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Akshay Deoras, Kieran M. R. Hunt, and Andrew G. Turner

Abstract

This study analyzes the prediction of Indian monsoon low pressure systems (LPSs) on an extended time scale of 15 days by models of the Subseasonal-to-Seasonal (S2S) prediction project. Using a feature-tracking algorithm, LPSs are identified in 11 S2S models during a common reforecast period of June–September 1999–2010, and then compared with 290 and 281 LPSs tracked in ERA-Interim and MERRA-2 reanalysis datasets. The results show that all S2S models underestimate the frequency of LPSs. They are able to represent transits, genesis, and lysis of LPSs; however, large biases are observed in the Australian Bureau of Meteorology, China Meteorological Administration (CMA), and Hydrometeorological Centre of Russia (HMCR) models. The CMA model exhibits large LPS track position error and the intensity of LPSs is overestimated (underestimated) by most models when verified against ERA-Interim (MERRA-2). The European Centre for Medium-Range Weather Forecasts and Met Office models have the best ensemble spread–error relationship for the track position and intensity, whereas the HMCR model has the worst. Most S2S models are underdispersive—more so for the intensity than the position. We find the influence of errors in the LPS simulation on the pattern of total precipitation biases in all S2S models. In most models, precipitation biases increase with forecast lead time over most of the monsoon core zone. These results demonstrate the potential for S2S models at simulating LPSs, thereby giving the possibility of improved disaster preparedness and water resources planning.

Open access
Zhaolu Hou, Jianping Li, and Bin Zuo

Abstract

Numerical seasonal forecasts in Earth science always contain forecast errors that cannot be eliminated by improving the ability of the numerical model. Therefore, correction of model forecast results is required. Analog correction is an effective way to reduce model forecast errors, but the key question is how to locate analogs. In this paper, we updated the local dynamical analog (LDA) algorithm to find analogs and depicted the process of model error correction as the LDA correction scheme. The LDA correction scheme was first applied to correct the operational seasonal forecasts of sea surface temperature (SST) over the period 1982–2018 from the state-of-the-art coupled climate model named NCEP Climate Forecast System, version 2. The results demonstrated that the LDA correction scheme improves forecast skill in many regions as measured by the correlation coefficient and root-mean-square error, especially over the extratropical eastern Pacific and tropical Pacific, where the model has high simulation ability. El Niño–Southern Oscillation (ENSO) as the focused physics process is also improved. The seasonal predictability barrier of ENSO is in remission, and the forecast skill of central Pacific ENSO also increases due to the LDA correction method. The intensity of the ENSO mature phases is improved. Meanwhile, the ensemble forecast results are corrected, which proves the positive influence from this LDA correction scheme on the probability forecast of cold and warm events. Overall, the LDA correction scheme, combining statistical and model dynamical information, is demonstrated to be readily integrable with other advanced operational models and has the capability to improve forecast results.

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Brandon R. Smith, Thea Sandmæl, Matthew C. Mahalik, Kimberly L. Elmore, Darrel M. Kingfield, Kiel L. Ortega, and Travis M. Smith
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Benjamin A. Schenkel, Roger Edwards, and Michael Coniglio
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Toshichika Iizumi, Yonghee Shin, Jaewon Choi, Marijn van der Velde, Luigi Nisini, Wonsik Kim, and Kwang-Hyung Kim

Abstract

Forecasting global food production is of growing importance in the context of globalizing food supply chains and observed increases in the frequency of climate extremes. The National Agriculture and Food Research Organization–Asia-Pacific Economic Cooperation Climate Center (NARO-APCC) Crop Forecasting Service provides yield forecasts for global cropland on a monthly basis using seasonal temperature and precipitation forecasts as the main inputs, and 1 year of testing the operation of the service was recently completed. Here we evaluate the forecasts for the 2019 yields of major commodity crops by comparing with the reported yields and forecasts from the European Commission’s Joint Research Centre (JRC) and the U.S. Department of Agriculture (USDA). Forecasts for maize, wheat, soybean, and rice were evaluated for 20 countries located in the Northern Hemisphere, including 39 crop-producing states in the United States, for which 2019 reported yields were already publicly available. The NARO-APCC forecasts are available several months earlier than the JRC and USDA forecasts. The skill of the NARO-APCC forecasts was good in absolute terms, but the forecast errors in the NARO-APCC forecasts were almost always larger than those of the JRC and USDA forecasts. The forecast errors in the JRC and USDA forecasts decreased as the harvest approached, whereas those in the NARO-APCC forecasts were rather stable over the season, with some exceptions. Although this feature seems to be a disadvantage, it may turn into an advantage if skillful forecasts are achievable in the earlier stages of a season. We conclude by discussing relative advantages and disadvantages and potential ways to improve global yield forecasting.

Open access
Cui Liu, Jianhua Sun, Xinlin Yang, Shuanglong Jin, and Shenming Fu

Abstract

Precipitation forecasts from the ECMWF model from March to September during 2015–18 were evaluated using observed precipitation at 2411 stations from the China Meteorological Administration. To eliminate the influence of varying climatology in different regions in China, the stable equitable error in probability space method was used to obtain criteria for 3- and 6-h accumulated precipitation at each station and classified precipitation into light, medium, and heavy precipitation. The model was evaluated for these categories using categorical and continuous methods. The threat score and the equitable threat score showed that the model’s forecasts of rainfall were generally more accurate at shorter lead times, and the best performance occurred in the middle and lower reaches of the Yangtze River basin. The miss ratio for heavy precipitation was higher in the northern region than in the southern region, while heavy precipitation false alarms were more frequent in southwestern China. Overall, the miss ratio and false alarm ratio for heavy precipitation were highest in northern China and western China, respectively. For light and medium precipitation, the model performed best in the middle and lower reaches of the Yangtze River basin. The model predicted too much light and medium precipitation, but too little heavy precipitation. Heavy precipitation was generally underestimated over all of China, especially in the western region of China, South China, and the Yungui Plateau. Heavy precipitation was systematically underestimated because of the resolution and the related parameterization of convection.

