Weather and Forecasting

Lackmann, Gary M.; Ancell, Brian; Bunkers, Matthew; Kirtman, Ben; Kosiba, Karen; McGovern, Amy; McMurdie, Lynn; Pu, Zhaoxia; Ritchie, Elizabeth; Huntington, Henry P.

2020 Weather and Forecasting

doi: 10.1175/WAF-D-20-0168.1

Trabing, Benjamin C.; Bell, Michael M.

2020 Weather and Forecasting

doi: 10.1175/WAF-D-19-0253.1

AbstractThe characteristics of official National Hurricane Center (NHC) intensity forecast errors are examined for the North Atlantic and east Pacific basins from 1989 to 2018. It is shown how rapid intensification (RI) and rapid weakening (RW) influence yearly NHC forecast errors for forecasts between 12 and 48 h in length. In addition to being the tail of the intensity change distribution, RI and RW are at the tails of the forecast error distribution. Yearly mean absolute forecast errors are positively correlated with the yearly number of RI/RW occurrences and explain roughly 20% of the variance in the Atlantic and 30% in the east Pacific. The higher occurrence of RI events in the east Pacific contributes to larger intensity forecast errors overall but also a better probability of detection and success ratio. Statistically significant improvements to 24-h RI forecast biases have been made in the east Pacific and to 24-h RW biases in the Atlantic. Over-ocean 24-h RW events cause larger mean errors in the east Pacific that have not improved with time. Environmental predictors from the Statistical Hurricane Intensity Prediction Scheme (SHIPS) are used to diagnose what conditions lead to the largest RI and RW forecast errors on average. The forecast error distributions widen for both RI and RW when tropical systems experience low vertical wind shear, warm sea surface temperature, and moderate low-level relative humidity. Consistent with existing literature, the forecast error distributions suggest that improvements to our observational capabilities, understanding, and prediction of inner-core processes is paramount to both RI and RW prediction.

Chang, Pao-Liang; Fang, Wei-Ting; Lin, Pin-Fang; Tang, Yu-Shuang

2020 Weather and Forecasting

doi: 10.1175/WAF-D-20-0064.1

AbstractAs Typhoon Goni (2015) passed over Ishigaki Island, a maximum gust speed of 71 m s−1 was observed by a surface weather station. During Typhoon Goni’s passage, mountaintop radar recorded antenna elevation angle oscillations, with a maximum amplitude of ~0.2° at an elevation angle of 0.2°. This oscillation phenomenon was reflected in the reflectivity and Doppler velocity fields as Typhoon Goni’s eyewall encompassed Ishigaki Island. The main antenna oscillation period was approximately 0.21–0.38 s under an antenna rotational speed of ~4 rpm. The estimated fundamental vibration period of the radar tower is approximately 0.25–0.44 s, which is comparable to the predominant antenna oscillation period and agrees with the expected wind-induced vibrations of buildings. The reflectivity field at the 0.2° elevation angle exhibited a phase shift signature and a negative correlation of −0.5 with the antenna oscillation, associated with the negative vertical gradient of reflectivity. FFT analysis revealed two antenna oscillation periods at 0955–1205 and 1335–1445 UTC 23 August 2015. The oscillation phenomenon ceased between these two periods because Typhoon Goni’s eye moved over the radar site. The VAD analysis-estimated wind speeds at a range of 1 km for these two antenna oscillation periods exceeded 45 m s−1, with a maximum value of approximately 70 m s−1. A bandpass filter QC procedure is proposed to filter out the predominant wavenumbers (between 40 and 70) for the reflectivity and Doppler velocity fields. The proposed QC procedure is indicated to be capable of mitigating the major signals resulting from antenna oscillations.

Fovell, Robert G.; Gallagher, Alex

2020 Weather and Forecasting

doi: 10.1175/waf-d-20-0101.1

AbstractWhile numerical weather prediction models have made considerable progress regarding forecast skill, less attention has been paid to the planetary boundary layer. This study leverages High-Resolution Rapid Refresh (HRRR) forecasts on native levels, 1-s radiosonde data, and (primarily airport) surface observations across the conterminous United States. We construct temporally and spatially averaged composites of wind speed and potential temperature in the lowest 1 km for selected months to identify systematic errors in both forecasts and observations in this critical layer. We find near-surface temperature and wind speed predictions to be skillful, although wind biases were negatively correlated with observed speed and temperature biases revealed a robust relationship with station elevation. Above ≈250 m above ground level, below which radiosonde wind data were apparently contaminated by processing, biases were small for wind speed and potential temperature at the analysis time (which incorporates sonde data) but became substantial by the 24-h forecast. Wind biases were positive through the layer for both 0000 and 1200 UTC, and morning potential temperature profiles were marked by excessively steep lapse rates that persisted across seasons and (again) exaggerated at higher elevation sites. While the source or cause of these systematic errors are not fully understood, this analysis highlights areas for potential model improvement and the need for a continued and accessible archive of the data that make analyses like this possible.

