Synoptic-Scale and Mesoscale Contributions to Objective Operational Maximum-Minimum Temperature Forecast Errors

Baker, Dennis G.

doi:10.1175/1520-0493(1982)110<0163:ssamct>2.0.co;2

Cited by 6 publications

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“…In the initial 1980s (Kim et al, 1984), statistical downscaling was mentioned to as 'Statistical Problem of Climate Inversion'. However, a same technique, called 'Model Output Statistics' (MOS) has been used in numerical weather forecasting since the early 1970s (Baker, 1982). One reason why downscaling is a comparatively young science is that it depends on the presence of global climate models, which themselves denote recent advances in the climate science community.…”

Section: Introductionmentioning

confidence: 99%

Future Projection of Mean Temperature of Post-Monsoon Season Over Bangladesh Using Statistical Downscaling of Global Climate Models

Rashid¹,

Hossain

2020

J. of Eng. Sci.

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In statistical downscaling technique, regional or local information are derived by determining a statistical model which relates large-scale climate variables or predictors generated by Global Climate Models (GCMs) to regional and local variables or predictands. In this paper, the results of GCMs were statistically downscaled to produce future climate projections of mean temperature in the post-monsoon season (October and November), for the time periods 2021-2050 and 2071-2100 for Bangladesh. The future climate projections are based on the three emission scenarios RCP2.6, RCP4.5 and RCP8.5 provided by the fifth Coupled Model Intercomparison Project (CMIP5). This paper established a method to analyze GCMs for use in statistical downscaling and utilized fifteen GCMs. The GCMs were assessed based upon their performance in simulated past climate in Bangladesh and adjoining areas. Downscaling was undertaken by linking large scale climate variables, taken from the ERA-Interim (resolution 79 km) reanalysis temperature, a gridded data set incorporating observations and climate models, to local scale observations. Overall, all fifteen GCMs, via statistical downscaling, show that mean temperature of the post-monsoon season in Bangladesh will increase under future climate scenarios. Comparing the ensemble of future projections with the reference period (1981- 2010), the mean post-monsoon temperature in Bangladesh is projected for RCP8.5 showing warming by 0.310C in near future and 1.790C in far future. On the other hand, estimated warming is 0.390C in near future and 1.140C is far future for RCP4.5. Low emission scenarios RCP2.6, near future temperature is nearly same the far future temperature. Journal of Engineering Science 11(2), 2020, 27-35

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Section: Introductionmentioning

confidence: 99%

Future Projection of Mean Temperature of Post-Monsoon Season Over Bangladesh Using Statistical Downscaling of Global Climate Models

Rashid¹,

Hossain

2020

J. of Eng. Sci.

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“…Since the indication from the first six years of the Massachusetts Institute of Technology (MIT) forecasting contest that the state of the art of short-range-forecast skill was not advancing rapidly (Sanders, 1973), there has been considerable interest in this and related issues, in the real world (e.g., Pierce, 1976;Ramage, 1976Ramage, , 1982Zurndorfer et al, 1979;Charba and Klein, 1980;Glahn, 1985;Gordon, 1985) as well as on the academic scene (Bosart, 1975(Bosart, , 1983Gedzelman, 1978;Firestone, 1979;Scanlon and Anawalt, 1980;Baker, 1982). Six years have passed since the last report on the state of the MIT contest, and it is appropriate to report whether the modest trends found at that time (Sanders, 1979) have continued.…”

Section: Introductionmentioning

confidence: 99%

focus on forecasting1

1986

Bulletin of the American Meteorological Society

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A forecast experiment was conducted from 1 June to 12 August 1983 at the Program for Regional Observing and Forecasting Services (PROFS) in Boulder, Colorado. The exercise included probabilistic forecasts of severe-weather convective outlooks prepared with the use of existing technology and severe-storm warnings made with the use of advanced technology to display a wide variety of data sets on the PROFS workstation. The convective outlooks were evaluated using signal-detection-theory analyses and Brier scores. Considerable skill using signal-detection theory was found for both severe-thunderstorm and tornado outlooks, treated as separate outlooks. On the other hand, Brier skill scores relative to sample climatology show small skill for the thunderstorm outlooks and no skill for the tornado outlooks. The signal-detection analyses provide valuable baseline values for future evaluation studies.Skill scores for the warnings issued at PROFS are compared with a similar set of skill scores for Warnings from the Denver WSFO (Weather Service Forecast Office). Considerably higher skill is found in PROFS severe-thunderstorm-warning scores. This result is attributed to advanced technology and the controlled environment in the PROFS forecast office. For tornado warnings, the probability of detection was slightly higher and the false-alarm rate was much lower at the Denver WSFO than at PROFS. The high false-alarm rate at PROFS is ascribed to the lack of familiarity with the new Doppler-radar data, the overreaction to those data, and the incompleteness of the radar data set. An analysis of area-and time-weighted skill scores is also presented to illustrate the concept that frequently issued time-and site-specific severe-storm warnings have a marked potential for improved service to the public.forecasts of severe weather (MacDonald, 1984). The summer forecast exercise included the following objectives: 1) to test a new version of the forecasting workstation, upgraded since the 1982 system reported by Reynolds (1983); 2) to obtain expert opinion on the scientific and technological obstacles to further progress in convective forecasting; 3) to develop procedures for evaluating progress in nowcasting and very-short-range forecasting of convection as these terms are defined by Zipser (1985). Three types of forecasts were prepared daily: 1) a convective-weather outlook for probabilities of tornadoes and severe thunderstorms; 2) warnings for severe thunderstorms, tornadoes, and flash floods as necessary; and 3) scheduled forecasts of significant weather probabilities for precipitation, wind, and visibility. Williams and McCoy (1984) have presented a preliminary report on evaluation of the probability forecasts. Schlatter et al. (1985) discuss topics related to the first two objectives of the 1983 exercise listed above.This paper presents an evaluation of the daily severeweather convective outlook forecasts and the severe-weather warnings (nowcasts to short-term forecasts) prepared during the experiment.

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Regional climate downscaling

Fowler

Wilby

2010

Modelling the Impact of Climate Change on Water Resources

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Synoptic-Scale and Mesoscale Contributions to Objective Operational Maximum-Minimum Temperature Forecast Errors

Cited by 6 publications

References 0 publications

Future Projection of Mean Temperature of Post-Monsoon Season Over Bangladesh Using Statistical Downscaling of Global Climate Models

Future Projection of Mean Temperature of Post-Monsoon Season Over Bangladesh Using Statistical Downscaling of Global Climate Models

focus on forecasting1

Regional climate downscaling

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