-km configuration is validated by much higher spatial correlation coefficients than in the 22-km configuration. During the first 24 forecast hours the 5-km model for...

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Section: A Diurnally Averaged Hovmöller Diagramsmentioning

confidence: 99%

Comparison of the Diurnal Precipitation Cycle in Convection-Resolving and Non-Convection-Resolving Mesoscale Models

Clark

Gallus

Chen

2007

Monthly Weather Review

118

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“…Some authors (e.g., Fritsch and Carbone 2004;Knievel et al 2004) have proposed the evaluation of numerical weather prediction (NWP) forecasts in terms of how well they reproduce statistical properties of observations as an alternative to skill scores based on point-to-point comparison. In this sense, Dai et al (1999), Davis et al (2003), Dai and Trenberth (2004), Knievel et al (2004), Janowiak et al (2007), Clark et al (2007Clark et al ( , 2009, and others have performed such evaluations by examining how NWP models depict the diurnal cycle of precipitation during the warm season over North America.…”

Section: Introductionmentioning

confidence: 99%

“…In this sense, Dai et al (1999), Davis et al (2003), Dai and Trenberth (2004), Knievel et al (2004), Janowiak et al (2007), Clark et al (2007Clark et al ( , 2009, and others have performed such evaluations by examining how NWP models depict the diurnal cycle of precipitation during the warm season over North America. Their analyses are indicative of model performance in characterizing convection initiation, the motion of organized convection, and the convection related to sea-breeze circulations near the Gulf of Mexico.…”

Section: Introductionmentioning

confidence: 99%

The Diurnal Cycle of Precipitation from Continental Radar Mosaics and Numerical Weather Prediction Models. Part II: Intercomparison among Numerical Models and with Nowcasting

Berenguer

Surcel

Zawadzki

et al. 2012

Monthly Weather Review

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This second part of a two-paper series compares deterministic precipitation forecasts from the Storm-Scale Ensemble Forecast System (4-km grid) run during the 2008 NOAA Hazardous Weather Testbed (HWT) Spring Experiment, and from the Canadian Global Environmental Multiscale (GEM) model (15 km), in terms of their ability to reproduce the average diurnal cycle of precipitation during spring 2008. Moreover, radar-based nowcasts generated with the McGill Algorithm for Precipitation Nowcasting Using SemiLagrangian Extrapolation (MAPLE) are analyzed to quantify the portion of the diurnal cycle explained by the motion of precipitation systems, and to evaluate the potential of the NWP models for very short-term forecasting.The observed diurnal cycle of precipitation during spring 2008 is characterized by the dominance of the 24-h harmonic, which shifts with longitude, consistent with precipitation traveling across the continent. Time-longitude diagrams show that the analyzed NWP models partially reproduce this signal, but show more variability in the timing of initiation in the zonal motion of the precipitation systems than observed from radar.Traditional skill scores show that the radar data assimilation is the main reason for differences in model performance, while the analyzed models that do not assimilate radar observations have very similar skill.The analysis of MAPLE forecasts confirms that the motion of precipitation systems is responsible for the dominance of the 24-h harmonic in the longitudinal range 1038-858W, where 8-h MAPLE forecasts initialized at 0100, 0900, and 1700 UTC successfully reproduce the eastward motion of rainfall systems. Also, on average, MAPLE outperforms radar data assimilating models for the 3-4 h after initialization, and nonradar data assimilating models for up to 5 h after initialization.

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“…A useful forecast model should be able to provide results that agree well with known analytical solutions, results of other independently developed models, reanalysis, analysis and observational data; it must conserve energy, mass and moisture; and its code and model description must be freely available [2,3]. WRF fulfills these criteria [4][5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of WRF-Forecasts Over Siberia: Air Mass Formation, Clouds and Precipitation

PaiMazumder¹,

Henderson²,

Mölders³

2012

TOASCJ

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Abstract:The Weather Research and Forecasting (WRF) model was run as a regional model without data assimilation or nudging (31 36h-simulations) for July and December 2005 over a limited area domain covering Siberia to examine weather formation in an air-mass source region. The WRF-results were compared to NCEP1/NCAR-reanalysis, International Satellite Cloud Climatology Project, Global Precipitation Climatology Centre and Canadian Meteorological Centre data to assess model performance and identify shortcomings. WRF is capable of predicting air-mass formation. Simulation errors are within the error range of other models. The timing of best/worst agreement differs among quantities depending on their sensitivity to systematic (model deficiencies) and/or unsystematic errors (e.g. initial conditions). Overall, the WRF-results agree better with reanalysis for July than December. WRF-results and reanalysis agree best under persistent high pressure and worst during frontal passages and transition from one pressure regime to another. In July, WRF provides smaller diurnal amplitudes of 2m-temperature with up to 5.4 K lower, and 3.5 K higher values at 0000 and 1200 UTC than the reanalysis. In December, WRF overestimates 2m-temperature by 1.4 K. WRF-temperatures excellently agree with the reanalysis from 700 hPa to 300 hPa. Except during frontal passages, wind-speed shows positive bias. Typically root-mean-square errors and standard deviation of errors of wind-speed (temperature) increase (decrease) with height. In December, WRF has difficulty predicting the position and strength of the polar jet. WRF underestimates cloudiness and snow-depth, but overestimates precipitation. In July, predicted convective precipitation is related strongly to boundaries between different land-cover. WRF-predicted snow-depth strongly correlates with terrain and misses the observed fine features.

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Using Temporal Modes of Rainfall to Evaluate the Performance of a Numerical Weather Prediction Model

Cited by 46 publications

References 41 publications

Comparison of the Diurnal Precipitation Cycle in Convection-Resolving and Non-Convection-Resolving Mesoscale Models

Comparison of the Diurnal Precipitation Cycle in Convection-Resolving and Non-Convection-Resolving Mesoscale Models

The Diurnal Cycle of Precipitation from Continental Radar Mosaics and Numerical Weather Prediction Models. Part II: Intercomparison among Numerical Models and with Nowcasting

Evaluation of WRF-Forecasts Over Siberia: Air Mass Formation, Clouds and Precipitation

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