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

1982 ◽  
Vol 110 (3) ◽  
pp. 163-169 ◽  
Author(s):  
Dennis G. Baker
Keyword(s):  
Minimum Temperature ◽  
Forecast Errors ◽  
Synoptic Scale ◽  
Temperature Forecast ◽  
Maximum Minimum

scholarly journals Seeking Consensus: A New Approach

2010 ◽  
Vol 138 (12) ◽  
pp. 4402-4415 ◽  
Author(s):  
Paul J. Roebber
Keyword(s):  
Minimum Temperature ◽  
Forecast Accuracy ◽  
Cost Savings ◽  
Absolute Error ◽  
Electricity Production ◽  
Forecast Errors ◽  
Consensus Forecasts ◽  
Temperature Forecast ◽  
Model Output Statistics ◽  
The Individual

Abstract Simulated evolution is used to generate consensus forecasts of next-day minimum temperature for a site in Ohio. The evolved forecast algorithm logic is interpretable in terms of physics that might be accounted for by experienced forecasters, but the logic of the individual algorithms that form the consensus is unique. As a result, evolved program consensus forecasts produce substantial increases in forecast accuracy relative to forecast benchmarks such as model output statistics (MOS) and those from the National Weather Service (NWS). The best consensus produces a mean absolute error (MAE) of 2.98°F on an independent test dataset, representing a 27% improvement relative to MOS. These results translate to potential annual cost savings for electricity production in the state of Ohio of the order of $2 million relative to the NWS forecasts. Perfect forecasts provide nearly $6 million in additional annual electricity production cost savings relative to the evolved program consensus. The frequency of outlier events (forecast busts) falls from 24% using NWS to 16% using the evolved program consensus. Information on when busts are most likely can be provided through a logistic regression equation with two variables: forecast wind speed and the deviation of the NWS minimum temperature forecast from persistence. A forecast of a bust is 4 times more likely to be correct than wrong, suggesting some utility in anticipating the most egregious forecast errors. Discussion concerning the probabilistic applications of evolved programs, the application of this technique to other forecast problems, and the relevance of these findings to the future role of human forecasting is provided.

Research on Error Correction and Integration Methods of Maximum and Minimum Temperature Forecast Based on Multi –model in arid area

2020 ◽  
Author(s):  
Jia lihong
Keyword(s):  
Error Correction ◽  
Minimum Temperature ◽  
Average Method ◽  
Arid Area ◽  
Ensemble Average ◽  
Maximum Temperature ◽  
Forecast Errors ◽  
Temperature Prediction ◽  
Temperature Forecast ◽  
Integration Schemes

<p>It is very difficult to predict accurate temperature, especially for maximum and minimum temperature, due to the large diurnal temperature range in arid area. Based on the temperature forecast products from ECMWF, T639, DOGRAFS and GRAPES models and hourly temperature observations at 105 automatic weather stations in Xinjiang during 2013~2015, two kinds of error correction and integration schemes were designed by using the decaying averaging method, ensemble average and weighted ensemble average method, the effects of error correction and integration on predicted maximum and minimum temperature in fore seasons in different partitions Xinjiang were tested contrastively. The first scheme was integrating forecast temperature before correcting errors, while the second scheme was correcting forecast errors firstly and then giving an integration. The results are follows as: (1)The accuracy of temperature predictions from ECMWF model was the best in Xinjiang as a whole, while that from DOGRAFS model was the worst, and the improvement to minimum temperature predictions was higher than that of maximum temperature prediction. (2) With regarding to different partitions Xinjiang, the accuracies of predicted maximum and minimum temperature in northern Xinjiang, west region and plain areas were correspondingly higher than those in southern Xinjiang, east region and mountain areas, and the correction capability to temperature prediction in winter was higher than that in other seasons. (3) The integrated prediction of maximum and minimum temperature by weighted ensemble average method was better than that of ensemble average method. The second scheme was superior to the first scheme. (4) The improvement to maximum(minimum) temperature prediction in the extreme high(low) temperature event process from 13 to 30 July 2017(from 22 to 24 April 2014) in Xinjiang was significant by using the second scheme.</p>

scholarly journals Surface high-resolution temperature forecast in southern Italy

2011 ◽  
Vol 6 (1) ◽  
pp. 211-217
Author(s):  
S. Federico ◽  
E. Avolio ◽  
F. Fusto ◽  
R. Niccoli ◽  
C. Bellecci
Keyword(s):  
Southern Italy ◽  
Background Field ◽  
Horizontal Resolution ◽  
Optimal Interpolation ◽  
Maximum Temperature ◽  
Forecast Errors ◽  
Field Observations ◽  
Forecast Performance ◽  
Temperature Forecast ◽  
Analysis System