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Matthew T. Bray, David D. Turner, and Gijs de Boer

Abstract

Despite a need for accurate weather forecasts for societal and economic interests in the U.S. Arctic, thorough evaluations of operational numerical weather prediction in the region have been limited. In particular, the Rapid Refresh Model (RAP), which plays a key role in short-term forecasting and decision-making, has seen very limited assessment in northern Alaska, with most evaluation efforts focused on lower latitudes. In the present study, we verify forecasts from version 4 of the RAP against radiosonde, surface meteorological, and radiative flux observations from two Arctic sites on the northern Alaskan coastline, with a focus on boundary layer thermodynamic and dynamic biases, model representation of surface inversions, and cloud characteristics. We find persistent seasonal thermodynamic biases near the surface that vary with wind direction, and may be related to the RAP’s handling of sea ice and ocean interactions. These biases seem to have diminished in the latest version of the RAP (version 5), which includes refined handling of sea ice, among other improvements. In addition, we find that despite capturing boundary layer temperature profiles well overall, the RAP struggles to consistently represent strong, shallow surface inversions. Further, while the RAP seems to forecast the presence of clouds accurately in most cases, there are errors in the simulated characteristics of these clouds, which we hypothesize may be related to the RAP’s treatment of mixed-phase clouds.

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Matthew D. Flournoy, Michael C. Coniglio, and Erik N. Rasmussen

Abstract

Although environmental controls on bulk supercell potential and hazards have been studied extensively, relationships between environmental conditions and temporal changes to storm morphology remain less explored. These relationships are examined in this study using a compilation of sounding data collected during field campaigns from 1994 to 2019 in the vicinity of 216 supercells. Environmental parameters are calculated from the soundings and related to storm-track characteristics like initial cell motion and the time of the right turn (i.e., the time elapsed between the cell initiation and the first time when the supercell obtains a quasi-steady motion that is directed clockwise from its initial motion.). We do not find any significant associations between environmental parameters and the time of the right turn. Somewhat surprisingly, no relationship is found between storm-relative environmental helicity and the time elapsed between cell initiation and the onset of deviant motion. Initial cell motion is best approximated by the direction of the 0–6-km mean wind at two-thirds the speed. This is a result of advection and propagation in the 0–4- and 0–2-km layers, respectively. Unsurprisingly, Bunkers-right storm motion is a good estimate of post-turn motion, but storms that exhibit a post-turn motion left of Bunkers-right are less likely to be tornadic. These findings are relevant for real-time forecasting efforts in predicting the path and tornado potential of supercells up to hours in advance.

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Seth P. Howard, Kim E. Klockow-McClain, Alison P. Boehmer, and Kevin M. Simmons

Abstract

Tornadoes cause billions of dollars in damage and over 100 fatalities on average annually. Yet, an indirect cost to these storms is found in lost sales and/or lost productivity from responding to over 2000 warnings per year. This project responds to the Weather Research and Forecasting Innovation Act of 2017, H.R. 353, which calls for the use of social and behavioral science to study and improve storm warning systems. Our goal is to provide an analysis of cost avoidance that could accrue from a change to the warning paradigm, particularly to include probabilistic hazard information at storm scales. A survey of nearly 500 firms was conducted in and near the Dallas–Fort Worth metropolitan area asking questions about experience with tornadoes, sources of information for severe weather, expected cost of responding to tornado warnings, and how the firm would respond to either deterministic or probabilistic warnings. We find a dramatic change from deterministic warnings compared to the proposed probabilistic and that a probabilistic information system produces annual cost avoidance in a range of $2.3–$7.6 billion (U.S. dollars) compared to the current deterministic warning paradigm.

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Shu-Ya Chen, Cheng-Peng Shih, Ching-Yuang Huang, and Wen-Hsin Teng

Abstract

Conventional soundings are rather limited over the western North Pacific and can be largely compensated by GNSS radio occultation (RO) data. We utilize the GSI hybrid assimilation system to assimilate RO data and the multiresolution global model (MPAS) to investigate the RO data impact on prediction of Typhoon Nepartak that passed over southern Taiwan in 2016. In this study, the performances of assimilation with local RO refractivity and bending angle operators are compared for the assimilation analysis and typhoon forecast. Assimilations with both RO data have shown similar and comparable temperature and moisture increments after cycling assimilation and largely reduce the RMSEs of the forecast without RO data assimilation at later times. The forecast results at 60–15-km resolution show that RO data assimilation largely improves the typhoon track prediction compared to that without RO data assimilation, and assimilation with bending angle has better performances than assimilation with refractivity, in particular for wind forecast. The improvement in the forecasted track is mainly due to the improved simulation for the translation of the typhoon. Diagnostics of wavenumber-1 potential vorticity (PV) tendency budget indicates that the northwestward typhoon translation dominated by PV horizontal advection is slowed down by the southward tendency induced by the stronger differential diabatic heating south of the typhoon center for bending-angle assimilation. Simulations with the enhanced resolution of 3 km in the region of the storm track show further improvements in both typhoon track and intensity prediction with RO data assimilation. Positive RO impacts on track prediction are also illustrated for two other typhoons using the MPAS-GSI system.

Open access