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Paul Gregory, Frederic Vitart, Rabi Rivett, Andrew Brown, and Yuriy Kuleshov

Abstract

Subseasonal tropical cyclone forecasts from two operational forecast models are verified for the 2017/18 and 2018/19 Southern Hemisphere cyclone seasons. The forecasts are generated using the ECMWF’s Medium- and Extended-Range Ensemble Integrated Forecasting System (IFS), and the Bureau of Meteorology’s seasonal forecasting system ACCESS-S1. Results show the IFS is more skillful than ACCESS-S1, which is attributed to the IFS’s greater ensemble size, increased spatial resolution, and data assimilation schemes. Applying a lagged ensemble with ACCESS-S1 increases forecast reliability, with the optimum number of lagged members being dependent on forecast lead time. To investigate the impacts of atmospheric assimilation at shorter lead times, comparisons were made between the Bureau of Meteorology’s ACCESS-S1 and ACCESS-GE2 systems, the latter a global Numerical Weather Prediction system running with the same resolution and model physics as ACCESS-S1 but using an ensemble Kalman filter for data assimilation. This comparison showed the data assimilation scheme used in the GE2 system gave improvements in forecast skill for days 8–10, despite the smaller ensemble size used in GE2 (24 members per forecast compared to 33). Finally, a multimodel ensemble was created by combining forecasts from both the IFS and ACCESS-S1. Using the multimodel ensemble gave improvements in forecast skill and reliability. This improvement is attributed to complementary spatial errors in both systems occurring across much of the Southern Hemisphere as well as an increase in the ensemble size.

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Synoptic-Scale Environments and Precipitation Morphologies of Tornado Outbreaks from Quasi-Linear Convective Systems in the United Kingdom

Ty J. Buckingham and David M. Schultz

Abstract

Nine tornado outbreaks (days with three or more tornadoes) have occurred in the United Kingdom from quasi-linear convective systems (QLCSs) in the 16 years between 2004 and 2019. Of the nine outbreaks, eight can be classified into two synoptic categories: type 1 and type 2. Synoptic categories are derived from the location of the parent extratropical cyclone and the orientation of the surface front associated with the QLCS. Environmental differences between the categories are assessed using ERA5 reanalysis data. Type 1 events are characterized by a confluent 500-hPa trough from the west, meridional cold front, strong cross-frontal wind veer (about 90°), cross-frontal temperature decrease of 2°–4°C, prefrontal 2-m dewpoint temperatures of 12°–14°C, a prefrontal low-level jet, and prefrontal 0–1- and 0–3-km bulk shears of 15 and 25 m s−1, respectively. In contrast, type 2 events are characterized by a diffluent 500-hPa trough from the northwest, zonal front, weaker cross-frontal wind veer (≤45°), much smaller cross-frontal temperature decrease, lower prefrontal 2-m dewpoint temperatures of 6°–10°C, and weaker prefrontal 0–1- and 0–3-km bulk shears of 10 and 15 m s−1, respectively. Analysis of the Met Office radar reflectivity mosaics revealed that narrow cold-frontal rainbands developed in all type 1 events and subsequently displayed precipitation core-and-gap structures. Conversely, type 2 events did not develop narrow cold-frontal rainbands, although precipitation cores developed sporadically within the wide cold-frontal rainband. Type 1 events produced tornadoes 2–4 h after core-and-gap development, whereas type 2 events produced tornadoes within 1 h of forming cores and gaps. All events produced tornadoes during a relatively short time period (1–3 h).

Open access
Lacey Holland, Steven Businger, Tamar Elias, and Tiziana Cherubini

Abstract

Kīlauea volcano, located on the island of Hawaii, is one of the most active volcanoes in the world. It was in a state of nearly continuous eruption from 1983 to 2018 with copious emissions of sulfur dioxide (SO2) that affected public health, agriculture, and infrastructure over large portions of the island. Since 2010, the University of Hawaiʻi at Mānoa provides publicly available vog forecasts that began in 2010 to aid in the mitigation of volcanic smog (or “vog”) as a hazard. In September 2017, the forecast system began to produce operational ensemble forecasts. The months that preceded Kīlauea’s historic lower east rift zone eruption of 2018 provide an opportunity to evaluate the newly implemented air quality ensemble prediction system and compare it another approach to the generation of ensemble members. One of the two approaches generates perturbations in the wind field while the other perturbs the sulfur dioxide (SO2) emission rate from the volcano. This comparison has implications for the limits of forecast predictability under the particularly dynamic conditions at Kīlauea volcano. We show that for ensemble forecasts of SO2 generated under these conditions, the uncertainty associated with the SO2 emission rate approaches that of the uncertainty in the wind field. However, the inclusion of a fluctuating SO2 emission rate has the potential to improve the prediction of the changes in air quality downwind of the volcano with suitable postprocessing.