Abstract. Since June 2008, 1-h temperature forecasts for the Calabria region (Southern Italy) are issued at 2.5 km horizontal resolution at CRATI/ISAC-CNR. Forecasts are available online at http://meteo.crati.it/previsioni.html (every 6-h). This paper shows the forecast performance out to three days for one climatological year (from 1 December 2008 to 30 November 2009, 365 run) for minimum, mean and maximum temperature. The forecast is evaluated against gridded analyses at the same horizontal resolution. Gridded analysis is based on Optimal Interpolation (OI) and uses a de-trending technique for computing the background field. Observations from 87 thermometers are used in the analysis system. In this paper cumulative statistics are shown to quantify forecast errors out to three days.

New NGM-Based MOS Guidance for Maximum/Minimum Temperature, Probability of Precipitation, Cloud Amount, and Surface Wind

1990 ◽  
Vol 5 (1) ◽  
pp. 128-138 ◽  
Author(s):  
Eli Jacks ◽  
J. Brent Bower ◽  
Valery J. Dagostaro ◽  
J. Paul Dallavalle ◽  
Mary C. Erickson ◽  
...  
Keyword(s):  
Minimum Temperature ◽  
Surface Wind ◽  
Cloud Amount ◽  
Probability Of Precipitation ◽  
Maximum Minimum

scholarly journals Ensemble Sensitivity Analysis Applied to a Southern Plains Convective Event

2015 ◽  
Vol 143 (1) ◽  
pp. 230-249 ◽  
Author(s):  
Christopher N. Bednarczyk ◽  
Brian C. Ancell
Keyword(s):  
Sensitivity Analysis ◽  
High Resolution ◽  
Wrf Model ◽  
Region Of Interest ◽  
Thermodynamic Characteristics ◽  
Forecast Errors ◽  
Synoptic Scale ◽  
Southern Plains ◽  
Synoptic Features ◽  
Upper Level

Abstract Forecast sensitivity of an April 2012 severe convection event in northern Texas is investigated with a high-resolution Weather Research and Forecasting (WRF) Model–based ensemble Kalman filter (EnKF). Through ensemble sensitivity analysis (ESA), which relates a forecast metric to initial and early forecast errors by linear regression, features of the flow are revealed that reflect dynamical relationships with the forecast convection. Results indicate that ESA can be successfully applied to high-resolution forecasts of convection, and the most important features are related to the synoptic-scale flow such as positioning of an upper-level low and lower-level thermodynamic characteristics of air masses. Comparisons of the maximum and minimum convectively active members in the region of interest show that the fields generated by ESA are consistent with the actual evolution of the event: members with more eastward progression of the synoptic-scale system produced a stronger convection forecast. The forecast metric of interest is modified in several ways to further evaluate the strength of the results of the sensitivity analysis. Three different variables acting as convection proxies (reflectivity, vertical velocity, and precipitation) are tested along with changing the location of the forecast metric and its spatial size. These additional tests highlight the same synoptic features of the flow with the only major differences reflecting the importance of magnitude versus position of the convective forecast.

scholarly journals Mesoscale predictability under various synoptic regimes

2001 ◽  
Vol 8 (6) ◽  
pp. 429-438 ◽  
Author(s):  
W. A. Nuss ◽  
D. K. Miller
Keyword(s):  
Surface Wind ◽  
Surface Wind Speed ◽  
Precipitation Forecast ◽  
Forecast Errors ◽  
Synoptic Scale ◽  
Topographic Features ◽  
Mesoscale Structure ◽  
Frontal Dynamics ◽  
Slight Rotation ◽  
Numerical Model Experiments

Abstract. Numerical model experiments using slightly rotated terrain are compared to gauge the sentivity of mesoscale forecasts to small perturbations that arise due to small synoptic-scale wind direction errors relative to topographic features. The surface and above surface wind speed errors, as well as the precipitation forecast errors, are examined for a landfalling cold front that occurred during the California Landfalling Jets (CALJET) experiment. The slight rotation in the terrain results in nearly identical synoptic-scale forecasts, but result in substantial forecast errors on the mesoscale in both wind and precipitation. The largest mesoscale errors occur when the front interacts with the topography, which feeds back on the frontal dynamics to produce differing frontal structures, which, in turn, result in mesoscale errors as large as 40% (60%) of the observed mesoscale variability in rainfall (winds). This sensitivity differs for the two rotations and a simple average can still have a substantial error. The magnitude of these errors is very large given the size of the perturbation, which raises concerns about the predictability of the detailed mesoscale structure for landfalling fronts.