Fovell, Robert G.; Gallagher, Alex

2020 Weather and Forecasting

doi: 10.1175/WAF-D-20-0101.1

AbstractWhile numerical weather prediction models have made considerable progress regarding forecast skill, less attention has been paid to the planetary boundary layer. This study leverages High-Resolution Rapid Refresh (HRRR) forecasts on native levels, 1-s radiosonde data, and (primarily airport) surface observations across the conterminous United States. We construct temporally and spatially averaged composites of wind speed and potential temperature in the lowest 1 km for selected months to identify systematic errors in both forecasts and observations in this critical layer. We find near-surface temperature and wind speed predictions to be skillful, although wind biases were negatively correlated with observed speed and temperature biases revealed a robust relationship with station elevation. Above ≈250 m above ground level, below which radiosonde wind data were apparently contaminated by processing, biases were small for wind speed and potential temperature at the analysis time (which incorporates sonde data) but became substantial by the 24-h forecast. Wind biases were positive through the layer for both 0000 and 1200 UTC, and morning potential temperature profiles were marked by excessively steep lapse rates that persisted across seasons and (again) exaggerated at higher elevation sites. While the source or cause of these systematic errors are not fully understood, this analysis highlights areas for potential model improvement and the need for a continued and accessible archive of the data that make analyses like this possible.

Køltzow, Morten; Casati, Barbara; Haiden, Thomas; Valkonen, Teresa

2020 Weather and Forecasting

doi: 10.1175/WAF-D-20-0060.1

AbstractAssessing the quality of precipitation forecasts requires observations, but all precipitation observations have associated uncertainties making it difficult to quantify the true forecast quality. One of the largest uncertainties is due to the wind-induced undercatch of solid precipitation gauge measurements. This study discusses how this impacts the verification of precipitation forecasts for Norway for one global model [the high-resolution version of the ECMWF Integrated Forecasting System (IFS-HRES)], and one high-resolution, limited-area model [Applications of Research to Operations at Mesoscale (MEPS)]. First, the forecasts are compared with high-quality reference measurements (less undercatch) and with more simple measurement equipment commonly available (substantial undercatch) at the Haukeliseter observation site. Then the verification is extended to include all Norwegian observation sites: 1) stratified by wind speed, since calm (windy) conditions experience less (more) undercatch; and 2) by applying transfer functions, which convert measured precipitation to what would have been measured with high-quality equipment with less undercatch, before the forecast–observation comparison is performed. Results show that the wind-induced undercatch of solid precipitation has a substantial impact on verification results. Furthermore, applying transfer functions to adjust for wind-induced undercatch of solid precipitation gives a more realistic picture of true forecast capabilities. In particular, estimates of systematic forecast biases are improved, and to a lesser degree, verification scores like correlation, RMSE, ETS, and stable equitable error in probability space (SEEPS). However, uncertainties associated with applying transfer functions are substantial and need to be taken into account in the verification process. Precipitation forecast verification for liquid and solid precipitation should be done separately whenever possible.

Roberts, Brett; Gallo, Burkely T.; Jirak, Israel L.; Clark, Adam J.; Dowell, David C.; Wang, Xuguang; Wang, Yongming

2020 Weather and Forecasting

doi: 10.1175/WAF-D-20-0069.1

AbstractThe High Resolution Ensemble Forecast v2.1 (HREFv2.1), an operational convection-allowing model (CAM) ensemble, is an “ensemble of opportunity” wherein forecasts from several independently designed deterministic CAMs are aggregated and postprocessed together. Multiple dimensions of diversity in the HREFv2.1 ensemble membership contribute to ensemble spread, including model core, physics parameterization schemes, initial conditions (ICs), and time lagging. In this study, HREFv2.1 forecasts are compared against the High Resolution Rapid Refresh Ensemble (HRRRE) and the Multiscale data Assimilation and Predictability (MAP) ensemble, two experimental CAM ensembles that ran during the 5-week Spring Forecasting Experiment (SFE) in spring 2018. The HRRRE and MAP are formally designed ensembles with spread achieved primarily through perturbed ICs. Verification in this study focuses on composite radar reflectivity and updraft helicity to assess ensemble performance in forecasting convective storms. The HREFv2.1 shows the highest overall skill for these forecasts, matching subjective real-time impressions from SFE participants. Analysis of the skill and variance of ensemble member forecasts suggests that the HREFv2.1 exhibits greater spread and more effectively samples model uncertainty than the HRRRE or MAP. These results imply that to optimize skill in forecasting convective storms at 1–2-day lead times, future CAM ensembles should employ either diverse membership designs or sophisticated perturbation schemes capable of representing model uncertainty with comparable efficacy.