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Greema Regmi, Sajan Shrestha, Sangeeta Maharjan, Anil Kumar Khadka, Ram Prasad Regmi, and Gopi Chandra Kaphle

Abstract

Safe flights over the Tribhuvan International Airport (TIA), Kathmandu, Nepal, remain a considerable challenge. Since the airport opened, there have been 13 aircraft accidents during landings and takeoffs that have claimed 392 lives. A detailed understanding and dependable forecast of atmospheric conditions that may develop over the complex terrain of the midhills of central Nepal Himalaya are yet to be achieved. The present study discusses the near-surface atmospheric conditions possibly associated with the most recent fatal crash at TIA on 12 March 2018 as revealed by the Advanced Research version of the Weather Research and Forecasting (WRF-ARW) Model routine forecast. At the time of the accident, two prominent gap winds were converging in the valley, thereby, forming a crosswind and a mix of strong up- and downdrafts over the airfield. As a result, the near-surface atmosphere was significantly turbulent. Unexpected encounters with such turbulent winds are a likely contributor to the fatal crash. This indicates that the knowledge of near-surface atmospheric conditions, critically needed by pilots in advance, for safe operations over the airfield may be generated with WRF-ARW forecasts.

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Tran Tan Tien, Dao Nguyen-Quynh Hoa, Cong Thanh, and Chanh Kieu

Abstract

In this study, the impacts of different augmented observations on forecasts of Typhoon Wutip’s (2013) formation are examined. Using the local ensemble transformed Kalman filter (LETKF) implemented for the Weather Research and Forecasting (WRF) Model, it is found that the prediction of Wutip’s formation location and timing is strongly governed by the strength of a monsoon trough that extends from the Bay of Bengal to the Philippine Sea. By properly capturing the strength of the monsoon trough after assimilating augmented observations available during Wutip’s early stage, the WRF Model could provide better forecasts of Wutip’s formation location and timing as compared to the forecasts initialized directly from global model analyses. Among different types of augmented observations, the satellite atmospheric motion wind vector (AMV) maintained by the Cooperative Institute for Meteorological Satellite Studies (CIMSS) appears to be the most critical in enhancing the large-scale representation of the monsoon trough. The benefit of augmented observations in Wutip’s formation forecast is most apparent at about 36 h prior to the observed formation time. At the shorter 24-h lead time, there is, however, no clear benefit of augmented observations in predicting the timing and the location of Wutip’s formation due to better global analyses. The results obtained in this study demonstrate the vital role of the CIMSS-AMV data in improving the large-scale environment required for TC formation that one should take into account for real-time TC forecasts.

Free access
Ahreum Lee, Byung-Ju Sohn, Ed Pavelin, Yoonjae Kim, Hyun-Suk Kang, Roger Saunders, and Young-Chan Noh

Abstract

The Unified Model (UM) data assimilation system incorporates a 1D-Var analysis of cloud variables for assimilating hyperspectral infrared radiances. For the Infrared Atmospheric Sounding Interferometer (IASI) radiance assimilation, a first guess of cloud top pressure (CTP) and cloud fraction (CF) is estimated using the minimum residual (MR) method, which simultaneously obtains CTP and CF by minimizing radiance difference between observation and model simulation. In this study, we examined how those MR-based cloud retrievals behave, using “optimum” CTP and CF that yield the best 1D-Var analysis results. It is noted that the MR method tends to overestimate cloud top height while underestimating cloud fraction, compared to the optimum results, necessitating an improved cloud retrieval. An artificial neural network (ANN) approach was taken to estimate CTP as close as possible to the optimum value, based on the hypothesis that CTP and CF closer to the optimum values will bring in better 1D-Var results. The ANN-based cloud retrievals indicated that CTP and CF biases shown in the MR method are much reduced, giving better 1D-Var analysis results. Furthermore, the computational time can be substantially reduced by the ANN method, compared to the MR method. The evaluation of the ANN method in a global weather forecasting system demonstrated that it helps to use more temperature channels in the assimilation, although its impact on UM forecasts was found to be near neutral. It is suggested that the neutral impact may be improved when error covariances for the cloudy sky are employed in the UM assimilation system.