Effects of day-night temperature combinations under constant day length on emergence and early growth of sericea lespedeza genotypes

2007 ◽  
Vol 87 (1) ◽  
pp. 77-81
Author(s):  
K. L. Kalburtji ◽  
J. A. Mosjidis ◽  
A. P. Mamolos
Keyword(s):  
Temperature Difference ◽  
Minimum Temperature ◽  
Seedling Emergence ◽  
Dry Weight ◽  
Day Length ◽  
Night Temperature ◽  
Sericea Lespedeza ◽  
Leaf Dry Weight ◽  
Maximum Minimum ◽  
Number Of Branches

Establishment of sericea lespedeza [Lespedeza cuneata (Dumont de Courset) G. Don.] in southeastern USA is difficult. Seedling emergence may be related to the range of temperatures prevalent during establishment. A growth chamber study was undertaken to measure the effect of temperature on seedling emergence of 56 sericea lespedeza genotypes. Main treatments were: (1) plants grown at three day-night temperature combinations with maximum/minimum temperature difference of 14°C. The temperature combinations were 22/8°C, 27/13°C, and 32/18°C; (2) plants grown at three day-night temperature combinations with maximum/minimum temperature difference of 7°C. This was accomplished by lowering the day temperature and keeping the night temperature the same as above. Emergence was reduced by about 27% with reduction of 7°C in day-night temperature within the range of temperatures used. Plant height, leaf dry weight, stem dry weight and number of branches were very sensitive to temperature combinations. Increases in temperature caused increases in height, leaf dry weight, stem dry weight and number of branches of all genotypes. Further screening of sericea for emergence and growth under low temperature may lead to cultivars with more vigorous seedlings that can be better established early in the season. Key words: Plant growth, temperatures, seedling emergence, Sericea, southern USA

scholarly journals Observed Changes in Return Values of Annual Temperature Extremes over Argentina

2008 ◽  
Vol 21 (21) ◽  
pp. 5455-5467 ◽  
Author(s):  
Matilde Rusticucci ◽  
Bárbara Tencer
Keyword(s):  
Minimum Temperature ◽  
Extreme Temperature ◽  
Value Theory ◽  
Extreme Value ◽  
Maximum Temperature ◽  
Temperature Extremes ◽  
Frequency Of Occurrence ◽  
Extreme Temperature Events ◽  
Huge Impact ◽  
Maximum Minimum

Abstract Extreme temperature events are one of the most studied extreme events since their occurrence has a huge impact on society. In this study, the frequency of occurrence of absolute extreme temperature events in Argentina is analyzed. Four annual extremes are defined based on minimum and maximum daily data: the highest maximum (minimum) temperature of the year, and the lowest maximum (minimum) temperature of the year. Applying the extreme value theory (EVT), a generalized extreme value (GEV) distribution is fitted to these extreme indices and return values are calculated for the period 1956–2003. Its spatial distribution indicates that, for warm extremes, maximum temperature (Tx) is expected to be greater than 32°C at least once every 100 yr throughout the country (reaching values even higher than 46°C in the central region), while minimum temperature (Tn) is expected to exceed 16°C (reaching 30°C in the central and northern regions). Cold annual extremes show larger gradients across the country, with Tx being lower than 8°C at least once every 100 yr, and Tn lower than 0°C every 2 yr, with values even less than −10°C in the southwestern part of the country. However, the frequency of occurrence of climatic extremes has changed throughout the globe during the twentieth century. Changes in return values of annual temperature extremes due to the 1976–77 climatic shift at six long-term datasets are then analyzed. The lowest Tx of the year is the variable in which the 1976–77 shift is less noticeable. At all the stations studied there is a decrease in the probability of occurrence of the highest Tx if the study is based on more recent records, while the frequency of occurrence of the highest Tn increases at some stations and decreases at others. This implies that in the “present climate” (after 1977) there is a greater frequency of occurrence of high values of Tn at Observatorio Central Buenos Aires and Río Gallegos together with a lower frequency of occurrence of high values of Tx, leading to a decrease in the annual temperature range. The most noticeable change in return values due to the 1976–77 shift is seen in Patagonia where the 10-yr return value for the highest Tn increases from 13.7°C before 1976 to 18.6°C after 1977. That is, values of the highest Tn that occurred at least once every 10 yr in the “past climate” (before 1976) now happened more than once every 2 yr.

Sign in / Sign up

Export Citation Format

Share Document