Lynn, Barry H.; Cohen, Seth; Druyan, Leonard; Phillips, Adam S.; Shea, Dennis; Krugliak, Haim-Zvi; Khain, Alexander P.

2020 Weather and Forecasting

doi: 10.1175/WAF-D-19-0154.1

AbstractA large set of deterministic and ensemble forecasts was produced to identify the optimal spacing for forecasting U.S. East Coast snowstorms. WRF forecasts were produced on cloud-allowing (~1-km grid spacing) and convection-allowing (3–4 km) grids, and compared against forecasts with parameterized convection (>~10 km). Performance diagrams were used to evaluate 19 deterministic forecasts from the winter of 2013–14. Ensemble forecasts of five disruptive snowstorms spanning the years 2015–18 were evaluated using various methods to evaluate probabilistic forecasts. While deterministic forecasts using cloud-allowing grids were not better than convection-allowing forecasts, both had lower bias and higher success ratios than forecasts with parameterized convection. All forecasts were underdispersive. Nevertheless, forecasts on the higher-resolution grids were more reliable than those with parameterized convection. Forecasts on the cloud-allowing grid were best able to discriminate areas that received heavy snow and those that did not, while the forecasts with parameterized convection were least able to do so. It is recommended to use convection-resolving and (if computationally possible) to use cloud-allowing forecast grids when predicting East Coast winter storms.

Vendrasco, Eder P.; Machado, Luiz A. T.; Ribeiro, Bruno Z.; Freitas, Edmilson D.; Ferreira, Rute C.; Negri, Renato G.

2020 Weather and Forecasting

doi: 10.1175/WAF-D-20-0017.1

AbstractThis research explores the benefits of radar data assimilation for short-range weather forecasts in southeastern Brazil using the Weather Research and Forecasting (WRF) Model’s three-dimensional variational data assimilation (3DVAR) system. Different data assimilation options are explored, including the cycling frequency, the number of outer loops, and the use of null-echo assimilation. Initially, four microphysics parameterizations are evaluated (Thompson, Morrison, WSM6, and WDM6). The Thompson parameterization produces the best results, while the other parameterizations generally overestimate the precipitation forecast, especially WDSM6. Additionally, the Thompson scheme tends to overestimate snow, while the Morrison scheme overestimates graupel. Regarding the data assimilation options, the results deteriorate and more spurious convection occurs when using a higher cycling frequency (i.e., 30 min instead of 60 min). The use of two outer loops produces worse precipitation forecasts than the use of one outer loop, and the null-echo assimilation is shown to be an effective way to suppress spurious convection. However, in some cases, the null-echo assimilation also removes convective clouds that are not observed by the radar and/or are still not producing rain, but have the potential to grow into an intense convective cloud with heavy rainfall. Finally, a cloud convective mask was implemented using ancillary satellite data to prevent null-echo assimilation from removing potential convective clouds. The mask was demonstrated to be beneficial in some circumstances, but it needs to be carefully evaluated in more cases to have a more robust conclusion regarding its use.

Vogel, Peter; Knippertz, Peter; Fink, Andreas H.; Schlueter, Andreas; Gneiting, Tilmann

2020 Weather and Forecasting

doi: 10.1175/WAF-D-20-0082.1

AbstractPrecipitation forecasts are of large societal value in the tropics. Here, we compare 1–5-day ensemble predictions from the European Centre for Medium-Range Weather Forecasts (ECMWF, 2009–17) and the Meteorological Service of Canada (MSC, 2009–16) over 30°S–30°N with an extended probabilistic climatology based on the Tropical Rainfall Measuring Mission 3 B42 gridded dataset. Both models predict rainfall occurrence better than the reference only over about half of all land points, with a better performance by MSC. After applying the postprocessing technique ensemble model output statistics, this fraction increases to 87% (ECMWF) and 82% (MSC). For rainfall amount there is skill in many tropical areas (about 60% of land points), which can be increased by postprocessing to 97% (ECMWF) and 88% (MSC). Forecasts for extremes (>20 mm) are only marginally worse than those of occurrence but do not improve as much through postprocessing, particularly over dry areas. Forecast performance is generally best over arid Australia and worst over oceanic deserts, the Andes and Himalayas, as well as over tropical Africa, where models misrepresent the high degree of convective organization, such that even postprocessed forecasts are hardly better than climatology. Skill of 5-day accumulated forecasts often exceeds that of shorter ranges, as timing errors matter less. An increase in resolution and major model update in 2010 has significantly improved ECMWF predictions. Especially over tropical Africa new techniques such as convection-permitting models or combined statistical-dynamical forecasts may be needed to generate skill beyond the climatological reference.