Open access
Hai Lin, William J. Merryfield, Ryan Muncaster, Gregory C. Smith, Marko Markovic, Frédéric Dupont, François Roy, Jean-François Lemieux, Arlan Dirkson, Viatcheslav V. Kharin, Woo-Sung Lee, Martin Charron, and Amin Erfani

Abstract

The second version of the Canadian Seasonal to Interannual Prediction System (CanSIPSv2) was implemented operationally at Environment and Climate Change Canada (ECCC) in July 2019. Like its predecessors, CanSIPSv2 applies a multimodel ensemble approach with two coupled atmosphere–ocean models, CanCM4i and GEM-NEMO. While CanCM4i is a climate model, which is upgraded from CanCM4 of the previous CanSIPSv1 with improved sea ice initialization, GEM-NEMO is a newly developed numerical weather prediction (NWP)-based global atmosphere–ocean coupled model. In this paper, CanSIPSv2 is introduced, and its performance is assessed based on the reforecast of 30 years from 1981 to 2010, with 10 ensemble members of 12-month integrations for each model. Ensemble seasonal forecast skill of 2-m air temperature, 500-hPa geopotential height, precipitation rate, sea surface temperature, and sea ice concentration is assessed. Verification is also performed for the Niño-3.4, the Pacific–North American pattern (PNA), the North Atlantic Oscillation (NAO), and the Madden–Julian oscillation (MJO) indices. It is found that CanSIPSv2 outperforms the previous CanSIPSv1 system in many aspects. Atmospheric teleconnections associated with the El Niño–Southern Oscillation (ENSO) are reasonably well captured by the two CanSIPSv2 models, and a large part of the seasonal forecast skill in boreal winter can be attributed to the ENSO impact. The two models are also able to simulate the Northern Hemisphere teleconnection associated with the tropical MJO, which likely provides another source of skill on the subseasonal to seasonal time scale.

Open access
Christopher S. Velden and Derrick Herndon

ABSTRACT

A consensus-based algorithm for estimating the current intensity of global tropical cyclones (TCs) from meteorological satellites is described. The method objectively combines intensity estimates from infrared and microwave-based techniques to produce a consensus TC intensity estimate, which is more skillful than the individual members. The method, called Satellite Consensus (SATCON), can be run in near–real time and employs information sharing between member algorithms and a weighting strategy that relies on the situational precision of each member. An evaluation of the consensus algorithm’s performance in comparison with its individual members and other available operational estimates of TC intensity is presented. It is shown that SATCON can provide valuable objective intensity estimates for poststorm assessments, especially in the absence of other data such as provided by reconnaissance aircraft. It can also serve as a near-real-time estimator of TC intensity for forecasters, with the ability to quickly reconcile differences in objective intensity methods and thus decrease the uncertainty and amount of time spent on the intensity analysis. Near-real-time SATCON estimates are being provided to global operational TC forecast centers.

Open access
Samuel J. Sangster and Christopher W. Landsea

Abstract

The Dvorak technique is used operationally by meteorological agencies throughout the world for estimating tropical cyclone intensity and position. The technique consists of constraints that put a maximum threshold for which the final T-number, relating directly to intensity, can change during a certain time interval (6, 12, 18, and 24 h). There are cases when these constraints could be broken, especially during rapid intensification. This research tests whether the constraints used for intensity change are warranted or need to be changed. A database of cases with the largest intensity changes for 2000–17 Atlantic tropical cyclones was compiled. A reconnaissance or scatterometer “fix” is required within 3 h of both the beginning and ending of the period for each case to inform the best track and to be included for analysis. Dvorak classifications from the Tropical Analysis and Forecast Branch are noted for each case, which includes the initial and final T-numbers, current intensity numbers, and data T-numbers. Statistical parameters, including correlations, intensity errors, absolute intensity errors, root-mean-square errors, and significance tests are calculated and analyzed for each period. Results suggest that the T-number constraints for the 18- and 24-h periods could be increased to a 2.5 and a 3.0, respectively. However, results also suggest that the constraints for the 6- and 12-h time intervals should remain the same.

Free access
Christophe Lavaysse, Tim Stockdale, Niall McCormick, and Jürgen Vogt

Abstract

This paper describes the assessment of the performance of a method for providing early warnings of unusually wet and dry precipitation conditions globally. The indicator that is used for forecasting these conditions is computed from forecasted standardized precipitation index (SPI) values for accumulation periods of 1, 3, and 6 months. The SPI forecasts are derived from forecasted precipitation produced by the latest probabilistic seasonal forecast of ECMWF. Early warnings of unusual precipitation periods are shown only when and where the forecast is considered robust (i.e., with at least 40% of ensemble members associated with intense forecasts), and corresponding with significant SPI values (i.e., below −1 for dry, or above +1 for wet conditions). The intensity of the forecasted events is derived based on the extreme forecast index and associated shift of tails products developed by ECMWF. Different warning levels are then assessed, depending on the return period of the forecast intensity, and the coherence of the ensemble forecast members. The assessment of the indicators performance is based on the 25-member ensemble forecast system that is carried out every month during the 36 years of the hindcast period (1981–2016). The results show that significant information is provided even for the longest lead time, albeit with a large variability across the globe with the highest scores over central Russia, Southeast Asia, and the northern part of South America or Australia. Because of the loss of predictability, each SPI is based on the first lead time. A sensitivity test highlights the influence on the robustness of the forecasts of the warning levels used, as well as the effects of prior conditions and of seasonality.

Open access